Unveiling biomarkers via plasma metabolome profiling for diabetic macrovascular and microvascular complications

Abstract

Background: Metabolic dysregulation plays a crucial role in the development of diabetic vascular complications. Current models for diabetic vascular complications predominantly rely on three conventional parameter classes: demographic characteristics, clinical measures, and standard laboratory indices. In contrast, the potential prognostic value of the plasma metabolome remains substantially under characterized in this context. This study aims to systemically reframe the value of circulating metabolites, providing new insights into both assessment and pathophysiology of diabetic complications.

Methods: This study included 333,870 participants from the UK Biobank (n = 115,078) and FinnGen Biobank (n = 218,792). The initial analysis utilizing longitudinal data from 7,711 patients with diabetes was used to screen 249 plasma metabolites associated with diabetic vascular complications. These metabolites were carefully quantified using nuclear magnetic resonance (NMR) to profile the metabolites of these participants. A total of 1,457 and 1,635 people were found to have developed macrovascular (including heart failure, stroke and coronary heart disease [CHD]) and microvascular complications (including diabetic neuropathy [DN], kidney disease and retinopathy) at follow-ups, respectively. A Least Absolute Shrinkage and Selection Operator-Cox (LASSO-Cox) regression was conducted to define the potential biomarkers, adjusting for conventional factors including age, sex, race, smoking status, diet intake, Townsend deprivation index, systolic and diastolic blood pressure, body mass index, plasma triglycerides, low-density lipoprotein (LDL) cholesterol, plasma creatinine and estimated glomerular filtration rate. Subsequently, a multivariate Cox proportional hazards regression model was used to estimate the hazard ratios (HRs). Finally, a bidirectional two-sample Mendelian randomization (MR) analysis was employed to evaluate the relationships between the selected metabolomics and diabetic complications to analyze causal associations.

Results: Over a 13.06 ± 3.59 years of follow-up, 15 out of 249 plasma metabolites demonstrated significant associations with incident macrovascular complications in LASSO-Cox regression, while 33 metabolites were linked to microvascular complications after 12.77 ± 3.90 years of follow-up (all P < 0.05). In the multivariate Cox proportional hazards regression, 6 metabolites including creatinine (HR = 1.32, 95% confidence interval [CI] 1.17-1.50, P < 0.001), albumin (HR = 0.87, 95% CI 0.81-0.94, P < 0.001), tyrosine (HR = 0.91, 95% CI 0.85-0.96, P = 0.001), glutamine (HR = 1.08, 95% CI 1.01-1.15, P = 0.020), lactate (HR = 1.07, 95% CI 1.01-1.14, P = 0.023), and the ratio of phospholipids to total lipids in small LDL (HR = 1.10, 95% CI 1.01-1.19, P = 0.023) were correlated with macrovascular complications, while 8 metabolites including glucose (HR = 1.25, 95% CI 1.18-1.33, P < 0.001), tyrosine (HR = 0.86, 95% CI 0.80-0.92, P < 0.001), concentration of very large high-density lipoprotein particles (HR = 0.78, 95% CI 0.68-0.90, P = 0.001), valine (HR = 1.21, 95% CI 1.08-1.36, P = 0.001), free cholesterol to total lipids in very small very low-density lipoprotein (VLDL, HR = 1.28, 95% CI 1.10-1.49, P = 0.001), alanine (HR = 1.08, 95% CI 1.01-1.15, P = 0.022), albumin (HR = 0.92, 95% CI 0.86-0.99, P = 0.027), and isoleucine (HR = 0.89, 95% CI 0.80-1.00, P = 0.041) were associated with microvascular complications. MR analysis suggested that genetic predisposition to several screened metabolites was linked to diabetic complications. For CHD, the ratio of phospholipids to total lipids in small LDL was associated with increased risk (odds ratio [OR] = 1.96, 95% CI 1.33-2.88, P = 0.015). As for reverse MR, DN was relevant to decreased level of serum ratio of docosahexaenoic acid to total fatty acids (OR = 0.97, 95% CI 0.95-0.99, P = 0.019), increased level of the ratio of triglycerides to total lipids in very large VLDL (OR = 1.03, 95% CI 1.01-1.05, P = 0.019), and pyruvate (OR = 1.03, 95% CI 1.01-1.05, P = 0.046).

