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. 2021 May 27;12:674343. doi: 10.3389/fimmu.2021.674343

Association of Circulating Vascular Endothelial Growth Factor Levels With Autoimmune Diseases: A Systematic Review and Meta-Analysis

Haoting Zhan 1,2,, Haolong Li 1,2,, Chenxi Liu 1,2, Linlin Cheng 1,2, Songxin Yan 1,2, Yongzhe Li 1,2,*
PMCID: PMC8191579  PMID: 34122433

Abstract

Background

Autoimmune diseases (ADs) are characterized by immune-mediated tissue damage, in which angiogenesis is a prominent pathogenic mechanism. Vascular endothelial growth factor (VEGF), an angiogenesis modulator, is significantly elevated in several ADs including rheumatoid arthritis (RA), systemic sclerosis (SSc), and systemic lupus erythematosus (SLE). We determined whether circulating VEGF levels were associated with ADs based on pooled evidence.

Methods

The analyses included 165 studies from the PubMed, EMBASE, Cochrane Library, and Web of Science databases and fulfilled the study criteria. Comparisons of circulating VEGF levels between patients with ADs and healthy controls were performed by determining pooled standard mean differences (SMDs) with 95% confidence intervals (CIs) in a random-effect model using STATA 16.0. Subgroup, sensitivity, and meta-regression analyses were performed to determine heterogeneity and to test robustness.

Results

Compared with healthy subjects, circulating VEGF levels were significantly higher in patients with SLE (SMD 0.84, 95% CI 0.25–1.44, P = 0.0056), RA (SMD 1.48, 95% CI 0.82–2.15, P <0.0001), SSc (SMD 0.56, 95% CI 0.36–0.75, P <0.0001), Behcet’s disease (SMD 1.65, 95% CI 0.88–2.41, P <0.0001), Kawasaki disease (SMD 2.41, 95% CI 0.10–4.72, P = 0.0406), ankylosing spondylitis (SMD 0.78, 95% CI 0.23–1.33, P = 0.0052), inflammatory bowel disease (SMD 0.57, 95% CI 0.43–0.71, P <0.0001), psoriasis (SMD 0.98, 95% CI 0.62–1.34, P <0.0001), and Graves’ disease (SMD 0.69, 95% CI 0.20–1.19, P = 0.0056). Circulating VEGF levels correlated with disease activity and hematological parameters in ADs.

Conclusion

Circulating VEGF levels were associated with ADs and could predict disease manifestations, severity and activity in patients with ADs.

Systematic Review Registration

PROSPERO, identifier CRD42021227843.

Keywords: diagnosis, disease activity, autoimmune disease, vascular endothelial growth factor, angiogenesis

Introduction

Angiogenesis, a hallmark of inflammatory activation, is an integral part of pathogenic processes including endothelial cell proliferation and migration and subsequent neoangiogenesis and remodeling in autoimmune diseases (ADs). Synovial pannus initiates the invasion of cartilage and subchondral bone to perpetuate rheumatoid arthritis (RA) (1, 2), whereas ankylosing spondylitis (AS) is characterized by increased vascularity and vascular lesions (3). Vascular endothelial dysfunction and injury are considered as the primum movens triggering Kawasaki disease (KD), systemic lupus erythematosus (SLE), inflammatory bowel disease (IBD), Behcet’s disease (BD), systemic sclerosis (SSc), and psoriasis (PsA) (49). Therefore, early detection of vascular involvement is pivotal in AD diagnosis.

Vascular endothelial growth factor (VEGF)-A, generally known as VEGF, is a crucial regulator of endothelial dysfunction, capillary permeability, and angiogenesis. For example, serum VEGF level and intrathyroid microvessel density were reported to be increased patients with Graves’ disease (GD) compared to healthy control (HC) subjects (10). Increased serum VEGF and significant difference in diffused and limited SSc suggest VEGF as a potential surrogate indicator of capillary damage (11). Strong VEGF expression in synovial fluid and serum of patients with RA was shown to lead to synovial neovascularization and destruction in cartilage and bones (12, 13). VEGF was reported to be overexpressed in the skin and peripheral blood of patients with PsA (14). Serum VEGF levels were shown to be elevated and to correlate with disease activity and severity in PsA, SLE, BD, IBD, KD, and AS (1419). These findings suggest VEGF as a potential pathogenic factor with promising diagnostic value in ADs. However, no clinical guidelines currently recommend serum VEGF evaluation in routine care and counseling of patients with ADs, and intensive studies are warranted to identify the clinical implications of the findings regarding VEGF’s role in ADs to date and to resolve contradictory results (2024).

Given the inconsistency among these findings and lower statistic power of the studies, we performed a systematic review and meta-analysis to generate independent results and recognize the source of heterogeneity. In the present study, we aimed to determine whether circulating VEGF was a causative factor in ADs.

Materials and Methods

Literature Search

The present systematic review with meta-analysis was performed according to the PRISMA guidelines (PROSPERO registration number, CRD42021227843). Two authors (HTZ and HLL) independently searched the PubMed, Embase, Cochrane Library, and the Web of Science databases for studies published until October 14. The detailed search strategies are provided in the online Supplemental Materials . Reference lists were manually retrieved.

Eligibility Criteria

Without restrictions on time, language, ethnicity, and geographical region, studies satisfying the following criteria were included: (1) case-control or cohort studies on the association between circulating VEGF and ADs including SLE, RA, SSc, BD, KD, AS, IBD, PsA, and GD; (2) HCs without ADs (2); available data on circulating VEGF levels (serum or plasma); (3) sufficient data on VEGF levels for both HCs and patients with ADs to evaluate standard mean differences (SMDs) with 95% confidence intervals (CIs). Studies based on animal and cellular models, those comprising HCs with insufficient data; and editorial letters with insufficient data were excluded.

Data Extraction and Quality Assessment

Two independent investigators (HTZ and HLL) individually screened the literature and extracted and evaluated the data. Any discrepancies were resolved by consensus or by a third opinion (YZL). Study number, name of the first author, publication year, country, study type, sample type, inclusion and exclusion criteria, demographic features, aggregated number of subjects and circulating VEGF levels in patients with ADs and HCs, diagnostic criteria, type of VEGF assay, and treatment history and strategy were extracted into pre-designed charts. For meta-analysis, continuous variables were translated from medians (interquartile range [IQR] or range) to means ± standard deviation (25). Newcastle–Ottawa quality assessment scale was used to evaluate study quality. Further details of the pooled studies were obtained by directly contacting the authors if warranted.

Data Analysis

STATA V.16.0 was used to perform the meta-analysis. SMDs with 95% CIs were used to estimate the pooled results and compare circulating VEGF levels between patients and HC groups. Random-effect model was used for analysis. Significant heterogeneity was ascertained based on a p value of ≤0.10 using the Cochrane Q test or an I2 value of >50%. Subgroup, sensitivity, and meta-regression analyses were performed to identify the source of heterogeneity and to test robustness. Spearman correlation coefficients were transformed into Pearson’s r values, which were converted to Fisher’s z values to obtain approximately normal distributions. Ultimately, the summary Fisher’s z values were converted into summary r values. Summary r values of 0.8–1.0, 0.6–0.8, 0.4–0.6, and 0.2–0.4 indicated extreme, high, and moderate relevance and poor correlation, respectively (details provided in the online Supplemental Materials ). Publication bias was assessed by Egger’s linear regression test and contour-enhanced funnel plots with collaborative meta-trim. A two-sided P <0.05 was considered to indicate statistical significance.

