Metabolic signatures and risk of type 2 diabetes in a Chinese population: an untargeted metabolomics study using both LC-MS and GC-MS

Genome-scale community modelling elucidates the metabolic interaction in Indian type-2 diabetic gut microbiota

Type-2 diabetes (T2D) is a rapidly growing multifactorial metabolic disorder that induces the onset of various diseases in the human body. The compositional and metabolic shift of the gut microbiota is a crucial factor behind T2D. Hence, gaining insight into the metabolic profile of the gut microbiota is essential for revealing their role in regulating the metabolism of T2D patients. Here, we have focused on the genome-scale community metabolic model reconstruction of crucial T2D-associated gut microbes. The model-based analysis of biochemical flux in T2D and healthy gut conditions showed distinct biochemical signatures and diverse metabolic interactions in the microbial community. The metabolic interactions encompass cross-feeding of short-chain fatty acids, amino acids, and vitamins among individual microbes within the community. In T2D conditions, a reduction in the metabolic flux of acetate, butyrate, vitamin B5, and bicarbonate was observed in the microbial community model, which can impact carbohydrate metabolism. The decline in butyrate levels is correlated with both insulin resistance and diminished glucose metabolism in T2D patients. Compared to the healthy gut, an overall reduction in glucose consumption and SCFA production flux was estimated in the T2D gut environment. Moreover, the decreased consumption profiles of branch chain amino acids (BCAAs) and aromatic amino acids (AAAs) in the T2D gut microbiota can be a distinct biomarker for T2D. Hence, the flux-level analysis of the microbial community model can provide insights into the metabolic reprogramming in diabetic gut microbiomes, which may be helpful in personalized therapeutics and diet design against T2D.

Unveiling the metabolic landscape of pulmonary hypertension: insights from metabolomics

Pulmonary hypertension (PH) is regarded as cardiovascular disease with an extremely poor prognosis, primarily due to irreversible vascular remodeling. Despite decades of research progress, the absence of definitive curative therapies remains a critical challenge, leading to high mortality rates. Recent studies have shown that serious metabolic disorders generally exist in PH animal models and patients of PH, which may be the cause or results of the disease. It is imperative for future research to identify critical biomarkers of metabolic dysfunction in PH pathophysiology and to uncover metabolic targets that could enhance diagnostic and therapeutic strategies. Metabolomics offers a powerful tool for the comprehensive qualitative and quantitative analysis of metabolites within specific organisms or cells. On the basis of the findings of the metabolomics research on PH, this review summarizes the latest research progress on metabolic pathways involved in processes such as amino acid metabolism, carbohydrate metabolism, lipid metabolism, and nucleotide metabolism in the context of PH.

Metabolomics Characterization of Disease Markers in Diabetes and Its Associated Pathologies

With the change in lifestyle of people, there has been a considerable increase in diabetes, which brings with it certain follow-up pathological conditions, which lead to a substantial medical burden. Identifying biomarkers that aid in screening, diagnosis, and prognosis of diabetes and its associated pathologies would help better patient management and facilitate a personalized treatment approach for prevention and treatment. With the advancement in techniques and technologies, metabolomics has emerged as an omics approach capable of large-scale high throughput data analysis and identifying and quantifying metabolites that provide an insight into the underlying mechanism of the disease and its progression. Diabetes and metabolomics keywords were searched in correspondence with the assigned keywords, including kidney, cardiovascular diseases and critical illness from PubMed and Scopus, from its inception to Dec 2023. The relevant studies from this search were extracted and included in the study. This review is focused on the biomarkers identified in diabetes, diabetic kidney disease, diabetes-related development of CVD, and its role in critical illness.

Therapeutic implications of dapagliflozin on the metabolomics profile of diabetic rats: A GC-MS investigation coupled with multivariate analysis

BACKGROUND

Diabetes mellitus is a complex metabolic disorder with systemic implications, necessitating the search for reliable biomarkers and therapeutic strategies. This study investigates the metabolomics profile alterations in diabetic rats, with a focus on the therapeutic effects of Dapagliflozin, a drug known to inhibit renal glucose reabsorption, using Gas Chromatography-Mass Spectrometry analysis.

METHODS

A GC-MS based metabolomics approach combined with multivariate and univariate statistical analyses was utilized to study serum samples from a diabetic model of Wistar rats, treated with dapagliflozin. Metabolomics pathways analysis was also performed to identify the altered metabolic pathways associated with the disease and the intervention.

RESULTS

Dapagliflozin treatment in diabetic rats resulted in normalized levels of metabolites associated with insulin resistance, notably branched-chain and aromatic amino acids. Improvements in glycine metabolism were observed, suggesting a modulatory role of the drug. Additionally, reduced palmitic acid levels indicated an alleviation of lipotoxic effects. The metabolic changes indicate a restorative effect of dapagliflozin on diabetes-induced metabolic perturbations.

CONCLUSIONS

The comprehensive metabolomics analysis demonstrated the potential of GC-MS in revealing significant metabolic pathway alterations due to dapagliflozin treatment in diabetic model rats. The therapy induced normalization of key metabolic disturbances, providing insights that could advance personalized diabetes mellitus management and therapeutic monitoring, highlighting the utility of metabolomics in understanding drug mechanisms and effects.

Adipose tissue-derived metabolite risk scores and risk for type 2 diabetes in South Asians

BACKGROUND

South Asians are at higher risk for type 2 diabetes (T2D) than many other race/ethnic groups. Ectopic adiposity, specifically hepatic steatosis and visceral fat may partially explain this. Our objective was to derive metabolite risk scores for ectopic adiposity and assess associations with incident T2D in South Asians.

METHODS

We examined 550 participants in the Mediators of Atherosclerosis in South Asians Living in America (MASALA) cohort study aged 40-84 years without known cardiovascular disease or T2D and with metabolomic data. Computed tomography scans at baseline assessed hepatic attenuation and visceral fat area, and fasting serum specimens at baseline and after 5 years assessed T2D. LC-MS-based untargeted metabolomic analysis was performed followed by targeted integration and reporting of known signals. Elastic net regularized linear regression analyses was used to derive risk scores for hepatic steatosis and visceral fat using weighted coefficients. Logistic regression models associated metabolite risk score and incident T2D, adjusting for age, gender, study site, BMI, physical activity, diet quality, energy intake and use of cholesterol-lowering medication.

RESULTS

Average age of participants was 55 years, 36% women with an average body mass index (BMI) of 25 kg/m2 and 6% prevalence of hepatic steatosis, with 47 cases of incident T2D at 5 years. There were 445 metabolites of known identity. Of these, 313 metabolites were included in the MET-Visc score and 267 in the MET-Liver score. In most fully adjusted models, MET-Liver (OR 2.04 [95% CI 1.38, 3.03]) and MET-Visc (OR 2.80 [1.75, 4.46]) were associated with higher odds of T2D. These associations remained significant after adjustment for measured adiposity.

CONCLUSIONS

Metabolite risk scores for intrahepatic fat and visceral fat were strongly related to incident T2D independent of measured adiposity. Use of these biomarkers to target risk stratification may help capture pre-clinical metabolic abnormalities.

Untargeted metabolomic profiling reveals molecular signatures associated with type 2 diabetes in Nigerians

BACKGROUND

Type 2 diabetes (T2D) has reached epidemic proportions globally, including in Africa. However, molecular studies to understand the pathophysiology of T2D remain scarce outside Europe and North America. The aims of this study are to use an untargeted metabolomics approach to identify: (a) metabolites that are differentially expressed between individuals with and without T2D and (b) a metabolic signature associated with T2D in a population of Sub-Saharan Africa (SSA).

METHODS

A total of 580 adult Nigerians from the Africa America Diabetes Mellitus (AADM) study were studied. The discovery study included 310 individuals (210 without T2D, 100 with T2D). Metabolites in plasma were assessed by reverse phase, ultra-performance liquid chromatography and mass spectrometry (RP)/UPLC-MS/MS methods on the Metabolon Platform. Welch's two-sample t-test was used to identify differentially expressed metabolites (DEMs), followed by the construction of a biomarker panel using a random forest (RF) algorithm. The biomarker panel was evaluated in a replication sample of 270 individuals (110 without T2D and 160 with T2D) from the same study.

RESULTS

Untargeted metabolomic analyses revealed 280 DEMs between individuals with and without T2D. The DEMs predominantly belonged to the lipid (51%, 142/280), amino acid (21%, 59/280), xenobiotics (13%, 35/280), carbohydrate (4%, 10/280) and nucleotide (4%, 10/280) super pathways. At the sub-pathway level, glycolysis, free fatty acid, bile metabolism, and branched chain amino acid catabolism were altered in T2D individuals. A 10-metabolite biomarker panel including glucose, gluconate, mannose, mannonate, 1,5-anhydroglucitol, fructose, fructosyl-lysine, 1-carboxylethylleucine, metformin, and methyl-glucopyranoside predicted T2D with an area under the curve (AUC) of 0.924 (95% CI: 0.845-0.966) and a predicted accuracy of 89.3%. The panel was validated with a similar AUC (0.935, 95% CI 0.906-0.958) in the replication cohort. The 10 metabolites in the biomarker panel correlated significantly with several T2D-related glycemic indices, including Hba1C, insulin resistance (HOMA-IR), and diabetes duration.