Conclusions: These findings may serve as potential biomarkers for predicting the development of vascular complications in patients with diabetes, thereby improving clinical management strategies for affected patients.

Trial registration: Not applicable.

Keywords: Diabetes complications; Macrovascular complications; Metabolomics; Microvascular complications.

Fig. 1

The overall workflow of the study. The diagram represents the inclusion criteria and analytical procedures of the study. ROC=receiver operating characteristics curve; NRI=net reclassification index; GWAS=genome-wide association studies; MR=Mendelian randomization; LASSO=least absolute shrinkage and selection operator; MR-PRESSO=Mendelian randomization pleiotropy residual sum and outlier; IDI=integrated discrimination index.

Fig. 2

Heatmap showing the correlations between selected metabolites and macrovascular/microvascular complications. Colors represent the mean level of corresponding metabolites after natural logarithmic transformation(ln[x + 1]) and scaled by Z transformation. To simplify complex terminology, we standardized data labels by using abbreviations. Lipid size categories were abbreviated as follows: "very small" to "XS", "small" to "S", "medium" to "M", "large" to "L", "very large" to "XL", and "extremely large" to "XXL". For lipid types, "free cholesterol" to "FC","cholesterol" to "C", "cholesteryl esters" to "CE" , "triglycerides" to "TG", "total lipids" to "TL","high-density lipoprotein" to "HDL", "low-density lipoprotein" to "LDL", "very low-density lipoprotein" to "VLDL" and "phospholipids" to "PLs". For fatty acids, "fatty acids" to "FA", "docosahexaenoic acid" to “DHA".

Fig. 3

Venn diagram showing the LASSO-Cox selected metabolites in different diabetic vascular complications. DKD = diabetic kidney disease; DN = diabetic neuropathy; DR = diabetic retinopathy; CHD = coronary heart disease; HF = heart failure.

Fig. 4

Hazard ratios of significant metabolites on the incidence of diabetic complications in Cox proportional hazards regression analyses. DKD = diabetic kidney disease; DN = diabetic neuropathy; DR = diabetic retinopathy; CHD = coronary heart disease; HF = heart failure. To simplify complex terminology, we standardized data labels by using abbreviations. Lipid size categories were abbreviated as follows: "very small" to "XS", "small" to "S", "medium" to "M", "large" to "L", "very large" to "XL", and "extremely large" to "XXL". For lipid types, "free cholesterol" to "FC", "cholesterol" to "C", "cholesteryl esters" to "CE", "triglycerides" to "TG", "total lipids" to "TL", "high-density lipoprotein" to "HDL", "low-density lipoprotein" to "LDL", "very low-density lipoprotein" to "VLDL", and "phospholipids" to "PLs".For fatty acids, "fatty acids" to "FA", "monounsaturated fatty acids" to "MUFA", "docosahexaenoic acid" to "DHA" and "polyunsaturated fatty acids" to"PUFA".

Fig. 5

The conventional and merged Cox proportional hazards regression models predicting the incidence of diabetic complications. Conventional models were developed by using conventional risk factors: age, sex, smoking status, race, diet, blood pressure, body mass index, blood lipids (combined plasma triglycerides and LDL cholesterol), plasma creatinine, eGFR, and Townsend deprivation index. The merged models incorporated both conventional risk factors and selected metabolomics. AUC = area under the curve; DKD = diabetic kidney disease; DN = diabetic neuropathy; DR = diabetic retinopathy; CHD = coronary heart disease; HF = heart failure.