A novel test for IGT utilizing metabolite markers of glucose tolerance

Baseline phenotypes with preserved β-cell function and high insulin concentrations have the best improvements in glucose tolerance after weight loss: results from the prospective DEXLIFE and EGIR-RISC studies

BACKGROUND

Weight loss and lifestyle intervention improve glucose tolerance delaying the onset of type 2 diabetes (T2D), but individual responses are highly variable. Determining the predictive factors linked to the beneficial effects of weight loss on glucose tolerance could provide tools for individualized prevention plans. Thus, the aim was to investigate the relationship between pre-intervention values of insulin sensitivity and secretion and the improvement in glucose metabolism after weight loss.

METHODS

In the DEXLIFE cohort (373 individuals at high risk of T2D, assigned 3:1 to a 12-week lifestyle intervention or a control arm, Trial Registration: ISRCTN66987085), K-means clustering and logistic regression analysis were performed based on pre-intervention indices of insulin sensitivity, insulin secretion (AUC-I), and glucose-stimulated insulin response (ratio of incremental areas of insulin and glucose, iAUC I/G). The response to the intervention was evaluated in terms of reduction of OGTT-glucose concentration. Clusters' validation was done in the prospective EGIR-RISC cohort (n = 1538).

RESULTS

Four replicable clusters with different glycemic and metabolomic profiles were identified. Individuals had similar weight loss, but improvement in glycemic profile and β-cell function was different among clusters, highly depending on pre-intervention insulin response to OGTT. Pre-intervention high insulin response was associated with the best improvement in AUC-G, while clusters with low AUC-I and iAUC I/G showed no beneficial effect of weight loss on glucose control, as also confirmed by the logistic regression model.

CONCLUSIONS

Individuals with preserved β-cell function and high insulin concentrations at baseline have the best improvement in glucose tolerance after weight loss.

Methylsulfonylmethane and Sesame Seed Oil Improve Dyslipidemia and Modulate Polyunsaturated Fatty Acid Metabolism in Two Mouse Models of Diabetes

The objective of this study was to identify alterations in lipids and polyunsaturated fatty acid (PUFA) metabolism in both the streptozotocin (STZ)-induced type 1 diabetic (T1D) mouse and the mutant db/db type 2 diabetic (T2D) mouse to establish a biological signature for the evaluation of natural products with purported lipid-altering activity. Eight-week-old male C57BL/6J mice were randomized to nondiabetic group or STZ-induced diabetic groups (n = 10/group). STZ-induced diabetic mice and 6-week-old male db/db mice (n = 10/group) were randomized to the following groups: (1) diabetic control, no treatment, (2) methylsulfonylmethane (MSM) treatment, (3) sesame seed oil (SSO) treatment, and (4) MSM+SSO combination treatment. Clinical parameters measured included weights, blood glucose, serum lipid panels, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) detection of free fatty acids in serum, liver, brain, and eyes. Blood glucose significantly decreased after 4 weeks of MSM treatment in T1D mice. Serum PUFA levels were significantly reduced in T2D mice compared with control mice. In contrast, treatment with SSO reversed this effect in T2D mice, exhibiting serum PUFA levels comparable to control mice. Serum triglycerides were significantly increased in both diabetic models compared to nondiabetic control, mimicking diabetes in people. High-density lipoprotein (HDL) was significantly increased in T1D receiving MSM+SSO and all T2D treatment groups. A corresponding significant decrease in non-HDL cholesterol was seen in T2D mice in all treatment groups. MSM+SSO treatment's effects on HDL and non-HDL cholesterol and PUFA metabolism could lead to improved clinical outcomes in diabetics by improving the lipid profile.

The Quantose Insulin Resistance Test for Maternal Insulin Resistance: A Pilot Study

OBJECTIVE

Insulin resistance (IR) increases during pregnancy which can lead to hyperinsulinemia, gestational diabetes mellitus (GDM), and neonatal hypoglycemia (NH), especially in obese women. Glucose tolerance testing (GTT) is used clinically to evaluate IR in pregnancy. Quantose IR score index is a novel blood screen of IR validated in nonpregnant individuals. The score is generated using an algorithm that combines insulin and three biomarkers of fatty acid pathways (α-hydroxybutyrate, oleic acid, linoleoyl-glycerophospocholine). Our objective was to determine the validity of Quantose IR test (Metabolan Inc. Morrisville, NC) in assessing IR in pregnant obese women, as compared with the homeostatic model assessment of insulin resistance (HOMA-IR), and its ability to predict GDM and NH.

STUDY DESIGN

Women between 10^0/7 and 13^6/7 weeks of gestation with a pre-pregnancy or early pregnancy body mass index more than 30 kg/m², and no pregestational diabetes, were included. Fasting blood samples were collected at 10^0/7 to 13^6/7 (T1) and 24^0/7 to 28^0/7 (T2) weeks. Quantose IR and HOMA-IR were calculated. All women underwent an early (T1; indicated for women with obesity) and a T2 glucose tolerance tests. GDM was diagnosed using the two-step approach, and NH was defined as a neonatal glucose less than 40 mg/dL in the first 24 hours of life. Linear regression and receiver operating characteristic curves were used for analysis.