CONCLUSIONS

We demonstrate that metabolomic dysregulation associated with T2D in Nigerians affects multiple processes, including glycolysis, free fatty acid and bile metabolism, and branched chain amino acid catabolism. Our study replicated previous findings in other populations and identified a metabolic signature that could be used as a biomarker panel of T2D risk and glycemic control thus enhancing our knowledge of molecular pathophysiologic changes in T2D. The metabolomics dataset generated in this study represents an invaluable addition to publicly available multi-omics data on understudied African ancestry populations.

The Role of Branched-chain Amino Acids and Their Metabolism in Cardiovascular Diseases

Branched-chain amino acids (BCAAs), including leucine, isoleucine, and valine, are essential amino acids for protein synthesis. Recent studies have yielded new insights into their diverse physiological and pathological roles in health and disease. Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality globally. An increasing number of clinical studies have demonstrated that high levels of circulating BCAAs are associated with an increased risk of CVDs. Animal studies have provided preliminary evidence linking BCAA intake and metabolism with cardiovascular diseases. Despite these insights, the causal relationship between BCAA metabolism and CVD remains poorly established, and the underlying mechanisms remain incompletely understood. Here, we aim to provide an update on the current understanding of the roles of BCAAs and their metabolism in the development and progression of various CVDs. We also discuss the potential strategies targeting BCAA nutrition and metabolism for the prevention and treatment of CVDs.

Heterogeneously elevated branched-chain/aromatic amino acids among new-onset type-2 diabetes mellitus patients are potentially skewed diabetes predictors

BACKGROUND

The lack of specific predictors for type-2 diabetes mellitus (T2DM) severely impacts early intervention/prevention efforts. Elevated branched-chain amino acids (BCAAs: Isoleucine, leucine, valine) and aromatic amino acids (AAAs: Tyrosine, tryptophan, phenylalanine)) show high sensitivity and specificity in predicting diabetes in animals and predict T2DM 10-19 years before T2DM onset in clinical studies. However, improvement is needed to support its clinical utility.

AIM

To evaluate the effects of body mass index (BMI) and sex on BCAAs/AAAs in new-onset T2DM individuals with varying body weight.

METHODS

Ninety-seven new-onset T2DM patients (< 12 mo) differing in BMI [normal weight (NW), n = 33, BMI = 22.23 ± 1.60; overweight, n = 42, BMI = 25.9 ± 1.07; obesity (OB), n = 22, BMI = 31.23 ± 2.31] from the First People's Hospital of Yunnan Province, Kunming, China, were studied. One-way and 2-way ANOVAs were conducted to determine the effects of BMI and sex on BCAAs/AAAs.

RESULTS

Fasting serum AAAs, BCAAs, glutamate, and alanine were greater and high-density lipoprotein (HDL) was lower (P < 0.05, each) in OB-T2DM patients than in NW-T2DM patients, especially in male OB-T2DM patients. Arginine, histidine, leucine, methionine, and lysine were greater in male patients than in female patients. Moreover, histidine, alanine, glutamate, lysine, valine, methionine, leucine, isoleucine, tyrosine, phenylalanine, and tryptophan were significantly correlated with abdominal adiposity, body weight and BMI, whereas isoleucine, leucine and phenylalanine were negatively correlated with HDL.

CONCLUSION

Heterogeneously elevated amino acids, especially BCAAs/AAAs, across new-onset T2DM patients in differing BMI categories revealed a potentially skewed prediction of T2DM development. The higher BCAA/AAA levels in obese T2DM patients would support T2DM prediction in obese individuals, whereas the lower levels of BCAAs/AAAs in NW-T2DM individuals may underestimate T2DM risk in NW individuals. This potentially skewed T2DM prediction should be considered when BCAAs/AAAs are to be used as the T2DM predictor.

Lysophosphatidylinositols Are Upregulated After Human β-Cell Loss and Potentiate Insulin Release

In this study, we identified new lipid species associated with the loss of pancreatic β-cells triggering diabetes. We performed lipidomics measurements on serum from prediabetic mice lacking β-cell prohibitin-2 (a model of monogenic diabetes) patients without previous history of diabetes but scheduled for pancreaticoduodenectomy resulting in the acute reduction of their β-cell mass (∼50%), and patients with type 2 diabetes (T2D). We found lysophosphatidylinositols (lysoPIs) were the main circulating lipid species altered in prediabetic mice. The changes were confirmed in the patients with acute reduction of their β-cell mass and in those with T2D. Increased lysoPIs significantly correlated with HbA1c (reflecting glycemic control), fasting glycemia, and disposition index, and did not correlate with insulin resistance or obesity in human patients with T2D. INS-1E β-cells as well as pancreatic islets isolated from nondiabetic mice and human donors exposed to exogenous lysoPIs showed potentiated glucose-stimulated and basal insulin secretion. Finally, addition of exogenous lysoPIs partially rescued impaired glucose-stimulated insulin secretion in islets from mice and humans in the diabetic state. Overall, lysoPIs appear to be lipid species upregulated in the prediabetic stage associated with the loss of β-cells and that support the secretory function of the remaining β-cells.

ARTICLE HIGHLIGHTS

Circulating lysophosphatidylinositols (lysoPIs) are increased in situations associated with β-cell loss in mice and humans such as (pre-)diabetes, and hemipancreatectomy. Pancreatic islets isolated from nondiabetic mice and human donors, as well as INS-1E β-cells, exposed to exogenous lysoPIs exhibited potentiated glucose-stimulated and basal insulin secretion. Addition of exogenous lysoPIs partially rescued impaired glucose-stimulated insulin secretion in islets from mice and humans in the diabetic state. LysoPIs appear as lipid species being upregulated already in the prediabetic stage associated with the loss of β-cells and supporting the function of the remaining β-cells.

Identification of blood metabolic biomarkers associated with diabetic distal symmetric sensorimotor polyneuropathy in patients with type 2 diabetes mellitus

BACKGROUND

Distal symmetric sensorimotor polyneuropathy (DSPN) is a common neurologic complication of type 2 diabetes mellitus (T2DM), but the underlying mechanisms and changes in serum metabolites remain largely undefined. This study aimed to characterize the plasma metabolite profiles of participants with T2DM using targeted metabolomics analysis and identify potential biomarkers for DSPN.

METHODS

A combined liquid chromatography MS/MS and direct flow injection were used to quantify plasma metabolite obtained from 63 participants with T2DM, 81 with DSPN, and 33 nondiabetic control participants. A total of 130 metabolites, including amino acids, biogenic amines, sphingomyelins (SM), phosphatidylcholines, carnitines, and hexose, were analyzed.

RESULTS

A total of 16 plasma metabolites and 3 cholesterol-related laboratory parameters were found to have variable importance in the projection score >1.0 and false discovery rate <5.0% between control, T2DM, and DSPN. Among these variables, five serum metabolites, including phenylalanine (AUC = 0.653), alanine (AUC = 0.630), lysine (AUC = 0.622) tryptophan (AUC = 0.620), and SM C16:0 (AUC = 0.630), are potential biomarkers (all p < .05) in distinguishing T2DM with DSPN from those without (AUC = 0.720).

CONCLUSIONS

In this cross-sectional study, derangement of several metabolites in the plasma was observed in T2DM with and without DSPN, and these metabolites may be potential biomarkers for predicting DSPN. Longitudinal studies are warranted.

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.

The progression from mild to severe hyperglycemia coupled with insulin resistance causes mitochondrial dysfunction and alters the metabolic secretome of epithelial kidney cells

Diabetic nephropathy (DN) and insulin resistance (IR) in kidney cells are considered main causes for end-stage renal failure. However, it is unclear how IR affects early stages of the disease. Here, we investigate the impact of mild (11 mM) and severe (22 mM) hyperglycemia, with and without induced IR, on cellular metabolism and mitochondrial bioenergetics in a human kidney cell line (HK-2). IR in HK-2 cells was induced with palmitic acid and cellular cytotoxicity was studied. We evaluated the impact of mild and severe hyperglycemia with and without IR on the metabolic secretome of the cells, their live-cell mitochondria function, mitochondrial membrane potential, and mitochondrial complex activities. Furthermore, we measured fatty acid oxidation and lipid accumulation. Cells cultured under mild hyperglycemic conditions exhibited increased mitochondrial bioenergetic parameters, such as basal respiration, ATP-linked production, maximal respiration capacity, and spare respiration capacity. However, these parameters decreased when cells were cultured under higher glucose concentrations when IR was induced. Our data suggests that progression from mild to severe hyperglycemia induces a metabolic shift, where gluconeogenic amino acids play a crucial role in supplying the energy requirements of HK-2. To our knowledge, this is the first study to evaluate the progression from mild to severe hyperglycemia allied to IR in human kidney cells. This work highlights that this progression leads to mitochondrial dysfunction and alters the metabolic profile of kidney cells. These results identify possible targets for early intervention in DN.

