Novel biomarkers for pre-diabetes identified by metabolomics

The link between the ANPEP gene and type 2 diabetes mellitus may be mediated by the disruption of glutathione metabolism and redox homeostasis

Aminopeptidase N (ANPEP), a membrane-associated ectoenzyme, has been identified as a susceptibility gene for type 2 diabetes (T2D) by genome-wide association and transcriptome studies; however, the mechanisms by which this gene contributes to disease pathogenesis remain unclear. The aim of this study was to determine the comprehensive contribution of ANPEP polymorphisms to T2D risk and annotate the underlying mechanisms. A total of 3206 unrelated individuals including 1579 T2D patients and 1627 controls were recruited for the study. Twenty-three common functional single nucleotide polymorphisms (SNP) of ANPEP were genotyped by the MassArray-4 system. Six polymorphisms, rs11073891, rs12898828, rs12148357, rs9920421, rs7111, and rs25653, were found to be associated with type 2 diabetes (Pperm ≤ 0.05). Common haplotype rs9920421G-rs4932143G-rs7111T was strongly associated with increased risk of T2D (Pperm = 5.9 × 10-12), whereas two rare haplotypes such as rs9920421G-rs4932143C-rs7111T (Pperm = 6.5 × 10-40) and rs12442778A-rs12898828A-rs6496608T-rs11073891C (Pperm = 1.0 × 10-7) possessed strong protection against disease. We identified 38 and 109 diplotypes associated with T2D risk in males and females, respectively (FDR ≤ 0.05). ANPEP polymorphisms showed associations with plasma levels of fasting blood glucose, aspartate aminotransferase, total protein and glutathione (P < 0.05), and several haplotypes were strongly associated with the levels of reactive oxygen species and uric acid (P < 0.0001). A deep literature analysis has facilitated the formulation of a hypothesis proposing that increased plasma levels of ANPEP as well as liver enzymes such as aspartate aminotransferase, alanine aminotransferase and gammaglutamyltransferase serve as an adaptive response directed towards the restoration of glutathione deficiency in diabetics by stimulating the production of amino acid precursors for glutathione biosynthesis.

Lc-ms-based untargeted metabolomics reveals potential mechanisms of histologic chronic inflammation promoting prostate hyperplasia

BACKGROUND

Chronic prostatitis may be a risk factor for developing proliferative changes in the prostate, although the underlying mechanisms are not entirely comprehended.

MATERIALS AND METHODS

Fifty individual prostate tissues were examined in this study, consisting of 25 patients diagnosed with prostatic hyperplasia combined with histologic chronic inflammation and 25 patients diagnosed with prostatic hyperplasia alone. We employed UPLC-Q-TOF-MS-based untargeted metabolomics using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry to identify differential metabolites that can reveal the mechanisms that underlie the promotion of prostate hyperplasia by histologic chronic inflammation. Selected differential endogenous metabolites were analyzed using bioinformatics and subjected to metabolic pathway studies.

RESULTS

Nineteen differential metabolites, consisting of nine up-regulated and ten down-regulated, were identified between the two groups of patients. These groups included individuals with combined histologic chronic inflammation and those with prostatic hyperplasia alone. Glycerolipids, glycerophospholipids, and sphingolipids were primarily the components present. Metabolic pathway enrichment was conducted on the identified differentially expressed metabolites. Topological pathway analysis revealed the differential metabolites' predominant involvement in sphingolipid, ether lipid, and glycerophospholipid metabolism. The metabolites involved in sphingolipid metabolism were Sphingosine, Cer (d18:1/24:1), and Phytosphingosine. The metabolites involved in ether lipid metabolism were Glycerophosphocholine and LysoPC (O-18:0/0:0). The metabolites involved in glycerophospholipid metabolism were LysoPC (P-18:0/0:0) and Glycerophosphocholine. with Impact > 0. 1 and FDR < 0. 05, the most important metabolic pathway was sphingolipid metabolism.

CONCLUSIONS

In conclusion, our findings suggest that patients with prostate hyperplasia and combined histologic chronic inflammation possess distinctive metabolic profiles. These differential metabolites appear to play a significant role in the pathogenesis of histologic chronic inflammation-induced prostate hyperplasia, primarily through the regulation of sphingolipids and glycerophospholipids metabolic pathways. The mechanism by which histologic chronic inflammation promotes prostate hyperplasia was elucidated through the analysis of small molecule metabolites. These findings support the notion that chronic prostatitis may contribute to an increased risk of prostate hyperplasia.

Role of human plasma metabolites in prediabetes and type 2 diabetes from the IMI-DIRECT study

AIMS/HYPOTHESIS

Type 2 diabetes is a chronic condition that is caused by hyperglycaemia. Our aim was to characterise the metabolomics to find their association with the glycaemic spectrum and find a causal relationship between metabolites and type 2 diabetes.

METHODS

As part of the Innovative Medicines Initiative - Diabetes Research on Patient Stratification (IMI-DIRECT) consortium, 3000 plasma samples were measured with the Biocrates AbsoluteIDQ p150 Kit and Metabolon analytics. A total of 911 metabolites (132 targeted metabolomics, 779 untargeted metabolomics) passed the quality control. Multivariable linear and logistic regression analysis estimates were calculated from the concentration/peak areas of each metabolite as an explanatory variable and the glycaemic status as a dependent variable. This analysis was adjusted for age, sex, BMI, study centre in the basic model, and additionally for alcohol, smoking, BP, fasting HDL-cholesterol and fasting triacylglycerol in the full model. Statistical significance was Bonferroni corrected throughout. Beyond associations, we investigated the mediation effect and causal effects for which causal mediation test and two-sample Mendelian randomisation (2SMR) methods were used, respectively.

RESULTS

In the targeted metabolomics, we observed four (15), 34 (99) and 50 (108) metabolites (number of metabolites observed in untargeted metabolomics appear in parentheses) that were significantly different when comparing normal glucose regulation vs impaired glucose regulation/prediabetes, normal glucose regulation vs type 2 diabetes, and impaired glucose regulation vs type 2 diabetes, respectively. Significant metabolites were mainly branched-chain amino acids (BCAAs), with some derivatised BCAAs, lipids, xenobiotics and a few unknowns. Metabolites such as lysophosphatidylcholine a C17:0, sum of hexoses, amino acids from BCAA metabolism (including leucine, isoleucine, valine, N-lactoylvaline, N-lactoylleucine and formiminoglutamate) and lactate, as well as an unknown metabolite (X-24295), were associated with HbA1c progression rate and were significant mediators of type 2 diabetes from baseline to 18 and 48 months of follow-up. 2SMR was used to estimate the causal effect of an exposure on an outcome using summary statistics from UK Biobank genome-wide association studies. We found that type 2 diabetes had a causal effect on the levels of three metabolites (hexose, glutamate and caproate [fatty acid (FA) 6:0]), whereas lipids such as specific phosphatidylcholines (PCs) (namely PC aa C36:2, PC aa C36:5, PC ae C36:3 and PC ae C34:3) as well as the two n-3 fatty acids stearidonate (18:4n3) and docosapentaenoate (22:5n3) potentially had a causal role in the development of type 2 diabetes.

CONCLUSIONS/INTERPRETATION

Our findings identify known BCAAs and lipids, along with novel N-lactoyl-amino acid metabolites, significantly associated with prediabetes and diabetes, that mediate the effect of diabetes from baseline to follow-up (18 and 48 months). Causal inference using genetic variants shows the role of lipid metabolism and n-3 fatty acids as being causal for metabolite-to-type 2 diabetes whereas the sum of hexoses is causal for type 2 diabetes-to-metabolite. Identified metabolite markers are useful for stratifying individuals based on their risk progression and should enable targeted interventions.

Abdominal obesity in youth: the associations of plasma Lysophophatidylcholine concentrations with insulin resistance

BACKGROUD

This study aimed to explore the associations of lysophosphatidylcholines (LPCs) with insulin resistance (IR) and abdominal obesity among children and adolescents.

METHODS

A cross-sectional study was conducted on 612 young individuals, aged 7 to 18 years in Tianjin City, China. LC-MS metabolomic analysis was used to measure LPCs levels. The Homeostasis Model Assessment was used to estimate IR. Waist circumference measurements were used to assess abdominal obesity. Logistic regression models were employed to explore the relationships between LPCs and IR and abdominal obesity. Mediation analyses were performed to analyze whether LPCs affected IR through abdominal obesity.

RESULTS

Compared to their counterparts, five specific LPCs were significantly different in youth with IR. The levels of LPC 24:0 and 26:0 were significantly associated with IR after adjustment. Both decreased levels of LPC 24:0 and 26:0 associated with the increased risks of IR (OR: 0.64, 95%CI: 0.38-0.95; OR: 0.66, 95%CI: 0.40-1.00), and the ORs for abdominal obesity were 0.68 (95%CI: 0.38-1.00) and 0.51 (95%CI: 0.28-0.90), respectively. Mediation analysis indicated that abdominal obesity mediated the association between LPC 26:0 and IR, with a total effect (c) of -0.109 (P < 0.05), a direct effect (c') of -0.055 (P > 0.05), and an indirect effect through obesity (a × b) path with "a" of -0.125 (P < 0.05) and "b" of 0.426 (P < 0.05).

CONCLUSION

Overall findings suggest that decreased levels of LPC 24:0 and 26:0 were associated with increased risks of IR and abdominal obesity. Importantly, addressing abdominal obesity may mediate the impact of IR driven by LPC 26:0.

