Serum and plasma amino acids as markers of prediabetes, insulin resistance, and incident diabetes

Targeted metabolomics reveals novel biomarkers for gestational diabetes mellitus

AIMS

The utilization of targeted metabolomics technology promises to facilitate the identification of novel metabolic markers in women with gestational diabetes mellitus (GDM), which may in turn facilitate a more comprehensive investigation of the underlying mechanisms of gestational diabetes GDM.

MATERIALS AND METHODS

In this study, we used targeted metabolomics to identify serum metabolites from women with or without GDM. The differential metabolites were categorized and analysed using pathway analyses, correlated with maternal glucose level, and assessed as predictors of GDM by receiver operating characteristics analysis.

RESULTS

Notably, we detected 46 differential metabolites (24 upregulated and 22 downregulated) between GDM and normal pregnancy, which were catalogued into amino acids, peptides and analogues, and organic acids and derivatives, and others. Pathway analysis showed that amino acid metabolites were abnormally active. In addition, most of the metabolites were closely related to maternal glucose level. Of these, two metabolites were associated with fasting blood glucose, 22 correlated with 1-h postprandial plasma glucose and 13 were related to 2-h postprandial plasma glucose. Next, we identified metabolites that could better diagnose GDM with the area under the receiver operating characteristics above 0.75, including 2-hydroxybutyric acid, itaconic acid, O-acetylcarnitine, glutathione disulfide, P-cresolsulfate, 2-furoic acid, l-asparagine, d-biotin, choline and homovanillic acid.

CONCLUSION

We identified abnormal serum metabolites caused by GDM, which may contribute to our understanding of the pathomechanisms of GDM.

Metabolomic analyses of multiple biologic matrices reveal metabolic heterogeneity in diabetic complications

AIMS

Type 2 diabetes mellitus (T2DM) arises from a complex interplay of genetic and environmental factors. Patients with T2DM are susceptible to hyperglycemia-related complications that can impair organ function, underscoring the need to explore the metabolic profiles of affected organs.

METHODS

In this study, a comprehensive metabolomic analysis was conducted on the serum, kidney, and heart tissues from a rat model of diabetic complications (DC). Pattern recognition and multivariate statistical analyses were applied to identify the potential biomarkers of DC, and metabolic network analysis served to understand the specific metabolic pathways associated with DC.

RESULTS

Fourteen significantly altered metabolites were identified in serum, 20 in the kidney, and 14 in the heart. The corresponding metabolic pathways included mineral absorption, mTOR signaling pathway, taurine and hypotaurine metabolism, glycine, serine and threonine metabolism, ABC transporters, glucagon signaling pathway, protein degradation and uptake, galactose metabolism, purine metabolism, nicotinic acid and nicotinamide metabolism, and glycolysis and gluconeogenesis. Differential metabolite network analysis revealed instinct metabolic patterns among the serum, kidney, and heart. Notably, the serum's metabolic correlation patterns were found to be somewhat similar to those observed in the kidney, whereas the heart exhibited less pronounced metabolite correlations compared to the other two biological matrices.

CONCLUSIONS

These findings provide insights into the mechanism underlying the development of diabetic complications. The integration of metabolomics and biological network analyses into diabetes research can potentially revolutionize the field by revealing novel biomarkers for early detection and personalized treatment of diabetes and its associated complications.

Interactive effect of phenylalanine with duration of diabetes on the risk of small vessel disease in Chinese patients with T2DM

Aims

Few prior studies have explored the relationship between phenylalanine and diabetic small vessel disease (SVD) in patients with different durations of type 2 diabetes mellitus(T2DM). Our study aimed to explore whether phenylalanine is associated with the risk of SVD and to further explore whether phenylalanine interacted with the duration of T2DM to alter the risk of SVD.

Materials and methods

A total of 1,032 T2DM patients were enrolled using the Liaoning Medical University First Affiliated Hospital (LMUFAH) system. SVD was defined as patients with diabetic nephropathy (DN) or diabetic retinopathy (DR) alone, or both. Serum amino acids were measured by mass spectrometry (MS) technology. A binary logistic regression model was used to examine associations of phenylalanine with SVD risk. Restricted cubic spline (RCS) regression was used to draw the odds ratio curves of plasma phenylalanine for SVD. Additive interaction analysis was employed to test the interaction of low phenylalanine with a long duration of T2DM for SVD.

Results

Among the 1,032 T2DM patients, 286 (27.7%) had SVD. Phenylalanine <42μmol/L was associated with a markedly increased risk of SVD (OR 1.76, 95%CI 1.23 to 2.51), which was enhanced by a duration of T2DM of ≥5 years to 4.83 (95%CI 2.97-7.87) with significant additive interactions. The inclusion of phenylalanine and duration of T2DM into a traditional risk factor model substantially increased the area under the receiver operating characteristic curve from 0.67 to 0.71 (95% CI 0.70 to 0.75) (P <0.05).

Conclusions

In Chinese patients with T2DM, phenylalanine <42μmol/L was associated with an increased risk of SVD, which was further amplified by a duration of T2DM of ≥5 years.

Efficacy of Fetal Wharton's Jelly Mesenchymal Stem Cells-Derived Small Extracellular Vesicles in Metabolic Syndrome

BACKGROUND/OBJECTIVE

Metabolic syndrome (MetS) is characterized by abdominal obesity, increased blood pressure (BP), fasting blood glucose (FBG) and triglyceride levels, and reduced high-density lipoprotein (HDL) levels. This study aims to investigate the efficacy of the Wharton's jelly mesenchymal stem cells (WJMSCs)-derived small extracellular vesicles' (sEVs) preparations in managing MetS.

METHOD

Twenty-four rats were fed with a high-fat and high-fructose diet to induce MetS for 16 weeks and randomized into three groups (n = 8/group): a MetS Control group treated with normal saline, MetS Low Dose (LD) group treated with a LD of sEVs preparations (3 × 109 particle/rat), and MetS High Dose (HD) group treated with a HD of sEVs preparations (9 × 109 particles/rat). The Control Non-Disease (ND) group was given a standard rat diet and autoclaved tap water with normal saline as treatment. Treatments were given via intravenous injection every three weeks for twelve weeks. Rats were assessed every six weeks for physical measurements, FBG, lipid profiles, CRP, leptin, adiponectin, and BP. Necropsy evaluation was performed on the lungs, liver, spleen, and kidney.

RESULTS

Significant reductions in FBG, triglycerides, BP, and increased HDL levels were observed in the treated groups compared to the control group. However, significant abdominal circumference (AC) improvement was not observed in the treated groups. Non-significant associations were found between fasting CRP, leptin, and adiponectin levels with MetS rats after treatment. In addition, sEVs preparations improved inflammation and hemorrhage in the lung and mineralisation in the renal of the treated group.

CONCLUSIONS

Human fetal WJMSCs-derived sEVs preparations improve all the clusters of MetS in rats except AC and could be further explored as a treatment for MetS.

Emerging Biomarkers in Metabolomics: Advancements in Precision Health and Disease Diagnosis

Metabolomics has come to the fore as an efficient tool in the search for biomarkers that are critical for precision health approaches and improved diagnostics. This review will outline recent advances in biomarker discovery based on metabolomics, focusing on metabolomics biomarkers reported in cancer, neurodegenerative disorders, cardiovascular diseases, and metabolic health. In cancer, metabolomics provides evidence for unique oncometabolites that are important for early disease detection and monitoring of treatment responses. Metabolite profiling for conditions such as neurodegenerative and mental health disorders can offer early diagnosis and mechanisms into the disease especially in Alzheimer's and Parkinson's diseases. In addition to these, lipid biomarkers and other metabolites relating to cardiovascular and metabolic disorders are promising for patient stratification and personalized treatment. The gut microbiome and environmental exposure also feature among the influential factors in biomarker discovery because they sculpt individual metabolic profiles, impacting overall health. Further, we discuss technological advances in metabolomics, current clinical applications, and the challenges faced by metabolomics biomarker validation toward precision medicine. Finally, this review discusses future opportunities regarding the integration of metabolomics into routine healthcare to enable preventive and personalized approaches.

Free amino acids accelerate the time-dependent inactivation of rat liver nucleotide pyrophosphatase/phosphodiesterase Enpp3 elicited by EDTA

Nucleotide-pyrophosphatases/phosphodiesterases (NPP/PDE) are membrane or secreted Zn2+-metallohydrolases of nucleoside-5´-monophosphate derivatives. They hydrolyze, for instance, ATP and 4-nitrophenyl-dTMP, and belong to the ecto-nucleotide pyrophosphatase/phosphodiesterase (ENPP) family that contains seven members (ENPP1-ENPP7). Earlier we had shown that an NPP/PDE activity solubilized and partially purified from rat liver membranes is inactivated by EDTA in a time-dependent fashion, an effect enhanced by glycine and blocked by the 4-nitrophenyl-dTMP. Here, we extended this observation to other free amino acids. Activity assays started after different incubation lengths with EDTA provided first-order, apparent inactivation constants (ki(ap)). With the exception of cysteine (a strong inhibitor) and histidine (itself evoking a time-dependent inactivation), free amino acids themselves did not affect activity but increased ki(ap). The results are compatible with a conformational change of NPP/PDE evoked by interaction with free amino acids. The enzyme preparation was analyzed to identify what ENPP family members were present. First, the hydrolytic activity on 2´,3´-cGAMP was assayed because until very recently ENPP1 was the only mammalian enzyme known to display it. 2´,3´-cGAMP hydrolase activity was clearly detected, but mass spectrometry data obtained by LC-MS/MS gave evidence that only rat Enpp3, Enpp4 and Enpp5 were present with low abundance. This finding coincided in time with a recent publication claiming that mouse Enpp3 hydrolyzes 2´,3´-cGAMP, and that Enpp1 and Enpp3 account for all the 2´,3´-cGAMP hydrolase activity in mice. So, our results are confirmatory of Enpp3 activity towards 2´,3´-cGAMP. Finally, the effect of amino acids could be relevant to NPP/PDE actions dependent on protein-protein interactions, like the known insulin-related effects of ENPP1 and possibly ENPP3.