Hibernation and plasma lipids in free-ranging brown bears-implications for diabetes

Brown bears (Ursus arctos) prepare for winter by overeating and increasing adipose stores, before hibernating for up to six months without eating, drinking, and with minimal movement. In spring, the bears exit the den without any damage to organs or physiology. Recent clinical research has shown that specific lipids and lipid profiles are of special interest for diseases such as diabetes type 1 and 2. Furthermore, rodent experiments show that lipids such as sulfatide protects rodents against diabetes. As free-ranging bears experience fat accumulation and month-long physical inactivity without developing diabetes, they could possibly be affected by similar protective measures. In this study, we investigated whether lipid profiles of brown bears are related to protection against hibernation-induced damage. We sampled plasma from 10 free-ranging Scandinavian brown bears during winter hibernation and repeated sampling during active state in the summer period. With quantitative shotgun lipidomics and liquid chromatography-mass spectrometry, we profiled 314 lipid species from 26 lipid classes. A principal component analysis revealed that active and hibernation samples could be distinguished from each other based on their lipid profiles. Six lipid classes were significantly altered when comparing plasma from active state and hibernation: Hexosylceramide, phosphatidylglycerol, and lysophosphatidylglycerol were higher during hibernation, while phosphatidylcholine ether, phosphatidylethanolamine ether, and phosphatidylinositol were lower. Additionally, sulfatide species with shorter chain lengths were lower, while longer chain length sulfatides were higher during hibernation. Lipids that are altered in bears are described by others as relevant for and associated with diabetes, which strengthens their position as potential effectors during hibernation. From this analysis, a range of lipids are suggested as potential protectors of bear physiology, and of potential importance in diabetes.

Targeting metabolic pathways: a novel therapeutic direction for type 2 diabetes

Background

Type 2 diabetes mellitus (T2DM) is a prevalent metabolic disease that causes multi-organ complications, seriously affecting patients' quality of life and survival. Understanding its pathogenesis remains challenging, with current clinical treatment regimens often proving ineffective.

Methods

In this study, we established a mouse model of T2DM and employed 16s rDNA sequencing to detect changes in the species and structure of gut flora. Additionally, we used UPLC-Q-TOF-MS to identify changes in urinary metabolites of T2DM mice, analyzed differential metabolites and constructed differential metabolic pathways. Finally, we used Pearman correlation analysis to investigate the relationship between intestinal flora and differential metabolites in T2DM mice, aiming to elucidate the pathogenesis of T2DM and provide an experimental basis for its clinical treatment.

Results

Our findings revealed a reduction in both the species diversity and abundance of intestinal flora in T2DM mice, with significantly decreased levels of beneficial bacteria such as Lactobacillus and significantly increased levels of harmful bacteria such as Helicobacter pylori. Urinary metabolomics results identified 31 differential metabolites between T2DM and control mice, including Phosphatidylcholine, CDP-ethanolamine and Leukotriene A4, which may be closely associated with the glycerophospholipid and arachidonic acid pathways. Pearman correlation analysis showed a strong correlation between dopamine and gonadal, estradiol and gut microbiota, may be a novel direction underlying T2DM.

Conclusion

In conclusion, our study suggests that alterations in gut microbiota and urinary metabolites are characteristic features of T2DM in mice. Furthermore, a strong correlation between dopamine, estradiol and gut microbiota, may be a novel direction underlying T2DM, the aim is to provide new ideas for clinical treatment and basic research.

Exploring Metabolomic Patterns in Type 2 Diabetes Mellitus and Response to Glucose-Lowering Medications-Review

The spectrum of information related to precision medicine in diabetes generally includes clinical data, genetics, and omics-based biomarkers that can guide personalized decisions on diabetes care. Given the remarkable progress in patient risk characterization, there is particular interest in using molecular biomarkers to guide diabetes management. Metabolomics is an emerging molecular approach that helps better understand the etiology and promises the identification of novel biomarkers for complex diseases. Both targeted or untargeted metabolites extracted from cells, biofluids, or tissues can be investigated by established high-throughput platforms, like nuclear magnetic resonance (NMR) and mass spectrometry (MS) techniques. Metabolomics is proposed as a valuable tool in precision diabetes medicine to discover biomarkers for diagnosis, prognosis, and management of the progress of diabetes through personalized phenotyping and individualized drug-response monitoring. This review offers an overview of metabolomics knowledge as potential biomarkers in type 2 diabetes mellitus (T2D) diagnosis and the response to glucose-lowering medications.

Differentiation in TCM patterns of chronic obstructive pulmonary disease by comprehensive metabolomic and lipidomic characterization

Introduction

Chronic obstructive pulmonary disease (COPD) is a complex disease involving inflammation, cell senescence, and autoimmunity. Dialectical treatment for COPD with traditional Chinese medicine (TCM) has the advantage of fewer side effects, more effective suppression of inflammation, and improved immune function. However, the biological base of TCM pattern differentiation in COPD remains unclear.

Methods

Liquid Chromatography-Quadrupole-Orbitrap mass spectrometry (LC-Q-Orbitrap MS/MS) based metabolomics and lipidomics were used to analyze the serum samples from COPD patients of three TCM patterns in Lung Qi Deficiency (n=65), Lung-Kidney Qi Deficiency (n=54), Lung-Spleen Qi Deficiency (n=52), and healthy subjects (n=41). Three cross-comparisons were performed to characterize metabolic markers for different TCM patterns of COPD vs healthy subjects.

Results

We identified 28, 8, and 16 metabolites with differential abundance between three TCM patterns of COPD vs healthy subjects, respectively, the metabolic markers included cortisol, hypoxanthine, fatty acids, alkyl-/alkenyl-substituted phosphatidylethanolamine, and phosphatidylcholine, etc. Three panels of metabolic biomarkers specific to the above three TCM patterns yielded areas under the receiver operating characteristic curve of 0.992, 0.881, and 0.928, respectively, with sensitivity of 97.1%, 88.6%, and 91.4%, respectively, and specificity of 96.4%, 81.8%, and 83.9%, respectively.

Discussion

Combining metabolomics and lipidomics can more comprehensively and accurately trace metabolic markers. As a result, the differences in metabolism were proven to underlie different TCM patterns of COPD, which provided evidence to aid our understanding of the biological basis of dialectical treatment, and can also serve as biomarkers for more accurate diagnosis.

Coffee intake and risk of diabetic nephropathy: a Mendelian randomization study

Rationale and objective

A causal relationship concerning coffee intake and diabetic nephropathy (DN) is controversial. We conducted a Mendelian randomization study to assess the causal nature of these associations.

Methods

40 independent single nucleotide polymorphisms (SNPs) associated with coffee intake were selected from the UK Biobank study. Summary-level data for diabetic nephropathy were obtained from publicly available genome-wide association studies (GWAS) and the FinnGen consortium. Inverse variance weighted (IVW), MR-Egger, and weighted median (WM) methods were used to examine a causal association. Sensitivity analyses included Cochran's Q test, the intercept of MR-Egger, MR-PRESSO, and the Outlier method. Leave-One-Out sensitivity analyses were also conducted to reduce the heterogeneity.

Results

Our current study demonstrated positive associations of genetically predicted coffee intake with diabetic nephropathy (OR=1.939; P = 0.045 and type 2 diabetes with renal complications (OR = 2.787, P= 0.047). These findings were robust across several sensitivity analyses.

Conclusions

This study found a positive correlation between coffee consumption and the risk of diabetic nephropathy using genetic data. For a more accurate and trustworthy conclusion, subgroup analysis on coffee intake, including preparing method, variety of coffee, and quantity, is required.

Untargeted metabonomic analysis of non-alcoholic fatty liver disease with iron overload in rats via UPLC/MS

BACKGROUND/AIMS

In recent years, many metabolites specific to nonalcoholic fatty liver disease (NAFLD) have been identified thanks to the application of metabolomics techniques. This study aimed to investigate the candidate targets and potential molecular pathways involved in NAFLD in the presence of iron overload.

METHODS

Male Sprague Dawley rats were fed with control or high-fat diet with or without excess iron. After 8,16,20 weeks of treatment, urine samples of rats were collected for metabolomics analysis using ultra-performance liquid chromatography/mass spectrometry (UPLC-MS). Blood and liver samples were also collected.

RESULTS

High-fat, high-iron diet resulted in increased triglyceride accumulation and increased oxidative damage. A total of 13 metabolites and four potential pathways were identified. Compared to the control group, the intensities of adenine, cAMP, hippuric acid, kynurenic acid, xanthurenic acid, uric acid, and citric acid were significantly lower (P < 0.05) and the concentration of other metabolites was significantly higher in the high-fat diet group. In the high-fat, high-iron group, the differences in the intensities of the above metabolites were amplified.

