Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study

Decreased Circulating Very Small Low-Density Lipoprotein is Likely Causal for Age-Related Macular Degeneration

Objective

Abnormal changes in metabolite levels in serum or plasma have been highlighted in several studies in age-related macular degeneration (AMD), the leading cause of irreversible vision loss. Specific changes in lipid profiles are associated with an increased risk of AMD. Metabolites could thus be used to investigate AMD disease mechanisms or incorporated into AMD risk prediction models. However, whether particular metabolites causally affect the disease has yet to be established.

Design

A 3-tiered analysis of blood metabolites in the United Kingdom (UK) Biobank cohort to identify metabolites that differ in AMD patients with evidence for a putatively causal role in AMD.

Participants

A total of 72 376 donors from the UK Biobank cohort including participants with AMD (N = 1353) and non-AMD controls (N = 71 023).

Methods

We analyzed 325 directly measured or derived blood metabolites from the UK Biobank for 72 376 donors to identify AMD-associated metabolites. Genome-wide association studies for 325 metabolites in 98 316 European participants from the UK Biobank were performed. The causal effects of these metabolites in AMD were tested using a 2-sample Mendelian randomization approach. The predictive value of these measurements together with sex and age was assessed by developing a machine learning classifier.

Main Outcome Measures

Evaluating metabolic biomarkers associated with AMD susceptibility and investigating their potential causal contribution to the development of the disease.

Results

This study noted age to be the prominent risk factor associated with AMD development. While accounting for age and sex, we identified 84 metabolic markers as significantly (false discovery rate-adjusted P value < 0.05) associated with AMD. Lipoprotein subclasses comprised the majority of the AMD-associated metabolites (39%) followed by several lipoprotein to lipid ratios. Nineteen metabolites showed a likely causative role in AMD etiology. Of these, 6 lipoproteins contain very small, very low-density lipoprotein (VLDL), and phospholipids to total lipid ratio in medium VLDL. Based on this we postulate that depletion of circulating very small VLDLs is likely causal for AMD. The risk prediction model constructed from the metabolites, age and sex, identified age as the primary predictive factor with a much smaller contribution by metabolites to AMD risk prediction.

Conclusions

This study underscores the pronounced role of lipids in AMD susceptibility and the likely causal contribution of particular subclasses of lipoproteins to AMD. Our study provides valuable insights into the metabopathological mechanisms of AMD disease development and progression.

Tracer-based metabolomics for profiling nitric oxide metabolites in a 3D microvessels-on-chip model

Endothelial dysfunction, prevalent in cardiovascular diseases (CVDs) and linked to conditions like diabetes, hypertension, obesity, renal failure, or hypercholesterolemia, is characterized by diminished nitric oxide (NO) bioavailability-a key signaling molecule for vascular homeostasis. Current two-dimensional (2D) in vitro studies on NO synthesis by endothelial cells (ECs) lack the crucial laminar shear stress, a vital factor in modulating the NO-generating enzyme, endothelial nitric oxide synthase (eNOS), under physiological conditions. Here we developed a tracer-based metabolomics approach to measure NO-specific metabolites with mass spectrometry (MS) and show the impact of fluid flow on metabolic parameters associated with NO synthesis using 2D and 3D platforms. Specifically, we tracked the conversion of stable-isotope labeled NO substrate L-Arginine to L-Citrulline and L-Ornithine to determine eNOS activity. We demonstrated clear responses in human coronary artery endothelial cells (HCAECs) cultured with 13C6, 15N4-L-Arginine, and treated with eNOS stimulator, eNOS inhibitor, and arginase inhibitor. Analysis of downstream metabolites, 13C6, 15N3 L-Citrulline and 13C5, 15N2 L-Ornithine, revealed distinct outcomes. Additionally, we evaluated the NO metabolic status in static 2D culture and 3D microvessel models with bidirectional and unidirectional fluid flow. Our 3D model exhibited significant effects, particularly in microvessels exposed to the eNOS stimulator, as indicated by the 13C6, 15N3 L-Citrulline/13C5, 15N2 L-Ornithine ratio, compared to the 2D culture. The obtained results indicate that the 2D static culture mimics an endothelial dysfunction status, while the 3D model with a unidirectional fluid flow provides a more representative physiological environment that provides a better model to study endothelial dysfunction.

Changes in Isoleucine, Sarcosine, and Dimethylglycine During OGTT as Risk Factors for Diabetes

CONTEXT

Current metabolomics studies in diabetes have focused on the fasting state, while only a few have addressed the satiated state.

OBJECTIVE

We combined the oral glucose tolerance test (OGTT) and metabolomics to examine metabolite-level changes in populations with different glucose tolerance statuses and to evaluate the potential risk of these changes for diabetes.

METHODS

We grouped participants into those with normal glucose tolerance (NGT), impaired glucose regulation (IGR), and newly diagnosed type 2 diabetes (NDM). During the OGTT, serum was collected at 0, 30, 60, 120, and 180 minutes. We evaluated the changes in metabolite levels during the OGTT and compared metabolic profiles among the 3 groups. The relationship between metabolite levels during the OGTT and risk of diabetes and prediabetes was analyzed using a generalized estimating equation (GEE). The regression results were adjusted for sex, body mass index, fasting insulin levels, heart rate, smoking status, and blood pressure.

RESULTS

Glucose intake altered metabolic profile and induced an increase in glycolytic intermediates and a decrease in amino acids, glycerol, ketone bodies, and triglycerides. Isoleucine levels differed between the NGT and NDM groups and between the NGT and IGR groups. Changes in sarcosine levels during the OGTT in the diabetes groups were opposite to those in glycine levels. GEE analysis revealed that during OGTT, isoleucine, sarcosine, and acetic acid levels were associated with NDM risks, and isoleucine and acetate levels with IGR risks.