RESULTS

The trial enrolled 100 patients. Ten subjects (10%) were diagnosed with GDM in the second trimester and none in the first trimester. At T1, Quantose IR (R²= 0.48), but not 1-hour glucose tolerance test (R²= 0.07), correlated with HOMA-IR. Similar correlations were observed at T2. The 1-hour glucose tolerance test followed by HOMA-IR and Quantose IR (area under the curve [AUC]: 0.82, 0.68, and 0.62, respectively) were predictors of GDM. Quantose IR (AUC: 0.74) and 1-hour glucose tolerance test (AUC: 0.72) at T1 and T2 (AUC: 0.75; AUC: 0.93; respectively) were best predictors of NH. The best cut offs, sensitivities, and specificities for prediction of NH were determined.

CONCLUSION

Similar to nonpregnant individuals, Quantose IR appears to be a valid measure of IR in obese pregnant women. First trimester Quantose IR is a predictor of GDM diagnosed in the second trimester and NH. Given that it requires a single blood draw and no glucose challenge, it may be a useful test to evaluate and monitor IR in pregnancy. Our findings may be used as pilot data to explore the potential use of Quantose IR in pregnancy further.

KEY POINTS

· Traditional testing methods for insulin resistance in pregnancy are often performed late, are time consuming, and unpleasant to patients.. · The first trimester one-step Quantose IR test reflects insulin resistance in pregnancy and predicts GDM and neonatal hypoglycemia.. · This is the first known prospective clinical study validating Quantose IR score index in an obstetrical population at risk for developing GDM..

Recent Developments in Biomarkers for Diagnosis and Screening of Type 2 Diabetes Mellitus

PURPOSE OF REVIEW

Diabetes mellitus is a complex, chronic illness characterized by elevated blood glucose levels that occurs when there is cellular resistance to insulin action, pancreatic β-cells do not produce sufficient insulin, or both. Diabetes prevalence has greatly increased in recent decades; consequently, it is considered one of the fastest-growing public health emergencies globally. Poor blood glucose control can result in long-term micro- and macrovascular complications such as nephropathy, retinopathy, neuropathy, and cardiovascular disease. Individuals with diabetes require continuous medical care, including pharmacological intervention as well as lifestyle and dietary changes.

RECENT FINDINGS

The most common form of diabetes mellitus, type 2 diabetes (T2DM), represents approximately 90% of all cases worldwide. T2DM occurs more often in middle-aged and elderly adults, and its cause is multifactorial. However, its incidence has increased in children and young adults due to obesity, sedentary lifestyle, and inadequate nutrition. This high incidence is also accompanied by an estimated underdiagnosis prevalence of more than 50% worldwide. Implementing successful and cost-effective strategies for systematic screening of diabetes mellitus is imperative to ensure early detection, lowering patients' risk of developing life-threatening disease complications. Therefore, identifying new biomarkers and assay methods for diabetes mellitus to develop robust, non-invasive, painless, highly-sensitive, and precise screening techniques is essential. This review focuses on the recent development of new clinically validated and novel biomarkers as well as the methods for their determination that represent cost-effective alternatives for screening and early diagnosis of T2DM.

Gas Chromatography-Mass Spectroscopy-Based Metabolomics Analysis Reveals Potential Biochemical Markers for Diagnosis of Gestational Diabetes Mellitus

Due to many adverse effects of gestational diabetes mellitus (GDM) on the mother and fetus, its diagnosis is crucial. The presence of GDM can be confirmed by an abnormal fasting plasma glucose level (aFPG) and/or oral glucose tolerance test (OGTT) performed mostly between 24 and 28 gestational week. Both aFPG and abnormal glucose tolerance (aGT) are used to diagnose GDM. In comparison to measurement of FPG, OGTT is time-consuming, usually inconvenient for the patient, and very often needs to be repeated. Therefore, it is necessary to seek tests that will be helpful and convenient to diagnose GDM. For this reason, we investigated the differences in fasting serum metabolites between GDM women with abnGM and normal FPG (aGT-GDM group), with aFPG and normal glucose metabolism (aFPG-GDM group) as well as pregnant women with normal glucose tolerance (NGT) being a control group. Serum metabolites were measured by an untargeted approach using gas chromatography–mass spectrometry (GC–MS). In the discovery phase, fasting serum samples collected from 79 pregnant women (aFPG-GDM, n = 24; aGT-GDM, n = 26; NGT, n = 29) between 24 and 28 weeks of gestation (gwk) were fingerprinted. A set of metabolites (α–hydroxybutyric acid (α–HB), β–hydroxybutyric acid (β–HB), and several fatty acids) significant in aGT-GDM vs NGT but not significant in aFPG-GDM vs NGT comparison in the discovery phase was selected for validation. These metabolites were quantified by a targeted GC–MS method in a validation cohort consisted of 163 pregnant women (aFPG-GDM, n = 51; aGT-GDM, n = 44; and NGT, n = 68). Targeted analyses were also performed on the serum collected from 92 healthy women in the first trimester (8–14 gwk) who were NGT at this time, but in the second trimester (24–28 gwk) they were diagnosed with GDM. It was found that α–HB, β–HB, and several fatty acids were associated with aGT-GDM. A combination of α–HB, β–HB, and myristic acid was found highly specific and sensitive for the diagnosis of GDM manifested by aGT-GDM (AUC = 0.828) or to select women at a risk of aGT-GDM in the first trimester (AUC = 0.791). Our findings provide new potential markers of GDM and may have implications for its early diagnosis.

