Metabolomics and Proteomics in Type 2 Diabetes

A sensitive, expandable AQC-based LC-MS/MS method to measure amino metabolites and sphingolipids in cell and serum samples

Sphingolipids are a major lipid species found in all eukaryotes. Among structurally complex and diversified lipids, sphingoid bases have been heavily linked to various metabolic diseases. However, most current LC-MS-based methods lack the sensitivity to detect low-abundant sphingoid bases. The 6-Aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) derivatization reagent, which efficiently forms covalent bonds with amino groups, has been widely used for amino acid detection. Nevertheless, the commonly used reverse-phase HPLC method for amino acid analysis is not suitable for amphipathic sphingolipids. To address this issue, we report a robust reverse-phase HPLC-MS/MS method capable of separating and detecting hydrophilic amino acids and sphingoid bases in a single run with high sensitivity. This method is also inclusive of other amino metabolites with an expandable target list. We tested this method under various conditions and samples, demonstrating its high reproducibility and sensitivity. Using this approach, we systematically analyzed human serum samples from healthy individuals, dyslipidemia, and type II diabetes mellitus (T2DM) patients, respectively. Two sphingolipids and five amino acids were identified with significant differences between the control and T2DM groups, highlighting the potential of this method in clinical studies.

Multi-omics correlates of insulin resistance and circadian parameters mapped directly from human serum

While it is generally known that metabolic disorders and circadian dysfunction are intertwined, how the two systems affect each other is not well understood, nor are the genetic factors that might exacerbate this pathological interaction. Blood chemistry is profoundly changed in metabolic disorders, and we have previously shown that serum factors change cellular clock properties. To investigate if circulating factors altered in metabolic disorders have circadian modifying effects, and whether these effects are of genetic origin, we measured circadian rhythms in U2OS cell in the presence of serum collected from diabetic, obese or control subjects. We observed that circadian period lengthening in U2OS cells was associated with serum chemistry that is characteristic of insulin resistance. Characterizing the genetic variants that altered circadian period length by genome-wide association analysis, we found that one of the top variants mapped to the E3 ubiquitin ligase MARCH1 involved in insulin sensitivity. Confirming our data, the serum circadian modifying variants were also enriched in type 2 diabetes and chronotype variants identified in the UK Biobank cohort. Finally, to identify serum factors that might be involved in period lengthening, we performed detailed metabolomics and found that the circadian modifying variants are particularly associated with branched chain amino acids, whose levels are known to correlate with diabetes and insulin resistance. Overall, our multi-omics data showed comprehensively that systemic factors serve as a path through which metabolic disorders influence circadian system, and these can be examined in human populations directly by simple cellular assays in common cultured cells.

Novel secreted regulators of glucose and lipid metabolism in the development of metabolic diseases

The tight regulation of glucose and lipid metabolism is crucial for maintaining metabolic health. Dysregulation of these processes can lead to the development of metabolic diseases. Secreted factors, or hormones, play an essential role in the regulation of glucose and lipid metabolism, thus also playing an important role in the development of metabolic diseases such as type 2 diabetes and obesity. Given the important roles of secreted factors, there has been significant interest in identifying new secreted factors and new functions for existing secreted factors that control glucose and lipid metabolism. In this review, we evaluate novel secreted factors or novel functions of existing factors that regulate glucose and lipid metabolism discovered in the last decade, including secreted isoform of endoplasmic reticulum membrane complex subunit 10, vimentin, cartilage intermediate layer protein 2, isthmin-1, lipocalin-2, neuregulin-1 and neuregulin-4. We discuss their discovery, tissues of origin, mechanisms of action and sex differences, emphasising their potential to regulate metabolic processes central to diabetes. Additionally, we discuss the translational barriers, particularly the absence of identified receptors, that hamper their functional characterisation and further therapeutic development. Ultimately, the identification of new secreted factors may give insights into previously unidentified pathways of disease progression and mechanisms of glucose and lipid homeostasis.

Proteomic analysis of cardiovascular disease-associated proteins in Korean patients with moderate-to-severe atopic dermatitis

Background

Cardiovascular diseases (CVDs) have been associated with atopic dermatitis (AD), including in Korean patients. Previous studies on AD have primarily focused on patients of European ancestry, while the Asian endotype exhibits distinct characteristics. This study aimed to characterize the blood proteomic signature of Korean patients with moderate-to-severe AD, with an emphasis on proteins related to CVDs.

Methods

A total of 78 participants, including 39 patients with moderate-to-severe AD and 39 age- and sex-matched healthy controls, were enrolled. Blood proteomics analysis was performed using the Olink CVD II panel, which measures the expression levels of 92 proteins associated with CVDs.

Results

Unsupervised hierarchical clustering revealed 44 upregulated and 5 downregulated proteins in AD patients compared to healthy controls. Principal component analysis (PCA) effectively distinguished AD patients from healthy subjects based on the complete set of proteins or the subset of upregulated proteins. A multiple linear regression model comprising CCL17 and FGF21 showed a strong correlation with disease severity (R = 0.619). Correlation analysis identified 25 highly correlated proteins, including STK4, ITGB1BP2, and DECR1, which were newly found to be upregulated in Korean AD patients. Pathway analysis highlighted the involvement of these proteins in vascular system, inflammation, and lipid metabolism pathways.

Conclusion

The blood proteomic profile of moderate-to-severe AD patients in Korea differed from healthy controls using the CVD II panel. This study provides potential biomarkers for the AD-CVD association and insights into the pathways contributing to this relationship in the Korean population.

Exploring protein relative relations in skeletal muscle proteomic analysis for insights into insulin resistance and type 2 diabetes

The escalating prevalence of insulin resistance (IR) and type 2 diabetes mellitus (T2D) underscores the urgent need for improved early detection techniques and effective treatment strategies. In this context, our study presents a proteomic analysis of post-exercise skeletal muscle biopsies from individuals across a spectrum of glucose metabolism states: normal, prediabetes, and T2D. This enabled the identification of significant protein relationships indicative of each specific glycemic condition. Our investigation primarily leveraged the machine learning approach, employing the white-box algorithm relative evolutionary hierarchical analysis (REHA), to explore the impact of regulated, mixed mode exercise on skeletal muscle proteome in subjects with diverse glycemic status. This method aimed to advance the diagnosis of IR and T2D and elucidate the molecular pathways involved in its development and the response to exercise. Additionally, we used proteomics-specific statistical analysis to provide a comparative perspective, highlighting the nuanced differences identified by REHA. Validation of the REHA model with a comparable external dataset further demonstrated its efficacy in distinguishing between diverse proteomic profiles. Key metrics such as accuracy and the area under the ROC curve confirmed REHA's capability to uncover novel molecular pathways and significant protein interactions, offering fresh insights into the effects of exercise on IR and T2D pathophysiology of skeletal muscle. The visualizations not only underscored significant proteins and their interactions but also showcased decision trees that effectively differentiate between various glycemic states, thereby enhancing our understanding of the biomolecular landscape of T2D.

Impact of Maternal Pre-Pregnancy Underweight on Cord Blood Metabolome: An Analysis of the Population-Based Survey of Neonates in Pomerania (SNiP)

Intrauterine growth restriction leads to an altered lipid and amino acid profile in the cord blood at the end of pregnancy. Pre-pregnancy underweight is an early risk factor for impaired fetal growth. The aim of this study was to investigate whether a pre-pregnancy body mass index (ppBMI) of <18.5 kg/m2, as early as at the beginning of pregnancy, is associated with changes in the umbilical cord metabolome. In a sample of the Survey of Neonates in Pomerania (SNIP) birth cohort, the cord blood metabolome of n = 240 newborns of mothers with a ppBMI of <18.5 kg/m2 with n = 208 controls (ppBMI of 18.5-24.9 kg/m2) was measured by NMR spectrometry. A maternal ppBMI of <18.5 kg/m2 was associated with increased concentrations of HDL4 cholesterol, HDL4 phospholipids, VLDL5 cholesterol, HDL 2, and HDL4 Apo-A1, as well as decreased VLDL triglycerides and HDL2 free cholesterol. A ppBMI of <18.5 kg/m2 combined with poor intrauterine growth (a gestational weight gain (GWG) < 25th percentile) was associated with decreased concentrations of total cholesterol; cholesterol transporting lipoproteins (LDL4, LDL6, LDL free cholesterol, and HDL2 free cholesterol); LDL4 Apo-B; total Apo-A2; and HDL3 Apo-A2. In conclusion, maternal underweight at the beginning of pregnancy already results in metabolic changes in the lipid profile in the cord blood, but the pattern changes when poor GWG is followed by pre-pregnancy underweight.

