1,5-Anhydroglucitol in saliva is a noninvasive marker of short-term glycemic control

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

AIMS/HYPOTHESIS

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

METHODS

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

RESULTS

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

CONCLUSIONS/INTERPRETATION

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

Nanoparticle enrichment mass-spectrometry proteomics identifies protein-altering variants for precise pQTL mapping

Proteogenomics studies generate hypotheses on protein function and provide genetic evidence for drug target prioritization. Most previous work has been conducted using affinity-based proteomics approaches. These technologies face challenges, such as uncertainty regarding target identity, non-specific binding, and handling of variants that affect epitope affinity binding. Mass spectrometry-based proteomics can overcome some of these challenges. Here we report a pQTL study using the Proteograph™ Product Suite workflow (Seer, Inc.) where we quantify over 18,000 unique peptides from nearly 3000 proteins in more than 320 blood samples from a multi-ethnic cohort in a bottom-up, peptide-centric, mass spectrometry-based proteomics approach. We identify 184 protein-altering variants in 137 genes that are significantly associated with their corresponding variant peptides, confirming target specificity of co-associated affinity binders, identifying putatively causal cis-encoded proteins and providing experimental evidence for their presence in blood, including proteins that may be inaccessible to affinity-based proteomics.

rox: A Statistical Model for Regression with Missing Values

High-dimensional omics datasets frequently contain missing data points, which typically occur due to concentrations below the limit of detection (LOD) of the profiling platform. The presence of such missing values significantly limits downstream statistical analysis and result interpretation. Two common techniques to deal with this issue include the removal of samples with missing values and imputation approaches that substitute the missing measurements with reasonable estimates. Both approaches, however, suffer from various shortcomings and pitfalls. In this paper, we present "rox", a novel statistical model for the analysis of omics data with missing values without the need for imputation. The model directly incorporates missing values as "low" concentrations into the calculation. We show the superiority of rox over common approaches on simulated data and on six metabolomics datasets. Fully leveraging the information contained in LOD-based missing values, rox provides a powerful tool for the statistical analysis of omics data.

Prognostic value of 1,5-anhydro-D-glucitol incorporating syntax score in acute coronary syndrome

The utility of adding information on 1,5-anhydro-D-glucitol (1,5-AG), a marker for postprandial hyperglycemia, to a pre-existing scoring system in acute coronary syndrome (ACS) patients is unknown. This retrospective cohort study included 266 ACS patients. The end point was major adverse cardiac and cerebral events (MACCE) through 5 years of follow-up. To evaluate incremental benefits of combining 1,5-AG with the syntax score, we applied time-dependent receiver operating curve (ROC) analysis, net reclassification improvement (NRI), integrated discrimination improvement (IDI) and decision curve analysis (DCA). Temporal changes to the area under time-dependent ROC curves showed that addition of 1,5-AG parameters to syntax score did not provide any incremental value (area under the curve for syntax alone, 0.673 (95% confidence interval (CI), 0.599-0.747) vs. with 1,5-AG combined, 0.671 (95%CI 0.596-0.746; Delong p = 0.65). Incorporating 1,5-AG into syntax score yielded a significant NRI of 0.291 (95%CI 0.015-0.567) and IDI of 0.055 (95%CI 0.018-0.093), while DCA analysis showed the limited net benefit in combination with 1,5-AG and syntax score. 1,5-AG values exhibited significant discriminatory utility for detecting MACCE within the ACS population. However, 1,5-AG levels contributed limited utility beyond syntax score based on time-dependent ROC and DCA analyses.Trial registration: UMIN000023837.

Salivary 1,5-Anhydroglucitol and its Correlation with Postprandial Hyperglycemia: Development and Validation of a Novel Assay

Background

Saliva has the potential to be used as a noninvasive sample for testing hyperglycemia in diabetes mellitus. Serum 1,5-anhydroglucitol (1,5-AG) decreases with an increase in blood sugar >180 mg/dl. We hypothesized that salivary 1,5-AG can be used to identify blood sugar higher than 180 mg/dl using a novel biochemical method.

Aim

This study aimed to develop a novel biochemical method for serum and salivary assessment of 1,5-AG and assess its correlation with postprandial blood sugar (PPBS) >180 mg/dl.

Methodology

The study comprised 45 controls (healthy individuals) and 45 cases (type 2 diabetic patients with PPBS >180 mg/dl). Blood and salivary samples were collected according to the study protocol. A new method was developed for the quantification of 1,5-AG in serum and saliva using liquid chromatography-mass spectrometry.

Results

The value of serum (mean -22.19 μg/ml and median -22.12 μg/ml) and salivary (mean -0.124 μg/ml and median -0.088 μg/ml) 1,5-AG was higher in healthy individuals compared to corresponding serum (mean -3.89 μg/ml and median -2.52 μg/ml) and salivary (mean -0.025 μg/ml and median - 0.025 μg/ml) levels in diabetics with PPBS >180 mg/dl. In diabetics, a significant negative correlation was noticed with PPBS levels and 1,5-AG levels in serum and saliva. Salivary 1,5-AG level <0.054 μg/ml had an 86.4% sensitivity and 87.2% specificity in predicting a blood sugar value >180 mg/dl.

Conclusion

The results of our study suggest that the short-term glycemic marker 1,5-AG can be detected in saliva and can be useful as an adjunct marker in monitoring of glycemic status in diabetic patients.

Salivary metabolic signatures of carotid atherosclerosis in patients with type 2 diabetes hospitalized for treatment

Atherosclerosis is a life-threatening disease associated with morbidity and mortality in patients with type 2 diabetes (T2D). This study aimed to characterize a salivary signature of atherosclerosis based on evaluation of carotid intima-media thickness (IMT) to develop a non-invasive predictive tool for diagnosis and disease follow-up. Metabolites in saliva and plasma samples collected at admission and after treatment from 25 T2D patients hospitalized for 2 weeks to undergo medical treatment for diabetes were comprehensively profiled using metabolomic profiling with gas chromatography-mass spectrometry. Orthogonal partial least squares analysis, used to explore the relationships of IMT with clinical markers and plasma and salivary metabolites, showed that the top predictors for IMT included salivary allantoin and 1,5-anhydroglucitol (1,5-AG) at both the baseline examination at admission and after treatment. Furthermore, though treatment induced alterations in salivary levels of allantoin and 1,5-AG, it did not modify the association between IMT and these metabolites (p _interaction > 0.05), and models with these metabolites combined yielded satisfactory diagnostic accuracy for the high IMT group even after treatment (area under curve = 0.819). Collectively, this salivary metabolite combination may be useful for non-invasive identification of T2D patients with a higher atherosclerotic burden in clinical settings.

Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population

Type 2 diabetes (T2D) has a heterogeneous etiology influencing its progression, treatment, and complications. A data driven cluster analysis in European individuals with T2D previously identified four subtypes: severe insulin deficient (SIDD), severe insulin resistant (SIRD), mild obesity-related (MOD), and mild age-related (MARD) diabetes. Here, the clustering approach was applied to individuals with T2D from the Qatar Biobank and validated in an independent set. Cluster-specific signatures of circulating metabolites and proteins were established, revealing subtype-specific molecular mechanisms, including activation of the complement system with features of autoimmune diabetes and reduced 1,5-anhydroglucitol in SIDD, impaired insulin signaling in SIRD, and elevated leptin and fatty acid binding protein levels in MOD. The MARD cluster was the healthiest with metabolomic and proteomic profiles most similar to the controls. We have translated the T2D subtypes to an Arab population and identified distinct molecular signatures to further our understanding of the etiology of these subtypes.

Variational autoencoders learn transferrable representations of metabolomics data

Dimensionality reduction approaches are commonly used for the deconvolution of high-dimensional metabolomics datasets into underlying core metabolic processes. However, current state-of-the-art methods are widely incapable of detecting nonlinearities in metabolomics data. Variational Autoencoders (VAEs) are a deep learning method designed to learn nonlinear latent representations which generalize to unseen data. Here, we trained a VAE on a large-scale metabolomics population cohort of human blood samples consisting of over 4500 individuals. We analyzed the pathway composition of the latent space using a global feature importance score, which demonstrated that latent dimensions represent distinct cellular processes. To demonstrate model generalizability, we generated latent representations of unseen metabolomics datasets on type 2 diabetes, acute myeloid leukemia, and schizophrenia and found significant correlations with clinical patient groups. Notably, the VAE representations showed stronger effects than latent dimensions derived by linear and non-linear principal component analysis. Taken together, we demonstrate that the VAE is a powerful method that learns biologically meaningful, nonlinear, and transferrable latent representations of metabolomics data.