CONCLUSION

Metabolic profiles differ after glucose induction in individuals with different glucose tolerance statuses. Changes in metabolite levels during OGTT are potential risk factors for diabetes development.

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

INTRODUCTION

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

RESEARCH DESIGN AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Alanine to glycine ratio is a novel predictive biomarker for type 2 diabetes mellitus

AIM

We aimed to evaluate the metabolite ratios that could predict the clinical incidence or remission of type 2 diabetes mellitus (T2D).

METHODS

The Cox proportional hazards regression model was used to assess 1813 individuals without T2D to test the predictive value of metabolite ratios for T2D incidence and 451 newly diagnosed T2D for remission. The receiver operating characteristic curve analysis was performed to determine the best cut-off values for the metabolite ratios. Survival analyses were performed to compare the four subgroups defined by baseline metabolite ratios and clinical status of obesity.

RESULTS

The alanine/glycine was the most significant marker for T2D incidence (hazard ratio per SD: 1.24; p < .001). On the other hand, metabolite hydroxy sphingomyelin C22:2 was most specific for T2D remission (hazard ratio per SD: 1.32; p = .029). Survival analysis of T2D incidence among the subgroups defined by the combination of alanine/glycine and obesity showed the group with a high alanine/glycine and obesity had the highest risk of T2D incidence (p < .001). The alanine/glycine as a T2D risk marker was also validated in the independent external data.

CONCLUSIONS

The combination of obesity and the alanine/glycine ratio can be used to evaluate the diabetes risk.

Cord Blood Metabolite Profiles and Their Association with Autistic Traits in Childhood

Autism Spectrum Disorder (ASD) is a diverse neurodevelopmental condition. Gene-environmental interactions in early stages of life might alter metabolic pathways, possibly contributing to ASD pathophysiology. Metabolomics may serve as a tool to identify underlying metabolic mechanisms contributing to ASD phenotype and could help to unravel its complex etiology. In a population-based, prospective cohort study among 783 mother-child pairs, cord blood serum concentrations of amino acids, non-esterified fatty acids, phospholipids, and carnitines were obtained using liquid chromatography coupled with tandem mass spectrometry. Autistic traits were measured at the children's ages of 6 (n = 716) and 13 (n = 648) years using the parent-reported Social Responsiveness Scale. Lower cord blood concentrations of SM.C.39.2 and NEFA16:1/16:0 were associated with higher autistic traits among 6-year-old children, adjusted for sex and age at outcome. After more stringent adjustment for confounders, no significant associations of cord blood metabolites and autistic traits at ages 6 and 13 were detected. Differences in lipid metabolism (SM and NEFA) might be involved in ASD-related pathways and are worth further investigation.

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

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

Investigation of the Exometabolomic Profiles of Rat Islets of Langerhans Cultured in Microfluidic Biochip

Diabetes mellitus (DM) is a complex disease with high prevalence of comorbidity and mortality. DM is predicted to reach more than 700 million people by 2045. In recent years, several advanced in vitro models and analytical tools were developed to investigate the pancreatic tissue response to pathological situations and identify therapeutic solutions. Of all the in vitro promising models, cell culture in microfluidic biochip allows the reproduction of in-vivo-like micro-environments. Here, we cultured rat islets of Langerhans using dynamic cultures in microfluidic biochips. The dynamic cultures were compared to static islets cultures in Petri. The islets' exometabolomic signatures, with and without GLP1 and isradipine treatments, were characterized by GC-MS. Compared to Petri, biochip culture contributes to maintaining high secretions of insulin, C-peptide and glucagon. The exometabolomic profiling revealed 22 and 18 metabolites differentially expressed between Petri and biochip on Day 3 and 5. These metabolites illustrated the increase in lipid metabolism, the perturbation of the pentose phosphate pathway and the TCA cycle in biochip. After drug stimulations, the exometabolome of biochip culture appeared more perturbed than the Petri exometabolome. The GLP1 contributed to the increase in the levels of glycolysis, pentose phosphate and glutathione pathways intermediates, whereas isradipine led to reduced levels of lipids and carbohydrates.

Associations of Early Pregnancy Metabolite Profiles with Gestational Blood Pressure Development

Blood pressure development plays a major role in both the etiology and prediction of gestational hypertensive disorders. Metabolomics might serve as a tool to identify underlying metabolic mechanisms in the etiology of hypertension in pregnancy and lead to the identification of novel metabolites useful for the prediction of gestational hypertensive disorders. In a population-based, prospective cohort study among 803 pregnant women, liquid chromatography—mass spectrometry was used to determine serum concentrations of amino-acids, non-esterified fatty acids, phospholipids and carnitines in early pregnancy. Blood pressure was measured in each trimester of pregnancy. Information on gestational hypertensive disorders was obtained from medical records. Higher individual metabolite concentrations of the diacyl-phosphatidylcholines and acyl-lysophosphatidylcholines group were associated with higher systolic blood pressure throughout pregnancy (Federal Discovery Rate (FDR)-adjusted p-values < 0.05). Higher concentrations of one non-esterified fatty acid were associated with higher diastolic blood pressure throughout pregnancy (FDR-adjusted p-value < 0.05). Using penalized regression, we identified 12 individual early-pregnancy amino-acids, non-esterified fatty acids, diacyl-phosphatidylcholines and acyl-carnitines and the glutamine/glutamic acid ratio, that were jointly associated with larger changes in systolic and diastolic blood pressure from first to third trimester. These metabolites did not improve the prediction of gestational hypertensive disorders in addition to clinical markers. In conclusion, altered early pregnancy serum metabolite profiles mainly characterized by changes in non-esterified fatty acids and phospholipids metabolites are associated with higher gestational blood pressure throughout pregnancy within the physiological ranges. These findings are important from an etiological perspective and, after further replication, might improve the early identification of women at increased risk of gestational hypertensive disorders.