Results

Search Results and Population Characteristics

The literature search is summarized in Figure 1 . After removing duplicate studies (n = 3,322) and irrelevant publications (n = 8,673), 298 articles were analyzed and the full texts of 273 articles were read. Thirty-two full-text articles were eliminated due to incomplete data or unrelated outcomes. Among 241 eligible studies meeting the inclusion criteria, 76 articles were excluded due to unextractable data, insufficient data on HCs, irrelevant VEGF sample type (urine/synovial fluid/tear fluid), or inappropriate disease control groups. Finally, 165 studies were included in the meta-analysis, with 28, 29, 40, 13, 8, 12, 16, 23, and six studies on SLE (20, 21, 2651), RA (12, 2224, 38, 43, 5274), SSc (11, 38, 39, 64, 75110), BD (111123), KD (18, 124130), AS (55, 73, 131140), IBD (141156), PsA (12, 14, 135, 136, 157175) and GD (10, 176180), respectively. The main study characteristics are summarized in Table 1 and Appendix 1 . The studies were medium-to-high quality based on the Newcastle–Ottawa quality assessment scale scores (range, 4–9).

Figure 1.

Figure 1

Flow diagram of included/excluded studies.

Table 1.

Population characteristics of the studies included in the meta-analysis.

Year Author Country Study type SLE HC
Sample size Female (%) Age (years) Sample size Female (%) Age (years)
2015 Barbulescu AL (20) Romania case-control 18 16 (88.88) 45.00 ± 10.81 17 16 (94.11) range: 19–64
2019 Barraclough M (21) UK case-control 36 34 (94) 40 ± 12.41 30 30 (100) 32 ± 14.44
2008 Ciprandi G (26) Italy case-control 40 40 (100) 41.95 ± 8.3 40 33 (82.5) 43 ± 8.2
2009 Colombo BM (27) Italy case-control 80 80 (100) 42.6 ± 9.1 80 80 (100) 40.1 ± 9.5
2014 De Jesus GR (28) Brazil case-control 54 54 (100) 34 34 (100)
2015 Ding Y (29) China case-control 41 30 (73.2) 11.1 ± 2.4 10
2009 Elhelaly NS (30) Egypt case-control 23 21 (91.3) Range 8–18 25
2012 Edelbauer M (31) Austria case-control 23 17 (73.9) 15 ± 5 20 5 (25) 12 ± 3
2018 El-Gazzar II (32) Egypt case-control 84 84 (100) 29.03 ± 5.4 33
2017 Ghazali WSW (33) Malaysia case-control 92 26 26 (100) 33.19 ± 10.3
LN 46 44 (96) 28.48 ± 9.93
Non-LN 46 46 (100) 32.39 ± 11.46
2007 Heshmat NM (34) Egypt case-control 25 24 (96) 14.1 ± 2.6 30 29 (96.7) 14.0 ± 2.5
2009 Hrycek A (35) Poland case-control 48 48 (100) 47 ± 14 24 24 (100) 51 ± 15
2009 Hrycek A (36) Poland case-control 21 21 (100) 51 ± 12.4 24 24 (100) 51 ± 15.3
2008 Ibrahim FF (37) Egypt case-control 30 30 (100) 25 ± 7.75 10 10 (100) 32 ± 7.5
1998 Kikuchi K (38) Japan case-control 17 14 (82.4) 47 ± 12.75 20 16 (80%) 50 ± 12.5
2013 Koca SS (39) Turkey case-control 23 21 (91.3) 37.9 ± 9.3 28 22 (78.6%) 42.5 ± 13.9
2007 Kuryliszyn-Moskal A (40) Poland case-control 47 44 (93.6) 40.8 ± 13.6 30
2014 Liu J (41) China case-control 75 59 (78.7) 35.42 ± 11.79 40 31 (77.5) 33.62 ± 10.21
2018 Merayo-Chalico J (42) Mexico case-control active SLE 6 6 (100) 34.6 ± 4.2 6 6 (100) 36 ± 4.1
remission SLE 6 6 (100) 34.1 ± 4.8
2016 Novikov A (43) Russia case-control 80 72 (90) 31.5 ± 36.3 28
2012 Moneib HA (44) Egypt case-control 30 21 (70) 28.9 ± 10.2 15 10 (66) 35.00 ± 9.48
2002 Navarro C (45) Mexico case-control 28 24 (85.7) 36.6± 16.1 24 19 (79.2) 29.2 ± 8.5
2005 Robak E (46) Poland case-control 41 38 (92.7) 40.5 ± 13.5 20
2003 Robak E (47) Poland case-control 60 55 (91.7) 41 ± 14.25 20 17 (85) 45 ± 5.75
2013 Robak E (48) Poland case-control 60 56 (93.3) 39.2 ± 11.25 20 17 (85)
2002 Robak E (49) Poland case-control 52 48 (92.3) 41 ± 14.75 20 18 (90) 38 ± 11.75
2017 Willis R (50) America case-control 312 30 83.3 43.5 ± 12.5
cohort1 267 252 (94.4) 47.6 ± 12.4
cohort2 45 44 (97.8) 44.0 ± 12.1
2014 Zhou L (51) China case-control 54 50 (92.6) 36.81 ± 12.52 28 22 (78.6) 37.82 ± 12.86
Year Author Country Study type RA HC
Sample size Female (%) Age (years) Sample size Female (%) Age (years)
2004 Ardicoglu O (52) case-conrol 38 40
2001 Ballara S (12) UK cohort early 44 61 61 ± 17.78 31 65 49 ± 12.59
longstanding 78 85 61 ± 14.07
2000 Bottomley MJ (53) UK case-conrol 61 51 (83.6) 59 ± 11.75 29 20 (69.0) 34 ± 8
2005 Kim HR (62) Korea case-conrol 30 24 (80) 50 ± 8 20 16 (80) 30 ± 8
2016 Deveci K (55) Turkey case-control 30 mean age of 30–50 30 mean age of 30–50
2002 Drouart M (56) France case-control 50 32 (64) 59.8 ± 12.8 64 30 (46.9) 42.1 ± 10.1
2016 do Prado AD (57) Brazil case-control 64 50 (78.1) 55.3 ± 9.8 30 23 (76.7) 55.9 ± 11.1
2009 Foster W (22) UK case-control 66 41 (62.1) 58 ± 14 49 34 (69.4) 54 ± 10
2018 Gumus A (58) Turkey case-control 59 25 20  (80.0) 46.4 ± 13.3
joint swelling (+) 31 27 (87.10) 45.06 ± 9.66
joint swelling (−) 28 25 (89.28) 45.10  ± 13.03
2014 Heard BJ (59) Canada case-control 100 46.5 ± 14.5 100 40.0 + 9.5
2008 Hetland ML (60) Denmark case-control 10 10
2003 Hashimoto N (61) Japan case-control active RA22 18 (81.8) 54 ± 12.75 11
1998 Kikuchi K (38) Japan case-control 11 10 (90.9) 51 ± 10.75 20 16 (80) 50 ± 12.5
2007 Cho ML (54) Korea case-control 72 49.6 ± 1.3 31 47.1 ± 2.1
2006 Kuryliszyn-Moskal A (63) Poland case-control 64 54 (84.4) 58.6 ± 12.6 32
2004 Kuwana M (64) Japan case-control 11 11 (100) 59.1 ± 12.0 11 11 (100) 52.7 ± 10.6
2010 Milman N (65) Canada case-control 47 78.70 54.3 ± 14.25
2018 Misra S (23) India case-control 50 46 (92) 35.90 ± 18.607 30 28 (93.3) 34.03 ± 10.3
2016 Novikov A (43) Russia case-control 74 59 (79.7) 54.0 ± 13.33
2001 Olszewski WL (66) Poland case-control 20 16 (80) 42 ± 7.5 20 25 ± 1
2012 Oranskiy SP (67) Russia case-control 39 (BMI normal) 82.0 53.0 ± 2.75 20 80.0 52.0 ± 2.5
2010 Ozgonenel L (68) Turkey case-control 40 32 (80) 46 ± 12.59 38 18 (47.4) 44 ± 11.11
2009 Young HR (69) America case-control 169 69.20 54.2 ± 11.8 92 63 53.2 ± 11.6
2016 Rodriguez-Carrio J (70) Spain case-control 212 175 (82.5) 54 ± 17.25 175 102 (58.3) 51 ± 14.25
2016 Smets P (71) France case-control 80:RA13 8 (61.5) 71 ± 7.97 37 24 (64.9) 73.35 ± 8.55
2004 Strunk J (72) Germany case-control active RA 21 16 (76.2) range: 38–79 12 6 (50) range: 17–58
2010 Tseng JC (73) China case-control 50 50