Metabolic Dysfunction Biomarkers as Predictors of Early Diabetes

During the pathophysiological course of type 2 diabetes (T2D), several metabolic imbalances occur. There is increasing evidence that metabolic dysfunction far precedes clinical manifestations. Thus, knowing and understanding metabolic imbalances is crucial to unraveling new strategies and molecules (biomarkers) for the early-stage prediction of the disease's non-clinical phase. Lifestyle interventions must be made with considerable involvement of clinicians, and it should be considered that not all patients will respond in the same manner. Individuals with a high risk of diabetic progression will present compensatory metabolic mechanisms, translated into metabolic biomarkers that will therefore show potential predictive value to differentiate between progressors/non-progressors in T2D. Specific novel biomarkers are being proposed to entrap prediabetes and target progressors to achieve better outcomes. This study provides a review of the latest relevant biomarkers in prediabetes. A search for articles published between 2011 and 2021 was conducted; duplicates were removed, and inclusion criteria were applied. From the 29 studies considered, a survey of the most cited (relevant) biomarkers was conducted and further discussed in the two main identified fields: metabolomics, and miRNA studies.

The role of metabolomics in personalized medicine for diabetes

Metabolomics is rapidly evolving omics technology in personalized medicine, it offers a new avenue for identification of multiple novel metabolic mediators of impaired glucose tolerance and dysglycemia. Liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy are most commonly used analytical methods in the field of metabolomics. Recent evidences showed that metabolomic profiles are link to the incidence of diabetes. In this review, an overview of metabolomics studies in diabetes revealed several diabetes-associated metabolites including 1,5-anhydroglycitol, branch chain amino acids, glucose, α-hydroxybutyric acid, 3-hydroundecanoyl-carnitine and phosphatidylcholine that could be potential biomarkers associated with diabetes. These identified metabolites can be used to develop personalized prognostics and diagnostic, and help in diabetes management.

D-Serine: A Cross Species Review of Safety

Background:D-Serine, a direct, full agonist at the D-serine/glycine modulatory site of the N-methyl-D-aspartate-type glutamate receptors (NMDAR), has been assessed as a treatment for multiple psychiatric and neurological conditions. Based on studies in rats, concerns of nephrotoxicity have limited D-serine research in humans, particularly using high doses. A review of D-serine's safety is timely and pertinent, as D-serine remains under active study for schizophrenia, both directly (R61 MH116093) and indirectly through D-amino acid oxidase (DAAO) inhibitors. The principal focus is on nephrotoxicity, but safety in other physiologic and pathophysiologic systems are also reviewed. Methods: Using the search terms "D-serine," "D-serine and schizophrenia," "D-serine and safety," "D-serine and nephrotoxicity" in PubMed, we conducted a systematic review on D-serine safety. D-serine physiology, dose-response and efficacy in clinical studies and dAAO inhibitor safety is also discussed. Results: When D-serine doses >500 mg/kg are used in rats, nephrotoxicity, manifesting as an acute tubular necrosis syndrome, seen within hours of administration is highly common, if not universal. In other species, however, D-serine induced nephrotoxicity has not been reported, even in other rodent species such as mice and rabbits. Even in rats, D--serine related toxicity is dose dependent and reversible; and does not appear to be present in rats at doses producing an acute Cmax of <2,000 nmol/mL. For comparison, the Cmax of D-serine 120 mg/kg, the highest dose tested in humans, is ~500 nmol/mL in acute dosing. Across all published human studies, only one subject has been reported to have abnormal renal values related to D-serine treatment. This abnormality did not clearly map on to the acute tubular necrosis syndrome seen in rats, and fully resolved within a few days of stopping treatment. DAAO inhibitors may be nephroprotective. D-Serine may have a physiologic role in metabolic, extra-pyramidal, cardiac and other systems, but no other clinically significant safety concerns are revealed in the literature. Conclusions: Even before considering human to rat differences in renal physiology, using current FDA guided monitoring paradigms, D-serine appears safe at currently studied maximal doses, with potential safety in combination with DAAO inhibitors.

Review of methods for detecting glycemic disorders

Prediabetes (intermediate hyperglycemia) consists of two abnormalities, impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) detected by a standardized 75-gram oral glucose tolerance test (OGTT). Individuals with isolated IGT or combined IFG and IGT have increased risk for developing type 2 diabetes (T2D) and cardiovascular disease (CVD). Diagnosing prediabetes early and accurately is critical in order to refer high-risk individuals for intensive lifestyle modification. However, there is currently no international consensus for diagnosing prediabetes with HbA1c or glucose measurements based upon American Diabetes Association (ADA) and the World Health Organization (WHO) criteria that identify different populations at risk for progressing to diabetes. Various caveats affecting the accuracy of interpreting the HbA1c including genetics complicate this further. This review describes established methods for detecting glucose disorders based upon glucose and HbA1c parameters as well as novel approaches including the 1-hour plasma glucose (1-h PG), glucose challenge test (GCT), shape of the glucose curve, genetics, continuous glucose monitoring (CGM), measures of insulin secretion and sensitivity, metabolomics, and ancillary tools such as fructosamine, glycated albumin (GA), 1,5- anhydroglucitol (1,5-AG). Of the approaches considered, the 1-h PG has considerable potential as a biomarker for detecting glucose disorders if confirmed by additional data including health economic analysis. Whether the 1-h OGTT is superior to genetics and omics in providing greater precision for individualized treatment requires further investigation. These methods will need to demonstrate substantially superiority to simpler tools for detecting glucose disorders to justify their cost and complexity.