Fetal Exposure to Maternal Smoking and Neonatal Metabolite Profiles

Fetal tobacco exposure has persistent effects on growth and metabolism. The underlying mechanisms of these relationships are yet unknown. We investigated the associations of fetal exposure to maternal smoking with neonatal metabolite profiles. In a population-based cohort study among 828 mother-infant pairs, we assessed maternal tobacco use by questionnaire. Metabolite concentrations of amino acids, non-esterified fatty acids, phospholipids and carnitines were determined by using LC-MS/MS in cord blood samples. Metabolite ratios reflecting metabolic pathways were computed. Compared to non-exposed neonates, those exposed to first trimester only tobacco smoking had lower neonatal mono-unsaturated acyl-alkyl-phosphatidylcholines (PC.ae) and alkyl-lysophosphatidylcholines (Lyso.PC.e) 18:0 concentrations. Neonates exposed to continued tobacco smoking during pregnancy had lower neonatal mono-unsaturated acyl-lysophosphatidylcholines (Lyso.PC.a), Lyso.PC.e.16:0 and Lyso.PC.e.18:1 concentration (False discovery rate (FDR) p-values < 0.05). Dose-response associations showed the strongest effect estimates in neonates whose mothers continued smoking ≥5 cigarettes per day (FDR p-values < 0.05). Furthermore, smoking during the first trimester only was associated with altered neonatal metabolite ratios involved in the Krebs cycle and oxidative stress, whereas continued smoking during pregnancy was associated with inflammatory, transsulfuration, and insulin resistance markers (p-value < 0.05). Thus, fetal tobacco exposure seems associated with neonatal metabolite profile adaptations. Whether these changes relate to later life metabolic health should be studied further.

Serum Metabolomic Signatures for Knee Cartilage Volume Loss over 10 Years in Community-Dwelling Older Adults

Osteoarthritis (OA) is the most prevalent joint disorder characterized by joint structural pathological changes with the loss of articular cartilage as its hallmark. Tools that can predict cartilage loss would help identify people at high risk, thus preventing OA development. The recent advance of the metabolomics provides a new avenue to systematically investigate metabolic alterations in disease and identify biomarkers for early diagnosis. Using a metabolomics approach, the current study aimed to identify serum metabolomic signatures for predicting knee cartilage volume loss over 10 years in the Tasmania Older Adult Cohort (TASOAC). Cartilage volume was measured in the medial, lateral, and patellar compartments of the knee by MRI at baseline and follow-up. Changes in cartilage volume over 10 years were calculated as percentage change per year. Fasting serum samples collected at 2.6-year follow-up were metabolomically profiled using the TMIC Prime Metabolomics Profiling Assay and pairwise metabolite ratios as the proxies of enzymatic reaction were calculated. Linear regression was used to identify metabolite ratio(s) associated with change in cartilage volume in each of the knee compartments with adjustment for age, sex, and BMI. The significance level was defined at α = 3.0 × 10−6 to control multiple testing. A total of 344 participants (51% females) were included in the study. The mean age was 62.83 ± 6.13 years and the mean BMI was 27.48 ± 4.41 kg/m2 at baseline. The average follow-up time was 10.84 ± 0.66 years. Cartilage volume was reduced by 1.34 ± 0.72%, 1.06 ± 0.58%, and 0.98 ± 0.46% per year in the medial, lateral, and patellar compartments, respectively. Our data showed that the increased ratios of hexadecenoylcarnitine (C16:1) to tetradecanoylcarnitine (C14) and C16:1 to dodecanoylcarnitine (C12) were associated with 0.12 ± 0.02% reduction per year in patellar cartilage volume (both p < 3.03 × 10−6). In conclusion, our data suggested that alteration of long chain fatty acid β-oxidation was involved in patellar cartilage loss. While confirmation is needed, the ratios of C16:1 to C14 and C12 might be used to predict long-term cartilage loss.

Maternal Body Mass Index, Early-Pregnancy Metabolite Profile, and Birthweight

CONTEXT

Maternal prepregnancy body mass index (BMI) has a strong influence on gestational metabolism, but detailed metabolic alterations are unknown.

OBJECTIVE

First, to examine the associations of maternal prepregnancy BMI with maternal early-pregnancy metabolite alterations. Second, to identify an early-pregnancy metabolite profile associated with birthweight in women with a higher prepregnancy BMI that improved prediction of birthweight compared to glucose and lipid concentrations.

DESIGN, SETTING, AND PARTICIPANTS

Prepregnancy BMI was obtained in a subgroup of 682 Dutch pregnant women from the Generation R prospective cohort study.

MAIN OUTCOME MEASURES

Maternal nonfasting targeted amino acids, nonesterified fatty acid, phospholipid, and carnitine concentrations measured in blood serum at mean gestational age of 12.8 weeks. Birthweight was obtained from medical records.

RESULTS

A higher prepregnancy BMI was associated with 72 altered amino acids, nonesterified fatty acid, phospholipid and carnitine concentrations, and 6 metabolite ratios reflecting Krebs cycle, inflammatory, oxidative stress, and lipid metabolic processes (P-values < 0.05). Using penalized regression models, a metabolite profile was selected including 15 metabolites and 4 metabolite ratios based on its association with birthweight in addition to prepregnancy BMI. The adjusted R2 of birthweight was 6.1% for prepregnancy BMI alone, 6.2% after addition of glucose and lipid concentrations, and 12.9% after addition of the metabolite profile.

CONCLUSIONS

A higher maternal prepregnancy BMI was associated with altered maternal early-pregnancy amino acids, nonesterified fatty acids, phospholipids, and carnitines. Using these metabolites, we identified a maternal metabolite profile that improved prediction of birthweight in women with a higher prepregnancy BMI compared to glucose and lipid concentrations.

