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. 2024 Sep 19;56(1):2326297. doi: 10.1080/07853890.2024.2326297

Risk factors for cardiovascular diseases in patients with vitiligo: an analysis of current evidence

Xin Liang a,#, Fei Guo b,#, Miao Zhang b,c, Chunxiao Wang b,c, Naixuan Lin b,c, Li Liu a, Yan Chen a, Fang Liu a, Yuhua Du a, Lei Li a,✉, Xin Li a,b,c,✉
PMCID: PMC11418058  PMID: 39300810

Abstract

Objective

The relationship between vitiligo and cardiovascular diseases remains controversial. This study aimed to systematically review the evidence comparing cardiovascular disease risk factors between patients with vitiligo and controls and to perform a meta-analysis of the results.

Data sources

A comprehensive database search was performed for all studies in PubMed, EMBASE, and Cochrane Central Register databases from inception to November, 2023. The main keywords used were vitiligo, hypertension, diabetes, hyperlipidemia, metabolic syndrome, obesity, smoking, alcohol consumption, C-reactive protein, and homocysteine.

Study selection

Only observational studies and no randomized controlled trials were included. Of the 1269 studies initially selected, the full texts of 108 were assessed for eligibility, and 74 were ultimately included in the analysis.

Data extraction and synthesis

Three reviewers independently extracted the following data: study design, number and characteristics of participants, inclusion indicators, and disease duration. A meta-analysis of the single-group rates was performed for the diabetes, hypertension, hyperlipidemia, and obesity groups. Random-effects or fixed-effects models were used to calculate the sample-size weighted averages for the indicators included in the studies.

Main outcomes and measures

The primary outcomes were co-morbidity analysis and co-morbidity rates of vitiligo with metabolic syndrome, obesity, hyperlipidemia, hypertension, and diabetes mellitus. Secondary outcomes were factors associated with vitiligo and cardiovascular disease.

Results

This meta-analysis concluded that comorbidities in patients with vitiligo included metabolic syndrome, diabetes, obesity, hyperlipidemia, and hypertension, with comorbidity rates of 28.3%, 6.0%, 38.5%, 43.0%, and 15.8%, respectively. Simultaneously, we showed that the vitiligo group differed significantly from the control group in the following aspects: fasting blood glucose, insulin, systolic and diastolic blood pressure, total cholesterol, triglycerides, low-density lipoprotein, high-density lipoprotein, homocysteine, C-reactive protein, smoking, and alcohol consumption. However, no significant differences were observed between the vitiligo and control groups in terms of waist circumference, body mass index, or phospholipid levels.

Limitations

The vast majority of the studies were from Eastern countries; therefore, extrapolation of these results to Western populations is questionable. The significant heterogeneity may be due to different protocols, doses, durations, center settings, population registries, etc., which severely compromise the validity of the results.

Conclusion

This study summarized not only the factors associated with, but also those not associated with, cardiovascular disease in patients with vitiligo. This study provides a foundation for the prevention and treatment of cardiovascular disease in patients with vitiligo.

Keywords: Vitiligo, cardiovascular disease risk, diabetes, obesity, hyperlipidemia, hypertension, systematic review

KEY MESSAGES

  • The relationship between vitiligo and cardiovascular diseases remains controversial.

  • This meta-analysis concluded that comorbidities in patients with vitiligo include metabolic syndrome, diabetes, obesity, hyperlipidemia, and hypertension, with comorbidity rates of 28.3%, 6.0%, 38.5%, 43.0%, and 15.8%.

  • Our study identified cardiovascular disease risk factors in patients with vitiligo, including smoking, alcohol consumption, high serum SBP, DBP, FBG, CRP, TC, TG, LDL, insulin, and Hcy, and low serum HDL levels.

Introduction

Vitiligo is a common, acquired depigmentation disorder that results in spots or flaky white patches on the skin caused by the loss of melanocytes. The incidence of vitiligo is approximately 0.1–2% globally, and men and women are equally affected, with no racial difference [1–7]. The international consensus classifies vitiligo into two types: segmental and nonsegmental. The latter category includes generalized, focal, acrofacial, mucosal and universal [8]. Nonsegmental vitiligo is clinically characterized by depigmented patches with a diameter of a few centimeters, usually involving both sides of the body in a symmetrical manner. Segmental vitiligo develops rapidly but has a limited course; depigmentation spreads within the segments in 6–24 months and ceases gradually with further expansion rarely seen [9].