Association of Acylcarnitines With Maternal Cardiometabolic Risk Factors Is Defined by Chain Length: The S-PRESTO Study

CONTEXT

Due to the essential role of carnitine as an intermediary in amino acid, carbohydrate, and lipid metabolism, a detailed characterization of circulating and urinary carnitine concentrations will aid in elucidating the molecular basis of impaired maternal metabolic flexibility and facilitating timely intervention for expectant mothers.

OBJECTIVE

To investigate the association of maternal plasma and urinary free carnitine and acylcarnitines with cardiometabolic risk factors.

METHODS

Liquid chromatography tandem mass spectrometry-based quantification of free carnitine and acylcarnitines (C2-C18) was performed on 765 plasma and 702 urine samples collected at preconception, 26 to 28 weeks' pregnancy, and 3 months postpartum in the Singapore PREconception Study of long-Term maternal and child Outcomes (S-PRESTO) cohort study.

RESULTS

Plasma concentrations of free carnitine and acylcarnitines decreased coupled with increased renal clearance in pregnancy compared with preconception and postpartum. Renal clearance of carnitine increased with an increase in prepregnancy body mass index (ppBMI) and gestational weight gain. Plasma short-chain acylcarnitines were positively associated with ppBMI, irrespective of the physiological state, while medium- and long-chain acylcarnitines were negatively associated with ppBMI at preconception and postpartum but showed a positive association in pregnancy. Similarly, plasma short-chain acylcarnitines were positively associated with Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) whereas medium- and long-chain acylcarnitines were negatively associated with HOMA-IR at preconception and in pregnancy. Mothers who developed gestational diabetes mellitus during pregnancy had ∼10% higher plasma propionylcarnitine concentration and ∼18% higher urine tiglylcarnitine concentration than mothers with normal glucose metabolism at preconception.

CONCLUSION

This study provides the metabolic and physiological basis of maternal carnitine homeostasis, which can be used in assessment of maternal cardiometabolic health at preconception to improve pregnancy outcomes.

Metabolomic Applications in Gut Microbiota-Host Interactions in Human Diseases

The human gut microbiota, consisting of trillions of microorganisms, encodes diverse metabolic pathways that impact numerous aspects of host physiology. One key way in which gut bacteria interact with the host is through the production of small metabolites. Several of these microbiota-dependent metabolites, such as short-chain fatty acids, have been shown to modulate host diseases. In this review, we examine how disease-associated metabolic signatures are identified using metabolomic platforms, and where metabolomics is applied in gut microbiota-disease interactions. We further explore how integration of metagenomic and metabolomic data in human studies can facilitate biomarkers discoveries in precision medicine.

Characterization of the plasma metabolome and lipidome in response to sleeve gastrectomy and gastric bypass surgeries reveals molecular patterns of surgical weight loss

OBJECTIVES

Bariatric surgery improves metabolic health, but the underlying mechanisms are not fully understood. We analyzed the effects of two types of bariatric surgery, sleeve gastrectomy (SG) and Roux-en-Y gastric bypass (RYGB), on the plasma metabolome and lipidome.

METHODS

We characterized the plasma metabolome (1268 metabolites) and lipidome (953 lipids) pre-operatively and at 3 and 12 months post-operatively in 104 obese adults who were previously recruited to a prospective cohort of bariatric surgery. The metabolomic and lipidomic responses to bariatric surgery over time were analyzed using multivariable linear mixed-effects models.

RESULTS

There were significant changes in multiple metabolites and lipids, including rapid early changes in amino acid and peptide metabolites, including decreases in branched-chain amino acids (BCAAs), aromatic AAs, alanine and aspartate, and increases in glycine, serine, arginine and citrulline. There were also significant decreases in many triglyceride species, with increases in phosphatidylcholines and phosphatidylethanolamines. There were significant changes in metabolites related to energy metabolism that were apparent only after 12 months. We observed differences by bariatric surgery type in the changes in a small number of primary and secondary bile acids, including glycohyocholate and glyco-beta-muricholate.

CONCLUSIONS

Our findings highlight the comprehensive changes in metabolites and lipids that occur over the 12 months following bariatric surgery. While both SG and RYGB caused profound changes in the metabolome and lipidome, RYGB was characterized by greater increases in bile acids following surgery.

Nanotopography Influences Host-Pathogen Quorum Sensing and Facilitates Selection of Bioactive Metabolites in Mesenchymal Stromal Cells and Pseudomonas aeruginosa Co-Cultures

Orthopedic implant-related bacterial infections and resultant antibiotic-resistant biofilms hinder implant-tissue integration and failure. Biofilm quorum sensing (QS) communication determines the pathogen colonization success. However, it remains unclear how implant modifications and host cells are influenced by, or influence, QS. High aspect ratio nanotopographies have shown to reduce biofilm formation of Pseudomonas aeruginosa, a sepsis causing pathogen with well-defined QS molecules. Producing such nanotopographies in relevant orthopedic materials (i.e., titanium) allows for probing QS using mass spectrometry-based metabolomics. However, nanotopographies can reduce host cell adhesion and regeneration. Therefore, we developed a polymer (poly(ethyl acrylate), PEA) coating that organizes extracellular matrix proteins, promoting bioactivity to host cells such as human mesenchymal stromal cells (hMSCs), maintaining biofilm reduction. This allowed us to investigate how hMSCs, after winning the race for the surface against pathogenic cells, interact with the biofilm. Our approach revealed that nanotopographies reduced major virulence pathways, such as LasR. The enhanced hMSCs support provided by the coated nanotopographies was shown to suppress virulence pathways and biofilm formation. Finally, we selected bioactive metabolites and demonstrated that these could be used as adjuncts to the nanostructured surfaces to reduce biofilm formation and enhance hMSC activity. These surfaces make excellent models to study hMSC-pathogen interactions and could be envisaged for use in novel orthopedic implants.

A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes

In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays "The Molecular Human". We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to "The Molecular Human" ( http://comics.metabolomix.com ), providing interactive data exploration and hypotheses generation possibilities.

Gut-muscle axis mechanism of exercise prevention of sarcopenia

Sarcopenia refers to an age-related systemic skeletal muscle disorder, which is characterized by loss of muscle mass and weakening of muscle strength. Gut microbiota can affect skeletal muscle through a variety of mechanisms. Gut microbiota present distinct features among elderly people and sarcopenia patients, including a decrease in microbial diversity, which might be associated with the quality and function of the skeletal muscle. There might be a gut-muscle axis; where gut microbiota and skeletal muscle may affect each other bi-directionally. Skeletal muscle can affect the biodiversity of the gut microbiota, and the latter can, in turn, affect the anabolism of skeletal muscle. This review examines recent studies exploring the relationship between gut microbiota and skeletal muscle, summarizes the effects of exercise on gut microbiota, and discusses the possible mechanisms of the gut-muscle axis.

Linoleoyl-lysophosphatidylcholine suppresses immune-related adverse events due to immune checkpoint blockade

Immune related adverse events (irAEs) after immune checkpoint blockade (ICB) therapy occur in a significant proportion of cancer patients. To date, the circulating mediators of ICB-irAEs remain poorly understood. Using non-targeted mass spectrometry, here we identify the circulating bio-active lipid linoleoyl-lysophosphatidylcholine (LPC 18:2) as a modulator of ICB-irAEs. In three independent human studies of ICB treatment for solid tumor, loss of circulating LPC 18:2 preceded the development of severe irAEs across multiple organ systems. In both healthy humans and severe ICB-irAE patients, low LPC 18:2 was found to correlate with high blood neutrophilia. Reduced LPC 18:2 biosynthesis was confirmed in preclinical ICB-irAE models, and LPC 18:2 supplementation in vivo suppressed neutrophilia and tissue inflammation without impacting ICB anti-tumor response. Results indicate that circulating LPC 18:2 suppresses human ICB-irAEs, and LPC 18:2 supplementation may improve ICB outcomes by preventing severe inflammation while maintaining anti-tumor immunity.

Quantified Metabolomics and Lipidomics Profiles Reveal Serum Metabolic Alterations and Distinguished Metabolites of Seven Chronic Metabolic Diseases

The co-occurrence of multiple chronic metabolic diseases is highly prevalent, posing a huge health threat. Clarifying the metabolic associations between them, as well as identifying metabolites which allow discrimination between diseases, will provide new biological insights into their co-occurrence. Herein, we utilized targeted serum metabolomics and lipidomics covering over 700 metabolites to characterize metabolic alterations and associations related to seven chronic metabolic diseases (obesity, hypertension, hyperuricemia, hyperglycemia, hypercholesterolemia, hypertriglyceridemia, fatty liver) from 1626 participants. We identified 454 metabolites were shared among at least two chronic metabolic diseases, accounting for 73.3% of all 619 significant metabolite-disease associations. We found amino acids, lactic acid, 2-hydroxybutyric acid, triacylglycerols (TGs), and diacylglycerols (DGs) showed connectivity across multiple chronic metabolic diseases. Many carnitines were specifically associated with hyperuricemia. The hypercholesterolemia group showed obvious lipid metabolism disorder. Using logistic regression models, we further identified distinguished metabolites of seven chronic metabolic diseases, which exhibited satisfactory area under curve (AUC) values ranging from 0.848 to 1 in discovery and validation sets. Overall, quantitative metabolome and lipidome data sets revealed widespread and interconnected metabolic disorders among seven chronic metabolic diseases. The distinguished metabolites are useful for diagnosing chronic metabolic diseases and provide a reference value for further clinical intervention and management based on metabolomics strategy.