2001 Sone H (24) Japan case-control 155 130 (83.9) 57.9 ± 12.0 75 62 (82.7) 55.8 ± 15.4
2007 Zayed A (74) Egypt case-control 40 range:21–57 20
Year Author Country Study type SSc HC
Sample size Female (%) Age (years) Sample size Female (%) Age (years)
2018 Alekperov R (75) Russia case-control 46 20
2004 Allanore Y (76) France case-control 40 33 (82.5) 57 ± 12 20 17 (85) 51 ± 7
2013 Aydogdu E (77) Turkey case-control 40 38 (95) 48.35 ± 13.2 20 19 (95) 49.3 ± 8.5
2017 Benyamine A (78) France case-control 45 44 (97.8) 61.49 ± 11.95 41 38 (92.7) 56.09 ± 7.82
2014 Bosello SL (79) Italy case-control 28 11
2014 Bosello SL (80) Italy case-control 24 10
2002 Choi JJ (11) Korea case-control 48 45 (81.8) 40.6 ± 13 55 30 38 ± 6
2017 Chora I (81) Italy case-control 55 49 (89.0) 64 ± 11 55 51 (92.7) 52 ± 10.25
VEDOSS 25 21 (84.0) 50 ± 14.5
2016 Cossu M (82) Italy case-control UCTD/SSC 47 52.7 ± 14.2 43
SSc without skin fibrisis 48 62 ± 13.2
limited 51 62.1 ± 10.4
diffused 35 54.6 ± 12.6
2013 De Lauretis A (83) UK case-control 74 59 (79.7) 51.4 ± 12.1 20 7 (35) 32.7 ± 6.3
2017 Delle Sedie A (84) Italy case-control 41 40 (97.6) 56 ± 15 31 25 (80.6) 50 ± 16
2011 Distler JHW (85) Germany case-control 40 34 (85) 46 ± 14.5 66 44 (66.7) 39 ± 13.75
2002 Distler O (86) Italy case-control 43 35 (81.4) 61 ± 13.75 21 16 (76.2) 55 ± 16.75
2012 Dunne JV (87) Canada case-control 40 35 (87.5) 40
diffused 14 45.5 ± 9.5
limited 26 53.8 ± 13.25
2005 Dziankowska-Bartkowiak B (88) Poland case-control 34 26 (76.5) 48 ± 13.5 20 19 (95.0) 46 ± 9.75
diffused 15 8 (53.3) 45 ± 12
limited 19 18 (94.7) 50 ± 10.75
2006 Dziankowska-Bartkowiak B (89) Poland case-control 28 22 (78.6) 47.5 ± 13 20 15 (75) 46 ± 9.75
diffused 12 7 (58.3) 48 ± 11.5
limited 16 15 (93.8) 47 ± 10.75
2013 Farouk HM (90) Egypt case-control 25 21 (84) 40.3 ± 5.86 20 17 (85) 38.9 ± 3.8
2014 Gkodkowska-Mrowka E (91) Poland case-control 66 60 (90) 53 ± 13.25 21 18 (85.7) 52 ± 10.25
2018 Gigante A (92) Italy case-control 15 15 (100) 41 ± 10.835 10 39 ± 10.484
2008 Hummers LK (93) America case-control 113 88.90 53.0 ± 12.2 27 63 57.5 ± 2.8
2017 Ibrahim SE (94) Egypt case-control 35 33 (94.2) 30.43 ± 4.53 35 29.8 ± 4.03
2018 Kawashiri S (95) Japan case-control 60 56 (93.3) 64 ± 8.889 25
diffused 16 15 (93.8) 64 ± 6.667
limitted 44 41 (93.2) 64 ± 10.37
1998 Kikuchi K (38) Japan case-control 40 37 (92.5) 53 ± 16.25 20 16 (80) 50 ± 12.5
2004 Kuryliszyn-Moskal A (96) Poland case-control 31 31 (100) 55.2 ± 10.4 30
2013 Koca SS (39) Turkey case-control 37 32 (86.5) 45.7 ± 13.6 28 22 (78.6) 42.5 ± 13.9
2020 Lv TT (97) China case-control 30 18 (75) 44 ± 12.0 15
2004 Kuwana M (64) Japan case-control 11 11 (100) 57.7 ± 11.8 11 11 (100) 52.7 ± 10.6
2019 Michalska-Jakubus M (98) Poland case-control 47 47 (100) 56.43 ± 11.01 27 27 (100) 52.37 ± 8.87
2010 Minier T (99) Hungary case-control 131 90.80 55.9 ± 11.7 30
diffused 41 82.80 52.6 ± 13.8
limited 90 94.40 57.4 ± 10.3
2012 Morgiel E (100) Poland case-control 30 26 (86.7) 54 ± 10.3 20
2009 Papaioannou AI (101) Greece case-control 40 33 (82.5) 56.75 ± 12.5 13
2015 Reiseter S (102) Norway cohort 298 243 (82) 56.0 ± 13.8 100
2001 Sato S (103) Japan case-control 32 29 (90.6) 47 ± 18 20
2010 Riccieri V (104) Italy case-control 65 63 (96.9) 57.3 ± 15.25 16
2017 Saranya C (105) India case-control 55 median 38 30 median 39
2016 Shenavandeh S (106) Iran case-control 44 40 (90.9) 40.7 ± 12.8 44 41 (93.2) 39.4 ± 11.76
2009 Solanilla A (107) France case-control 35 25
2016 Yalcinkaya Y (108) Turkey case-control 72 66 (92) 44.9 ± 12.7 20
2020 Waszczykowska A Poland case-control 25 21 (84) 57.1 ± 10.8 25 20 (80) 59.4 ± 9.9
(109) diffused 8 7 (87.5) 50.6 ± 11.4
limited 17 14 (82.4) 60.2 ± 9.4
2008 Wipff J (110) France case-control 187 157 (84) 55.9 ± 13.2 48 40 (83.3) 59.4 ± 11.6
Year Author Country Study type BD HC
Sample size Female (%) Age (years) Sample size Female (%) Age (years)
2018 Arica DA (111) Turkey case-control 45 22 (48.9) 36.7 ± 10.3 28 35.7 ± 7.51
2003 Cekmen M (112) Turkey case-control 39 18 (46.2) 38.1 ± 10.4 15 7 (46.7) 39.2 ± 9.3
2013 Eldin AB (113) Egypt case-control 30 6 (20) 30.6 ± 9.36 20 4 (20) 26.9 ± 8.38
2003 Erdem F (114) Turkey case-control 33 16 (48.5) 33.2 ± 10.4 30 9 (30) 34.0 ± 11.1
2012 Ganeb SS (115) Egypt case-control 70 27 (38.6) 32.84 ± 3.63 70 29 (41.4) 32.81 ± 3.89
2019 Gheita TA (116) Egypt case-control 96 34.9 ± 10.1 60 9 (25) 36.7 ± 12.6
active 59 11 (18.6) 33.03 ± 9.8
inactive 37 6 (16.2) 36.2 ± 10.1
2011 Ibrahim SE (117) Egypt case-control 40 8 (20) 40.35 ± 7.34 40 9 (22.5) 37.3 ± 7.06
2017 Kul A (118) Turkey case-control active 40 16 (40) 37.6 ± 8.7 40 18 (45) 38.8 ± 7.9
2009 Ozdamar Y (119) Turkey case-control active prosterior segment of BD 20 7 (35) 33 ± 6
inactive ocular BD 23 10 (43.5) 35 ± 7
2007 Ozturk MA (120) Turkey case-control 21 6 (28.6) 35.8 ± 8.6 21
2018 Sertoglu E (121) Turkey case-control 55 18 (32.7) 40 ± 10 31 12 (38.7) 40 ± 13
2006 Shaker O (122) Egypt case-control 30 20 32.6 ± 9.14 15 20 30.13 ± 12.32
2013 Yalcindag A (123) Turkey case-control 65 32 (49) 40.3 ± 9.8 21 11 (48) 38.5 ± 9.3
Year Author Country Study type KD HC
Sample size Female (%) Age (years) Sample size Female (%) Age (years)
2011 Breunis WB (124) Netherlands case-control early101 18
2001 Hamamichi Y (125) Japan case-control acute 49 1.9 ± 0.2 38 4.5 ± 0.7
convalesent 30 4.8 ± 0.7
1998 Maeno N (126) Japan case-control 22 10 (45.5) 2.2 ± 1.425 healthy 19 9 (47.7) 1.4 ± 1.4
acute 20 10 (50) 1.5 ± 1.15 febrile 22 10 (45.5) 1.3 ± 1.4
subacute 13 5 (38.5) 2.5 ± 1.325
convalesent 15 8 (53.3) 1.9 ± 1.4
1999 Ohno T (18) Japan case-control acute 66 24 (36.4) 1.79 ± 2.375 healthy 18 8 (44.4) 4.25 ± 1.75
acute phase31 febrile 18 9 (50) 3.375 ± 2.29
convalescent phase31
2002 Takuro Ohno (127) Japan case-control acute phase 41 14 (34.1) 1.83 ± 2.17 25 8 (32) 9 ± 1.75
convalescent phase 41
2019 Su Y (128) China case-control 90 51 (56.7) 2.55 ± 1.72 healthy 60 28 (46.7) 2.19 ± 2.22
febrile 40 20 (50) 2.84 ± 1.63
2009 Ueno K (129) Japan case-control 80 37 (46.25) 2.1 ± 1.8 febrile 26 10 (38.5) 1.9 ± 1.1
2016 Zeng H (130) China case-control 52
