Metabolomic Biomarkers in the Progression to Type 1 Diabetes

Longitudinal pathway analysis using structural information with case studies in early type 1 diabetes

Pathway analysis is a frequent step in studies involving gene or protein expression data, but most of the available pathway methods are designed for simple case versus control studies of two sample groups without further complexity. The few available methods allowing the pathway analysis of more complex study designs cannot use pathway structures or handle the situation where the variable of interest is not defined for all samples. Such scenarios are common in longitudinal studies with so long follow up time that healthy controls are required to identify the effect of normal aging apart from the effect of disease development, which is not defined for controls. To address the need, we introduce a new method for Pathway Analysis of Longitudinal data (PAL), which is suitable for complex study designs, such as longitudinal data. The main advantages of PAL are the use of pathway structures and the suitability of the approach for study settings beyond currently available tools. We demonstrate the performance of PAL with simulated data and three longitudinal datasets related to the early development of type 1 diabetes, which involve different study designs and only subtle biological signals, and include both transcriptomic and proteomic data. An R package implementing PAL is publicly available at https://github.com/elolab/PAL .

Multifaceted Role of Apolipoprotein C3 in Cardiovascular Disease Risk and Metabolic Disorder in Diabetes

Apolipoprotein C3 (APOC3) plays a critical role in regulating triglyceride levels and serves as a key predictor of cardiovascular disease (CVD) risk, particularly in patients with diabetes. While APOC3 is known to inhibit lipoprotein lipase, recent findings reveal its broader influence across lipoprotein metabolism, where it modulates the structure and function of various lipoproteins. Therefore, this review examines the complex metabolic cycle of APOC3, emphasizing the impact of APOC3-containing lipoproteins on human metabolism, particularly in patients with diabetes. Notably, APOC3 affects triglyceride-rich lipoproteins and causes structural changes in high-, very low-, intermediate-, and low-density lipoproteins, thereby increasing CVD risk. Evidence suggests that elevated APOC3 levels-above the proposed safe range of 10-15 mg/dL-correlate with clinically significant CVD outcomes. Recognizing APOC3 as a promising biomarker for CVD, this review underscores the urgent need for high-throughput, clinically feasible methods to further investigate its role in lipoprotein physiology in both animal models and human studies. Additionally, we analyze the relationship between APOC3-related genes and lipoproteins, reinforcing the value of large-population studies to understand the impact of APOC3 on metabolic diseases. Ultimately, this review supports the development of therapeutic strategies targeting APOC3 reduction as a preventive approach for diabetes-related CVD.

Prediction of progression to type 1 diabetes with dynamic biomarkers and risk scores

Identifying biomarkers of functional β-cell loss is an important step in the risk stratification of type 1 diabetes. Genetic risk scores (GRS), generated by profiling an array of single nucleotide polymorphisms, are a widely used type 1 diabetes risk-prediction tool. Type 1 diabetes screening studies have relied on a combination of biochemical (autoantibody) and GRS screening methodologies for identifying individuals at high-risk of type 1 diabetes. A limitation of these screening tools is that the presence of autoantibodies marks the initiation of β-cell loss, and is therefore not the best biomarker of progression to early-stage type 1 diabetes. GRS, on the other hand, represents a static biomarker offering a single risk score over an individual's lifetime. In this Personal View, we explore the challenges and opportunities of static and dynamic biomarkers in the prediction of progression to type 1 diabetes. We discuss future directions wherein newer dynamic risk scores could be used to predict type 1 diabetes risk, assess the efficacy of new and emerging drugs to retard, or prevent type 1 diabetes, and possibly replace or further enhance the predictive ability offered by static biomarkers, such as GRS.

Metabolomics and Lipidomics Studies in Pediatric Type 1 Diabetes: Biomarker Discovery for the Early Diagnosis and Prognosis

Aim

Type 1 diabetes (T1D) is an autoimmune disease with heterogeneous risk factors. Metabolic perturbations in the pathogenesis of the disease are remarkable to illuminate the interaction between genetic and environmental factors and how islet immunity and overt diabetes develop. This review aimed to integrate the metabolic changes of T1D to identify potential biomarkers for predicting disease progression based on recent metabolomics and lipidomics studies with parallel methodologies.

Methods

A total of 18 metabolomics and lipidomics studies of childhood T1D during the last 15 years were reviewed. The metabolic fingerprints consisting of 41 lipids and/or metabolite classes of subjects with islet autoantibodies, progressors of T1D, and T1D children were mapped in four-time dimensions based on a tentative effect-score rule.