Insulin resistance and insulin sensitizing agents

Insulin resistance is a common feature of obesity and type 2 diabetes, but novel approaches of diabetes subtyping (clustering) revealed variable degrees of insulin resistance in people with diabetes. Specifically, the severe insulin resistant diabetes (SIRD) subtype not only exhibits metabolic abnormalities, but also bears a higher risk for cardiovascular, renal and hepatic comorbidities. In humans, insulin resistance comprises dysfunctional adipose tissue, lipotoxic insulin signaling followed by glucotoxicity, oxidative stress and low-grade inflammation. Recent studies show that aside from metabolites (free fatty acids, amino acids) and signaling proteins (myokines, adipokines, hepatokines) also exosomes with their cargo (proteins, mRNA and microRNA) contribute to altered crosstalk between skeletal muscle, liver and adipose tissue during the development of insulin resistance. Reduction of fat mass mainly, but not exclusively, explains the success of lifestyle modification and bariatric surgery to improve insulin sensitivity. Moreover, some older antihyperglycemic drugs (metformin, thiazolidinediones), but also novel therapeutic concepts (new peroxisome proliferator-activated receptor agonists, incretin mimetics, sodium glucose cotransporter inhibitors, modulators of energy metabolism) can directly or indirectly reduce insulin resistance. This review summarizes molecular mechanisms underlying insulin resistance including the roles of exosomes and microRNAs, as well as strategies for the management of insulin resistance in humans.

Plasma triacylglycerols are biomarkers of β-cell function in mice and humans

Objectives

To find plasma biomarkers prognostic of type 2 diabetes, which could also inform on pancreatic β-cell deregulations or defects in the function of insulin target tissues.

Methods

We conducted a systems biology approach to characterize the plasma lipidomes of C57Bl/6J, DBA/2J, and BALB/cJ mice under different nutritional conditions, as well as their pancreatic islet and liver transcriptomes. We searched for correlations between plasma lipids and tissue gene expression modules.

Results

We identified strong correlation between plasma triacylglycerols (TAGs) and islet gene modules that comprise key regulators of glucose- and lipid-regulated insulin secretion and of the insulin signaling pathway, the two top hits were Gck and Abhd6 for negative and positive correlations, respectively. Correlations were also found between sphingomyelins and islet gene modules that overlapped in part with the gene modules correlated with TAGs. In the liver, the gene module most strongly correlated with plasma TAGs was enriched in mRNAs encoding fatty acid and carnitine transporters as well as multiple enzymes of the β-oxidation pathway. In humans, plasma TAGs also correlated with the expression of several of the same key regulators of insulin secretion and the insulin signaling pathway identified in mice. This cross-species comparative analysis further led to the identification of PITPNC1 as a candidate regulator of glucose-stimulated insulin secretion.

Conclusion

TAGs emerge as biomarkers of a liver-to-β-cell axis that links hepatic β-oxidation to β-cell functional mass and insulin secretion.

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Plasma triacylglycerols correlated with genes controlling β-cell mass and function.

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Plasma triacylglycerols correlated with genes controlling liver β-oxidation.

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In humans, triacylglycerols also correlated with key regulators of insulin secretion.

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Mouse and human data identified PITPNC1 as a candidate regulator of insulin secretion.

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Triacylglycerols are biomarkers of the liver-to-β-cell axis and β-cell function.

Metabolic responsiveness to training depends on insulin sensitivity and protein content of exosomes in insulin-resistant males

High-intensity interval training (HIIT) improves cardiorespiratory fitness (VO₂max), but its impact on metabolism remains unclear. We hypothesized that 12-week HIIT increases insulin sensitivity in males with or without type 2 diabetes [T2D and NDM (nondiabetic humans)]. However, despite identically higher VO₂max, mainly insulin-resistant (IR) persons (T2D and IR NDM) showed distinct alterations of circulating small extracellular vesicles (SEVs) along with lower inhibitory metabolic (protein kinase Cε activity) or inflammatory (nuclear factor κB) signaling in muscle of T2D or IR NDM, respectively. This is related to the specific alterations in SEV proteome reflecting down-regulation of the phospholipase C pathway (T2D) and up-regulated antioxidant capacity (IR NDM). Thus, SEV cargo may contribute to modulating the individual metabolic responsiveness to exercise training in humans.

Association of circulating metabolites with incident type 2 diabetes in an obese population from a national cohort

AIMS

Obesity is the most common risk factor for type 2 diabetes. However, not all obese individuals develop diabetes. In the era of precision medicine, metabolomics may reveal the fundamental metabolic status of an individual. Our aim was to assess the association of metabolites with incident type 2 diabetes in obese individuals using Korean Genome and Epidemiology Cohort Study.

METHODS

Using 12 years of metabolomic data from 2,580 individuals, we performed a metabolomic study to define metabolically healthy obesity in an obese population (n = 704) with incident type 2 diabetes. Cox proportional hazards regression model and survival analysis were performed adjusted for the traditional risk factors of type 2 diabetes.

RESULTS

Our study revealed that spermine, acyl-alkyl phosphatidylcholines (C34:3, C36:3, C42:1), hydroxy sphingomyelin (C22:2, C14:1), and sphingomyelin (C16:0) were associated with incident type 2 diabetes in obese individuals after the adjustment for risk factors and correction of multiple comparisons by Bonferroni method. Five metabolites (except hydroxy sphingomyelin C14:1 and sphingomyelin C16:0) were also significantly associated with incident type 2 diabetes in lean individuals.

CONCLUSIONS

This study highlights the need for defining metabolically healthy obesity based on serum metabolites and elucidates potential biomarkers for type 2 diabetes in an obese population.

Prospective analysis of circulating metabolites and endometrial cancer risk

BACKGROUND

Endometrial cancer is strongly associated with obesity and dysregulation of metabolic factors such as estrogen and insulin signaling are causal risk factors for this malignancy. To identify additional novel metabolic pathways associated with endometrial cancer we performed metabolomic analyses on pre-diagnostic plasma samples from 853 case-control pairs from the European Prospective Investigation into Cancer and Nutrition (EPIC).