Untargeted Metabolomics Reveals the Role of Lipocalin-2 in the Pathological Changes of Lens and Retina in Diabetic Mice

Purpose

To identify the role of lipocalin-2 (LCN2) in diabetic cataract (DC) and diabetic retinopathy (DR), diabetes models were established in wild-type (WT) and LCN2 gene knockout (LCN2-/-) mice by streptozotocin (STZ), this study aimed to investigate the metabolic alterations and underlying pathways in the lens and retina.

Methods

Untargeted metabolomic analysis was performed on the lenses and retinas of WT and LCN2-/- diabetic mice, and relevant pathways were predicted through bioinformatics analysis.

Results

LCN2 was notably elevated in the anterior capsules of DC and the vitreous humor of DR. Metabolic profiling of the lenses and retinas of diabetic mice indicated that the differential metabolites were mostly amino acids, fatty acids, carbohydrates, and their derivatives. In the lenses of STZ-induced WT mice, the differential abundance score (DA-score) revealed an increase in metabolites associated with the citrate (or TCA) cycle and glucagon signaling pathway, whereas a decrease was observed in metabolites related to cholesterol metabolism. After the knockout of LCN2, the DA-score indicated that the majority of metabolites involved in cholesterol metabolism, cysteine and methionine metabolism, and tryptophan metabolism were diminished. In the STZ-induced retina, there was an increase in metabolites associated with the mTOR signaling pathway, and this increase was inhibited by the knockout of LCN2.

Conclusions

Numerous metabolites exhibited substantial alterations in the lenses and retinas of diabetic mice. Untargeted metabolomics has provided insights into the function of LCN2 in DC and DR. These changes in metabolites, along with their related pathways, could be the mechanisms by which LCN2 modulated DC and DR.

The Chinese herbal prescription Kang-Gong-Yan alleviates cervicitis by modulating metabolites and gut microbiota

CONTEXT

Cervicitis is a common gynecological inflammatory disease. The Chinese herbal prescription Kang-Gong-Yan (KGY) is clinically effective against cervicitis; however, the chemical constituents and therapeutic mechanism of KGY remain elusive.

OBJECTIVE

To analyze the chemical constituents of KGY and explore the potential mechanism of KGY in treating cervicitis.

MATERIALS AND METHODS

UHPLC-Q-Exactive Plus Orbitrap MS was used to identify the active compounds of KGY; Sprague-Dawley (SD) female rats were randomly divided into the control, model, and KGY groups. Phenol mucilage (25%) was slowly injected into the vagina and cervix of the rats to establish the cervicitis model. Then, rats in the KGY groups (low dose: 1 g/kg/d; medium dose: 5 g/kg/d; high dose: 10 g/kg/d) were continuously gavaged KGY for one week. HE staining was used to observe the cervical tissues of rats; ELISA was used to detect inflammatory factors in plasma; non-targeted metabolomics was used to analyze metabolites; 16S rRNA sequencing was used to analyze intestinal microorganisms.

RESULTS

KGY exerted anti-cervicitis effects and decreased the levels of IL-6, IL-1β, and TNF-α. The mechanism of KGY in treating cervicitis is mainly associated with betaine, amino acid, pyrimidine, and phospholipid metabolism by regulating fifteen metabolites. Moreover, KGY reversed cervicitis-induced gut dysbiosis by mediating five bacteria.

DISCUSSION AND CONCLUSIONS

The Chinese herbal prescription KGY may alleviate cervicitis by modulating metabolites and gut microbiota disorders. These findings provide a scientific basis for the clinical application of KGY and a new strategy for treating cervicitis in Chinese medicine.

Metabolic profiling identifies potential biomarkers associated with progression from gestational diabetes mellitus to prediabetes postpartum

The current study aims to identify potential metabolic biomarkers that predict the progression to prediabetes in women with a history of gestational diabetes mellitus (GDM). We constructed a prediabetes group ( n = 42) and a control group ( n = 40) based on a2-hour 75 g oral glucose tolerance test for women with a history of GDM from six weeks to six months postpartum, and collected their clinical data and biochemical test results. We performed the plasma metabolomics analysis of the subjects at the fasting and 2-hour post-load time points by using ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS/MS). We found that the prediabetes group was older and had higher 2-hour post-load glucose levels during pregnancy than the control group. The metabolomic analysis identified 164 differential metabolites between the groups. Compared with the control group, 15 metabolites in the prediabetes group exhibited consistent change trends at both time points, including three increased and 12 decreased metabolites. By building a prediction model of the progression from GDM to prediabetes, we found a combination of three clinical markers yielded an area under thecurve (AUC) of 0.71 (95% confidence interval [CI], 0.60-0.82). We also assessed the discriminative power of the panel of 15 metabolites for distinguishing between postpartum prediabetes and normal glucose tolerance of the subjects at the fasting (AUC, 0.98; 95% CI, 0.94-1.00) and 2-hour post-load (AUC, 0.99; 95% CI, 0.97-1.00) time points. The metabolic pathway analysis indicated that energy metabolism and branched-chain amino acids played a role in the development of prediabetes in women with a history of GDM during early postpartum. In conclusion, this study identified potential metabolic biomarkers and pathways associated with the progression from GDM to prediabetes in the early postpartum period. A panel of 15 metabolites showed promising discriminative power for distinguishing between postpartum prediabetes and normal glucose tolerance. These findings provide insights into the underlying pathophysiology of this transition and suggest the feasibility of developing a metabolic profiling test for the early identification of women at high risk of prediabetes following GDM.

Associations of Amino Acids with the Risk of Prediabetes: A Case-Control Study from Kazakhstan

BACKGROUND

The high global prevalence of prediabetes requires its early identification. Amino acids (AAs) have emerged as potential predictors of prediabetes. This study investigates the association between amino acids and prediabetes in the Kazakh population.

MATERIALS AND METHODS

In this case-control study, serum AAs levels were measured using the Trace GC 1310 gas chromatography system coupled with the TSQ 8000 triple quadrupole mass spectrometer (Thermo Scientific, Austin, TX, USA) followed by silylation with the BSTFA + 1% TMCS derivatization method. Biochemical parameters, including total cholesterol, HDL-C, LDL-C, triglycerides, fasting glucose, HbA1c, and Creatinine, were assessed for each participant. Trained professionals conducted anthropometric and physical examinations (which included taking blood pressure and heart rate measurements) and family history collection.

RESULTS

A total of 112 Kazakh individuals with prediabetes and 55 without prediabetes, aged 36-65 years, were included in the study. Only Alanine and valine showed a significant association with prediabetes risk among the 13 AAs analyzed. Our findings revealed an inverse relationship between Alanine and Valine and prediabetes in individuals of Kazakh ethnicity.

CONCLUSION

A lower serum level of Alanine and Valine may serve as a predictive biomarker for prediabetes in the Kazakh population.

Microbiome-derived metabolites in early to mid-pregnancy and risk of gestational diabetes: a metabolome-wide association study

BACKGROUND

Pre-diagnostic disturbances in the microbiome-derived metabolome have been associated with an increased risk of diabetes in non-pregnant populations. However, the roles of microbiome-derived metabolites, the end-products of microbial metabolism, in gestational diabetes (GDM) remain understudied. We examined the prospective association of microbiome-derived metabolites in early to mid-pregnancy with GDM risk in a diverse population.

METHODS

We conducted a prospective discovery and validation study, including a case-control sample of 91 GDM and 180 non-GDM individuals within the multi-racial/ethnic The Pregnancy Environment and Lifestyle Study (PETALS) as the discovery set, a random sample from the PETALS (42 GDM, 372 non-GDM) as validation set 1, and a case-control sample (35 GDM, 70 non-GDM) from the Gestational Weight Gain and Optimal Wellness randomized controlled trial as validation set 2. We measured untargeted fasting serum metabolomics at gestational weeks (GW) 10-13 and 16-19 by gas chromatography/time-of-flight mass spectrometry (TOF-MS), liquid chromatography (LC)/quadrupole TOF-MS, and hydrophilic interaction LC/quadrupole TOF-MS. GDM was diagnosed using the 3-h, 100-g oral glucose tolerance test according to the Carpenter-Coustan criteria around GW 24-28.

RESULTS

Among 1362 annotated compounds, we identified 140 of gut microbiome metabolism origin. Multivariate enrichment analysis illustrated that carbocyclic acids and branched-chain amino acid clusters at GW 10-13 and the unsaturated fatty acids cluster at GW 16-19 were positively associated with GDM risk (FDR < 0.05). At GW 10-13, the prediction model that combined conventional risk factors and LASSO-selected microbiome-derived metabolites significantly outperformed the model with only conventional risk factors including fasting glucose (discovery AUC: 0.884 vs. 0.691; validation 1: 0.945 vs. 0.731; validation 2: 0.987 vs. 0.717; all P < 0.01). At GW 16-19, similar results were observed (discovery AUC: 0.802 vs. 0.691, P < 0.01; validation 1: 0.826 vs. 0.780; P = 0.10).

CONCLUSIONS

Dysbiosis in microbiome-derived metabolites is present early in pregnancy among individuals progressing to GDM.

Discovery and validation of plasma, saliva and multi-fluid plasma-saliva metabolomic scores predicting insulin resistance and diabetes progression or regression among Puerto Rican adults

AIMS/HYPOTHESIS

Many studies have examined the relationship between plasma metabolites and type 2 diabetes progression, but few have explored saliva and multi-fluid metabolites.

