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. 2025 Apr 2;24(1):152.
doi: 10.1186/s12933-025-02701-z.

Lipidomic analysis reveals metabolism alteration associated with subclinical carotid atherosclerosis in type 2 diabetes

Maria Barranco-Altirriba  1   2   3   4 , Joana Rossell  1   5   6 , Núria Alonso  5   7   8 , Ralf J M Weber  9   10   11 , Emilio Ortega  12   13 , Gavin R Lloyd  9   10 , Marta Hernandez  14 , Oscar Yanes  5   15 , Jordi Capellades  5   16 , Catherine Winder  9   10   17 , Alexandra Junza  5   18 , Mireia Falguera  19 , Josep Franch-Nadal  5   20 , Warwick B Dunn  9   10   17 , Alexandre Perera-Lluna  2   3   4 , Esmeralda Castelblanco  21   22 , Didac Mauricio  23   24   25   26
Affiliations

Lipidomic analysis reveals metabolism alteration associated with subclinical carotid atherosclerosis in type 2 diabetes

Maria Barranco-Altirriba et al. Cardiovasc Diabetol. .

Abstract

Background: Disruption of lipid metabolism contributes to increased cardiovascular risk in diabetes.

Methods: We evaluated the associations between serum lipidomic profile and subclinical carotid atherosclerosis (SCA) in type 1 (T1D) and type 2 (T2D) diabetes, and in subjects without diabetes (controls) in a cross-sectional study. All subjects underwent a lipidomic analysis using ultra-high performance liquid chromatography-electrospray ionization tandem mass spectrometry, carotid ultrasound (mode B) to assess SCA, and clinical assessment. Multiple linear regression models were used to assess the association between features and the presence and burden of SCA in subjects with T1D, T2D, and controls separately. Additionally, multiple linear regression models with interaction terms were employed to determine features significantly associated with SCA within risk groups, including smoking habit, hypertension, dyslipidaemia, antiplatelet use and sex. Depending on the population under study, different confounding factors were considered and adjusted for, including sample origin, sex, age, hypertension, dyslipidaemia, body mass index, waist circumference, glycated haemoglobin, glucose levels, smoking habit, diabetes duration, antiplatelet use, and alanine aminotransferase levels.

Results: A total of 513 subjects (151 T1D, 155 T2D, and 207 non-diabetic control) were included, in whom the percentage with SCA was 48.3%, 49.7%, and 46.9%, respectively. A total of 27 unique lipid species were associated with SCA in subjects with T2D, in former/current smokers with T2D, and in individuals with T2D without dyslipidaemia. Phosphatidylcholines and diacylglycerols were the main SCA-associated lipidic classes. Ten different species of phosphatidylcholines were up-regulated, while 4 phosphatidylcholines containing polyunsaturated fatty acids were down-regulated. One diacylglycerol was down-regulated, while the other 3 were positively associated with SCA in individuals with T2D without dyslipidaemia. We discovered several features significantly associated with SCA in individuals with T1D, but only one sterol could be partially annotated.

Conclusions: We revealed a significant disruption of lipid metabolism associated with SCA in subjects with T2D, and a larger SCA-associated disruption in former/current smokers with T2D and individuals with T2D who do not undergo lipid-lowering treatment.

Keywords: Lipidomic profile; Smoking habit; Subclinical carotid atherosclerosis; Type 1 diabetes; Type 2 diabetes.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This study was approved by the local ethics committee of the University Hospital Germans Trias i Pujol (PI-15-147), following the principles of the Declaration of Helsinki. All participants provided written informed consent. Consent for publication: Not applicable. Competing interests: Prof. Mauricio is a co-author of this study and an Editorial Board member of the Cardiovascular diabetology journal. He was not involved in handling this manuscript during the submission and the review processes.

Figures

Fig. 1
Fig. 1
Bar plot of the linear regressors of lipids associated with SCA in six analyses. From left to right, the columns show lipids associated with: the burden of SCA in subjects with T1D, the presence of SCA in subjects with T2D, the presence of SCA in smokers with T2D, the presence of SCA in subjects with T2D and without dyslipidaemia, the burden of SCA in smokers with T2D and the burden of SCA in subjects with T2D and without dyslipidaemia. Colours differentiate lipid classes. Darker bars with the linear regressor value in white indicate significance. TG, triacylglycerol; SM, sphingomyelin; PE, phosphatidylethanolamine, PC, phosphatidylcholine; LPE, lysophosphatidylethanolamine; LPC, lysophosphatidylcholine, DG, diacylglycerol; CerPE, ceramide-phosphoethanolamine; Cer, ceramide; CE, cholesterol ester. Mix indicates an LC–MS feature that includes multiple lipid species with the same sum composition notation. For example, Mix PC(38:3) represents PC species containing a total of 38 carbon atoms and 3 double bonds
Fig. 2
Fig. 2
Boxplots of the scaled and centred log-transformed intensity of each lipid significantly associated with the presence of SCA in smokers with T2D and in individuals with T2D without dyslipidaemia. All lipids shown correspond to LC–MS features acquired in positive ionization mode. The first 14 plots correspond to the smoking habit comparison, while the last 2 represent the dyslipidaemia comparison. Each plot title displays the lipid name and the number of observations. The x-axis labels indicate the sub-group of individuals analysed. The q-values for each comparison are displayed at the top of the plots. CE, cholesterol esters; Cer, ceramides; LPC, lysophosphatidylcholine; LPE, lysophosphatidylethanolamine; PC, phosphatidylcholine; PE, phosphatidylethanolamine; TG, triacylglycerols. Mix indicates an LC–MS feature that includes multiple lipid species with the same sum composition notation
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