Detection of cellular metabolites by redox enzymatic cascades

Detection of cellular metabolites that are disease biomarkers is important for human healthcare monitoring and assessing prognosis and therapeutic response. Accurate and rapid detection of microbial metabolites and pathway intermediates is also crucial for the process optimization required for development of bioconversion methods using metabolically engineered cells. Various redox enzymes can generate electrons that can be employed in enzyme-based biosensors and in the detection of cellular metabolites. These reactions can directly transform target compounds into various readout signals. By incorporating engineered enzymes into enzymatic cascades, the readout signals can be improved in terms of accuracy and sensitivity. This review critically discusses selected redox enzymatic and chemoenzymatic cascades currently employed for detection of human- and microbe-related cellular metabolites including, amino acids, d-glucose, inorganic ions (pyrophosphate, phosphate, and sulfate), nitro- and halogenated phenols, NAD(P)H, fatty acids, fatty aldehyde, alkane, short chain acids, and cellular metabolites.

Dental, oral pH, orthodontic and salivary values in children with obstructive sleep apnea

OBJECTIVES

Mouth breathing is a key feature of obstructive sleep apnea (OSA). The current study evaluated dental, salivary and orthodontic characteristics of children with OSA, and compared them to those of children without OSA.

MATERIALS AND METHODS

Twenty-two children (mean age 5.3 years, 13 males) with OSA and 21 children without OSA who served as a control group (mean age 6.8 years, 11 males) underwent dental examinations. The OSA group was classified according to the apnea-hypopnea Index. Clinical examination included plaque index, gingival index, caries status, pH at 7 oral sites, salivary carries bacterial counts and inflammatory cytokine levels. Orthodontics measurements were calculated as the percentage of children with values in the normal range, in each group.

RESULTS

The mean values of the decayed, missing and filled teeth (DMFT)/dmft index, the gingival index and the plaque index were higher in the OSA than the control group. Salivary Mutans streptococci and lactobacilli counts were significantly higher in the OSA than the control group; as were pH values in the hard and soft palate, and in the posterior and middle tongue. Significantly lower values were observed in the OSA than the control group for most of the orthodontic variables examined. Similarly, stratification of AHI according to severity shows the lowest values among those with mild OSA, and the highest among those with severe AHI.

CONCLUSIONS

Compared to a control group, mouth breathing children with obstructive sleep apnea had differences in oral microbiota, greater acidity and poorer dental status.

CLINICAL RELEVANCE

Clinicians should be aware of the various oral disturbances that may accompany OSA, and implement preventive measures.

Saliva and Plasma Reflect Metabolism Altered by Diabetes and Periodontitis

Periodontitis is an inflammatory disorder caused by disintegration of the balance between the periodontal microbiome and host response. While growing evidence suggests links between periodontitis and various metabolic disorders including type 2 diabetes (T2D), non-alcoholic liver disease, and cardiovascular disease (CVD), which often coexist in individuals with abdominal obesity, factors linking periodontal inflammation to common metabolic alterations remain to be fully elucidated. More detailed characterization of metabolomic profiles associated with multiple oral and cardiometabolic traits may provide better understanding of the complexity of oral-systemic crosstalk and its underlying mechanism. We performed comprehensive profiling of plasma and salivary metabolomes using untargeted gas chromatography/mass spectrometry to investigate multivariate covariation with clinical markers of oral and systemic health in 31 T2D patients with metabolic comorbidities and 30 control subjects. Orthogonal partial least squares (OPLS) results enabled more accurate characterization of associations among 11 oral and 25 systemic clinical outcomes, and 143 salivary and 78 plasma metabolites. In particular, metabolites that reflect cardiometabolic changes were identified in both plasma and saliva, with plasma and salivary ratios of (mannose + allose):1,5-anhydroglucitol achieving areas under the curve of 0.99 and 0.92, respectively, for T2D diagnosis. Additionally, OPLS analysis of periodontal inflamed surface area (PISA) as the numerical response variable revealed shared and unique responses of metabolomic and clinical markers to PISA between healthy and T2D groups. When combined with linear regression models, we found a significant correlation between PISA and multiple metabolites in both groups, including threonate, cadaverine and hydrocinnamate in saliva, as well as lactate and pentadecanoic acid in plasma, of which plasma lactate showed a predominant trend in the healthy group. Unique metabolites associated with PISA in the T2D group included plasma phosphate and salivary malate, while those in the healthy group included plasma gluconate and salivary adenosine. Remarkably, higher PISA was correlated with altered hepatic lipid metabolism in both groups, including higher levels of triglycerides, aspartate aminotransferase and alanine aminotransferase, leading to increased risk of cardiometabolic disease based on a score summarizing levels of CVD-related biomarkers. These findings revealed the potential utility of saliva for evaluating the risk of metabolic disorders without need for a blood test, and provide evidence that disrupted liver lipid metabolism may underlie the link between periodontitis and cardiometabolic disease.

SGI: automatic clinical subgroup identification in omics datasets

SUMMARY

The 'Subgroup Identification' (SGI) toolbox provides an algorithm to automatically detect clinical subgroups of samples in large-scale omics datasets. It is based on hierarchical clustering trees in combination with a specifically designed association testing and visualization framework that can process an arbitrary number of clinical parameters and outcomes in a systematic fashion. A multi-block extension allows for the simultaneous use of multiple omics datasets on the same samples. In this article, we first describe the functionality of the toolbox and then demonstrate its capabilities through application examples on a type 2 diabetes metabolomics study as well as two copy number variation datasets from The Cancer Genome Atlas.

AVAILABILITY AND IMPLEMENTATION

SGI is an open-source package implemented in R. Package source codes and hands-on tutorials are available at https://github.com/krumsieklab/sgi. The QMdiab metabolomics data is included in the package and can be downloaded from https://doi.org/10.6084/m9.figshare.5904022.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Plasma Proteomics of Renal Function: A Transethnic Meta-Analysis and Mendelian Randomization Study

BACKGROUND

Studies on the relationship between renal function and the human plasma proteome have identified several potential biomarkers. However, investigations have been conducted largely in European populations, and causality of the associations between plasma proteins and kidney function has never been addressed.

METHODS

A cross-sectional study of 993 plasma proteins among 2882 participants in four studies of European and admixed ancestries (KORA, INTERVAL, HUNT, QMDiab) identified transethnic associations between eGFR/CKD and proteomic biomarkers. For the replicated associations, two-sample bidirectional Mendelian randomization (MR) was used to investigate potential causal relationships. Publicly available datasets and transcriptomic data from independent studies were used to examine the association between gene expression in kidney tissue and eGFR.

RESULTS

In total, 57 plasma proteins were associated with eGFR, including one novel protein. Of these, 23 were additionally associated with CKD. The strongest inferred causal effect was the positive effect of eGFR on testican-2, in line with the known biological role of this protein and the expression of its protein-coding gene (SPOCK2) in renal tissue. We also observed suggestive evidence of an effect of melanoma inhibitory activity (MIA), carbonic anhydrase III, and cystatin-M on eGFR.

CONCLUSIONS

In a discovery-replication setting, we identified 57 proteins transethnically associated with eGFR. The revealed causal relationships are an important stepping stone in establishing testican-2 as a clinically relevant physiological marker of kidney disease progression, and point to additional proteins warranting further investigation.

Saliva 1,5-anhydroglucitol is associated with early-phase insulin secretion in Chinese patients with type 2 diabetes

INTRODUCTION

Saliva collection is a non-invasive test and is convenient. 1,5-anhydroglucitol (1,5-AG) is a new indicator reflecting short-term blood glucose levels. This study aimed to explore the relationship between saliva 1,5-AG and insulin secretion function and insulin sensitivity.

