| Literature DB >> 32718041 |
Anna Maria Rychter1, Marzena Skrzypczak-Zielińska2, Aleksandra Zielińska2, Piotr Eder1, Eliana B Souto3,4, Agnieszka Zawada1, Alicja Ewa Ratajczak1, Agnieszka Dobrowolska1, Iwona Krela-Kaźmierczak1.
Abstract
Although many preventive and treatment approaches have been proposed, cardiovascular disease (CVD) remains one of the leading causes of deaths worldwide. Current epidemiological data require the specification of new causative factors, as well as the development of improved diagnostic tools to provide better cardiovascular management. Excessive accumulation of adipose tissue among patients suffering from obesity not only constitutes one of the main risk factors of CVD development but also alters adipokines. Increased attention is devoted to bioactive adipokines, which are also produced by the adipose tissue. The retinol-binding protein 4 (RBP4) has been associated with numerous CVDs and is presumably associated with an increased cardiovascular risk. With this in mind, exploring the role of RBP4, particularly among patients with obesity, could be a promising direction and could lead to better CVD prevention and management in this patient group. In our review, we summarized the current knowledge about RBP4 and its association with essential aspects of cardiovascular disease-lipid profile, intima-media thickness, atherosclerotic process, and diet. We also discussed the RBP4 gene polymorphisms essential from a cardiovascular perspective.Entities:
Keywords: RBP4; atherosclerosis; cardiovascular disease; lipoprotein metabolism; metabolic syndrome; obesity
Year: 2020 PMID: 32718041 PMCID: PMC7432399 DOI: 10.3390/ijms21155229
Source DB: PubMed Journal: Int J Mol Sci ISSN: 1422-0067 Impact factor: 5.923
Figure 1Retinol-binding protein 4 (RBP4).
The summary of measurement methods and serum retinol-binding protein 4 (RBP4) range in studies assessing cardiovascular (CV) risk.
| Authors | Study Population | Groups, Sex (Group Size, | Age (Years) | BMI (kg/m2) | CV Risk Assessment Method | CV Risk (Group Size, | RBP4 Measurement Method (Unit) | RBP4 Specimen | Serum RBP4 Range | Relation between RBP4 and CV Risk |
|---|---|---|---|---|---|---|---|---|---|---|
| Feng et al. 2015 [ | T2DM | 498 F; | 62.80 ± 13.60 | 27.50 ± 4.20 | cIMT (mm) | G 1 (332): no abnormalities | ELISA (mg/L) | serum | G 1 | + |
| 27.90 ± 3.40 | G 2 (386): ≥ 1 | G 2 | ||||||||
| 27.60 ± 3.60 | G 3 (358): ≥ 1.5 | G 3 | ||||||||
| Xiao et al. 2013 [ | T2DM | 140 F; | 35.00–70.00 | 25.10 ± 2.80 | cIMT (mm) | subAS (78) | ELISA with monoclonal antibodies (mg/L) | serum | 37.1 | + |
| 24.50 ± 2.80 | Non-subAS | 23.2 | + | |||||||
| Won et al. 2012 [ | Healthy | 175 F; | 40.00 ± 11.00 | 27.00 ± 2.60 | The Framingham Risk Score | MetS (57) | EIA (µg/mL) | plasma | MetS | + |
| 23.60 ± 3.00 | Non-MetS (234) | Non-MetS | ||||||||
| Su et al. 2020 [ | CKD | 58 F; | 59.50–78.00 | 27.40 ± 2.90 | CV events (fatal and nonfatal) | (total 80) | ELISA (mg/L) | serum | >33.86 | + |
| 25.90 ± 2.10 | (total 89) | <33.86 | + | |||||||
| Solini et al. 2009 [ | HYP | 35 F | 47.40 ± 5.00 | 25.00 ± 1.60 | cIMT (mm) | 0.54 ± 0.15 | ELISA (µg/mL) | plasma | Median value | + |
| CTL | 35 F | 46.90 ± 6.30 | 25.70 ± 1.40 | 0.5 ± 0.13 | Median value | None | ||||
| Mansouri et al. 2012 [ | T2DM | 53 F; | 53.60 ± 8.40 | 27.70 ± 4.10 | cIMT (mm) | 0.8 ± 0.2 | ELISA (µg/mL) | serum | 71.9 ± 35.6 | None |
| Bobbert et al. 2010 [ | T2DM and non-T2DM | 52 F; | 55.00 ± 1.30 | 30.80 ± 0.70 | cIMT (mm) | 0.72 ± 0.02 | ELISA (µmol/L) | serum | 1.89 ± 0.05 | + |
| Chu et al. 2011 [ | T2DM with CKD | 86 (sex NM) | 70.00 ± 11.00 | 26.20 ± 6.20 | cIMT (mm) | 0.75 ± 0.16 | ELISA (µg/mL) | serum | 44.8 ± 6.4 | None |
| T2DM without CKD | 153 (sex NM) | 60.00 ± 12.00 | 26.30 ± 5.90 | 0.69 ± 0.14 | 39.5 ± 4.9 | None | ||||
| Li et al. 2020 [ | CHF | 227 F; | ≥60 | 22.49–26.67 | MACE | - | ELISA (µg/mL) | serum | 46.66 ± 12.38 | + (log RBP4 associated with 1.6 times higher risk of MACE) |