Results

From birth, high-risk T1D subjects had decreased unsaturated triacylglycerols, unsaturated phosphatidylcholines (PCs), sphingomyelins (SMs), amino acids, and metabolites in the tricarboxylic acid (TCA) cycle. On the contrary, lysophosphatidylcholines (LPCs) and monosaccharides increased. And LPCs and branched-chain amino acids (BCAAs) were elevated before the appearance of islet autoantibodies but were lowered after seroconversion. Choline-related lipids (including PCs, SMs, and LPCs), BCAAs, and metabolites involved in the TCA cycle were identified as consensus biomarkers potentially predicting the development of islet autoimmunity and T1D. Decreased LPCs and amino acids indicated poor glycemic control of T1D, while elevated lysophosphatidylethanolamines and saturated PCs implied good glycemic control. Further pathway analysis revealed that biosynthesis of aminoacyl-tRNA, BCAAs, and alanine, aspartate, and glutamate metabolism were significantly enriched. Moreover, established cohort studies and predictive statistical models of pediatric T1D were also summarized.

Conclusion

The metabolic profile of high-risk T1D subjects and patients demonstrated significant changes compared with healthy controls. This integrated analysis provides a comprehensive overview of metabolic features and potential biomarkers in the pathogenesis and progression of T1D.

Metabolic Signatures of the Exposome-Quantifying the Impact of Exposure to Environmental Chemicals on Human Health

Human health and well-being are intricately linked to environmental quality. Environmental exposures can have lifelong consequences. In particular, exposures during the vulnerable fetal or early development period can affect structure, physiology and metabolism, causing potential adverse, often permanent, health effects at any point in life. External exposures, such as the “chemical exposome” (exposures to environmental chemicals), affect the host’s metabolism and immune system, which, in turn, mediate the risk of various diseases. Linking such exposures to adverse outcomes, via intermediate phenotypes such as the metabolome, is one of the central themes of exposome research. Much progress has been made in this line of research, including addressing some key challenges such as analytical coverage of the exposome and metabolome, as well as the integration of heterogeneous, multi-omics data. There is strong evidence that chemical exposures have a marked impact on the metabolome, associating with specific disease risks. Herein, we review recent progress in the field of exposome research as related to human health as well as selected metabolic and autoimmune diseases, with specific emphasis on the impacts of chemical exposures on the host metabolome.

Longitudinal Metabolome-Wide Signals Prior to the Appearance of a First Islet Autoantibody in Children Participating in the TEDDY Study

Children at increased genetic risk for type 1 diabetes (T1D) after environmental exposures may develop pancreatic islet autoantibodies (IA) at a very young age. Metabolic profile changes over time may imply responses to exposures and signal development of the first IA. Our present research in The Environmental Determinants of Diabetes in the Young (TEDDY) study aimed to identify metabolome-wide signals preceding the first IA against GAD (GADA-first) or against insulin (IAA-first). We profiled metabolomes by mass spectrometry from children's plasma at 3-month intervals after birth until appearance of the first IA. A trajectory analysis discovered each first IA preceded by reduced amino acid proline and branched-chain amino acids (BCAAs), respectively. With independent time point analysis following birth, we discovered dehydroascorbic acid (DHAA) contributing to the risk of each first IA, and γ-aminobutyric acid (GABAs) associated with the first autoantibody against insulin (IAA-first). Methionine and alanine, compounds produced in BCAA metabolism and fatty acids, also preceded IA at different time points. Unsaturated triglycerides and phosphatidylethanolamines decreased in abundance before appearance of either autoantibody. Our findings suggest that IAA-first and GADA-first are heralded by different patterns of DHAA, GABA, multiple amino acids, and fatty acids, which may be important to primary prevention of T1D.

Feasibility of Localized Metabolomics in the Study of Pancreatic Islets and Diabetes

(1) Background: Disruption of insulin production by native or transplanted pancreatic islets caused by auto/allo-immunity leads to hyperglycemia, a serious health condition and important therapeutic challenge due to the lifelong need for exogeneous insulin administration. Early metabolic biomarkers can prompt timely interventions to preserve islet function, but reliable biomarkers are currently lacking. We explored the feasibility of "localized metabolomics" where initial biomarker discovery is made in aqueous humor samples for further validation in the circulation. (2) Methods: We conducted non-targeted metabolomic studies in parallel aqueous humor and plasma samples from diabetic and nondiabetic mice. Metabolite levels and associated pathways were compared in both compartments as well as to an earlier longitudinal dataset in hyperglycemia-progressor versus non-progressor non-obese diabetic (NOD) mice. (3) Results: We confirmed that aqueous humor samples can be used to assess metabolite levels. About half of the identified metabolites had well-correlated levels in the aqueous humor and plasma. Several plasma metabolites were significantly different between diabetic and nondiabetic animals and between males and females, and many of them were correlated with the aqueous humor. (4) Conclusions: This study provides proof-of-concept evidence that aqueous humor samples enriched with islet-related metabolites and representative of the immediate islet microenvironment following intraocular islet transplant can be used to assess metabolic changes that could otherwise be overlooked in the general circulation. The findings support localized metabolomics, with and without intraocular islet transplant, to identify biomarkers associated with diabetes and islet allograft rejection.