CONCLUSION

Our findings suggest that NAFLD rats have impaired antioxidant system and liver function, lipid disorders, abnormal energy, and glucose metabolism, and that iron overload may further exacerbate these disorders.

Fatty Acid Profile and Genetic Variants of Proteins Involved in Fatty Acid Metabolism Could Be Considered as Disease Predictor

Circulating fatty acids (FA) have an endogenous or exogenous origin and are metabolized under the effect of many enzymes. They play crucial roles in many mechanisms: cell signaling, modulation of gene expression, etc., which leads to the hypothesis that their perturbation could be the cause of disease development. FA in erythrocytes and plasma rather than dietary FA could be used as a biomarker for many diseases. Cardiovascular disease was associated with elevated trans FA and decreased DHA and EPA. Increased arachidonic acid and decreased Docosahexaenoic Acids (DHA) were associated with Alzheimer's disease. Low Arachidonic acid and DHA are associated with neonatal morbidities and mortality. Decreased saturated fatty acids (SFA), increased monounsaturated FA (MUFA) and polyunsaturated FA (PUFA) (C18:2 n-6 and C20:3 n-6) are associated with cancer. Additionally, genetic polymorphisms in genes coding for enzymes implicated in FA metabolism are associated with disease development. FA desaturase (FADS1 and FADS2) polymorphisms are associated with Alzheimer's disease, Acute Coronary Syndrome, Autism spectrum disorder and obesity. Polymorphisms in FA elongase (ELOVL2) are associated with Alzheimer's disease, Autism spectrum disorder and obesity. FA-binding protein polymorphism is associated with dyslipidemia, type 2 diabetes, metabolic syndrome, obesity, hypertension, non-alcoholic fatty liver disease, peripheral atherosclerosis combined with type 2 diabetes and polycystic ovary syndrome. Acetyl-coenzyme A carboxylase polymorphisms are associated with diabetes, obesity and diabetic nephropathy. FA profile and genetic variants of proteins implicated in FA metabolism could be considered as disease biomarkers and may help with the prevention and management of diseases.

Enhanced protein acetylation initiates fatty acid-mediated inhibition of cardiac glucose transport

Fatty acids (FAs) rapidly and efficiently reduce cardiac glucose uptake in the Randle cycle or glucose-FA cycle. This fine-tuned physiological regulation is critical to allow optimal substrate allocation during fasted and fed states. However, the mechanisms involved in the direct FA-mediated control of glucose transport have not been totally elucidated yet. We previously reported that leucine and ketone bodies, other cardiac substrates, impair glucose uptake by increasing global protein acetylation from acetyl-CoA. As FAs generate acetyl-CoA as well, we postulated that protein acetylation is enhanced by FAs and participates in their inhibitory action on cardiac glucose uptake. Here, we demonstrated that both palmitate and oleate promoted a rapid increase in protein acetylation in primary cultured adult rat cardiomyocytes, which correlated with an inhibition of insulin-stimulated glucose uptake. This glucose absorption deficit was caused by an impairment in the translocation of vesicles containing the glucose transporter GLUT4 to the plasma membrane, although insulin signaling remained unaffected. Interestingly, pharmacological inhibition of lysine acetyltransferases (KATs) prevented this increase in protein acetylation and glucose uptake inhibition induced by FAs. Similarly, FA-mediated inhibition of insulin-stimulated glucose uptake could be prevented by KAT inhibitors in perfused hearts. To summarize, enhanced protein acetylation can be considered as an early event in the FA-induced inhibition of glucose transport in the heart, explaining part of the Randle cycle.NEW & NOTEWORTHY Our results show that cardiac metabolic overload by oleate or palmitate leads to increased protein acetylation inhibiting GLUT4 translocation to the plasma membrane and glucose uptake. This observation suggests an additional regulation mechanism in the physiological glucose-FA cycle originally discovered by Randle.

The relationship between islet β-cell function and metabolomics in overweight patients with Type 2 diabetes

A cross-sectional study was performed using metabolomics in overweight patients with Type 2 diabetes (T2D) at different stages of the disease. We aimed to identify potential metabolites for assessing islet β-cell function in order to investigate the correlation between islet β-cell dysfunction and metabolite changes in overweight patients with T2D. We selected 60 overweight adults (24 ≤ body mass index [BMI] < 28 kg/m2) with T2D who had been admitted to our hospital. The participants were equally divided into three groups according to disease duration: H1 (duration ≤ 5 years), H2 (5 years < duration ≤ 10 years), and H3 (duration > 10 years). Questionnaires, physical examinations, laboratory tests, and imaging studies were administered to all participants. The modified homeostasis model of assessment (HOMA) index was calculated using fasting C-peptide levels, and metabolite assays were performed using mass spectrometry. The results showed that HOMA-β and visceral fat area (VFA) were negatively correlated with diabetes duration. The VFA was positively correlated with arginine, cysteine, methionine, proline, and succinyl/methylmalonylcarnitine levels. The HOMA-β was negatively correlated with the serine and tetradecanoyldiacylcarnitine levels, and positively correlated with the aspartic acid, cysteine, homocysteine, piperamide, proline, and valine levels. The HOMA-IR was negatively correlated with hydroxypalmitoylcarnitine levels and positively correlated with the myristoylcarnitine levels. Thus, at different stages of T2D progression in overweight patients, serine, aspartic acid, cysteine, homocysteine, piperamide, proline, valine, and tetradecanoyldiacylcarnitine may be associated with HOMA-β and represent potential novel biomarkers for evaluating islet β-cell function.

Untargeted metabolomic profiling identifies serum metabolites associated with type 2 diabetes in a cross-sectional study of the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study

Type 2 diabetes (T2D) is a complex chronic disease with substantial phenotypic heterogeneity affecting millions of individuals. Yet, its relevant metabolites and etiological pathways are not fully understood. The aim of this study is to assess a broad spectrum of metabolites related to T2D in a large population-based cohort. We conducted a metabolomic analysis of 4,281 male participants within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. The serum metabolomic analysis was performed using an LC-MS/GC-MS platform. Associations between 1,413 metabolites and T2D were examined using linear regression, controlling for important baseline risk factors. Standardized β-coefficients and standard errors (SEs) were computed to estimate the difference in metabolite concentrations. We identified 74 metabolites that were significantly associated with T2D based on the Bonferroni-corrected threshold (P < 3.5 × 10-5). The strongest signals associated with T2D were of carbohydrates origin, including glucose, 1,5-anhydroglucitol (1,5-AG), and mannose (β = 0.34, -0.91, and 0.41, respectively; all P < 10-75). We found several chemical class pathways that were significantly associated with T2D, including carbohydrates (P = 1.3 × 10-11), amino acids (P = 2.7 × 10-6), energy (P = 1.5 × 10-4), and xenobiotics (P = 1.2 × 10-3). The strongest subpathway associations were seen for fructose-mannose-galactose metabolism, glycolysis-gluconeogenesis-pyruvate metabolism, fatty acid metabolism (acyl choline), and leucine-isoleucine-valine metabolism (all P < 10-8). Our findings identified various metabolites and candidate chemical class pathways that can be characterized by glycolysis and gluconeogenesis metabolism, fructose-mannose-galactose metabolism, branched-chain amino acids, diacylglycerol, acyl cholines, fatty acid oxidation, and mitochondrial dysfunction.NEW & NOTEWORTHY These metabolomic patterns may provide new additional evidence and potential insights relevant to the molecular basis of insulin resistance and the etiology of T2D.

Insulin resistance in Alzheimer's disease: The genetics and metabolomics links

Alzheimer's disease (AD) is a neurodegenerative disease with significant socioeconomic burden worldwide. Although genetics and environmental factors play a role, AD is highly associated with insulin resistance (IR) disorders such as metabolic syndrome (MS), obesity, and type two diabetes mellitus (T2DM). These findings highlight a shared pathogenesis. The use of metabolomics as a downstream systems' biology (omics) approach can help to identify these shared metabolic traits and assist in the early identification of at-risk groups and potentially guide therapy. Targeting the shared AD-IR metabolic trait with lifestyle interventions and pharmacological treatments may offer promising AD therapeutic approach. In this narrative review, we reviewed the literature on the AD-IR pathogenic link, the shared genetics and metabolomics biomarkers between AD and IR disorders, as well as the lifestyle interventions and pharmacological treatments which target this pathogenic link.

Specific triacylglycerol, diacylglycerol, and lyso-phosphatidylcholine species for the prediction of type 2 diabetes: a ~ 16-year prospective study in Chinese

Background

Bioactive lipids play an important role in insulin secretion and sensitivity, contributing to the pathophysiology of type 2 diabetes (T2D). This study aimed to identify novel lipid species associated with incident T2D in a nested case–control study within a long-term prospective Chinese community-based cohort with a median follow-up of ~ 16 years.