Quantitative Proteomics Analysis of Serum and Urine With DIA Mass Spectrometry Reveals Biomarkers for Pediatric Obstructive Sleep Apnea

OBJECTIVES

Identification of suitable biomarkers that facilitate the screening and evaluation of pediatric obstructive sleep apnea (OSA) and its severity was explored.

METHODS

Data-independent acquisition quantitative proteomic analysis was employed to identify serum and urine proteins with differential expression patterns between children with OSA and controls. Differentially expressed proteins that gradually increased or decreased with the severity of OSA were retained as potential biomarkers and underwent ELISA validation.

RESULTS

We found that with increasing severity of OSA, there was a gradual upregulation of 34 proteins in the serum and 124 proteins in the urine, along with a respective downregulation of 10 serum proteins and 64 urinary proteins in the initial cohort of 40 children. These proteins primarily participate in immune activation, the complement pathway, oxygen transport, and reactive oxygen metabolism. Notably, cathepsin Z exhibited a positive correlation with the obstructive apnea hypopnea index, whereas sex hormone-binding globulin (SHBG) was negatively correlated. These proteins were then validated by ELISA in an independent cohort (n=21). Circulating cathepsin Z and SHBG levels displayed acceptable diagnostic performance of OSA with AUC values of 0.863 and 0.738, respectively.

CONCLUSIONS

We identified two promising circulating proteins as novel biomarkers for clinical diagnosis and assessment of pediatric OSA severity. Furthermore, the comprehensive proteomic profile in pediatric OSA should aid in exploring the underlying pathophysiological mechanisms associated with this prevalent condition.

The role and mechanism of VDAC1 in type 2 diabetes: An underestimated target of environmental pollutants

Type 2 diabetes (T2D) is a chronic metabolic disease that accounts for more than 90% of diabetic patients. Its main feature is hyperglycemia due to insulin resistance or insulin deficiency. With changes in diet and lifestyle habits, the incidence of T2D in adolescents has burst in recent decades. The deterioration in the exposure to the environmental pollutants further aggravates the prevalence of T2D, and consequently, it imposes a significant economic burden. Therefore, early prevention and symptomatic treatment are essential to prevent diabetic complications. Mitochondrial number and electron transport chain activity are decreased in the patients with T2D. Voltage-Dependent Anion Channel 1 (VDAC1), as a crucial channel protein on the outer membrane of mitochondria, regulates signal transduction between mitochondria and other cellular components, participating in various biological processes. When VDAC1 exists in oligomeric form, it additionally facilitates the entry and exit of macromolecules into and from mitochondria, modulating insulin secretion. We summarize and highlight the interplay between VDAC1 and T2D, especially in the environmental pollutants-related T2D, shed light on the potential therapeutic implications of targeting VDAC1 monomers and oligomers, providing a new possible target for the treatment of T2D.

Unveiling novel metabolic alterations in postmenopausal osteoporosis and type 2 diabetes mellitus through NMR-based metabolomics: A pioneering approach for identifying early diagnostic markers

BACKGROUND AND AIMS

Postmenopausal osteoporosis (PMO) and type 2 diabetes mellitus (T2DM) frequently coexist in postmenopausal women. The study aimed to explore metabolic variations linked to these circumstances and their simultaneous presence through proton nuclear magnetic resonance metabolomics (1H NMR).

MATERIALS AND METHODS

Serum samples from 80 postmenopausal women, including 20 PMO individuals, 20 T2DM, 20 T2DM + PMO, and 20 healthy postmenopausal women, were analyzed using 1H NMR spectroscopy.

RESULTS

Our study revealed significant metabolic profile differences among the four groups. Notably, the T2DM + PMO group showed elevated levels of alanine, pyruvate, glutamate, lactate, and aspartate, indicating their involvement in lipid metabolism, energy, and amino acids. Importantly, our multivariate statistical analysis identified a metabolite set that accurately distinguished the groups, suggesting its potential as an early diagnostic marker.

CONCLUSION

The 1H NMR metabolomics approach uncovered metabolic biomarkers intricately linked to postmenopausal osteoporosis (PMO), type 2 diabetes mellitus (T2DM), and their concurrent presence. Among these biomarkers, alanine emerged as a pivotal player, showing its significant role in the metabolic landscape associated with PMO and T2DM. These findings shed light on the pathophysiological mechanisms underlying these conditions and underscore alanine's potential as a diagnostic biomarker.

Proteomic and metabolomic proof of concept for unified airways in chronic rhinosinusitis and asthma

BACKGROUND

The pathogenesis of chronic rhinosinusitis with nasal polyps (CRSwNP) with comorbid asthma remains unclear.

OBJECTIVE

To assess upper and lower airway unity and identify a possible common pathogenesis in CRSwNP with asthma.

METHODS

This study analyzed the expression of proteins and metabolites in nasal lavage fluid cells (NLFCs) and induced sputum cells (ISCs). Differentially expressed proteins and their function-related metabolites in the upper and lower airways of patients having CRSwNP with or without asthma were identified; relevant signaling pathways were analyzed, and key pathway-related proteins were identified. Parallel reaction monitoring was used to verify these target proteins.

RESULTS

Protein or metabolite expression between NLFCs and ISCs was highly correlated and conservative on the basis of expression profiles and weighted gene coexpression network analysis. There were 17 differentially coexpressed proteins and their function-related 13 metabolites that were identified in the NLFCs and ISCs of CRSwNP, whereas 11 proteins and 11 metabolites were identified in CRSwNP with asthma. An asthma pathway was involved in the copathogenesis of upper and lower airways in whether CRSwNP or CRSwNP with asthma. The asthma pathway-related proteins proteoglycan 2 and eosinophil peroxidase, as the core of the protein-metabolism interaction networks between the upper and lower airways, were both highly coexpressed in NLFCs and ISCs in patients having either CRSwNP or CRSwNP with asthma by parallel reaction monitoring validation.

CONCLUSION

Proteomics and metabolomics reveal upper and lower airway unity. Asthma pathway-related proteins proteoglycan 2 and eosinophil peroxidase from the upper airway could be used to assess the potential risk of lower airway dysfunction in CRSwNP.

Autophagy and mitophagy as potential therapeutic targets in diabetic heart condition: Harnessing the power of nanotheranostics

Autophagy and mitophagy pose unresolved challenges in understanding the pathology of diabetic heart condition (DHC), which encompasses a complex range of cardiovascular issues linked to diabetes and associated cardiomyopathies. Despite significant progress in reducing mortality rates from cardiovascular diseases (CVDs), heart failure remains a major cause of increased morbidity among diabetic patients. These cellular processes are essential for maintaining cellular balance and removing damaged or dysfunctional components, and their involvement in the development of diabetic heart disease makes them attractive targets for diagnosis and treatment. While a variety of conventional diagnostic and therapeutic strategies are available, DHC continues to present a significant challenge. Point-of-care diagnostics, supported by nanobiosensing techniques, offer a promising alternative for these complex scenarios. Although conventional medications have been widely used in DHC patients, they raise several concerns regarding various physiological aspects. Modern medicine places great emphasis on the application of nanotechnology to target autophagy and mitophagy in DHC, offering a promising approach to deliver drugs beyond the limitations of traditional therapies. This article aims to explore the potential connections between autophagy, mitophagy and DHC, while also discussing the promise of nanotechnology-based theranostic interventions that specifically target these molecular pathways.

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.

Effects of short-term moderate intensity exercise on the serum metabolome in older adults: a pilot randomized controlled trial

BACKGROUND

We previously reported changes in the serum metabolome associated with impaired myocardial relaxation in an asymptomatic older community cohort. In this prospective parallel-group randomized control pilot trial, we subjected community adults without cardiovascular disease to exercise intervention and evaluated the effects on serum metabolomics.