METHODS

A total of 129 metabolites (acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexoses, and sphingolipids) were measured by liquid chromatography-mass spectrometry. Conditional logistic regression estimated the associations of metabolites with endometrial cancer risk. An analysis focusing on clusters of metabolites using the bootstrap lasso method was also employed.

RESULTS

After adjustment for body mass index, sphingomyelin [SM] C18:0 was positively (OR1SD: 1.18, 95% CI: 1.05-1.33), and glycine, serine, and free carnitine (C0) were inversely (OR1SD: 0.89, 95% CI: 0.80-0.99; OR1SD: 0.89, 95% CI: 0.79-1.00 and OR1SD: 0.91, 95% CI: 0.81-1.00, respectively) associated with endometrial cancer risk. Serine, C0 and two sphingomyelins were selected by the lasso method in >90% of the bootstrap samples. The ratio of esterified to free carnitine (OR1SD: 1.14, 95% CI: 1.02-1.28) and that of short chain to free acylcarnitines (OR1SD: 1.12, 95% CI: 1.00-1.25) were positively associated with endometrial cancer risk. Further adjustment for C-peptide or other endometrial cancer risk factors only minimally altered the results.

CONCLUSION

These findings suggest that variation in levels of glycine, serine, SM C18:0 and free carnitine may represent specific pathways linked to endometrial cancer development. If causal, these pathways may offer novel targets for endometrial cancer prevention.

Whole blood co-expression modules associate with metabolic traits and type 2 diabetes: an IMI-DIRECT study

Background

The rising prevalence of type 2 diabetes (T2D) poses a major global challenge. It remains unresolved to what extent transcriptomic signatures of metabolic dysregulation and T2D can be observed in easily accessible tissues such as blood. Additionally, large-scale human studies are required to further our understanding of the putative inflammatory component of insulin resistance and T2D. Here we used transcriptomics data from individuals with (n = 789) and without (n = 2127) T2D from the IMI-DIRECT cohorts to describe the co-expression structure of whole blood that mainly reflects processes and cell types of the immune system, and how it relates to metabolically relevant clinical traits and T2D.

Methods

Clusters of co-expressed genes were identified in the non-diabetic IMI-DIRECT cohort and evaluated with regard to stability, as well as preservation and rewiring in the cohort of individuals with T2D. We performed functional and immune cell signature enrichment analyses, and a genome-wide association study to describe the genetic regulation of the modules. Phenotypic and trans-omics associations of the transcriptomic modules were investigated across both IMI-DIRECT cohorts.

Results

We identified 55 whole blood co-expression modules, some of which clustered in larger super-modules. We identified a large number of associations between these transcriptomic modules and measures of insulin action and glucose tolerance. Some of the metabolically linked modules reflect neutrophil-lymphocyte ratio in blood while others are independent of white blood cell estimates, including a module of genes encoding neutrophil granule proteins with antibacterial properties for which the strongest associations with clinical traits and T2D status were observed. Through the integration of genetic and multi-omics data, we provide a holistic view of the regulation and molecular context of whole blood transcriptomic modules. We furthermore identified an overlap between genetic signals for T2D and co-expression modules involved in type II interferon signaling.

Conclusions

Our results offer a large-scale map of whole blood transcriptomic modules in the context of metabolic disease and point to novel biological candidates for future studies related to T2D.

Pre-diagnostic biomarkers of type 2 diabetes identified in the UAE's obese national population using targeted metabolomics

Currently, type 2 diabetes mellitus (T2DM) and obesity are major global public health issues, and their prevalence in the United Arab Emirates (UAE) are among the highest in the world. In 2019, The UAE diabetes national prevalence was 15.4%. In recent years there has been a considerable investigation of predictive biomarkers associated with these conditions. This study analysed fasting (8 h) blood samples from an obese, normoglycemic cohort and an obese, T2DM cohort of UAE nationals, employing clinical chemistry analysis, 1D ¹H NMR and mass spectroscopy (FIA-MS/MS and LC-MS/MS) techniques. The novel findings reported for the first time in a UAE population revealed significant differences in a number of metabolites in the T2DM cohort. Metabolic fingerprints identified by NMR included BCAAs, trimethylamine N-oxide, β-hydroxybutyrate, trimethyl uric acid, and alanine. A targeted MS approach showed significant differences in lysophosphatidylcholines, phosphatidylcholines, acylcarnitine, amino acids and sphingomyelins; Lyso.PC.a.C18.0, PC.ae.C34.2, C3.DC..C4.OH, glutamine and SM.C16.1, being the most significant metabolites. Pearson's correlation studies showed associations between these metabolites and the clinical chemistry parameters across both cohorts. This report identified differences in metabolites in response to T2DM in agreement with many published population studies. This contributes to the global search for a bank of metabolite biomarkers that can predict the advent of T2DM and give insight to its pathogenic mechanisms.

Serum acylcarnitines and amino acids and risk of type 2 diabetes in a multiethnic Asian population

INTRODUCTION

We evaluated whether concentrations of serum acylcarnitines and amino acids are associated with risk of type 2 diabetes and can improve predictive diabetes models in an Asian population.

RESEARCH DESIGN AND METHODS

We used data from 3313 male and female participants from the Singapore Prospective Study Program cohort who were diabetes-free at baseline. The average age at baseline was 48.0 years (SD: 11.9 years), and participants were of Chinese, Malay, and Indian ethnicity. Diabetes cases were identified through self-reported physician diagnosis, fasting glucose and glycated hemoglobin concentrations, and linkage to national disease registries. We measured fasting serum concentrations of 45 acylcarnitines and 14 amino acids. The association between metabolites and incident diabetes was modeled using Cox proportional hazards regression with adjustment for age, sex, ethnicity, height, and parental history of diabetes, and correction for multiple testing. Metabolites were added to the Atherosclerosis Risk in Communities (ARIC) predictive diabetes risk model to assess whether they could increase the area under the receiver operating characteristic curve (AUC).

