Age- and islet autoimmunity-associated differences in amino acid and lipid metabolites in children at risk for type 1 diabetes

A Composite Biomarker Signature of Type 1 Diabetes Risk Identified via Augmentation of Parallel Multi-Omics Data from a Small Cohort

Background

Biomarkers of early pathogenesis of type 1 diabetes (T1D) are crucial to enable effective prevention measures in at-risk populations before significant damage occurs to their insulin producing beta-cell mass. We recently introduced the concept of integrated parallel multi-omics and employed a novel data augmentation approach which identified promising candidate biomarkers from a small cohort of high-risk T1D subjects. We now validate selected biomarkers to generate a potential composite signature of T1D risk.

Methods

Twelve candidate biomarkers, which were identified in the augmented data and selected based on their fold-change relative to healthy controls and cross-reference to proteomics data previously obtained in the expansive TEDDY and DAISY cohorts, were measured in the original samples by ELISA.

Results

All 12 biomarkers had established connections with lipid/lipoprotein metabolism, immune function, inflammation, and diabetes, but only 7 were found to be markedly changed in the high-risk subjects compared to the healthy controls: ApoC1 and PON1 were reduced while CETP, CD36, FGFR1, IGHM, PCSK9, SOD1, and VCAM1 were elevated.

Conclusions

Results further highlight the promise of our data augmentation approach in unmasking important patterns and pathologically significant features in parallel multi-omics datasets obtained from small sample cohorts to facilitate the identification of promising candidate T1D biomarkers for downstream validation. They also support the potential utility of a composite biomarker signature of T1D risk characterized by the changes in the above markers.

Possible heterogeneity of initial pancreatic islet beta-cell autoimmunity heralding type 1 diabetes

The etiology of type 1 diabetes (T1D) foreshadows the pancreatic islet beta-cell autoimmune pathogenesis that heralds the clinical onset of T1D. Standardized and harmonized tests of autoantibodies against insulin (IAA), glutamic acid decarboxylase (GADA), islet antigen-2 (IA-2A), and ZnT8 transporter (ZnT8A) allowed children to be followed from birth until the appearance of a first islet autoantibody. In the Environmental Determinants of Diabetes in the Young (TEDDY) study, a multicenter (Finland, Germany, Sweden, and the United States) observational study, children were identified at birth for the T1D high-risk HLA haploid genotypes DQ2/DQ8, DQ2/DQ2, DQ8/DQ8, and DQ4/DQ8. The TEDDY study was preceded by smaller studies in Finland, Germany, Colorado, Washington, and Sweden. The aims were to follow children at increased genetic risk to identify environmental factors that trigger the first-appearing autoantibody (etiology) and progress to T1D (pathogenesis). The larger TEDDY study found that the incidence rate of the first-appearing autoantibody was split into two patterns. IAA first peaked already during the first year of life and tapered off by 3-4 years of age. GADA first appeared by 2-3 years of age to reach a plateau by about 4 years. Prior to the first-appearing autoantibody, genetic variants were either common or unique to either pattern. A split was also observed in whole blood transcriptomics, metabolomics, dietary factors, and exposures such as gestational life events and early infections associated with prolonged shedding of virus. An innate immune reaction prior to the adaptive response cannot be excluded. Clarifying the mechanisms by which autoimmunity is triggered to either insulin or GAD65 is key to uncovering the etiology of autoimmune T1D.

Glucose metabolic reprogramming in autoimmune diseases

Autoimmune diseases are conditions in which the immune system mistakenly targets and damages healthy tissue in the body. In recent decades, the incidence of autoimmune diseases has increased, resulting in a significant disease burden. The current autoimmune therapies focus on targeting inflammation or inducing immunosuppression rather than addressing the underlying cause of the diseases. The activity of metabolic pathways is elevated in autoimmune diseases, and metabolic changes are increasingly recognized as important pathogenic processes underlying these. Therefore, metabolically targeted therapies may represent an important strategy for treating autoimmune diseases. This review provides a comprehensive overview of the evidence surrounding glucose metabolic reprogramming and its potential applications in drug discovery and development for autoimmune diseases, such as type 1 diabetes, multiple sclerosis, systemic lupus erythematosus, rheumatoid arthritis, and systemic sclerosis.

Metabolomics in diabetes mellitus: clinical insight

INTRODUCTION

Diabetes Mellitus (DM) is a chronic heterogeneous metabolic disorder characterized by hyperglycemia due to the destruction of insulin-producing pancreatic β cells and/or insulin resistance. It is now considered a global epidemic disease associated with serious threats to a patient's life. Understanding the metabolic pathways involved in disease pathogenesis and progression is important and would improve prevention and management strategies. Metabolomics is an emerging field of research that offers valuable insights into the metabolic perturbation associated with metabolic diseases, including DM.

AREA COVERED

Herein, we discussed the metabolomics in type 1 and 2 DM research, including its contribution to understanding disease pathogenesis and identifying potential novel biomarkers clinically useful for disease screening, monitoring, and prognosis. In addition, we highlighted the metabolic changes associated with treatment effects, including insulin and different anti-diabetic medications.

EXPERT OPINION

By analyzing the metabolome, the metabolic disturbances involved in T1DM and T2DM can be explored, enhancing our understanding of the disease progression and potentially leading to novel clinical diagnostic and effective new therapeutic approaches. In addition, identifying specific metabolites would be potential clinical biomarkers for predicting the disease and thus preventing and managing hyperglycemia and its complications.

Issues and challenges in diabetic neuropathy management: A narrative review

Diabetic neuropathy (DN) is a devastating disorder with an increasing prevalence globally. This epidemic can pose a critical burden on individuals and com-munities, subsequently affecting the productivity and economic output of a country. With more people living a sedentary lifestyle, the incidence of DN is escalating worldwide. Many researchers have relentlessly worked on ways to combat this devastating disease. Their efforts have given rise to a number of commercially available therapies that can alleviate the symptoms of DN. Unfortunately, most of these therapies are only partially effective. Worse still, some are associated with unfavorable side effects. This narrative review aims to highlight current issues and challenges in the management of DN, especially from the perspective of molecular mechanisms that lead to its progression, with the hope of providing future direction in the management of DN. To improve the approaches to diabetic management, the suggested resolutions in the literature are also discussed in this review. This review will provide an in-depth understanding of the causative mechanisms of DN, apart from the insights to improve the quality and strategic approaches to DN management.

Serum Metabolomics Reveals a Potential Benefit of Methionine in Type 1 Diabetes Patients with Poor Glycemic Control and High Glycemic Variability

Glycemic variability (GV) in some patients with type 1 diabetes (T1D) remains heterogeneous despite comparable clinical indicators, and whether other factors are involved is yet unknown. Metabolites in the serum indicate a broad effect of GV on cellular metabolism and therefore are more likely to indicate metabolic dysregulation associated with T1D. To compare the metabolomic profiles between high GV (GV-H, coefficient of variation (CV) of glucose ≥ 36%) and low GV (GV-L, CV < 36%) groups and to identify potential GV biomarkers, metabolomics profiling was carried out on serum samples from 17 patients with high GV, 16 matched (for age, sex, body mass index (BMI), diabetes duration, insulin dose, glycated hemoglobin (HbA1c), fasting, and 2 h postprandial C-peptide) patients with low GV (exploratory set), and another 21 (GV-H/GV-L: 11/10) matched patients (validation set). Subsequently, 25 metabolites were significantly enriched in seven Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways between the GV-H and GV-L groups in the exploratory set. Only the differences in spermidine, L-methionine, and trehalose remained significant after validation. The area under the curve of these three metabolites combined in distinguishing GV-H from GV-L was 0.952 and 0.918 in the exploratory and validation sets, respectively. L-methionine was significantly inversely related to HbA1c and glucose CV, while spermidine was significantly positively associated with glucose CV. Differences in trehalose were not as reliable as those in spermidine and L-methionine because of the relatively low amounts of trehalose and the inconsistent fold change sizes in the exploratory and validation sets. Our findings suggest that metabolomic disturbances may impact the GV of T1D. Additional in vitro and in vivo mechanistic studies are required to elucidate the relationship between spermidine and L-methionine levels and GV in T1D patients with different geographical and nutritional backgrounds.

Circulating biomarkers during progression to type 1 diabetes: A systematic review

AIM

Progression to type 1 diabetes (T1D) is defined in stages and clinical disease is preceded by a period of silent autoimmunity. Improved prediction of the risk and rate of progression to T1D is needed to reduce the prevalence of diabetic ketoacidosis at presentation as well as for staging participants for clinical trials. This systematic review evaluates novel circulating biomarkers associated with future progression to T1D.