METHODS

Between February 2019 to November 2019, thirty (83% females) middle-aged adults (53 ± 4 years) were randomized with sex stratification to either twelve weeks of moderate-intensity exercise training (Intervention) (n = 15) or Control (n = 15). The Intervention group underwent once-weekly aerobic and strength training sessions for 60 min each in a dedicated cardiac exercise laboratory for twelve weeks (ClinicalTrials.gov: NCT03617653). Serial measurements were taken pre- and post-intervention, including serum sampling for metabolomic analyses.

RESULTS

Twenty-nine adults completed the study (Intervention n = 14; Control n = 15). Long-chain acylcarnitine C20:2-OH/C18:2-DC was reduced in the Intervention group by a magnitude of 0.714 but increased in the Control group by a magnitude of 1.742 (mean difference -1.028 age-adjusted p = 0.004). Among Controls, alanine correlated with left ventricular mass index (r = 0.529, age-adjusted p = 0.018) while aspartate correlated with Lateral e' (r = -764, age-adjusted p = 0.016). C20:3 correlated with E/e' ratio fold-change in the Intervention group (r = -0.653, age-adjusted p = 0.004). Among Controls, C20:2/C18:2 (r = 0.795, age-adjusted p = 0.005) and C20:2-OH/C18:2-DC fold-change (r = 0.742, age-adjusted p = 0.030) correlated with change in E/A ratio.

CONCLUSIONS

Corresponding relationships between serum metabolites and cardiac function in response to exercise intervention provided pilot observations. Future investigations into cellular fuel oxidation or central carbon metabolism pathways that jointly impact the heart and related metabolic systems may be critical in preventive trials.

Exploring the Hypocholesterolemic Potential of a Fucus vesiculosus Extract: Omic Insights into Molecular Mechanisms at the Intestinal Level

High blood cholesterol levels are a major risk factor for cardiovascular diseases. A purified aqueous extract of Fucus vesiculosus, rich in phlorotannins and peptides, has been described for its potential to inhibit cholesterol biosynthesis and intestinal absorption. In this work, the effect of this extract on intestinal cells' metabolites and proteins was analysed to gain a deeper understanding of its mode of action on lipids' metabolism, particularly concerning the absorption and transport of exogenous cholesterol. Caco-2 cells, differentiated into enterocytes, were exposed to the extract, and analysed by untargeted metabolomics and proteomics. The results of the metabolomic analysis showed statistically significant differences in glutathione content of cells exposed to the extract compared to control cells, along with an increased expression of fatty acid amides in exposed cells. A proteomic analysis showed an increased expression in cells exposed to the extract compared to control cells of FAB1 and NPC1, proteins known to be involved in lipid metabolism and transport. To the extent of our knowledge, this study is the first use of untargeted metabolomics and a proteomic analysis to investigate the effects of F. vesiculosus on differentiated Caco-2 cells, offering insights into the molecular mechanism of the extract's compounds on intestinal cells.

Biochemotaxis-Oriented Engineering Bacteria Expressing GLP-1 Enhance Diabetes Therapy by Regulating the Balance of Immune

Glucagon like peptide-1 (GLP-1) is an effective hypoglycemic drug that can repair the pancreas β cells and promote insulin secretion. However, GLP-1 has poor stability and lacks of target ability, which makes it difficult to reach the site of action to exert its efficacy. Here, GLP-1-expressing plasmids are introduced into the Escherichia coli Nissle 1917 (EcN) and a lipid membrane is formed through simple self-assembly on its surface, resulting in an oral delivery system (LEG) capable of resisting the harsh environment of the gastrointestinal tract. The system utilizes the chemotactic properties of probiotics to achieve efficient enrichment at the pancreatic site, and protects islet β cells from destruction by regulating the balance of immune cells. More interestingly, LEG not only continuously produces GLP-1 to restore pancreatic islet β cell function and secrete insulin to control blood sugar levels, but also regulates the intestinal flora and increases the richness and diversity of probiotics. In mice diabetes models, oral administration of LEG only once every other day has good biosafety and compliance, and achieves long-term control of blood glucose. Therefore, this strategy not only provides an oral delivery platform for pancreatic targeting, but also opens up new avenues for reversing diabetes.

Hydrophilic Interaction Liquid Chromatography-Hydrogen/Deuterium Exchange-Mass Spectrometry (HILIC-HDX-MS) for Untargeted Metabolomics

Liquid chromatography with mass spectrometry (LC-MS)-based metabolomics detects thousands of molecular features (retention time-m/z pairs) in biological samples per analysis, yet the metabolite annotation rate remains low, with 90% of signals classified as unknowns. To enhance the metabolite annotation rates, researchers employ tandem mass spectral libraries and challenging in silico fragmentation software. Hydrogen/deuterium exchange mass spectrometry (HDX-MS) may offer an additional layer of structural information in untargeted metabolomics, especially for identifying specific unidentified metabolites that are revealed to be statistically significant. Here, we investigate the potential of hydrophilic interaction liquid chromatography (HILIC)-HDX-MS in untargeted metabolomics. Specifically, we evaluate the effectiveness of two approaches using hypothetical targets: the post-column addition of deuterium oxide (D2O) and the on-column HILIC-HDX-MS method. To illustrate the practical application of HILIC-HDX-MS, we apply this methodology using the in silico fragmentation software MS-FINDER to an unknown compound detected in various biological samples, including plasma, serum, tissues, and feces during HILIC-MS profiling, subsequently identified as N1-acetylspermidine.

Lessons and Applications of Omics Research in Diabetes Epidemiology

PURPOSE OF REVIEW

Recent advances in genomic technology and molecular techniques have greatly facilitated the identification of disease biomarkers, advanced understanding of pathogenesis of different common diseases, and heralded the dawn of precision medicine. Much of these advances in the area of diabetes have been made possible through deep phenotyping of epidemiological cohorts, and analysis of the different omics data in relation to detailed clinical information. In this review, we aim to provide an overview on how omics research could be incorporated into the design of current and future epidemiological studies.

RECENT FINDINGS

We provide an up-to-date review of the current understanding in the area of genetic, epigenetic, proteomic and metabolomic markers for diabetes and related outcomes, including polygenic risk scores. We have drawn on key examples from the literature, as well as our own experience of conducting omics research using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank, as well as other cohorts, to illustrate the potential of omics research in diabetes. Recent studies highlight the opportunity, as well as potential benefit, to incorporate molecular profiling in the design and set-up of diabetes epidemiology studies, which can also advance understanding on the heterogeneity of diabetes. Learnings from these examples should facilitate other researchers to consider incorporating research on omics technologies into their work to advance the field and our understanding of diabetes and its related co-morbidities. Insights from these studies would be important for future development of precision medicine in diabetes.

Predictive model for vitamin C levels in hyperinsulinemic individuals based on age, sex, waist circumference, low-density lipoprotein, and immune-associated serum proteins

Vitamin C (ascorbic acid) is an important water-soluble antioxidant associated with decreased oxidative stress in type 2 diabetes (T2D) patients. A previous targeted plasma proteomic study has indicated that ascorbic acid is associated with markers of the immune system in healthy subjects. However, the association between the levels of ascorbic acid and blood biomarkers in subjects at risk of developing T2D is still unknown. Serum ascorbic acid was measured by ultra-performance liquid chromatography and serum proteins were quantified by untargeted liquid-chromatography mass spectrometry in 25 hyperinsulinemia subjects that were randomly assigned a high dairy intake diet or an adequate dairy intake diet for 6 weeks, then crossed-over after a 6-week washout period. Spearman correlation followed by gene ontology analyses were performed to identify biological pathways associated with ascorbic acid. Finally, machine learning analysis was performed to obtain a specific serum protein signature that could predict ascorbic acid levels. After adjustments for waist circumference, LDL, HDL, fasting insulin, fasting blood glucose, age, gender, and dairy intake; serum ascorbic acid correlated positively with different aspects of the immune system. Machine learning analysis indicated that a signature composed of 21 features that included 17 proteins (mainly from the immune system), age, sex, waist circumference, and LDL could predict serum ascorbic acid levels in hyperinsulinemia subjects. In conclusion, the result reveals a correlation as well as modulation between serum ascorbic acid levels and proteins that play vital roles in regulating different aspects of the immune response in individuals at risk of T2D. The development of a predictive signature for ascorbic acid will further help the assessment of ascorbic acid status in clinical settings.

