Serological Phenotyping Analysis Uncovers a Unique Metabolomic Pattern Associated With Early Onset of Type 2 Diabetes Mellitus

Metabolomic network reveals novel biomarkers for type 2 diabetes mellitus in the UK Biobank study

AIMS

To identify hub metabolic biomarkers that constructively shape the type 2 diabetes mellitus (T2DM) risk network.

MATERIALS AND METHODS

We analysed data from 98 831 UK Biobank participants, confirming T2DM diagnoses via medical records and International Classification of Diseases codes. Totally 168 circulating metabolites were quantified by nuclear magnetic resonance at baseline. Metabolome-wide association studies with Cox proportional hazards models were performed to identify statistically significant metabolites. Network analysis was applied to compute topological attributes (degree, betweenness, closeness and eigencentrality) and to detect small-world features (high clustering, short path lengths). Identified metabolites were used with XGBoost models to assess risk prediction performance.

RESULTS

Over a median 12-year follow-up, 114 metabolites were significantly associated with T2DM risk and clustered into three distinct small-world modules. Total triglycerides and large high-density lipoprotein (HDL) cholesterol emerged as the pivotal biomarkers in the 'risk' and 'protective' modules, respectively, as evidenced by their high eigencentrality. Moreover, total branched-chain amino acids (BCAAs) exhibited small-world network characteristics exclusively in pre-T2DM individuals, suggesting them as a potent early indicators. GlycA demonstrated high closeness centrality in females, implying a female-specific risk biomarker.

CONCLUSIONS

By constructing a metabolic network that captures the complex interrelationships among circulating metabolites, our study identified total triglycerides and large HDL cholesterol as central hubs in the T2DM risk metabolome network. BCAA and GlycA emerged as alarm indicators for pre-T2DM individuals and females, respectively. Network analysis not only elucidates the topological functional roles of biomarkers but also addresses the limitations of false positives and collinearity in single-metabolite studies, offering insights for metabolic pathway research and precision interventions.

Clinical metabolomics in type 2 diabetes mellitus: from pathogenesis to biomarkers

As a multidimensional metabolic disorder, the disability and death rate of type 2 diabetes mellitus (T2DM) has increased over time. T2DM covers a wide range of pathological manifestations ranging from hyperglycemia to multi-organ failure, and it has the potential to evolve into acute complications, including ketosis and chronic complications such as peripheral neuropathy, retinopathy, and nephropathy. T2DM mainly occurs in microvascular and large vessels and thus it is restricted for the clinician to diagnose and prescribe. However, the pathological mechanism and clinical diagnosis are inadequate. High-throughput metabolomics, characterized by non-invasive diagnostic techniques to identify potential biomarkers and distinct stages of T2DM, has been increasingly recognized as a vigorous tool with latent capacity for clinical translation. The pathological stratification of T2DM can significantly reduce disability and mortality rates. By tracing the metabolome and associated pathways from impaired fasting blood glucose or impaired glucose tolerance to severe organ failure, the chief contributions of large, independent population-based cohorts are summarized herein. These results facilitate understanding the pathophysiology and mechanism and supports research in accurate diagnosis, risk prediction, curative effect, distinct stages, and prognosis judgment of T2DM.

Metabolomic approaches to dissect dysregulated metabolism in the progression of pre-diabetes to T2DM

Many individuals with pre-diabetes eventually develop diabetes. Therefore, profiling of prediabetic metabolic disorders may be an effective targeted preventive measure. We aimed to elucidate the metabolic mechanism of progression of pre-diabetes to type 2 diabetes mellitus (T2DM) from a metabolic perspective. Four sets of plasma samples (20 subjects per group) collected according to fasting blood glucose (FBG) concentration were subjected to metabolomic analysis. An integrative approach of metabolome and WGCNA was employed to explore candidate metabolites. Compared with the healthy group (FBG < 5.6 mmol L-1), 113 metabolites were differentially expressed in the early stage of pre-diabetes (5.6 mmol L-1 ⩽ FBG < 6.1 mmol L-1), 237 in the late stage of pre-diabetes (6.1 mmol L-1 ⩽ FBG < 7.0 mmol L-1), and 245 in the T2DM group (FBG ⩾ 7.0 mmol L-1). A total of 27 differentially expressed metabolites (DEMs) were shared in all comparisons. Among them, L-norleucine was downregulated, whereas ethionamide, oxidized glutathione, 5-methylcytosine, and alpha-D-glucopyranoside beta-D-fructofuranosyl were increased with the rising levels of FBG. Surprisingly, 15 (11 lyso-phosphatidylcholines, L-norleucine, oxidized glutathione, arachidonic acid, and 5-oxoproline) of the 27 DEMs were ferroptosis-associated metabolites. WGCNA clustered all metabolites into 8 modules and the pathway enrichment analysis of DEMs showed a significant annotation to the insulin resistance-related pathway. Integrated analysis of DEMs, ROC and WGCNA modules determined 12 potential biomarkers for pre-diabetes and T2DM, including L-norleucine, 8 of which were L-arginine or its metabolites. L-Norleucine and L-arginine could serve as biomarkers for pre-diabetes. The inventory of metabolites provided by our plasma metabolome offers insights into T2DM physiology metabolism.

Essential Role of Multi-Omics Approaches in the Study of Retinal Vascular Diseases

Retinal vascular disease is a highly prevalent vision-threatening ocular disease in the global population; however, its exact mechanism remains unclear. The expansion of omics technologies has revolutionized a new medical research methodology that combines multiple omics data derived from the same patients to generate multi-dimensional and multi-evidence-supported holistic inferences, providing unprecedented opportunities to elucidate the information flow of complex multi-factorial diseases. In this review, we summarize the applications of multi-omics technology to further elucidate the pathogenesis and complex molecular mechanisms underlying retinal vascular diseases. Moreover, we proposed multi-omics-based biomarker and therapeutic strategy discovery methodologies to optimize clinical and basic medicinal research approaches to retinal vascular diseases. Finally, the opportunities, current challenges, and future prospects of multi-omics analyses in retinal vascular disease studies are discussed in detail.

Non-coding RNAs in diabetes mellitus and diabetic cardiovascular disease

More than 10% of the world's population already suffers from varying degrees of diabetes mellitus (DM), but there is still no cure for the disease. Cardiovascular disease (CVD) is one of the most common and dangerous of the many health complications that can be brought on by DM, and has become the leading cause of death in people with diabetes. While research on DM and associated CVD is advancing, the specific mechanisms of their development are still unclear. Given the threat of DM and CVD to humans, the search for new predictive markers and therapeutic ideas is imminent. Non-coding RNAs (ncRNAs) have been a popular subject of research in recent years. Although they do not encode proteins, they play an important role in living organisms, and they can cause disease when their expression is abnormal. Numerous studies have observed aberrant ncRNAs in patients with DM complications, suggesting that they may play an important role in the development of DM and CVD and could potentially act as biomarkers for diagnosis. There is additional evidence that treatment with existing drugs for DM, such as metformin, alters ncRNA expression levels, suggesting that regulation of ncRNA expression may be a key mechanism in future DM treatment. In this review, we assess the role of ncRNAs in the development of DM and CVD, as well as the evidence for ncRNAs as potential therapeutic targets, and make use of bioinformatics to analyze differential ncRNAs with potential functions in DM.