METHODS

PubMed, Ovid, and EBSCO databases were used to identify a comprehensive list of articles. The eligibility criteria included observational studies that evaluated the usefulness of circulating markers in predicting T1D progression in at-risk subjects <20 years old.

RESULTS

Twenty-six studies were identified, seventeen were cohort studies and ten were case control studies. From the 26 studies, 5 found evidence for protein and lipid dysregulation, 11 identified molecular markers while 12 reported on changes in immune parameters during progression to T1D. An increased risk of T1D progression was associated with the presence of altered gene expression, immune markers including regulatory T cell dysfunction and higher short-lived effector CD8⁺ T cells in progressors.

DISCUSSION

Several circulating biomarkers are dysregulated before T1D diagnosis and may be useful in predicting either the risk or rate of progression to T1D. Further studies are required to validate these biomarkers and assess their predictive accuracy before translation into broader use.

SYSTEMATIC REVIEW REGISTRATION

https://www.crd.york.ac.uk/prospero, identifier (CRD42020166830).

Lipid metabolism in type 1 diabetes mellitus: Pathogenetic and therapeutic implications

Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease with insulin deficiency due to pancreatic β cell destruction. Multiple independent cohort studies revealed specific lipid spectrum alterations prior to islet autoimmunity in T1DM. Except for serving as building blocks for membrane biogenesis, accumulative evidence suggests lipids and their derivatives can also modulate different biological processes in the progression of T1DM, such as inflammation responses, immune attacks, and β cell vulnerability. However, the types of lipids are huge and majority of them have been largely unexplored in T1DM. In this review, based on the lipid classification system, we summarize the clinical evidence on dyslipidemia related to T1DM and elucidate the potential mechanisms by which they participate in regulating inflammation responses, modulating lymphocyte function and influencing β cell susceptibility to apoptosis and dysfunction. This review systematically recapitulates the role and mechanisms of various lipids in T1DM, providing new therapeutic approaches for T1DM from a nutritional perspective.

Hypothesis: Induction of Autoimmunity in Type 1 Diabetes-A Lipid Focus

Several unrelated findings led us to hypothesize that induction of autoimmunity is a consequence of a prior major inflammatory event in individuals with susceptible HLA phenotypes and elevated sensitivity to cytokines and free fatty acids (FFA). We observed provocative enhanced responsiveness of cultured human fibroblasts from individuals with type 1 diabetes (T1D), but not control subjects, to FFA and the inflammatory cytokines TNFα and IL1-β. Major infections increase inflammatory cytokines as well as circulating FFA. Endotoxin-treated animal models of sepsis also exhibit elevated inflammatory cytokines that inhibit FFA oxidation and elevate FFA. The pancreatic β-cell possesses low reactive oxygen species (ROS) scavenging capacity and responds to both elevated FFA and cytokines with increased ROS production, a combination that increases exocytosis and trafficking of secretory vesicles to the plasma membrane. Increased trafficking is accompanied by increased cycling of secretory granule proteins and may be linked with increased surface presentation of granule proteins to the immune system. We propose that this ultimately targets β-cell granular proteins at the cell surface and is consistent with the preponderance of autoantibodies to granule proteins. Our hypothesis encourages testing of potential early therapeutic interventions to prevent progression of β-cell destruction.

Alterations in Biomarkers Related to Glycemia, Lipid Metabolism, and Inflammation up to 20 Years Before Diagnosis of Type 1 Diabetes in Adults: Findings From the AMORIS Cohort

OBJECTIVE

Type 1 diabetes is described to have an acute onset, but autoantibodies can appear several years preceding diagnosis. This suggests a long preclinical phase, which may also include metabolic parameters. Here we assessed whether elevations in glycemic, lipid, and other metabolic biomarkers were associated with future type 1 diabetes risk in adults.

RESEARCH DESIGN AND METHODS

We studied 591,239 individuals from the Swedish AMORIS cohort followed from 1985-1996 to 2012. Through linkage to national patient, diabetes, and prescription registers, we identified incident type 1 diabetes. Using Cox regression models, we estimated hazard ratios for biomarkers at baseline and incident type 1 diabetes. We additionally assessed trajectories of biomarkers during the 25 years before type 1 diabetes diagnosis in a nested case-control design.

RESULTS

We identified 1,122 type 1 diabetes cases during follow-up (average age of patient at diagnosis: 53.3 years). The biomarkers glucose, fructosamine, triglycerides, the ratio of apolipoprotein (apo)B to apoA-I, uric acid, alkaline phosphatase, and BMI were positively associated with type 1 diabetes risk. Higher apoA-I was associated with lower type 1 diabetes incidence. Already 15 years before diagnosis, type 1 diabetes cases had higher mean glucose, fructosamine, triglycerides, and uric acid levels compared with control subjects.

CONCLUSIONS

Alterations in biomarker levels related to glycemia, lipid metabolism, and inflammation are associated with clinically diagnosed type 1 diabetes risk, and these may be elevated many years preceding diagnosis.

Fatty Acid Profiles and Their Association With Autoimmunity, Insulin Sensitivity and β Cell Function in Latent Autoimmune Diabetes in Adults

BACKGROUND

The pathogenesis of the progressive loss of beta cell function latent autoimmune diabetes in adults (LADA) remains still elusive. We aim to study the fatty acid (FA) profile in LADA.

SUBJECTS AND METHODS

Data from 116 patients with diabetes and GADA and 249 diabetes controls without GADA selected by Propensity Score Matching were collected. FA was analyzed with liquid chromatography-tandem mass spectrometry analysis.

RESULTS

Principal factor analysis found component 1 explains 82.6% of total variance contained fatty acids from a mixed of lard oil, seafood, and vegetable diet, followed by diet predominantly from vegetable oil, a diet of high fat diet, and a diet of seafood diet. The FA heatmap looked clearly different among the three groups with more similar type 1 (t1dm) and LADA fatty acid profile. n-3 α-linolenic acid (ALA), n-3 long chain polyunsaturated fatty acid (n-3 LC-PUFA), such as Eicosapentaenoic Acid and Docosapentaenoic Acid, n-3/n-6 ratio and triene/tetraene ratio were higher in patients with type 2 diabetes (t2dm) compared with LADA and t1dm. Saturated FAs were lower in t2dm than t1dm and LADA. Arachidic acid and n-6 LC-PUFAs were lower in t2dm than in t1dm and LADA. The characteristics of FAs in LADA were in between of classical t1dm and t2dm. Patients were classified into 6 clusters by FA clusters. Only cluster 2, 3, 5 contained enough patients to be analyzed. Cluster 5 showed an insulin deficient phenotype containing more than 60% of patients with t1dm and LADA and only 12.8% of t2dm. Cluster 2 and 3 were similar. β cell function and glycemic control was better in cluster 3 homing 25% of t2dm. Cluster 2 held 28% of t1dm and LADA, in this cluster more than 60% of patients was t2dm. n-3 linolenic acid, n-3 LC-PUFAs, some n-6 LC-PUFAs, n-3/n-6 ratio and triene/tetraene ratio were negatively associated with GADA positivity while n-6 Arachidonic Acid was associated positively with GADA. Similar findings were found for insulin sensitivity and beta cell function.

CONCLUSION

PUFA are associated with insulin sensitivity and beta cell function, and like other clinical features, FA profile distributed differently, but could not be used as makers to differentiate LADA from t1dm and t2dm.

ETHICS AND DISSEMINATION

This study has been approved by the Ethical Review Committee of Second Hospital of Dalian Medical University (approval number: 2021-005).

CLINICAL TRIAL REGISTRATION

none.

A Contemporary Insight of Metabolomics Approach for Type 1 Diabetes: Potential for Novel Diagnostic Targets

High-throughput omics has been widely applied in metabolic disease, type 1 diabetes (T1D) was one of the most typical diseases. Effective prevention and early diagnosis are very important because of infancy and persistent characteristics of T1D. The occurrence and development of T1D is a chronic and continuous process, in which the production of autoantibodies (ie serum transformation) occupies the central position. Metabolomics can evaluate the metabolic characteristics of serum before seroconversion, the changes with age and T1D complications. And the addition of natural drug metabolomics is more conducive to the systematic and comprehensive diagnosis and treatment of T1D. This paper reviewed the metabolic changes and main pathogenesis from pre-diagnosis to treatment in T1D. The metabolic spectrum of significant abnormal energy and glucose-related metabolic pathway, down-regulation of lipid metabolism and up-regulation of some antioxidant pathways has appeared before seroconversion, indicating that the body has been in the dual state of disease progression and disease resistance before T1D onset. Some metabolites (such as methionine) are closely related to age, and the types of autoantibodies produced are age-specific. Some metabolites may jointly predict DN with eGFR, and metabolomics can further contribute to the pathogenesis based on the correlation between DN and DR. Many natural drug components have been proved to act on abnormal metabolic pathways of T1D and have a positive impact on some metabolic levels, which is very important for further finding therapeutic targets and developing new drugs with small side effects. Metabolomics can provide auxiliary value for the diagnosis of T1D and provide a new direction to reveal the pathogenesis of T1D and find new therapeutic targets. The development of T1D metabolomics shows that high-throughput research methods are expected to be introduced into clinical practice.

