Metabolomic biomarkers for personalised glucose lowering drugs treatment in type 2 diabetes

Proline Analogues

Within the canonical repertoire of the amino acid involved in protein biogenesis, proline plays a unique role as an amino acid presenting a modified backbone rather than a side-chain. Chemical structures that mimic proline but introduce changes into its specific molecular features are defined as proline analogues. This review article summarizes the existing chemical, physicochemical, and biochemical knowledge about this peculiar family of structures. We group proline analogues from the following compounds: substituted prolines, unsaturated and fused structures, ring size homologues, heterocyclic, e.g., pseudoproline, and bridged proline-resembling structures. We overview (1) the occurrence of proline analogues in nature and their chemical synthesis, (2) physicochemical properties including ring conformation and cis/trans amide isomerization, (3) use in commercial drugs such as nirmatrelvir recently approved against COVID-19, (4) peptide and protein synthesis involving proline analogues, (5) specific opportunities created in peptide engineering, and (6) cases of protein engineering with the analogues. The review aims to provide a summary to anyone interested in using proline analogues in systems ranging from specific biochemical setups to complex biological systems.

Gut microbiome-serum metabolic profiles: insight into the hypoglycemic effect of Porphyra haitanensis glycoprotein on hyperglycemic mice

The hypoglycemic activity of natural algal glycoproteins has attracted interest, but studies of their mechanism of regulating glucose metabolism are lacking. This study investigated the hypoglycemic activity of Porphyra haitanensis glycoprotein (PG) in a mouse hyperglycemia model. The underlying mechanism was elucidated by monitoring changes in the gut microbiome and untargeted serum metabolomics. The results indicated that 30-300 mg kg-1 PG regulated blood glucose levels by increasing insulin secretion, reducing glycated hemoglobin, and improving streptozotocin-induced hyperglycemia in a concentration-dependent manner. In particular, 300 mg kg-1 PG decreased fasting blood glucose by 63.11% and glycosylated hemoglobin by 24.50% and increased insulin secretion by 163.97%. The mechanism of the improvement of hyperglycemia by PG may involve regulating beneficial intestinal bacteria (e.g., norank_f__Muribaculaceae and Lachnospiraceae) and altering the serum metabolic profile (e.g., upregulation of hypotaurine, 3-hydroxy-2-naphthoic acid, and L-glycine), to regulate taurine and hypotaurine, the TCA cycle, AMPK, and pyruvate metabolism. Our findings supported the development of Porphyra haitanensis and its glycoprotein as novel natural antidiabetic compounds to regulate the glycemic balance.

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.

Recharacterization of the Mammalian Cytosolic Type 2 (R)-β-Hydroxybutyrate Dehydrogenase (BDH2) as 4-Oxo-L-Proline Reductase (EC 1.1.1.104)

Early studies revealed that chicken embryos incubated with a rare analog of l-proline, 4-oxo-l-proline, showed increased levels of the metabolite 4-hydroxy-l-proline. In 1962, 4-oxo-l-proline reductase, an enzyme responsible for the reduction of 4-oxo-l-proline, was partially purified from rabbit kidneys and characterized biochemically. However, only recently was the molecular identity of this enzyme solved. Here, we report the purification from rat kidneys, identification, and biochemical characterization of 4-oxo-l-proline reductase. Following mass spectrometry analysis of the purified protein preparation, the previously annotated mammalian cytosolic type 2 (R)-β-hydroxybutyrate dehydrogenase (BDH2) emerged as the only candidate for the reductase. We subsequently expressed rat and human BDH2 in Escherichia coli, then purified it, and showed that it catalyzed the reversible reduction of 4-oxo-l-proline to cis-4-hydroxy-l-proline via chromatographic and tandem mass spectrometry analysis. Specificity studies with an array of compounds carried out on both enzymes showed that 4-oxo-l-proline was the best substrate, and the human enzyme acted with 12,500-fold higher catalytic efficiency on 4-oxo-l-proline than on (R)-β-hydroxybutyrate. In addition, human embryonic kidney 293T (HEK293T) cells efficiently metabolized 4-oxo-l-proline to cis-4-hydroxy-l-proline, whereas HEK293T BDH2 KO cells were incapable of producing cis-4-hydroxy-l-proline. Both WT and KO HEK293T cells also produced trans-4-hydroxy-l-proline in the presence of 4-oxo-l-proline, suggesting that the latter compound might interfere with the trans-4-hydroxy-l-proline breakdown in human cells. We conclude that BDH2 is a mammalian 4-oxo-l-proline reductase that converts 4-oxo-l-proline to cis-4-hydroxy-l-proline and not to trans-4-hydroxy-l-proline, as originally thought. We also hypothesize that this enzyme may be a potential source of cis-4-hydroxy-l-proline in mammalian tissues.