GALNT2 expression is associated with glucose control and serum metabolites in patients with type 2 diabetes

AIMS

Aim of this study was to investigate in type 2 diabetes whether expression level of GALNT2, a positive modulator of insulin sensitivity, is associated with a metabolic signature.

METHODS

Five different metabolite families, including acylcarnitines, aminoacids, biogenic amines, phospholipids and sphingolipids were investigated in fasting serum of 70 patients with type 2 diabetes, by targeted metabolomics. GALNT2 expression levels were measured in peripheral white blood cells by RT-PCR. The association between GALNT2 expression and serum metabolites was assessed using false discovery rate followed by stepwise selection and, finally, multivariate model including several clinical parameters as confounders. The association between GALNT2 expression and the same clinical parameters was also investigated.

RESULTS

GALNT2 expression was independently correlated with HbA1c levels (P value = 0.0052), a finding that is the likely consequence of the role of GALNT2 on insulin sensitivity. GALNT2 expression was also independently associated with serum levels of the aminoacid glycine (P value = 0.014) and two biogenic amines phenylethylamine (P value = 0.0065) and taurine (P value = 0.0011). The association of GALNT2 expression with HbA1c was not mediated by these three metabolites.

CONCLUSIONS

Our data indicate that in type 2 diabetes the expression of GALNT2 is associated with several serum metabolites. This association needs to be further investigated to understand in depth its role in mediating the effect of GALNT2 on insulin sensitivity, glucose control and other clinical features in people with diabetes.

Integrated biomarker profiling for predicting the response of type 2 diabetes to metformin

AIM

To explore biomarkers that can predict the response of type 2 diabetes (T2D) patients to metformin at an early stage to provide better treatment for T2D.

METHODS

T2D patients with (responders) or without response (non-responders) to metformin were recruited, and their serum samples were used for metabolomic analysis to identify candidate biomarkers. Moreover, the efficacy of metformin was verified by insulin-resistant mice, and the candidate biomarkers were verified to determine the biomarkers. Five different machine learning methods were used to construct the integrated biomarker profiling (IBP) with the biomarkers to predict the response of T2D patients to metformin.

RESULTS

A total of 73 responders and 63 non-responders were recruited, and 88 differential metabolites were identified in the serum samples. After being verified in mice, 19 of the 88 were considered as candidate biomarkers. Next, after metformin regulation, nine candidate biomarkers were confirmed as the biomarkers. After comparing five machine learning models, the nine biomarkers were constructed into the IBP for predicting the response of T2D patients to metformin based on the Naïve Bayes classifier, which was verified with an accuracy of 89.70%.

CONCLUSIONS

The IBP composed of nine biomarkers can be used to predict the response of T2D patients to metformin, enabling clinicians to start a combined medication strategy as soon as possible if T2D patients do not respond to metformin.

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.

Serum metabolomics reveals the metabolic profile and potential biomarkers of ankylosing spondylitis

Ankylosing spondylitis (AS) is a chronic systemic inflammatory disease that significantly impairs physical function in young individuals. However, the identification of radiographic changes in AS is frequently delayed, and the diagnostic efficacy of biomarkers like HLA-B27 remains moderately effective, with unsatisfactory sensitivity and specificity. In contrast to existing literature, our current experiment utilized a larger sample size and employed both untargeted and targeted UHPLC-QTOF-MS/MS based metabolomics to identify the metabolite profile and potential biomarkers of AS. The results indicated a notable divergence between the two groups, and a total of 170 different metabolites were identified, which were associated with the 6 primary metabolic pathways exhibiting a correlation with AS. Among these, 26 metabolites exhibited high sensitivity and specificity with area under curve (AUC) values greater than 0.8. Subsequent targeted quantitative analysis discovered 3 metabolites, namely 3-amino-2-piperidone, hypoxanthine and octadecylamine, exhibiting excellent distinguishing ability based on the results of the ROC curve and the Random Forest model, thus qualifying as potential biomarkers for AS. Summarily, our untargeted and targeted metabolomics investigation offers novel and precise insights into potential biomarkers for AS, potentially enhancing diagnostic capabilities and furthering the comprehension of the condition's pathophysiology.

The role of branched-chain amino acids and their downstream metabolites in mediating insulin resistance

Elevated levels of circulating branched-chain amino acids (BCAAs) and their associated metabolites have been strongly linked to insulin resistance and type 2 diabetes. Despite extensive research, the precise mechanisms linking increased BCAA levels with these conditions remain elusive. In this review, we highlight the key organs involved in maintaining BCAA homeostasis and discuss how obesity and insulin resistance disrupt the intricate interplay among these organs, thus affecting BCAA balance. Additionally, we outline recent research shedding light on the impact of tissue-specific or systemic modulation of BCAA metabolism on circulating BCAA levels, their metabolites, and insulin sensitivity, while also identifying specific knowledge gaps and areas requiring further investigation. Finally, we summarize the effects of BCAA supplementation or restriction on obesity and insulin sensitivity.

The Lipidomic Profile Is Associated with the Dietary Pattern in Subjects with and without Diabetes Mellitus from a Mediterranean Area

Lipid functions can be influenced by genetics, age, disease states, and lifestyle factors, particularly dietary patterns, which are crucial in diabetes management. Lipidomics is an expanding field involving the comprehensive exploration of lipids from biological samples. In this cross-sectional study, 396 participants from a Mediterranean region, including individuals with type 1 diabetes (T1D), type 2 diabetes (T2D), and non-diabetic individuals, underwent lipidomic profiling and dietary assessment. Participants completed validated food frequency questionnaires, and lipid analysis was conducted using ultra-high-performance liquid chromatography coupled with mass spectrometry (UHPLC/MS). Multiple linear regression models were used to determine the association between lipid features and dietary patterns. Across all subjects, acylcarnitines (AcCa) and triglycerides (TG) displayed negative associations with the alternate Healthy Eating Index (aHEI), indicating a link between lipidomic profiles and dietary habits. Various lipid species (LS) showed positive and negative associations with dietary carbohydrates, fats, and proteins. Notably, in the interaction analysis between diabetes and the aHEI, we found some lysophosphatidylcholines (LPC) that showed a similar direction with respect to aHEI in non-diabetic individuals and T2D subjects, while an opposite direction was observed in T1D subjects. The study highlights the significant association between lipidomic profiles and dietary habits in people with and without diabetes, particularly emphasizing the role of healthy dietary choices, as reflected by the aHEI, in modulating lipid concentrations. These findings underscore the importance of dietary interventions to improve metabolic health outcomes, especially in the context of diabetes management.

Advanced Mass Spectrometry-Based Biomarker Identification for Metabolomics of Diabetes Mellitus and Its Complications

Over the years, there has been notable progress in understanding the pathogenesis and treatment modalities of diabetes and its complications, including the application of metabolomics in the study of diabetes, capturing attention from researchers worldwide. Advanced mass spectrometry, including gas chromatography-tandem mass spectrometry (GC-MS/MS), liquid chromatography-tandem mass spectrometry (LC-MS/MS), and ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q-TOF-MS), etc., has significantly broadened the spectrum of detectable metabolites, even at lower concentrations. Advanced mass spectrometry has emerged as a powerful tool in diabetes research, particularly in the context of metabolomics. By leveraging the precision and sensitivity of advanced mass spectrometry techniques, researchers have unlocked a wealth of information within the metabolome. This technology has enabled the identification and quantification of potential biomarkers associated with diabetes and its complications, providing new ideas and methods for clinical diagnostics and metabolic studies. Moreover, it offers a less invasive, or even non-invasive, means of tracking disease progression, evaluating treatment efficacy, and understanding the underlying metabolic alterations in diabetes. This paper summarizes advanced mass spectrometry for the application of metabolomics in diabetes mellitus, gestational diabetes mellitus, diabetic peripheral neuropathy, diabetic retinopathy, diabetic nephropathy, diabetic encephalopathy, diabetic cardiomyopathy, and diabetic foot ulcers and organizes some of the potential biomarkers of the different complications with the aim of providing ideas and methods for subsequent in-depth metabolic research and searching for new ways of treating the disease.

Global status and trends of metabolomics in diabetes: A literature visualization knowledge graph study

BACKGROUND

Diabetes is a metabolic disease characterized by hyperglycemia, which has increased the global medical burden and is also the main cause of death in most countries.

AIM

To understand the knowledge structure of global development status, research focus, and future trend of the relationship between diabetes and metabolomics in the past 20 years.

METHODS

The articles about the relationship between diabetes and metabolomics in the Web of Science Core Collection were retrieved from 2002 to October 23, 2023, and the relevant information was analyzed using CiteSpace6.2.2R (CiteSpace), VOSviewer6.1.18 (VOSviewer), and Bibliometrix software under R language.