Untargeted metabolomics reveals gender- and age- independent metabolic changes of type 1 diabetes in Chinese children

INTRODUCTION

Type 1 diabetes (T1D) is a chronic condition associated with multiple complications that substantially affect both the quality of life and the life-span of children. Untargeted Metabolomics has provided new insights into disease pathogenesis and risk assessment.

METHODS

In this study, we characterized the serum metabolic profiles of 76 children with T1D and 65 gender- and age- matched healthy controls using gas chromatography coupled with timeof-flight mass spectrometry. In parallel, we comprehensively evaluated the clinical phenome of T1D patients, including routine blood and urine tests, and concentrations of cytokines, hormones, proteins, and trace elements.

RESULTS

A total of 70 differential metabolites covering 11 metabolic pathways associated with T1D were identified, which were mainly carbohydrates, indoles, unsaturated fatty acids, amino acids, and organic acids. Subgroup analysis revealed that the metabolic changes were consistent among pediatric patients at different ages or gender but were closely associated with the duration of the disease.

DISCUSSION

Carbohydrate metabolism, unsaturated fatty acid biosynthesis, and gut microbial metabolism were identified as distinct metabolic features of pediatric T1D. These metabolic changes were also associated with T1D, which may provide important insights into the pathogenesis of the complications associated with diabetes.

Application of Metabolomics in Various Types of Diabetes

Metabolomics is the analysis of numerous small molecules known as metabolites. Over the past few years, with the continuous development in metabolomics, it has been widely used in the detection, diagnosis, and treatment of diabetes and has demonstrated great benefits. At the same time, studies on diabetes and its complications have discovered the metabolic markers that are characteristic of diabetes. However, the pathogenesis of diabetes has yet to be clarified, as well as no complete cure. The mechanism of diabetes has not been completely elucidated, and its eradication treatment is not available. Thus, prevention of the onset of the disease and its treatment have become very important. In this review, we focused on the recent progress in the use of metabolites in diabetes and their complications, as well as understanding the impact of diabetes metabolites.

Insulin-Binding Peptide Probes Provide a Novel Strategy for Pancreatic β-Cell Imaging

Type 1 diabetes develops in childhood and adolescence, with peak incidence in the early teenage years. There is an urgent need for an accurate method to detect insulin-producing β-cells in patients that is not affected by alterations in β-cell function. As part of our research program to design specific probes to measure β-cell mass, we recently developed a novel insulin-binding peptide probe (IBPP) for the detection of β-cells in vivo. Here, we applied our innovative method to show specific labeling of this IBPP to human and mouse fixed β-cells in pancreatic islets. Importantly, we showed staining of human and mouse islets in culture without any negative functional or cell viability impact. Moreover, the IBPP-stained mouse islets after tail vein injection in vivo, albeit with batch differences in staining efficiency. In conclusion, we provide evidence showing that the IBPP can be used for future accurate detection of β-cell mass in a variety of preclinical models of diabetes.

Knockout of the Amino Acid Transporter SLC6A19 and Autoimmune Diabetes Incidence in Female Non-Obese Diabetic (NOD) Mice

High protein feeding has been shown to accelerate the development of type 1 diabetes in female non-obese diabetic (NOD) mice. Here, we investigated whether reducing systemic amino acid availability via knockout of the Slc6a19 gene encoding the system B(0) neutral amino acid transporter AT1 would reduce the incidence or delay the onset of type 1 diabetes in female NOD mice. Slc6a19 gene deficient NOD mice were generated using the CRISPR-Cas9 system which resulted in marked aminoaciduria. The incidence of diabetes by week 30 was 59.5% (22/37) and 69.0% (20/29) in NOD.Slc6a19^+/+ and NOD.Slc6a19^-/- mice, respectively (hazard ratio 0.77, 95% confidence interval 0.41-1.42; Mantel-Cox log rank test: p = 0.37). The median survival time without diabetes was 28 and 25 weeks for NOD.Slc6a19^+/+ and NOD.Slc6a19^-/- mice, respectively (ratio 1.1, 95% confidence interval 0.6-2.0). Histological analysis did not show differences in islet number or the degree of insulitis between wild type and Slc6a19 deficient NOD mice. We conclude that Slc6a19 deficiency does not prevent or delay the development of type 1 diabetes in female NOD mice.

Prediction of Type 1 Diabetes at Birth: Cord Blood Metabolites vs Genetic Risk Score in the Norwegian Mother, Father, and Child Cohort

BACKGROUND AND AIM

Genetic markers are established as predictive of type 1 diabetes, but unknown early life environment is believed to be involved. Umbilical cord blood may reflect perinatal metabolism and exposures. We studied whether selected polar metabolites in cord blood contribute to prediction of type 1 diabetes.

METHODS

Using a targeted UHPLC-QQQ-MS platform, we quantified 27 low-molecular-weight metabolites (including amino acids, small organic acids, and bile acids) in 166 children, who later developed type 1 diabetes, and 177 random control children in the Norwegian Mother, Father, and Child cohort. We analyzed the data using logistic regression (estimating odds ratios per SD [adjusted odds ratio (aOR)]), area under the receiver operating characteristic curve (AUC), and k-means clustering. Metabolites were compared to a genetic risk score based on 51 established non-HLA single-nucleotide polymorphisms, and a 4-category HLA risk group.

RESULTS

The strongest associations for metabolites were aminoadipic acid (aOR = 1.23; 95% CI, 0.97-1.55), indoxyl sulfate (aOR = 1.15; 95% CI, 0.87-1.51), and tryptophan (aOR = 0.84; 95% CI, 0.65-1.10), with other aORs close to 1.0, and none significantly associated with type 1 diabetes. K-means clustering identified 6 clusters, none of which were associated with type 1 diabetes. Cross-validated AUC showed no predictive value of metabolites (AUC 0.49), whereas the non-HLA genetic risk score AUC was 0.56 and the HLA risk group AUC was 0.78.

CONCLUSIONS

In this large study, we found no support of a predictive role of cord blood concentrations of selected bile acids and other small polar metabolites in the development of type 1 diabetes.

Circulating Free Fatty Acid and Phospholipid Signature Predicts Early Rapid Kidney Function Decline in Patients With Type 1 Diabetes

OBJECTIVES

Patients with type 1 diabetes (T1D) exhibit modest lipid abnormalities as measured by traditional metrics. This study aimed to identify lipidomic predictors of rapid decline of kidney function in T1D.

RESEARCH DESIGN AND METHODS

In a case-control study, 817 patients with T1D from three large cohorts were randomly split into training and validation subsets. Case was defined as >3 mL/min/1.73 m² per year decline in estimated glomerular filtration rate (eGFR), while control was defined as <1 mL/min/1.73 m² per year decline over a minimum 4-year follow-up. Lipids were quantified in baseline serum samples using a targeted mass spectrometry lipidomic platform.

RESULTS

At individual lipids, free fatty acid (FFA)20:2 was directly and phosphatidylcholine (PC)16:0/22:6 was inversely and independently associated with rapid eGFR decline. When examined by lipid class, rapid eGFR decline was characterized by higher abundance of unsaturated FFAs, phosphatidylethanolamine (PE)-Ps, and PCs with an unsaturated acyl chain at the sn1 carbon, and by lower abundance of saturated FFAs, longer triacylglycerols, and PCs, PEs, PE-Ps, and PE-Os with an unsaturated acyl chain at the sn1 carbon at eGFR ≥90 mL/min/1.73 m². A multilipid panel consisting of unsaturated FFAs and saturated PE-Ps predicted rapid eGFR decline better than individual lipids (C-statistic, 0.71) and improved the C-statistic of the clinical model from 0.816 to 0.841 (P = 0.039). Observations were confirmed in the validation subset.

CONCLUSIONS

Distinct from previously reported predictors of GFR decline in type 2 diabetes, these findings suggest differential incorporation of FFAs at the sn1 carbon of the phospholipids' glycerol backbone as an independent predictor of rapid GFR decline in T1D.