RESEARCH DESIGN AND METHODS

Adult patients with type 2 diabetes who were hospitalized were enrolled. Based on blood glucose and C-peptide, homeostasis model assessment 2 for β cell secretion function, C-peptidogenic index (CGI), △2-hour C-peptide (2hCP)/△2-hour postprandial glucose (2hPG), ratio of 0-30 min area under the curve for C-peptide and area under the curve for glucose (AUC_CP30/AUC_PG30), and AUC_2hCP/AUC_2hPG were calculated to evaluate insulin secretion function, while indicators such as homeostasis model assessment 2 for insulin resistance were used to assess insulin sensitivity.

RESULTS

We included 284 subjects (178 men and 106 women) with type 2 diabetes aged 20-70 years. The saliva 1,5-AG level was 0.133 (0.089-0.204) µg/mL. Spearman's correlation analysis revealed a significantly negative correlation between saliva 1,5-AG and 0, 30, and 120 min blood glucose, glycated hemoglobin A_1c, and glycated albumin (all p<0.05), and a significantly positive association between saliva 1,5-AG and CGI (r=0.171, p=0.004) and AUC _CP30 /AUC _PG30 (r=0.174, p=0.003). The above correlations still existed after adjusting for age, sex, body mass index, and diabetes duration. In multiple linear regression, saliva 1,5-AG was an independent factor of CGI (standardized β=0.135, p=0.015) and AUC _CP30 /AUC _PG30 (standardized β=0.110, p=0.020).

CONCLUSIONS

Saliva 1,5-AG was related to CGI and AUC_CP30/AUC_PG30 in patients with type 2 diabetes.

TRIAL REGISTRATION NUMBER

ChiCTR-SOC-17011356.

Revealing the role of the human blood plasma proteome in obesity using genetic drivers

Blood circulating proteins are confounded readouts of the biological processes that occur in different tissues and organs. Many proteins have been linked to complex disorders and are also under substantial genetic control. Here, we investigate the associations between over 1000 blood circulating proteins and body mass index (BMI) in three studies including over 4600 participants. We show that BMI is associated with widespread changes in the plasma proteome. We observe 152 replicated protein associations with BMI. 24 proteins also associate with a genome-wide polygenic score (GPS) for BMI. These proteins are involved in lipid metabolism and inflammatory pathways impacting clinically relevant pathways of adiposity. Mendelian randomization suggests a bi-directional causal relationship of BMI with LEPR/LEP, IGFBP1, and WFIKKN2, a protein-to-BMI relationship for AGER, DPT, and CTSA, and a BMI-to-protein relationship for another 21 proteins. Combined with animal model and tissue-specific gene expression data, our findings suggest potential therapeutic targets further elucidating the role of these proteins in obesity associated pathologies.

Secondary Metabolites in the Treatment of Diabetes Mellitus: A Paradigm Shift

Diabetes mellitus (DM) is a chronic, polygenic and non-infectious group of diseases that occurs due to insulin resistance or its low production by the pancreas and is also associated with lifelong damage, dysfunction and collapse of various organs. Management of diabetes is quite complex having many bodily and emotional complications and warrants efficient measures for prevention and control of the same. As per the estimates of the current and future diabetes prevalence, around 425 million people were diabetic in 2017 which is anticipated to rise up to 629 million by 2045. Various studies have vaguely proven the fact that several vitamins, minerals, botanicals and secondary metabolites demonstrate hypoglycemic activity in vivo as well as in vitro. Flavonoids, anthocyanin, catechin, lipoic acid, coumarin metabolites, etc. derived from herbs were found to elicit a significant influence on diabetes. However, the prescription of herbal compounds depend on various factors, including the degree of diabetes progression, comorbidities, feasibility, economics as well as their ADR profile. For instance, cinnamon could be a more favorable choice for diabetic hypertensive patients. Diabecon®, Glyoherb® and Diabeta Plus® are some of the herbal products that had been launched in the market for the favorable or adjuvant therapy of diabetes. Moreover, Aloe vera leaf gel extract demonstrates significant activity in diabetes. The goal of this review was to inscribe various classes of secondary metabolites, in particular those obtained from plants, and their role in the treatment of DM. Recent advancements in recognizing the markers which can be employed for identifying altered metabolic pathways, biomarker discovery, limitations, metabolic markers of drug potency and off-label effects are also reviewed.

Circulating Protein Signatures and Causal Candidates for Type 2 Diabetes

The increasing prevalence of type 2 diabetes poses a major challenge to societies worldwide. Blood-based factors like serum proteins are in contact with every organ in the body to mediate global homeostasis and may thus directly regulate complex processes such as aging and the development of common chronic diseases. We applied a data-driven proteomics approach, measuring serum levels of 4,137 proteins in 5,438 elderly Icelanders, and identified 536 proteins associated with prevalent and/or incident type 2 diabetes. We validated a subset of the observed associations in an independent case-control study of type 2 diabetes. These protein associations provide novel biological insights into the molecular mechanisms that are dysregulated prior to and following the onset of type 2 diabetes and can be detected in serum. A bidirectional two-sample Mendelian randomization analysis indicated that serum changes of at least 23 proteins are downstream of the disease or its genetic liability, while 15 proteins were supported as having a causal role in type 2 diabetes.

Structure and function relationships of sugar oxidases and their potential use in biocatalysis

Several sugar oxidases that catalyze the oxidation of sugars have been isolated and characterized. These enzymes can be classified as flavoenzyme due to the presence of flavin adenine dinucleotide (FAD) as a cofactor. Sugar oxidases have been proposed to be the key biocatalyst in biotransformation of carbohydrates which can potentially convert sugars to provide a pool of intermediates for synthesis of rare sugars, fine chemicals and drugs. Moreover, sugar oxidases have been applied in biosensing of various biomolecules in food industries, diagnosis of diseases and environmental pollutant detection. This review provides the discussions on general properties, current mechanistic understanding, structural determination, biocatalytic application, and biosensor integration of representative sugar oxidase enzymes, namely pyranose 2-oxidase (P2O), glucose oxidase (GO), hexose oxidase (HO), and oligosaccharide oxidase. The information regarding the relationship between structure and function of these sugar oxidases points out the key properties of this particular group of enzymes that can be modified by engineering, which had resulted in a remarkable economic importance.

Identification of genetic variants controlling RNA editing and their effect on RNA structure stabilization

Post-transcriptional modification of RNA (RNA editing, RNAe) results in differences between the RNA transcript and the genomic DNA sequence (RDD). Enzymatic modification of adenosine to inosine (A2I) by ADAR is the most studied type of RNAe. However, few genetic association studies with A2I RNAe events have been conducted. Some studies have analyzed the inter-population RNAe-QTL diversity in humans, but the sample size of these studies was limited. Other types of RNA and DNA differences have been reported but are largely understudied. Here, we report a comprehensive analysis of all types of RDD, based on two independent datasets. We found that A2I was by far the most observed type of RDD. Moreover, manual curation suggests that A2I is likely the only enzymatically driven RNAe type observed in blood derived DNA, all other non-A2I RDD could either be attributed to sequencing and processing artifacts, or are a result of somatic DNA rearrangements. We then conducted an in-cis genetic association study and identified 472 genetic associations (RNAe-QTL), that were replicated in both datasets. We confirm the potential effect of the RNAe-QTL on RNA structure by showing that allele specific RNAe occurs in heterozygotes. Although the generally assumed function of RNAe is to destabilize double stranded RNA structure, we found clear evidence for the potential additional involvement of RNAe in maintaining RNA hairpin that has been altered by the RNAe-QTL. Our study confirms, in two independent datasets, the potential role of RNAe in maintaining RNA structure in the presence of genetic variation.

Proteome-wide assessment of diabetes mellitus in Qatari identifies IGFBP-2 as a risk factor already with early glycaemic disturbances

BACKGROUND

Proteomics is expected to provide novel insights in the underlying pathophysiology of type 2 diabetes mellitus. In the present study, we aimed to identify and biochemically characterize proteins associated with diabetes mellitus in a Qatari population.

METHODS

In a diabetes case-control study (175 cases, 164 controls; Arab, South Asian and Philippine ethnicities), we conducted a discovery study to screen 1141 blood protein levels for associations with diabetes mellitus. Additional analyses were done in controls in relation to Hb1Ac, and biochemical characterization of the main findings was performed with metabolomics (501 metabolites). We performed two-sample Mendelian Randomization to provide evidence of potential causality using data from European descent of the DIAGRAM consortium (74,124 cases of diabetes mellitus and 824,006 controls) for the identified proteins for T2D and Hb1Ac.

