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

Exploring new frontiers in type 1 diabetes through advanced mass-spectrometry-based molecular measurements

Type 1 diabetes (T1D) is a devastating autoimmune disease for which advanced mass spectrometry (MS) methods are increasingly used to identify new biomarkers and better understand underlying mechanisms. For example, integration of MS analysis and machine learning has identified multimolecular biomarker panels. In mechanistic studies, MS has contributed to the discovery of neoepitopes, and pathways involved in disease development and identifying therapeutic targets. However, challenges remain in understanding the role of tissue microenvironments, spatial heterogeneity, and environmental factors in disease pathogenesis. Recent advancements in MS, such as ultra-fast ion-mobility separations, and single-cell and spatial omics, can play a central role in addressing these challenges. Here, we review recent advancements in MS-based molecular measurements and their role in understanding T1D.

Lipid profile alterations and biomarker identification in type 1 diabetes mellitus patients under glycemic control

BACKGROUND

Type 1 diabetes mellitus (T1DM) is well-known to trigger a disruption of lipid metabolism. This study aimed to compare lipid profile changes in T1DM patients after achieving glucose control and explore the underlying mechanisms. In addition, we seek to identify novel lipid biomarkers associated with T1DM under conditions of glycemic control.

METHODS

A total of 27 adults with T1DM (age: 34.3 ± 11.2 yrs) who had maintained glucose control for over a year, and 24 healthy controls (age: 35.1 + 5.56 yrs) were recruited. Clinical characteristics of all participants were analyzed and plasma samples were collected for untargeted lipidomic analysis using mass spectrometry.

RESULTS

We identified 594 lipid species from 13 major classes. Differential analysis of plasma lipid profiles revealed a general decline in lipid levels in T1DM patients with controlled glycemic levels, including a notable decrease in triglycerides (TAGs) and diglycerides (DAGs). Moreover, these T1DM patients exhibited lower levels of six phosphatidylcholines (PCs) and three phosphatidylethanolamines (PEs). Random forest analysis determined DAG(14:0/20:0) and PC(18:0/20:3) to be the most prominent plasma markers of T1DM under glycemic control (AUC = 0.966).

CONCLUSIONS

The levels of all metabolites from the 13 lipid classes were changed in T1DM patients under glycemic control, with TAGs, DAGs, PCs, PEs, and FFAs demonstrating the most significant decrease. This research identified DAG(14:0/20:0) and PC(18:0/20:3) as effective plasma biomarkers in T1DM patients with controled glycemic levels.

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.

Integrating Omics into Functional Biomarkers of Type 1 Diabetes

Biomarkers are critical to the staging and diagnosis of type 1 diabetes (T1D). Functional biomarkers offer insights into T1D immunopathogenesis and are often revealed using "omics" approaches that integrate multiple measures to identify involved pathways and functions. Application of the omics biomarker discovery may enable personalized medicine approaches to circumvent the more recently appreciated heterogeneity of T1D progression and treatment. Use of omics to define functional biomarkers is still in its early years, yet findings to date emphasize the role of cytokine signaling and adaptive immunity in biomarkers of progression and response to therapy. Here, we share examples of the use of omics to define functional biomarkers focusing on two signatures, T-cell exhaustion and T-cell help, which have been associated with outcomes in both the natural history and treatment contexts.

Insulitis in human type 1 diabetes: lessons from an enigmatic lesion

Type 1 diabetes is caused by a deficiency of insulin secretion which has been considered traditionally as the outcome of a precipitous decline in the viability of β-cells in the islets of Langerhans, brought about by autoimmune-mediated attack. Consistent with this, various classes of lymphocyte, as well as cells of the innate immune system have been found in association with islets during disease progression. However, analysis of human pancreas from subjects with type 1 diabetes has revealed that insulitis is often less intense than in equivalent animal models of the disease and can affect many fewer islets than expected, at disease onset. This is especially true in subjects developing type 1 diabetes in, or beyond, their teenage years. Such studies imply that both the phenotype and the number of immune cells present within insulitic lesions can vary among individuals in an age-dependent manner. Additionally, the influent lymphocytes are often mainly arrayed peripherally around islets rather than gaining direct access to the endocrine cell core. Thus, insulitis remains an enigmatic phenomenon in human pancreas and this review seeks to explore the current understanding of its likely role in the progression of type 1 diabetes.

Association of RPS26 gene polymorphism with different types of diabetes in Chinese individuals

AIMS/INTRODUCTION

Different types of diabetes show distinct genetic characteristics, but the specific genetic susceptibility factors remain unclear. Our study aimed to explore the associations between the ribosomal protein S26 (RPS26) gene rs1131017 polymorphisms and susceptibility to type 1 diabetes mellitus, latent autoimmune diabetes in adults (LADA) and type 2 diabetes mellitus in the Chinese Han population, and their correlations with clinical features.

MATERIALS AND METHODS

Genotyping of the rs1131017 variant was carried out for 1,006 type 1 diabetes mellitus patients, 210 LADA patients, 642 type 2 diabetes mellitus patients and 2,099 control individuals.

RESULTS

We found that the rs1131017 C allele was a risk locus for both type 1 diabetes mellitus and LADA (odds ratio [OR] 1.50, 95% confidence interval [CI] 1.33-1.69, P < 0.001; OR 1.31, 95% CI 1.04-1.64, P = 0.021, respectively). Nevertheless, this association was not found for type 2 diabetes mellitus. Carrying the C allele genotype was associated with a lower postprandial C-peptide for type 1 diabetes mellitus (OR 1.41, 95% CI 1.11-1.80, P = 0.006) and lower fasting C-peptide for LADA (OR 1.55, 95% CI 1.01-2.38, P = 0.047). Interestingly, a lower GC frequency was noted for LADA than for type 1 diabetes mellitus, regardless of classification based on age at diagnosis, C-peptide or glutamic acid decarboxylase antibody positivity.

CONCLUSIONS

The RPS26 polymorphism was associated with susceptibility and clinical characteristics of type 1 diabetes mellitus and LADA in the Chinese population, but was not related to type 2 diabetes mellitus. Thus, it might serve as a novel biomarker for particular types of diabetes.