METHODS

We used LC/MS to measure plasma (n=1051) and saliva (n=635) metabolites among Puerto Rican adults from the San Juan Overweight Adults Longitudinal Study. We used elastic net regression to identify plasma, saliva and multi-fluid plasma-saliva metabolomic scores predicting baseline HOMA-IR in a training set (n=509) and validated these scores in a testing set (n=340). We used multivariable Cox proportional hazards models to estimate HRs for the association of baseline metabolomic scores predicting insulin resistance with incident type 2 diabetes (n=54) and prediabetes (characterised by impaired glucose tolerance, impaired fasting glucose and/or high HbA1c) (n=130) at 3 years, along with regression from prediabetes to normoglycaemia (n=122), adjusting for traditional diabetes-related risk factors.

RESULTS

Plasma, saliva and multi-fluid plasma-saliva metabolomic scores predicting insulin resistance included highly weighted metabolites from fructose, tyrosine, lipid and amino acid metabolism. Each SD increase in the plasma (HR 1.99 [95% CI 1.18, 3.38]; p=0.01) and multi-fluid (1.80 [1.06, 3.07]; p=0.03) metabolomic scores was associated with higher risk of type 2 diabetes. The saliva metabolomic score was associated with incident prediabetes (1.48 [1.17, 1.86]; p=0.001). All three metabolomic scores were significantly associated with lower likelihood of regressing from prediabetes to normoglycaemia in models adjusting for adiposity (HRs 0.72 for plasma, 0.78 for saliva and 0.72 for multi-fluid), but associations were attenuated when adjusting for lipid and glycaemic measures.

CONCLUSIONS/INTERPRETATION

The plasma metabolomic score predicting insulin resistance was more strongly associated with incident type 2 diabetes than the saliva metabolomic score. Only the saliva metabolomic score was associated with incident prediabetes.

Association between metabolic score for insulin resistance and regression to normoglycemia from prediabetes in Chinese adults: A retrospective cohort study

BACKGROUND

Metabolic score for insulin resistance (METS-IR) is a surrogate index to estimate insulin sensitivity. The aim of this study was to examine the association between METS-IR and regression to normoglycemia in Chinese adults with prediabetes.

METHODS

A total of 15,415 Chinese adults with prediabetes defined by their fasting blood glucose were included in this retrospective study. The association between METS-IR and regression to normoglycemia from prediabetes was evaluated using the Cox proportional hazards regression model. A Cox proportional hazards regression with cubic spline function was performed to explore the nonlinear association between METS-IR and regression to normoglycemia. Kaplan-Meier curves was used to describe the probability of regression to normoglycemia from prediabetes.

RESULTS

In multivariate Cox proportional hazards regression analyses, the increase in METS-IR was independently associated with a reduced probability of regression to normoglycemia from prediabetes (all p < 0.01 in models 1-3). A nonlinear association between METS-IR and the probability of regression to normoglycemia was observed, with an inflection point of 49.3. The hazard ratio on the left side of the inflection point was 0.965 (95% CI 0.953-0.976). Subgroup analyses demonstrated the robustness of our findings.

CONCLUSION

This study demonstrated a negative and nonlinear association between METS-IR and regression to normoglycemia in Chinese adults with prediabetes. When METS-IR is below 49.3, reducing METS-IR could significantly increase the probability of regression to normoglycemia from prediabetes.

Pharmacodynamic and targeted amino acid metabolomics researches on the improvement of diabetic retinopathy with Fufang Xueshuantong component compatibility

The Fufang Xueshuantong capsule (FXT) has significant preventive and therapeutic effects on diabetic retinopathy(DR), but the compatibility of its active components remains to be thoroughly explored. In this study, a zebrafish diabetic retinopathy model was established using high-mixed sugars, and the optimal ratios of notoginseng total saponins, total salvianolic acid, astragaloside, and harpagide were selected through orthogonal experiments. Furthermore, we used UPLC-QqQ/MS to detect the changes in amino acid content of DR zebrafish tissues after administration of FXT and its compatible formula to analyze the effects of FXT and its compatible formula on amino acid metabolites. The results showed that the final compatibility ratios of the components were 8: 5: 1: 6.6 by comprehensive evaluation of the indicators. FXT and its compatibility formula had beneficial effects on retinal vasodilatation, lipid accumulation in the liver, total glucose, and VEGF levels in DR zebrafish, and all of them could call back some amino acid levels in DR zebrafish. In this research, we determined the compatible formulation of the active ingredients in the FXT and investigated their efficacy in DR zebrafish for further clinical applications.

Investigating the mechanism of cornel iridoid glycosides on type 2 diabetes mellitus using serum and urine metabolites in rats

ETHNOPHARMACOLOGICAL RELEVANCE

Cornel iridoid glycosides (CIG) are extracted from Corni fructus, a herbal medicine used in traditional Chinese medicine to treat diabetes. However, the antidiabetic effects of CIG and the underlying metabolic mechanisms require further exploration.

AIM OF THE STUDY

This study aimed to assess the antidiabetic effects and metabolic mechanism of CIG by performing metabolomic analyses of serum and urine samples of rats.

MATERIALS AND METHODS

A rat model of type 2 diabetes mellitus (T2DM) was established by administering a low dose of streptozotocin (30 mg/kg) intraperitoneally after 4 weeks of feeding a high-fat diet. The model was evaluated based on several parameters, including fasting blood glucose (FBG), random blood glucose (RBG), urine volume, liver index, body weight, histopathological sections, and serum biochemical parameters. Subsequently, serum and urine metabolomics were analyzed using ultra-high-pressure liquid chromatography coupled with linear ion trap-Orbitrap tandem mass spectrometry (UHPLC-LTQ-Orbitrap-MS). Data were analyzed using unsupervised principal component analysis (PCA) and supervised orthogonal partial least squares discriminant analysis (OPLS-DA). Differential metabolites were examined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways to explore the underlying mechanisms.

RESULTS

After 4 weeks of treatment with different doses of CIG, varying degrees of antidiabetic effects were observed, along with reduced liver and pancreatic injury, and improved oxidative stress levels. Compared with the T2DM group, 19 and 23 differential metabolites were detected in the serum and urine of the CIG treatment group, respectively. The key metabolites involved in pathway regulation include taurine, chenodeoxycholic acid, glycocholic acid, and L-tyrosine in the serum and glycine, hippuric acid, phenylacetylglycine, citric acid, and D-glucuronic acid in the urine, which are related to lipid, amino acid, energy, and carbohydrate metabolism.

CONCLUSIONS

This study confirmed the antidiabetic effects of CIG and revealed that CIG effectively controlled metabolic disorders in T2DM rats. This seems to be meaningful for the clinical application of CIG, and can benefit further studies on CIG mechanism.

The role of immuno-metabolic depression features in the effects of light therapy in patients with depression and type 2 diabetes mellitus: A randomized controlled trial

OBJECTIVE

Immuno-metabolic depression (IMD) is proposed to be a form of depression encompassing atypical, energy-related symptoms (AES), low-grade inflammation and metabolic dysregulations. Light therapy may alleviate AES by modulating inflammatory and metabolic pathways. We investigated whether light therapy improves clinical and biological IMD features and whether effects of light therapy on AES or depressive symptom severity are moderated by baseline IMD features. Associations between changes in symptoms and biomarkers were explored.

METHODS

In secondary analyses, clinical trial data was used from 77 individuals with depression and type 2 diabetes mellitus (T2DM) randomized to four weeks of light therapy or placebo. AES severity and depressive symptom severity were based on the Inventory of Depressive Symptomatology. Biomarkers included 73 metabolites (Nightingale) summarized in three principal components and CRP, IL-6, TNF-α, INF-γ. Linear regression analyses were performed.

RESULTS

Light therapy had no effect on AES severity, inflammatory markers and metabolite principle components versus placebo. None of these baseline features moderated the effects of light therapy on AES severity. Only a principle component reflecting metabolites implicated in glucose homeostasis moderated the effects of light therapy on depressive symptom severity (βinteraction = 0.65, P = 0.001, FDR = 0.003). Changes in AES were not associated with changes in biomarkers.

CONCLUSION

Findings do not support the efficacy of light therapy in reducing IMD features in patients with depression and T2DM. We find limited evidence that light therapy is a more beneficial depression treatment among those with more IMD features. Changes in clinical and biological IMD features did not align over four-weeks' time.

TRIAL REGISTRATION

The Netherlands Trial Register (NTR) NTR4942.

Critical review on the research of chemical structure, bioactivities, and mechanism of actions of Dendrobium officinale polysaccharide

Dendrobium officinale (Tie-Pi-Shi-Hu) is a precious traditional Chinese medicine (TCM). The principal active components are polysaccharides (DOP), which have a high potency in therapeutic applications. However, limitations in structure analysis and underlying mechanism investigation impede its further research. This review systemically and critically summarises current understanding in both areas, and points out the influence of starch impurities and the role of gut microbiota in DOP research. As challenges faced in studying natural polysaccharide investigations are common, this review contributes to a broader understanding of polysaccharides beyond DOP.

Artesunate improves glucose and lipid metabolism in db/db mice by regulating the metabolic profile and the MAPK/PI3K/Akt signalling pathway

BACKGROUND

Diabetes is a metabolic disorder characterized by chronic hyperglycaemia. Chronic metabolic abnormalities and long-term hyperglycaemia may result in a wide range of acute and chronic consequences. Previous studies have demonstrated that artesunate(ART) has antidiabetic, anti-inflammatory, antiatherosclerotic, and other beneficial effects, but the specific regulatory mechanism is not completely clear.