Untargeted metabolomic analysis in non-fasted diabetic dogs by UHPLC-HRMS

INTRODUCTION

We recently identified variances in serum metabolomic profiles between fasted diabetic and healthy dogs, some having similarities to those identified in human type 1 diabetes.

OBJECTIVES

Compare untargeted metabolomic profiles in the non-fasted state.

METHODS

Serum from non-fasted diabetic (n = 6) and healthy control (n = 6) dogs were analyzed by liquid chromatography-high resolution mass spectrometry.

RESULTS

Clear clustering of metabolites between groups were observed, with multiple perturbations identified that were similar to those previously observed in fasted diabetic dogs.

CONCLUSION

These findings further support the development of targeted assays capable of detecting metabolites that may be useful as biomarkers of canine diabetes.

Determinants of the urinary and serum metabolome in children from six European populations

BACKGROUND

Environment and diet in early life can affect development and health throughout the life course. Metabolic phenotyping of urine and serum represents a complementary systems-wide approach to elucidate environment-health interactions. However, large-scale metabolome studies in children combining analyses of these biological fluids are lacking. Here, we sought to characterise the major determinants of the child metabolome and to define metabolite associations with age, sex, BMI and dietary habits in European children, by exploiting a unique biobank established as part of the Human Early-Life Exposome project ( http://www.projecthelix.eu ).

METHODS

Metabolic phenotypes of matched urine and serum samples from 1192 children (aged 6-11) recruited from birth cohorts in six European countries were measured using high-throughput ¹H nuclear magnetic resonance (NMR) spectroscopy and a targeted LC-MS/MS metabolomic assay (Biocrates AbsoluteIDQ p180 kit).

RESULTS

We identified both urinary and serum creatinine to be positively associated with age. Metabolic associations to BMI z-score included a novel association with urinary 4-deoxyerythreonic acid in addition to valine, serum carnitine, short-chain acylcarnitines (C3, C5), glutamate, BCAAs, lysophosphatidylcholines (lysoPC a C14:0, lysoPC a C16:1, lysoPC a C18:1, lysoPC a C18:2) and sphingolipids (SM C16:0, SM C16:1, SM C18:1). Dietary-metabolite associations included urinary creatine and serum phosphatidylcholines (4) with meat intake, serum phosphatidylcholines (12) with fish, urinary hippurate with vegetables, and urinary proline betaine and hippurate with fruit intake. Population-specific variance (age, sex, BMI, ethnicity, dietary and country of origin) was better captured in the serum than in the urine profile; these factors explained a median of 9.0% variance amongst serum metabolites versus a median of 5.1% amongst urinary metabolites. Metabolic pathway correlations were identified, and concentrations of corresponding metabolites were significantly correlated (r > 0.18) between urine and serum.

CONCLUSIONS

We have established a pan-European reference metabolome for urine and serum of healthy children and gathered critical resources not previously available for future investigations into the influence of the metabolome on child health. The six European cohort populations studied share common metabolic associations with age, sex, BMI z-score and main dietary habits. Furthermore, we have identified a novel metabolic association between threonine catabolism and BMI of children.

Comprehensive Metabolomics Study To Assess Longitudinal Biochemical Changes and Potential Early Biomarkers in Nonobese Diabetic Mice That Progress to Diabetes

A global nontargeted longitudinal metabolomics study was carried out in male and female NOD mice to characterize the time-profile of the changes in the metabolic signature caused by onset of type 1 diabetes (T1D) and identify possible early biomarkers in T1D progressors. Metabolomics profiling of samples collected at five different time-points identified 676 and 706 biochemicals in blood and feces, respectively. Several metabolites were expressed at significantly different levels in progressors at all time-points, and their proportion increased strongly following onset of hyperglycemia. At the last time-point, when all progressors were diabetic, a large percentage of metabolites had significantly different levels: 57.8% in blood and 27.8% in feces. Metabolic pathways most strongly affected included the carbohydrate, lipid, branched-chain amino acid, and oxidative ones. Several biochemicals showed considerable (>4×) change. Maltose, 3-hydroxybutyric acid, and kojibiose increased, while 1,5-anhydroglucitol decreased more than 10-fold. At the earliest time-point (6-week), differences between the metabolic signatures of progressors and nonprogressors were relatively modest. Nevertheless, several compounds had significantly different levels and show promise as possible early T1D biomarkers. They include fatty acid phosphocholine derivatives from the phosphatidylcholine subpathway (elevated in both blood and feces) as well as serotonin, ribose, and arabinose (increased) in blood plus 13-HODE, tocopherol (increased), diaminopimelate, valerate, hydroxymethylpyrimidine, and dulcitol (decreased) in feces. A combined metabolic signature based on these compounds might serve as an early predictor of T1D-progressors.