Metabolomics of Type 1 and Type 2 Diabetes: Insights into Risk Prediction and Mechanisms

PURPOSE OF REVIEW

Metabolomics enables rapid interrogation of widespread metabolic processes making it well suited for studying diabetes. Here, we review the current status of metabolomic investigation in diabetes, highlighting its applications for improving risk prediction and mechanistic understanding.

RECENT FINDINGS

Findings of metabolite associations with type 2 diabetes risk have confirmed experimental observations (e.g., branched-chain amino acids) and also pinpointed novel pathways of diabetes risk (e.g., dimethylguanidino valeric acid). In type 1 diabetes, abnormal metabolite patterns are observed prior to the development of autoantibodies and hyperglycemia. Diabetes complications display specific metabolite signatures that are distinct from the metabolic derangements of diabetes and differ across vascular beds. Lastly, metabolites respond acutely to pharmacologic treatment, providing opportunities to understand inter-individual treatment responses. Metabolomic studies have elucidated biological mechanisms underlying diabetes development, complications, and therapeutic response. While not yet ready for clinical translation, metabolomics is a powerful and promising precision medicine tool.

Ketogenic diet: A promising neuroprotective composition for managing Alzheimer's diseases and its pathological mechanisms

Ketogenic diet and ketone bodies gained significant attention in recent years due to their ability to influence the specific energy metabolism and restoration of mitochondrial homeostasis that can help in hindering the progression of many metabolic diseases including diabetes and neurodegenerative diseases. Ketogenic diet consists of high fat and low carbohydrate contents which makes the body glucose deprived and rely on alternative sources (ketone bodies) for energy. It has been initially designed and supplemented for the treatment of epilepsy and later its influence on many energy-deriving biochemical pathways made it a highly sorted food supplement for many metabolic diseases and even by healthy individuals for body building and calorie restriction. Among the reported therapeutic action over a range of diseases, neurodegenerative disorders especially Alzheimer's disease gained the attention of many researchers and clinicians because of its potency and its easier supplementation as a food additive. Complex pathology and multiple influencing factors of Alzheimer's disease make exploration of its therapeutic strategies a demanding task. It was a common phenomenon that energy deprivation in neurological disorders including Alzheimer's disease, to progress rapidly. The ability of ketone bodies to stabilize the mitochondrial energy metabolism makes it a suitable intervening agent. In this review, we will discuss various research progress made with regards to ketone bodies/ketogenic diet for management of Alzheimer's disease and elaborate in detail about the mechanisms that are influenced during their therapeutic action.

Visible-light-induced C(sp3)-H functionalizations of piperidines to 3,3-dichloro-2-hydroxy-piperidines with N-chlorosuccinimide

A visible-light-induced synthesis of 3,3-dichloro-2-hydroxy-piperidines via site-selective functionalizations of C(sp³)-H in N-substituted piperidines using easily available N-chlorosuccinimide as chlorine source was developed. Mechanistic investigations suggest that chlorine radical is involved in this transformation.

Metabolomics: small molecules that matter more

Metabolomics, an analytical study with high-throughput profiling, helps to understand interactions within a biological system. Small molecules, called metabolites or metabolomes with the size of <1500 Da, depict the status of a biological system in a different manner. Currently, we are in need to globally analyze the metabolome and the pathways involved in healthy, as well as diseased conditions, for possible therapeutic applications. Metabolome analysis has revealed high-abundance molecules during different conditions such as diet, environmental stress, microbiota, and disease and treatment states. As a result, it is hard to understand the complete and stable network of metabolites of a biological system. This review helps readers know the available techniques to study metabolomics in addition to other major omics such as genomics, transcriptomics, and proteomics. This review also discusses the metabolomics in various pathological conditions and the importance of metabolomics in therapeutic applications.

Serum Metabolites as an Indicator of Developing Gestational Diabetes Mellitus Later in the Pregnancy: A Prospective Cohort of a Chinese Population

OBJECTIVE

Gestational diabetes mellitus (GDM) is a common metabolic disorder with onset during pregnancy. However, the etiology and pathogenesis of GDM have not been fully elucidated. In this study, we used a metabolomics approach to investigate the relationship between maternal serum metabolites and GDM in early pregnancy.

