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Significant Interplay Between Lipids, Cytokines, Chemokines, Growth Factors, and Blood Cells in an Outpatient Cohort
Mats B Eriksson1,2, Lars B Eriksson1, Anders O Larsson3
1Department of Surgical Sciences, Uppsala University, Uppsala University Hospital, SE-751 85 Uppsala, Sweden.
Insights
This study reveals a complex link between immune factors, lipids, and blood cell counts in healthy adults, suggesting these interactions may predict early cardiovascular disease risk.
Area of Science:
- Immunometabolism
- Cardiovascular Disease Pathophysiology
- Lipidomics
Background:
- Cardiovascular disease (CVD) is a leading cause of death, driven by atherosclerosis, involving lipids and immune cells.
- Traditional lipid biomarkers (LDL, HDL) are used for CVD risk, but the role of cytokines, chemokines, growth factors (CCGFs), and blood cells in healthy individuals is unclear.
Purpose of the Study:
- To investigate associations between plasma CCGFs, lipid biomarkers, and blood cell counts in healthy young adults.
- To explore the complex immunometabolic network in individuals without diagnosed cardiac conditions.
Main Methods:
- Analyzed plasma CCGFs using proximity extension assay (PEA) in 164 healthy adults (18-44 years).
- Assessed correlations between CCGFs, lipid biomarkers (HDL, triglycerides, remnants, non-HDL, apolipoprotein A1), and blood cell counts (leukocytes, neutrophils, platelets).
- Applied false discovery rate (FDR) correction for multiple testing.
Main Results:
- Negative associations were found between CCGFs and HDL/apolipoprotein A1.
- Pro-inflammatory cytokines (CCL3, IL-6, TNFSF10) positively correlated with triglycerides, remnants, non-HDL, and BMI.
- Triglycerides and remnants showed positive correlations with elevated leukocyte, neutrophil, and platelet counts.
- HGF and FGF-21 showed complex associations with BMI and HDL.
Conclusions:
- A complex immunometabolic network involving lipids, CCGFs, and blood cells exists even in healthy individuals.
- These interactions may be crucial in early pathophysiological processes of CVD.
- Understanding these cytokine-lipid-cell interactions could enhance cardiovascular risk assessment beyond traditional metrics.
Abstract:
Cardiovascular disease (CVD) remains the leading global cause of morbidity and mortality, largely driven by atherosclerosis, a chronic inflammatory process involving lipids and immune cells. Although traditional lipid biomarkers such as low-density lipoprotein (LDL) and high-density lipoprotein (HDL) are well-established in CVD risk stratification, the interplay between cytokines, chemokines, growth factors (CCGFs), lipid metabolism, and hematological parameters in non-cardiac populations remains underexplored. We investigated associations between plasma cytokines and lipid-related biomarkers and their relationships with circulating blood cell counts in a cohort of 164 essentially healthy adults aged 18-44 years. CCGF profiling was performed using a proximity extension assay (PEA), and statistical correlations were adjusted for multiple testing using false discovery rate (FDR) correction. The CCGFs that were associated with HDL and apolipoprotein A1 all displayed negative associations. Several pro-inflammatory cytokines, including CCL3, IL-6, and TNFSF10, showed strong positive associations with triglycerides, remnants, non-HDL, and body mass index (BMI). Furthermore, triglycerides and remnants were consistently correlated with elevated leukocyte, neutrophil, and platelet counts. HGF and FGF-21, mainly considered as anti-inflammatory, were positively associated with BMI and negatively associated with HDL, which is compliant with a multitude of actions, depending on the local milieu and the cellular interplay. Our results support the existence of a complex immunometabolic network involving lipids, CCGFs, and blood cells, even in non-diseased individuals. The observed patterns underscore the importance of understanding the intricate cytokine-lipid-cell interactions that may occur in early pathophysiological processes and highlight their potential utility in refining cardiovascular risk assessment beyond traditional lipid metrics.
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