AIM

This study investigated the effects of ART on metabolic disorders in type 2 diabetes mellitus (T2DM) model db/db mice and explored the underlying mechanisms involved.

METHODS

C57BL/KsJ-db/db mice were used to identify the targets and molecular mechanism of ART. Metabolomic methods were used to evaluate the efficacy of ART in improving T2DM-related metabolic disorders. Network pharmacology and transcriptomic sequencing were used to analyse the targets and pathways of ART in T2DM. Finally, molecular biology experiments were performed to verify the key targets and pathways selected by network pharmacology and transcriptomic analyses.

RESULTS

After a 7-week ART intervention (160 mg/kg), the glucose and lipid metabolism levels of the db/db mice improved. Additionally, the oxidative stress indices, namely, the MDA and SOD levels, significantly improved (p<0.01). Linoleic acid and glycerophospholipid metabolism, amino acid metabolism, bile acid synthesis, and purine metabolism disorders in db/db mice were partially corrected after ART treatment. Network pharmacology analysis identified important targets of ART for the treatment of metabolic disorders in T2DM . These targets are involved in key signalling pathways, including the highest scores observed for the PI3K/Akt signalling pathway. Transcriptomic analysis revealed that ART could activate the MAPK signalling pathway and two key gene targets, HGK and GADD45. Immunoblotting revealed that ART increases p-PI3K, p-AKT, Glut2, and IRS1 protein expression and suppresses the phosphorylation of p38, ERK1/2, and JNK, returning HGK and GADD45 to their preartesunate levels.

CONCLUSION

Treatment of db/db mice with 160 mg/kg ART for 7 weeks significantly reduced fasting blood glucose and lipid levels. It also improved metabolic imbalances in amino acids, lipids, purines, and bile acids, thereby improving metabolic disorders. These effects are achieved by activating the PI3K/AKT pathway and inhibiting the MAPK pathway, thus demonstrating the efficacy of the drug.

Associations of Lifestyle Patterns with Glucose and Lipid Metabolism in Finnish Adults at Increased Risk of Type 2 Diabetes

SCOPE

Various lifestyle and sociodemographic factors have been associated with risk factors for type 2 diabetes (T2D). However, their combined associations with T2D risk factors have been studied much less.

MATERIALS AND RESULTS

This study investigates cross-sectional associations of lifestyle patterns with T2D risk factors among 2925 adults at increased risk participating in the Stop Diabetes study. Lifestyle patterns are determined using principal component analysis (PCA) with several lifestyle and sociodemographic factors. The associations of lifestyle patterns with measures of glucose and lipid metabolism and serum metabolites analyzed by nuclear magnetic resonance (NMR) spectroscopy are studied using linear regression analysis. "Healthy eating" pattern is associated with better glucose and insulin metabolism, more favorable lipoprotein and fatty acid profiles and lower serum concentrations of metabolites related to inflammation, insulin resistance, and T2D. "High socioeconomic status and low physical activity" pattern is associated with increased serum concentrations of branched-chain amino acids, as are "Meat and poultry" and "Sleeping hours" patterns. "Snacks" pattern is associated with lower serum concentrations of ketone bodies.

CONCLUSIONS

Our results show, in large scale primary care setting, that healthy eating is associated with better glucose and lipid metabolism and reveal novel associations of lifestyle patterns with metabolites related to glucose metabolism.

The paradigm change from reactive medical services to 3PM in ischemic stroke: a holistic approach utilising tear fluid multi-omics, mitochondria as a vital biosensor and AI-based multi-professional data interpretation

Worldwide stroke is the second leading cause of death and the third leading cause of death and disability combined. The estimated global economic burden by stroke is over US$891 billion per year. Within three decades (1990-2019), the incidence increased by 70%, deaths by 43%, prevalence by 102%, and DALYs by 143%. Of over 100 million people affected by stroke, about 76% are ischemic stroke (IS) patients recorded worldwide. Contextually, ischemic stroke moves into particular focus of multi-professional groups including researchers, healthcare industry, economists, and policy-makers. Risk factors of ischemic stroke demonstrate sufficient space for cost-effective prevention interventions in primary (suboptimal health) and secondary (clinically manifested collateral disorders contributing to stroke risks) care. These risks are interrelated. For example, sedentary lifestyle and toxic environment both cause mitochondrial stress, systemic low-grade inflammation and accelerated ageing; inflammageing is a low-grade inflammation associated with accelerated ageing and poor stroke outcomes. Stress overload, decreased mitochondrial bioenergetics and hypomagnesaemia are associated with systemic vasospasm and ischemic lesions in heart and brain of all age groups including teenagers. Imbalanced dietary patterns poor in folate but rich in red and processed meat, refined grains, and sugary beverages are associated with hyperhomocysteinaemia, systemic inflammation, small vessel disease, and increased IS risks. Ongoing 3PM research towards vulnerable groups in the population promoted by the European Association for Predictive, Preventive and Personalised Medicine (EPMA) demonstrates promising results for the holistic patient-friendly non-invasive approach utilising tear fluid-based health risk assessment, mitochondria as a vital biosensor and AI-based multi-professional data interpretation as reported here by the EPMA expert group. Collected data demonstrate that IS-relevant risks and corresponding molecular pathways are interrelated. For examples, there is an evident overlap between molecular patterns involved in IS and diabetic retinopathy as an early indicator of IS risk in diabetic patients. Just to exemplify some of them such as the 5-aminolevulinic acid/pathway, which are also characteristic for an altered mitophagy patterns, insomnia, stress regulation and modulation of microbiota-gut-brain crosstalk. Further, ceramides are considered mediators of oxidative stress and inflammation in cardiometabolic disease, negatively affecting mitochondrial respiratory chain function and fission/fusion activity, altered sleep-wake behaviour, vascular stiffness and remodelling. Xanthine/pathway regulation is involved in mitochondrial homeostasis and stress-driven anxiety-like behaviour as well as molecular mechanisms of arterial stiffness. In order to assess individual health risks, an application of machine learning (AI tool) is essential for an accurate data interpretation performed by the multiparametric analysis. Aspects presented in the paper include the needs of young populations and elderly, personalised risk assessment in primary and secondary care, cost-efficacy, application of innovative technologies and screening programmes, advanced education measures for professionals and general population-all are essential pillars for the paradigm change from reactive medical services to 3PM in the overall IS management promoted by the EPMA.

Sex-related differences in within-subject biological variation estimates for 22 essential and non-essential amino acids

BACKGROUND

Measurement of serum amino acid (AA) concentrations is important in particular for the diagnosis and monitoring of inborn errors of AA metabolism. To ensure optimal clinical interpretation of AAs, reliable biological variation (BV) data are essential. In the present study, we derived BV data for 22 non-essential, conditionally essential, and essential AAs and assessed differences in BV of AAs related to sex.

METHODS

Morning blood samples were drawn from 66 subjects (31 males and 35 females) once a week for 10 consecutive weeks. All samples were analyzed in duplicate using liquid chromatography-tandem mass-spectrometry. The data were assessed for outliers, trends, normality and variance homogeneity analysis prior to estimating within-subject (CVI) and between-subject (CVG) BV.

RESULTS

CVI estimates ranged from 9.0 % for histidine (male) to 33.0 % for taurine (male). CVI estimates in males and females were significantly different for all AAs except for aspartic acid, citrulline and phenylalanine, in most cases higher in females than in males. Apart from for arginine, CVG estimates in males and females were similar.

CONCLUSIONS

In this highly powered BV study, we provide updated BV estimates for 22 AAs and demonstrate that for most AAs, CVI estimates differ between males and females, with implications for interpretation and use of AAs in clinical practice.

Heat stress reduces brown adipose tissue activity by exacerbating mitochondrial damage in type 2 diabetic mice

Epidemiological evidence shows that diabetic patients are susceptible to high temperature weather, and brown adipose tissue (BAT) activity is closely related to type 2 diabetes (T2DM). Activation of BAT under cold stress helps improve T2DM. However, the impact of high temperature on the activity of BAT is still unclear. The study aimed to investigate the impact of heat stress on glucose and lipid metabolism in T2DM mice by influencing BAT activity. High-fat feeding and injecting streptozotocin (STZ) induced model of T2DM mice. All mice were randomly divided into three groups: a normal(N) group, a diabetes (DM) group and a heat stress diabetes (DMHS) group. The DMHS group received heat stress intervention for 3 days. Fasting blood glucose, fasting serum insulin and blood lipids were measured in all three groups. The activity of BAT was assessed by using quantitative real-time PCR (qRT-PCR), electron microscopy, and PET CT. Furthermore, the UHPLC-Q-TOF MS technique was employed to perform metabolomics analysis of BAT on both DM group and DMHS group. The results of this study indicated that heat stress aggravated the dysregulation of glucose and lipid metabolism, exacerbated mitochondrial dysfunction in BAT and reduced the activity of BAT in T2DM mice. This may be related to the abnormal accumulation of branched-chain amino acids (BCAAs) in the mitochondria of BAT.

Application of Metabolomics and Traditional Chinese Medicine for Type 2 Diabetes Mellitus Treatment

Diabetes is a major global public health problem with high incidence and case fatality rates. Traditional Chinese medicine (TCM) is used to help manage Type 2 Diabetes Mellitus (T2DM) and has steadily gained international acceptance. Despite being generally accepted in daily practice, the TCM methods and hypotheses for understanding diseases lack applicability in the current scientific characterization systems. To date, there is no systematic evaluation system for TCM in preventing and treating T2DM. Metabonomics is a powerful tool to predict the level of metabolites in vivo, reveal the potential mechanism, and diagnose the physiological state of patients in time to guide the follow-up intervention of T2DM. Notably, metabolomics is also effective in promoting TCM modernization and advancement in personalized medicine. This review provides updated knowledge on applying metabolomics to TCM syndrome differentiation, diagnosis, biomarker discovery, and treatment of T2DM by TCM. Its application in diabetic complications is discussed. The combination of multi-omics and microbiome to fully elucidate the use of TCM to treat T2DM is further envisioned.