Metabolic profiling of tissue-specific insulin resistance in human obesity: results from the Diogenes study and the Maastricht Study

BACKGROUND

Recent evidence indicates that insulin resistance (IR) in obesity may develop independently in different organs, representing different etiologies toward type 2 diabetes and other cardiometabolic diseases. The aim of this study was to investigate whether IR in the liver and IR in skeletal muscle are associated with distinct metabolic profiles.

METHODS

This study includes baseline data from 634 adults with overweight or obesity (BMI ≥ 27 kg/m²) (≤65 years; 63% women) without diabetes of the European Diogenes Study. Hepatic insulin resistance index (HIRI) and muscle insulin sensitivity index (MISI), were derived from a five-point OGTT. At baseline 17 serum metabolites were identified and quantified by nuclear-magnetic-resonance spectroscopy. Linear mixed model analyses (adjusting for center, sex, body mass index (BMI), waist-to-hip ratio) were used to associate HIRI and MISI with these metabolites. In an independent sample of 540 participants without diabetes (BMI ≥ 27 kg/m²; 40-65 years; 46% women) of the Maastricht Study, an observational prospective population-based cohort study, 11 plasma metabolites and a seven-point OGTT were available for validation.

RESULTS

Both HIRI and MISI were associated with higher levels of valine, isoleucine, oxo-isovaleric acid, alanine, lactate, and triglycerides, and lower levels of glycine (all p < 0.05). HIRI was also associated with higher levels of leucine, hydroxyisobutyrate, tyrosine, proline, creatine, and n-acetyl and lower levels of acetoacetate and 3-OH-butyrate (all p < 0.05). Except for valine, these results were replicated for all available metabolites in the Maastricht Study.

CONCLUSIONS

In persons with obesity without diabetes, both liver and muscle IR show a circulating metabolic profile of elevated (branched-chain) amino acids, lactate, and triglycerides, and lower glycine levels, but only liver IR associates with lower ketone body levels and elevated ketogenic amino acids in circulation, suggestive of decreased ketogenesis. This knowledge might enhance developments of more targeted tissue-specific interventions to prevent progression to more severe disease stages.

An unsupervised learning approach to identify novel signatures of health and disease from multimodal data

BACKGROUND

Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. Integrated analysis of data from different modalities has the potential of uncovering novel biomarkers and disease signatures.

METHODS

We collected 1385 data features from diverse modalities, including metabolome, microbiome, genetics, and advanced imaging, from 1253 individuals and from a longitudinal validation cohort of 1083 individuals. We utilized a combination of unsupervised machine learning methods to identify multimodal biomarker signatures of health and disease risk.

RESULTS

Our method identified a set of cardiometabolic biomarkers that goes beyond standard clinical biomarkers. Stratification of individuals based on the signatures of these biomarkers identified distinct subsets of individuals with similar health statuses. Subset membership was a better predictor for diabetes than established clinical biomarkers such as glucose, insulin resistance, and body mass index. The novel biomarkers in the diabetes signature included 1-stearoyl-2-dihomo-linolenoyl-GPC and 1-(1-enyl-palmitoyl)-2-oleoyl-GPC. Another metabolite, cinnamoylglycine, was identified as a potential biomarker for both gut microbiome health and lean mass percentage. We identified potential early signatures for hypertension and a poor metabolic health outcome. Additionally, we found novel associations between a uremic toxin, p-cresol sulfate, and the abundance of the microbiome genera Intestinimonas and an unclassified genus in the Erysipelotrichaceae family.

CONCLUSIONS

Our methodology and results demonstrate the potential of multimodal data integration, from the identification of novel biomarker signatures to a data-driven stratification of individuals into disease subtypes and stages-an essential step towards personalized, preventative health risk assessment.

Plasma Metabolomics to Identify and Stratify Patients With Impaired Glucose Tolerance

OBJECTIVE

Impaired glucose tolerance (IGT) is one of the presymptomatic states of type 2 diabetes mellitus and requires an oral glucose tolerance test (OGTT) for diagnosis. Our aims were twofold: (i) characterize signatures of small molecules predicting the OGTT response and (ii) identify metabolic subgroups of participants with IGT.

METHODS

Plasma samples from 827 participants of the Study of Health in Pomerania free of diabetes were measured using mass spectrometry and proton-nuclear magnetic resonance spectroscopy. Linear regression analyses were used to screen for metabolites significantly associated with the OGTT response after 2 hours, adjusting for baseline glucose and insulin levels as well as important confounders. A signature predictive for IGT was established using regularized logistic regression. All cases with IGT (N = 159) were selected and subjected to unsupervised clustering using a k-means approach.

RESULTS AND CONCLUSION

In total, 99 metabolites and 22 lipoprotein measures were significantly associated with either 2-hour glucose or 2-hour insulin levels. Those comprised variations in baseline concentrations of branched-chain amino ketoacids, acylcarnitines, lysophospholipids, or phosphatidylcholines, largely confirming previous studies. By the use of these metabolites, subjects with IGT segregated into two distinct groups. Our IGT prediction model combining both clinical and metabolomics traits achieved an area under the curve of 0.84, slightly improving the prediction based on established clinical measures. The present metabolomics approach revealed molecular signatures associated directly to the response of the OGTT and to IGT in line with previous studies. However, clustering of subjects with IGT revealed distinct metabolic signatures of otherwise similar individuals, pointing toward the possibility of metabolomics for patient stratification.