RESULTS

After accounting for multiple testing, 30 protein levels were different (p-values<8.6e^-5) between cases and controls. Of these, a higher Hb1Ac in controls was associated with a lower IGFBP-2 level (p-value = 4.1e^-6). IGFBP-2 protein level was found lower among cases compared with controls across all ethnicities. In controls, IGFBP-2 was associated with 21 metabolite levels, but specifically connected to the metabolite citrulline in network analyses. We observed no evidence, however, that the association between IGFBP-2 and diabetes mellitus was causal.

CONCLUSIONS

We specifically identified IGFBP-2 to be associated with diabetes mellitus, although with no evidence for causality, which was specifically connected to citrulline metabolism.

Diabetes Screening: Detection and Application of Saliva 1,5-Anhydroglucitol by Liquid Chromatography-Mass Spectrometry

CONTEXT

Unlike other commonly used invasive blood glucose-monitoring methods, saliva detection prevents patients from suffering physical uneasiness. However, there are few studies on saliva 1,5-anhydroglucitol (1,5-AG) in patients with diabetes mellitus (DM).

OBJECTIVE

This study aimed to evaluate the effectiveness of saliva 1,5-AG in diabetes screening in a Chinese population.

DESIGN AND PARTICIPANTS

This was a population-based cross-sectional study. A total of 641 subjects without a valid diabetic history were recruited from September 2018 to June 2019. Saliva 1,5-AG was measured with liquid chromatography-mass spectrometry.

MAIN OUTCOME MEASURES

DM was defined per American Diabetes Association criteria. The efficiency of saliva 1,5-AG for diabetes screening was analyzed by receiver operating characteristic curves, and the optimal cutoff point was determined according to the Youden index.

RESULTS

Saliva 1,5-AG levels in subjects with DM were lower than those in subjects who did not have DM (both P < .05). Saliva 1,5-AG was positively correlated with serum 1,5-AG and negatively correlated with blood glucose and glycated hemoglobin (HbA1c) (all P < .05). The optimal cutoff points of saliva 1,5-AG0 and 1,5-AG120 for diabetes screening were 0.436 μg/mL (sensitivity: 63.58%, specificity: 60.61%) and 0.438 μg/mL (sensitivity: 62.25%, specificity: 60.41%), respectively. Fasting plasma glucose (FPG) combined with fasting saliva 1,5-AG reduced the proportion of people who required an oral glucose tolerance test by 47.22% compared with FPG alone.

CONCLUSION

Saliva 1,5-AG combined with FPG or HbA1c improved the efficiency of diabetes screening. Saliva 1,5-AG is robust in nonfasting measurements and a noninvasive and convenient tool for diabetes screening.

Metabolomics analyses in non-diabetic middle-aged individuals reveal metabolites impacting early glucose disturbances and insulin sensitivity

INTRODUCTION

Several plasma metabolites have been associated with insulin resistance and type 2 diabetes mellitus.

OBJECTIVES

We aimed to identify plasma metabolites associated with different indices of early disturbances in glucose metabolism and insulin sensitivity.

METHODS

This cross-sectional study was conducted in a subsample of the Leiden Longevity Study comprising individuals without a history of diabetes mellitus (n = 233) with a mean age of 63.3 ± 6.7 years of which 48.1% were men. We tested for associations of fasting glucose, fasting insulin, HOMA-IR, Matsuda Index, Insulinogenic Index and glycated hemoglobin with metabolites (Swedish Metabolomics Platform) using linear regression analysis adjusted for age, sex and BMI. Results were validated internally using an independent metabolomics platform (Biocrates platform) and replicated externally in the independent Netherlands Epidemiology of Obesity (NEO) study (Metabolon platform) (n = 545, mean age of 55.8 ± 6.0 years of which 48.6% were men). Moreover, in the NEO study, we replicated our analyses in individuals with diabetes mellitus (cases: n = 36; controls = 561).

RESULTS

Out of the 34 metabolites, a total of 12 plasma metabolites were associated with different indices of disturbances in glucose metabolism and insulin sensitivity in individuals without diabetes mellitus. These findings were validated using a different metabolomics platform as well as in an independent cohort of non-diabetics. Moreover, tyrosine, alanine, valine, tryptophan and alpha-ketoglutaric acid levels were higher in individuals with diabetes mellitus.

CONCLUSION

We found several plasma metabolites that are associated with early disturbances in glucose metabolism and insulin sensitivity of which five were also higher in individuals with diabetes mellitus.

Epigenetics meets proteomics in an epigenome-wide association study with circulating blood plasma protein traits

DNA methylation and blood circulating proteins have been associated with many complex disorders, but the underlying disease-causing mechanisms often remain unclear. Here, we report an epigenome-wide association study of 1123 proteins from 944 participants of the KORA population study and replication in a multi-ethnic cohort of 344 individuals. We identify 98 CpG-protein associations (pQTMs) at a stringent Bonferroni level of significance. Overlapping associations with transcriptomics, metabolomics, and clinical endpoints suggest implication of processes related to chronic low-grade inflammation, including a network involving methylation of NLRC5, a regulator of the inflammasome, and associated pQTMs implicating key proteins of the immune system, such as CD48, CD163, CXCL10, CXCL11, LAG3, FCGR3B, and B2M. Our study links DNA methylation to disease endpoints via intermediate proteomics phenotypes and identifies correlative networks that may eventually be targeted in a personalized approach of chronic low-grade inflammation.

The Clinical Utility of Salivary Biomarkers in the Identification of Type 2 Diabetes Risk and Metabolic Syndrome

Type 2 diabetes is traditionally diagnosed by the use of an oral glucose tolerance test and/or HbA1c, both of which require serum collection. Various biomarkers, which are measurable biological substances that provide clinical insight on disease state, have also been effective in the early identification and risk prediction of inflammatory diseases. Measuring biomarker concentrations has traditionally been obtained through serum collection as well. However, numerous biomarkers are detectable in saliva. Salivary analysis has more recently been introduced into research as a potential non-invasive, cost-effective diagnostic for the early identification of type 2 diabetes risk in adults and youth. Therefore, the purpose of this review was to compare 6 established inflammatory biomarkers of type 2 diabetes, in serum and saliva, and determine if similar diagnostic effectiveness is seen in saliva. A lack of standardized salivary analysis, processing, and collection accounts for errors and inconsistencies in conclusive data amongst studies. Proposing a national standardization in salivary analysis, coupled with increased data and research on the utility of saliva as a diagnostic, poses the potential for salivary analysis to be used in diagnostic settings.

A deep neural network approach to predicting clinical outcomes of neuroblastoma patients

Background

The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also important to better understand the biological mechanisms underlying disease etiology and development, as well as treatment responses. Recently, different predictive models, relying on distinct algorithms (including Support Vector Machines and Random Forests) have been investigated. In this context, deep learning strategies are of special interest due to their demonstrated superior performance over a wide range of problems and datasets. One of the main challenges of such strategies is the “small n large p” problem. Indeed, omics datasets typically consist of small numbers of samples and large numbers of features relative to typical deep learning datasets. Neural networks usually tackle this problem through feature selection or by including additional constraints during the learning process.

Methods

We propose to tackle this problem with a novel strategy that relies on a graph-based method for feature extraction, coupled with a deep neural network for clinical outcome prediction. The omics data are first represented as graphs whose nodes represent patients, and edges represent correlations between the patients’ omics profiles. Topological features, such as centralities, are then extracted from these graphs for every node. Lastly, these features are used as input to train and test various classifiers.

Results

We apply this strategy to four neuroblastoma datasets and observe that models based on neural networks are more accurate than state of the art models (DNN: 85%-87%, SVM/RF: 75%-82%). We explore how different parameters and configurations are selected in order to overcome the effects of the small data problem as well as the curse of dimensionality.

Conclusions

Our results indicate that the deep neural networks capture complex features in the data that help predicting patient clinical outcomes.