New insights into the role of immunity and inflammation in diabetic kidney disease in the omics era

Diabetic kidney disease (DKD) is becoming the leading cause of chronic kidney disease, especially in the industrialized world. Despite mounting evidence has demonstrated that immunity and inflammation are highly involved in the pathogenesis and progression of DKD, the underlying mechanisms remain incompletely understood. Substantial molecules, signaling pathways, and cell types participate in DKD inflammation, by integrating into a complex regulatory network. Most of the studies have focused on individual components, without presenting their importance in the global or system-based processes, which largely hinders clinical translation. Besides, conventional technologies failed to monitor the different behaviors of resident renal cells and immune cells, making it difficult to understand their contributions to inflammation in DKD. Recently, the advancement of omics technologies including genomics, epigenomics, transcriptomics, proteomics, and metabolomics has revolutionized biomedical research, which allows an unbiased global analysis of changes in DNA, RNA, proteins, and metabolites in disease settings, even at single-cell and spatial resolutions. They help us to identify critical regulators of inflammation processes and provide an overview of cell heterogeneity in DKD. This review aims to summarize the application of multiple omics in the field of DKD and emphasize the latest evidence on the interplay of inflammation and DKD revealed by these technologies, which will provide new insights into the role of inflammation in the pathogenesis of DKD and lead to the development of novel therapeutic approaches and diagnostic biomarkers.

Genotype-based precision nutrition strategies for the prediction and clinical management of type 2 diabetes mellitus

Globally, type 2 diabetes mellitus (T2DM) is one of the most common metabolic disorders. T2DM physiopathology is influenced by complex interrelationships between genetic, metabolic and lifestyle factors (including diet), which differ between populations and geographic regions. In fact, excessive consumptions of high fat/high sugar foods generally increase the risk of developing T2DM, whereas habitual intakes of plant-based healthy diets usually exert a protective effect. Moreover, genomic studies have allowed the characterization of sequence DNA variants across the human genome, some of which may affect gene expression and protein functions relevant for glucose homeostasis. This comprehensive literature review covers the impact of gene-diet interactions on T2DM susceptibility and disease progression, some of which have demonstrated a value as biomarkers of personal responses to certain nutritional interventions. Also, novel genotype-based dietary strategies have been developed for improving T2DM control in comparison to general lifestyle recommendations. Furthermore, progresses in other omics areas (epigenomics, metagenomics, proteomics, and metabolomics) are improving current understanding of genetic insights in T2DM clinical outcomes. Although more investigation is still needed, the analysis of the genetic make-up may help to decipher new paradigms in the pathophysiology of T2DM as well as offer further opportunities to personalize the screening, prevention, diagnosis, management, and prognosis of T2DM through precision nutrition.

Combined untargeted and targeted lipidomics approaches reveal potential biomarkers in type 2 diabetes mellitus cynomolgus monkeys

BACKGROUND

The significantly increasing incidence of type 2 diabetes mellitus (T2DM) over the last few decades triggers the demands of T2DM animal models to explore the pathogenesis, prevention, and therapy of the disease. The altered lipid metabolism may play an important role in the pathogenesis and progression of T2DM. However, the characterization of molecular lipid species in fasting serum related to T2DM cynomolgus monkeys is still underrecognized.

METHODS

Untargeted and targeted LC-mass spectrometry (MS)/MS-based lipidomics approaches were applied to characterize and compare the fasting serum lipidomic profiles of T2DM cynomolgus monkeys and the healthy controls.

RESULTS

Multivariate analysis revealed that 196 and 64 lipid molecules differentially expressed in serum samples using untargeted and targeted lipidomics as the comparison between the disease group and healthy group, respectively. Furthermore, the comparative analysis of differential serum lipid metabolites obtained by untargeted and targeted lipidomics approaches, four common serum lipid species (phosphatidylcholine [18:0_22:4], lysophosphatidylcholine [14:0], phosphatidylethanolamine [PE] [16:1_18:2], and PE [18:0_22:4]) were identified as potential biomarkers and all of which were found to be downregulated. By analyzing the metabolic pathway, glycerophospholipid metabolism was associated with the pathogenesis of T2DM cynomolgus monkeys.

CONCLUSION

The study found that four downregulated serum lipid species could serve as novel potential biomarkers of T2DM cynomolgus monkeys. Glycerophospholipid metabolism was filtered out as the potential therapeutic target pathway of T2DM progression. Our results showed that the identified biomarkers may offer a novel tool for tracking disease progression and response to therapeutic interventions.

Characterizing the metabolic divide: distinctive metabolites differentiating CAD-T2DM from CAD patients

OBJECTIVE

To delineate the metabolomic differences in plasma samples between patients with coronary artery disease (CAD) and those with concomitant CAD and type 2 diabetes mellitus (T2DM), and to pinpoint distinctive metabolites indicative of T2DM risk.

METHOD

Plasma samples from CAD and CAD-T2DM patients across three centers underwent comprehensive metabolomic and lipidomic analyses. Multivariate logistic regression was employed to discern the relationship between the identified metabolites and T2DM risk. Characteristic metabolites' metabolic impacts were further probed through hepatocyte cellular experiments. Subsequent transcriptomic analyses elucidated the potential target sites explaining the metabolic actions of these metabolites.

RESULTS

Metabolomic analysis revealed 192 and 95 significantly altered profiles in the discovery (FDR < 0.05) and validation (P < 0.05) cohorts, respectively, that were associated with T2DM risk in univariate logistic regression. Further multivariate regression analyses identified 22 characteristic metabolites consistently associated with T2DM risk in both cohorts. Notably, pipecolinic acid and L-pipecolic acid, lysine derivatives, exhibited negative association with CAD-T2DM and influenced cellular glucose metabolism in hepatocytes. Transcriptomic insights shed light on potential metabolic action sites of these metabolites.

CONCLUSIONS

This research underscores the metabolic disparities between CAD and CAD-T2DM patients, spotlighting the protective attributes of pipecolinic acid and L-pipecolic acid. The comprehensive metabolomic and transcriptomic findings provide novel insights into the mechanism research, prophylaxis and treatment of comorbidity of CAD and T2DM.

Multi-omics Investigations in Endocrine Systems and Their Clinical Implications

Innovative techniques such as the "omics" can be a powerful tool for the understanding of intracellular pathways involved in homeostasis maintenance and identification of new potential therapeutic targets against endocrine-metabolic disorders. Over the last decades, proteomics has been extensively applied in the study of a wide variety of human diseases, including those involving the endocrine system. Among the most endocrine-related disorders investigated by proteomics in humans are diabetes mellitus and thyroid, pituitary, and reproductive system disorders. In diabetes, proteins implicated in insulin signaling, glucose metabolism, and β-cell activity have been investigated. In thyroid diseases, protein expression alterations were described in thyroid malignancies and autoimmune thyroid illnesses. Additionally, proteomics has been used to investigate the variations in protein expression in adrenal cancers and conditions, including Cushing's syndrome and Addison's disease. Pituitary tumors and disorders including acromegaly and hypopituitarism have been studied using proteomics to examine changes in protein expression. Reproductive problems such as polycystic ovarian syndrome and endometriosis are two examples of conditions where alterations in protein expression have been studied using proteomics. Proteomics has, in general, shed light on the molecular underpinnings of many endocrine-related illnesses and revealed promising biomarkers for both their detection and treatment. The capacity of proteomics to thoroughly and objectively examine complex protein mixtures is one of its main benefits. Mass spectrometry (MS) is a widely used method that identifies and measures proteins based on their mass-to-charge ratio and their fragmentation pattern. MS can perform the separation of proteins according to their physicochemical characteristics, such as hydrophobicity, charge, and size, in combination with liquid chromatography. Other proteomics techniques include protein arrays, which enable the simultaneous identification of several proteins in a single assay, and two-dimensional gel electrophoresis (2D-DIGE), which divides proteins depending on their isoelectric point and molecular weight. This chapter aims to summarize the most relevant proteomics data from targeted tissues, as well as the daily rhythmic variation of relevant biomarkers in both physiological and pathophysiological conditions within the involved endocrine system, especially because the actual modern lifestyle constantly imposes a chronic unentrained condition, which virtually affects all the circadian clock systems within human's body, being also correlated with innumerous endocrine-metabolic diseases.