Metabolomics-Based Prospective Studies and Prediction of Type 2 Diabetes Mellitus Risks

The preceding decade has witnessed an intense upsurge in the diabetic population across the world making type 2 diabetes mellitus (T2DM) more of an epidemic than a lifestyle disease. Metabolic disorders are often latent for a while before becoming clinically evident, thus reinforcing the pursuit of early biomarkers of metabolic alterations. A prospective study along with metabolic profiling is the most appropriate way to detect the early pathophysiological changes in metabolic diseases such as T2DM. The aim of this review was to summarize the different potential biomarkers of T2DM identified in prospective studies, which used tools of metabolomics. The review also demonstrates on how metabolomic profiling-based prospective studies can be used to address a concern like population-specific disease mechanism. We performed a literature search on metabolomics-based prospective studies on T2DM using the key words "metabolomics," "Type 2 diabetes," "diabetes mellitus", "metabolite profiling," "prospective study," "metabolism," and "biomarker." Additional articles that were obtained from the reference lists of the articles obtained using the above key words were also examined. Articles on dietary intake, type 1 diabetes mellitus, and gestational diabetes were excluded. The review revealed that many studies showed a direct association of branched-chain amino acids and an inverse association of glycine with T2DM. Majority of the prospective studies conducted were targeted metabolomics-based, with Caucasians as their study cohort. The whole disease risk in populations, including Asians, could therefore not be identified. This review proposes the utility of prospective studies in conjunction with metabolomics platform to unravel the altered metabolic pathways that contribute to the risk of T2DM.

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.

1-Hour Post-OGTT Glucose Improves the Early Prediction of Type 2 Diabetes by Clinical and Metabolic Markers

CONTEXT

Early prediction of dysglycemia is crucial to prevent progression to type 2 diabetes. The 1-hour postload plasma glucose (PG) is reported to be a better predictor of dysglycemia than fasting plasma glucose (FPG), 2-hour PG, or glycated hemoglobin (HbA1c).

OBJECTIVE

To evaluate the predictive performance of clinical markers, metabolites, HbA1c, and PG and serum insulin (INS) levels during a 75-g oral glucose tolerance test (OGTT).

DESIGN AND SETTING

We measured PG and INS levels at 0, 30, 60, and 120 minutes during an OGTT in 543 participants in the Botnia Prospective Study, 146 of whom progressed to type 2 diabetes within a 10-year follow-up period. Using combinations of variables, we evaluated 1527 predictive models for progression to type 2 diabetes.

RESULTS

The 1-hour PG outperformed every individual marker except 30-minute PG or mannose, whose predictive performances were lower but not significantly worse. HbA1c was inferior to 1-hour PG according to DeLong test P value but not false discovery rate. Combining the metabolic markers with PG measurements and HbA1c significantly improved the predictive models, and mannose was found to be a robust metabolic marker.

CONCLUSIONS

The 1-hour PG, alone or in combination with metabolic markers, is a robust predictor for determining the future risk of type 2 diabetes, outperforms the 2-hour PG, and is cheaper to measure than metabolites. Metabolites add to the predictive value of PG and HbA1c measurements. Shortening the standard 75-g OGTT to 1 hour improves its predictive value and clinical usability.

A review of validated biomarkers obtained through metabolomics

INTRODUCTION

Studying changes in the whole set of small molecules, final products of biochemical reactions in living systems or metabolites, is extremely appealing because they represent the best approach to identifying what occurs in an organism when samples are collected. However, their usefulness as potential biomarkers is limited by discoveries obtained in small groups without proper validation or even confirmation of the chemical structure. Areas covered: During the past 5 years, more than 900 papers have been published on metabolomics for biomarker discovery, but the numbers are much lower when some criteria of validation are applied. In total, 102 papers have been included in this review. The most frequent disease areas in which these markers have been discovered include the following: cancer, diabetes, and related diseases and neurodegenerative, cardiovascular, autoimmune, liver, and kidney diseases. Expert commentary: Metabolomics has been demonstrated as rapidly growing due to the improvements in instrumentation, mainly mass spectrometry, and data mining software. For application in the clinic, the results should be validated in different stages, from analytical validation to validation in independent sets of samples, using thousands of samples from different sources.

Metabolite changes in risk of type 2 diabetes mellitus in cohort studies: A systematic review and meta-analysis

AIMS

Fasting plasma glucose, oral glucose tolerance test, and glycated hemoglobin are diagnostic markers for type 2 diabetes mellitus (T2DM). However, it is necessary to detect physiological changes in T2DM rapidly and stratify diabetic stage using other biomarkers. We performed a systematic review and meta-analysis to contribute to the development of objective and sensitive diagnostic indicators by integrating metabolite biomarkers derived from large-scale cohort studies.

METHODS

We searched for metabolomics studies of T2DM cohort in PubMed, Scopus, and Web of Science for studies published within the last 10 years from January 2008 to February 2017. The concentrations of metabolites and odds ratios (ORs) were integrated and risk ratio (RR) values were estimated to distinguish subjects with T2DM and normal participants.

