Abstract
Cardiometabolic risk factors diabetes, obesity, and hypertension are highly prevalent and contribute to increased cardiovascular disease (CVD). Endothelial dysfunction precedes CVD development. The current study aimed to investigate the EC transcriptome among individuals with varying degree of cardiometabolic risk.
Cardiometabolic risk factors including diabetes mellitus (DM), obesity, and hypertension are highly prevalent and contribute to cardiovascular disease (CVD). In clinical practice, these risk factors often coexist likely contributing to shared mechanisms of inflammation. Endothelial dysfunction precedes the development of CVD, often occurring as a direct result of these cardiometabolic risk factors. Direct brachial vein endothelial cell (EC) harvesting offers an opportunity to examine vascular health via assessment of EC gene expression to explore mechanisms contributing to cardiovascular risk. The current study investigated the EC transcriptome among individuals with varying degree of cardiometabolic risk.
As part of an ongoing clinical trial examining residual risk in DM (NCT04369664), adult participants with and without DM without established CVD were recruited. The study was approved by the Institutional Review Board of New York University Langone Health, and all patients provided written informed consent. The current analysis included 18 patients who underwent brachial vein EC harvesting and RNA sequencing. Individuals were categorized based on the presence of cardiometabolic risk factor (hypertension, DM, obesity) burden as 0, 1, 2, or 3 risk factors. Hypertension was defined by documented history, use of anti-hypertensive medication or a measured systolic blood pressure ≥140 mmHg obtained during the research visit. DM was defined by documented history. Obesity was defined as BMI ≥30 kg/m2.
As previously described,1 EC’s were obtained via insertion of intravenous angiocatheter into a peripheral brachial vein in the upper extremity. A J-shaped vascular guidewire was then inserted past the end of the angiocatheter with sequential scraping of the intima for retrieval of EC’s followed by mRNA extraction with RNA sequencing libraries generated with a low input Clonetech SMART-seq HT with Nxt HT (Takara Bio USA, San Jose, CA) and run on a NovaSeq SP100 with mapping of sequencing reads to the human reference genome (GRCh37/hg19) using STAR (v2.6.1d). Read count tables were generated using FeatureCounts from Subread (v1.6.3) and normalized using DESeq2 R package (v1.30.1) followed by differential expression analysis. Pathway analysis was performed using GSEA from ClusterProfiler R package (v3.18.1) using the full database of human GO pathways downloaded from MSigDB as the reference. Each sample had a mean of 20 million reads/sample with a mean of 4.8 million reads/sample after removal of duplicates, mapping to genome, and removal of multi-mapping. To evaluate the association between cardiometabolic comorbidity burden and outcome transcripts we performed linear regression with multivariable models, adjusting for age, sex, and race/ethnicity. Support for this study was provided by the National Institutes of Health (R35HL144993 to JSB, and K23HL152013 to MG) and the American Heart Association (20SFRN35210609) to JSB. IG was supported by R01HL164939. The authors are solely responsible for the design and conduct of this study, all study analyses, the drafting and editing of the paper and its final contents.
A total of 18 individuals were included in the present analysis (mean age 47 ± 14 years, 44% female, and 61% white). Among participants, 3 had absent risk factors, 8 had 1 risk factor, 5 had 2 risk factors and 2 had 3 risk factors. (Table 1) RNA sequencing revealed 588 differentially expressed transcripts (p-adj <0.05) with 585 upregulated and 3 downregulated. (Figure 1a) Unsupervised hierarchical clustering analysis revealed excellent discrimination across groups. (Figure 1b)
Table 1.
Baseline Characteristics Stratified By Risk Factor Burden
| Overall (N=18) | 0 Risk Factors (N=3) | 1 Risk Factor (N=8) | 2 Risk Factors (N=5) | 3 Risk Factors (N=2) | |
|---|---|---|---|---|---|
| Age | 46.8 (14.4) | 45.3 (17.9) | 45.0 (14.9) | 48.0 (17.1) | 53.0 (7.1) |
| Female Sex | 8 (44%) | 1 (33%) | 3 (38%) | 3 (60%) | 1 (50%) |
| Asian | 3 (17%) | 0 | 2 25%) | 1 (20%) | 0 |
| Non-Hispanic/Latino | |||||
| BMI (kg/m2) | 29.2 (6.2) | 29.0 (0.6) | 27.2 (3.2) | 27.6 (13.7) | 41.2 (13.7) |
| Waist Circumference (cm) | 100.3 (13.0) | 99.8 (7.2) | 94.7 (9.6) | 99.1 (7.3) | 126.5 (16.2) |
| HbA1c (%) | 6.0 (0.9) | 5.2 (0.2) | 5.9 (0.6) | 6.4 (1.3) | 6.5 (0.9) |
| Total Cholesterol (mg/dL) | 207.4 (34.5) | 206.3 (33.6) | 201.0 (37.8) | 226.2 (34.6) | 187.5 (17.7) |
| LDL (mg/dL) | 128.7 (26.5) | 136.7 (33.9) | 122.3 (30.1) | 134.0 (23.8) | 129.5 (19.1) |
| HDL (mg/dL) | 57.6 (22.3) | 50.7 (4.9) | 57.5 (17.4) | 70.8 (32.6) | 35 (1.4) |
| Risk Factors | |||||
| Diabetes | 11 | 0 | 5 | 4* | 2 |
| Hypertension | 9 | 0 | 2 | 5* | 2 |
| Obesity | 4 | 0 | 1 | 1* | 2 |
Among the 5 patients with 2 risk factors, 4 had diabetes and hypertension, and 1 had obesity and hypertension
Figure 1.