Year Author Country Study type AS HC
Sample size Female (%) Age (years) Sample size Female (%) Age (years)
2016 Akar S (13)] Turkey case-control 98 27.7 ± 8.6 49
2016 Deveci K (55) Turkey case-control 30 mean age of 30–50 30 mean age of 30–50
2002 Goldberger C (132) Austria case-control 16 2 (12.5) 50.4 ± 2.7 8
2015 Lin TT (133) China case-control 140 102 (72.9) 31.8 ± 9.3 90 72 (80) 30.2 ± 8.2
2016 Przepiera-Bedzak H (134) Poland case-control 80 16 (20) 50.9 ± 12.8 21 8 (38.1) 48.2 ± 13.5
2015 Przepiera-Bedzak H (135) Poland case-control 61 12 (19.7) 43.3 ± 13.2 29 19 (65.5) 48.2 ± 13.5
2016 Przepiera-Bedzak H (136) Poland case-control 81 20 (24.7) 44.7 ± 13.2 30 19 (63.3) 43.5 ± 9.4
2016 Sakellariou GT (137) Greece case-control 57 4 (7.0) 39.1 ± 1.4 34 2 (6.0) 38.8 ± 1.0
2015 Solmaz D (138) Turkey case-control 98 21 (21.4) 39.3 ± 10.0 49 12 (24.5) 39.0 ± 5.9
2018 Solmaz D (139) Turkey case-control 97 21 (21.6) 38 ± 10.4 48 12 (25) 41 ± 5.0
2019 Torres L (140) Sweden case-control 204 87 (43) 49 ± 15.56 80
2010 Tseng JC (73) China case-control 50 50
Year Author Country Study type IBD HC
Sample size Female (%) Age (years) Sample size Female (%) Age (years)
2018 Aksoy EK (141) Turkey case-control UC 39 15 (38.5) 46.1 ± 12.6 15 7 (46.7) 41.4 ± 12.6
2014 Algaba A (142) Spain case-control 37 (UC = 6) 20 (54) 36 ± 13 40 24 (60) 43 ± 9
2004 Di Sabatino A (143) Italy case-control CD 25 37.8 ± 11.25 22 38.3 ± 11.25
2007 Dueñas Pousa I (144) Spain case-control CD 30 15 (50) 44 ± 14 30 15 (50) 43 ± 14
2006 Ferrante M (145) Belgium cohort 824 466 (56.6) 38.9 ± 12.07 271 156 (57.6) 28 ± 10.37
1999 Griga T (146) Germany case-control 27 10 5 (50) 29.3 ± 6.1
CD 19 8 (42.1) 34.8 ± 11.0
UC 8 3 (37.5) 46.6 ± 19.5
1998 Griga T (147) Germany case-control 46 9 5 (55.6) 31.5 ± 8.0
CD 31 13 (41.9) 33.1 ± 7.9
UC 15 7 (46.7) 34.5 ± 12.0
2001 Kanazawa S (148) Japan case-control 22 20 12 (60) 60 ± 8
CD 11 7 (63.6) 38.5 ± 5.75
UC 11 6 (54.5) 56.5 ± 10.75
2003 Kapsoritakis A (149) Greece case-control 94 23 38 ± 9
CD 44
UC 50
2015 Kleiner G (150) Italy case-control 26;CD15;UC11 12 (46.2) 9 ± 3.75 37 22 (59.5) 11 ± 4
2004 Magro F (151) Portugal case-control 218 115 59 (51.3) 32 ± 9.75
CD 145 84 (57.9) 33 ± 14.5
UC 73 43 (58.9) 35 ± 11.75
2011 Pousa ID (152) Spain case-control active UC 13 46 46 ± 12 26
2007 Pousa ID (153) Spain case-control CD 70 39 (55.7) 42 ± 13 30 15 (50) 43 ± 14
1997 Schurer-Maly CC (154) Switzer-land case-control CD 24 32
UC 23
2020 deZoeten EF (155) America case-control pediatric 5/18 (27.8) 12.7 ± 12.7 pediatric 17 7/18 (38.9) 12.7 ± 16.5
active IBD 17
adult adult 19 7/19 (36.8) 56.9 ± 14.4
actuve UC 10 36.4 ± 11.7
inactive UC 10 52.6 ± 17.7
2007 Wiercinska-Drapalo A (156) Poland case-control UC 33 13 (39.4) 43 ± 12.75 20 5 (25) 38 ± 6
Year Author Country Study type PsA HC
Sample size Female (%) Age (years) Sample size Female (%) Age (years)
2009 Ablin JN (14) Israel case-control skin10 4 (40) 48.6 ± 18.6 16 12 (75) 41.69 ± 9.71
arthritis22 10 (45.5) 47.18 ± 8.15
2007 Akman A (157) Turkey case-control 46 30 (65.2) 43.2 ± 14.4 20 7 (35) 34.6 ± 14.5
2010 Anderson KS (158) Sweden case-control plaque(PV) 14 4 (28.6) 47 ± 10.75 14
2001 Ballara S (12) UK cohort arthritis13 62 46 ± 17.04 31 65 49 ± 12.59
2016 Batycka-Baran A (159) Poland case-control arthritis 24 37.5 48.29 ± 9.05 36
2012 Batycka-Baran A (160) Poland case-control plaque-type psoriasis 63 41.3 42.16 ± 15.42 31 48.4 41.35 ± 15.23
2016 Capkin AA (161) Turkey case-control 48 16 (33.3) 48.6 ± 12.5 48 20 (41.7) 52.3 ± 8.4
1999 Bhushan M (162) UK case-control chronic plaque 15 6 (30) 45 ± 13.75 13 7 (53.8) 43 ± 14.75
2002 Creamer D (163) UK case-control 22 7 (31.8) 47 ± 12 17 7 (41.2) 42 ± 10
severe 11
moderate 11
arthritis 10
non-arthritis 12
2010 Flisiak I (164) Poland case-control chronic plaque 59 16 (27.1) 49.1 ± 2.1 20
mild 24
moderate 20
severe 15
2007 Fink AM (165) Austria case-control arthritis 28 10 (35.7) 54 ± 13 9 2 (22.2) 56 ± 9
active 14 4 (28.6)
inactive 14 6 (42.9)
2012 Kaur S (166) Estonia case-control Plaque (PV) 58 23 (39.7) 41.7 ± 12.0 58 30 (51.7) 41.4 ± 12.1
2014 Meki AR (167) Saudi Arabia case-control Plaque (PV)58 22 (37.9) 30.17 ± 10.71 22 11 (50) 29.36 ± 8.83
2020 Midde HS (168) India cohort 54 16 (29.6) 41.28 ± 11.83 54 16 (29.6) 41.22 ± 11.77
2002 Nielsen HJ (169) Denmark cohort Plaque (PV)16 9 (56.25) 24–70 years 13
2008 Nofal A (170) Egypt case-control Plaque (PV)30 11 (37) 42 ± 12.2 10 4 (40) 38.5 ± 11.6
2015 Przepiera-Bedzak H (135) Poland case-control arthritis 69 39 (56.5) 52.0 ± 12.0 29 19 (65.5) 48.2 ± 13.5
2016 Przepiera-Bedzak H (136) Poland case-control arthritis 76 43 (56.6) 50.8 ± 12.7 30 19 (63.3) 43.5 ± 9.4
2013 Przepiera-Bedzak H (171) Poland case-control arthritis 80 43 (53.8) 50.1 ± 12.0 20 12 (60) 48.1 ± 14.0
2016 Shahidi-Dadras M (172) Iran case-control severe chronic plaque psoriasis 60 27 (45) 38.35 ± 14.96 60 27 (45) 39.55 ± 15.24
2016 Shahidi-Dadras M (173) Iran case-control moderate-severe chronic plaque psoriasis 58 27 (46.6) 37.5 ± 14.1 60 27 (45) 39.6 ± 15.2
2009 Takahashi H (174) Japan case-control 122 41 (33.6) 47.5 ± 7.6 78 24 (30.8) 38.6 ± 12.25
2017 Zheng YZ (175) China case-control Plaque (PV)194 74 (38.1) 39.5 ± 12.70 175 81 (46.3) 40.2 ± 7.58
Year Author Country Study type GD HC
Sample size Female (%) Age (years) Sample size Female (%) Age (years)
2020 Cheng CW (10) China case-control 40 100 40.9 ± 13.5 14 100 44.1 ± 13.8
2009 Figueroa-Vega N (176) Spain case-control 44 32 (72.7) 45.11 ± 15.20 22 14 (63.6) 43.47 ± 8.62
active GO 13 9 (69.2) 46.42 ± 12.58
inactive GO 13 10 (76.9) 48.77 ± 19.31
No GO 18 13 (72.2) 41.85 ± 10.76
1998 Iitaka M (177) Japan case-control 49 39 (79.6) 34.7 ± 11.9 37 26 (70.3) 35.7 ± 11.2
2014 Kajdaniuk D (178) Poland case-control active GO16 12 (75) 37 ± 9 22
2016 Rancier M (179) Tunisia case-control 21 4 (19.0) 44.84 ± 12.10 55 29 (52.7) 46.36 ± 11.03
2014 Ye X (180) China case-control 64 30 20 (66.7) 32.8 ± 10.8
GD 30 19 (63.3) 34.50 ± 13.45
active GO34 23 (67.6) 31.06 ± 15.15
inactive GO14 9 (64.3) 30.79 ± 17.80