Omics to Unveil Diabetes Mellitus Pathogenesis and Biomarkers: Focus on Proteomics, Lipidomics, and Metabolomics

Diabetes mellitus (DM) is a chronic metabolic disorder characterized by elevated blood sugar levels, resulting from either body's inability to produce or effectively utilize insulin. There are several types of DM, but the most common are type 1 diabetes (T1D), type 2 diabetes (T2D), and gestational diabetes mellitus (GDM). DM is a complex disease and a global health concern, and the current clinical markers, such as fasting glucose, are helpful in the diagnosis of DM, but are not specific and sensitive, especially when measured on the beginning of the pathogenesis. Therefore, there is a pressing need to discover new early biomarkers that can provide an early diagnosis. Omics is an important field for the discovery of potential new biomarkers, especially proteomics, metabolomics, and lipidomics, where techniques such as liquid chromatography, mass spectrometry, and nuclear magnetic resonance are utilized to identify novel DM biomarkers and their pathways. In this review, we report papers that applied omics in the context of DM to identify new markers and their relationship with this disease, with the aim of elucidating new diagnostic techniques for the main types of DM.

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

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

Inter-organ crosstalk during development and progression of type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is characterized by tissue-specific insulin resistance and pancreatic β-cell dysfunction, which result from the interplay of local abnormalities within different tissues and systemic dysregulation of tissue crosstalk. The main local mechanisms comprise metabolic (lipid) signalling, altered mitochondrial metabolism with oxidative stress, endoplasmic reticulum stress and local inflammation. While the role of endocrine dysregulation in T2DM pathogenesis is well established, other forms of inter-organ crosstalk deserve closer investigation to better understand the multifactorial transition from normoglycaemia to hyperglycaemia. This narrative Review addresses the impact of certain tissue-specific messenger systems, such as metabolites, peptides and proteins and microRNAs, their secretion patterns and possible alternative transport mechanisms, such as extracellular vesicles (exosomes). The focus is on the effects of these messengers on distant organs during the development of T2DM and progression to its complications. Starting from the adipose tissue as a major organ relevant to T2DM pathophysiology, the discussion is expanded to other key tissues, such as skeletal muscle, liver, the endocrine pancreas and the intestine. Subsequently, this Review also sheds light on the potential of multimarker panels derived from these biomarkers and related multi-omics for the prediction of risk and progression of T2DM, novel diabetes mellitus subtypes and/or endotypes and T2DM-related complications.

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

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

Effect of dapagliflozin on proteomics and metabolomics of serum from patients with type 2 diabetes

BACKGROUND

Sodium-glucose co-transporter 2 (SGLT2) inhibitors reduced the risk of cardiovascular and renal outcomes in patients with type 2 diabetes (T2D), but the underlying mechanism has not been well elucidated. The circulating levels of proteins and metabolites reflect the overall state of the human body. This study aimed to evaluate the effect of dapagliflozin on the proteome and metabolome in patients with newly diagnosed T2D.

METHODS

A total of 57 newly diagnosed T2D patients were enrolled, and received 12 weeks of dapagliflozin treatment (10 mg/d, AstraZeneca). Serum proteome and metabolome were investigated at the baseline and after dapagliflozin treatment.

RESULTS

Dapagliflozin significantly decreased HbA1c, BMI, and HOMA-IR in T2D patients (all p < 0.01). Multivariate models indicated clear separations of proteomics and metabolomics data between the baseline and after dapagliflozin treatment. A total of 38 differentially abundant proteins including 23 increased and 15 decreased proteins, and 35 differentially abundant metabolites including 17 increased and 18 decreased metabolites, were identified. In addition to influencing glucose metabolism (glycolysis/gluconeogenesis and pentose phosphate pathway), dapagliflozin significantly increased sex hormone-binding globulin, transferrin receptor protein 1, disintegrin, and metalloprotease-like decysin-1 and apolipoprotein A-IV levels, and decreased complement C3, fibronectin, afamin, attractin, xanthine, and uric acid levels.

CONCLUSIONS

The circulating proteome and metabolome in newly diagnosed T2D patients were significantly changed after dapagliflozin treatment. These changes in proteins and metabolites might be associated with the beneficial effect of dapagliflozin on cardiovascular and renal outcomes.

Metabolomics signature of cardiovascular disease in patients with diabetes, a narrative review

Objectives

The exact underlying mechanism of developing diabetes-related cardiovascular disease (CVD) among patients with type 2 diabetes (T2D) is not clear. Metabolomics can provide a platform enabling the prediction, diagnosis, and understanding of the risk of CVD in patients with diabetes mellitus. The aim of this review is to summarize the available evidence on the relationship between metabolomics and cardiovascular diseases in patients with diabetes.

Methods

The literature was searched to find out studies that have investigated the relationship between the alteration of specific metabolites and cardiovascular diseases in patients with diabetes.

Results

Evidence proposed that changes in the metabolism of certain amino acids, lipids, and carbohydrates, independent of traditional CVD risk factors, are associated with increased CVD risk.

Conclusions

Metabolomics can provide a platform to enable the prediction, diagnosis, and understanding of the risk of CVD in patients with diabetes mellitus. The association of the alteration in specific metabolites with CVD may be considered in the investigations for the development of new therapeutic targets for the prevention of CVD in patients with diabetes mellitus.

Ten metabolites-based algorithm predicts the future development of type 2 diabetes in Chinese

INTRODUCTION

Type 2 diabetes (T2D) is a heterogeneous metabolic disease with large variations in the relative contributions of insulin resistance and β-cell dysfunction across different glucose tolerance subgroups and ethnicities. A more precise yet feasible approach to categorize risk preceding T2D onset is urgently needed. This study aimed to identify potential metabolic biomarkers that could contribute to the development of T2D and investigate whether their impact on T2D is mediated through insulin resistance and β-cell dysfunction.

METHODS

A non-targeted metabolomic analysis was performed in plasma samples of 196 incident T2D cases and 196 age- and sex-matched non-T2D controls recruited from a long-term prospective Chinese community-based cohort with a follow-up period of ∼ 16 years.

RESULTS

Metabolic profiles revealed profound perturbation of metabolomes before T2D onset. Overall metabolic shifts were strongly associated with insulin resistance rather than β-cell dysfunction. In addition, 188 out of the 578 annotated metabolites were associated with insulin resistance. Bi-directional mediation analysis revealed putative causal relationships among the metabolites, insulin resistance and T2D risk. We built a machine-learning based prediction model, integrating the conventional clinical risk factors (age, BMI, TyG index and 2hG) and 10 metabolites (acetyl-tryptophan, kynurenine, γ-glutamyl-phenylalanine, DG(18:2/22:6), DG(38:7), LPI(18:2), LPC(P-16:0), LPC(P-18:1), LPC(P-20:0) and LPE(P-20:0)) (AUROC = 0.894, 5.6% improvement comparing to the conventional clinical risk model), that successfully predicts the development of T2D.

CONCLUSIONS

Our findings support the notion that the metabolic changes resulting from insulin resistance, rather than β-cell dysfunction, are the primary drivers of T2D in Chinese adults. Metabolomes as a valuable phenotype hold potential clinical utility in the prediction of T2D.

Roadmap to the Effective Use of Continuous Glucose Monitoring: Innovation, Investigation, and Implementation

For 25 years, continuous glucose monitoring (CGM) has been evolving into what it is now: a key tool to both measure individuals' glycemic status and to help guide their day-to-day management of diabetes. Through a series of engineering innovations, clinical investigations, and efforts to optimize workflow implementation, the use of CGM is helping to transform diabetes care. This article presents a roadmap to the effective use of CGM that outlines past, present, and possible future advances in harnessing the potential of CGM to improve the lives of many people with diabetes, with an emphasis on ensuring that CGM technology is available to all who could benefit from its use.