Endothelial cell Transcriptome Among Individuals with Varying Degree of Cardiometabolic Risk. (a) volcano plot (p-adjusted <0.05), (b) heatmap of log2 normalized counts of 193 top differentially expressed genes (p-adjusted <0.01), (c) top differentially enriched pathways via gene ontology pathway analysis, (d) mean log normalized counts for differentially expressed genes categorized by number of risk factors, including VCAM1 (p-trend=0.013), CEACAM1 (p-trend 0.089), ADAM17 (p-trend 0.005) and CD99L2 (p-trend 0.03). Risk factors categorized as 0, 1, 2, or 3 based on presence or absence of hypertension, diabetes mellitus, and obesity.
Gene ontology enrichment analysis revealed upregulated pathways associated with T-cell activation (NES = 2.22, p<0.001), leukocyte differentiation (NES= 2.16, p<0.001), leukocyte migration (NES= 2.12, p<0.001), regulation of cell-cell adhesion (NES= 1.91, p=0.006), platelet aggregation (NES= 1.66, p=0.03), and integrin-mediated signaling pathway (NES=1.61, p=0.03). Downregulated pathways included endothelial cell proliferation (NES= −1.68, p=0.03) and response to interleukin-1 (NES= −1.61, p=0.04). (Figure 1c)
Since the T-cell activation pathway was among the most differentially expressed pathways with the highest normalized enrichment scores (Figure 1c), we evaluated significant genes in that pathway which included VCAM1, CEACAM1, ADAM 17, and CD99L2, all with a log-2-fold change >3 and p-adj <0.05. These genes demonstrated a graded increase in mean normalized counts with increasing number of risk factors (Figure 1d).
Previous data demonstrated a proinflammatory EC transcriptome in patients with DM.2 Whole blood transcriptomics found upregulation in pathways associated with inflammation and coagulation processes in those with obesity and metabolic syndrome.3 We extend those findings and identified genes upregulated in those with greater cardiometabolic risk factors. For example, VCAM is an adhesion molecule that plays an important role in vascular inflammation, serves as a marker of endothelial injury, and has been shown to be elevated in hypertension, diabetes, obesity and cardiovascular disease.4 ADAM17 is a TNF-alpha converting enzyme shown to play a role in diabetes, obesity, and atherosclerosis.5
Inflammation plays an important role in the pathogenesis of CVD with upregulation of adhesion molecules and inflammatory cytokines contributing to endothelial dysfunction. Enrichment analysis in our study identified several pathways related to inflammation and immune activation associated with greater cardiometabolic risk factor burden. The findings of the present study reinforce a potential mechanism by which cardiometabolic risk factors contribute to CVD development.
Our study has several limitations including a small sample size with a limited number of individuals with zero or three risk factors. Additionally, the current analysis does not account for duration of risk factor nor degree of control. Nevertheless, we demonstrate a proinflammatory and pro-adhesive EC transcriptome associated with increased cardiometabolic risk factor burden. These findings offer insight into a potential mechanism linking these risk factors with the development of cardiovascular disease and highlight the potential role for risk factor reduction to mitigate cardiovascular disease risk.
FUNDING:
Support for this study was provided by the National Institutes of Health (R35HL144993 to JSB, and K23HL152013 to MG) and the American Heart Association (20SFRN35210609) to JSB. IG was supported by R01HL164939.
Footnotes
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REFERENCES:
- 1.Garshick MS, Barrett TJ, Wechter T, Azarchi S, Scher JU, Neimann A, Katz S, Fuentes-Duculan J, Cannizzaro MV, Jelic S, et al. Inflammasome Signaling and Impaired Vascular Health in Psoriasis. Arterioscler Thromb Vasc Biol. 2019;39:787–798. doi: 10.1161/atvbaha.118.312246 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Beckman JA, Doherty SP, Feldman ZB, Banks ES, Moslehi J, Jaffe IZ, Hamburg NM, Sheng Q, Brown JD. Comparative Transcriptomics of Ex Vivo, Patient-Derived Endothelial Cells Reveals Novel Pathways Associated With Type 2 Diabetes Mellitus. JACC Basic Transl Sci. 2019;4:567–574. doi: 10.1016/j.jacbts.2019.05.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Paczkowska-Abdulsalam M, Niemira M, Bielska A, Szałkowska A, Raczkowska BA, Junttila S, Gyenesei A, Adamska-Patruno E, Maliszewska K, Citko A, et al. Evaluation of Transcriptomic Regulations behind Metabolic Syndrome in Obese and Lean Subjects. Int J Mol Sci. 2020;21. doi: 10.3390/ijms21041455 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Troncoso MF, Ortiz-Quintero J, Garrido-Moreno V, Sanhueza-Olivares F, Guerrero-Moncayo A, Chiong M, Castro PF, García L, Gabrielli L, Corbalán R, et al. VCAM-1 as a predictor biomarker in cardiovascular disease. Biochim Biophys Acta Mol Basis Dis. 2021;1867:166170. doi: 10.1016/j.bbadis.2021.166170 [DOI] [PubMed] [Google Scholar]
- 5.Menghini R, Fiorentino L, Casagrande V, Lauro R, Federici M. The role of ADAM17 in metabolic inflammation. Atherosclerosis. 2013;228:12–17. doi: 10.1016/j.atherosclerosis.2013.01.024 [DOI] [PubMed] [Google Scholar]