Phospholipid Levels at Seroconversion Are Associated With Resolution of Persistent Islet Autoimmunity: The Diabetes Autoimmunity Study in the Young

Reversion of islet autoimmunity (IA) may point to mechanisms that prevent IA progression. We followed 199 individuals who developed IA during the Diabetes Autoimmunity Study in the Young. Untargeted metabolomics was performed in serum samples following IA. Cox proportional hazards models were used to test whether the metabolites (2,487) predicted IA reversion: two or more consecutive visits negative for all autoantibodies. We conducted a principal components analysis (PCA) of the top metabolites; |hazard ratio (HR) >1.25| and nominal P < 0.01. Phosphatidylcholine (16:0_18:1(9Z)) was the strongest individual metabolite (HR per 1 SD 2.16, false discovery rate (FDR)-adjusted P = 0.0037). Enrichment analysis identified four clusters (FDR P < 0.10) characterized by an overabundance of sphingomyelin (d40:0), phosphatidylcholine (16:0_18:1(9Z)), phosphatidylcholine (30:0), and l-decanoylcarnitine. Overall, 63 metabolites met the criteria for inclusion in the PCA. PC1 (HR 1.4, P < 0.0001), PC2 (HR 0.85, P = 0.0185), and PC4 (HR 1.28, P = 0.0103) were associated with IA reversion. Given the potential influence of diet on the metabolome, we investigated whether nutrients were correlated with PCs. We identified 20 nutrients that were correlated with the PCs (P < 0.05). Total sugar intake was the top nutrient. Overall, we identified an association between phosphatidylcholine, sphingomyelin, and carnitine levels and reversion of IA.

Serum fatty acids and risk of developing islet autoimmunity: A nested case-control study within the TRIGR birth cohort

BACKGROUND

Circulating fatty acids have been linked to development of type 1 diabetes.

OBJECTIVES

To study the prospective associations of serum fatty acids with the risk of islet autoimmunity in high-risk children.

METHODS

A nested case-control selection was carried out within the TRIGR cohort, which included infants with HLA (DQB1 or DQA1)-conferred disease susceptibility and a first-degree relative with type 1 diabetes, born between 2002 and 2007 in 15 countries and followed-up until 2017. The present study included 244 case children positive for at least two islet autoantibodies (ICA, IAA, GADA, and IA-2A) and two control children were matched for country and age. Proportions of 26 serum fatty acids at cord blood and at 6, 12, and 18 months of age were assessed using gas-chromatography.

RESULTS

The average proportions of the following fatty acids were associated with an increased risk of islet autoimmunity, adjusted for sex, HLA risk, and maternal type 1 diabetes: pentadecanoic acid (15:0) (OR 3.41: 95% CI 1.70, 6.85), heptadecanoic acid (iso 17:0) (2.64: 1.62, 4.28) and (anteiso 17:0) (2.27: 1.39, 3.70), stearic acid (18:0) (23.8: 2.32, 244.6), and conjugated linoleic acid (18:2n-7) (2.60: 1.47, 4.59). Breastfeeding and not having maternal type 1 diabetes were positively associated with levels of the above-mentioned fatty acids. N-3 fatty acids were not consistently associated with islet autoimmunity.

CONCLUSIONS

We found direct associations of pentadecanoic acid, heptadecanoic acid, stearic acid, and conjugated linoleic acid with the risk of islet autoimmunity. Further studies are needed to understand the complex role of fatty acids in the development of type 1 diabetes.

Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes

BACKGROUND

Biomarkers are crucial for detecting early type-1 diabetes (T1D) and preventing significant β-cell loss before the onset of clinical symptoms. Here, we present proof-of-concept studies to demonstrate the potential for identifying integrated biomarker signature(s) of T1D using parallel multi-omics.

METHODS

Blood from human subjects at high risk for T1D (and healthy controls; n = 4 + 4) was subjected to parallel unlabeled proteomics, metabolomics, lipidomics, and transcriptomics. The integrated dataset was analyzed using Ingenuity Pathway Analysis (IPA) software for disturbances in the at-risk subjects compared to controls.

RESULTS

The final quadra-omics dataset contained 2292 proteins, 328 miRNAs, 75 metabolites, and 41 lipids that were detected in all samples without exception. Disease/function enrichment analyses consistently indicated increased activation, proliferation, and migration of CD4 T-lymphocytes and macrophages. Integrated molecular network predictions highlighted central involvement and activation of NF-κB, TGF-β, VEGF, arachidonic acid, and arginase, and inhibition of miRNA Let-7a-5p. IPA-predicted candidate biomarkers were used to construct a putative integrated signature containing several miRNAs and metabolite/lipid features in the at-risk subjects.

CONCLUSIONS

Preliminary parallel quadra-omics provided a comprehensive picture of disturbances in high-risk T1D subjects and highlighted the potential for identifying associated integrated biomarker signatures. With further development and validation in larger cohorts, parallel multi-omics could ultimately facilitate the classification of T1D progressors from non-progressors.

Children's erythrocyte fatty acids are associated with the risk of islet autoimmunity

Our aim was to investigate the associations between erythrocyte fatty acids and the risk of islet autoimmunity in children. The Environmental Determinants of Diabetes in the Young Study (TEDDY) is a longitudinal cohort study of children at high genetic risk for type 1 diabetes (n = 8676) born between 2004 and 2010 in the U.S., Finland, Sweden, and Germany. A nested case-control design comprised 398 cases with islet autoimmunity and 1178 sero-negative controls matched for clinical site, family history, and gender. Fatty acids composition was measured in erythrocytes collected at the age of 3, 6, and 12 months and then annually up to 6 years of age. Conditional logistic regression models were adjusted for HLA risk genotype, ancestry, and weight z-score. Higher eicosapentaenoic and docosapentaenoic acid (n - 3 polyunsaturated fatty acids) levels during infancy and conjugated linoleic acid after infancy were associated with a lower risk of islet autoimmunity. Furthermore, higher levels of some even-chain saturated (SFA) and monounsaturated fatty acids (MUFA) were associated with increased risk. Fatty acid status in early life may signal the risk for islet autoimmunity, especially n - 3 fatty acids may be protective, while increased levels of some SFAs and MUFAs may precede islet autoimmunity.

Plasma Metabolome and Circulating Vitamins Stratified Onset Age of an Initial Islet Autoantibody and Progression to Type 1 Diabetes: The TEDDY Study

Children's plasma metabolome, especially lipidome, reflects gene regulation and dietary exposures, heralding the development of islet autoantibodies (IA) and type 1 diabetes (T1D). The Environmental Determinants of Diabetes in the Young (TEDDY) study enrolled 8,676 newborns by screening of HLA-DR-DQ genotypes at six clinical centers in four countries, profiled metabolome, and measured concentrations of ascorbic acid, 25-hydroxyvitamin D [25(OH)D], and erythrocyte membrane fatty acids following birth until IA seroconversion under a nested case-control design. We grouped children having an initial autoantibody only against insulin (IAA-first) or GAD (GADA-first) by unsupervised clustering of temporal lipidome, identifying a subgroup of children having early onset of each initial autoantibody, i.e., IAA-first by 12 months and GADA-first by 21 months, consistent with population-wide early seroconversion age. Differential analysis showed that infants having reduced plasma ascorbic acid and cholesterol experienced IAA-first earlier, while early onset of GADA-first was preceded by reduced sphingomyelins at infancy. Plasma 25(OH)D prior to either autoantibody was lower in T1D progressors compared with nonprogressors, with simultaneous lower diglycerides, lysophosphatidylcholines, triglycerides, and alanine before GADA-first. Plasma ascorbic acid and 25(OH)D at infancy were lower in HLA-DR3/DR4 children among IA case subjects but not in matched control subjects, implying gene expression dysregulation of circulating vitamins as latent signals for IA or T1D progression.

Metabolomics-related nutrient patterns at seroconversion and risk of progression to type 1 diabetes

OBJECTIVE

Our aim was to elucidate the role of diet in type 1 diabetes (T1D) by examining combinations of nutrient intake in the progression from islet autoimmunity (IA) to T1D.

METHODS

We measured 2457 metabolites and dietary intake at the time of seroconversion in 132 IA-positive children in the prospective Diabetes Autoimmunity Study in the Young. IA was defined as the first of two consecutive visits positive for at least one autoantibody (insulin, GAD, IA-2, or ZnT8). By December 2018, 40 children progressed to T1D. Intakes of 38 nutrients were estimated from semiquantitative food frequency questionnaires. We tested the association of each metabolite with progression to T1D using multivariable Cox regression. Nutrient patterns that best explained variation in candidate metabolites were identified using reduced rank regression (RRR), and their association with progression to T1D was tested using Cox regression adjusting for age at seroconversion and high-risk HLA genotype.

RESULTS

In stepwise selection, 22 nutrients significantly predicted at least two of the 13 most significant metabolites associated with progression to T1D, and were included in RRR. A nutrient pattern corresponding to intake lower in linoleic acid, niacin, and riboflavin, and higher in total sugars, explained 18% of metabolite variability. Children scoring higher on this metabolite-related nutrient pattern at seroconversion had increased risk for progressing to T1D (HR = 3.17, 95%CI = 1.42-7.05).