Pyranose oxidase: A versatile sugar oxidoreductase for bioelectrochemical applications

Pyranose oxidase (POx) is an FAD-dependent oxidoreductase, and like glucose oxidase (GOx) it is a member of the glucose-methanol-choline (GMC) superfamily of oxidoreductases. POx oxidizes several monosaccharides including D-glucose, D-galactose, and D-xylose, while concurrently oxygen is reduced to hydrogen peroxide. In addition to this oxidase activity, POx shows pronounced activity with alternative electron acceptors that include various quinones or (complexed) metal ions. Even though POx in general shows properties that are more favourable than those of GOx (e.g., a considerably higher catalytic efficiency (k_cat/K_m) for D-glucose, significantly lower Michaelis constants K_m for D-glucose, reactivity with both anomeric forms of D-glucose) it is much less frequently used for both biosensor and biofuel cell applications than GOx. POx has been applied in biosensing of D-glucose, D-galactose, and D-xylose, and in combination with α-glucosidase also maltose. An attractive application is in biosensors constructed for the measurement of 1,5-anhydro-D-glucitol, a recognised biomarker in diabetes. Bioelectrochemical applications of POx had been restricted to enzymes of fungal origin. The recent discovery and characterisation of POx from bacterial sources, which show properties that are very distinct from the fungal enzymes, might open new possibilities for further applications in bioelectrochemistry.

n-Butylamine for Improving the Efficiency of Untargeted Mass Spectrometry Analysis of Plasma Metabolite Composition

A comparative study of the impact of n-butylamine and traditionally used additives (ammonium hydroxide and formic acid) on the efficiency of the electrospray ionization (ESI) process for the enhancement of metabolite coverage was performed by direct injection mass spectrometry (MS) analysis in negative mode. Evaluation of obtained MS data showed that n-butylamine is one of the most effective additives for the analysis of metabolite composition in ESI in negative ion mode (ESI(-)) The limitations of the use of n-butylamine and other alkylamines in the analysis of metabolic composition and a decontamination procedure that can reduce MS device contamination after their application are discussed. The proposed procedure allows the performance of high-sensitivity analysis of low-molecular-weight compounds on the same MS device in both polarities.

Clinical and metabolomics analysis of hepatocellular carcinoma patients with diabetes mellitus

INTRODUCTION

Diabetes and cancer are among the most frequent causes of death worldwide. Recent epidemiological findings have indicated a link between diabetes and cancer in several organs, particularly the liver. A number of epidemiological studies have demonstrated that diabetes is an established independent risk factor for hepatocellular carcinoma (HCC). However, the metabolites connecting diabetes and HCC remains less well understood.

OBJECTIVES

The study aimed to identify clinical and metabolomics differences of HCC from patients with/without diabetes using comprehensive global metabolomics analysis.

METHODS

Metabolite profiling was conducted with the Metabolon platform for 120 human diabetes/non-diabetes HCC tumor/normal tissues. Standard statistical analyses were performed using the Partek Genomics Suite on log-transformed data. Principal component analysis (PCA) was conducted using all and dysregulated metabolites.

RESULTS

We identified a group of metabolites that are differentially expressed in the tumor tissues of diabetes HCC compared to non-diabetes HCC patients. Meanwhile, we also identified a group of metabolites that are differentially expressed in the matched normal liver tissues of diabetes HCC compared to non-diabetes HCC patients. Some metabolites are consistently dysregulated in the tumor or matched normal tissues of HCC with or without diabetes. However, some metabolites, including 2-hydroxystearate, were only overexpressed in the tumor tissues of HCC with diabetes and associated with the glucose level.

CONCLUSION

Metabolic profiling identifies distinct dysregulated metabolites in HCC patients with/without diabetes.

Omics-based biomarkers in the diagnosis of diabetes

Diabetes mellitus (DM) is a group of metabolic diseases related to the dysfunction of insulin, causing hyperglycaemia and life-threatening complications. Current early screening and diagnostic tests for DM are based on changes in glucose levels and autoantibody detection. This review evaluates recent studies on biomarker candidates in diagnosing type 1, type 2 and gestational DM based on omics classification, whilst highlighting the relationship of these biomarkers with the development of diabetes, diagnostic accuracy, challenges and future prospects. In addition, it also focuses on possible non-invasive biomarker candidates besides common blood biomarkers.

MoDentify: phenotype-driven module identification in metabolomics networks at different resolutions

Summary

Associations of metabolomics data with phenotypic outcomes are expected to span functional modules, which are defined as sets of correlating metabolites that are coordinately regulated. Moreover, these associations occur at different scales, from entire pathways to only a few metabolites; an aspect that has not been addressed by previous methods. Here, we present MoDentify, a free R package to identify regulated modules in metabolomics networks at different layers of resolution. Importantly, MoDentify shows higher statistical power than classical association analysis. Moreover, the package offers direct interactive visualization of the results in Cytoscape. We present an application example using complex, multifluid metabolomics data. Due to its generic character, the method is widely applicable to other types of data.

Availability and implementation

https://github.com/krumsieklab/MoDentify (vignette includes detailed workflow).

Supplementary information

Supplementary data are available at Bioinformatics online.

Intra- and inter-individual metabolic profiling highlights carnitine and lysophosphatidylcholine pathways as key molecular defects in type 2 diabetes

Type 2 diabetes (T2D) mellitus is a complex metabolic disease commonly caused by insulin resistance in several tissues. We performed a matched two-dimensional metabolic screening in tissue samples from 43 multi-organ donors. The intra-individual analysis was assessed across five key metabolic tissues (serum, visceral adipose tissue, liver, pancreatic islets and skeletal muscle), and the inter-individual across three different groups reflecting T2D progression. We identified 92 metabolites differing significantly between non-diabetes and T2D subjects. In diabetes cases, carnitines were significantly higher in liver, while lysophosphatidylcholines were significantly lower in muscle and serum. We tracked the primary tissue of origin for multiple metabolites whose alterations were reflected in serum. An investigation of three major stages spanning from controls, to pre-diabetes and to overt T2D indicated that a subset of lysophosphatidylcholines was significantly lower in the muscle of pre-diabetes subjects. Moreover, glycodeoxycholic acid was significantly higher in liver of pre-diabetes subjects while additional increase in T2D was insignificant. We confirmed many previously reported findings and substantially expanded on them with altered markers for early and overt T2D. Overall, the analysis of this unique dataset can increase the understanding of the metabolic interplay between organs in the development of T2D.

Fine-Mapping of the Human Blood Plasma N-Glycome onto Its Proteome

Most human proteins are glycosylated. Attachment of complex oligosaccharides to the polypeptide part of these proteins is an integral part of their structure and function and plays a central role in many complex disorders. One approach towards deciphering this human glycan code is to study natural variation in experimentally well characterized samples and cohorts. High-throughput capable large-scale methods that allow for the comprehensive determination of blood circulating proteins and their glycans have been recently developed, but so far, no study has investigated the link between both traits. Here we map for the first time the blood plasma proteome to its matching N-glycome by correlating the levels of 1116 blood circulating proteins with 113 N-glycan traits, determined in 344 samples from individuals of Arab, South-Asian, and Filipino descent, and then replicate our findings in 46 subjects of European ancestry. We report protein-specific N-glycosylation patterns, including a correlation of core fucosylated structures with immunoglobulin G (IgG) levels, and of trisialylated, trigalactosylated, and triantennary structures with heparin cofactor 2 (SERPIND2). Our study reveals a detailed picture of protein N-glycosylation and suggests new avenues for the investigation of its role and function in the associated complex disorders.

Defining the genetic control of human blood plasma N-glycome using genome-wide association study

Glycosylation is a common post-translational modification of proteins. Glycosylation is associated with a number of human diseases. Defining genetic factors altering glycosylation may provide a basis for novel approaches to diagnostic and pharmaceutical applications. Here we report a genome-wide association study of the human blood plasma N-glycome composition in up to 3811 people measured by Ultra Performance Liquid Chromatography (UPLC) technology. Starting with the 36 original traits measured by UPLC, we computed an additional 77 derived traits leading to a total of 113 glycan traits. We studied associations between these traits and genetic polymorphisms located on human autosomes. We discovered and replicated 12 loci. This allowed us to demonstrate an overlap in genetic control between total plasma protein and IgG glycosylation. The majority of revealed loci contained genes that encode enzymes directly involved in glycosylation (FUT3/FUT6, FUT8, B3GAT1, ST6GAL1, B4GALT1, ST3GAL4, MGAT3 and MGAT5) and a known regulator of plasma protein fucosylation (HNF1A). However, we also found loci that could possibly reflect other more complex aspects of glycosylation process. Functional genomic annotation suggested the role of several genes including DERL3, CHCHD10, TMEM121, IGH and IKZF1. The hypotheses we generated may serve as a starting point for further functional studies in this research area.

Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies

INTRODUCTION

Obesity is a disorder characterized by a disproportionate increase in body weight in relation to height, mainly due to the accumulation of fat, and is considered a pandemic of the present century by many international health institutions. It is associated with several non-communicable chronic diseases, namely, metabolic syndrome, type 2 diabetes mellitus (T2DM), cardiovascular diseases (CVD), and cancer. Metabolomics is a useful tool to evaluate changes in metabolites due to being overweight and obesity at the body fluid and cellular levels and to ascertain metabolic changes in metabolically unhealthy overweight and obese individuals (MUHO) compared to metabolically healthy individuals (MHO).

OBJECTIVES

We aimed to conduct a systematic review (SR) of human studies focused on identifying metabolomic signatures in obese individuals and obesity-related metabolic alterations, such as inflammation or oxidative stress.

METHODS

We reviewed the literature to identify studies investigating the metabolomics profile of human obesity and that were published up to May 7th, 2019 in SCOPUS and PubMed through an SR. The quality of reporting was evaluated using an adapted of QUADOMICS.

RESULTS

Thirty-three articles were included and classified according to four types of approaches. (i) studying the metabolic signature of obesity, (ii) studying the differential responses of obese and non-obese subjects to dietary challenges (iii) studies that used metabolomics to predict weight loss and aimed to assess the effects of weight loss interventions on the metabolomics profiles of overweight or obese human subjects (iv) articles that studied the effects of specific dietary patterns or dietary compounds on obesity-related metabolic alterations in humans.

CONCLUSION

The present SR provides state-of-the-art information about the use of metabolomics as an approach to understanding the dynamics of metabolic processes involved in human obesity and emphasizes metabolic signatures related to obesity phenotypes.

Characterization of Bulk Phosphatidylcholine Compositions in Human Plasma Using Side-Chain Resolving Lipidomics

Kit-based assays, such as AbsoluteIDQ^TM p150, are widely used in large cohort studies and provide a standardized method to quantify blood concentrations of phosphatidylcholines (PCs). Many disease-relevant associations of PCs were reported using this method. However, their interpretation is hampered by lack of functionally-relevant information on the detailed fatty acid side-chain compositions as only the total number of carbon atoms and double bonds is identified by the kit. To enable more substantiated interpretations, we characterized these PC sums using the side-chain resolving Lipidyzer^TM platform, analyzing 223 samples in parallel to the AbsoluteIDQ^TM. Combining these datasets, we estimated the quantitative composition of PC sums and subsequently tested their replication in an independent cohort. We identified major constituents of 28 PC sums, revealing also various unexpected compositions. As an example, PC 16:0_22:5 accounted for more than 50% of the PC sum with in total 38 carbon atoms and 5 double bonds (PC aa 38:5). For 13 PC sums, we found relatively high abundances of odd-chain fatty acids. In conclusion, our study provides insights in PC compositions in human plasma, facilitating interpretation of existing epidemiological data sets and potentially enabling imputation of PC compositions for future meta-analyses of lipidomics data.

An Update on Measures of Preoperative Glycemic Control

Glycemic control represents a modifiable preoperative risk factor in surgery. Traditionally, hemoglobin A1c (HbA1c) and plasma glucose are utilized as measures of glycemic control. However, studies show mixed results regarding the ability of these conventional measures to predict adverse surgical outcomes. This may be explained by the time window captured by HbA1c and serum glucose: long-term and immediate glycemic control, respectively. Fructosamine, glycosylated albumin, and 1,5-anhydroglucitol constitute alternative metrics of glycemic control that are of growing interest but are underutilized in the field of surgery. These nontraditional measures reflect the temporal variations in glycemia over the preceding days to weeks. Therefore, they may more accurately reflect glycemic control within the time window that most significantly affects surgical outcomes. Additionally, these alternative measures are predictive of negative outcomes, even in the nondiabetic population and in patients with chronic renal disease and anemia, for whom HbA1c performs poorly. Adopting these newer metrics of glycemia may enhance the value of preoperative evaluation, such that the effectiveness of any preoperative glycemic control interventions can be assessed, and adverse outcomes associated with hyperglycemia better predicted. The goal of this review is to provide an update on the preoperative management of glycemia and to describe alternative metrics that may improve our ability to predict and control for the negative outcomes associated with poor glycemic control.

Comparison of the microbiome, metabolome, and lipidome of obese and non-obese horses

Metabolic diseases such as obesity and type 2 diabetes in humans have been linked to alterations in the gastrointestinal microbiota and metabolome. Knowledge of these associations has improved our understanding of the pathophysiology of these diseases and guided development of diagnostic biomarkers and therapeutic interventions. The cellular and molecular pathophysiology of equine metabolic syndrome (EMS) and obesity in horses, however, remain ill-defined. Thus, the objectives of this study were to characterize the fecal microbiome, fecal metabolome, and circulating lipidome in obese and non-obese horses. The fecal microbiota, fecal metabolome, and serum lipidome were evaluated in obese (case) horses (n = 20) and non-obese (control) horses (n = 20) matched by farm of origin (n = 7). Significant differences in metabolites of the mitochondrial tricarboxylic acid cycle and circulating free fatty acids were identified in the obese horses compared to the non-obese horses. These results indicate that the host and bacterial metabolism should be considered important in obese horses. Further studies to determine whether these associations are causal and the mechanistic basis of the association are warranted because they might reveal diagnostic biomarkers and therapeutic interventions to mitigate obesity, EMS, and sequelae including laminitis.

Exposure to disinfection byproducts and risk of type 2 diabetes: a nested case-control study in the HUNT and Lifelines cohorts

INTRODUCTION

Environmental chemicals acting as metabolic disruptors have been implicated with diabetogenesis, but evidence is weak among short-lived chemicals, such as disinfection byproducts (trihalomethanes, THM composed of chloroform, TCM and brominated trihalomethanes, BrTHM).

OBJECTIVES

We assessed whether THM were associated with type 2 diabetes (T2D) and we explored alterations in metabolic profiles due to THM exposures or T2D status.

METHODS

A prospective 1:1 matched case-control study (n = 430) and a cross-sectional 1:1 matched case-control study (n = 362) nested within the HUNT cohort (Norway) and the Lifelines cohort (Netherlands), respectively, were set up. Urinary biomarkers of THM exposure and mass spectrometry-based serum metabolomics were measured. Associations between THM, clinical markers, metabolites and disease status were evaluated using logistic regressions with Least Absolute Shrinkage and Selection Operator procedure.

RESULTS

Low median THM exposures (ng/g, IQR) were measured in both cohorts (cases and controls of HUNT and Lifelines, respectively, 193 (76, 470), 208 (77, 502) and 292 (162, 595), 342 (180, 602). Neither BrTHM (OR = 0.87; 95% CI: 0.67, 1.11 | OR = 1.09; 95% CI: 0.73, 1.61), nor TCM (OR = 1.03; 95% CI: 0.88, 1.2 | OR = 1.03; 95% CI: 0.79, 1.35) were associated with incident or prevalent T2D, respectively. Metabolomics showed 48 metabolites associated with incident T2D after adjusting for sex, age and BMI, whereas a total of 244 metabolites were associated with prevalent T2D. A total of 34 metabolites were associated with the progression of T2D. In data driven logistic regression, novel biomarkers, such as cinnamoylglycine or 1-methylurate, being protective of T2D were identified. The incident T2D risk prediction model (HUNT) predicted well incident Lifelines cases (AUC = 0.845; 95% CI: 0.72, 0.97).

CONCLUSION

Such exposome-based approaches in cohort-nested studies are warranted to better understand the environmental origins of diabetogenesis.