RESULTS

Fourteen cohort studies were investigated in this meta-analysis. There were 4592 patients in the case group and 11,492 participants in the control group. We noted a 1.89-, 1.63-, and 1.87-fold higher risk of T2DM associated with leucine (RR 1.89 [95% CI 1.57-2.29]), alanine (RR 1.63 [95% CI 1.48-1.79]), and oleic acid (RR 1.87 [95% CI 1.62-2.17]), respectively. Lysophosphatidylcholine C18:0 (RR 0.80 [95% CI 0.72-0.90]) and creatinine (RR 0.63 [95% CI 0.53-0.74]) were associated with 20% and 37% decreased T2DM risks, respectively.

CONCLUSIONS

Most amino acids in patients were positively related to diabetes, while creatinine and some lysophosphatidylcholines showed a negative relationship. This suggests that diabetic risk prediction using metabolites that sensitively reflect changes in the body will improve individual diagnosis and personalize medicine.

Lipidome as a predictive tool in progression to type 2 diabetes in Finnish men

BACKGROUND

There is a need for early markers to track and predict the development of type 2 diabetes mellitus (T2DM) from the state of normal glucose tolerance through prediabetes. In this study we tested whether the plasma molecular lipidome has biomarker potential to predicting the onset of T2DM.

METHODS

We applied global lipidomic profiling on plasma samples from well-phenotyped men (107 cases, 216 controls) participating in the longitudinal METSIM study at baseline and at five-year follow-up. To validate the lipid markers, an additional study with a representative sample of adult male population (n=631) was also conducted. A total of 277 plasma lipids were analyzed using the lipidomics platform based on ultra-performance liquid chromatography coupled to time-of-flight mass spectrometry. Lipids with the highest predictive power for the development of T2DM were computationally selected, validated and compared to standard risk models without lipids.

RESULTS

A persistent lipid signature with higher levels of triacylglycerols and diacyl-phospholipids as well as lower levels of alkylacyl phosphatidylcholines was observed in progressors to T2DM. Lysophosphatidylcholine acyl C18:2 (LysoPC(18:2)), phosphatidylcholines PC(32:1), PC(34:2e) and PC(36:1), and triacylglycerol TG(17:1/18:1/18:2) were selected to the full model that included metabolic risk factors and FINDRISC variables. When further adjusting for BMI and age, these lipids had respective odds ratios of 0.32, 2.4, 0.50, 2.2 and 0.31 (all p<0.05) for progression to T2DM. The independently-validated predictive power improved in all pairwise comparisons between the lipid model and the respective standard risk model without the lipids (integrated discrimination improvement IDI>0; p<0.05). Notably, the lipid models remained predictive of the development of T2DM in the fasting plasma glucose-matched subset of the validation study.

CONCLUSION

This study indicates that a lipid signature characteristic of T2DM is present years before the diagnosis and improves prediction of progression to T2DM. Molecular lipid biomarkers were shown to have predictive power also in a high-risk group, where standard risk factors are not helpful at distinguishing progressors from non-progressors.

Plasma Dihydroceramides Are Diabetes Susceptibility Biomarker Candidates in Mice and Humans

Plasma metabolite concentrations reflect the activity of tissue metabolic pathways and their quantitative determination may be informative about pathogenic conditions. We searched for plasma lipid species whose concentrations correlate with various parameters of glucose homeostasis and susceptibility to type 2 diabetes (T2D). Shotgun lipidomic analysis of the plasma of mice from different genetic backgrounds, which develop a pre-diabetic state at different rates when metabolically stressed, led to the identification of a group of sphingolipids correlated with glucose tolerance and insulin secretion. Quantitative analysis of these and closely related lipids in the plasma of individuals from two population-based prospective cohorts revealed that specific long-chain fatty-acid-containing dihydroceramides were significantly elevated in the plasma of individuals who will progress to diabetes up to 9 years before disease onset. These lipids may serve as early biomarkers of, and help identify, metabolic deregulation in the pathogenesis of T2D.

Expression and functional assessment of candidate type 2 diabetes susceptibility genes identify four new genes contributing to human insulin secretion

Objectives

Genome-wide association studies (GWAS) have identified >100 loci independently contributing to type 2 diabetes (T2D) risk. However, translational implications for precision medicine and for the development of novel treatments have been disappointing, due to poor knowledge of how these loci impact T2D pathophysiology. Here, we aimed to measure the expression of genes located nearby T2D associated signals and to assess their effect on insulin secretion from pancreatic beta cells.

Methods

The expression of 104 candidate T2D susceptibility genes was measured in a human multi-tissue panel, through PCR-free expression assay. The effects of the knockdown of beta-cell enriched genes were next investigated on insulin secretion from the human EndoC-βH1 beta-cell line. Finally, we performed RNA-sequencing (RNA-seq) so as to assess the pathways affected by the knockdown of the new genes impacting insulin secretion from EndoC-βH1, and we analyzed the expression of the new genes in mouse models with altered pancreatic beta-cell function.

Results

We found that the candidate T2D susceptibility genes' expression is significantly enriched in pancreatic beta cells obtained by laser capture microdissection or sorted by flow cytometry and in EndoC-βH1 cells, but not in insulin sensitive tissues. Furthermore, the knockdown of seven T2D-susceptibility genes (CDKN2A, GCK, HNF4A, KCNK16, SLC30A8, TBC1D4, and TCF19) with already known expression and/or function in beta cells changed insulin secretion, supporting our functional approach. We showed first evidence for a role in insulin secretion of four candidate T2D-susceptibility genes (PRC1, SRR, ZFAND3, and ZFAND6) with no previous knowledge of presence and function in beta cells. RNA-seq in EndoC-βH1 cells with decreased expression of PRC1, SRR, ZFAND6, or ZFAND3 identified specific gene networks related to T2D pathophysiology. Finally, a positive correlation between the expression of Ins2 and the expression of Prc1, Srr, Zfand6, and Zfand3 was found in mouse pancreatic islets with altered beta-cell function.