Methods

Plasma samples from 196 incident T2D cases and 196 age- and sex-matched non-T2D controls recruited from the Hong Kong Cardiovascular Risk Factor Prevalence Study (CRISPS) were first analyzed using untargeted lipidomics. Potential predictive lipid species selected by the Boruta analysis were then verified by targeted lipidomics. The associations between these lipid species and incident T2D were assessed. Effects of novel lipid species on insulin secretion in mouse islets were investigated.

Results

Boruta analysis identified 16 potential lipid species. After adjustment for body mass index (BMI), triacylglycerol/high-density lipoprotein (TG/HDL) ratio and the presence of prediabetes, triacylglycerol (TG) 12:0_18:2_22:6, TG 16:0_11:1_18:2, TG 49:0, TG 51:1 and diacylglycerol (DG) 18:2_22:6 were independently associated with increased T2D risk, whereas lyso-phosphatidylcholine (LPC) O-16:0, LPC P-16:0, LPC O-18:0 and LPC 18:1 were independently associated with decreased T2D risk. Addition of the identified lipid species to the clinical prediction model, comprised of BMI, TG/HDL ratio and the presence of prediabetes, achieved a 3.8% improvement in the area under the receiver operating characteristics curve (AUROC) (p = 0.0026). Further functional study revealed that, LPC O-16:0 and LPC O-18:0 significantly potentiated glucose induced insulin secretion (GSIS) in a dose-dependent manner, whereas neither DG 18:2_22:6 nor TG 12:0_18:2_22:6 had any effect on GSIS.

Conclusions

Addition of the lipid species substantially improved the prediction of T2D beyond the model based on clinical risk factors. Decreased levels of LPC O-16:0 and LPC O-18:0 may contribute to the development of T2D via reduced insulin secretion.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01677-4.

Plasma metabolomic signatures of obesity and risk of type 2 diabetes

OBJECTIVE

The mechanisms linking obesity to type 2 diabetes (T2D) are not fully understood. This study aimed to identify obesity-related metabolomic signatures (MESs) and evaluated their relationships with incident T2D.

METHODS

In a nested case-control study of 2076 Chinese adults, 140 plasma metabolites were measured at baseline, linear regression was applied with the least absolute shrinkage and selection operator to identify MESs for BMI and waist circumference (WC), and conditional logistic regression was applied to examine their associations with T2D risk.

RESULTS

A total of 32 metabolites associated with BMI or WC were identified and validated, among which 14 showed positive associations and 3 showed inverse associations with T2D; 8 and 18 metabolites were selected to build MESs for BMI and WC, respectively. Both MESs showed strong linear associations with T2D: odds ratio (95% CI) comparing extreme quartiles was 4.26 (2.00-9.06) for BMI MES and 9.60 (4.22-21.88) for WC MES (both p-trend < 0.001). The MES-T2D associations were particularly evident among individuals with normal WC: odds ratio (95% CI) reached 6.41 (4.11-9.98) for BMI MES and 10.38 (6.36-16.94) for WC MES. Adding MESs to traditional risk factors and plasma glucose improved C statistics from 0.79 to 0.83 (p < 0.001).

CONCLUSIONS

Multiple obesity-related metabolites and MESs strongly associated with T2D in Chinese adults were identified.

Prepregnancy Protein Source and BCAA Intake Are Associated with Gestational Diabetes Mellitus in the CARDIA Study

Diet quality and protein source are associated with type 2 diabetes, however relationships with GDM are less clear. This study aimed to determine whether prepregnancy diet quality and protein source are associated with gestational diabetes mellitus (GDM). Participants were 1314 Black and White women without diabetes, who had at least one birth during 25 years of follow-up in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort study. The CARDIA A Priori Diet Quality Score (APDQS) was assessed in the overall cohort at enrollment and again at Year 7. Protein source and branched-chain amino acid (BCAA) intake were assessed only at the Year 7 exam (n = 565). Logistic regression analysis was used to determine associations between prepregnancy dietary factors and GDM. Women who developed GDM (n = 161) were more likely to have prepregnancy obesity and a family history of diabetes (p < 0.05). GDM was not associated with prepregnancy diet quality at enrollment (Year 0) (odds ratio [OR]: 1.01; 95% confidence interval [CI] 0.99, 1.02) or Year 7 (odds ratio [OR]: 0.97; 95% confidence interval [CI] 0.94, 1.00) in an adjusted model. Conversely, BCAA intake (OR:1.59, 95% CI 1.03, 2.43) and animal protein intake (OR: 1.06, 95% CI 1.02, 1.10) as a proportion of total protein intake, were associated with increased odds of GDM, while proportion of plant protein was associated with decreased odds of GDM (OR: 0.95, 95% CI 0.91, 0.99). In conclusion, GDM is strongly associated with source of prepregnancy dietary protein intake but not APDQS in the CARDIA study.

The Association between Circulating Branched Chain Amino Acids and the Temporal Risk of Developing Type 2 Diabetes Mellitus: A Systematic Review & Meta-Analysis

Introduction: Recent studies have concluded that elevated circulating branched chain amino acids (BCAA) are associated with the pathogenesis of type 2 diabetes mellitus (T2DM) and obesity. However, the development of this association over time and the quantification of the strength of this association for individual BCAAs prior to T2DM diagnosis remains unexplored. Methods: A systematic search was conducted using the Healthcare Databases Advance Search (HDAS) via the National Institute for Health and Care Excellence (NICE) website. The data sources included EMBASE, MEDLINE and PubMed for all papers from inception until November 2021. Nine studies were identified in this systematic review and meta-analysis. Stratification was based on follow-up times (0−6, 6−12 and 12 or more years) and controlling of body mass index (BMI) through the specific assessment of overweight cohorts was also undertaken. Results: The meta-analysis revealed a statistically significant positive association between BCAA concentrations and the development of T2DM, with valine OR = 2.08 (95% CI = 2.04−2.12, p < 0.00001), leucine OR = 2.25 (95% CI = 1.76−2.87, p < 0.00001) and isoleucine OR = 2.12, 95% CI = 2.00−2.25, p < 0.00001. In addition, we demonstrated a positive consistent temporal association between circulating BCAA levels and the risk of developing T2DM with differentials in the respective follow-up times of 0−6 years, 6−12 years and ≥12 years follow-up for valine (OR = 2.08, 1.86 and 2.14, p < 0.05 each), leucine (OR = 2.10, 2.25 and 2.16, p < 0.05 each) and isoleucine (OR = 2.12, 1.90 and 2.16, p < 0.05 each) demonstrated. Conclusion: Plasma BCAA concentrations are associated with T2DM incidence across all temporal subgroups. We suggest the potential utility of BCAAs as an early biomarker for T2DM irrespective of follow-up time.

Advances in multi-omics study of biomarkers of glycolipid metabolism disorder

Glycolipid metabolism disorder are major threats to human health and life. Genetic, environmental, psychological, cellular, and molecular factors contribute to their pathogenesis. Several studies demonstrated that neuroendocrine axis dysfunction, insulin resistance, oxidative stress, chronic inflammatory response, and gut microbiota dysbiosis are core pathological links associated with it. However, the underlying molecular mechanisms and therapeutic targets of glycolipid metabolism disorder remain to be elucidated. Progress in high-throughput technologies has helped clarify the pathophysiology of glycolipid metabolism disorder. In the present review, we explored the ways and means by which genomics, transcriptomics, proteomics, metabolomics, and gut microbiomics could help identify novel candidate biomarkers for the clinical management of glycolipid metabolism disorder. We also discuss the limitations and recommended future research directions of multi-omics studies on these diseases.

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.

Metabolomics analysis identifies glutamic acid and cystine imbalances in COVID-19 patients without comorbid conditions. Implications on redox homeostasis and COVID-19 pathophysiology

It is well known that the presence of comorbidities and age-related health issues may hide biochemical and metabolic features triggered by SARS-CoV-2 infection and other diseases associated to hypoxia, as they are by themselves chronic inflammatory conditions that may potentially disturb metabolic homeostasis and thereby negatively impact on COVID-19 progression. To unveil the metabolic abnormalities inherent to hypoxemia caused by COVID-19, we here applied gas chromatography coupled to mass spectrometry to analyze the main metabolic changes exhibited by a population of male patients less than 50 years of age with mild/moderate and severe COVID-19 without pre-existing comorbidities known to predispose to life-threatening complications from this infection. Several differences in serum levels of particular metabolites between normal controls and patients with COVID-19 as well as between mild/moderate and severe COVID-19 were identified. These included increased glutamic acid and reduced glutamine, cystine, threonic acid, and proline levels. In particular, using the entire metabolomic fingerprint obtained, we observed that glutamine/glutamate metabolism was associated with disease severity as patients in the severe COVID-19 group presented the lowest and higher serum levels of these amino acids, respectively. These data highlight the hypoxia-derived metabolic alterations provoked by SARS-CoV-2 infection in the absence of pre-existing co-morbidities as well as the value of amino acid metabolism in determining reactive oxygen species recycling pathways, which when impaired may lead to increased oxidation of proteins and cell damage. They also provide insights on new supportive therapies for COVID-19 and other disorders that involve altered redox homeostasis and lower oxygen levels that may lead to better outcomes of disease severity.