RESULTS

Participants were followed up for an average of 8.4 years (SD: 2.1 years), during which time 314 developed diabetes. Branched-chain amino acids (HR: 1.477 per SD; 95% CI 1.325 to 1.647) and the alanine to glycine ratio (HR: 1.572; 95% CI 1.426 to 1.733) were most strongly associated with diabetes risk. Additionally, the acylcarnitines C4 and C16-OH, and the amino acids alanine, combined glutamate/glutamine, ornithine, phenylalanine, proline, and tyrosine were significantly associated with higher diabetes risk, and the acylcarnitine C8-DC and amino acids glycine and serine with lower risk. Adding selected metabolites to the ARIC model resulted in a significant increase in AUC from 0.836 to 0.846.

CONCLUSIONS

We identified acylcarnitines and amino acids associated with risk of type 2 diabetes in an Asian population. A subset of these modestly improved the prediction of diabetes when added to an established diabetes risk model.

Quantile contours and allometric modelling for risk classification of abnormal ratios with an application to asymmetric growth-restriction in preterm infants

We develop an approach to risk classification based on quantile contours and allometric modelling of multivariate anthropometric measurements. We propose the definition of allometric direction tangent to the directional quantile envelope, which divides ratios of measurements into half-spaces. This in turn provides an operational definition of directional quantile that can be used as cutoff for risk assessment. We show the application of the proposed approach using a large dataset from the Vermont Oxford Network containing observations of birthweight (BW) and head circumference (HC) for more than 150,000 preterm infants. Our analysis suggests that disproportionately growth-restricted infants with a larger HC-to-BW ratio are at increased mortality risk as compared to proportionately growth-restricted infants. The role of maternal hypertension is also investigated.

Insulin resistance and systemic metabolic changes in oral glucose tolerance test in 5340 individuals: an interventional study

BACKGROUND

Insulin resistance (IR) is predictive for type 2 diabetes and associated with various metabolic abnormalities in fasting conditions. However, limited data are available on how IR affects metabolic responses in a non-fasting setting, yet this is the state people are mostly exposed to during waking hours in the modern society. Here, we aim to comprehensively characterise the metabolic changes in response to an oral glucose test (OGTT) and assess the associations of these changes with IR.

METHODS

Blood samples were obtained at 0 (fasting baseline, right before glucose ingestion), 30, 60, and 120 min during the OGTT. Seventy-eight metabolic measures were analysed at each time point for a discovery cohort of 4745 middle-aged Finnish individuals and a replication cohort of 595 senior Finnish participants. We assessed the metabolic changes in response to glucose ingestion (percentage change in relative to fasting baseline) across the four time points and further compared the response profile between five groups with different levels of IR and glucose intolerance. Further, the differences were tested for covariate adjustment, including gender, body mass index, systolic blood pressure, fasting, and 2-h glucose levels. The groups were defined as insulin sensitive with normal glucose (IS-NGT), insulin resistant with normal glucose (IR-NGT), impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and new diabetes (NDM). IS-NGT and IR-NGT were defined as the first and fourth quartile of fasting insulin in NGT individuals.

RESULTS

Glucose ingestion induced multiple metabolic responses, including increased glycolysis intermediates and decreased branched-chain amino acids, ketone bodies, glycerol, and triglycerides. The IR-NGT subgroup showed smaller responses for these measures (mean + 23%, interquartile 9-34% at 120 min) compared to IS-NGT (34%, 23-44%, P < 0.0006 for difference, corrected for multiple testing). Notably, the three groups with glucose abnormality (IFG, IGT, and NDM) showed similar metabolic dysregulations as those of IR-NGT. The difference between the IS-NGT and the other subgroups was largely explained by fasting insulin, but not fasting or 2 h glucose. The findings were consistent after covariate adjustment and between the discovery and replication cohort.

CONCLUSIONS

Insulin-resistant non-diabetic individuals are exposed to a similar adverse postprandial metabolic milieu, and analogous cardiometabolic risk, as those with type 2 diabetes. The wide range of metabolic abnormalities associated with IR highlights the necessity of diabetes diagnostics and clinical care beyond glucose management.

Activation of The Phosphatidylcholine to Lysophosphatidylcholine Pathway Is Associated with Osteoarthritis Knee Cartilage Volume Loss Over Time

To identify serum biomarker(s) for predicting knee cartilage volume loss over time, we studied 139 knee osteoarthritis (OA) patients from a previous 24-month clinical trial cohort. Targeted metabolomic profiling was performed on serum collected at baseline. The pairwise metabolite ratios as proxies for enzymatic reaction were calculated and used in the analysis. Cartilage volume loss between baseline and 24 months was assessed quantitatively by magnetic resonance imaging (MRI). Data revealed an association between the serum ratio of lysophosphatidylcholine 18:2 (lysoPC 18:2) to phosphatidylcholine 44:3 (PC44:3) and the cartilage volume loss in the lateral compartment (β = -0.21 ± 0.04, p = 8.53*10^-7) and with joint degradation markers, COMP (r = 0.32, p = 0.0002) and MMP1 (r = 0.26, p = 0.002). The significance remained after adjustment for age, sex, BMI, diabetes, hypertension, dyslipidemia, and the treatment taken in the original study. As the ratio indicated the over activation of the conversion pathway of PC to lysoPC catalyzed by phospholipase A₂ (PLA₂), we assessed and found that a specific PLA₂, PLA₂G5, was significantly increased in human OA cartilage and synovial membrane (85% and 19% respectively, both p < 0.04) compared to controls, and its overexpression correlated with IL-6 (r = 0.63, p = 0.0008). Our data suggest that the serum lysoPC 18:2 to PC44:3 ratio is highly associated with a greater risk of cartilage volume loss of the knee and warrants further investigation in an independent cohort.