CONCLUSIONS

Combinations of nutrient intake reflecting candidate metabolites are associated with increased risk of T1D, and may help focus dietary prevention efforts.

Newborn Screening Samples for Diabetes Research: An Underused Resource

Inborn errors of metabolism and diabetes share common derangements in analytes of metabolic networks that are tested for in newborn screening, usually performed 48-72 h after birth. There is limited research examining the metabolic imprint of diabetes on newborn screening results. This paper aims to demonstrate the links between diabetes, biochemical genetics and newborn screening in investigating disease pathophysiology in diabetes, provide possible reasons for the lack of research in diabetes in newborn screening and offer recommendations on potential research areas. We performed a systematic search of the available literature from 1 April 1998 to 31 December 2018 involving newborn screening and diabetes using OVID, MEDLINE, Cochrane and the PROSPERO register, utilizing a modified extraction tool adapted from Cochrane. Eight studies were included after screening 1312 records. Five studies reanalyzed dried blood spots (DBS) on filter paper cards, and three studies utilized pre-existing results. The results of these studies and how they relate to cord blood studies, the use of cord blood versus newborn screening dried blood spots as a sample and considerations on newborn screening and diabetes research is further discussed. The timing of sampling of newborn screening allows insight into neonatal physiology in a catabolic state with minimal maternal and placental influence. This, combined with the wide coverage of newborn screening worldwide, may aid in our understanding of the origins of diabetes.

Presumption of innocence for beta cells: why are they vulnerable autoimmune targets in type 1 diabetes?

It is increasingly appreciated that the pathogenic mechanisms of type 1 diabetes involve both the autoimmune aggressors and their beta cell targets, which engage in a conflicting dialogue within and possibly outside the pancreas. Indeed, autoimmune CD8⁺ T cells, which are the final mediators of beta cell destruction, circulate at similar frequencies in type 1 diabetic and healthy individuals. Hence a universal state of 'benign' islet autoimmunity exists, and we hypothesise that its progression to type 1 diabetes may at least partially rely on a higher vulnerability of beta cells, which play a key, active role in disease development and/or amplification. We posit that this autoimmune vulnerability is rooted in some features of beta cell biology: the stress imposed by the high rate of production of insulin and other granule proteins, their dense vascularisation and the secretion of their products directly into the bloodstream. Gene variants that may predispose individuals to this vulnerability have been identified, e.g. MDA5, TYK2, PTPN2. They interact with environmental cues, such as viral infections, that may drive this genetic potential towards exacerbated local inflammation and progressive beta cell loss. On top of this, beta cells set up compensatory responses, such as the unfolded protein response, that become deleterious in the long term. The relative contribution of immune and beta cell drivers may vary and phenotypic subtypes (endotypes) are likely to exist. This dual view argues for the use of circulating biomarkers of both autoimmunity and beta cell stress for disease staging, and for the implementation of both immunomodulatory and beta cell-protective therapeutic strategies. Graphical abstract.

Lipidomic profiling of plasma free fatty acids in type-1 diabetes highlights specific changes in lipid metabolism

Type-1 diabetes mellitus (T1DM) is associated with metabolic changes leading to alterations in glucose and lipid handling. While T1DM-associated effects on many major plasma lipids have been characterised, such effects on plasma free fatty acids (FFA) have not been fully examined. Using gas chromatography–mass spectrometry, we measured the plasma concentrations of FFA species in individuals with T1DM (n = 44) and age/sex-matched healthy controls (n = 44). Relationships between FFA species and various parameters were evaluated. Plasma concentrations of myristate (14:0), palmitoleate (16:1), palmitate (16:0), linoleate (18:2), oleate (18:1c9), cis-vaccenate (18:1c11), eicosapentaenoate (20:5), arachidonate (20:4) and docosahexanoate (22:6) were reduced in the T1DM group (p < 0.0001 for all, except p = 0.0020 for eicosapentaenoate and p = 0.0068 for arachidonate); α-linolenate (18:3) and dihomo-γ-linolenate (20:3) concentrations were unchanged. The saturated/unsaturated FFA ratio, n-3/n-6 ratio, de novo lipogenesis index (palmitate (main lipogenesis product)/linoleate (only found in diet)) and elongase index (oleate/palmitoleate) were increased in the T1DM group (p = 0.0166, p = 0.0089, p < 0.0001 and p = 0.0008 respectively). The stearoyl-CoA desaturase 1 (SCD1) index 1 (palmitoleate/palmitate) and index 2 (oleate/stearate) were reduced in T1DM (p < 0.0001 for both). The delta-(5)-desaturase (D5D) index (arachidonate/dihomo-γ-linolenate) was unchanged. Age and sex had no effect on plasma FFA concentrations in T1DM, while SCD1 index 1 was positively correlated (p = 0.098) and elongase index negatively correlated with age (p = 0.0363). HbA1c was negatively correlated with all plasma FFA concentrations measured except α-linolenate and dihomo-γ-linolenate. Correlations were observed between plasma FFA concentrations and cholesterol and HDL concentrations, but not LDL concentration or diabetes duration. Collectively, these results aid our understanding of T1DM and its effects on lipid metabolism.

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Plasma concentrations of major FFA species are lower in T1DM compared to controls.

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Plasma FFA concentrations negatively correlates with HbA1c in T1DM.

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The SCD1 index is reduced in T1DM.

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Lipogenesis, elongase, n3/n6, saturated/unsaturated indices are increased in T1DM.

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Collectively, the data highlight specific changes in lipid metabolism in T1DM

Metabolomic Profiling in Neuromyelitis Optica Spectrum Disorder Biomarker Discovery

There is no specific test for diagnosing neuromyelitis optica spectrum disorder (NMOSD), a disabling autoimmune disease of the central nervous system. Instead, diagnosis relies on ruling out other related disorders with overlapping clinical symptoms. An urgency for NMOSD biomarker discovery is underscored by adverse responses to treatment following misdiagnosis and poor prognosis following the delayed onset of treatment. Pathogenic autoantibiotics that target the water channel aquaporin-4 (AQP4) and myelin oligodendrocyte glycoprotein (MOG) contribute to NMOSD pathology. The importance of early diagnosis between AQP4-Ab⁺ NMOSD, MOG-Ab⁺ NMOSD, AQP4-Ab⁻ MOG-Ab⁻ NMOSD, and related disorders cannot be overemphasized. Here, we provide a comprehensive data collection and analysis of the currently known metabolomic perturbations and related proteomic outcomes of NMOSD. We highlight short chain fatty acids, lipoproteins, amino acids, and lactate as candidate diagnostic biomarkers. Although the application of metabolomic profiling to individual NMOSD patient care shows promise, more research is needed.

GMSimpute: a generalized two-step Lasso approach to impute missing values in label-free mass spectrum analysis

Motivation

Missingness in label-free mass spectrometry is inherent to the technology. A computational approach to recover missing values in metabolomics and proteomics datasets is important. Most existing methods are designed under a particular assumption, either missing at random or under the detection limit. If the missing pattern deviates from the assumption, it may lead to biased results. Hence, we investigate the missing patterns in free mass spectrometry data and develop an omnibus approach GMSimpute, to allow effective imputation accommodating different missing patterns.

Results

Three proteomics datasets and one metabolomics dataset indicate missing values could be a mixture of abundance-dependent and abundance-independent missingness. We assess the performance of GMSimpute using simulated data (with a wide range of 80 missing patterns) and metabolomics data from the Cancer Genome Atlas breast cancer and clear cell renal cell carcinoma studies. Using Pearson correlation and normalized root mean square errors between the true and imputed abundance, we compare its performance to K-nearest neighbors’ type approaches, Random Forest, GSimp, a model-based method implemented in DanteR and minimum values. The results indicate GMSimpute provides higher accuracy in imputation and exhibits stable performance across different missing patterns. In addition, GMSimpute is able to identify the features in downstream differential expression analysis with high accuracy when applied to the Cancer Genome Atlas datasets.

Availability and implementation

GMSimpute is on CRAN: https://cran.r-project.org/web/packages/GMSimpute/index.html.

Supplementary information

Supplementary data are available at Bioinformatics online.

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.