RESULTS

A total of 3123 publications were included from 2002 to 2022. In the past two decades, the number of publications and citations in this field has continued to increase. The United States, China, Germany, the United Kingdom, and other relevant funds, institutions, and authors have significantly contributed to this field. Scientific Reports and PLoS One are the journals with the most publications and the most citations. Through keyword co-occurrence and cluster analysis, the closely related keywords are "insulin resistance", "risk", "obesity", "oxidative stress", "metabolomics", "metabolites" and "biomarkers". Keyword clustering included cardiovascular disease, gut microbiota, metabonomics, diabetic nephropathy, molecular docking, gestational diabetes mellitus, oxidative stress, and insulin resistance. Burst detection analysis of keyword depicted that "Gene", "microbiota", "validation", "kidney disease", "antioxidant activity", "untargeted metabolomics", "management", and "accumulation" are knowledge frontiers in recent years.

CONCLUSION

The relationship between metabolomics and diabetes is receiving extensive attention. Diabetic nephropathy, diabetic cardiovascular disease, and kidney disease are key diseases for future research in this field. Gut microbiota, molecular docking, and untargeted metabolomics are key research directions in the future. Antioxidant activity, gene, validation, mass spectrometry, management, and accumulation are at the forefront of knowledge frontiers in this field.

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.

Untargeted lipidomics analysis in women with morbid obesity and type 2 diabetes mellitus: A comprehensive study

There is a phenotype of obese individuals termed metabolically healthy obese that present a reduced cardiometabolic risk. This phenotype offers a valuable model for investigating the mechanisms connecting obesity and metabolic alterations such as Type 2 Diabetes Mellitus (T2DM). Previously, in an untargeted metabolomics analysis in a cohort of morbidly obese women, we observed a different lipid metabolite pattern between metabolically healthy morbid obese individuals and those with associated T2DM. To validate these findings, we have performed a complementary study of lipidomics. In this study, we assessed a liquid chromatography coupled to a mass spectrometer untargeted lipidomic analysis on serum samples from 209 women, 73 normal-weight women (control group) and 136 morbid obese women. From those, 65 metabolically healthy morbid obese and 71 with associated T2DM. In this work, we find elevated levels of ceramides, sphingomyelins, diacyl and triacylglycerols, fatty acids, and phosphoethanolamines in morbid obese vs normal weight. Conversely, decreased levels of acylcarnitines, bile acids, lyso-phosphatidylcholines, phosphatidylcholines (PC), phosphatidylinositols, and phosphoethanolamine PE (O-38:4) were noted. Furthermore, comparing morbid obese women with T2DM vs metabolically healthy MO, a distinct lipid profile emerged, featuring increased levels of metabolites: deoxycholic acid, diacylglycerol DG (36:2), triacylglycerols, phosphatidylcholines, phosphoethanolamines, phosphatidylinositols, and lyso-phosphatidylinositol LPI (16:0). To conclude, analysing both comparatives, we observed decreased levels of deoxycholic acid, PC (34:3), and PE (O-38:4) in morbid obese women vs normal-weight. Conversely, we found elevated levels of these lipids in morbid obese women with T2DM vs metabolically healthy MO. These profiles of metabolites could be explored for the research as potential markers of metabolic risk of T2DM in morbid obese women.

Metabolomics Signature in Prediabetes and Diabetes: Insights From Tandem Mass Spectrometry Analysis

OBJECTIVE

This study investigates the metabolic differences between normal, prediabetic and diabetic patients with good and poor glycaemic control (GGC and PGC).

DESIGN

In this study, 1102 individuals were included, and 50 metabolites were analysed using tandem mass spectrometry. The diabetes diagnosis and treatment standards of the American Diabetes Association (ADA) were used to classify patients.

METHODS

The nearest neighbour method was used to match controls and cases in each group on the basis of age, sex and BMI. Factor analysis was used to reduce the number of variables and find influential underlying factors. Finally, Pearson's correlation coefficient was used to check the correlation between both glucose and HbAc1 as independent factors with binary classes.

RESULTS

Amino acids such as glycine, serine and proline, and acylcarnitines (AcylCs) such as C16 and C18 showed significant differences between the prediabetes and normal groups. Additionally, several metabolites, including C0, C5, C8 and C16, showed significant differences between the diabetes and normal groups. Moreover, the study found that several metabolites significantly differed between the GGC and PGC diabetes groups, such as C2, C6, C10, C16 and C18. The correlation analysis revealed that glucose and HbA1c levels significantly correlated with several metabolites, including glycine, serine and C16, in both the prediabetes and diabetes groups. Additionally, the correlation analysis showed that HbA1c significantly correlated with several metabolites, such as C2, C5 and C18, in the controlled and uncontrolled diabetes groups.

CONCLUSIONS

These findings could help identify new biomarkers or underlying markers for the early detection and management of diabetes.

Prediction of Myocardial Infarction Using a Combined Generative Adversarial Network Model and Feature-Enhanced Loss Function

Accurate risk prediction for myocardial infarction (MI) is crucial for preventive strategies, given its significant impact on global mortality and morbidity. Here, we propose a novel deep-learning approach to enhance the prediction of incident MI cases by incorporating metabolomics alongside clinical risk factors. We utilized data from the KORA cohort, including the baseline S4 and follow-up F4 studies, consisting of 1454 participants without prior history of MI. The dataset comprised 19 clinical variables and 363 metabolites. Due to the imbalanced nature of the dataset (78 observed MI cases and 1376 non-MI individuals), we employed a generative adversarial network (GAN) model to generate new incident cases, augmenting the dataset and improving feature representation. To predict MI, we further utilized multi-layer perceptron (MLP) models in conjunction with the synthetic minority oversampling technique (SMOTE) and edited nearest neighbor (ENN) methods to address overfitting and underfitting issues, particularly when dealing with imbalanced datasets. To enhance prediction accuracy, we propose a novel GAN for feature-enhanced (GFE) loss function. The GFE loss function resulted in an approximate 2% improvement in prediction accuracy, yielding a final accuracy of 70%. Furthermore, we evaluated the contribution of each clinical variable and metabolite to the predictive model and identified the 10 most significant variables, including glucose tolerance, sex, and physical activity. This is the first study to construct a deep-learning approach for producing 7-year MI predictions using the newly proposed loss function. Our findings demonstrate the promising potential of our technique in identifying novel biomarkers for MI prediction.

Omentin associates with serum metabolite profiles indicating lower diabetes risk: KORA F4 Study

INTRODUCTION

Circulating omentin levels have been positively associated with insulin sensitivity. Although a role for adiponectin in this relationship has been suggested, underlying mechanisms remain elusive. In order to reveal the relationship between omentin and systemic metabolism, this study aimed to investigate associations of serum concentrations of omentin and metabolites.

RESEARCH DESIGN AND METHODS

This study is based on 1124 participants aged 61-82 years from the population-based KORA (Cooperative Health Research in the Region of Augsburg) F4 Study, for whom both serum omentin levels and metabolite concentration profiles were available. Associations were assessed with five multivariable regression models, which were stepwise adjusted for multiple potential confounders, including age, sex, body mass index, waist-to-hip ratio, lifestyle markers (physical activity, smoking behavior and alcohol consumption), serum adiponectin levels, high-density lipoprotein cholesterol, use of lipid-lowering or anti-inflammatory medication, history of myocardial infarction and stroke, homeostasis model assessment 2 of insulin resistance, diabetes status, and use of oral glucose-lowering medication and insulin.

RESULTS

Omentin levels significantly associated with multiple metabolites including amino acids, acylcarnitines, and lipids (eg, sphingomyelins and phosphatidylcholines (PCs)). Positive associations for several PCs, such as diacyl (PC aa C32:1) and alkyl-alkyl (PC ae C32:2), were significant in models 1-4, whereas those with hydroxytetradecenoylcarnitine (C14:1-OH) were significant in all five models. Omentin concentrations were negatively associated with several metabolite ratios, such as the valine-to-PC ae C32:2 and the serine-to-PC ae C32:2 ratios in most models.

CONCLUSIONS

Our results suggest that omentin may influence insulin sensitivity and diabetes risk by changing systemic lipid metabolism, but further mechanistic studies investigating effects of omentin on metabolism of insulin-sensitive tissues are needed.

Longitudinal characterization of the metabolome of dairy cows transitioning from one lactation to the next: Investigations in blood serum