METHODS

A nested case-control study was performed. To establish an early pregnancy cohort, pregnant women in early pregnancy (10-13⁺⁶ weeks) were recruited. In total, 51 patients with GDM and 51 healthy controls were included. Serum samples were analyzed using an untargeted high-performance liquid chromatography mass spectrometry metabolomics approach. The relationships between metabolites and GDM were analyzed by an orthogonal partial least-squares discriminant analysis. Differential metabolites were evaluated using a KEGG pathway analysis.

RESULTS

A total of 44 differential metabolites were identified between GDM cases and healthy controls during early pregnancy. Of these, 26 significant metabolites were obtained in early pregnancy after false discovery rate (FDR < 0.1) correction. In the GDM group, the levels of L-pyroglutamic acid, L-glutamic acid, phenylacetic acid, pantothenic acid, and xanthine were significantly higher and the levels of 1,5-anhydro-D-glucitol, calcitriol, and 4-oxoproline were significantly lower than those in the control group. These metabolites were involved in multiple metabolic pathways, including those for amino acid, carbohydrate, lipid, energy, nucleotide, cofactor, and vitamin metabolism.

CONCLUSIONS

We identified significant differentially expressed metabolites associated with the risk of GDM, providing insight into the mechanisms underlying GDM in early pregnancy and candidate predictive markers.

Mapping Metabolite and ICD-10 Associations

The search for novel metabolic biomarkers is intense but has had limited practical outcomes for medicine. Part of the problem is that we lack knowledge of how different comorbidities influence biomarkers’ performance. In this study, 49 metabolites were measured by targeted LC/MS protocols in the serum of 1011 volunteers. Their performance as potential biomarkers was evaluated by the area under the curve of receiver operator characteristics (AUC-ROC) for 105 diagnosis codes or code groups from the 10th revision of the international classification of diseases (ICD-10). Additionally, the interferences between diagnosis codes were investigated. The highest AUC-ROC values for individual metabolites and ICD-10 code combinations reached a moderate (0.7) range. Most metabolites that were found to be potential markers remained so independently of the control group composition or comorbidities. The precise value of the AUC-ROC, however, could vary depending on the comorbidities. Moreover, networks of metabolite and disease associations were built in order to map diseases, which may interfere with metabolic biomarker research on other diseases.

Mining treatment patterns of glucose-lowering medications for type 2 diabetes in the Netherlands

RATIONALE AND OBJECTIVES

Different classes of glucose-lowering medications are used for patients with type 2 diabetes mellitus (T2DM) management. It is unclear how often these medications are prescribed in clinical practice. In this study, we aimed to describe treatment patterns of glucose-lowering medications in patients with T2DM in the Netherlands.

METHODS

We studied a cohort of 73 819 patients with T2DM, aged ≥45 years with a first prescription for oral glucose-lowering medication between 2011 and 2017. We used the NControl database with dispensing data from 800 pharmacies in the Netherlands. Prevalence of each glucose-lowering medication class during 6 years after the index date was calculated. Using SQL Server, we identified stepwise patterns of medication prescription in this population.

FINDINGS

During the study period, prevalence of biguanides (BIGU) decreased from 95.6% to 80.8% and use of sulfonylureas (SU) increased from 27.3% to 42.3%. 55.2% of all patients only received BIGUs, 19.1% of all patients started on BIGUs but switched to BIGU +SU. 13.5% of patients with T2DM initiated insulins, on average 532 days (almost 18 months) after the index date.

CONCLUSIONS

Our findings showed that in the Netherlands, medication treatment in patients with T2DM is mainly consistent with the clinical guidelines in the Netherlands during the study period.

Metabolite Profiles of Incident Diabetes and Heterogeneity of Treatment Effect in the Diabetes Prevention Program

Novel biomarkers of type 2 diabetes (T2D) and response to preventative treatment in individuals with similar clinical risk may highlight metabolic pathways that are important in disease development. We profiled 331 metabolites in 2,015 baseline plasma samples from the Diabetes Prevention Program (DPP). Cox models were used to determine associations between metabolites and incident T2D, as well as whether associations differed by treatment group (i.e., lifestyle [ILS], metformin [MET], or placebo [PLA]), over an average of 3.2 years of follow-up. We found 69 metabolites associated with incident T2D regardless of treatment randomization. In particular, cytosine was novel and associated with the lowest risk. In an exploratory analysis, 35 baseline metabolite associations with incident T2D differed across the treatment groups. Stratification by baseline levels of several of these metabolites, including specific phospholipids and AMP, modified the effect that ILS or MET had on diabetes development. Our findings highlight novel markers of diabetes risk and preventative treatment effect in individuals who are clinically at high risk and motivate further studies to validate these interactions.