SLE, systemic lupus erythematosus; LN, lupus nephritis; HC, healthy control; RA, rheumatoid arthritis; HC, healthy control; SSc, systemic sclerosis; VEDOSS, very early diagnosis of systemic sclerosis; UCTD, undifferentiated connective tissue disease; HC, healthy control; BD, Behcet’s disease; HC, healthy control; KD, Kawasaki disease; HC, healthy control. AS, ankylosing spondylitis; HC, healthy control; IBD, inflammatory bowel disease; CD, Crohn’s disease; UC, ulcerative colitis; HC, healthy control; PsA, psoriasis; PV, psoriasis vulgaris; HC, healthy control; GD, Graves’ disease; GO, Graves’ ophthalmopathy; HC, healthy control.

Meta-Analysis of the Association Between Circulating VEGF and SLE

Circulating VEGF levels were significantly higher in SLE than in HC (SMD 0.84, 95%CI 0.25–1.44, P = 0.0056) ( Figure 2A ). Additionally, circulating VEGF was higher in active SLE than in inactive SLE (SMD 0.80, 95%CI 0.02–1.59, P = 0.0454) ( Figure 2B-i ), serum VEGF levels remained remarkable higher in active SLE than in inactive SLE (SMD 0.51, 95% CI 0.33–0.70, P <0.0001) ( Figure 2B-ii ), whereas serum VEGF levels were significantly higher in SLE with renal involvement than that without renal involvement (SMD 1.43, 95% CI 0.58–2.28, P = 0.0010) ( Figure 2C ). Due to the observed heterogeneity, the sample types were stratified (serum versus plasma); the heterogeneity in serum VEGF levels in active and inactive SLE disappeared after removing studies using plasma (before, I2 = 94.04%, P = 0.0002; after, I2 = 0.00%, P = 0.3178).