The role of NMR-based circulating metabolic biomarkers in development and risk prediction of new onset type 2 diabetes

Associations of circulating metabolic biomarkers with type 2 diabetes (T2D) and their added value for risk prediction are uncertain among Chinese adults. A case-cohort study included 882 T2D cases diagnosed during 8-years' follow-up and a subcohort of 789 participants. NMR-metabolomic profiling quantified 225 plasma biomarkers in stored samples taken at recruitment into the study. Cox regression yielded adjusted hazard ratios (HRs) for T2D associated with individual biomarkers, with a set of biomarkers incorporated into an established T2D risk prediction model to assess improvement in discriminatory ability. Mean baseline BMI (SD) was higher in T2D cases than in the subcohort (25.7 [3.6] vs. 23.9 [3.6] kg/m²). Overall, 163 biomarkers were significantly and independently associated with T2D at false discovery rate (FDR) controlled p < 0.05, and 138 at FDR-controlled p < 0.01. Branched chain amino acids (BCAA), apolipoprotein B/apolipoprotein A1, triglycerides in VLDL and medium and small HDL particles, and VLDL particle size were strongly positively associated with T2D (HRs 1.74-2.36 per 1 SD, p < 0.001). HDL particle size, cholesterol concentration in larger HDL particles and docosahexaenoic acid levels were strongly inversely associated with T2D (HRs 0.43-0.48, p < 0.001). With additional adjustment for plasma glucose, most associations (n = 147 and n = 129 at p < 0.05 and p < 0.01, respectively) remained significant. HRs appeared more extreme among more centrally adipose participants for apolipoprotein B/apolipoprotein A1, BCAA, HDL particle size and docosahexaenoic acid (p for heterogeneity ≤ 0.05). Addition of 31 selected biomarkers to an established T2D risk prediction model modestly, but significantly, improved risk discrimination (c-statistic 0.86 to 0.91, p < 0.001). In relatively lean Chinese adults, diverse metabolic biomarkers are associated with future risk of T2D and can help improve established risk prediction models.

Untargeted plasma metabolomics for risk prediction of hepatocellular carcinoma: A prospective study in two Chinese cohorts

Characterization of metabolic perturbation prior to hepatocellular carcinoma (HCC) may deepen the understanding of causal pathways and identify novel biomarkers for early prevention. We conducted two 1:1 matched nested case-control studies (108 and 55 pairs) to examine the association of plasma metabolome (profiled using LC-MS) with the risk of HCC based on two prospective cohorts in China. Differential metabolites were identified by paired t-tests and orthogonal partial least-squares discriminant analysis (OPLS-DA). Weighted gene co-expression network analysis (WGCNA) was performed to classify metabolites into modules for identifying biological pathways involved in hepatocarcinogenesis. We assessed the risk predictivity of metabolites using multivariable logistic regression models. Among 612 named metabolites, 44 differential metabolites were identified between cases and controls, including 12 androgenic/progestin steroid hormones, 8 bile acids, 10 amino acids, 6 phospholipids, and 8 others. These metabolites were associated with HCC in the multivariable logistic regression analyses, with odds ratios ranging from 0.19 (95% CI: 0.11-0.35) to 5.09 (95% CI: 2.73-9.50). WGCNA including 612 metabolites showed 8 significant modules related to HCC risk, including those representing metabolic pathways of androgen and progestin, primary and secondary bile acids, and amino acids. A combination of 18 metabolites of independent effects showed the potential to predict HCC risk, with an AUC of 0.87 (95% CI: 0.82-0.92) and 0.86 (95% CI: 0.80-0.93) in the training and validation sets, respectively. In conclusion, we identified a panel of plasma metabolites that could be implicated in hepatocellular carcinogenesis and have the potential to predict HCC risk.

Ethnic disparities attributed to the manifestation in and response to type 2 diabetes: insights from metabolomics

Type 2 diabetes (T2D) associated health disparities among different ethnicities have long been known. Ethnic variations also exist in T2D related comorbidities including insulin resistance, vascular complications and drug response. Genetic heterogeneity, dietary patterns, nutrient metabolism and gut microbiome composition attribute to ethnic disparities in both manifestation and progression of T2D. These factors differentially regulate the rate of metabolism and metabolic health. Metabolomics studies have indicated significant differences in carbohydrate, lipid and amino acid metabolism among ethnicities. Interestingly, genetic variations regulating lipid and amino acid metabolism might also contribute to inter-ethnic differences in T2D. Comprehensive and comparative metabolomics analysis between ethnicities might help to design personalized dietary regimen and newer therapeutic strategies. In the present review, we explore population based metabolomics data to identify inter-ethnic differences in metabolites and discuss how (a) genetic variations, (b) dietary patterns and (c) microbiome composition may attribute for such differences in T2D.

Fatty acids of type 2 diabetic serum decrease the stemness properties of human adipose-derived mesenchymal stem cells

In type 2 diabetes, dyslipidemia and increased serum free fatty acids (FFAs) exacerbate the development of the disease through a negative effect on insulin secretion. Adipose-derived mesenchymal stem cells (AdMSCs) play a key role in regenerative medicine, and these cells can potentially be applied as novel therapeutic resources in the treatment of diabetes. In this study, AdMSCs were treated with diabetic or nondiabetic serum FFAs isolated from women of menopausal age. Serum FFAs were analyzed using gas-liquid chromatography. The expression level of the stemness markers CD49e and CD90 and the Wnt signaling target genes Axin-2 and c-Myc were evaluated using real-time PCR. The proliferation rate and colony formation were also assessed using a BrdU assay and crystal violet staining, respectively. The level of glutathione was assessed using cell fluorescence staining. Compared to nondiabetic serum, diabetic serum contained a higher percentage of oleate (1.5-fold, p < 0.01). In comparison with nondiabetic FFAs, diabetic FFAs demonstrated decreasing effects on the expression of CD90 (-51%, p < 0.001) and c-Myc (-48%, p < 0.05), and proliferation rate (-35%, p < 0.001), colony formation capacity (-50%, p < 0.01), and GSH levels (-62%, p < 0.05). The negative effect of the FFAs of diabetic serum on the stemness characteristics may impair the regenerative capabilities of AdMSCs.

Circulating Metabolic Biomarkers Are Consistently Associated With Type 2 Diabetes Risk in Asian and European Populations

CONTEXT

While Asians have a higher risk of type 2 diabetes (T2D) than Europeans for a given body mass index (BMI), it remains unclear whether the same markers of metabolic pathways are associated with diabetes.

OBJECTIVE

We evaluated associations between metabolic biomarkers and incidence of T2D in 3 major Asian ethnic groups (Chinese, Malay, and Indian) and a European population.

METHODS

We analyzed data from adult males and females of 2 cohorts from Singapore (n = 6393) consisting of Chinese, Malays, and Indians and 3 cohorts of European-origin participants from Finland (n = 14 558). We used nuclear magnetic resonance to quantify 154 circulating metabolic biomarkers at baseline and performed logistic regression to assess associations with T2D risk adjusted for age, sex, BMI and glycemic markers.

RESULTS

Of the 154 metabolic biomarkers, 59 were associated with higher risk of T2D in both Asians and Europeans (P < 0.0003, Bonferroni-corrected). These included branched chain and aromatic amino acids, the inflammatory marker glycoprotein acetyls, total fatty acids, monounsaturated fatty acids, apolipoprotein B, larger very low-density lipoprotein particle sizes, and triglycerides. In addition, 13 metabolites were associated with a lower T2D risk in both populations, including omega-6 polyunsaturated fatty acids and larger high-density lipoprotein particle sizes. Associations were consistent within the Asian ethnic groups (all Phet ≥ 0.05) and largely consistent for the Asian and European populations (Phet ≥ 0.05 for 128 of 154 metabolic biomarkers).

CONCLUSION

Metabolic biomarkers across several biological pathways were consistently associated with T2D risk in Asians and Europeans.

Microbiome risk profiles as biomarkers for inflammatory and metabolic disorders

The intestine harbours a complex array of microorganisms collectively known as the gut microbiota. The past two decades have witnessed increasing interest in studying the gut microbiota in health and disease, largely driven by rapid innovation in high-throughput multi-omics technologies. As a result, microbial dysbiosis has been linked to many human pathologies, including type 2 diabetes mellitus and inflammatory bowel disease. Integrated analyses of multi-omics data, including metagenomics and metabolomics along with measurements of host response and cataloguing of bacterial isolates, have identified many bacteria and bacterial products that are correlated with disease. Nevertheless, insight into the mechanisms through which microbes affect intestinal health requires going beyond correlation to causation. Current understanding of the contribution of the gut microbiota to disease causality remains limited, largely owing to the heterogeneity of microbial community structures, interindividual differences in disease evolution and incomplete understanding of the mechanisms that integrate microbiota-derived signals into host signalling pathways. In this Review, we provide a broad insight into the microbiome signatures linked to inflammatory and metabolic disorders, discuss outstanding challenges in this field and propose applications of multi-omics technologies that could lead to an improved mechanistic understanding of microorganism-host interactions.