Metabolic signatures suggest o-phosphocholine to UDP-N-acetylglucosamine ratio as a potential biomarker for high-glucose and/or palmitate exposure in pancreatic β-cells

INTRODUCTION

Chronic exposure to high-glucose and free fatty acids (FFA) alone/or in combination; and the resulting gluco-, lipo- and glucolipo-toxic conditions, respectively, have been known to induce dysfunction and apoptosis of β-cells in Diabetes. The molecular mechanisms and the development of biomarkers that can be used to predict similarities and differences behind these conditions would help in easier and earlier diagnosis of Diabetes.

OBJECTIVES

This study aims to use metabolomics to gain insight into the mechanisms by which β-cells respond to excess-nutrient stress and identify associated biomarkers.

METHODS

INS-1E cells were cultured in high-glucose, palmitate alone/or in combination for 24 h to mimic gluco-, lipo- and glucolipo-toxic conditions, respectively. Biochemical and cellular experiments were performed to confirm the establishment of these conditions. To gain molecular insights, abundant metabolites were identified and quantified using ¹H-NMR.

RESULTS

No loss of cellular viability was observed in high-glucose while exposure to FFA alone/in combination with high-glucose was associated with increased ROS levels, membrane damage, lipid accumulation, and DNA double-strand breaks. Forty-nine abundant metabolites were identified and quantified using ¹H-NMR. Chemometric pair-wise analysis in glucotoxic and lipotoxic conditions, when compared with glucolipotoxic conditions, revealed partial overlap in the dysregulated metabolites; however, the dysregulation was more significant under glucolipotoxic conditions.

CONCLUSION

The current study compared gluco-, lipo- and glucolipotoxic conditions in parallel and elucidated differences in metabolic pathways that play major roles in Diabetes. o-phosphocholine and UDP-N-acetylglucosamine were identified as common dysregulated metabolites and their ratio was proposed as a potential biomarker for these conditions.

Association between serum haptoglobin and carotid arterial functions: usefulness of a targeted metabolomics approach

Background

Serum haptoglobin (Hp) has been closely associated with cardio-cerebrovascular diseases. We investigated a metabolic profile associated with circulating Hp and carotid arterial functions via a targeted metabolomics approach to provide insight into potential mechanisms.

Methods

A total of 240 participants, including 120 patients with type 2 diabetes mellitus (T2DM) and 120 non-diabetes mellitus (non-DM) subjects were recruited in this study. Targeted metabolic profiles of serum metabolites were determined using an AbsoluteIDQ™ p180 Kit (BIOCRATES Life Sciences AG, Innsbruck, Austria). Ultrasound of the bilateral common carotid artery was used to measure intima-media thickness and inter-adventitial diameter. Serum Hp levels were tested by enzyme-linked immunosorbent assay.

Results

Serum Hp levels in T2DM patients and non-DM subjects were 103.40 (72.46, 131.99) mg/dL and 100.20 (53.99, 140.66) mg/dL, respectively. Significant differences of 19 metabolites and 17 metabolites were found among serum Hp tertiles in T2DM patients and non-DM subjects, respectively (P < 0.05). Of these, phosphatidylcholine acyl-alkyl C32:2 (PC ae C32:2) was the common metabolite observed in two populations, which was associated with the serum Hp groups and lipid traits (P < 0.05). Furthermore, the metabolite ratios of two acidic amino acids, including aspartate to PC ae C32:2 (Asp/PC ae C32:2) and glutamate to PC ae C32:2 (Glu/PC ae C32:2) were correlated with serum Hp, carotid arterial functions and other biochemical index in both populations significantly (P < 0.05).

Conclusions

Targeted metabolomics analyses might provide a new insight into the potential mechanisms underlying the association between serum Hp and carotid arterial functions.

Electronic supplementary material

The online version of this article (10.1186/s12933-019-0808-2) contains supplementary material, which is available to authorized users.

Weighted volume under the three-way receiver operating characteristic surface

It is often necessary to differentiate subjects from multiple categories using medical tests. We may then adopt statistical measures to characterize the performance of these tests. The three-way ROC analysis has been proposed to evaluate the diagnostic accuracy of medical tests with three categories, reflecting the correct classification probabilities across all possible decision thresholds. The geometry of the ROC surface is carefully studied, leading to numerical summary measures such as the volume under the surface. This paper generalizes the global volume under the surface of three-way ROC analysis to the weighted volume under the surface (WVUS) by introducing a weight function emphasizing particular regions of correct classification probabilities. This generalization practically allows researchers to calculate the diagnostic accuracy for a medical or clinical biomarker while satisfactorily high probabilities of correct classification for one or two classes are conditionally ensured. We provide the asymptotic properties of the proposed nonparametric and parametric estimators of WVUS, which could easily lend support to statistical inferences. Some simulations have been conducted to assess the proposed estimators and also to demonstrate the necessity of WVUS. A real data analysis about liver cancer illustrates our methodology.

Chronic d-serine supplementation impairs insulin secretion

OBJECTIVE

The metabolic role of d-serine, a non-proteinogenic NMDA receptor co-agonist, is poorly understood. Conversely, inhibition of pancreatic NMDA receptors as well as loss of the d-serine producing enzyme serine racemase have been shown to modulate insulin secretion. Thus, we aim to study the impact of chronic and acute d-serine supplementation on insulin secretion and other parameters of glucose homeostasis.

METHODS

We apply MALDI FT-ICR mass spectrometry imaging, NMR based metabolomics, 16s rRNA gene sequencing of gut microbiota in combination with a detailed physiological characterization to unravel the metabolic action of d-serine in mice acutely and chronically treated with 1% d-serine in drinking water in combination with either chow or high fat diet feeding. Moreover, we identify SNPs in SRR, the enzyme converting L-to d-serine and two subunits of the NMDA receptor to associate with insulin secretion in humans, based on the analysis of 2760 non-diabetic Caucasian individuals.