Salivary 1,5-Anhydroglucitol and Vitamin Levels in Relation to Caries Risk in Children

The objective of this study was to evaluate the association between salivary 1,5-anhydroglucitol (AG), vitamins A (VA), C (VC), and E (VE), and caries risk in children. 100 healthy children aged between 6 and 13 years were divided into two equal groups of caries-free (DMFS/dmfs=0) and caries active (DMFS/dmfs>3). Unstimulated midmorning saliva was collected from all the children and the levels of salivary AG and vitamins A, C, and E were measured. Caries risk assessment was done using American Academy of Pediatric Dentistry Caries Assessment Tool. Analysis of salivary AG and vitamins was performed using a commercially available ELISA kit. Low levels of AG were present in caries active and high caries risk groups compared to caries-free and low/medium caries risk groups. This difference is statistically significant (p < 0.05). A strong negative correlation between AG and caries activity was observed in the caries active group. VA was not related to caries activity, while VC and VE displayed a statistically significant correlation (p < 0.05). Similarly, a strong negative correlation was observed between the levels of AG and high caries risk group. Salivary AG, VC, and VE together are related to caries risk in caries active children. These salivary parameters can act as indicator of caries status in children.

Effects of a long-term lifestyle intervention on metabolically healthy women with obesity: Metabolite profiles according to weight loss response

BACKGROUND & AIMS

The benefits of weight loss in subjects with metabolically healthy obesity (MHO) are still a matter of controversy. We aimed to identify metabolic fingerprints and their associated pathways that discriminate women with MHO with high or low weight loss response after a lifestyle intervention, based on a hypocaloric Mediterranean diet (MedDiet) and physical activity.

METHODS

A UPLC-Q-Exactive-MS/MS metabolomics workflow was applied to plasma samples from 27 women with MHO before and after 12 months of a hypocaloric weight loss intervention with a MedDiet and increased physical activity. The subjects were stratified into two age-matched groups according to weight loss: <10% (low weight loss group, LWL) and >10% (high weight loss group, HWL). Random forest analysis was performed to identify metabolites discriminating between the LWL and the HWL as well as within-status effects. Modulated pathways and associations between metabolites and anthropometric and biochemical variables were also investigated.

RESULTS

Thirteen metabolites discriminated between the LWL and the HWL, including 1,5-anhydroglucitol, carotenediol, 3-(4-hydroxyphenyl)lactic acid, N-acetylaspartate and several lipid species (steroids, a plasmalogen, sphingomyelins, a bile acid and long-chain acylcarnitines). 1,5-anhydroglucitol, 3-(4-hydroxyphenyl)lactic acid and sphingomyelins were positively associated with weight variables whereas N-acetylaspartate and the plasmalogen correlated negatively with them. Changes in very long-chain acylcarnitines and hydroxyphenyllactic levels were observed in the HWL and positively correlated with fasting glucose, and changes in levels of the plasmalogen negatively correlated with insulin resistance. Additionally, the cholesterol profile was positively associated with changes in acid hydroxyphenyllactic, sphingolipids and 1,5-AG.

CONCLUSIONS

Higher weight loss after a hypocaloric MedDiet and increased physical activity for 12 months is associated with changes in the plasma metabolome in women with MHO. These findings are associated with changes in biochemical variables and may suggest an improvement of the cardiometabolic risk profile in those patients that lose greater weight. Further studies are needed to investigate whether the response of those subjects with MHO to this intervention differs from those with unhealthy obesity.

Metabolomic and lipidomic assessment of the metabolic syndrome in Dutch middle-aged individuals reveals novel biological signatures separating health and disease

BACKGROUND

We aimed to identify novel metabolite and lipid signatures connected with the metabolic syndrome in a Dutch middle-aged population.

METHODS

115 individuals with a metabolic syndrome score ranging from 0 to 5 [50 cases of the metabolic syndrome (score ≥ 3) and 65 controls] were enrolled from the Leiden Longevity Study, and LC/GC-MS metabolomics and lipidomics profiling were performed on fasting plasma samples. Data were analysed with principal component analysis and orthogonal projections to latent structures (OPLS) to study metabolite/lipid signatures associated with the metabolic syndrome. In addition, univariate analyses were done with linear regression, adjusted for age and sex, for the study of individual metabolites/lipids in relation to the metabolic syndrome.

RESULTS

Data was available on 103 metabolites and 223 lipids. In the OPLS model with metabolic syndrome score (Y-variable), 9 metabolites were negatively correlated and 26 metabolites (mostly acylcarnitines, amino acids and keto acids) were positively correlated with the metabolic syndrome score. In addition, a total of 100 lipids (mainly triacylglycerides) were positively correlated and 10 lipids from different lipid classes were negatively correlated with the metabolic syndrome score. In the univariate analyses, the metabolic syndrome (score) was associated with multiple individual metabolites (e.g., valeryl carnitine, pyruvic acid, lactic acid, alanine) and lipids [e.g., diglyceride(34:1), diglyceride(36:2)].

CONCLUSION

In this first study on metabolomics/lipidomics of the metabolic syndrome, we identified multiple novel metabolite and lipid signatures, from different chemical classes, that were connected to the metabolic syndrome and are of interest to cardiometabolic disease biology.

Deep molecular phenotypes link complex disorders and physiological insult to CpG methylation

Epigenetic regulation of cellular function provides a mechanism for rapid organismal adaptation to changes in health, lifestyle and environment. Associations of cytosine-guanine di-nucleotide (CpG) methylation with clinical endpoints that overlap with metabolic phenotypes suggest a regulatory role for these CpG sites in the body's response to disease or environmental stress. We previously identified 20 CpG sites in an epigenome-wide association study (EWAS) with metabolomics that were also associated in recent EWASs with diabetes-, obesity-, and smoking-related endpoints. To elucidate the molecular pathways that connect these potentially regulatory CpG sites to the associated disease or lifestyle factors, we conducted a multi-omics association study including 2474 mass-spectrometry-based metabolites in plasma, urine and saliva, 225 NMR-based lipid and metabolite measures in blood, 1124 blood-circulating proteins using aptamer technology, 113 plasma protein N-glycans and 60 IgG-glyans, using 359 samples from the multi-ethnic Qatar Metabolomics Study on Diabetes (QMDiab). We report 138 multi-omics associations at these CpG sites, including diabetes biomarkers at the diabetes-associated TXNIP locus, and smoking-specific metabolites and proteins at multiple smoking-associated loci, including AHRR. Mendelian randomization suggests a causal effect of metabolite levels on methylation of obesity-associated CpG sites, i.e. of glycerophospholipid PC(O-36: 5), glycine and a very low-density lipoprotein (VLDL-A) on the methylation of the obesity-associated CpG loci DHCR24, MYO5C and CPT1A, respectively. Taken together, our study suggests that multi-omics-associated CpG methylation can provide functional read-outs for the underlying regulatory response mechanisms to disease or environmental insults.

From Discovery to Translation: Characterization of C-Mannosyltryptophan and Pseudouridine as Markers of Kidney Function

Using a non-targeted metabolomics platform, we recently identified C-mannosyltryptophan and pseudouridine as non-traditional kidney function markers. The aims of this study were to obtain absolute concentrations of both metabolites in blood and urine from individuals with and without CKD to provide reference ranges and to assess their fractional excretions (FE), and to assess the agreement with their non-targeted counterparts. In individuals without/with CKD, mean plasma and urine concentrations for C-mannosyltryptophan were 0.26/0.72 µmol/L and 3.39/4.30 µmol/mmol creatinine, respectively. The respective concentrations for pseudouridine were 2.89/5.67 µmol/L and 39.7/33.9 µmol/mmol creatinine. Median (25^th, 75^th percentiles) FEs were 70.8% (65.6%, 77.8%) for C-mannosyltryptophan and 76.0% (68.6%, 82.4%) for pseudouridine, indicating partial net reabsorption. Association analyses validated reported associations between single metabolites and eGFR. Targeted measurements of both metabolites agreed well with the non-targeted measurements, especially in urine. Agreement for composite nephrological measures FE and urinary metabolite-to-creatinine ratio was lower, but could be improved by replacing non-targeted creatinine measurements with a standard clinical creatinine test. In summary, targeted quantification and additional characterization in relevant populations are necessary steps in the translation of non-traditional biomarkers in nephrology from non-targeted discovery to clinical application.

Metabolomic profiles of plasma, exhaled breath condensate, and saliva are correlated with potential for air toxics detection

INTRODUCTION

Advances in the development of high-resolution metabolomics (HRM) have provided new opportunities for their use in characterizing exposures to environmental air pollutants and air pollution-related disease etiologies. Exposure assessment studies have considered blood, breath, and saliva as biological matrices suitable for measuring responses to air pollution exposures. The current study examines comparability among these three matrices using HRM and explores their potential for measuring mobile-source air toxics.