Metabolomics profiles associated with HbA1c levels in patients with type 2 diabetes

Glycated hemoglobin (HbA1c) is an indicator of the average blood glucose concentration. Failing to control HbA1c levels can accelerate the development of complications in patients with diabetes. Although metabolite profiles associated with HbA1c level in diabetes patients have been characterized using different platforms, more studies using high-throughput technology will be helpful to identify additional metabolites related to diabetes. Type 2 diabetes (T2D) patients were divided into two groups based on the HbA1c level: normal (HbA1c ≤6%) and high (HbA1c ≥9%) in both discovery and replication sets. A targeted metabolomics approach was used to quantify serum metabolites and multivariate logistic regression was used to identify significant differences between groups. The concentrations of 22 metabolites differed significantly between the two groups in the discovery set. In the replication set, the levels of 21 metabolites, including 16 metabolites identified in the discovery set, differed between groups. Among these, concentrations of eleven amino acids and one phosphatidylcholine (PC), lysoPC a C16:1, were higher and four metabolites, including three PCs (PC ae C36:1, PC aa C26:0, PC aa C34:2) and hexose, were lower in the group with normal HbA1c group than in the group with high HbA1c. Metabolites with high concentrations in the normal HbA1c group, such as glycine, valine, and PCs, may contribute to reducing HbA1c levels in patients with T2D. The metabolite signatures identified in this study provide insight into the mechanisms underlying changes in HbA1c levels in T2D.

Clinical and genetic predictors of diabetes drug's response

Diabetes is a major health problem worldwide. Glycemic control is the main goal in the management of type 2 diabetes. While many anti-diabetic drugs and guidelines are available, almost half of diabetic patients do not reach their treatment goal and develop complications. The glucose-lowering response to anti-diabetic drug differs significantly between individuals. Relatively little is known about the factors that might underlie this response. The identification of predictors of response to anti-diabetic drugs is essential for treatment personalization. Unfortunately, the evidence on predictors of drugs response in type 2 diabetes is scarce. Only a few trials were designed for specific groups of patients (e.g. patients with renal impairment or older patients), while subgroup analyses of larger trials are frequently unreported. Physicians need help in picking the drug which provides the maximal benefit, with minimal side effects, in the right dose, for a specific patient, using an omics-based approach besides the phenotypic characteristics.

Blood Metabolomic Measures Associate With Present and Future Glycemic Control in Type 2 Diabetes

OBJECTIVE

We studied whether blood metabolomic measures in people with type 2 diabetes (T2D) are associated with insufficient glycemic control and whether this association is influenced differentially by various diabetes drugs. We then tested whether the same metabolomic profiles were associated with the initiation of insulin therapy.

METHODS

A total of 162 metabolomic measures were analyzed using a nuclear magnetic resonance-based method in people with T2D from four cohort studies (n = 2641) and one replication cohort (n = 395). Linear and logistic regression analyses with adjustment for potential confounders, followed by meta-analyses, were performed to analyze associations with hemoglobin A1c levels, six glucose-lowering drug categories, and insulin initiation during a 7-year follow-up period (n = 698).

RESULTS

After Bonferroni correction, 26 measures were associated with insufficient glycemic control (HbA1c >53 mmol/mol). The strongest association was with glutamine (OR, 0.66; 95% CI, 0.61 to 0.73; P = 7.6 × 10-19). In addition, compared with treatment-naive patients, 31 metabolomic measures were associated with glucose-lowering drug use (representing various metabolite categories; P ≤ 3.1 × 10-4 for all). In drug-stratified analyses, associations with insufficient glycemic control were only mildly affected by different glucose-lowering drugs. Five of the 26 metabolomic measures (apolipoprotein A1 and medium high-density lipoprotein subclasses) were also associated with insulin initiation during follow-up in both discovery and replication. The strongest association was observed for medium high-density lipoprotein cholesteryl ester (OR, 0.54; 95% CI, 0.42 to 0.71; P = 4.5 × 10-6).