Dairy Intake Modifies the Level of the Bile Acid Precursor and Its Correlation with Serum Proteins Associated with Cholesterol Clearance in Subjects with Hyperinsulinemia

Bile acids regulate glucose homeostasis and lipid metabolism. Further, the levels of bile acids can be influenced by the intake of dairy products. Although the serum proteome can provide information on the biological pathways associated with different metabolites, it is unknown whether the intake of dairy modifies such associations between bile acids and the proteome. The objectives of this study were to examine plasma bile acid profiles, find the correlations between bile acids and lipid as well as glycemic markers, and to uncover the correlation between bile acids and proteins after high dairy (HD) and adequate dairy (AD) intake among 25 overweight individuals with hyperinsulinemia. In this randomized crossover-trial study, hyperinsulinemia adults were randomized to both HD (≥4 servings/day) and AD (≤2 servings/day) for 6 weeks. Measurements and analyses were performed on before- as well as after- AD and HD conditions. The results indicated that plasma 7α-hydroxy-4-cholesten-3-one (7AC4) increased after HD in comparison with before HD intake (p = 0.03). After adjusting for BMI, age, and sex, 7AC4 positively correlated with triglyceride levels in the pre-AD (r = 0.44; p = 0.03) and post-HD (r = 0.42; p = 0.04). Further, 7AC4 correlated positively with proteins associated with high-density lipoprotein particle remodeling pathway and reverse cholesterol transport only after HD consumption. Thus, the consumption of higher dairy intake modifies the association between 7AC4-a biomarker for bile acid synthesis-and serum proteins involved in cholesterol clearance. Overall, higher dairy consumption may have a positive effect on cholesterol metabolism in subjects at risk of type 2 diabetes.

mtDNA Single-Nucleotide Variants Associated with Type 2 Diabetes

Type 2 diabetes (T2D) is a chronic systemic disease with a complex etiology, characterized by insulin resistance and mitochondrial dysfunction in various cell tissues. To explore this relationship, we conducted a secondary analysis of complete mtDNA sequences from 1261 T2D patients and 1105 control individuals. Our findings revealed significant associations between certain single-nucleotide polymorphisms (SNPs) and T2D. Notably, the variants m.1438A>G (rs2001030) (controls: 32 [27.6%], T2D: 84 [72.4%]; OR: 2.46; 95%CI: 1.64-3.78; p < 0.001), m.14766C>T (rs193302980) (controls: 498 [36.9%], T2D: 853 [63.1%]; OR: 2.57, 95%CI: 2.18-3.04, p < 0.001), and m.16519T>C (rs3937033) (controls: 363 [43.4%], T2D: 474 [56.6%]; OR: 1.24, 95%CI: 1.05-1.47, p = 0.012) were significantly associated with the likelihood of developing diabetes. The variant m.16189T>C (rs28693675), which has been previously documented in several studies across diverse populations, showed no association with T2D in our analysis (controls: 148 [13.39] T2D: 171 [13.56%]; OR: 1.03; 95%CI: 0.815-1.31; p = 0.83). These results provide evidence suggesting a link between specific mtDNA polymorphisms and T2D, possibly related to association rules, topological patterns, and three-dimensional conformations associated with regions where changes occur, rather than specific point mutations in the sequence.

Proteomic Profiling Identifies Distinct Regulation of Proteins in Obese Diabetic Patients Treated with Metformin

Background: Obesity and type 2 diabetes mellitus (T2DM) are characterized by underlying low-grade chronic inflammation. Metformin has been used as the first line of therapy in T2DM as it decreases hepatic glucose production and glucose intestinal absorption, enhances insulin sensitivity and weight loss, and is known to ameliorate inflammation. The mechanisms through which metformin exerts its effect remain unclear. Proteomics has emerged as a unique approach to explore the biological changes associated with diseases, including T2DM. It provides insight into the circulating biomarkers/mediators which could be utilized for disease screening, diagnosis, and prognosis. Methods: This study evaluated the proteomic changes in obese (Ob), obese diabetics (OD), and obese diabetic patients on metformin (ODM) using a 2D DIGE MALDI-TOF mass spectrometric approach. Results: Significant changes in sixteen plasma proteins (15 up and 1 down, ANOVA, p ≤ 0.05; fold change ≥ 1.5) were observed in the ODM group when compared to the Ob and OD groups. Bioinformatic network pathway analysis revealed that the majority of these altered plasma proteins are involved in distinct pathways involving acute-phase response, inflammation, and oxidative response and were centered around HNF4A, ERK, JNK, and insulin signaling pathways. Conclusions: Our study provides important information about the possible biomarkers altered by metformin treatment in obese patients with and without T2DM. These altered plasma proteins are involved in distinct pathways involving acute-phase response, inflammation, and oxidative response and were centered around HNF4A, ERK, JNK, and insulin signaling pathways. The presented proteomic profiling approach may help in identifying potential biomarkers/mediators affected by metformin treatment in T2DM and inform the understanding of metformin's mechanisms of action.

Altered serum metabolic profile in patients with IgA nephropathy

BACKGROUND

We investigated alterations in the serum metabolomic profile of IgA nephropathy (IgAN) patients and screen biomarkers of IgA nephropathy based on ultra-performance liquid chromatography-mass spectrometry (UPLC-MS).

METHODS

Serum samples from 65 IgAN patients and 31 healthy controls were analyzed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). Univariate and multivariate analysis were performed to screen the differential metabolites. Differential metabolites should meet both the following two criteria: adjusted P < 0.05 in the univariate analysis and VIP value > 1 in the multivariate model. Pathway analysis was performed to reveal the metabolic pathways that were significantly influenced in IgAN. Spearman correlation analysis was applied to explore the correlation between metabolites and between the metabolites and clinicopathological features of IgAN. A random forest model and Logistics regression analysis were conducted to evaluate the predictive ability of the metabolites.

RESULTS

The metabolic profile was significantly altered in IgAN patients compared with healthy controls. Thirty-nine metabolites were identified, including glycerophospholipids, sphingolipids, vitamin K1, vitamin K2, bile acids and amino acids. Sphingolipid metabolism, ubiquinone and other terpenoid-quinone biosynthesis, and glycerophospholipid metabolism were found to be significantly disturbed in the pathway analysis. Differential metabolites were found to be associated with the clinical and pathological features of IgAN patients. Lanosterol, vitamin K1, vitamin K2, and β-elemonic acid were found to have promising predictive ability for IgAN.

CONCLUSIONS

We confirmed the differences in the metabolic profiles of IgAN patients and healthy controls and identified the differential metabolites of IgAN, which may help with the further exploration of the pathogenesis and treatment of IgAN.

A paper-based chemical tongue based on the charge transfer complex of ninhydrin with an array of metal-doped carbon dots discriminates natural amino acids and several of their enantiomers

Simultaneous detection of multiple amino acids (AAs) instead of individual AAs is inherently worthwhile for improving diagnostic accuracy in clinical applications. Here, a facile and reliable colorimetric microfluidic paper-based analytical device (μPAD) using carbon dots doped with transition metals (Cr3+, Mn2+, Fe3+, Co2+, Ni2+, Cu2+, and Zn2+) has been provided to detect and discriminate 20 natural amino acids. To make the colourless metal-doped carbon dots suitable for colorimetric assays, they were mixed with ninhydrin to form a charge transfer complex. This optical tongue system, which was constructed by dropping mixtures of ninhydrin with a series of metal-doped carbon dots on a paper substrate in an array format, represented obvious but different colorimetric signatures for every examined amino acid. Since bovine serum albumin was used as a chiral selector reagent for synthesizing the CDs, the sensor device represented excellent selectivity to identify enantiomeric species of AAs. This is the first optical array device that can simultaneously discriminate AAs and several of their enantiomers. We employed various statistical and chemometric methods to analyze the digital data library collected by Image J software, including principal component analysis (PCA), linear discriminant analysis (LDA), and hierarchical cluster analysis (HCA). Twenty AAs could be well distinguished at various concentrations (10.00, 5.00, 2.50, and 1.25 mM). The colorimetric patterns were highly repeatable and were characteristic of individual AAs. Besides qualitative analysis, the designed μPAD-based optical tongue represented quantitative analysis ability, e.g., for lysine in the concentration ranges of 0.005-20.0 mM with a detection limit of 1.0 × 10-6 M and for arginine in the concentration range of 0.12-20.00 mM with a detection limit of 80.0 × 10-6 M. In addition, the binary, ternary, and quaternary mixtures of AAs could also be well recognized with this sensor.