Figure 2.

Figure 2

Forest plot of SLE associated with the circulating VEGF. (A) SLE vs. HC, forest plot; (Bi) Active SLE vs. Inactive SLE; (ii) Serum VEGF in active SLE vs. inactive SLE, forest plot; (C) Renal SLE vs. Non-renal SLE, forest plot; (D) Subgroup analysis: (i) Serum vs. Plasma (a for serum and b for plasma); (ii) Sample size n≤50 vs. n>50 (a for n≤50 and b for n>50).

The subgroup analysis indicated significantly higher serum (SMD 0.64, 95% CI 0.37–0.91, P <0.0001) and plasma (SMD 1.56, 95% CI 0.49–2.63, P = 0.0040) VEGF levels in SLE ( Figure 2D-i ). Significantly higher circulating VEGF levels were present in small (n ≤50) (SMD 0.96, 95% CI 0.56–1.35, P <0.0001) and large (n >50) (SMD 0.39, 95% CI 0.07–0.72, P = 0.0170) studies ( Figure 2D-ii ).

Meta-regression analysis adjusted for age and percentage of female patients demonstrated age (P = 0.0030) but not sex (P = 0.9700) had a significant effect.

Meta-Analysis of the Association Between Circulating VEGF and RA

Circulating VEGF levels were significantly higher in RA than in HC (SMD 1.48, 95% CI 0.82–2.15, P <0.0001) ( Figure 3A ). Overall heterogeneity was apparent.

Figure 3.

Figure 3

Forest plot of RA associated with the circulating VEGF. (A) RA vs. HC, forest plot; (B) Subgroup analysis: (i) Serum vs. Plasma (a for serum and b for plasma); (ii) Sample size n≤50 vs. n>50 (a for n≤50 and b for n>50).

The subgroup analysis indicated significantly higher VEGF levels in serum (SMD 1.49, 95% CI 1.09–1.88, P <0.0001) but not plasma (P = 0.0820) in RA ( Figure 3B-i ). Higher circulating VEGF levels were present in small (n ≤50) (SMD 1.58, 95% CI 1.10–2.05, P <0.0001) and large (n >50) (SMD 1.03, 95% CI 0.47–1.60, P <0.0001) studies on RA ( Figure 3B-ii ).

Meta-regression analysis adjusted for age and female sex demonstrated neither age (P = 0.4090) nor sex (P = 0.7570) had a significant effect.

Meta-Analysis of the Association Between Circulating VEGF and SSc

Circulating VEGF levels were significantly higher in SSc than in HC (SMD 0.56, 95% CI 0.36–0.75, P <0.0001) ( Figure 4A ). The comparison of serum VEGF levels between limited and diffused SSc did not reach statistical significance (P = 0.2735) ( Figure 4B ).

Figure 4.

Figure 4

Forest plot of SSc associated with the circulating VEGF. (A) SSc vs. HC, forest plot; (B) Limited SSc vs. Diffused SSc, forest plot; (C) Subgroup analysis: (i) Serum vs. Plasma (a for serum and b for plasma); (ii) Sample size n≤50 vs. n>50 (a for n≤50 and b for n>50).

The subgroup analysis performed due to the obvious overall heterogeneity (I2 = 98.35%, P <0.0001) revealed significantly higher VEGF levels in serum (SMD 0.48, 95% CI 0.28–0.67, P <0.0001) and plasma (SMD 0.86, 95% CI 0.49–1.24, P <0.0001) samples of patients with SSc ( Figure 4C-i ). Elevated circulating VEGF levels were observed in small (n ≤50) (SMD 0.57, 995% CI 0.33–0.81, P <0.0001) and large (n >50) (SMD 0.52, 95% CI 0.28–0.75, P <0.0001) studies on SSc ( Figure 4C-ii ).

Meta-regression analysis adjusted for age and female sex demonstrated neither age (P = 0.2740) nor sex (P = 0.7020) had a significant effect.

Meta-Analysis of the Association Between Circulating VEGF and BD

Circulating VEGF levels were significantly higher in BD than in HC (SMD 1.65, 95% CI 0.88–2.41, P <0.0001) ( Figure 5A ) as well as in active BD than in inactive BD (SMD 0.91, 95% CI 0.26–1.55, P = 0.0064) ( Figure 5B ). Heterogeneity was present.

Figure 5.

Figure 5

Forest plot of BD associated with the circulating VEGF. (A) BD vs. HC, forest plot; (B) Active BD vs. Inactive BD, forest plot; (C) Subgroup analysis: (i) Serum vs. Plasma (a for serum and b for plasma); (ii) Sample size n≤50 vs. n>50 (a for n≤50 and b for n>50).

The subgroup analysis revealed significantly elevated serum VEGF levels (SMD 1.60, 95% CI 0.85–2.34, P <0.0001) ( Figure 5C-i ), specifically in small (n ≤50) (SMD 1.86, 95% CI 1.15–2.57, P <0.0001) and not in large (n >50) studies (P = 0.1200) ( Figure 5C-ii ).

Meta-regression analysis adjusted for age and female sex demonstrated neither age (P = 0.2700) nor sex (P = 0.0720) had a significant effect.

Meta-Analysis of the Association Between Circulating VEGF and KD

Circulating VEGF levels were elevated in KD than in HC (SMD 2.41, 95% CI 0.10–4.72, P = 0.0406) ( Figure S1A ) and febrile controls (SMD 1.08, 95% CI 0.02–2.14, P = 0.0452) ( Figure S1B ). The comparison of serum VEGF levels between acute and convalescent KD revealed no statistical significance (P = 0.0831) ( Figure S1C ). Heterogeneity was prominent.

The subgroup analysis indicated serum VEGF levels were higher in KD than in HC (SMD 2.26, 95% CI 0.93–3.58, P = 0.0010) ( Figure S1D-i ). Increased circulating VEGF levels were found in small (n ≤50) (SMD 1.36, 95% CI 0.45–2.27, P = 0.0030) and large (n >50) studies (SMD 3.19, 95% CI 1.01–5.38, P = 0.0040) ( Figure S1D-ii ). Meta-regression analysis adjusted for age and female sex demonstrated female sex (P = 0.0100) but not age (P = 0.1280) had a significant effect.

Meta-Analysis of the Association Between Circulating VEGF and AS

Circulating VEGF levels were significantly elevated in AS than in HC (SMD 0.78, 95% CI 0.23–1.33, P = 0.0052) ( Figure S2A ). The overall heterogeneity was apparent (I2 = 95.68%, P <0.0001).

The subgroup analysis revealed significantly higher serum VEGF levels in AS than in HC (SMD 0.60, 95% CI 0.36–0.84, P <0.0001) ( Figure S2B-i ). Significantly elevated circulating VEGF levels were found in small (n ≤50) (SMD 1.66, 95% CI 0.35–2.98, P = 0.0130) and large (n >50) studies (SMD 0.55, 95% CI 0.29–0.80, P <0.0001) on AS ( Figure S2B-ii ).

Meta-regression analysis adjusted for age and female sex demonstrated neither age (P = 0.8040) nor sex (P = 0.8500) had a significant effect.