| Bachmayer et al. 2013 [ | Patients with obesity | 65 F; | 43.00 ± 10.00 | 50.00 ± 7.00 | Endothelial dysfunction: CRAE (µm); CRVE (µm); AVR | CRAE 178 ± 19 | ELISA (ng/mL) | NM | 24,773 ± 14,025 | None |
| CRVE 221 ± 24 | None | |||||||||
| AVR 0.81 ± 0.09 | None |
F—female, M—men, ± SDs, T2DM—Type 2 diabetes mellitus, G—group, ELISA—enzyme-linked immuno-absorbent assay, CV—cardiovascular, IMT—intima-media thickness, cIMT—carotid intima-media thickness, fIMT—femoral intima-media thickness, iIMT—common iliac intima-media thickness, subAS—subclinical atherosclerosis, EIA—enzyme immunoassay, MetS—metabolic syndrome, CKD—chronic kidney disease, HYP—hypertensive, CTL—normotensive, +—positive, NM—not mentioned, CHF—chronic heart failure, MACE—major adverse cardiac event(s) (cardiovascular death and rehospitalization due to the deterioration of CHF), CRAE, CRVE—central retinal artery/vein equivalent, AVR—arterio–venous-ratio.
Figure 2RBP4 gene structure, chromosome location, and cardiovascular disease (CVD) variants distribution. rs—number of the reference sequence in the National Center of Biotechnological Information database.
RBP4 gene variants investigated as risk factors of cardiovascular diseases in obesity.
| Variant | Genetic Location | Study Group | Pathophysiology Association | Reference | |
|---|---|---|---|---|---|
| Diagnosis | |||||
| rs10882280 | g.6681G > T | 1422 F; | healthy | Higher high-density lipoprotein level associated with minor allele T ( | Shea et al. 2010 |
| rs11187545 | g.8889T > C | ||||
| rs10882283 | g.5030T > G | 457 F; | T2DM | G-allele associated | Kovacs et al. 2007 |
| rs10882273 | g.27484T > C | 457 F; | T2DM | C-allele | Kovacs et al. 2007 |
| 1787 F;1423 M (3210) | Chinese Hans population 50–70 years old | Higher body-mass index values. Higher insulin and free fatty acids levels. | Wu et al. 2009 | ||
| rs10882272 | g.26761T > C | 593 F; | French-Canadian founder population | Association with circulating retinol levels. Modulation between vitamin A intake and abdominal adiposity. | Goodwin et al. 2015 |
| 5 006 | Caucasian cohorts from Finland, USA, and Italy | Association with circulating retinol levels. | Mondul et al. 2011 | ||
| rs3758538 | g.3944A > C | 97 with obesity; | Spanish Caucasian children | Association with triglycerides levels and plasma RBP4 levels. C allele associated with obesity and higher BMI z-score. | Codõner-Franch et al. 2016 |
| 1787 F; | Chinese Hans population 50–70 years old | Association with hypertriglyceridemia and plasma RBP4 levels. | Wu et al. 2009 | ||
| rs3758539 | g.4406G > A | 97 cases | Obesity, | Association with triglycerides levels in children. | Codõner-Franch et al. 2016 |
| 66 F; | Obesity, | Association with an increased susceptibility for obesity and an increased BMI. | Shajarian et al. 2015 | ||
| rs12265684 | g.12177G > A | 97 cases | Obesity, | Association with triglycerides levels and blood pressure. | Codõner-Franch et al. 2016 |
| rs34571439 | g.14684T > G | Association with triglycerides and plasma RBP4 levels as well as plasma C-reactive protein values. | |||
| rs7094671 | g.10377C > T | 297 M; | CAD, Chinese patients | G allele associated with a higher risk of CAD | Wan et al. 2014 |
rs—number of the reference sequence in the National Center of Biotechnological Information database, UTR—untranslated region, F—female, M—men, CTL—controls, T2DM—Type 2 diabetes mellitus, CAD—coronary artery disease.
Figure 3Lipid disorders impacted by RBP4 as the main levels of risk of cardiovascular disease caused by obesity. LDL—low-density lipoprotein; HDL—high-density lipoprotein; VLDL—very-low-density lipoprotein; ULDL—ultra low-density lipoprotein.
Figure 4The potential pathomechanism of RBP4’s impact on atherosclerosis and CVD risk. E-selectin: endothelial-leukocyte adhesion molecule; MCP-1: monocyte chemoattractant protein 1; VCAM-1: vascular cell adhesion molecule 1; ICAM-1: intercellular adhesion molecule 1; CRP: C reactive protein; IL-6: interleukin-6; TNF-α—tumor necrosis factor α.
Figure 5Diet, lifestyle, and RBP4.