The objective of this study was to characterize changes in the serum metabolome and various indicators of oxidative balance in dairy cows starting 2 wk before dry-off and continuing until wk 16 of lactation. Twelve Holstein dairy cows (body weight 745 ± 71 kg, body condition score 3.43 ± 0.66; mean ± SD) were housed in a tiestall barn from 10 wk before to 16 wk after parturition. Cows were dried off 6 wk before the expected calving date (mean dry period length = 42 d). From 8 wk before calving to 16 wk after calving, blood samples were taken weekly to study redox metabolism by determining antioxidant capacity, measured as the ferric-reducing ability of plasma, reactive oxidative metabolites, oxidative stress index, oxidative damage of lipids, measured as thiobarbituric acid reactive substances, and glutathione peroxidase activity. According to these results, dairy cows had the lowest serum antioxidant capacity and greater levels of oxidative stress during the dry-off period and the early postpartum period. For metabolomics, a subset of serum samples including wk -7 (before dry-off), -5 (after dry-off), -1, 1, 5, 10, and 15 relative to calving were used. A targeted metabolomics approach was performed using liquid chromatography and flow injection with electrospray ionization triple quadrupole mass spectrometry using the MxP Quant 500 kit (Biocrates Life Sciences AG). A total of 240 metabolites in serum were used in the final data analysis. Principal component analysis revealed a clear separation by days of sampling, indicating a remarkable shift in metabolic phenotype between the dry period and late and early lactation. Changes in many non-lipid metabolites associated with one-carbon metabolism, the tricarboxylic acid cycle, the urea cycle, and AA catabolism were observed in the study, with changes in AA serum concentrations likely related to factors such as energy and nitrogen balance, digestive efficiency, and changing diets. The study confirmed an extensive remodeling of the serum lipidome in peripartum dairy cows, highlighting the importance of changes in acylcarnitine (acylCN), phosphatidylcholines (PC), and triacylglycerols (TG), as they play a crucial role in lipid metabolism. Results showed that short-chain acylCN increased after dry-off and decreased thereafter, whereas lipid-derived acylCN increased around parturition, suggesting that more fatty acids could enter mitochondria. Phospholipids and sphingolipids in serum showed changes during lactation. In particular, concentrations of sphingomyelins, PC, and lysoPC decreased around calving but increased in mid- and late lactation. In contrast, concentrations of TG remained consistently low after parturition. The serum concentrations of bile acids fluctuated during the dry period and lactation, with glycocholic acid, cholic acid, glycodeoxycholic acid, and taurocholic acid showing the greatest concentrations. These changes are likely due to the interplay of diet, liver function, and the ability of the gut microbiota to convert primary to secondary bile acids. Overall, these descriptive results may aid in hypothesis generation and in the design and interpretation of future metabolite-based studies in dairy cows. Furthermore, they contribute to our understanding of the physiological ranges in serum metabolites relative to the lactation cycle of the dairy cow.

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.

Effects of Inorganic Arsenic on Type 2 Diabetes Mellitus In Vivo: the Roles and Mechanisms of miRNAs

Accumulating studies have shown that chronic exposure to iAs correlates with an increased incidence of diabetes. In recent years, miRNA dysfunction has emerged both as a response to iAs exposure and independently as candidate drivers of metabolic phenotypes such as T2DM. However, few miRNAs have been profiled during the progression of diabetes after iAs exposure in vivo. In the present study, high iAs (10 mg/L NaAsO2) exposure mice models of C57BKS/Leprdb (db/db) and C57BLKS/J (WT) were established through the drinking water, the exposure duration was 14 weeks. The results showed that high iAs exposure induced no significant changes in FBG levels in either db/db or WT mice. FBI levels, C-peptide content, and HOMA-IR levels were significantly increased, and glycogen levels in the livers were significantly lower in arsenic-exposed db/db mice. HOMA-β% was decreased significantly in WT mice exposed to high iAs. In addition, more different metabolites were found in the arsenic-exposed group than the control group in db/db mice, mainly involved in the lipid metabolism pathway. Highly expressed glucose, insulin, and lipid metabolism-related miRNAs were selected, including miR-29a-3p, miR-143-3p, miR-181a-3p, miR-122-3p, miR-22-3p, and miR-16-3p. And a series of target genes were chosen for analysis, such as ptp1b, irs1, irs2, sirt1, g6pase, pepck and glut4. The results showed that, the axles of miR-181a-3p-irs2, miR-181a-3p-sirt1, miR-22-3p-sirt1, and miR-122-3p-ptp1b in db/db mice, and miR-22-3p-sirt1, miR-16-3p-glut4 in WT mice could be considered promising targets to explore the mechanisms and therapeutic aspects of T2DM after exposure to high iAs.

Microbiome and metabolome dysbiosis analysis in impaired glucose tolerance for the prediction of progression to diabetes mellitus

Gut microbiota and circulating metabolite dysbiosis predate important pathological changes in glucose metabolic disorders; however, comprehensive studies on impaired glucose tolerance (IGT), a diabetes mellitus (DM) precursor, are lacking. Here, we perform metagenomic sequencing and metabolomics on 47 pairs of individuals with IGT and newly diagnosed DM and 46 controls with normal glucose tolerance (NGT); patients with IGT are followed up after 4 years for progression to DM. Analysis of baseline data reveals significant differences in gut microbiota and serum metabolites among the IGT, DM, and NGT groups. In addition, 13 types of gut microbiota and 17 types of circulating metabolites showed significant differences at baseline before IGT progressed to DM, including higher levels of Eggerthella unclassified, Coprobacillus unclassified, Clostridium ramosum, L-valine, L-norleucine, and L-isoleucine, and lower levels of Eubacterium eligens, Bacteroides faecis, Lachnospiraceae bacterium 3_1_46FAA, Alistipes senegalensis, Megaspaera elsdenii, Clostridium perfringens, α-linolenic acid, 10E,12Z-octadecadienoic acid, and dodecanoic acid. A random forest model based on differential intestinal microbiota and circulating metabolites can predict the progression from IGT to DM (AUC = 0.87). These results suggest that microbiome and metabolome dysbiosis occur in individuals with IGT and have important predictive values and potential for intervention in preventing IGT from progressing to DM.

Plasma Metabolites Related to the Consumption of Different Types of Dairy Products and Their Association with New-Onset Type 2 Diabetes: Analyses in the Fenland and EPIC-Norfolk Studies, United Kingdom

SCOPE

To identify metabolites associated with habitual dairy consumption and investigate their associations with type 2 diabetes (T2D) risk.

METHODS AND RESULTS

Metabolomics assays were conducted in the Fenland (n = 10,281) and EPIC-Norfolk (n = 1,440) studies. Using 82 metabolites assessed in both studies, we developed metabolite scores to classify self-reported consumption of milk, yogurt, cheese, butter, and total dairy (Fenland Study-discovery set; n = 6035). Internal and external validity of the scores was evaluated (Fenland-validation set, n = 4246; EPIC-Norfolk, n = 1440). The study assessed associations between each metabolite score and T2D incidence in EPIC-Norfolk (n = 641 cases; 16,350 person-years). The scores classified low and high consumers for all dairy types with internal validity, and milk, butter, and total dairy with external validity. The scores were further associated with lower incident T2D: hazard ratios (95% confidence interval) per standard deviation: milk 0.71 (0.65, 0.77); butter 0.62 (0.57, 0.68); total dairy 0.66 (0.60, 0.72). These associations persisted after adjustment for known dairy-fat biomarkers.

CONCLUSION

Metabolite scores identified habitual consumers of milk, butter, and total dairy products, and were associated with lower T2D risk. These findings hold promise for identifying objective indicators of the physiological response to dairy consumption.

Hallmarks of the metabolic secretome

The identification of novel secreted factors is advancing at an unprecedented pace. However, there is a critical need to consolidate and integrate this knowledge to provide a framework of their diverse mechanisms, functional significance, and inter-relationships. Complicating this effort are challenges related to nonstandardized methods, discrepancies in sample handling, and inconsistencies in the annotation of unknown molecules. This Review aims to synthesize the rapidly expanding field of the metabolic secretome, encompassing the five major types of secreted factors: proteins, peptides, metabolites, lipids, and extracellular vesicles. By systematically defining the functions and detection of the components within the metabolic secretome, this Review provides a primer into the advances of the field, and how integration of the techniques discussed can provide a deeper understanding of the mechanisms underlying metabolic homeostasis and its disorders.

A Review from a Clinical Perspective: Recent Advances in Biosensors for the Detection of L-Amino Acids

The field of biosensors is filled with reports and designs of various sensors, with the vast majority focusing on glucose sensing. However, in addition to glucose, there are many other important analytes that are worth investigating as well. In particular, L-amino acids appear as important diagnostic markers for a number of conditions. However, the progress in L-amino acid detection and the development of biosensors for L-amino acids are still somewhat insufficient. In recent years, the need to determine L-amino acids from clinical samples has risen. More clinical data appear to demonstrate that abnormal concentrations of L-amino acids are related to various clinical conditions such as inherited metabolic disorders, dyslipidemia, type 2 diabetes, muscle damage, etc. However, to this day, the diagnostic potential of L-amino acids is not yet fully established. Most likely, this is because of the difficulties in measuring L-amino acids, especially in human blood. In this review article, we extensively investigate the 'overlooked' L-amino acids. We review typical levels of amino acids present in human blood and broadly survey the importance of L-amino acids in most common conditions which can be monitored or diagnosed from changes in L-amino acids present in human blood. We also provide an overview of recent biosensors for L-amino acid monitoring and their advantages and disadvantages, with some other alternative methods for L-amino acid quantification, and finally we outline future perspectives related to the development of biosensing devices for L-amino acid monitoring.

Circadian organization of lipid landscape is perturbed in type 2 diabetic patients

Lipid homeostasis in humans follows a diurnal pattern in muscle and pancreatic islets, altered upon metabolic dysregulation. We employ tandem and liquid-chromatography mass spectrometry to investigate daily regulation of lipid metabolism in subcutaneous white adipose tissue (SAT) and serum of type 2 diabetic (T2D) and non-diabetic (ND) human volunteers (n = 12). Around 8% of ≈440 lipid metabolites exhibit diurnal rhythmicity in serum and SAT from ND and T2D subjects. The spectrum of rhythmic lipids differs between ND and T2D individuals, with the most substantial changes observed early morning, as confirmed by lipidomics in an independent cohort of ND and T2D subjects (n = 32) conducted at a single morning time point. Strikingly, metabolites identified as daily rhythmic in both serum and SAT from T2D subjects exhibit phase differences. Our study reveals massive temporal and tissue-specific alterations of human lipid homeostasis in T2D, providing essential clues for the development of lipid biomarkers in a temporal manner.