Meta-Analysis of the Association Between Circulating VEGF and IBD

Serum VEGF levels were significantly higher in IBD than in HC (SMD 0.57, 95% CI 0.43–0.71, P <0.0001) ( Figure S3A ). The overall heterogeneity was extremely low (I2 = 3.12%, P <0.0001). Meta-regression analysis adjusted for age or females demonstrated insignificant effect of age (P = 0.0760) and sex (P = 0.2610).

Serum VEGF levels were significantly higher in ulcerative colitis (UC) than in HC (SMD 0.69, 95% CI 0.21–1.16, P = 0.0048) ( Figure S3B-i ). Both the studies on active UC and those that did not specify disease activity reported significantly higher serum VEGF levels in UC (SMD 0.75, 95% CI 0.17–1.34, P = 0.0120 and SMD 0.56, 95% CI 0.20–0.93, P = 0.0030, respectively) ( Figure S3D-i ). The serum VEGF levels were not significantly different between active and inactive UC (P = 0.1658) ( Figure S3C-i ). Meta-regression analysis adjusted for age and female sex demonstrated insignificant effects of age (P = 0.8330) and sex (P = 0.2150).

Serum VEGF levels were significantly higher in Crohn’s disease (CD) than in HC (SMD 0.72, 95% CI 0.29–1.16, P = 0.0011) ( Figure S3B-ii ). Both the studies on active CD and those that did not specify disease activity reported significantly higher serum VEGF levels in CD (SMD 0.62, 95% CI 0.10–1.15, P = 0.0200 and SMD 0.78, 95% CI 0.33–1.22, P = 0.0010, respectively) ( Figure S3D-ii ). Significantly increased serum VEGF levels were present in small (n ≤50) (SMD 0.86, 95% CI 0.32–1.40, P = 0.002) but not in large (n >50) studies (P = 0.0600) ( Figure S3D-iii ). Moreover, serum VEGF levels were significantly higher in active CD than in inactive CD (SMD 0.53, 95% CI 0.09–0.96, P = 0.0176) ( Figure S3C-ii ). Meta-regression analysis adjusted for age and female sex demonstrated age (P = 0.0120) and sex (P = 0.0010) had significant effects.

Meta-Analysis of the Association Between Circulating VEGF and PsA

Circulating VEGF levels were significantly higher in PsA (SMD 0.98, 95% CI 0.62–1.34, P <0.0001) ( Figure S4A ), in psoriatic arthritis (SMD 0.72, 95% CI 0.12–1.32, P = 0.0192) ( Figure S4B ), and psoriasis with skin involvement (SMD 1.26, 95% CI 0.65–1.86, P = 0.0001) than in HC ( Figure S4C ). Heterogeneity was observed in the analyses.

The subgroup analysis indicated significantly higher serum (SMD 1.02, 95% CI 0.50–1.55, P <0.0001) and plasma (SMD 0.67, 95% CI 0.37–0.97, P <0.0001) VEGF levels in PsA ( Figure S4D-i ). Significantly higher circulating VEGF levels were found in small (n ≤50) (SMD 0.80, 95% CI 0.49–1.11, P <0.0001) and large (n >50) (SMD 1.12, 95% CI 0.40–1.83, P = 0.0020) studies on PsA ( Figure S4D-ii ). Meta-regression analysis adjusted for age and female sex demonstrated that neither age (P = 0.0570) nor sex (P = 0.1890) had a significant effect.

Meta-Analysis of the Association Between Circulating VEGF and GD

Circulating VEGF levels were significantly higher in GD than in HC (SMD 0.69, 95% CI 0.20–1.19, P = 0.0056), with considerable heterogeneity ( Figure S5A ). Circulating VEGF levels were higher in active than in inactive Graves’ ophthalmopathy (GO) (SMD 0.80, 95% CI 0.29–1.30, P = 0.0019), without any heterogeneity (I2 = 0.00%, P = 0.7548) ( Figure S5B ).

Serum (SMD 0.77, 95% CI 0.27–1.28, P = 0.0020) but not plasma (P = 0.3880) VEGF levels were significantly higher in GD than in HC ( Figure S5C ). Meta-regression analysis adjusted for age and female sex demonstrated the significant effect of age (P = 0.0070) but not sex (P = 0.2420).

Correlation Analyses Between Circulation VEGF and AD Clinical Features

We explored the potential correlation of VEGF in clinical implications and hematological indicators of ADs. For SLE ( Figure S6 ), the summary Fisher’s z showed a positive, moderate correlation between circulating VEGF level and disease activity (SLEDAI/SLAM, ES 0.55, 95% CI 0.29–0.81, P <0.0001; summary r = 0.50), erythrocyte sedimentation rate (ESR; ES 0.40, 95% CI 0.18–0.63, P = 0.0004; summary r = 0.38). A negative, poor correlation was found for C3 (ES −0.45, 95% CI −0.81 to −0.08, P = 0.0162, summary r = −0.42). There was no correlation between circulating VEGF level and platelet count (P = 0.1163).

In RA ( Figure S7 ), there was a positive, weak correlation between circulating VEGF and disease activity (DAS-28; ES 0.33, 95% CI 0.22–0.44, P <0.0001, summary r = 0.32), ESR (ES 0.35, 95% CI 0.18–0.51, P <0.0001; summary r = 0.34) as well as C-reactive protein (CRP; ES 0.38, 95% CI 0.24–0.52, P <0.0001; summary r = 0.36).

In SSc ( Figure S8 ), there was a positive, moderate relationship between circulating VEGF level and pulmonary artery pressure (ES 0.62, 95% CI 0.37–0.87, P <0.0001; summary r = 0.55) and Medical Research Council dyspnea score (ES 0.65, 95% CI 0.08–1.22, P = 0.0246; summary r = 0.57). There was no relationship between circulating VEGF level and modified Ronan skin score (P = 0.3100).

In BD ( Figure S9 ), summary correlation coefficients indicated a significant, positive, and strong correlation with disease activity based on Behcet’s disease current activity form score (ES 1.22, 95% CI 0.03–2.41, P = 0.0446, summary r = 0.84) and moderate correlation with ESR (ES 0.47, 95% CI 0.11–0.82, P = 0.0108, summary r = 0.44).

In AS ( Figure S10 ), circulating VEGF level was poorly correlated with disease activity (BASDAI/BASMI; ES 0.35, 95% CI 0.09–0.60, P = 0.0080; summary r = 0.34), ESR (ES 0.26, 95% CI 0.17–0.36, P <0.0001; summary r = 0.25), and CRP (ES 0.24, 95% CI 0.14–0.35, P <0.0001; summary r = 0.24).

In IBD ( Figure S11 ), circulating VEGF level exhibited a positive, poor correlation with Crohn’s disease activity index (CDAI; ES 0.34, 95% CI 0.10–0.57, P = 0.0053, summary r = 0.33), medium correlation with UC activity index (UDAI; ES 0.57, 95% CI 0.29–0.86, P = 0.0001; summary r = 0.52), strong correlation with ESR (ES 0.87, 95% CI 0.63–1.12, P <0.0001; summary r = 0.70), and weak correlation with platelet count (ES 0.32, 95% CI 0.16–0.49, P = 0.0001; summary r 0.31).

In PsA ( Figure S12 ), circulating VEGF level was positively correlated with psoriasis area and severity index score (ES 1.12, 95% CI 0.64–1.60, P <0.0001; summary r = 0.81) and had a positive, moderate correlation with disease duration (ES 0.51, 95% CI 0.32–0.69, P <0.0001; summary r = 0.47).