Development and validation of a diabetes mellitus and prediabetes risk prediction function for case finding in primary care in Hong Kong: a cross-sectional study and a prospective study protocol paper

INTRODUCTION

Diabetes mellitus (DM) is a major non-communicable disease with an increasing prevalence. Undiagnosed DM is not uncommon and can lead to severe complications and mortality. Identifying high-risk individuals at an earlier disease stage, that is, pre-diabetes (pre-DM), is crucial in delaying progression. Existing risk models mainly rely on non-modifiable factors to predict only the DM risk, and few apply to Chinese people. This study aims to develop and validate a risk prediction function that incorporates modifiable lifestyle factors to detect DM and pre-DM in Chinese adults in primary care.

METHODS AND ANALYSIS

A cross-sectional study to develop DM/Pre-DM risk prediction functions using data from the Hong Kong's Population Health Survey (PHS) 2014/2015 and a 12-month prospective study to validate the functions in case finding of individuals with DM/pre-DM. Data of 1857 Chinese adults without self-reported DM/Pre-DM will be extracted from the PHS 2014/2015 to develop DM/Pre-DM risk models using logistic regression and machine learning methods. 1014 Chinese adults without a known history of DM/Pre-DM will be recruited from public and private primary care clinics in Hong Kong. They will complete a questionnaire on relevant risk factors and blood tests on Oral Glucose Tolerance Test (OGTT) and haemoglobin A1C (HbA1c) on recruitment and, if the first blood test is negative, at 12 months. A positive case is DM/pre-DM defined by OGTT or HbA1c in any blood test. Area under receiver operating characteristic curve, sensitivity, specificity, positive predictive value and negative predictive value of the models in detecting DM/pre-DM will be calculated.

ETHICS AND DISSEMINATION

Ethics approval has been received from The University of Hong Kong/Hong Kong Hospital Authority Hong Kong West Cluster (UW19-831) and Hong Kong Hospital Authority Kowloon Central/Kowloon East Cluster (REC(KC/KE)-21-0042/ER-3). The study results will be submitted for publication in a peer-reviewed journal.

TRIAL REGISTRATION NUMBER

US ClinicalTrial.gov: NCT04881383; HKU clinical trials registry: HKUCTR-2808; Pre-results.

Lipidomics Analysis of Free Fatty Acids in Human Plasma of Healthy and Diabetic Subjects by Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS)

Targeted analytical methods for the determination of free fatty acids (FFAs) in human plasma are of high interest because they may help in identifying biomarkers for diseases and in monitoring the progress of a disease. The determination of FFAs is of particular importance in the case of metabolic disorders because FFAs have been associated with diabetes. We present a liquid chromatography-high resolution mass spectrometry (LC-HRMS) method, which allows the simultaneous determination of 74 FFAs in human plasma. The method is fast (10-min run) and straightforward, avoiding any derivatization step and tedious sample preparation. A total of 35 standard saturated and unsaturated FFAs, as well as 39 oxygenated (either hydroxy or oxo) saturated FFAs, were simultaneously detected and quantified in plasma samples from 29 subjects with type 2 diabetes mellitus (T2D), 14 with type 1 diabetes mellitus (T1D), and 28 healthy subjects. Alterations in the levels of medium-chain FFAs (C6:0 to C10:0) were observed between the control group and T2D and T1D patients.

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.

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.

Metabolomics and Type 2 Diabetes Risk: An Updated Systematic Review and Meta-analysis of Prospective Cohort Studies

BACKGROUND

Due to the rapidly increasing availability of metabolomics data in prospective studies, an update of the meta evidence on metabolomics and type 2 diabetes risk is warranted.

PURPOSE

To conduct an updated systematic review and meta-analysis of plasma, serum, and urine metabolite markers and incident type 2 diabetes.

DATA SOURCES

We searched PubMed and Embase until 6 March 2021.

STUDY SELECTION

We selected prospective observational studies where investigators used high-throughput techniques to investigate the relationship between plasma, serum, or urine metabolites and incident type 2 diabetes.

DATA EXTRACTION

Baseline metabolites per-SD risk estimates and 95% CIs for incident type 2 diabetes were extracted from all eligible studies.

DATA SYNTHESIS

A total of 61 reports with 71,196 participants and 11,771 type 2 diabetes cases/events were included in the updated review. Meta-analysis was performed for 412 metabolites, of which 123 were statistically significantly associated (false discovery rate-corrected P < 0.05) with type 2 diabetes risk. Higher plasma and serum levels of certain amino acids (branched-chain, aromatic, alanine, glutamate, lysine, and methionine), carbohydrates and energy-related metabolites (mannose, trehalose, and pyruvate), acylcarnitines (C4-DC, C4-OH, C5, C5-OH, and C8:1), the majority of glycerolipids (di- and triacylglycerols), (lyso)phosphatidylethanolamines, and ceramides included in meta-analysis were associated with higher risk of type 2 diabetes (hazard ratio 1.07-2.58). Higher levels of glycine, glutamine, betaine, indolepropionate, and (lyso)phosphatidylcholines were associated with lower type 2 diabetes risk (hazard ratio 0.69-0.90).

LIMITATIONS

Substantial heterogeneity (I2 > 50%, τ2 > 0.1) was observed for some of the metabolites.

CONCLUSIONS

Several plasma and serum metabolites, including amino acids, lipids, and carbohydrates, are associated with type 2 diabetes risk.

Potential Novel Serum Metabolic Markers Associated With Progression of Prediabetes to Overt Diabetes in a Chinese Population

Background

Identifying the metabolite profile of individuals with prediabetes who turned to type 2 diabetes (T2D) may give novel insights into early T2D interception. The purpose of this study was to identify metabolic markers that predict the development of T2D from prediabetes in a Chinese population.

Methods

We used an untargeted metabolomics approach to investigate the associations between serum metabolites and risk of prediabetes who turned to overt T2D (n=153, mean follow up 5 years) in a Chinese population (REACTION study). Results were compared with matched controls who had prediabetes at baseline [age: 56 ± 7 years old, body mass index (BMI): 24.2 ± 2.8 kg/m2] and at a 5-year follow-up [age: 61 ± 7 years old, BMI: 24.5 ± 3.1 kg/m2]. Confounding factors were adjusted and the associations between metabolites and diabetes risk were evaluated with multivariate logistic regression analysis. A 10-fold cross-validation random forest classification (RFC) model was used to select the optimal metabolites panels for predicting the development of diabetes, and to internally validate the discriminatory capability of the selected metabolites beyond conventional clinical risk factors.

Findings

Metabolic alterations, including those associated with amino acid and lipid metabolism, were associated with an increased risk of prediabetes progressing to diabetes. The most important metabolites were inosine [odds ratio (OR) = 19.00; 95% confidence interval (CI): 4.23-85.37] and carvacrol (OR = 17.63; 95% CI: 4.98-62.34). Thirteen metabolites were found to improve T2D risk prediction beyond eight conventional T2D risk factors [area under the curve (AUC) was 0.98 for risk factors + metabolites vs 0.72 for risk factors, P < 0.05].

Interpretations

Use of the metabolites identified in this study may help determine patients with prediabetes who are at highest risk of progressing to diabetes.

Untargeted Metabolomics Analysis of the Serum Metabolic Signature of Childhood Obesity

Obesity rates among children are growing rapidly worldwide, placing massive pressure on healthcare systems. Untargeted metabolomics can expand our understanding of the pathogenesis of obesity and elucidate mechanisms related to its symptoms. However, the metabolic signatures of obesity in children have not been thoroughly investigated. Herein, we explored metabolites associated with obesity development in childhood. Untargeted metabolomic profiling was performed on fasting serum samples from 27 obese Caucasian children and adolescents and 15 sex- and age-matched normal-weight children. Three metabolomic assays were combined and yielded 726 unique identified metabolites: gas chromatography–mass spectrometry (GC–MS), hydrophilic interaction liquid chromatography coupled to mass spectrometry (HILIC LC–MS/MS), and lipidomics. Univariate and multivariate analyses showed clear discrimination between the untargeted metabolomes of obese and normal-weight children, with 162 significantly differentially expressed metabolites between groups. Children with obesity had higher concentrations of branch-chained amino acids and various lipid metabolites, including phosphatidylcholines, cholesteryl esters, triglycerides. Thus, an early manifestation of obesity pathogenesis and its metabolic consequences in the serum metabolome are correlated with altered lipid metabolism. Obesity metabolite patterns in the adult population were very similar to the metabolic signature of childhood obesity. Identified metabolites could be potential biomarkers and used to study obesity pathomechanisms.