2,5-Dihydroxyterephthalic Acid: A Matrix for Improved Detection and Imaging of Amino Acids

Amino acids (AAs), which are low-molecular-weight (low-MW) metabolites, serve as essential building blocks not only for protein synthesis but also for maintaining the nitrogen balance in living systems. In situ detection and imaging of AAs are crucial for understanding more complex biological processes. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a label-free mass spectrometric imaging technique that enables the simultaneous detection and imaging of the spatial distribution and relative abundance of different endogenous/exogenous compounds in biological samples. The excellent efficiency of MALDI-MSI is attributed to the choice of the MALDI matrix. However, to the best of our knowledge, no matrix has been specifically developed for AAs. Herein, we report a MALDI matrix, 2,5-dihydroxyterephthalic acid (DHT), which can improve the detection and imaging of AAs in biological samples by MALDI-MS. Our results indicated that DHT exhibited strong ultraviolet-visible (UV-vis) absorption, uniform matrix deposition, and high vacuum stability. Moreover, the matrix-related ion signals produced from DHT were reduced by 50 and 71.8% at m/z < 500 compared to the commonly used matrices of 2,5-dihydroxybenzoic acid (DHB) and α-cyano-4-hydroxycinnamic acid (CHCA), respectively, in their respective organic solvents. In terms of quantitative performance, arginine, glutamic acid, glutamine, and proline can be detected with limits of detection of 6, 4, 6, and 4 ng/mL, respectively, using the DHT as the matrix. Using DHT as the matrix, all 20 protein AAs were successfully detected in human serum by MALDI-MS, whereas only 7 and 10 AAs were detected when DHB and CHCA matrices were used, respectively. Furthermore, 20 protein AAs and taurine were successfully detected and imaged in a section of edible Crassostrea gigas (oyster) tissue for the first time. Our study demonstrates that using DHT as a matrix can improve the detection and imaging of AAs in biological samples by MALDI-MS.

Exploration of the Shared Hub Genes and Biological Mechanism in Osteoporosis and Type 2 Diabetes Mellitus based on Machine Learning

A substantial amount of evidence suggests a close relationship between osteoporosis (OP) and Type 2 Diabetes Mellitus (T2DM), but the mechanisms involved remain unknown. Therefore, we conducted this study with the aim of screening for hub genes common to both diseases and conducting a preliminary exploration of common regulatory mechanisms. In the present study, we first screened genes significantly associated with OP and T2DM by the univariate logistic regression algorithm. And then, based on cross-analysis and random forest algorithm, we obtained three hub genes (ACAA2, GATAD2A, and VPS35) and validated the critical roles and predictive performance of the three genes in both diseases by differential expression analysis, receiver operating characteristic (ROC) curves, and genome wide association study (GWAS) analysis. Finally, based on gene set enrichment analysis (GSEA) and the construction of the miRNA-mRNA regulatory network, we conducted a preliminary exploration of the co-regulatory mechanisms of three hub genes in two diseases. In conclusion, this study provides promising biomarkers for predicting and treating both diseases and offers novel directions for exploring the common regulatory mechanisms of both diseases.

Amino acid metabolism dysregulation associated with inflammation and insulin resistance in HIV-infected individuals with metabolic disorders

Amino acid metabolic profile, particularly its association with clinical characteristics, remains unclear in patients with human immunodeficiency virus (HIV) infection and acquired immune deficiency syndrome (AIDS) combined with metabolic disorders. In this study, we performed targeted metabolomic analyses on 64 patients with HIV/AIDS and 21 healthy controls. Twenty-four amino acids and selected intermediate metabolites in the serum were quantitatively detected using high-performance liquid chromatography-tandem mass spectrometry, and characteristic changes and metabolic pathways were analyzed in HIV-infected patients with different degrees of abnormal glucose and lipid metabolism. Spearman's partial correlation was used to analyze the association between amino acids, biochemical parameters, and inflammatory cytokines. The results showed that the main metabolic pathways of the eighteen differential metabolites involved were arginine biosynthesis and metabolism, methionine cycle, and tryptophan metabolism. Fourteen differential amino acid metabolites were positively correlated with nine inflammatory cytokines, including TNF-α, C-reactive protein, IL-1β, and galectin-3 (FDR < 0.1). Kynurenine, ornithine, and homocysteine were positively correlated with fasting blood glucose and insulin resistance index (FDR < 0.1). Our study revealed a multi-pathway imbalance in amino acid metabolism in patients with HIV/AIDS, which was significantly correlated with inflammation and insulin resistance.

Reinstating acute-phase insulin release among sedentary adults at high risk for type 2 diabetes with Yoga and Walking based lifestyle modification

BACKGROUND

We compared the impact of 2 lifestyle modifying physical activities, yoga (YBLM) or walking (WBLM) on impaired beta cell function (IBF) or insulin resistance (IR) in restoring acute phase insulin release (APIR) among prediabetes at high risk for type 2 diabetes (HRDM).

METHOD

Male and female adults (N = 42, 38.1 ± 4.8 years) with abdominal obesity (Male:103 ± 8.1 cm) Female: 92 ± 11.1 cm), randomized into YBLM (n = 20) and WBLM (n = 22), were monitored for the practice of the intervention, 45 min a day/5 days a week for 12 weeks. Blood sample was collected at 0th minute to estimate the level of Fasting Blood Glucose (FBG), Sr. Insulin and lipid profile and at 10th minute (APIR). IBF, IR and sensitivity (IS) reading were checked in HOMA-2 calculator.

RESULT

Wilcoxon sign rank t-test denoted an improved APIR among the subjects with IBF (p = 0.008) and not among the subjects with IR (p = 0.332). However, regression analysis yielded an improved APIR among subjects with IBF (F(1,10) = 7.816, p = 0.002) with the management of body weight and lipid profile and IR (F(1,13) = 17.003, p = 0.001) being found influenced with selected lipid components. In all, during the post assessment period, an impressive boost in APIR was manifested among people shifted to Normoglycemia (n = 14, 35.9%).

CONCLUSION

Intriguingly, we postulate the potential role of YBLM over WBLM in the management of lipid profile and body weight on accelerating APIR either through improved Beta cell compensation or by sensitizing skeletal muscles regulating IR, helping improve glucose tolerance resulting in either remission or management of prediabetes.

The Regulation and Secretion of Glucagon in Response to Nutrient Composition: Unraveling Their Intricate Mechanisms

Glucagon was initially regarded as a hyperglycemic substance; however, recent research has revealed its broader role in metabolism, encompassing effects on glucose, amino acids (AAs), and lipid metabolism. Notably, the interplay of glucagon with nutrient intake, particularly of AAs, and non-nutrient components is central to its secretion. Fasting and postprandial hyperglucagonemia have long been linked to the development and progression of type 2 diabetes (T2DM). However, recent studies have brought to light the positive impact of glucagon agonists on lipid metabolism and energy homeostasis. This review explores the multifaceted actions of glucagon, focusing on its regulation, signaling pathways, and effects on glucose, AAs, and lipid metabolism. The interplay between glucagon and other hormones, including insulin and incretins, is examined to provide a mechanistic understanding of its functions. Notably, the liver-α-cell axis, which involves glucagon and amino acids, emerges as a critical aspect of metabolic regulation. The dysregulation of glucagon secretion and its impact on conditions such as T2DM are discussed. The review highlights the potential therapeutic applications of targeting the glucagon pathway in the treatment of metabolic disorders.

Dietary amino acids and anthropometric indices: Tehran Lipid and Glucose Study

Objective

Recent studies investigated the role of amino acids (AAs) in weight management. We aimed to determine the association between AAs and three-year change of anthropometric indices and incident obesity.

Materials and methods

Height, weight, hip, and waist circumference (WC) were collected at baseline and follow up. Three-year changes in anthropometric indices and obesity incident according to body mass index (BMI) (overweight & obesity) and WC cutoffs (obesity-WC) were ascertained. Dietary intakes of AAs were collected at baseline, using a food frequency questionnaire. Data analyses were conducted on 4976 adult participants and two subsamples, including 1,570 and 2,918 subjects, for assessing the AAs relationship with 3-year changes on anthropometric indices and obesity incident.

Results

Lysine and aspartic acid were positively associated with higher weight change, whereas acidic AAs, cysteine, and glutamic acid showed a negative correlation with weight change. Furthermore, a weak positive correlation was shown for alkaline AAs, lysine, and valine with WC; however, acidic AAs, tryptophan, cysteine, and glutamic acid were negatively associated with WC. Aromatic and acidic AAs also demonstrated a weak negative relation with changes in BAI. Phenylalanine and Aromatic AAs showed a negative association with overweight &obesity incidence adjusting for potential confounders. Each quartile increases the dietary lysine, arginine, alanine, methionine, aspartic acid, and alkaline AAs related to a greater risk of obesity-WC, while tryptophan, glutamic acid, proline, and acidic AAs associated with lower obesity-WC risk.

Conclusion

Our results suggested that certain dietary AAs may potentially change anthropometric indices and risk of obesity incident.