CONCLUSION

Blood metabolomic measures were associated with present and future glycemic control and might thus provide relevant cues to identify those at increased risk of treatment failure.

Gut microbiota and serum metabolite differences in African Americans and White Americans with high blood pressure

BACKGROUND

Black Americans have greater rates, severity and resistance to treatment of hypertension than White Americans. The gut microbiota and its metabolites may contribute to this. This concept was tested in a pilot study.

METHODS

Subjects with high (HBP, >140/80 mm Hg) and normal (NBP, <120/80 mm Hg) blood pressure (BP) provided stool and blood samples for whole genome sequencing (WGS) of gut microbiota and global untargeted metabolomics of serum. Patients were either black (B) with NBP (n = 10 for WGS, 5 for metabolomics) and HBP (n = 10 and 9, BHBP) or white (W) with NBP (n = 20 and 13, WNBP) and HBP (n = 12 and 8, WHBP).

RESULTS

All four subject groups had distinct gut microbiota taxonomy by partial least squares discriminant analysis (PLS-DA). More importantly, linear discriminant analysis effect size showed marked differences in function of the microbiota of BHBP and WHBP (PLS-DA) with LDA scores <1. This included pathways for synthesis and interconversion of amino acids and inflammatory antigens. Similarly, metabolites differed (PLS-DA) with BHBP having significantly higher sulfacetaldehyde, quinolinic acid, 5-aminolevulinic acid, leucine and phenylalanine and lower 4-oxoproline and l-anserine.

DISCUSSION

Combination analyses of functional gut metabolic pathways and metabolomics data in this small pilot study suggest that BHBP may have greater oxidative stress markers in plasma, greater inflammatory potential of the gut microbiome and altered metabolites with gut microbial functions implying insulin resistance. A fuller understanding of these potential differences could lead to race-based treatments for hypertension.

Effect of metformin on plasma metabolite profile in the Copenhagen Insulin and Metformin Therapy (CIMT) trial

AIM

Metformin is the first-line treatment for Type 2 diabetes. However, not all people benefit from this drug. Our aim was to investigate the effects of metformin on the plasma metabolome and whether the pretreatment metabolite profile can predict HbA_1c outcome.

METHODS

Post hoc analysis of the Copenhagen Insulin and Metformin Therapy (CIMT) trial, a multicentre study from May 2008 to December 2012, was carried out. We used a non-target method to analyse 87 plasma metabolites in participants with Type 2 diabetes (n = 370) who were randomized in a 1 : 1 ratio to 18 months of metformin or placebo treatment. Metabolites were measured by liquid chromatography-mass spectrometry at baseline and at 18-month follow-up and the data were analysed using a linear mixed-effect model.

RESULTS

At baseline, participants who were on metformin before the trial (n = 312) had higher levels of leucine/isoleucine and five lysophosphatidylethanolamines (LPEs), and lower levels of carnitine and valine compared with metformin-naïve participants (n = 58). At follow-up, participants randomized to metformin (n = 188) had elevated levels of leucine/isoleucine and reduced carnitine, tyrosine and valine compared with placebo (n = 182). At baseline, participants on metformin treatment with the highest levels of carnitine C10:1 and leucine/isoleucine had the lowest HbA_1c (P-interaction = 0.02 and 0.03, respectively). This association was not significant with HbA_1c at follow-up.

CONCLUSIONS

Metformin treatment is associated with decreased levels of valine, tyrosine and carnitine, and increased levels of leucine/isoleucine. None of the identified metabolites can predict the HbA_1c -lowering effect of metformin. Further studies of the association between metformin, carnitine and leucine/isoleucine are warranted.

