Circulating metabolomic and lipidomic changes in subjects with new-onset type 1 diabetes after optimization of glycemic control

Construction of Multi-Modal Transcriptome-Small Molecule Interaction Networks from High-Throughput Measurements to Study Human Complex Traits

Small molecules (SMs) are integral to biological processes, influencing metabolism, homeostasis, and regulatory networks. Despite their importance, a significant knowledge gap exists regarding their downstream effects on biological pathways and gene expression, largely due to differences in scale, variability, and noise between untargeted metabolomics and sequencing-based technologies. To address these challenges, we developed a multi-omics framework comprising a machine learning-based protocol for data processing, a semi-supervised network inference approach, and network-guided analysis of complex traits. The ML protocol harmonized metabolomic, lipidomic, and transcriptomic data through batch correction, principal component analysis, and regression-based adjustments, enabling unbiased and effective integration. Building on this, we proposed a semi-supervised method to construct transcriptome-SM interaction networks (TSI-Nets) by selectively integrating SM profiles into gene-level networks using a meta-analytic approach that accounts for scale differences and missing data across omics layers. Benchmarking against three conventional unsupervised methods demonstrated the superiority of our approach in generating diverse, biologically relevant, and robust networks. While single-omics analyses identified 18 significant genes and 3 significant SMs associated with insulin sensitivity (IS), network-guided analysis revealed novel connections between these markers. The top-ranked module highlighted a cross-talk between fiber-degrading gut microbiota and immune regulatory pathways, inferred by the interaction of the protective SM, N-acetylglycine (NAG), with immune genes (FCER1A, HDC, MS4A2, and CPA3), linked to improved IS and reduced obesity and inflammation. Together, this framework offers a robust and scalable solution for multi-modal network inference and analysis, advancing SM pathway discovery and their implications for human health. Leveraging data from a population of thousands of individuals with extended longevity, the inferred TSI-Nets demonstrate generalizability across diverse conditions and complex traits. These networks are publicly available as a resource for the research community.

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.

Lipidome characterisation and sex-specific differences in type 1 and type 2 diabetes mellitus

BACKGROUND

In this study, we evaluated the lipidome alterations caused by type 1 diabetes (T1D) and type 2 diabetes (T2D), by determining lipids significantly associated with diabetes overall and in both sexes, and lipids associated with the glycaemic state.

METHODS

An untargeted lipidomic analysis was performed to measure the lipid profiles of 360 subjects (91 T1D, 91 T2D, 74 with prediabetes and 104 controls (CT)) without cardiovascular and/or chronic kidney disease. Ultra-high performance liquid chromatography-electrospray ionization mass spectrometry (UHPLC-ESI-MS) was conducted in two ion modes (positive and negative). We used multiple linear regression models to (1) assess the association between each lipid feature and each condition, (2) determine sex-specific differences related to diabetes, and (3) identify lipids associated with the glycaemic state by considering the prediabetes stage. The models were adjusted by sex, age, hypertension, dyslipidaemia, body mass index, glucose, smoking, systolic blood pressure, triglycerides, HDL cholesterol, LDL cholesterol, alternate Mediterranean diet score (aMED) and estimated glomerular filtration rate (eGFR); diabetes duration and glycated haemoglobin (HbA1c) were also included in the comparison between T1D and T2D.

RESULTS

A total of 54 unique lipid subspecies from 15 unique lipid classes were annotated. Lysophosphatidylcholines (LPC) and ceramides (Cer) showed opposite effects in subjects with T1D and subjects with T2D, LPCs being mainly up-regulated in T1D and down-regulated in T2D, and Cer being up-regulated in T2D and down-regulated in T1D. Also, Phosphatidylcholines were clearly down-regulated in subjects with T1D. Regarding sex-specific differences, ceramides and phosphatidylcholines exhibited important diabetes-associated differences due to sex. Concerning the glycaemic state, we found a gradual increase of a panel of 1-deoxyceramides from normoglycemia to prediabetes to T2D.

CONCLUSIONS

Our findings revealed an extensive disruption of lipid metabolism in both T1D and T2D. Additionally, we found sex-specific lipidome changes associated with diabetes, and lipids associated with the glycaemic state that can be linked to previously described molecular mechanisms in diabetes.

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.