Predictive Modeling of Type 1 Diabetes Stages Using Disparate Data Sources

This study aims to model genetic, immunologic, metabolomics, and proteomic biomarkers for development of islet autoimmunity (IA) and progression to type 1 diabetes in a prospective high-risk cohort. We studied 67 children: 42 who developed IA (20 of 42 progressed to diabetes) and 25 control subjects matched for sex and age. Biomarkers were assessed at four time points: earliest available sample, just prior to IA, just after IA, and just prior to diabetes onset. Predictors of IA and progression to diabetes were identified across disparate sources using an integrative machine learning algorithm and optimization-based feature selection. Our integrative approach was predictive of IA (area under the receiver operating characteristic curve [AUC] 0.91) and progression to diabetes (AUC 0.92) based on standard cross-validation (CV). Among the strongest predictors of IA were change in serum ascorbate, 3-methyl-oxobutyrate, and the PTPN22 (rs2476601) polymorphism. Serum glucose, ADP fibrinogen, and mannose were among the strongest predictors of progression to diabetes. This proof-of-principle analysis is the first study to integrate large, diverse biomarker data sets into a limited number of features, highlighting differences in pathways leading to IA from those predicting progression to diabetes. Integrated models, if validated in independent populations, could provide novel clues concerning the pathways leading to IA and type 1 diabetes.

Circulating metabolites in progression to islet autoimmunity and type 1 diabetes

AIMS/HYPOTHESIS

Metabolic dysregulation may precede the onset of type 1 diabetes. However, these metabolic disturbances and their specific role in disease initiation remain poorly understood. In this study, we examined whether children who progress to type 1 diabetes have a circulatory polar metabolite profile distinct from that of children who later progress to islet autoimmunity but not type 1 diabetes and a matched control group.

METHODS

We analysed polar metabolites from 415 longitudinal plasma samples in a prospective cohort of children in three study groups: those who progressed to type 1 diabetes; those who seroconverted to one islet autoantibody but not to type 1 diabetes; and an antibody-negative control group. Metabolites were measured using two-dimensional GC high-speed time of flight MS.

RESULTS

In early infancy, progression to type 1 diabetes was associated with downregulated amino acids, sugar derivatives and fatty acids, including catabolites of microbial origin, compared with the control group. Methionine remained persistently upregulated in those progressing to type 1 diabetes compared with the control group and those who seroconverted to one islet autoantibody. The appearance of islet autoantibodies was associated with decreased glutamic and aspartic acids.

CONCLUSIONS/INTERPRETATION

Our findings suggest that children who progress to type 1 diabetes have a unique metabolic profile, which is, however, altered with the appearance of islet autoantibodies. Our findings may assist with early prediction of the disease.

Metabolite-related dietary patterns and the development of islet autoimmunity

The role of diet in type 1 diabetes development is poorly understood. Metabolites, which reflect dietary response, may help elucidate this role. We explored metabolomics and lipidomics differences between 352 cases of islet autoimmunity (IA) and controls in the TEDDY (The Environmental Determinants of Diabetes in the Young) study. We created dietary patterns reflecting pre-IA metabolite differences between groups and examined their association with IA. Secondary outcomes included IA cases positive for multiple autoantibodies (mAb+). The association of 853 plasma metabolites with outcomes was tested at seroconversion to IA, just prior to seroconversion, and during infancy. Key compounds in enriched metabolite sets were used to create dietary patterns reflecting metabolite composition, which were then tested for association with outcomes in the nested case-control subset and the full TEDDY cohort. Unsaturated phosphatidylcholines, sphingomyelins, phosphatidylethanolamines, glucosylceramides, and phospholipid ethers in infancy were inversely associated with mAb+ risk, while dicarboxylic acids were associated with an increased risk. An infancy dietary pattern representing higher levels of unsaturated phosphatidylcholines and phospholipid ethers, and lower sphingomyelins was protective for mAb+ in the nested case-control study only. Characterization of this high-risk infant metabolomics profile may help shape the future of early diagnosis or prevention efforts.

Impaired Amino Acid and TCA Metabolism and Cardiovascular Autonomic Neuropathy Progression in Type 1 Diabetes

While diabetes is characterized by hyperglycemia, nutrient metabolic pathways like amino acid and tricarboxylic acid (TCA) cycle are also profoundly perturbed. As glycemic control alone does not prevent complications, we hypothesized that these metabolic disruptions are responsible for the development and progression of diabetic cardiovascular autonomic neuropathy (CAN). We performed standardized cardiovascular autonomic reflex tests and targeted fasting plasma metabolomic analysis of amino acids and TCA cycle intermediates in subjects with type 1 diabetes and healthy control subjects followed for 3 years. Forty-seven participants with type 1 diabetes (60% female and mean ± SD age 35 ± 13 years, diabetes duration 13 ± 7 years, and HbA_1c 7.9 ± 1.2%) had lower fumarate levels and higher threonine, serine, proline, asparagine, aspartic acid, phenylalanine, tyrosine, and histidine levels compared with 10 age-matched healthy control subjects. Higher baseline fumarate levels and lower baseline amino acid levels-asparagine and glutamine-correlate with CAN (lower baseline SD of normal R-R interval [SDNN]). Baseline glutamine and ornithine levels also associated with the progression of CAN (lower SDNN at 3 years) and change in SDNN, respectively, after adjustment for baseline HbA_1c, blood glucose, BMI, cholesterol, urine microalbumin-to- creatinine ratio, estimated glomerular filtration rate, and years of diabetes. Therefore, significant changes in the anaplerotic flux into the TCA cycle could be the critical defect underlying CAN progression.

Metabolomics of Type 1 and Type 2 Diabetes

Type 1 and type 2 diabetes mellitus (DM) are chronic diseases that affect nearly 425 million people worldwide, leading to poor health outcomes and high health care costs. High-throughput metabolomics screening can provide vital insight into the pathophysiological pathways of DM and help in managing its effects. The primary aim of this study was to contribute to the understanding and management of DM by providing reliable evidence of the relationships between metabolites and type 1 diabetes (T1D) and metabolites and type 2 diabetes (T2D). Information for the study was obtained from the PubMed, MEDLINE, and EMBASE databases, and leads to additional articles that were obtained from the reference lists of the studies examined. The results from the selected studies were used to assess the relationships between diabetes (T1D and/or T2D) and metabolite markers—such as glutamine, glycine, and aromatic amino acids—in patients. Seventy studies were selected from the three databases and from the reference lists in the records retrieved. All studies explored associations between various metabolites and T1D or T2D. This review identified several plasma metabolites associated with T2D prediabetes and/or T1D and/or T2D in humans. The evidence shows that metabolites such as glucose, fructose, amino acids, and lipids are typically altered in individuals with T1D and T2D. These metabolites exhibit significant predictive associations with T2D prediabetes, T1D, and/or T2D. The current review suggests that changes in plasma metabolites can be identified by metabolomic techniques and used to identify and analyze T1D and T2D biomarkers. The results of the metabolomic studies can be used to help create effective interventions for managing these diseases.

Plasma lipid species at type 1 diabetes onset predict residual beta-cell function after 6 months

INTRODUCTION

The identification of metabolomic dysregulation appears promising for the prediction of type 1 diabetes and may also reveal metabolic pathways leading to beta-cell destruction. Recent studies indicate that regulation of multiple phospholipids precede the presence of autoantigens in the development of type 1 diabetes.

OBJECTIVES

We hypothesize that lipid biomarkers in plasma from children with recent onset type 1 diabetes will reflect their remaining beta-cell function and predict future changes in beta-cell function.

METHODS

We performed targeted lipidomic profiling by electrospray ionization tandem mass spectrometry to acquire comparative measures of 354 lipid species covering 25 lipid classes and subclasses in plasma samples from 123 patients < 17 years of age followed prospectively at 1, 3, 6 and 12 months after diagnosis. Lipidomic profiles were analysed using liner regression to investigate the relationship between plasma lipids and meal stimulated C-peptide levels at each time point. P-values were corrected for multiple comparisons by the method of Benjamini and Hochberg.

RESULTS

Linear regression analysis showed that the relative levels of cholesteryl ester, diacylglycerol and triacylglycerol at 1 month were associated to the change in c-peptide levels from 1 to 6 months (corrected p-values of 4.06E-03, 1.72E-02 and 1.72E02, respectively). Medium chain saturated and monounsaturated fatty acids were the major constituents of the di- and triacylglycerol species suggesting a link with increased lipogenesis.

CONCLUSION

These observations support the hypothesis of lipid disturbances as explanatory factors for residual beta-cell function in children with new onset type 1 diabetes.

Serum biomarkers for diagnosis and prediction of type 1 diabetes

Type 1 diabetes (T1D) culminates in the autoimmune destruction of the pancreatic βcells, leading to insufficient production of insulin and development of hyperglycemia. Serum biomarkers including a combination of glucose, glycated molecules, C-peptide, and autoantibodies have been well established for the diagnosis of T1D. However, these molecules often mark a late stage of the disease when ∼90% of the pancreatic insulin-producing β-cells have already been lost. With the prevalence of T1D increasing worldwide and because of the physical and psychological burden induced by this disease, there is a great need for prognostic biomarkers to predict T1D development or progression. This would allow us to identify individuals at high risk for early prevention and intervention. Therefore, considerable efforts have been dedicated to the understanding of disease etiology and the discovery of novel biomarkers in the last few decades. The advent of high-throughput and sensitive "-omics" technologies for the study of proteins, nucleic acids, and metabolites have allowed large scale profiling of protein expression and gene changes in T1D patients relative to disease-free controls. In this review, we briefly discuss the classical diagnostic biomarkers of T1D but mainly focus on the novel biomarkers that are identified as markers of β-cell destruction and screened with the use of state-of-the-art "-omics" technologies.