A holistic view of muscle metabolic reprogramming through personalized metabolic modeling in newly diagnosed diabetic patients

Type 2 diabetes mellitus (T2DM) is a challenging and progressive metabolic disease caused by insulin resistance. Skeletal muscle is the major insulin-sensitive tissue that plays a pivotal role in blood sugar homeostasis. Dysfunction of muscle metabolism is implicated in the disturbance of glucose homeostasis, the development of insulin resistance, and T2DM. Understanding metabolism reprogramming in newly diagnosed patients provides opportunities for early diagnosis and treatment of T2DM as a challenging disease to manage. Here, we applied a system biology approach to investigate metabolic dysregulations associated with the early stage of T2DM. We first reconstructed a human muscle-specific metabolic model. The model was applied for personalized metabolic modeling and analyses in newly diagnosed patients. We found that several pathways and metabolites, mainly implicating in amino acids and lipids metabolisms, were dysregulated. Our results indicated the significance of perturbation of pathways implicated in building membrane and extracellular matrix (ECM). Dysfunctional metabolism in these pathways possibly interrupts the signaling process and develops insulin resistance. We also applied a machine learning method to predict potential metabolite markers of insulin resistance in skeletal muscle. 13 exchange metabolites were predicted as the potential markers. The efficiency of these markers in discriminating insulin-resistant muscle was successfully validated.

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.

Plasma Amino Acids in Early Pregnancy and Midpregnancy and Their Interplay With Phospholipid Fatty Acids in Association With the Risk of Gestational Diabetes Mellitus: Results From a Longitudinal Prospective Cohort

OBJECTIVE

We prospectively evaluated plasma amino acids (AAs) in early pregnancy and midpregnancy and their interplay with phospholipid fatty acids (FAs) in association with gestational diabetes mellitus (GDM) risk.

RESEARCH DESIGN AND METHODS

From a longitudinal pregnancy cohort of 2,802 individuals, concentrations of 24 plasma AAs at 10-14 and 15-26 gestational weeks (GW) were assessed among 107 GDM case subjects and 214 non-GDM control subjects. We estimated adjusted odds ratios (OR) and 95% CI for the associations of plasma AAs and the joint associations of plasma AAs and phospholipid FAs with GDM risk, adjusting for risk factors including age, prepregnancy BMI, and family history of diabetes.

RESULTS

Glycine at 10-14 GW was inversely associated with GDM (adjusted OR [95% CI] per SD increment: 0.55 [0.39-0.79]). Alanine, aspartic acid, and glutamic acid at 10-14 GW were positively associated with GDM (1.43 [1.08-1.88], 1.41 [1.11-1.80], and 1.39 [0.98-1.98]). At 15-26 GW, findings for glycine, alanine, aspartic acid, and the glutamine-to-glutamic acid ratio were consistent with the directions observed at 10-14 GW. Isoleucine, phenylalanine, and tyrosine were positively associated with GDM (1.64 [1.19-2.27], 1.15 [0.87-1.53], and 1.56 [1.16-2.09]). All P values for linear trend were <0.05. Several AAs and phospholipid FAs were significantly and jointly associated with GDM. For instance, the lowest risk was observed among women with higher glycine and lower even-chain saturated FAs at 10-14 GW (adjusted OR [95% CI] 0.15 [0.06, 0.37]).

CONCLUSIONS

Plasma AAs may be implicated in GDM development starting in early pregnancy. Associations of AAs with GDM may be enhanced in the copresence of phospholipid FA profile.

Chronometabolism: The Timing of the Consumption of Meals Has a Greater Influence Than Glycemic Index (GI) on the Postprandial Metabolome

Eating late in the day is associated with circadian desynchrony, resulting in dysregulated metabolism and increased cardiometabolic disease risk. However, the underlying mechanisms remain unclear. Using targeted metabolomics of postprandial plasma samples from a secondary analysis of a randomised 2 × 2 crossover study in 36 healthy older Chinese adults, we have compared postprandial metabolic responses between high (HI) glycemic index (GI) or low-GI (LO) meals, consumed either at breakfast (BR) or at dinner (DI). 29 out of 234 plasma metabolites exhibited significant differences (p < 0.05) in postprandial AUC between BR and DI sessions, whereas only five metabolites were significantly different between HI and LO sessions. There were no significant interactions between intake timing and meal GI. Lower glutamine: glutamate ratio, lower lysine and higher trimethyllysine (TML) levels were found during DI compared with BR, along with greater postprandial reductions (δAUC) in creatine and ornithine levels during DI, indicating a worse metabolic state during the evening DI period. Greater reductions (δAUC) in postprandial creatine and ornithine were also observed during HI compared with LO (both p < 0.05). These metabolomic changes may indicate potential molecular signatures and/or pathways linking metabolic responses with cardiometabolic disease risk between different meal intake timings and/or meals with variable GI.

Short communication: unique metabolic signature of proliferative retinopathy in the tear fluid of diabetic patients with comorbidities - preliminary data for PPPM validation

Type 2 diabetes (T2DM) defined as the adult-onset type that is primarily not insulin-dependent, comprises over 95% of all diabetes mellitus (DM) cases. According to global records, 537 million adults aged 20-79 years are affected by DM that means at least 1 out of 15 persons. This number is projected to grow by 51% by the year 2045. One of the most common complications of T2DM is diabetic retinopathy (DR) with an overall prevalence over 30%. The total number of the DR-related visual impairments is on the rise, due to the growing T2DM population. Proliferative diabetic retinopathy (PDR) is the progressing DR and leading cause of preventable blindness in working-age adults. Moreover, PDR with characteristic systemic attributes including mitochondrial impairment, increased cell death and chronic inflammation, is an independent predictor of the cascading DM-complications such as ischemic stroke. Therefore, early DR is a reliable predictor appearing upstream of this "domino effect". Global screening, leading to timely identification of DM-related complications, is insufficiently implemented by currently applied reactive medicine. A personalised predictive approach and cost-effective targeted prevention shortly - predictive, preventive and personalised medicine (PPPM / 3PM) could make a good use of the accumulated knowledge, preventing blindness and other severe DM complications. In order to reach this goal, reliable stage- and disease-specific biomarker panels are needed characterised by an easy way of the sample collection, high sensitivity and specificity of analyses. In the current study, we tested the hypothesis that non-invasively collected tear fluid is a robust source for the analysis of ocular and systemic (DM-related complications) biomarker patterns suitable for differential diagnosis of stable DR versus PDR. Here, we report the first results of the comprehensive ongoing study, in which we correlate individualised patient profiles (healthy controls versus patients with stable D as well as patients with PDR with and without co-morbidities) with their metabolic profiles in the tear fluid. Comparative mass spectrometric analysis performed has identified following metabolic clusters which are differentially expressed in the groups of comparison: acylcarnitines, amino acid & related compounds, bile acids, ceramides, lysophosphatidyl-choline, nucleobases & related compounds, phosphatidyl-cholines, triglycerides, cholesterol esters, and fatty acids. Our preliminary data strongly support potential clinical utility of metabolic patterns in the tear fluid indicating a unique metabolic signature characteristic for the DR stages and PDR progression. This pilot study creates a platform for validating the tear fluid biomarker patterns to stratify T2DM-patients predisposed to the PDR. Moreover, since PDR is an independent predictor of severe T2DM-related complications such as ischemic stroke, our international project aims to create an analytical prototype for the "diagnostic tree" (yes/no) applicable to healthrisk assessment in diabetes care.

Solvent Effects Used for Optimal Simultaneous Analysis of Amino Acids via 19F NMR Spectroscopy

¹⁹F NMR has been extensively used in simultaneous analysis of multicomponent due to its 100% natural isotope abundance, high NMR-sensitivity, and wide-range chemical shifts. The solvent effects are usually observed in NMR spectroscopy and cause large changes in ¹⁹F chemical shifts. Herein, we propose that the simultaneous analysis of a complex mixture can be achieved using solvent effects via ¹⁹F NMR spectroscopy, such as a mixture solution of amino acids (AAs). AAs are not only cell-signaling molecules, but are also considered as biomarkers of some diseases. Hence, the analysis of AAs is important for human health and the diagnosis of diseases. In this work, the key to the success of sensing 19 biogenic AAs is the use of 2-fluorobenzaldehyde (2FBA) as a highly sensitive derivatizing agent and solvent effects to produce distinguishable ¹⁹F NMR signals. As a result, the resolution of ¹⁹F NMR spectroscopy of multiple 2FBA-labeled AAs is obviously higher than other methods based on ¹⁹F NMR. Moreover, 14 and 18 AAs can be satisfactorily differentiated and unambiguously identified in different complicated media supporting the growth of mammalian cells. Furthermore, quantification of the concentration of AAs can be made, and the limit of detection reaches 10 μM. Our work provides new insights into the simultaneous analysis of a multicomponent mixture based on solvent effects by ¹⁹F NMR spectroscopy.

Identification of FGF13 as a Potential Biomarker and Target for Diagnosis of Impaired Glucose Tolerance

Early identification of pre-diabetes provides an opportunity for intervention and treatment to delay its progression to type 2 diabetes mellitus (T2DM). We aimed to identify the biomarkers of impaired glucose tolerance (IGT) through bioinformatics analysis. The GSE76896 dataset, including non-diabetic (ND), IGT, and T2DM clinical samples, was deeply analyzed to identify 309 Co-DEGs for IGT and T2DM. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses indicated that inflammatory responses and the PI3K-AKT signaling pathway are important patho-physiological features of IGT and T2DM. Protein-protein interaction (PPI) network analysis and cytoHubba technolgy identified seven hub genes: namely, CCL2, CXCL1, CXCL8, EDN1, FGF13, MMP1, and NGF. The expression and ROC curves of these hub genes were validated using the GSE38642 dataset. Through an immunofluorescence assay, we found that the expression of FGF13 in islets of mice in the HFD and T2DM groups was significantly lower than in the control group. Similarly, the level of FGF13 in the sera of IGT and T2DM patients was lower than that in the healthy group. Together, these results suggest that FGF13 can be treated as a novel biomarker of IGT, which may provide new targets for the diagnosis and treatment of pre-diabetes and T2DM.