A Mitochondrial Nanoguard Modulates Redox Homeostasis and Bioenergy Metabolism in Diabetic Peripheral Neuropathy

As a major late complication of diabetes, diabetic peripheral neuropathy (DPN) is the primary reason for amputation. Nevertheless, there are no wonder drugs available. Regulating dysfunctional mitochondria is a key therapeutic target for DPN. Resveratrol (RSV) is widely proven to guard mitochondria, yet the unsatisfactory bioavailability restricts its clinical application. Tetrahedral framework nucleic acids (tFNAs) are promising carriers due to their excellent cell entrance efficiency, biological safety, and structure editability. Here, RSV was intercalated into tFNAs to form the tFNAs-RSV complexes. tFNAs-RSV achieved enhanced stability, bioavailability, and biocompatibility compared with tFNAs and RSV alone. With its treatment, reactive oxygen species (ROS) production was minimized and reductases were activated in an in vitro model of DPN. Besides, respiratory function and adenosine triphosphate (ATP) production were enhanced. tFNAs-RSV also exhibited favorable therapeutic effects on sensory dysfunction, neurovascular deterioration, demyelination, and neuroapoptosis in DPN mice. Metabolomics analysis revealed that redox regulation and energy metabolism were two principal mechanisms that were impacted during the process. Comprehensive inspections indicated that tFNAs-RSV inhibited nitrosation and oxidation and activated reductase and respiratory chain. In sum, tFNAs-RSV served as a mitochondrial nanoguard (mito-guard), representing a viable drilling target for clinical drug development of DPN.

Characteristics of glucolipid metabolism and complications in novel cluster-based diabetes subgroups: a retrospective study

BACKGROUND

Glucolipid metabolism plays an important role in the occurrence and development of diabetes mellitus. However, there is limited research on the characteristics of glucolipid metabolism and complications in different subgroups of newly diagnosed diabetes. This study aimed to investigate the characteristics of glucolipid metabolism and complications in novel cluster-based diabetes subgroups and explore the contributions of different glucolipid metabolism indicators to the occurrence of complications and pancreatic function.

METHODS

This retrospective study included 547 newly diagnosed type 2 diabetes patients. Age, body mass index (BMI), glycated hemoglobin (HbA1C), homeostasis model assessment-2 beta-cell function (HOMA2-β), and homeostasis model assessment-2 insulin resistance (HOMA2-IR) were used as clustering variables. The participants were divided into 4 groups by k-means cluster analysis. The characteristics of glucolipid indicators and complications in each subgroup were analyzed. Regression analyses were used to evaluate the impact of glucolipid metabolism indicators on complications and pancreatic function.

RESULTS

Total cholesterol (TC), triglycerides (TG), triglyceride glucose index (TyG), HbA1C, fasting plasma glucose (FPG), and 2-h postprandial plasma glucose (2hPG) were higher in the severe insulin-resistant diabetes (SIRD) and severe insulin-deficient diabetes (SIDD) groups. Fasting insulin (FINS), fasting C-peptide (FCP), 2-h postprandial insulin (2hINS), 2-h postprandial C-peptide (2hCP), and the monocyte-to-high-density lipoprotein cholesterol ratio (MHR) were higher in mild obesity-related diabetes (MOD) and SIRD. 2hCP, FCP, and FINS were positively correlated with HOMA2-β, while FPG, TyG, HbA1C, and TG were negatively correlated with HOMA2-β. FINS, FPG, FCP, and HbA1C were positively correlated with HOMA2-IR, while high-density lipoprotein (HDL) was negatively correlated with HOMA2-IR. FINS (odds ratio (OR),1.043;95% confidence interval (CI) 1.006 ~ 1.081), FCP (OR,2.881;95%CI 2.041 ~ 4.066), and TyG (OR,1.649;95%CI 1.292 ~ 2.104) contributed to increase the risk of nonalcoholic fatty liver disease (NAFLD); 2hINS (OR,1.015;95%CI 1.008 ~ 1.022) contributed to increase the risk of atherosclerotic cardiovascular disease (ASCVD); FCP (OR,1.297;95%CI 1.027 ~ 1.637) significantly increased the risk of chronic kidney disease (CKD).

CONCLUSIONS

There were differences in the characteristics of glucolipid metabolism as well as complications among different subgroups of newly diagnosed type 2 diabetes. 2hCP, FCP, FINS, FPG, TyG, HbA1C, HDL and TG influenced the function of insulin. FINS, TyG, 2hINS, and FCP were associated with ASCVD, NAFLD, and CKD in newly diagnosed T2DM patients.

Longitudinal associations between metabolites and immediate, short- and medium-term exposure to ambient air pollution: Results from the KORA cohort study

BACKGROUND

Short-term exposure to air pollution has been reported to be associated with cardiopulmonary diseases, but the underlying mechanisms remain unclear. This study aimed to investigate changes in serum metabolites associated with immediate, short- and medium-term exposures to ambient air pollution.

METHODS

We used data from the German population-based Cooperative Health Research in the Region of Augsburg (KORA) S4 survey (1999-2001) and two follow-up examinations (F4: 2006-08 and FF4: 2013-14). Mass-spectrometry-based targeted metabolomics was used to quantify metabolites among serum samples. Only participants with repeated metabolites measurements were included in this analysis. We collected daily averages of fine particles (PM2.5), coarse particles (PMcoarse), nitrogen dioxide (NO2), and ozone (O3) at urban background monitors located in Augsburg, Germany. Covariate-adjusted generalized additive mixed-effects models were used to examine the associations between immediate (2-day average of same day and previous day as individual's blood withdrawal), short- (2-week moving average), and medium-term exposures (8-week moving average) to air pollution and metabolites. We further performed pathway analysis for the metabolites significantly associated with air pollutants in each exposure window.

RESULTS

Of 9,620 observations from 4,261 study participants, we included 5,772 (60.0%) observations from 2,583 (60.6%) participants in this analysis. Out of 108 metabolites that passed quality control, multiple significant associations between metabolites and air pollutants with several exposure windows were identified at a Bonferroni corrected p-value threshold (p < 3.9 × 10-5). We found the highest number of associations for NO2, particularly at the medium-term exposure windows. Among the identified metabolic pathways based on the metabolites significantly associated with air pollutants, the glycerophospholipid metabolism was the most robust pathway in different air pollutants exposures.

CONCLUSIONS

Our study suggested that short- and medium-term exposure to air pollution might induce alterations of serum metabolites, particularly in metabolites involved in metabolic pathways related to inflammatory response and oxidative stress.

Unveiling the future of metabolic medicine: omics technologies driving personalized solutions for precision treatment of metabolic disorders

Metabolic disorders are increasingly prevalent worldwide, leading to high rates of morbidity and mortality. The variety of metabolic illnesses can be addressed through personalized medicine. The goal of personalized medicine is to give doctors the ability to anticipate the best course of treatment for patients with metabolic problems. By analyzing a patient's metabolomic, proteomic, genetic profile, and clinical data, physicians can identify relevant diagnostic, and predictive biomarkers and develop treatment plans and therapy for acute and chronic metabolic diseases. To achieve this goal, real-time modeling of clinical data and multiple omics is essential to pinpoint underlying biological mechanisms, risk factors, and possibly useful data to promote early diagnosis and prevention of complex diseases. Incorporating cutting-edge technologies like artificial intelligence and machine learning is crucial for consolidating diverse forms of data, examining multiple variables, establishing databases of clinical indicators to aid decision-making, and formulating ethical protocols to address concerns. This review article aims to explore the potential of personalized medicine utilizing omics approaches for the treatment of metabolic disorders. It focuses on the recent advancements in genomics, epigenomics, proteomics, metabolomics, and nutrigenomics, emphasizing their role in revolutionizing personalized medicine.

The association between dietary amino acid profile and the risk of type 2 diabetes: Ravansar non-communicable disease cohort study

BACKGROUND

Type 2 diabetes (T2D) is one of the most common chronic diseases and the main risk factors for T2D consist of a combination of lifestyle, unhealthy diet, and genetic factors. Amino acids are considered to be a major component of dietary sources for many of the associations between dietary protein and chronic disease. Therefore, this study amied to determine the association between dietary amino acid intakes and the incidence of T2D.

METHODS

The present nested case-control study was conducted using data from the Ravansar Non-Communicable Disease (RaNCD) Cohort Study. The information required for this study was collected from individuals who participated in the Adult Cohort Study from the start of the study until September 2023. Over a 6-year follow-up period, data from 113 new T2D cases were available. Four controls were then randomly selected for each case using density sampling. Cases and controls were matched for sex and age at the interview. Food frequency questionnaire (FFQ) was used to collect data related to all amino acids including tryptophan, threonine, isoleucine, leucine, lysine, methionine, cysteine, phenylalanine, tyrosine, valine, arginine, histidine, alanine, aspartic acid, glutamic acid, glycine, proline, and serine were also extracted. Binary logistic regression was used to estimate the crude and adjusted odds ratio for the risk of T2D.