Conclusions

This study showed the ability of post-GWAS functional studies to identify new genes and pathways involved in human pancreatic beta-cell function and in T2D pathophysiology.

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Expression of genes located nearby T2D associated signals is enriched in β cells.

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Knockdown of 7 T2D genes with known role in β cell changes insulin secretion.

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Knockdown of 4 T2D genes with unknown role in β cell impairs insulin secretion.

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RNA-seq in cells with knockdown of these 4 genes detected T2D-related networks.

Serum metabolite profile associates with the development of metabolic co-morbidities in first-episode psychosis

Psychotic patients are at high risk for developing obesity, metabolic syndrome and type 2 diabetes. These metabolic co-morbidities are hypothesized to be related to both treatment side effects as well as to metabolic changes occurring during the psychosis. Earlier metabolomics studies have shown that blood metabolite levels are predictive of insulin resistance and type 2 diabetes in the general population as well as sensitive to the effects of antipsychotics. In this study, we aimed to identify the metabolite profiles predicting future weight gain and other metabolic abnormalities in psychotic patients. We applied comprehensive metabolomics to investigate serum metabolite profiles in a prospective study setting in 36 first-episode psychosis patients during the first year of the antipsychotic treatment and 19 controls. While corroborating several earlier findings when comparing cases and controls and the effects of the antipsychotic medication, we also found that prospective weight gain in psychotic patients was associated with increased levels of triacylglycerols with low carbon number and double-bond count at baseline, that is, lipids known to be associated with increased liver fat. Our study suggests that metabolite profiles may be used to identify the psychotic patients most vulnerable to develop metabolic co-morbidities, and may point to a pharmacological approach to counteract the antipsychotic-induced weight gain.

LC-MS/MS method for quantitation of seven biomarkers in human plasma for the assessment of insulin resistance and impaired glucose tolerance

Early detection of insulin resistance (IR) and/or impaired glucose tolerance (IGT) is crucial for delaying and preventing the progression toward type 2 diabetes. We recently developed and validated a straightforward metabolite-based test for the assessment of IR and IGT in a single LC-MS/MS method. Plasma samples were diluted with isotopically-labeled internal standards and extracted by simple protein precipitation. The extracts were analyzed by LC-MS/MS for the quantitation of 2-hydroxybutyric acid (0.500-40.0μg/mL), 3-hydroxybutyric acid (1.00-80.0μg/mL), 4-methyl-2-oxopentanoic acid (0.500-20.0μg/mL), 1-linoleoyl-2-hydroxy-sn-glycero-3-phosphocholine (2.50-100μg/mL), oleic acid (10.0-400μg/mL), pantothenic acid (0.0100-0.800μg/mL), and serine (2.50-100μg/mL). Liquid chromatography was carried out on a reversed phase column with a run time of 3.1min and the mass spectrometer operated in negative MRM mode. Method validation was performed on three identical LC-MS/MS systems with five runs each. Sufficient linearity (R2>0.99) was observed for all the analytes over the ranges. The imprecision (CVs) was found to be less than 5.5% for intra-run and less than 5.8% for inter-run for the seven analytes. The analytical recovery was determined to be between 96.3 and 103% for the seven analytes. This fast and robust method has subsequently been used for patient sample analysis for the assessment of IR and IGT.

Lysophosphatidylcholines activate PPARδ and protect human skeletal muscle cells from lipotoxicity

Metabolomics studies of human plasma demonstrate a correlation of lower plasma lysophosphatidylcholines (LPC) concentrations with insulin resistance, obesity, and inflammation. This relationship is not unraveled on a molecular level. Here we investigated the effects of the abundant LPC(16:0) and LPC(18:1) on human skeletal muscle cells differentiated to myotubes. Transcriptome analysis of human myotubes treated with 10μM LPC for 24h revealed enrichment of up-regulated peroxisome proliferator-activated receptor (PPAR) target transcripts, including ANGPTL4, PDK4, PLIN2, and CPT1A. The increase in both PDK4 and ANGPTL4 RNA expression was abolished in the presence of either PPARδ antagonist GSK0660 or GSK3787. The induction of PDK4 by LPCs was blocked with siRNA against PPARD. The activation of PPARδ transcriptional activity by LPC was shown as PPARδ-dependent luciferase reporter gene expression and enhanced DNA binding of the PPARδ/RXR dimer. On a functional level, further results show that the LPC-mediated activation of PPARδ can reduce fatty acid-induced inflammation and ER stress in human skeletal muscle cells. The protective effect of LPC was prevented in the presence of the PPARδ antagonist GSK0660. Taking together, LPCs can activate PPARδ, which is consistent with the association of high plasma LPC levels and PPARδ-dependent anti-diabetic and anti-inflammatory effects.