Role of Impaired Glycolysis in Perturbations of Amino Acid Metabolism in Diabetes Mellitus

The most frequent alterations in plasma amino acid concentrations in type 1 and type 2 diabetes are decreased L-serine and increased branched-chain amino acid (BCAA; valine, leucine, and isoleucine) levels. The likely cause of L-serine deficiency is decreased synthesis of 3-phosphoglycerate, the main endogenous precursor of L-serine, due to impaired glycolysis. The BCAA levels increase due to decreased supply of pyruvate and oxaloacetate from glycolysis, enhanced supply of NADH + H⁺ from beta-oxidation, and subsequent decrease in the flux through the citric acid cycle in muscles. These alterations decrease the supply of α-ketoglutarate for BCAA transamination and the activity of branched-chain keto acid dehydrogenase, the rate-limiting enzyme in BCAA catabolism. L-serine deficiency contributes to decreased synthesis of phospholipids and increased synthesis of deoxysphinganines, which play a role in diabetic neuropathy, impaired homocysteine disposal, and glycine deficiency. Enhanced BCAA levels contribute to increased levels of aromatic amino acids (phenylalanine, tyrosine, and tryptophan), insulin resistance, and accumulation of various metabolites, whose influence on diabetes progression is not clear. It is concluded that amino acid concentrations should be monitored in patients with diabetes, and systematic investigation is needed to examine the effects of L-serine and glycine supplementation on diabetes progression when these amino acids are decreased.

The relationship between glucose and the liver-alpha cell axis - A systematic review

Until recently, glucagon was considered a mere antagonist to insulin, protecting the body from hypoglycemia. This notion changed with the discovery of the liver-alpha cell axis (LACA) as a feedback loop. The LACA describes how glucagon secretion and pancreatic alpha cell proliferation are stimulated by circulating amino acids. Glucagon in turn leads to an upregulation of amino acid metabolism and ureagenesis in the liver. Several increasingly common diseases (e.g., non-alcoholic fatty liver disease, type 2 diabetes, obesity) disrupt this feedback loop. It is important for clinicians and researchers alike to understand the liver-alpha cell axis and the metabolic sequelae of these diseases. While most of previous studies have focused on fasting concentrations of glucagon and amino acids, there is limited knowledge of their dynamics after glucose administration. The authors of this systematic review applied PRISMA guidelines and conducted PubMed searches to provide results of 8078 articles (screened and if relevant, studied in full). This systematic review aims to provide better insight into the LACA and its mediators (amino acids and glucagon), focusing on the relationship between glucose and the LACA in adult and pediatric subjects.

The crucial role and mechanism of insulin resistance in metabolic disease

Insulin resistance (IR) plays a crucial role in the development and progression of metabolism-related diseases such as diabetes, hypertension, tumors, and nonalcoholic fatty liver disease, and provides the basis for a common understanding of these chronic diseases. In this study, we provide a systematic review of the causes, mechanisms, and treatments of IR. The pathogenesis of IR depends on genetics, obesity, age, disease, and drug effects. Mechanistically, any factor leading to abnormalities in the insulin signaling pathway leads to the development of IR in the host, including insulin receptor abnormalities, disturbances in the internal environment (regarding inflammation, hypoxia, lipotoxicity, and immunity), metabolic function of the liver and organelles, and other abnormalities. The available therapeutic strategies for IR are mainly exercise and dietary habit improvement, and chemotherapy based on biguanides and glucagon-like peptide-1, and traditional Chinese medicine treatments (e.g., herbs and acupuncture) can also be helpful. Based on the current understanding of IR mechanisms, there are still some vacancies to follow up and consider, and there is also a need to define more precise biomarkers for different chronic diseases and lifestyle interventions, and to explore natural or synthetic drugs targeting IR treatment. This could enable the treatment of patients with multiple combined metabolic diseases, with the aim of treating the disease holistically to reduce healthcare expenditures and to improve the quality of life of patients to some extent.

Specific Alteration of Branched-Chain Amino Acid Profile in Polycystic Ovary Syndrome

Polycystic ovary syndrome (PCOS) is one of the most common endocrinopathies in reproductive age women; it is a complex health issue with numerous comorbidities. Attention has recently been drawn to amino acids as they are molecules essential to maintain homeostasis. The aim of the study was to investigate the branch chain amino acid (BCAA) profile in women with PCOS. A total of 326 women, 208 diagnosed with PCOS and 118 healthy controls, participated in the study; all the patients were between 18 and 40 years old. Anthropometrical, biochemical and hormonal parameters were assessed. Gas-liquid chromatography combined with tandem mass spectrometry was used to investigate BCAA levels. Statistical analysis showed significantly higher plasma levels of BCAAs (540.59 ± 97.23 nmol/mL vs. 501.09 ± 85.33 nmol/mL; p < 0.001) in women with PCOS. Significant correlations (p < 0.05) were found between BCAA and BMI, HOMA-IR, waist circumference and total testosterone levels. In the analysis of individuals with abdominal obesity, there were significant differences between PCOS and controls in BCAA (558.13 ± 100.51 vs. 514.22 ± 79.76 nmol/mL) and the concentrations of all the analyzed amino acids were higher in the PCOS patients. Hyperandrogenemia in PCOS patients was associated with significantly higher leucine, isoleucine and total BCAA levels. The increase of BCAA levels among PCOS patients in comparison to healthy controls might be an early sign of metabolic alteration and a predictive factor for other disturbances.

Obesity and type 2 diabetes mellitus: connections in epidemiology, pathogenesis, and treatments

The prevalence of obesity and diabetes mellitus (DM) has been consistently increasing worldwide. Sharing powerful genetic and environmental features in their pathogenesis, obesity amplifies the impact of genetic susceptibility and environmental factors on DM. The ectopic expansion of adipose tissue and excessive accumulation of certain nutrients and metabolites sabotage the metabolic balance via insulin resistance, dysfunctional autophagy, and microbiome-gut-brain axis, further exacerbating the dysregulation of immunometabolism through low-grade systemic inflammation, leading to an accelerated loss of functional β-cells and gradual elevation of blood glucose. Given these intricate connections, most available treatments of obesity and type 2 DM (T2DM) have a mutual effect on each other. For example, anti-obesity drugs can be anti-diabetic to some extent, and some anti-diabetic medicines, in contrast, have been shown to increase body weight, such as insulin. Meanwhile, surgical procedures, especially bariatric surgery, are more effective for both obesity and T2DM. Besides guaranteeing the availability and accessibility of all the available diagnostic and therapeutic tools, more clinical and experimental investigations on the pathogenesis of these two diseases are warranted to improve the efficacy and safety of the available and newly developed treatments.

Optimization of a GC-MS method for the profiling of microbiota-dependent metabolites in blood samples: An application to type 2 diabetes and prediabetes

Changes in serum or plasma metabolome may reflect gut microbiota dysbiosis, which is also known to occur in patients with prediabetes and type 2 diabetes (T2DM). Thus, developing a robust method for the analysis of microbiota-dependent metabolites (MDMs) is an important issue. Gas chromatography with mass spectrometry (GC–MS) is a powerful approach enabling detection of a wide range of MDMs in biofluid samples with good repeatability and reproducibility, but requires selection of a suitable solvents and conditions. For this reason, we conducted for the first time the study in which, we demonstrated an optimisation of samples preparation steps for the measurement of 75 MDMs in two matrices. Different solvents or mixtures of solvents for MDMs extraction, various concentrations and volumes of derivatizing reagents as well as temperature programs at methoxymation and silylation step, were tested. The stability, repeatability and reproducibility of the 75 MDMs measurement were assessed by determining the relative standard deviation (RSD). Finally, we used the developed method to analyse serum samples from 18 prediabetic (PreDiab group) and 24 T2DM patients (T2DM group) from our 1000PLUS cohort. The study groups were homogeneous and did not differ in age and body mass index. To select statistically significant metabolites, T2DM vs. PreDiab comparison was performed using multivariate statistics. Our experiment revealed changes in 18 MDMs belonging to different classes of compounds, and seven of them, based on the SVM classification model, were selected as a panel of potential biomarkers, able to distinguish between patients with T2DM and prediabetes.

Plasma Metabolic Signatures of Healthy Overweight Subjects Challenged With an Oral Glucose Tolerance Test

Insulin secretion following ingestion of a carbohydrate load affects a multitude of metabolic pathways that simultaneously change direction and quantity of interorgan fluxes of sugars, lipids and amino acids. In the present study, we aimed at identifying markers associated with differential responses to an OGTT a population of healthy adults. By use of three metabolite profiling platforms, we assessed these postprandial responses of a total of 202 metabolites in plasma of 72 healthy volunteers undergoing comprehensive phenotyping and of which half enrolled into a weight-loss program over a three-month period. A standard oral glucose tolerance test (OGTT) served as dietary challenge test to identify changes in postprandial metabolite profiles. Despite classified as healthy according to WHO criteria, two discrete clusters (A and B) were identified based on the postprandial glucose profiles with a balanced distribution of volunteers based on gender and other measures. Cluster A individuals displayed 26% higher postprandial glucose levels, delayed glucose clearance and increased fasting plasma concentrations of more than 20 known biomarkers of insulin resistance and diabetes previously identified in large cohort studies. The volunteers identified by canonical postprandial responses that form cluster A may be called pre-pre-diabetics and defined as “at risk” for development of insulin resistance. Moreover, postprandial changes in selected fatty acids and complex lipids, bile acids, amino acids, acylcarnitines and sugars like mannose revealed marked differences in the responses seen in cluster A and cluster B individuals that sustained over the entire challenge test period of 240 min. Almost all metabolites, including glucose and insulin, returned to baseline values at the end of the test (at 240 min), except a variety of amino acids and here those that have been linked to diabetes development. Analysis of the corresponding metabolite profile in a fasting blood sample may therefore allow for early identification of these subjects at risk for insulin resistance without the need to undergo an OGTT.