A Pharmacometabolomic Approach to Predict Response to Metformin in Early-Phase Type 2 Diabetes Mellitus Patients

Metformin is a first-line medication for type 2 diabetes mellitus (T2DM). Based on its universal use, the consideration of inter-individual variability and development of predictive biomarkers are clinically significant. We aimed to identify endogenous markers of metformin responses using a pharmacometabolomic approach. Twenty-nine patients with early-phase T2DM were enrolled and orally administered metformin daily for 6 months. A total of 22 subjects were included in the final analysis. Patients were defined as responders or non-responders based on changes in their glycated haemoglobin A1c (HbA1c) from baseline, over 3 months. Urine metabolites at baseline, as well as at the 3 and 6 month follow-ups after the start of treatment were analysed using gas chromatography-mass spectrometry and evaluated with multivariate analyses. Metabolites distinguishable between the two response groups were obtained at baseline, as well as at the 3 and 6 month follow-ups, and significantly different metabolites were listed as markers of metformin response. Among the identified metabolites, citric acid, myoinositol, and hippuric acid levels showed particularly significant differences between the non-responder and responder groups. We thus identified different metabolite profiles in the two groups of T2DM patients after metformin administration, using pharmacometabolomics. These results might facilitate a better understanding and prediction of metformin response and its variability in individual patients.

Precision nutrition for prevention and management of type 2 diabetes

Precision nutrition aims to prevent and manage chronic diseases by tailoring dietary interventions or recommendations to one or a combination of an individual's genetic background, metabolic profile, and environmental exposures. Recent advances in genomics, metabolomics, and gut microbiome technologies have offered opportunities as well as challenges in the use of precision nutrition to prevent and manage type 2 diabetes. Nutrigenomics studies have identified genetic variants that influence intake and metabolism of specific nutrients and predict individuals' variability in response to dietary interventions. Metabolomics has revealed metabolomic fingerprints of food and nutrient consumption and uncovered new metabolic pathways that are potentially modified by diet. Dietary interventions have been successful in altering abundance, composition, and activity of gut microbiota that are relevant for food metabolism and glycaemic control. In addition, mobile apps and wearable devices facilitate real-time assessment of dietary intake and provide feedback which can improve glycaemic control and diabetes management. By integrating these technologies with big data analytics, precision nutrition has the potential to provide personalised nutrition guidance for more effective prevention and management of type 2 diabetes. Despite these technological advances, much research is needed before precision nutrition can be widely used in clinical and public health settings. Currently, the field of precision nutrition faces challenges including a lack of robust and reproducible results, the high cost of omics technologies, and methodological issues in study design as well as high-dimensional data analyses and interpretation. Evidence is needed to support the efficacy, cost-effectiveness, and additional benefits of precision nutrition beyond traditional nutrition intervention approaches. Therefore, we should manage unrealistically high expectations and balance the emerging field of precision nutrition with public health nutrition strategies to improve diet quality and prevent type 2 diabetes and its complications.

The extracellular domain of Staphylococcus aureus LtaS binds insulin and induces insulin resistance during infection

Insulin resistance is a risk factor for obesity and diabetes and predisposes individuals to Staphylococcus aureus colonization; however, the contribution of S. aureus to insulin resistance remains unclear. Here, we show that S. aureus infection causes impaired glucose tolerance via secretion of an insulin-binding protein extracellular domain of LtaS, eLtaS, which blocks insulin-mediated glucose uptake. Notably, eLtaS transgenic mice (eLtaS ^trans ) exhibited a metabolic syndrome similar to that observed in patients, including increased food and water consumption, impaired glucose tolerance and decreased hepatic glycogen synthesis. Furthermore, transgenic mice showed significant metabolic differences compared to their wild-type counterparts, particularly for the early insulin resistance marker α-hydroxybutyrate. We subsequently developed a full human monoclonal antibody against eLtaS that blocked the interaction between eLtaS and insulin, which effectively restored glucose tolerance in eLtaS ^trans and S. aureus-challenged mice. Thus, our results reveal a mechanism for S. aureus-induced insulin resistance.

Personalised Interventions-A Precision Approach for the Next Generation of Dietary Intervention Studies

Diet is a key modifiable risk factor for non-communicable diseases. However, we currently are not benefitting from the full potential of its protective effects. This is due to a number of reasons, including high individual variability in response to certain diets. It is now well acknowledged that in order to gain the full benefit of dietary regimes it is essential to take into account individual responses. With this in mind, the present review examines the concept of precision nutrition and the performance of n-of-1 studies, and discusses the development of certain approaches that will be critical for development of the concepts.

Novel Applications of Metabolomics in Personalized Medicine: A Mini-Review

Interindividual variability in drug responses and disease susceptibility is common in the clinic. Currently, personalized medicine is highly valued, the idea being to prescribe the right medicine to the right patient. Metabolomics has been increasingly applied in evaluating the therapeutic outcomes of clinical drugs by correlating the baseline metabolic profiles of patients with their responses, i.e., pharmacometabonomics, as well as prediction of disease susceptibility among population in advance, i.e., patient stratification. The accelerated advance in metabolomics technology pinpoints the huge potential of its application in personalized medicine. In current review, we discussed the novel applications of metabolomics with typical examples in evaluating drug therapy and patient stratification, and underlined the potential of metabolomics in personalized medicine in the future.