Sensitivity Analysis and Publication Bias

The sensitivity analysis revealed the stability of pooled results (data not shown). For SLE, RA, SSc, KD, and AS, the contour-enhanced funnel plots revealed no publication bias ( Figure S13 ), the meta-trim practice demonstrated that all imputed studies fell into the significant region. In contrast, Egger’s test suggested publication bias for SLE, RA, and KD (P <0.0001 for all) as well as for AS (P = 0.0001). However, there was consistency in publication bias for SSc by Egger’s test (P = 0.1413). This remind us to be cautious with using Egger’s test to determine publication bias in small number of studies (<20). There was no publication bias with PsA and GD (P = 0.4874 and P = 0.5419, respectively), in contrast to that observed with BD (P = 0.0006). The imputed studies on IBD fell into the non-significant region, and Egger’s test also represented evidence of it (P = 0.0017) in UC; the existence of publication bias was proven by Egger’s test (P = 0.0113) in CD.

Discussion

In the current meta-analysis, we found a close relationship between circulating VEGF level and ADs. First, our analyses revealed significantly increased circulating VEGF levels in SLE, RA, SSc, BD, KD, AS, IBD, PsA, and GD. Additionally, we showed that serum VEGF could distinguish active from inactive SLE and renal from non-renal SLE; it could also discriminate between active and inactive CD. Likewise, circulating VEGF had a strong ability to differentiate active from inactive BD and GO. Serum VEGF exhibited its dipartite boundedness in limited/diffused cutaneous SSc, active/inactive UC, and acute/convalescent KD. Furthermore, we demonstrated the correlation of circulating VEGF levels with metrics of disease activity and severity (SLEDAI/SLAM, DAS-28, MRC dyspnea score, modified Ronan skin score, BD current activity form score, BASDAI/BASMI, CDAI, UDAI, psoriasis area and severity index) as well as with hematological parameters (ESR, CRP, platelet count, pulmonary artery pressure). Overall, these results indicate that circulating VEGF reflects pathogenesis and should be considered as a potent hematological marker for diagnosis and disease progression in ADs.

Structural and functional abnormities in neovasculature may lead to damage in chronic inflammatory diseases. Consecutive angiogenesis and immune-mediated vascular endothelial cell injury and dysfunction as well as persistent inflammation play important pathological roles in SLE (20), whereas expansion and invasion of synovial vessels facilitate inflammation and erosive joint destruction in RA (12). Early generalized microvascular endothelial damage leading to immune activation and defective angiogenesis are significant events in cumulative systemic fibrosis and microangiopathy in SSc (76). Additionally, BD is characterized by systemic vasculitis, inflammatory infiltrates, subsequent vascular lesions, and neovascularization (113, 115), whereas subendothelial edema and fenestrated endothelium constitute acute systemic vasculitis observed in KD (181). Structural changes in vascular endothelium due to inflammation and hypoxia stimulate angiogenesis to permeate vascular and mediate tissue repair in IBD (6). Finally, early psoriatic skin plaque formation is triggered by inappropriate expansion and vascular alterations, pronounced permeability, and endothelial cell proliferation (162). Therefore, angiogenesis and angiopathy are considered as major pathogenic events predisposing to ADs.

VEGF, an increasingly recognized proangiogenic inducer of endothelial proliferation and microvascular hyperpermeability, may reverse the tide of inducers against inhibitors and promote angiogenesis (182). Despite the unclear role of angiogenesis in AS and GD, higher-than-normal VEGF levels support its role in bone and enchondral ossification in AS (183) and increased microvessel density in GD (184). Over the past decades, numerous studies have reported increased VEGF levels in ADs, beyond its well-known role in tumorigenesis. In the present study, our meta-analysis reveals differences in circulating VEGF levels between patients with ADs and HC subjects, providing further evidence for its utility in determining disease activity and severity in ADs.

In the present meta-analysis, there were variations in circulating VEGF levels due to differences in sample collection methods and demographic characteristics across the studies, requiring adjustment for the interpretation of the final laboratory results. Serum VEGF levels are 7–10 times higher than plasma VEGF levels in RA (60). Serum VEGF is a combination of efflux from platelets, neutrophils during coagulation, and circulating VEGF, which rarely occurs in vivo; in contrast, plasma VEGF directly reflects circulating VEGF in the absence of coagulation in vivo. In support of this difference, the present meta-analysis also revealed that the removal of plasma samples from the analysis led to the disappearance of heterogeneity in serum VEGF levels in active and inactive SLE. Plasma samples with citrate anticoagulants had the lowest VEGF levels, reflecting that that reservation of platelets VEGF releasing is effective and that different anticoagulation procedures should be considered in evaluating variations in VEGF levels across studies. Higher plasma VEGF levels in female patients compared with male patients, increasing VEGF levels with age in adults, and decreasing VEGF levels with age in children illustrate the contributory roles of sex and age to discrepancy (185). The cohort size in specific studies might also impact the mean and standard deviation. Therefore, we addressed these variables in subgroup and meta-regression analyses. The subgroup analyses explored the source of heterogeneity in serum VEGF levels for only studies on active and inactive SLE (before, I2 = 94.04%, P = 0.0002; after, I2 = 0.00%, P = 0.3178). We also observed apparent associations of circulating VEGF levels with age and female sex in SLE and CD, with sex in KD, and with age in GD.

There are several limitations in the present meta-analysis. First, although subgroup and meta-regression analyses were performed to explore heterogeneity, much of it remains to be explained and reported. Second, the funnel plots indicated publication bias in studies on BD and IBD, including UC as well as CD, which might have led to the overestimation of pooled SMDs. Third, data could not be fully retrieved, which might have resulted in missing values in meta-regression and the omission of covariates in tests assessing heterogeneity. Availability of complete data on patient inclusion and exclusion criteria, ethnicity, AD treatment details, and exact timing and method of VEGF measurement would greatly reduce the bias in our analyses. Although the existing heterogeneity could be partially explained by age, sex, sample type, and sample size of the individual studies, an exact conclusion could not be drawn due to the lacking explanation for the remaining heterogeneity. Further studies using more comprehensive data should be performed to elucidate the association of circulating VEGF levels with ADs.

In conclusion, our meta-analysis unveiled a close association between circulating VEGF levels and ADs including disease activity and severity as well as clinical hematological manifestations. Serum VEGF is a reliable marker that can distinguish active from inactive in SLE and GO and can potentially differentiate IBD from HC. Early and regular measurement of circulating VEGF levels may be considered as a noninvasive method to monitor vascular involvement and activity in ADs. Future studies should focus on the prognostic and diagnostic utility of circulating VEGF, its role in pathogenesis, and the utility of VEGF-targeted therapeutic strategies in ADs.

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.

Author Contributions

YL conceived and designed the research. HZ and HL extracted data and conducted quality assessment. CL, LC, SY, HL, and HZ analyzed the data. HZ wrote the paper. All authors are accountable for all aspects of the study, and attest to the accuracy and integrity of the results. All authors contributed to the article and approved the submitted version.

Funding

This research was supported by grants from the National Natural Science Foundation of China Grants (81871302) and Beijing Key Clinical Specialty for Laboratory Medicine - Excellent Project (No. ZK201000).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2021.674343/full#supplementary-material

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Data Availability Statement

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