METHODS

Four participants provided saliva, exhaled breath concentrate (EBC), and plasma before and after a 2 h road traffic exposure. Samples were analyzed on a Thermo Scientific QExactive MS system in positive electrospray ionization mode and resolution of 70 000 full-width at half-maximum with C18 chromatography. Data were processed using an apLCMS and xMSanalyzer on the R statistical platform.

RESULTS

The analysis yielded 7110, 6019, and 7747 reproducible features in plasma, EBC, and saliva, respectively. Correlations were moderate-to-strong (R = 0.41-0.80) across all pairwise comparisons of feature intensity within profiles, with the strongest between EBC and saliva. The associations of mean intensities between matrix pairs were positive and significant, controlling for subject and sampling time effects. Six out of 20 features shared in all three matrices putatively matched a list of known mobile-source air toxics.

CONCLUSIONS

Plasma, saliva, and EBC have largely comparable metabolic profiles measurable through HRM. These matrices have the potential to be used in identification and measurement of exposures to mobile-source air toxics, though further, targeted study is needed.

Phenotype-driven identification of modules in a hierarchical map of multifluid metabolic correlations

The identification of phenotype-driven network modules in complex, multifluid metabolomics data poses a considerable challenge for statistical analysis and result interpretation. This is the case for phenotypes with only few associations ('sparse' effects), but, in particular, for phenotypes with a large number of metabolite associations ('dense' effects). Herein, we postulate that examining the data at different layers of resolution, from metabolites to pathways, will facilitate the interpretation of modules for both the sparse and the dense cases. We propose an approach for the phenotype-driven identification of modules on multifluid networks based on untargeted metabolomics data of plasma, urine, and saliva samples from the German Study of Health in Pomerania (SHIP-TREND) study. We generated a hierarchical, multifluid map of metabolism covering both metabolite and pathway associations using Gaussian graphical models. First, this map facilitates a fundamental understanding of metabolism within and across fluids for our study, and can serve as a valuable and downloadable resource. Second, based on this map, we then present an algorithm to identify regulated modules that associate with factors such as gender and insulin-like growth factor I (IGF-I) as examples of traits with dense and sparse associations, respectively. We found IGF-I to associate at the rather fine-grained metabolite level, while gender shows well-interpretable associations at pathway level. Our results confirm that a holistic and interpretable view of metabolic changes associated with a phenotype can only be obtained if different layers of metabolic resolution from multiple body fluids are considered.

Metabolism consists of complex interactions across various organs and body fluids, which poses a substantial challenge for the analysis of metabolic data. To address this problem, Jan Krumsiek from Helmholtz Zentrum München and colleagues used metabolomics measurements of plasma, urine, and saliva from 1000 people to statistically reconstruct a map of interactions in human metabolism. Based on this map, a novel approach that identifies highly correlated biochemical modules that are associated with a given phenotype, was tested for gender and insulin-like growth factor I (IGF-I). The identified modules provided insights into the interaction between metabolome and phenotype that reach beyond what can be found by commonly used statistical approaches for metabolomics. The approach is generic and can be readily applied to new datasets by other colleagues from the field.

The association between various smoking behaviors, cotinine biomarkers and skin autofluorescence, a marker for advanced glycation end product accumulation

BACKGROUND

Skin autofluorescence, a biomarker for advanced glycation end products (AGEs) accumulation, has been shown to predict diabetes-related cardiovascular complications and is associated with several environmental and lifestyle factors. In the present study, we examined the association between various smoking behaviors and skin autofluorescence, as well as the association between several cotinine biomarkers and skin autofluorescence, using both epidemiological and metabolomics data.

METHODS

In a cross-sectional study, we evaluated participants from the LifeLines Cohort Study and the Qatar Metabolomics Study on Diabetes (QMDiab). In the LifeLines Cohort Study smoking behavior and secondhand smoking were assessed in 8,905 individuals including 309 individuals (3.5%) with type 2 diabetes. In QMDiab, cotinine biomarkers were measured in saliva, plasma and urine in 364 individuals of whom 188 (51%) had type 2 diabetes. Skin autofluorescence was measured non-invasively in all participants using the AGE Reader.

RESULTS

Skin autofluorescence levels increased with a higher number of hours being exposed to secondhand smoking. Skin autofluorescence levels of former smokers approached levels of never smokers after around 15 years of smoking cessation. Urinary cotinine N-oxide, a biomarker of nicotine exposure, was found to be positively associated with skin autofluorescence in the QMDiab study (p = 0.03).

CONCLUSIONS

In the present study, we have demonstrated that secondhand smoking is associated with higher skin autofluorescence levels whereas smoking cessation has a beneficial effect on skin autofluorescence. Finally, urinary cotinine N-oxide might be used as an alternative way for questionnaires to examine the effect of (environmental) tobacco smoking on skin autofluorescence.

Connecting genetic risk to disease end points through the human blood plasma proteome

Genome-wide association studies (GWAS) with intermediate phenotypes, like changes in metabolite and protein levels, provide functional evidence to map disease associations and translate them into clinical applications. However, although hundreds of genetic variants have been associated with complex disorders, the underlying molecular pathways often remain elusive. Associations with intermediate traits are key in establishing functional links between GWAS-identified risk-variants and disease end points. Here we describe a GWAS using a highly multiplexed aptamer-based affinity proteomics platform. We quantify 539 associations between protein levels and gene variants (pQTLs) in a German cohort and replicate over half of them in an Arab and Asian cohort. Fifty-five of the replicated pQTLs are located in trans. Our associations overlap with 57 genetic risk loci for 42 unique disease end points. We integrate this information into a genome-proteome network and provide an interactive web-tool for interrogations. Our results provide a basis for novel approaches to pharmaceutical and diagnostic applications.

Individual genetic variation can affect the levels of protein in blood, but detailed data sets linking these two types of data are rare. Here, the authors carry out a genome-wide association study of levels of over a thousand different proteins, and describe many new SNP-protein interactions.

Impact of statistical models on the prediction of type 2 diabetes using non-targeted metabolomics profiling

OBJECTIVE

Characterizing specific metabolites in sub-clinical phases preceding the onset of type 2 diabetes to enable efficient preventive and personalized interventions.

RESEARCH DESIGN AND METHODS

We developed predictive models of type 2 diabetes using two strategies. One strategy focused on the probability of incidence only and was based on logistic regression (MRS1); the other strategy accounted for the age at diagnosis of diabetes and was based on Cox regression (MRS2). We assessed 293 metabolites using non-targeted metabolomics in fasting plasma samples of 1,044 participants (including 231 incident cases over 9 years) used as training population; and fasting serum samples of 128 participants (64 incident cases versus 64 controls) used as validation population. We applied a LASSO-based variable selection aiming at maximizing the out-of-sample area under the receiver operating characteristic curve (AROC) and integrated AROC.

RESULTS

Sixteen and 17 metabolites were selected for MRS1 and MRS2, respectively, with AROC = 90% and 73% in the training and validation populations, respectively for MRS1. MRS2 had a similar performance and was significantly associated with a younger age of onset of type 2 diabetes (β = -3.44 years per MRS2 SD in the training population, p = 1.56 × 10(-7); β = -4.73 years per MRS2 SD in the validation population, p = 4.04 × 10(-3)).

CONCLUSIONS

Overall, this study illustrates that metabolomics improves prediction of type 2 diabetes incidence of 4.5% on top of known clinical and biological markers, reaching 90% in total AROC, which is considered the threshold for clinical validity, suggesting it may be used in targeting interventions to prevent type 2 diabetes.

Noninvasive metabolic profiling for painless diagnosis of human diseases and disorders

Metabolic profiling provides a powerful diagnostic tool complementary to genomics and proteomics. The pain, discomfort and probable iatrogenic injury associated with invasive or minimally invasive diagnostic methods, render them unsuitable in terms of patient compliance and participation. Metabolic profiling of biomatrices like urine, breath, saliva, sweat and feces, which can be collected in a painless manner, could be used for noninvasive diagnosis. This review article covers the noninvasive metabolic profiling studies that have exhibited diagnostic potential for diseases and disorders. Their potential applications are evident in different forms of cancer, metabolic disorders, infectious diseases, neurodegenerative disorders, rheumatic diseases and pulmonary diseases. Large scale clinical validation of such diagnostic methods is necessary in future.