Increased cardiac PFK-2 protects against high-fat diet-induced cardiomyopathy and mediates beneficial systemic metabolic effects

A healthy heart adapts to changes in nutrient availability and energy demands. In metabolic diseases like type 2 diabetes (T2D), increased reliance on fatty acids for energy production contributes to mitochondrial dysfunction and cardiomyopathy. A principal regulator of cardiac metabolism is 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase (PFK-2), which is a central driver of glycolysis. We hypothesized that increasing PFK-2 activity could mitigate cardiac dysfunction induced by high-fat diet (HFD). Wild type (WT) and cardiac-specific transgenic mice expressing PFK-2 (GlycoHi) were fed a low fat or HFD for 16 weeks to induce metabolic dysfunction. Metabolic phenotypes were determined by measuring mitochondrial bioenergetics and performing targeted quantitative proteomic and metabolomic analysis. Increasing cardiac PFK-2 had beneficial effects on cardiac and mitochondrial function. Unexpectedly, GlycoHi mice also exhibited sex-dependent systemic protection from HFD, including increased glucose homeostasis. These findings support improving glycolysis via PFK-2 activity can mitigate mitochondrial and functional changes that occur with metabolic syndrome.

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.

Metabolomics in diabetes mellitus: clinical insight

INTRODUCTION

Diabetes Mellitus (DM) is a chronic heterogeneous metabolic disorder characterized by hyperglycemia due to the destruction of insulin-producing pancreatic β cells and/or insulin resistance. It is now considered a global epidemic disease associated with serious threats to a patient's life. Understanding the metabolic pathways involved in disease pathogenesis and progression is important and would improve prevention and management strategies. Metabolomics is an emerging field of research that offers valuable insights into the metabolic perturbation associated with metabolic diseases, including DM.

AREA COVERED

Herein, we discussed the metabolomics in type 1 and 2 DM research, including its contribution to understanding disease pathogenesis and identifying potential novel biomarkers clinically useful for disease screening, monitoring, and prognosis. In addition, we highlighted the metabolic changes associated with treatment effects, including insulin and different anti-diabetic medications.

EXPERT OPINION

By analyzing the metabolome, the metabolic disturbances involved in T1DM and T2DM can be explored, enhancing our understanding of the disease progression and potentially leading to novel clinical diagnostic and effective new therapeutic approaches. In addition, identifying specific metabolites would be potential clinical biomarkers for predicting the disease and thus preventing and managing hyperglycemia and its complications.

12,13-diHOME and noradrenaline are associated with the occurrence of acute myocardial infarction in patients with type 2 diabetes mellitus

BACKGROUND

Acute myocardial infarction (AMI) is the most prevalent cause of mortality and morbidity in patients with type 2 diabetes mellitus (T2DM). However, strict blood glucose control does not always prevent the development and progression of AMI. Therefore, the present study aimed to explore potential new biomarkers associated with the occurrence of AMI in T2DM patients.

METHODS

A total of 82 participants were recruited, including the control group (n = 28), T2DM without AMI group (T2DM, n = 30) and T2DM with initial AMI group (T2DM + AMI, n = 24). The untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) analysis was performed to evaluate the changes in serum metabolites. Then, candidate metabolites were determined using ELISA method in the validation study (n = 126/T2DM group, n = 122/T2DM + AMI group).

RESULTS

The results showed that 146 differential serum metabolites were identified among the control, T2DM and T2DM + AMI, Moreover, 16 differentially-expressed metabolites were significantly altered in T2DM + AMI compared to T2DM. Amino acid and lipid pathways were the major involved pathways. Furthermore, three candidate differential metabolites, 12,13-dihydroxy-9Z-octadecenoic acid (12,13-diHOME), noradrenaline (NE) and estrone sulfate (ES), were selected for validation study. Serum levels of 12,13-diHOME and NE in T2DM + AMI were significantly higher than those in T2DM. Multivariate logistic analyses showed that 12,13-diHOME (OR, 1.491; 95% CI 1.230-1.807, P < 0.001) and NE (OR, 8.636; 95% CI 2.303-32.392, P = 0.001) were independent risk factors for AMI occurrence in T2T2DM patients. The area under receiver operating characteristic (ROC) curve (AUC) were 0.757 (95% CI 0.697-0.817, P < 0.001) and 0.711(95% CI 0.648-0.775, P < 0.001), respectively. The combination of both significantly improved the AUC to 0.816 (95% CI 0.763-0.869, P < 0.001).

CONCLUSIONS

12,13-diHOME and NE may lead to understanding the possible metabolic alterations associated with AMI onset in T2DM population and serve as promising risk factors and therapeutic targets.

Discovery and validation of glucose-sensitive peptide biomarkers from human serum albumin to diagnose type 2 diabetes mellitus

Glycated albumin (GA), which represents the global glycation level of albumin, has emerged as a biomarker for diagnosing prediabetes and diabetes. In our previous study, we developed a peptide-based strategy and found three putative peptide biomarkers from the tryptic peptides of GA to diagnose type 2 diabetes mellitus (T2DM). However, the trypsin cleavage sites at the carboxyl side of lysine (K) and arginine (R) are consistent with the nonenzymatic glycation modification site residues, which considerably increases the number of missed cleavage sites and half-cleaved peptides. To solve this problem, the endoproteinase Glu-C was used to digest GA from human serum to screen putative peptides to diagnose T2DM. In the discovery phase, we found eighteen and fifteen glucose-sensitive peptides from purified albumin and human serum incubated with ¹³C glucose in vitro, respectively. In the validation phase, eight glucose-sensitive peptides were screened and validated in 72 clinical samples (28 healthy controls and 44 patients with diabetes) using label-free LC-ESI-MRM. Three putative sensitive peptides (VAHRFKDLGEE, FKPLVEEPQNLIKQNCE and NQDSISSKLKE) from albumin exhibited good specificity and sensitivity based on receiver operating characteristic analysis. In summary, three peptides were found as promising biomarkers for the diagnosis and assessment of T2DM based on mass spectrometry.

Potential of in vitro nuclear magnetic resonance of biofluids and tissues in clinical research

Body fluids, cells, and tissues contain a wide variety of metabolites that consist of a mixture of various low-molecular-weight compounds, including amino acids, peptides, lipids, nucleic acids, and organic acids, which makes comprehensive analysis more difficult. Quantitative nuclear magnetic resonance (NMR) spectroscopy is a well-established analytical technique for analyzing the metabolic profiles of body fluids, cells, and tissues. It enables fast and comprehensive detection, characterization, a high level of experimental reproducibility, minimal sample preparation, and quantification of various endogenous metabolites. In recent times, NMR-based metabolomics has been appreciably utilized in diverse branches of medicine, including microbiology, toxicology, pathophysiology, pharmacology, nutritional intervention, and disease diagnosis/prognosis. In this review, the utility of NMR-based metabolomics in clinical studies is discussed. The significance of in vitro NMR-based metabolomics as an effective tool for detecting metabolites and their variations in different diseases are discussed, together with the possibility of identifying specific biomarkers that can contribute to early detection and diagnosis of disease.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Effects of aging and type 2 diabetes on cardiac structure and function: Underlying mechanisms

We characterized long-term changes in cardiac structure and function in a high-fat diet/streptozotocin mouse model of aging and type 2 diabetes mellitus (T2D) and examined how the intersection of both conditions alters plasma metabolomics. We also evaluated the possible roles played by oxidative stress, arginase activity and pro-inflammatory cytokines. C57BL/6 male mice (13-month-old) were used. Control animals (n = 13) were fed regular chow for 10 months (aged group). T2D animals (n = 25) were provided a single injection of streptozotocin and fed a high fat diet for 10 months. In select endpoints, young animals were used for comparison. To monitor changes in left ventricular (LV) structure and function, echocardiography was used. At the terminal study (23 months), blood was collected and hearts processed for biochemical or histological analysis. Echo yielded diminished diastolic function with aging and T2D. LV fractional shortening and ejection fraction decreased with T2D by 16 months peaking at 23 months. Western blots noted increases in fibronectin and type I collagen with aging/T2D and greater levels with T2D in α-smooth muscle actin. Increases in plasma and/or myocardial protein carbonyls, arginase activity and pro-inflammatory cytokines occurred with aging and T2D. Untargeted metabolomics and cheminformatics revealed differences in the plasma metabolome of T2D vs. aged mice while select classes of lipid metabolites linked to insulin resistance, were dysregulated. We thus, document changes in LV structure and function with aging that in select endpoints, are accentuated with T2D and link them to increases in OS, arginase activity and pro-inflammatory cytokines.