RESULTS

Using the univariable model, a significant association was found between T2D risk and branched-chain, alkaline, sulfuric, and essential amino acids in the fourth quartile. Accordingly, individuals in the fourth quartile had a 1.81- to 1.87-fold higher risk of developing new T2D than individuals in the lowest quartile (P<0.05). After adjustment for several variables, the risk of developing a new T2D was 2.70 (95% CI: 1.16-6.31), 2.68 (95% CI: 1.16-6.21), 2.98 (95% CI: 1.27-6.96), 2.45 (95% CI: 1.02-5.90), and 2.66 (95% CI: 1.13-6.25) times higher, for individuals in the fourth quartile of branched-chain, alkaline, sulfuric, alcoholic, and essential amino acids compared with those in the lowest quartile, respectively.

CONCLUSIONS

The results showed that the risk of developing a new T2D was higher for individuals in the fourth quartile of branched-chain amino acids, alkaline, sulfate, and essential amino acids than in the lower quartile.

Regulation of Macronutrients in Insulin Resistance and Glucose Homeostasis during Type 2 Diabetes Mellitus

Insulin resistance is an important feature of metabolic syndrome and a precursor of type 2 diabetes mellitus (T2DM). Overnutrition-induced obesity is a major risk factor for the development of insulin resistance and T2DM. The intake of macronutrients plays a key role in maintaining energy balance. The components of macronutrients distinctly regulate insulin sensitivity and glucose homeostasis. Precisely adjusting the beneficial food compound intake is important for the prevention of insulin resistance and T2DM. Here, we reviewed the effects of different components of macronutrients on insulin sensitivity and their underlying mechanisms, including fructose, dietary fiber, saturated and unsaturated fatty acids, and amino acids. Understanding the diet-gene interaction will help us to better uncover the molecular mechanisms of T2DM and promote the application of precision nutrition in practice by integrating multi-omics analysis.

Metabolite profiles of diabetes mellitus and response to intervention in anti-hyperglycemic drugs

Type 2 diabetes mellitus (T2DM) has become a major health problem, threatening the quality of life of nearly 500 million patients worldwide. As a typical multifactorial metabolic disease, T2DM involves the changes and interactions of various metabolic pathways such as carbohydrates, amino acid, and lipids. It has been suggested that metabolites are not only the endpoints of upstream biochemical processes, but also play a critical role as regulators of disease progression. For example, excess free fatty acids can lead to reduced glucose utilization in skeletal muscle and induce insulin resistance; metabolism disorder of branched-chain amino acids contributes to the accumulation of toxic metabolic intermediates, and promotes the dysfunction of β-cell mitochondria, stress signal transduction, and apoptosis. In this paper, we discuss the role of metabolites in the pathogenesis of T2DM and their potential as biomarkers. Finally, we list the effects of anti-hyperglycemic drugs on serum/plasma metabolic profiles.

Alzheimer's Disease in Diabetic Patients: A Lipidomic Prospect

Diabetes Mellitus (DM) and Alzheimer's disease (AD) have been two of the most common chronic diseases affecting people worldwide. Type 2 DM (T2DM) is a metabolic disease depicted by insulin resistance, dyslipidemia, and chronic hyperglycemia while AD is a neurodegenerative disease marked by Amyloid β (Aβ) accumulation, neurofibrillary tangles aggregation, and tau phosphorylation. Various clinical, epidemiological, and lipidomics studies have linked those diseases claiming shared pathological pathways raising the assumption that diabetic patients are at an increased risk of developing AD later in their lives. Insulin resistance is the tipping point beyond where advanced glycation end (AGE) products and free radicals are produced leading to oxidative stress and lipid peroxidation. Additionally, different types of lipids are playing a crucial role in the development and the relationship between those diseases. Lipidomics, an analysis of lipid structure, formation, and interactions, evidently exhibits these lipid changes and their direct and indirect effect on Aβ synthesis, insulin resistance, oxidative stress, and neuroinflammation. In this review, we have discussed the pathophysiology of T2DM and AD, the interconnecting pathological pathways they share, and the lipidomics where different lipids such as cholesterol, phospholipids, sphingolipids, and sulfolipids contribute to the underlying features of both diseases. Understanding their role can be beneficial for diagnostic purposes or introducing new drugs to counter AD.

High-Fat Diet Alters Acylcarnitine Metabolism of the Retina and Retinal Pigment Epithelium/Choroidal Tissues in Laser-Induced Choroidal Neovascularization Rat Models

SCOPE

Choroidal neovascularization (CNV) is age-related macular degeneration's (AMD) main pathological change. High-fat diet (HFD) is associated with a form of CNV; however, the specific mechanism is unclear. Mitochondrial dysfunction, characterized by abnormal acylcarnitine, occurs during metabolic screening of serum or other body tissues in AMD. This study investigates HFD's role in retinal and retinal pigment epithelium (RPE)/choroidal acylcarnitine metabolism in CNV formation.

METHODS AND RESULTS

Chow diet and HFD-BN rats are laser-treated to induce CNV. Acylcarnitine species are quantitatively characterized by ultrahigh-performance liquid chromatography-tandem mass spectrometry. Optical coherence tomography and fundus fluorescein angiography evaluate CNV severity. HFD promotes weight gain, dyslipidemia, and CNV formation. In CNV rats, few medium-chain fatty acids (MCFAs) acylcarnitine in the RPE/choroid are initially affected. When an HFD is administered to these, even MCFA acylcarnitine in the RPE/choroid is found to decline. However, in the retina, odd acylcarnitines are increased, revealing "an opposite" change within the RPE/choroid, accompanied by influencing glycolytic key enzymes. The HFD+CNV group incorporated fewer long-chain acylcarnitines, like C18:2, into the retina than controls.

CONCLUSIONS

HFD hastens choroidal neovascularization. The study comprehensively documented acylcarnitine profiles in a CNV rat model. Acylcarnitine's odd-even and carbon-chain length properties may guide future therapeutics.

Inference of disease-associated microbial gene modules based on metagenomic and metatranscriptomic data

The identification of microbial characteristics associated with diseases is crucial for disease diagnosis and therapy. However, the presence of heterogeneity, high dimensionality, and large amounts of microbial data presents tremendous challenges in discovering key microbial features. In this paper, we present IDAM, a novel computational method for inferring disease-associated gene modules from metagenomic and metatranscriptomic data. This method integrates gene context conservation (uber-operons) and regulatory mechanisms (gene co-expression patterns) within a mathematical graph model to explore gene modules associated with specific diseases. It alleviates reliance on prior meta-data. We applied IDAM to publicly available datasets from inflammatory bowel disease, melanoma, type 1 diabetes mellitus, and irritable bowel syndrome. The results demonstrated the superior performance of IDAM in inferring disease-associated characteristics compared to existing popular tools. Furthermore, we showcased the high reproducibility of the gene modules inferred by IDAM using independent cohorts with inflammatory bowel disease. We believe that IDAM can be a highly advantageous method for exploring disease-associated microbial characteristics. The source code of IDAM is freely available at https://github.com/OSU-BMBL/IDAM, and the web server can be accessed at https://bmblx.bmi.osumc.edu/idam/.

Early Perturbations in Red Blood Cells in Response to Murine Malarial Parasite Infection: Proof-of-Concept 1H NMR Metabolomic Study

BACKGROUND

The major focus of metabolomics research has been confined to the readily available biofluids-urine and blood serum. However, red blood cells (RBCs) are also readily available, and may be a source of a wealth of information on vertebrates. However, the comprehensive metabolomic characterization of RBCs is minimal although they exhibit perturbations in various physiological states. RBCs act as the host of malarial parasites during the symptomatic stage. Thus, understanding the changes in RBC metabolism during infection is crucial for a better understanding of disease progression.

METHODS

The metabolome of normal RBCs obtained from Swiss mice was investigated using 1H NMR spectroscopy. Several 1 and 2-dimensional 1H NMR experiments were employed for this purpose. The information from this study was used to investigate the changes in the RBC metabolome during the early stage of infection (~1% infected RBCs) by Plasmodium bergheii ANKA.

RESULTS

We identified over 40 metabolites in RBCs. Several of these metabolites were quantitated using 1H NMR spectroscopy. The results indicate changes in the choline/membrane components and other metabolites during the early stage of malaria.

CONCLUSIONS

The paper reports the comprehensive characterization of the metabolome of mouse RBCs. Changes during the early stage of malarial infection suggest significant metabolic alteration, even at low parasite content (~1%).

GENERAL SIGNIFICANCE

This study should be of use in maximizing the amount of information available from metabolomic experiments on the cellular components of blood. The technique can be directly applied to real-time investigation of infectious diseases that target RBCs.

Plasma lipidic fingerprint associated with type 2 diabetes in patients with coronary heart disease: CORDIOPREV study

OBJECTIVE

We aimed to identify a lipidic profile associated with type 2 diabetes mellitus (T2DM) development in coronary heart disease (CHD) patients, to provide a new, highly sensitive model which could be used in clinical practice to identify patients at T2DM risk.

METHODS

This study considered the 462 patients of the CORDIOPREV study (CHD patients) who were not diabetic at the beginning of the intervention. In total, 107 of them developed T2DM after a median follow-up of 60 months. They were diagnosed using the American Diabetes Association criteria. A novel lipidomic methodology employing liquid chromatography (LC) separation followed by HESI, and detection by mass spectrometry (MS) was used to annotate the lipids at the isomer level. The patients were then classified into a Training and a Validation Set (60-40). Next, a Random Survival Forest (RSF) was carried out to detect the lipidic isomers with the lowest prediction error, these lipids were then used to build a Lipidomic Risk (LR) score which was evaluated through a Cox. Finally, a production model combining the clinical variables of interest, and the lipidic species was carried out.