RESULTS

We show that chronic elevation of d-serine results in reduced high fat diet intake. In addition, d-serine leads to diet-independent hyperglycemia due to blunted insulin secretion from pancreatic beta cells. Inhibition of alpha 2-adrenergic receptors rapidly restores glycemia and glucose tolerance in d-serine supplemented mice. Moreover, we show that single nucleotide polymorphisms (SNPs) in SRR as well as in individual NMDAR subunits are associated with insulin secretion in humans.

CONCLUSION

Thus, we identify a novel role of d-serine in regulating systemic glucose metabolism through modulating insulin secretion.

Single Cell Analysis Identifies the miRNA Expression Profile of a Subpopulation of Muscle Precursor Cells Unique to Humans With Type 2 Diabetes

MicroRNAs (miRNAs) take part in regulating central cellular processes such as differentiation and metabolism. We have previously shown that muscle progenitor cells derived from individuals with type 2 diabetes (T2DM) have a dysregulated miRNA profile. We hypothesized that the T2DM muscle progenitor population is heterogeneous in its miRNA expression and differs from the progenitor population of healthy controls. MiRNA expression profiles of CD56+ muscle progenitor cells from people with T2DM and from healthy controls were therefore investigated at a single cell level. Single-cell analysis revealed three subpopulations expressing distinct miRNA profiles: two subpopulations including both T2DM and healthy control muscle precursors presented miRNA expression profiles mostly overlapping between groups. A distinct third subpopulation consisted solely of cells from donors with T2DM and showed enriched expression of miRNAs previously shown to be associated with type 2 diabetes. Among the enriched miRNAs was miR-29, a regulator of GLUT4 mRNA expression. Interestingly, this subpopulation also revealed several miRNAs with predicted targets in the PI3K/Akt pathway, not previously described in relation to T2DM muscle dysfunction. We concluded that a subpopulation of T2DM muscle precursor cells is severely dysregulated in terms of their miRNA expression, and accumulation of this population might thus contribute to the dysfunctional muscular phenotype in type 2 diabetes.

The serum metabolomics signature of type 2 diabetes is obscured in Alzheimer's disease

There is evidence for systemic metabolic impairment in Alzheimer's disease (AD), and type 2 diabetes (T2D) increases AD risk. Although studies analyzing blood metabolomics signatures have shown differences between cognitively healthy (CH) and AD subjects, these signatures have not been compared with individuals with T2D. We utilized untargeted analysis platforms (primary metabolism and complex lipids) to characterize the serum metabolome of 126 overnight-fasted elderly subjects classified into four groups based upon AD status (CH or AD) and T2D status [nondiabetic (ND) or T2D]. Cognitive diagnosis groups were a priori weighted equally with T2D subjects. We hypothesized that AD subjects would display a metabolic profile similar to cognitively normal elderly individuals with T2D. However, partial least squares-discriminant analysis (PLS-DA) modeling resulted in poor classification across the four groups (<50% classification accuracy of test subjects). Binary classification of AD vs. CH was poor, but binary classification of T2D vs. ND was good, providing >79.5% and >76.9% classification accuracy for held-out samples using primary metabolism and complex lipids, respectively. When modeling was limited to CH subjects, T2D discrimination improved for the primary metabolism platform (>89.5%) and remained accurate for complex lipids (>73% accuracy). Greater abundances of glucose, fatty acids (C20:2), and phosphatidylcholines and lower abundances of glycine, maleimide, octanol, and tryptophan, cholesterol esters, phosphatidylcholines, and sphingomyelins were identified in CH subjects with T2D relative to those without T2D. In contrast, T2D was not accurately discriminated within AD subjects. Results herein suggest that AD may obscure the typical metabolic phenotype of T2D.

Models including plasma levels of sphingomyelins and phosphatidylcholines as diagnostic and prognostic biomarkers of endometrial cancer

In endometrial cancer, biomarkers for preoperative identification of patients with low risk for disease progression would enable stratification according to the extent of surgery needed, and would avoid the complications that can be associated with radical surgery. A panel of proteins, amino acids, enzymes, and miRNA has been investigated as potential biomarkers for endometrial cancer. At the time of the manuscript submission targeted metabolomics/lipidomics approaches have not been applied to biomarker research in endometrial cancer. Using electrospray ionization-tandem mass spectrometry we quantified 163 metabolites in 126 plasma samples (61 patients with endometrial cancer, 65 control patients). Three single phosphatidylcholines were identified with significantly decreased levels in patients with endometrial cancer. A diagnostic model was defined as the ratio between acylcarnitine C16 and phosphatidylcholine PCae C40:1, the ratio between proline and tyrosine, and the ratio between the two phosphatidylcholines PCaa C42:0 and PCae C44:5; which provided sensitivity of 85.25%, specificity of 69.23%, and AUC of 0.837. Addition of smoking status further improved the constructed diagnostic model (AUC = 0.855). The presence of the major prognostic factors of deep myometrial invasion and lymphovascular invasion were also associated with altered metabolite concentrations. A prognostic model for deep myometrial invasion included the ratio between two hydroxysphingomyelins SMOH C14:1 and SMOH C24:1, and the ratio between two phosphatidylcholines PCaa C40:2 and PCaa C42:6, which provided sensitivity of 81.25%, specificity of 86.36%, and AUC of 0.857. The model for lymphovascular invasion included the ratio between two phosphatidylcholines PCaa C34:4 and PCae C38:3, and the ratio between acylcarnitine C16:2 and phosphatidylcholine PCaa C38:1, which provided sensitivity of 88.89%, specificity of 84.31%, and AUC of 0.935.