Targeted metabolomics analysis of amino acids and acylcarnitines as risk markers for diabetes by LC-MS/MS technique

Diabetes is a common chronic disease affecting millions of people worldwide. It underlies various complications and imposes many costs on individuals and society. Discovering early diagnostic biomarkers takes excellent insight into preventive plans and the best use of interventions. Therefore, in the present study, we aimed to evaluate the association between the level of amino acids and acylcarnitines and diabetes to develop diabetes predictive models. Using the targeted LC-MS/MS technique, we analyzed fasting plasma samples of 206 cases and 206 controls that were matched by age, sex, and BMI. The association between metabolites and diabetes was evaluated using univariate and multivariate regression analysis with adjustment for systolic and diastolic blood pressure and lipid profile. To deal with multiple comparisons, factor analysis was used. Participants' average age and BMI were 61.6 years, 28.9 kg/m2, and 55% were female. After adjustment, Factor 3 (tyrosine, valine, leucine, methionine, tryptophan, phenylalanine), 5 (C3DC, C5, C5OH, C5:1), 6 (C14OH, C16OH, C18OH, C18:1OH), 8 (C2, C4OH, C8:1), 10 (alanine, proline) and 11 (glutamic acid, C18:2OH) were positively associated with diabetes. Inline, factor 9 (C4DC, serine, glycine, threonine) and 12 (citrulline, ornithine) showed a reverse trend. Some amino acids and acylcarnitines were found as potential risk markers for diabetes incidents that reflected the disturbances in the several metabolic pathways among the diabetic population and could be targeted to prevent, diagnose, and treat diabetes.

Genetic variants in ALDH1L1 and GLDC influence the serine-to-glycine ratio in Hispanic children

BACKGROUND

Glycine is a proteogenic amino acid that is required for numerous metabolic pathways, including purine, creatine, heme, and glutathione biosynthesis. Glycine formation from serine, catalyzed by serine hydroxy methyltransferase, is the major source of this amino acid in humans. Our previous studies in a mouse model have shown a crucial role for the 10-formyltetrahydrofolate dehydrogenase enzyme in serine-to-glycine conversion.

OBJECTIVES

We sought to determine the genomic influence on the serine-glycine ratio in 803 Hispanic children from 319 families of the Viva La Familia cohort.

METHODS

We performed a genome-wide association analysis for plasma serine, glycine, and the serine-glycine ratio in Sequential Oligogenic Linkage Analysis Routines while accounting for relationships among family members.

RESULTS

All 3 parameters were significantly heritable (h2 = 0.22-0.78; P < 0.004). The strongest associations for the serine-glycine ratio were with single nucleotide polymorphisms (SNPs) in aldehyde dehydrogenase 1 family member L1 (ALDH1L1) and glycine decarboxylase (GLDC) and for glycine with GLDC (P < 3.5 × 10-8; effect sizes, 0.03-0.07). No significant associations were found for serine. We also conducted a targeted genetic analysis with ALDH1L1 exonic SNPs and found significant associations between the serine-glycine ratio and rs2886059 (β = 0.68; SE, 0.25; P = 0.006) and rs3796191 (β = 0.25; SE, 0.08; P = 0.003) and between glycine and rs3796191 (β = -0.08; SE, 0.02; P = 0.0004). These exonic SNPs were further associated with metabolic disease risk factors, mainly adiposity measures (P < 0.006). Significant genetic and phenotypic correlations were found for glycine and the serine-glycine ratio with metabolic disease risk factors, including adiposity, insulin sensitivity, and inflammation-related phenotypes [estimate of genetic correlation = -0.37 to 0.35 (P < 0.03); estimate of phenotypic correlation = -0.19 to 0.13 (P < 0.006)]. The significant genetic correlations indicate shared genetic effects among glycine, the serine-glycine ratio, and adiposity and insulin sensitivity phenotypes.

CONCLUSIONS

Our study suggests that ALDH1L1 and GLDC SNPs influence the serine-to-glycine ratio and metabolic disease risk.

Metabolomic Analysis of Serum and Tear Samples from Patients with Obesity and Type 2 Diabetes Mellitus

Metabolomics strategies are widely used to examine obesity and type 2 diabetes (T2D). Patients with obesity (n = 31) or T2D (n = 26) and sex- and age-matched controls (n = 28) were recruited, and serum and tear samples were collected. The concentration of 23 amino acids and 10 biogenic amines in serum and tear samples was analyzed. Statistical analysis and Pearson correlation analysis along with network analysis were carried out. Compared to controls, changes in the level of 6 analytes in the obese group and of 10 analytes in the T2D group were statistically significant. For obesity, the energy generation, while for T2D, the involvement of NO synthesis and its relation to insulin signaling and inflammation, were characteristic. We found that BCAA and glutamine metabolism, urea cycle, and beta-oxidation make up crucial parts of the metabolic changes in T2D. According to our data, the retromer-mediated retrograde transport, the ethanolamine metabolism, and, consequently, the endocannabinoid signaling and phospholipid metabolism were characteristic of both conditions and can be relevant pathways to understanding and treating insulin resistance. By providing potential therapeutic targets and new starting points for mechanistic studies, our results emphasize the importance of complex data analysis procedures to better understand the pathomechanism of obesity and diabetes.

Integrating network pharmacology and non-targeted metabolomics to explore the common mechanism of Coptis Categorized Formula improving T2DM zebrafish

ETHNOPHARMACOLOGICAL RELEVANCE

Coptis Categorized Formula (CCF) is one of the core prescriptions in Treatise on Febrile Diseases. Its efficacy can be available not only in exogenous diseases but widely in various internal injuries and miscellaneous diseases. CCF (i.e., Huanglian Jiedu Decoction, Huanglian Ejiao Decoction, Dahuang Huanglian Xiexin Decoction, Gegen Qinlian Decoction) is different in composition, but they all play a favorable role in curative effect on type 2 diabetes mellitus (T2DM). Therefore, it is of great significance to reveal the common mechanism of CCF in treating T2DM.

AIM OF THE STUDY

Based on network pharmacology and non-targeted metabolomics research strategy, the common mechanism of the CCF treating T2DM was discussed.

MATERIALS AND METHODS

Firstly, Ultra-high performance liquid chromatography-quadrupole-time of flight/mass spectrometry was used to identify the chemical constituents of the CCF. Then, the targets of these chemical components were used for network pharmacology analysis associated with therapeutic effect. Finally, the diabetic zebrafish model was constructed to further verify the common mechanism of the CCF in treating T2DM.

RESULTS

A total of 160 chemical compositions were identified and 16 of them were common chemical compositions of the four CCF, including berberine, baicalin, coptisine and so forth. Network pharmacology results showed that Dipeptidyl peptidase (DPP)-4, cysteinyl aspartate specific proteinase (CASP)3, nitric oxide synthase (NOS)2, NOS3, and other 37 targets were common targets of CCF, and advanced glycation end products (AGE)-receptor of advanced glycation end products (RAGE) signaling pathway in diabetic complications, mitogen-activated protein kinase (MAPK) signaling pathway and hypoxia inducible factor (HIF)-1 signaling pathway were critical pathways of four CCF in the treatment of T2DM. CCF can lessen the blood glucose of diabetic zebrafish. The contents of 25 differential metabolites in diabetic zebrafish were altered. These metabolites were mainly related to phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arachidonic acid metabolism, sphingolipid metabolism, and tyrosine metabolism.

CONCLUSION

Our research shows that the common mechanism of CCF in improving T2DM is as follows: berberine, baicalin, coptisine and other chemical components can directionally regulate DPP-4, CASP3, NOS2, NOS3 and other targets, which are mediated by AGE-RAGE signaling pathway in diabetic complications, MAPK signaling pathway and HIF-1 signaling pathway. The content of endogenous metabolites such as L-valine and L-sorbitose changes, and further regulates the metabolism of amino acid metabolism, lipid metabolism, purine metabolism, sphingosine metabolism and arachidonic acid metabolism, so as to play a significant role in regulating glycolipid metabolism, improving insulin resistance, inhibiting cell apoptosis, anti-oxidation and anti-inflammation, and finally ameliorating T2DM.

Proteomic Analysis of Mouse Kidney Tissue Associates Peroxisomal Dysfunction with Early Diabetic Kidney Disease

Background: The absence of efficient inhibitors for diabetic kidney disease (DKD) progression reflects the gaps in our understanding of DKD molecular pathogenesis. Methods: A comprehensive proteomic analysis was performed on the glomeruli and kidney cortex of diabetic mice with the subsequent validation of findings in human biopsies and omics datasets, aiming to better understand the underlying molecular biology of early DKD development and progression. Results: LC–MS/MS was employed to analyze the kidney proteome of 2 DKD models: Ins2Akita (early and late DKD) and db/db mice (late DKD). The abundance of detected proteins was defined. Pathway analysis of differentially expressed proteins in the early and late DKD versus the respective controls predicted dysregulation in DKD hallmarks (peroxisomal lipid metabolism and β-oxidation), supporting the functional relevance of the findings. Comparing the observed protein changes in early and late DKD, the consistent upregulation of 21 and downregulation of 18 proteins was detected. Among these were downregulated peroxisomal and upregulated mitochondrial proteins. Tissue sections from 16 DKD patients were analyzed by IHC confirming our results. Conclusion: Our study shows an extensive differential expression of peroxisomal proteins in the early stages of DKD that persists regardless of the disease severity, providing new perspectives and potential markers of diabetic kidney dysfunction.