RESULTS

LC-tandem MS annotated 440 lipid species. From those, the RSF identified 15 lipid species with the lowest prediction error. These lipids were combined in an LR score which showed association with the development of T2DM. The LR hazard ratio per unit standard deviation was 2.87 and 1.43, in the Training and Validation Set respectively. Likewise, patients with higher LR Score values had lower insulin sensitivity (P = 0.006) and higher liver insulin resistance (P = 0.005). The receiver operating characteristic (ROC) curve obtained by combining clinical variables and the selected lipidic isomers using a generalised lineal model had an area under the curve (AUC) of 81.3%.

CONCLUSION

Our study showed the potential of comprehensive lipidomic analysis in identifying patients at risk of developing T2DM. In addition, the lipid species combined with clinical variables provided a new, highly sensitive model which can be used in clinical practice to identify patients at T2DM risk. Moreover, these results also indicate that we need to look closely at isomers to understand the role of this specific compound in T2DM development. Trials registration NCT00924937.

Plasma metabolic fingerprints for large-scale screening and personalized risk stratification of metabolic syndrome

Direct diagnosis and accurate assessment of metabolic syndrome (MetS) allow for prompt clinical interventions. However, traditional diagnostic strategies overlook the complex heterogeneity of MetS. Here, we perform metabolomic analysis in 13,554 participants from the natural cohort and identify 26 hub plasma metabolic fingerprints (PMFs) associated with MetS and its early identification (pre-MetS). By leveraging machine-learning algorithms, we develop robust diagnostic models for pre-MetS and MetS with convincing performance through independent validation. We utilize these PMFs to assess the relative contributions of the four major MetS risk factors in the general population, ranked as follows: hyperglycemia, hypertension, dyslipidemia, and obesity. Furthermore, we devise a personalized three-dimensional plasma metabolic risk (PMR) stratification, revealing three distinct risk patterns. In summary, our study offers effective screening tools for identifying pre-MetS and MetS patients in the general community, while defining the heterogeneous risk stratification of metabolic phenotypes in real-world settings.

Dietary intakes of branched-chain amino acids and plasma lipid profiles among filipino women in Korea: the Filipino Women's Diet and Health Study (FiLWHEL)

BACKGROUND

The potential role of dietary branched-chain amino acids (BCAA) in metabolic health, including cardiovascular disease and diabetes, is evolving, and it is yet to be understood if dietary BCAA intakes are associated with plasma lipid profiles or dyslipidaemia. This study tested the association of dietary BCAA intakes with plasma lipid profiles and dyslipidaemia among Filipino women in Korea.

METHODS

Energy-adjusted dietary BCAA intakes (isoleucine, leucine, valine, and total BCAA) and fasting blood profiles of triglycerides (TG), total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-C), and low-density lipoprotein-cholesterol (LDL-C) were determined in a sample of 423 women enrolled in the Filipino Women's Diet and Health Study (FiLWHEL). The generalized linear model was applied to estimate least-square (LS) means and 95% confidence intervals (CIs) and compare plasma TG, TC, HDL-C, and LDL-C across tertile distribution of energy-adjusted dietary BCAA intakes at P < 0.05.

RESULTS

Mean of energy-adjusted dietary total BCAA intake was 8.3 ± 3.9 g/d. Average plasma lipid profiles were 88.5 ± 47.4 mg/dl for TG, 179.7 ± 34.5 mg/dl for TC, 58.0 ± 13.7 mg/dl for HDL-C, and 104.0 ± 30.5 mg/dl for LDL-C. LS means, and 95% CIs across tertiles of energy-adjusted total BCAA intakes were 89.9 mg/dl, 88.8 mg/dl and 85.8 mg/dl (P-trend = 0.45) for TG, 179.1 mg/dl, 183.6 mg/dl and 176.5 mg/dl (P-trend = 0.48) for TC, 57.5 mg/dl, 59.6 mg/dl and 57.1 mg/dl (P-trend = 0.75) for HDL-C and 103.6 mg/dl, 106.2 mg/dl and 102.3 mg/dl (P-trend = 0.68) for LDL-C. Furthermore, the multivariable-adjusted prevalence ratios and 95% confidence intervals for dyslipidaemia across increasing tertile distribution of energy-adjusted total BCAA intake were; 1.00, 0.67 (0.40, 1.13) and 0.45 (0.16, 1.27; P-trend = 0.03) for the first, second and third tertile, respectively.

CONCLUSIONS

Higher dietary intakes of BCAA presented a statistically significant inverse trend with the prevalence of dyslipidaemia among Filipino women in this study and testing these associations in longitudinal studies may be necessary to confirm these findings.

Lipidomics and pancreatic cancer risk in two prospective studies

Pancreatic ductal carcinoma (PDAC) is highly fatal with limited understanding of mechanisms underlying its carcinogenesis. We comprehensively investigated whether lipidomic measures were associated with PDAC in two prospective studies. We measured 904 lipid species and 252 fatty acids across 15 lipid classes in pre-diagnostic serum (up to 24 years) in a PDAC nested-case control study within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO, NCT00002540) with 332 matched case-control sets including 272 having serial blood samples and Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC, NCT00342992) with 374 matched case-control sets. Controls were matched to cases by cohort, age, sex, race, and date at blood draw. We used conditional logistic regression to calculate odds ratios (OR) and 95% confidence intervals (CI) per one-standard deviation increase in log-lipid concentrations within each cohort, and combined ORs using fixed-effects meta-analyses. Forty-three lipid species were associated with PDAC (false discovery rate, FDR ≤ 0.10), including lysophosphatidylcholines (LPC, n = 2), phosphatidylethanolamines (PE, n = 17), triacylglycerols (n = 13), phosphatidylcholines (PC, n = 3), diacylglycerols (n = 4), monoacylglycerols (MAG, n = 2), cholesteryl esters (CE, n = 1), and sphingomyelins (n = 1). LPC(18:2) and PE(O-16:0/18:2) showed significant inverse associations with PDAC at the Bonferroni threshold (P value < 5.5 × 10-5). The fatty acids LPC[18:2], LPC[16:0], PC[15:0], MAG[18:1] and CE[22:0] were significantly associated with PDAC (FDR < 0.10). Similar associations were observed in both cohorts. There was no significant association for the differences between PLCO serial lipidomic measures or heterogeneity by follow-up time overall. Results support that the pre-diagnostic serum lipidome, including 43 lipid species from 8 lipid classes and 5 fatty acids, is associated with PDAC.

Metabolomic Profiling in Children with Celiac Disease: Beyond the Gluten-Free Diet

Celiac disease (CD) is included in the group of complex or multifactorial diseases, i.e., those caused by the interaction of genetic and environmental factors. Despite a growing understanding of the pathophysiological mechanisms of the disease, diagnosis is still often delayed and there are no effective biomarkers for early diagnosis. The only current treatment, a gluten-free diet (GFD), can alleviate symptoms and restore intestinal villi, but its cellular effects remain poorly understood. To gain a comprehensive understanding of CD's progression, it is crucial to advance knowledge across various scientific disciplines and explore what transpires after disease onset. Metabolomics studies hold particular significance in unravelling the complexities of multifactorial and multisystemic disorders, where environmental factors play a significant role in disease manifestation and progression. By analyzing metabolites, we can gain insights into the reasons behind CD's occurrence, as well as better comprehend the impact of treatment initiation on patients. In this review, we present a collection of articles that showcase the latest breakthroughs in the field of metabolomics in pediatric CD, with the aim of trying to identify CD biomarkers for both early diagnosis and treatment monitoring. These advancements shed light on the potential of metabolomic analysis in enhancing our understanding of the disease and improving diagnostic and therapeutic strategies. More studies need to be designed to cover metabolic profiles in subjects at risk of developing the disease, as well as those analyzing biomarkers for follow-up treatment with a GFD.

Aromatic Amino Acids and Their Interactions with Gut Microbiota-Related Metabolites for Risk of Gestational Diabetes: A Prospective Nested Case-Control Study in a Chinese Cohort

INTRODUCTION

The aim of this study was to explore associations of aromatic amino acids (AAA) in early pregnancy with gestational diabetes mellitus (GDM), and whether high AAA and gut microbiota-related metabolites had interactive effects on GDM risk.

METHODS

We conducted a 1:1 case-control study (n = 486) nested in a prospective cohort of pregnant women from 2010 to 2012. According to the International Association of Diabetes and Pregnancy Study Group's criteria, 243 women were diagnosed with GDM. Binary conditional logistic regression was performed to examine associations of AAA with GDM risk. Interactions between AAA and gut microbiota-related metabolites for GDM were examined using additive interaction measures.

RESULTS

High phenylalanine and tryptophan were associated with increased GDM risk (OR: 1.72, 95% CI: 1.07-2.78 and 1.66, 1.02-2.71). The presence of high trimethylamine (TMA) markedly increased the OR of high phenylalanine alone up to 7.95 (2.79-22.71), while the presence of low glycoursodeoxycholic acid (GUDCA) markedly increased the OR of high tryptophan alone up to 22.88 (5.28-99.26), both with significant additive interactions. Furthermore, high lysophosphatidylcholines (LPC18:0) mediated both interactive effects.

CONCLUSIONS

High phenylalanine may have an additive interaction with high TMA, while high tryptophan may have an additive interaction with low GUDCA toward increased risk of GDM, both being mediated via LPC18:0.