α-Hydroxybutyric Acid Is a Selective Metabolite Biomarker of Impaired Glucose Tolerance

OBJECTIVE

Plasma metabolites that distinguish isolated impaired glucose tolerance (iIGT) from isolated impaired fasting glucose (iIFG) may be useful biomarkers to predict IGT, a high-risk state for the development of type 2 diabetes.

RESEARCH DESIGN AND METHODS

Targeted metabolomics with 23 metabolites previously associated with dysglycemia was performed with fasting plasma samples from subjects without diabetes at time 0 of an oral glucose tolerance test (OGTT) in two observational cohorts: RISC (Relationship Between Insulin Sensitivity and Cardiovascular Disease) and DMVhi (Diabetes Mellitus and Vascular Health Initiative). Odds ratios (ORs) for a one-SD change in the metabolite level were calculated using multiple logistic regression models controlling for age, sex, and BMI to test for associations with iIGT or iIFG versus normal. Selective biomarkers of iIGT were further validated in the Botnia study.

RESULTS

α-Hydroxybutyric acid (α-HB) was most strongly associated with iIGT in RISC (OR 2.54 [95% CI 1.86-3.48], P value 5E-9) and DMVhi (2.75 [1.81-4.19], 4E-5) while having no significant association with iIFG. In Botnia, α-HB was selectively associated with iIGT (2.03 [1.65-2.49], 3E-11) and had no significant association with iIFG. Linoleoyl-glycerophosphocholine (L-GPC) and oleic acid were also found to be selective biomarkers of iIGT. In multivariate IGT prediction models, addition of α-HB, L-GPC, and oleic acid to age, sex, BMI, and fasting glucose significantly improved area under the curve in all three cohorts.

CONCLUSIONS

α-HB, L-GPC, and oleic acid were shown to be selective biomarkers of iIGT, independent of age, sex, BMI, and fasting glucose, in 4,053 subjects without diabetes from three European cohorts. These biomarkers can be used in predictive models to identify subjects with IGT without performing an OGTT.

Developmental programming, adiposity, and reproduction in ruminants

Although sheep have been widely adopted as an animal model for examining the timing of nutritional interventions through pregnancy on the short- and long-term outcomes, only modest programming effects have been seen. This is due in part to the mismatch in numbers of twins and singletons between study groups as well as unequal numbers of males and females. Placental growth differs between singleton and twin pregnancies which can result in different body composition in the offspring. One tissue that is especially affected is adipose tissue which in the sheep fetus is primarily located around the kidneys and heart plus the sternal/neck region. Its main role is the rapid generation of heat due to activation of the brown adipose tissue-specific uncoupling protein 1 at birth. The fetal adipose tissue response to suboptimal maternal food intake at defined stages of development differs between the perirenal abdominal and pericardial depots, with the latter being more sensitive. Fetal adipose tissue growth may be mediated in part by changes in leptin status of the mother which are paralleled in the fetus. Then, over the first month of life plasma leptin is higher in females than males despite similar adiposity, when fat is the fastest growing tissue with the sternal/neck depot retaining uncoupling protein 1, whereas other depots do not. Future studies should take into account the respective effects of fetal number and sex to provide more detailed insights into the mechanisms by which adipose and related tissues can be programmed in utero.

Bioanalytical techniques in nontargeted clinical lipidomics

Lipidomic analysis aims at comprehensive characterization of molecular lipids in biological systems. Due to the central role of lipid metabolism in many devastating diseases, lipidomics is being increasingly applied in biomedical research. Over the past years, advances in analytical techniques and bioinformatics enabled increasingly comprehensive and accurate coverage of lipids both in tissues and biofluids, yet many challenges remain. This review highlights recent progress in the domain of analytical lipidomics, with main emphasis on non-targeted methodologies for large scale clinical applications, as well as discusses some of the key challenges and opportunities in this field.

Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical Application

Type 2 diabetes (T2D) and its comorbidities have reached epidemic proportions, with more than half a billion cases expected by 2030. Metabolomics is a fairly new approach for studying metabolic changes connected to disease development and progression and for finding predictive biomarkers to enable early interventions, which are most effective against T2D and its comorbidities. In metabolomics, the abundance of a comprehensive set of small biomolecules (metabolites) is measured, thus giving insight into disease-related metabolic alterations. This review shall give an overview of basic metabolomics methods and will highlight current metabolomics research successes in the prediction and diagnosis of T2D. We summarized key metabolites changing in response to T2D. Despite large variations in predictive biomarkers, many studies have replicated elevated plasma levels of branched-chain amino acids and their derivatives, aromatic amino acids and α-hydroxybutyrate ahead of T2D manifestation. In contrast, glycine levels and lysophosphatidylcholine C18:2 are depressed in both predictive studies and with overt disease. The use of metabolomics for predicting T2D comorbidities is gaining momentum, as are our approaches for translating basic metabolomics research into clinical applications. As a result, metabolomics has the potential to enable informed decision-making in the realm of personalized medicine.

A new metabolite panel test for identification of patients with impaired glucose tolerance? Analysis of the article by Cobb et al

The article by Cobb et al represents solid research work applying the most sophisticated laboratory technologies, a very sound clinical research methodology, and valid statistical analysis procedures. The authors have identified a combination of metabolites suitable to replace the oral glucose tolerance test procedure in the identification of patients with impaired glucose tolerance (IGT) from a fasting blood draw. However, the discussed pathophysiological, clinical, and economic aspects may induce mechanisms restricting the probability of a global acceptance of this test for daily routine.