Dynamics of Plasma Lipidome in Progression to Islet Autoimmunity and Type 1 Diabetes - Type 1 Diabetes Prediction and Prevention Study (DIPP)

Type 1 diabetes (T1D) is one of the most prevalent autoimmune diseases among children in Western countries. Earlier metabolomics studies suggest that T1D is preceded by dysregulation of lipid metabolism. Here we used a lipidomics approach to analyze molecular lipids in a prospective series of 428 plasma samples from 40 children who progressed to T1D (PT1D), 40 children who developed at least a single islet autoantibody but did not progress to T1D during the follow-up (P1Ab) and 40 matched controls (CTR). Sphingomyelins were found to be persistently downregulated in PT1D when compared to the P1Ab and CTR groups. Triacylglycerols and phosphatidylcholines were mainly downregulated in PT1D as compared to P1Ab at the age of 3 months. Our study suggests that distinct lipidomic signatures characterize children who progressed to islet autoimmunity or overt T1D, which may be helpful in the identification of at-risk children before the initiation of autoimmunity.

Metabolomics reveals new metabolic perturbations in children with type 1 diabetes

OBJECTIVE

Using an untargeted metabolomics approach we investigated the metabolome of children with type 1 diabetes (T1D) in comparison with healthy peers and explored the contribution of HbA1c and clinical features to the observed difference.

RESEARCH DESIGN AND METHODS

We enrolled children with T1D aged 6-15 years, attending the pediatric diabetes clinic of University of Padova (Italy). Healthy controls were enrolled on voluntary basis and matched for age, sex, pubertal status, body mass index (BMI). We performed a liquid chromatography and mass spectrometry analysis (LC-MS) on fasting urinary samples of the 2 groups.

RESULTS

A total of 56 patients with T1D aged (11.4 ± 2.2) years, and 30 healthy controls (10.7 ± 2.8) years were enrolled. We identified 59 urinary metabolites having a higher level in children with T1D, mainly represented by gluco- and mineralcorticoids, phenylalanine and tryptophan catabolites (kynurenine), small peptides, glycerophospholipids, fatty acids, and gut bacterial products. We did not find any association between HbA1c, pubertal status, disease duration, and metabolome profile within the case group.

CONCLUSIONS

T1D profoundly disrupts the metabolome of pediatric patients. The excess of cortisol and aldosterone may contribute to the development of macrovascular complications in adulthood, while the increase of tryptophan derivates may have a role in neuronal damage associated to hyperglycemia. Determinants of such findings, other than HbA1c, should be explored.

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.

Untargeted metabolomic analysis in naturally occurring canine diabetes mellitus identifies similarities to human Type 1 Diabetes

While predominant as a disease entity, knowledge voids exist regarding the pathogenesis of canine diabetes. To test the hypothesis that diabetic dogs have similar metabolomic perturbations to humans with type 1 diabetes (T1D), we analyzed serum metabolomic profiles of breed- and body weight-matched, diabetic (n = 6) and healthy (n = 6) dogs by liquid chromatography-mass spectrometry (LC-MS) profiling. We report distinct clustering of diabetic and control groups based on heat map analysis of known and unknown metabolites. Random forest classification identified 5/6 dogs per group correctly with overall out of bag error rate = 16.7%. Diabetic dogs demonstrated significant upregulation of glycolysis/gluconeogenesis intermediates (e.g., glucose/fructose, C6H12O6, keto-hexose, deoxy-hexose, (P < 0.01)), with significant downregulation of tryptophan metabolism metabolites (e.g., picolinic acid, indoxyl sulfate, anthranilate, (P < 0.01)). Multiple amino acids (AA), AA metabolites, and bile acids were also significantly lower in diabetic versus healthy dogs (P < 0.05) with the exception of the branched chain AA valine, which was elevated in diabetic animals (P < 0.05). Metabolomic profiles in diabetic versus healthy dogs shared similarities with those reported in human T1D (e.g., alterations in glycolysis/gluconeogensis metabolites, bile acids, and elevated branched chain AA). Further studies are warranted to evaluate the utility of canine diabetes to provide novel mechanistic insights to the human disorder.

Late-onset islet autoimmunity in childhood: the Diabetes Autoimmunity Study in the Young (DAISY)

AIMS/HYPOTHESIS

We sought to assess the frequency, determinants and prognosis for future diabetes in individuals with islet autoimmunity and whether these factors differ depending on the age of onset of islet autoimmunity.

METHODS

A prospective cohort (n = 2547) of children from the general population who had a high-risk HLA genotype and children who had a first-degree relative with type 1 diabetes were followed for up to 21 years. Those with the persistent presence of one or more islet autoantibodies were categorised as early-onset (<8 years of age, n = 143, median 3.3 years) or late-onset (≥8 years of age, n = 64, median 11.1 years), and were followed for a median of 7.4 and 4.7 years, respectively. Progression to diabetes was evaluated by Kaplan-Meier analysis with logrank test. Factors associated with progression to diabetes were analysed using the parametric accelerated failure time model.

RESULTS

Children with late-onset islet autoimmunity were more likely to be Hispanic or African-American than non-Hispanic white (p = 0.004), and less likely to be siblings of individuals with type 1 diabetes (p = 0.04). The frequencies of the HLA-DR3/4 genotype and non-HLA gene variants associated with type 1 diabetes did not differ between the two groups. However, age and HLA-DR3/4 were important predictors of rate of progression to both the presence of additional autoantibodies and type 1 diabetes. Late-onset islet autoimmunity was more likely to present with a single islet autoantibody (p = 0.01) and revert to an antibody-negative state (p = 0.01). Progression to diabetes was significantly slower in children with late-onset islet autoimmunity (p < 0.001).

CONCLUSIONS/INTERPRETATION

A late onset of islet autoimmunity is more common in African-American and Hispanic individuals. About half of those with late-onset islet autoimmunity progress to show multiple islet autoantibodies and develop diabetes in adolescence or early adulthood. Further investigation of environmental determinants of late-onset autoimmunity may lead to an understanding of and ability to prevent adolescent and adult-onset type 1 diabetes.

Longitudinal plasma metabolic profiles, infant feeding, and islet autoimmunity in the MIDIA study

AIMS

The aim of this study was to investigate the longitudinal plasma metabolic profiles in healthy infants and the potential association with breastfeeding duration and islet autoantibodies predictive of type 1 diabetes.

METHOD

Up to four longitudinal plasma samples from age 3 months from case children who developed islet autoimmunity (n = 29) and autoantibody-negative control children (n = 29) with the HLA DR4-DQ8/DR3-DQ2 genotype were analyzed using two-dimensional gas chromatography coupled to a time-of-flight mass spectrometer for detection of small polar metabolites.

RESULTS

Plasma metabolite levels were found to depend strongly on age, with fold changes varying up to 50% from age 3 to 24 months (p < 0.001 after correction for multiple testing). Tyrosine levels tended to be lower in case children, but this was not significant after correction for multiple testing. Ornithine levels were lower in case children compared with the controls at the time of seroconversion, but the difference was not statistically significant after correcting for multiple testing. Breastfeeding for at least 3 months as compared with shorter duration was associated with higher plasma levels of isoleucine, and lower levels of methionine and 3,4-dihydroxybutyric acid at 3 months of age.

CONCLUSIONS

Plasma levels of several small, polar metabolites changed with age during early childhood, independent of later islet autoimmunity status and sex. Breastfeeding was associated with higher levels of branched-chain amino acids, and lower levels of methionine and 3,4-dihydroxybutyric acid.

Magnetic resonance imaging evaluation of the distal oblique bundle in the distal interosseous membrane of the forearm

Background

Some cadaveric studies have reported the role of the distal oblique bundle (DOB) in the distal radioulnar joint stability. We aimed to determine whether the presence of the DOB can be identified and its thickness can be measured in magnetic resonance imaging (MRI) examinations.

Methods

We retrospectively reviewed 468 wrist and forearm MRIs. Inclusion criteria were wrist or forearm MRIs taken in patients older than 18 years of age, and exclusion criteria were patients with acute wrist or forearm fractures, infections, or malignant tumors. We selected 80 MRIs that provided adequate coverage of the distal interosseous membrane (DIOM). The thickness of the DIOM in the T2-weighted transverse plane was measured on the picture archiving and communicating system. We used a model-based clustering method to determine whether some individuals have thicker DIOMs that can be considered as the DOB.