Association between the metabolic profile of serum fatty acids and diabetic nephropathy: a study conducted in northeastern China

BACKGROUND AND PURPOSE

With the progressive increase in the prevalence of type 2 diabetes mellitus (T2DM), diabetic nephropathy (DN) - one of the most common chronic microvascular complications - has evolved into a significant cause of death worldwide among end-stage renal disease patients. Academic researchers have for decades focused on the development of DN and recently found that free fatty acids (FFAs) constituted an independent risk factor for vascular complications in T2DM patients. It is therefore critical to determine whether the metabolic profile of FFAs is related to DN.

METHODS

This study comprised 611 research subjects in Dalian, a city in northeast China: 52 DN patients, 115 T2DM patients, and 444 healthy controls. We determined 15 forms of serum FFAs, including arachidonic acid (AA, C20:4), docosahexaenoic acid (DHA, C22:6), erucic acid (C22:1), nervonic acid (NA, C24:1), estimated total omega-3s, total omega-6s, the omega-3/omega-6 ratio, and total FFA content by liquid chromatography-mass spectrometry (LC-MS).

RESULTS

The levels of NA (mean = 45.27, range = 0.84-76.57) and DHA (mean = 324.58, range = 205.38-450.03) in DN patients were slightly lower than those in T2DM patients or healthy controls. The serum omega-3 polyunsaturated fatty acid (PUFA) DHA (C22:6) was significantly negatively correlated with microalbuminuria (MAU), the albumin/creatinine ratio (ACR), body mass index (BMI), fasting plasma glucose (FPG), and glycosylated hemoglobin (HbA1c). The serum monounsaturated fatty acid (MUFA) NA (C24:1) was significantly negatively correlated with BMI, FPG, and HbA1c. After adjustment of variables, multiple logistic regression analysis revealed significant odds ratios (ORs) [with confidence intervals (CIs)] for DHA (0.991, 0.985-0.997; p = 0.002) and NA (0.978, 0.958-0.999; p = 0.037).

CONCLUSION

In this study, we ascertained that the contents of NA and DHA in patients with DN were relatively low, and that DHA was negatively correlated with MAU and the ACR. However, large-scale, population-based studies focusing on the role of NA and DHA in the pathogenesis of DN are still required in the future.

Use of metabolomics in refining the effect of an organic food intervention on biomarkers of exposure to pesticides and biomarkers of oxidative damage in primary school children in Cyprus: A cluster-randomized cross-over trial

BACKGROUND

Exposure to pesticides has been associated with oxidative stress in animals and humans. Previously, we showed that an organic food intervention reduced pesticide exposure and oxidative damage (OD) biomarkers over time; however associated metabolic changes are not fully understood yet.

OBJECTIVES

We assessed perturbations of the urine metabolome in response to an organic food intervention for children and its association with pesticides biomarkers [3-phenoxybenzoic acid (3-PBA) and 6-chloronicotinic acid (6-CN)]. We also evaluated the molecular signatures of metabolites associated with biomarkers of OD (8-iso-PGF2a and 8-OHdG) and related biological pathways.

METHODS

We used data from the ORGANIKO LIFE + trial (NCT02998203), a cluster-randomized cross-over trial conducted among primary school children in Cyprus. Participants (n = 149) were asked to follow an organic food intervention for 40 days and their usual food habits for another 40 days, providing up to six first morning urine samples (>850 samples in total). Untargeted GC-MS metabolomics analysis was performed. Metabolites with RSD ≤ 20% and D-ratio ≤ 50% were retained for analysis. Associations were examined using mixed-effect regression models and corrected for false-discovery rate of 0.05. Pathway analysis followed.

RESULTS

Following strict quality checks, 156 features remained out of a total of 610. D-glucose was associated with the organic food intervention (β = -0.23, 95% CI: -0.37,-0.10), aminomalonic acid showed a time-dependent increase during the intervention period (β_int = 0.012; 95% CI:0.002, 0.022) and was associated with the two OD biomarkers (β = -0.27, 95% CI:-0.34,-0.20 for 8-iso-PGF2a and β = 0.19, 95% CI:0.11,0.28 for 8-OHdG) and uric acid with 8-OHdG (β = 0.19, 95% CI:0.11,0.26). Metabolites were involved in pathways such as the starch and sucrose metabolism and pentose and glucuronate interconversions.

DISCUSSION

This is the first metabolomics study providing evidence of differential expression of metabolites by an organic food intervention, corroborating the reduction in biomarkers of OD. Further mechanistic evidence is warranted to better understand the biological plausibility of an organic food treatment on children's health outcomes.

Examining the Interaction of the Gut Microbiome with Host Metabolism and Cardiometabolic Health in Metabolic Syndrome

(1) Background: The microbiota-host cross-talk has been previously investigated, while its role in health is not yet clear. This study aimed to unravel the network of microbial-host interactions and correlate it with cardiometabolic risk factors. (2) Methods: A total of 47 adults with overweight/obesity and metabolic syndrome from the METADIET study were included in this cross-sectional analysis. Microbiota composition (151 genera) was assessed by 16S rRNA sequencing, fecal (m = 203) and plasma (m = 373) metabolites were profiled. An unsupervised sparse generalized canonical correlation analysis was used to construct a network of microbiota-metabolite interactions. A multi-omics score was derived for each cluster of the network and associated with cardiometabolic risk factors. (3) Results: Five multi-omics clusters were identified. Thirty-one fecal metabolites formed these clusters and were correlated with plasma sphingomyelins, lysophospholipids and medium to long-chain acylcarnitines. Seven genera from Ruminococcaceae and a member from the Desulfovibrionaceae family were correlated with fecal and plasma metabolites. Positive correlations were found between the multi-omics scores from two clusters with cholesterol and triglycerides levels. (4) Conclusions: We identified a correlated network between specific microbial genera and fecal/plasma metabolites in an adult population with metabolic syndrome, suggesting an interplay between gut microbiota and host lipid metabolism on cardiometabolic health.

Gut-Microbial Metabolites, Probiotics and Their Roles in Type 2 Diabetes

Type 2 diabetes (T2D) is a worldwide prevalent metabolic disorder defined by high blood glucose levels due to insulin resistance (IR) and impaired insulin secretion. Understanding the mechanism of insulin action is of great importance to the continuing development of novel therapeutic strategies for the treatment of T2D. Disturbances of gut microbiota have been widely found in T2D patients and contribute to the development of IR. In the present article, we reviewed the pathological role of gut microbial metabolites including gaseous products, branched-chain amino acids (BCAAs) products, aromatic amino acids (AAAs) products, bile acids (BA) products, choline products and bacterial toxins in regulating insulin sensitivity in T2D. Following that, we summarized probiotics-based therapeutic strategy for the treatment of T2D with a focus on modulating gut microbiota in both animal and human studies. These results indicate that gut-microbial metabolites are involved in the pathogenesis of T2D and supplementation of probiotics could be beneficial to alleviate IR in T2D via modulation of gut microbiota.

Effect of Metabolite Levels on Type 2 Diabetes Mellitus and Glycemic Traits: A Mendelian Randomization Study

OBJECTIVE

To assess the causal associations of plasma levels of metabolites with type 2 diabetes mellitus (T2DM) and glycemic traits.

METHODS

Two-sample mendelian randomization (MR) was conducted to assess the causal associations. Genetic variants strongly associated with metabolites at genome-wide significance level (P < 5 × 10-8) were selected from public genome-wide association studies, and single-nucleotide polymorphisms of outcomes were obtained from the Diabetes Genetics Replication and Meta-analysis consortium for T2DM and from the Meta-Analyses of Glucose and Insulin-related Traits Consortium for fasting glucose, insulin, and glycated hemoglobin (HbA1c). The Wald ratio and inverse-variance weighted methods were used for analyses, and MR-Egger was used for sensitivity analysis.

RESULTS

The β estimates per 1-SD increase of arachidonic acid (AA) level was 0.16 (95% CI, 0.078-0.242; P < 0.001). Genetic predisposition to higher plasma AA levels were associated with higher fasting glucose levels (β 0.10 [95% CI, 0.064-0.134], P < 0.001), higher HbA1c levels (β 0.04 [95% CI, 0.027-0.061]), and lower fasting insulin levels (β -0.025 [95% CI, -0.047 to -0.002], P = 0.033). Besides, 2-hydroxybutyric acid (2-HBA) might have a positive causal effect on glycemic traits.

CONCLUSIONS

Our findings suggest that AA and 2-HBA may have causal associations on T2DM and glycemic traits. This is beneficial for clarifying the pathogenesis of T2DM, which would be valuable for early identification and prevention for T2DM.

Metabolomics in Diabetes and Diabetic Complications: Insights from Epidemiological Studies

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