VLDL cholesterol associates with higher plasmatic expression of inflammatory proteins and atherosclerotic pathways compared to LDL cholesterol

Selected Abstract - SITeCS Congress 2023

Elisa Mattavelli
Department of Pharmacological and Biomolecular Sciences, University of Milan, Italy and IRCCS MultiMedica Hospital, Milano, Italy
Elena Olmastroni
Department of Pharmacological and Biomolecular Sciences, University of Milan, Italy and IRCCS MultiMedica Hospital, Milano, Italy
Nick Nurmohamed
Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, the Netherlands
Liliana Grigore
IRCCS MultiMedica Hospital, Milano, Italy
Fabio Pellegatta
IRCCS MultiMedica Hospital, Milano, Italy
Erik S. G. Stroes
Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, the Netherlands
Alberico L. Catapano
Department of Pharmacological and Biomolecular Sciences, University of Milan, Italy and IRCCS MultiMedica Hospital, Milano, Italy
Andrea Baragetti
Department of Pharmacological and Biomolecular Sciences, University of Milan, Italy and IRCCS MultiMedica Hospital, Milano, Italy

Abstract

Background and aim: High cholesterol in Low-Density Lipoproteins (LDL-C) is the key target of current pharmacological treatments aimed at reducing atherosclerotic cardiovascular disease (ACVD) risk. Increased cholesterol in Very low-density lipoproteins (“VLDL-C”) is an independent predictor of ACVD. VLDL-C was previously associated with markers of inflammation (for instance C-reactive protein). We now tested the relationship between either VLDL-C or LDL-C with a large spectrum of inflammatory proteins in plasma collected from subjects at different ACVD risks.
Methods: We measured 276 proteins (OlinkTM) in plasma from a primary ACVD risk prevention cohort (“PLIC” in Milan; n=656 (8.2% on statins)) and a secondary ACVD risk prevention cohort (the Second Manifestations of ARTerial disease, “SMART”, the Netherlands, n=630 (50.8% on statins)). Cohorts were divided into three groups for VLDL-C (“Normal” VLDL-C<15 mg/dL, “High” VLDL-C 15-30 mg/dL, “Very high” VLDL-C >30 mg/dL) and LDL-C (“Normal” LDL-C <115 mg/dL, “High” LDL-C 115-155 mg/dL, “Very high” LDL-C>155 mg/dL). The expression (Normalized Protein eXpression, NPX) of each protein was compared among these groups by artificial intelligence. The performance to discriminate subjects with higher VLDL-C or LDL-C was evaluated by comparing the Areas Under the Curve (AUCs) of the Receiver Operating Characteristics curve (ROC) considering proteomics on top of ACVD risk factors (“CVRFs”: age, body mass index, systolic blood pressure, glycemia, therapies), versus the AUC of the ROCs with CVRFs alone.
Results: The number of plasma proteins differentially expressed increased, as a function of higher VLDL-C in PLIC, as the NPXs of 84 were higher in “High” and the NPXs of 136 were higher in “Very high” vs “Normal” VLDL-C respectively. A similar trend was found in SMART, where the NPXs of 30 proteins were higher in “High” and the NPXs of 64 were higher in “Very high” vs “Normal” VLDL-C respectively. 26 proteins were shared between the two populations and recapitulated key atherosclerotic pathways (including chemotaxis of immune cells).
The relationship between LDL-C was less marked; in PLIC, 14 proteins were more expressed in “High” and 33 in “Very high” vs “Normal” LDL-C respectively, while in SMART, the NPXs of 11 proteins were higher in “High” and the NPXs of 36 were higher in “Very high” vs “Normal” LDL-C respectively. Only 4 proteins were shared between high and very high LDL-C in the two populations. Finally, none of the proteins were shared between the groups of “High”/“Very high” VLDL-C and “High”/“Very high” LDL-C in the two cohorts.
Canonical CVRFs alone slightly improved the ability to identify subjects with increased VLDL-C both in PLIC and SMART (AUCs between 0.6 on average), but adding plasma proteomics markedly improved the performance to identify subjects with “High” VLDL-C, in PLIC (AUC=0.767 (0.709-0.837)) and in SMART (AUC=0.781 (0.681-0.873)), and with “Very high” VLDL-C (AUC=0.950 (0.899-0.976) in PLIC, and AUC=0.938 (0.894-0.971) in SMART).
The ROC of plasma proteomics with CVRFs was also superior to the ROC of the CVRFs alone to identify subjects with “High” and “Very high” LDL-C, but, as compared to the ROCs that discriminated subjects with “High” and “Very-high” VLDL-C, the AUCs were attenuated in both cohort (for “High” LDL-C: AUC=0.665 (0.558-0.774) in PLIC and AUC=0.775 (0.704-0.842) in SMART; for “Very high” LDL-C: AUC =0.776 (0.694-0.854) in PLIC and AUC=0.882 (0.825-0.931) in SMART).
Conclusion: High VLDL-C associates with a higher number of differentially expressed plasma proteins versus high LDL-C and none of the proteins were in common. Our data do not underestimate the value of LDL-C in ACVD but reinforce the concept that VLDL-C may also promote different atherosclerotic pathways involved in determining ACVD.

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