Results

The thickness of the DIOM demonstrated a bimodal distribution, indicating the presence of patients with a thick DIOM (DOB). The model-based clustering method indicated that the optimal cutoff point was 1.0 mm. Twenty-six individuals (32.5%) had thick DIOMs with a mean thickness of 1.4 mm (standard deviation, 0.2 mm), while 54 individuals (67.5%) had thin DIOMs with a mean thickness of 0.6 mm (standard deviation, 0.2 mm).

Conclusion

Our study demonstrates that it is possible to identify the DOB and measure its thickness using MRI. Future in-vivo studies of the DOB using MRI in patients with distal radioulnar joint pathologies may reveal its role in the distal radioulnar joint stability.

Metabolic profiling of type 1 diabetes mellitus in children and adolescents: a case-control study

BACKGROUND

Type 1 diabetes mellitus (T1DM) is one of the most common pediatric diseases and its incidence is rising in many countries. Recently, it has been shown that metabolites other than glucose play an important role in insulin deficiency and the development of diabetes. The aim of our study was to look for discriminating variation in the concentrations of small-molecule metabolites in the plasma of T1DM children as compared to non-diabetic matched controls using proton nuclear magnetic resonance (¹H-NMR)-based metabolomics.

METHODS

A cross-sectional study was set-up to examine the metabolic profile in fasting plasma samples from seven children with poorly controlled T1DM and seven non-diabetic controls aged 8-18 years, and matched for gender, age and BMI-SDS. The obtained plasma ¹H-NMR spectra were rationally divided into 110 integration regions, representing the metabolic phenotype. These integration regions reflect the relative metabolite concentrations and were used as statistical variables to construct (train) a classification model in discriminating between T1DM patients and controls.

RESULTS

The total amount of variation explained by the model between the groups is 81.0% [R²Y(cum)] and within the groups is 75.8% [R²X(cum)]. The predictive ability of the model [Q²(cum)] obtained by cross-validation is 50.7%, indicating that the discrimination between the groups on the basis of the metabolic phenotype is valid. Besides the expected higher concentration of glucose, the relative concentrations of lipids (triglycerides, phospholipids and cholinated phospholipids) are clearly lower in the plasma of T1DM patients as compared to controls. Also the concentrations of the amino acids serine, tryptophan and cysteine are slightly decreased.

CONCLUSIONS

The present study demonstrates that metabolic profiling of plasma by ¹H-NMR spectroscopy allows to discriminate between T1DM patients and controls. The metabolites that significantly differ between both groups might point to disturbances in biochemical pathways including (1) choline deficiency, (2) increased gluconeogenesis, and (3) glomerular hyperfiltration. Although the sample size of this study is still somewhat limited and a validation should be performed, the proof of principle looks promising and justifies a deeper investigation of the diagnostic possibilities of ¹H-NMR metabolomics in follow-up studies. Trial registration NCT03014908. Registered 06/01/2017. Retrospectively registered.

The Folate Cycle As a Cause of Natural Killer Cell Dysfunction and Viral Etiology in Type 1 Diabetes

The folate pathway is critical to proper cellular function and metabolism. It is responsible for multiple functions, including energy (ATP) production, methylation reactions for DNA and protein synthesis and the production of immunomodulatory molecules, inosine and adenosine. These play an important role in immune signaling and cytotoxicity. Herein, we hypothesize that defects in the folate pathway in genetically susceptible individuals could lead to immune dysfunction, permissive environments for chronic cyclical latent/lytic viral infection, and, ultimately, the development of unchecked autoimmune responses to infected tissue, in this case islet beta cells. In the context of type 1 diabetes (T1D), there has been a recent increase in newly diagnosed cases of T1D in the past 20 years that has exceeded previous epidemiological predictions with yet unidentified factor(s). This speaks to a potential environmental trigger that adversely affects immune responses. Most research into the immune dysfunction of T1D has focused on downstream adaptive responses of T and B cells neglecting the role of the upstream innate players such as natural killer (NK) cells. Constantly, surveilling the blood and tissues for pathogens, NK cells remove threats through direct cytotoxic responses and recruitment of adaptive responses using cytokines, such as IL-1β and IFN-γ. One long-standing hypothesis suggests viral infection as a potential trigger for the autoimmune response in T1D. Recent data suggest multiple viruses as potential causal agents. Intertwined with this is an observed reduced NK cell enumeration, cytotoxicity, and cytokine signaling in T1D patients. Many of the viruses implicated in T1D are chronic latent/lysogenic infections with demonstrated capacity to reduce NK cell response and number through mechanisms that resemble those of pregnancy tolerance. Defects in the folate pathway in T1D patients could result in decreased immune response to viral infection or viral reactivation. Dampened NK responses to infections result in improper signaling, improper antigen presentation, and amplified CD8⁺ lymphocyte proliferation and cytotoxicity, a hallmark of beta cell infiltrates in patients with T1D onset. This would suggest a critical role for NK cells in T1D development linked to viral infection and the importance of the folate pathway in maintaining proper NK response.

Metabolomic Biomarkers in the Progression to Type 1 Diabetes

Metabolomics is the snapshot of all detectable metabolites and lipids in biological materials and has potential in reflecting genetic and environmental factors contributing to the development of complex diseases, such as type 1 diabetes. The progression to seroconversion to development of type 1 diabetes has been studied using this technique, although in relatively small cohorts and at limited time points. Overall, three observations have been consistently reported; phospholipids at birth are lower in children developing type 1 diabetes early in childhood, methionine levels are lower in children at seroconversion, and triglycerides are increased at seroconversion and associated to microbiome diversity, indicating an association between the metabolome and microbiome in type 1 diabetes progression.

Elevated systemic glutamic acid level in the non-obese diabetic mouse is Idd linked and induces beta cell apoptosis

Although type 1 diabetes (T1D) is a T-cell-mediated disease in the effector stage, the mechanism behind the initial beta cell assault is less understood. Metabolomic differences, including elevated levels of glutamic acid, have been observed in patients with T1D before disease onset, as well as in pre-diabetic non-obese diabetic (NOD) mice. Increased levels of glutamic acid damage both neurons and beta cells, implying that this could contribute to the initial events of T1D pathogenesis. We investigated the underlying genetic factors and consequences of the increased levels of glutamic acid in NOD mice. Serum glutamic acid levels from a (NOD×B6)F₂ cohort (n = 182) were measured. By genome-wide and Idd region targeted microsatellite mapping, genetic association was detected for six regions including Idd2, Idd4 and Idd22. In silico analysis of potential enzymes and transporters located in and around the mapped regions that are involved in glutamic acid metabolism consisted of alanine aminotransferase, glutamic-oxaloacetic transaminase, aldehyde dehydrogenase 18 family, alutamyl-prolyl-tRNA synthetase, glutamic acid transporters GLAST and EAAC1. Increased EAAC1 protein expression was observed in lysates from livers of NOD mice compared with B6 mice. Functional consequence of the elevated glutamic acid level in NOD mice was tested by culturing NOD. Rag2^-/- Langerhans' islets with glutamic acid. Induction of apoptosis of the islets was detected upon glutamic acid challenge using TUNEL assay. Our results support the notion that a dysregulated metabolome could contribute to the initiation of T1D. We suggest that targeting of the increased glutamic acid in pre-diabetic patients could be used as a potential therapy.

Early signs of disease in type 1 diabetes

As a severe chronic disease with long-term complications, type 1 diabetes (T1D) is a burden to the patients and their families as well as a challenge to the health care system. T1D is a heterogeneous disease with a variety of etiologies and a wide range in the rate of progression to the disease. In order to prevent and treat T1D it would be important to identify measures that could be used to predict and monitor disease progression, as well as to further understand the molecular mechanisms involved. During the past 20 yr since its initiation, the Finnish Diabetes Prediction and Prevention Project (DIPP) has collected longitudinal biological samples from children with a human leukocyte antigen gene-conferred risk of T1D. This large sample collection has provided detailed sample series that enable studies to map the progression from health to disease, as well as the healthy maturation of risk-matched children. The DIPP samples have been used in a large body of research to elucidate the factors involved in the development of T1D. Interestingly, results from recent studies exploiting omics platforms have revealed that signs of the disease process can be detected very early on, even prior to appearance of the first T1D-associated antibodies, which are currently considered the earliest indications of the emerging disease. Identification and validation of multi-modal molecular markers will we hope provide a means to subgroup the heterogeneous group of T1D patients and enable prediction, diagnosis, and monitoring of T1D. Discovery of such markers is important in the design and testing of prevention and therapies for T1D.