Antidiabetic effects of Andrographis paniculata supplementation on biochemical parameters, inflammatory responses, and oxidative stress in canine diabetes

Introduction: Diabetes mellitus is a common endocrine disorder that causes hyperglycemia in dogs. Persistent hyperglycemia can induce inflammation and oxidative stress. This study aimed to investigate the effects of A. paniculata (Burm.f.) Nees (Acanthaceae) (A. paniculata) on blood glucose, inflammation, and oxidative stress in canine diabetes. A total of 41 client-owned dogs (23 diabetic and 18 clinically healthy) were included in this double-blind, placebo-controlled trial. Methods: The diabetic dogs were further divided into two treatments protocols: group 1 received A. paniculata extract capsules (50 mg/kg/day; n = 6) or received placebo for 90 days (n = 7); and group 2 received A. paniculata extract capsules (100 mg/kg/day; n = 6) or received a placebo for 180 days (n = 4). Blood and urine samples were collected every month. No significant differences in fasting blood glucose, fructosamine, interleukin-6, tumor necrosis factor-alpha, superoxide dismutase, and malondialdehyde levels were observed between the treatment and placebo groups (p > 0.05). Results and Discussion: The levels of alanine aminotransferase, alkaline phosphatase, blood urea nitrogen, and creatinine were stable in the treatment groups. The blood glucose levels and concentrations of inflammatory and oxidative stress markers in the client-owned diabetic dogs were not altered by A. paniculata supplementation. Furthermore, treatment with this extract did not have any adverse effects on the animals. Non-etheless, the effects of A. paniculata on canine diabetes must be appropriately evaluated using a proteomic approach and involving a wider variety of protein markers.

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.

Forkhead box P3 gene polymorphisms predispose to type 2 diabetes and diabetic nephropathy in the Han Chinese populations: a genetic-association and gender-based evaluation study

BACKGROUND

Functional mutations or polymorphisms affecting forkhead box P3 (FOXP3) can lead to their abnormal FOXP3 gene expression and/or defective Treg cells generation, thus resulting in autoimmune disease and inflammatory disorders. FOXP3 also plays a key role in Type 2 diabetes mellitus (T2DM) and its complications, because the disease usually involves chronic low-grade inflammatory disorders and is associated with long-term immune system imbalance. This study aimed to investigate the association between FOXP3 polymorphisms and the susceptibility to T2DM and type 2 diabetes nephropathy (T2DN) within the Han Chinese populations.

METHODS

Polymorphisms in rs3761548C/A and rs2294021C/T were examined in 400 patients (which include an equal number of T2DM and T2DN groups) and 200 healthy controls using PCR-HRM and sequence analysis.

RESULTS

The genotype and allelic frequencies of the two single nucleotide polymorphisms (SNPs) were significantly different in T2DM and the progression of diabetes developing to T2DN. The further gender-based evaluation showed that in female subjects, rs3761548C/A was associated with an approximately 3-fold higher threat for T2DM and 4.5-fold for T2DN, while there was no noticeable association with rs2294021C/T; in males, the promoter polymorphism showed an increased predisposition of 5.4-fold and 3.4-fold predisposition to T2DM and T2DN, respectively, while rs2294021 polymorphism could impart a nearly 2-fold risk of developing T2DN. An additional analysis of combined genotypes (rs3761548 C/A-rs2294021C/T) revealed that CC-CC and CC-CT can be considered protective combinations in the predisposition of males with diabetes towards T2DN, while AA-CC and AA-TT have the opposite effect.

CONCLUSIONS

This study demonstrated the possible involvement of individual and combined genetic associations of rs3761548C/A and rs2294021C/T polymorphisms with the susceptibility to diabetes and diabetic nephropathy in the Han Chinese population, as well as gender bias.

High Plasma Levels of Soluble Lectin-like Oxidized Low-Density Lipoprotein Receptor-1 Are Associated With Inflammation and Cardiometabolic Risk Profiles in Pediatric Overweight and Obesity

Background Lectin-like oxidized low-density lipoprotein (ox-LDL) receptor-1 is a scavenger receptor for oxidized low-density lipoprotein. In adults, higher soluble lectin-like ox-LDL receptor-1 (sLOX-1) levels are associated with cardiovascular disease, type 2 diabetes, and obesity, but a similar link in pediatric overweight/obesity remains uncertain. Methods and Results Analyses were based on the cross-sectional HOLBAEK Study, including 4- to 19-year-olds from an obesity clinic group with body mass index >90th percentile (n=1815) and from a population-based group (n=2039). Fasting plasma levels of sLOX-1 and inflammatory markers were quantified, cardiometabolic risk profiles were assessed, and linear and logistic regression analyses were performed. Pubertal/postpubertal children and adolescents from the obesity clinic group exhibited higher sLOX-1 levels compared with the population (P<0.001). sLOX-1 positively associated with proinflammatory cytokines, matrix metalloproteinases, body mass index SD score, waist SD score, body fat %, plasma alanine aminotransferase, serum high-sensitivity C-reactive protein, plasma low-density lipoprotein cholesterol, triglycerides, systolic and diastolic blood pressure SD score, and inversely associated with plasma high-density lipoprotein cholesterol (all P<0.05). sLOX-1 positively associated with high alanine aminotransferase (odds ratio [OR], 1.16, P=4.1 E-04), insulin resistance (OR, 1.16, P=8.6 E-04), dyslipidemia (OR, 1.25, P=1.8 E-07), and hypertension (OR, 1.12, P=0.02). Conclusions sLOX-1 levels were elevated during and after puberty in children and adolescents with overweight/obesity compared with population-based peers and associated with inflammatory markers and worsened cardiometabolic risk profiles. sLOX-1 may serve as an early marker of cardiometabolic risk and inflammation in pediatric overweight/obesity. Registration The HOLBAEK Study, formerly known as The Danish Childhood Obesity Biobank, ClinicalTrials.gov identifier number NCT00928473, https://clinicaltrials.gov/ct2/show/NCT00928473 (registered June 2009).

Systematic metabolomic studies identified adult adiposity biomarkers with acetylglycine associated with fat loss in vivo

Obesity is associated with various adverse health outcomes. Body fat (BF) distribution is recognized as an important factor of negative health consequences of obesity. Although metabolomics studies, mainly focused on body mass index (BMI) and waist circumference, have explored the biological mechanisms involved in the development of obesity, these proxy composite measures are not accurate and cannot reflect BF distribution, and thus may hinder accurate assessment of metabolic alterations and differential risk of metabolic disorders among individuals presenting adiposity differently throughout the body. Thus, the exact relations between metabolites and BF remain to be elucidated. Here, we aim to examine the associations of metabolites and metabolic pathways with BF traits which reflect BF distribution. We performed systematic untargeted serum metabolite profiling and dual-energy X-ray absorptiometry (DXA) whole body fat scan for 517 Chinese women. We jointly analyzed DXA-derived four BF phenotypes to detect cross-phenotype metabolite associations and to prioritize important metabolomic factors. Topology-based pathway analysis was used to identify important BF-related biological processes. Finally, we explored the relationships of the identified BF-related candidate metabolites with BF traits in different sex and ethnicity through two independent cohorts. Acetylglycine, the top distinguished finding, was validated for its obesity resistance effect through in vivo studies of various diet-induced obese (DIO) mice. Eighteen metabolites and fourteen pathways were discovered to be associated with BF phenotypes. Six of the metabolites were validated in varying sex and ethnicity. The obesity-resistant effects of acetylglycine were observed to be highly robust and generalizable in both human and DIO mice. These findings demonstrate the importance of metabolites associated with BF distribution patterns and several biological pathways that may contribute to obesity and obesity-related disease etiology, prevention, and intervention. Acetylglycine is highlighted as a potential therapeutic candidate for preventing excessive adiposity in future studies.