Metabolite profiling in identifying metabolic biomarkers in older people with late-onset type 2 diabetes mellitus

Regulation of blood glucose requires precise coordination between different endocrine systems and multiple organs. Type 2 diabetes mellitus (T2D) arises from a dysregulated response to elevated glucose levels in the circulation. Globally, the prevalence of T2D has increased dramatically in all age groups. T2D in older adults is associated with higher mortality and reduced functional status, leading to higher rate of institutionalization. Despite the potential healthcare challenges associated with the presence of T2D in the elderly, the pathogenesis and phenotype of late-onset T2D is not well studied. Here we applied untargeted metabolite profiling of urine samples from people with and without late-onset T2D using ultra-performance liquid-chromatography mass-spectrometry (UPLC-MS) to identify urinary biomarkers for late-onset T2D in the elderly. Statistical modeling of measurements and thorough validation of structural assignment using liquid chromatography tandem mass-spectrometry (LC-MS/MS) have led to the identification of metabolite biomarkers associated with late-onset T2D. Lower levels of phenylalanine, acetylhistidine, and cyclic adenosine monophosphate (cAMP) were found in urine samples of T2D subjects validated with commercial standards. Elevated levels of 5′-methylthioadenosine (MTA), which previously has only been implicated in animal model of diabetes, was found in urine of older people with T2D.

Discovery and identification of potential biomarkers for alcohol-induced oxidative stress based on cellular metabolomics

Biomarkers involved in alcohol-induced oxidative stress play an important role in alcoholic liver disease prevention and diagnosis. Alcohol-induced oxidative stress in human liver L-02 cells was used to discover the potential biomarkers. Metabolites from L-02 cells induced by alcohol were measured by high-performance liquid chromatography and mass spectrometry. Fourteen metabolites that allowed discrimination between control and model groups were discovered by multivariate statistical data analysis (i.e. principal components analysis, orthogonal partial least-squares discriminate analysis). Based on the retention time, UV spectrum and LC-MS findings of the samples and compared with the authentic standards, eight biomarkers involved in alcohol-induced oxidative stress, namely, malic acid, oxidized glutathione, γ-glutamyl-cysteinyl-glycine, adenosine triphosphate, phenylalanine, adenosine monophosphate, nitrotyrosine and tryptophan, were identified. These biomarkers offered important targets for disease diagnosis and other researches.

Challenges of implementing personalized (precision) medicine: a focus on diabetes

The concept of personalized (precision) medicine (PM) emphasizes the scientific and technological innovations that enable the physician to tailor disease prediction, diagnosis and treatment to the individual patient, based on a personalized data-driven approach. The major challenge for the medical systems is to translate the molecular and genomic advances into clinical available means. Patients and healthcare providers, the pharmaceutical and diagnostic industries manifest a growing interest in PM. Multiple stakeholders need adaptation and re-engineering for successful clinical implementation of PM. Drawing primarily from the field of ‘diabetes’, this article will summarize the main challenges to implementation of PM into current medical practice and some of the approaches currently being implemented to overcome these challenges.

Metabolomics: Bridging the Gap between Pharmaceutical Development and Population Health

Metabolomics has emerged as an essential tool for studying metabolic processes, stratification of patients, as well as illuminating the fundamental metabolic alterations in disease onset, progression, or response to therapeutic intervention. Metabolomics materialized within the pharmaceutical industry as a standalone assay in toxicology and disease pathology and eventually evolved towards aiding in drug discovery and pre-clinical studies via supporting pharmacokinetic and pharmacodynamic characterization of a drug or a candidate. Recent progress in the field is illustrated by coining of the new term—Pharmacometabolomics. Integration of data from metabolomics with large-scale omics along with clinical, molecular, environmental and behavioral analysis has demonstrated the enhanced utility of deconstructing the complexity of health, disease, and pharmaceutical intervention(s), which further highlight it as an essential component of systems medicine. This review presents the current state and trend of metabolomics applications in pharmaceutical development, and highlights the importance and potential of clinical metabolomics as an essential part of multi-omics protocols that are directed towards shaping precision medicine and population health.