The pathogenesis of vitiligo has not yet been confirmed, and the current evidence suggests that it may be related to oxidative stress, genetic factors, degenerative theory, autoimmune responses, generation of inflammatory mediators, and melanocyte detachment mechanisms [9–11]. Vitiligo is often associated with autoimmune diseases, particularly autoimmune thyroid diseases (Hashimoto’s thyroiditis and Graves’ disease), rheumatoid arthritis, diabetes, psoriasis, pernicious anemia, systemic lupus erythematosus, and Addison’s disease [12–14]. Tumor necrosis factor-α, interleukin-1, and interleukin-6 are cytokines that promote inflammatory responses. They have been implicated in the pathogenesis of vitiligo and are associated with insulin resistance, atherosclerosis, and other metabolic complications [15].

Few studies have suggested that patients with vitiligo might be at a higher risk of dyslipidemia and atherosclerosis, thus increasing their risk for cardiovascular diseases [13, 16,17]. Additionally, patients with vitiligo, especially those with chronic and severe diseases or concomitant metabolic syndromes, are at an increased risk of cardiovascular diseases [18]. An Iranian study concluded that individuals with vitiligo were more likely to develop high blood pressure, which is a risk factor for cardiovascular diseases [19], while other studies considered patients with vitiligo to have a lower risk for cardiovascular diseases [20].

Considering the controversial reports on the relationship between vitiligo and cardiovascular diseases and whether patients with vitiligo are at a higher risk of cardiovascular disease than healthy controls, a systematic review and meta-analysis are urgently needed to illustrate this relationship, increase our understanding of the strength of their correlation, and provide updated data references for performing relevant medical services and clinical interventions. This systematic review is the first to systematically summarize the factors associated with and unrelated to the risk of cardiovascular diseases in patients with vitiligo.

Materials and methods

We performed a systematic review and meta-analysis to assess the association between vitiligo and cardiovascular disease risk factors. This study was conducted in accordance with the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines and registered with PROSPERO (CRD42023390952), an international registry for prospective systematic evaluations (https://www.crd.york.ac.uk/PROSPERO/).

Data sources and searches

PubMed, Embase, and the Cochrane Central Register of Controlled Trials were searched from inception to November, 2023, for studies on the association between vitiligo and cardiovascular disease risk factors. We added the term ‘vitiligo’ to the keywords of risk factors, including ‘diabetes’, ‘hypertension’, ‘homocysteine’, ‘LDL’, ‘HDL’, ‘C-reactive protein’, ‘metabolic syndrome’, ‘fasting glucose’, ‘triglycerides’, ‘cholesterol’, ‘body mass index’, ‘obesity’, and other keywords for database search. This review was limited to studies published in English.

Inclusion and exclusion criteria

The included studies were selected according to the following criteria: (1) human studies only; (2) provide raw data; (3) include a reference group, single-arm studies to calculate disease incidence; (4) provide odds ratio (OR), hazard ratio, or hazard ratio estimates and confidence intervals (CIs) (or sufficient data to calculate them); and treat cardiovascular disease (atherosclerosis, acute coronary syndrome) and its risk factors as specific outcome events. The exclusion criteria were as follows: (1) multiple submissions, (2) not a clinical study, and (3) inability to contact the corresponding author. Initially, 708 articles were included (Figure 1). Three additional articles were identified after a manual review of the reference lists of the selected studies. These studies were then carefully reviewed. Finally, 74 studies were included in this systematic review (Table 1). A flowchart describing the screening process is shown in Figure 1.

Figure 1.

Figure 1.

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Literature search and study selection.

Table 1.

Characteristics of the included studies and the Newcastle–Ottawa scale (NOS) quality assessment table.

Author (pub. year) Study setting Study period Study design Indicators used in the study Controls: total number Cases: total number Mean age of controls, years, mean (SD) Mean age of cases, years, mean (SD) Disease duration, mean (SD) Controls gender M/F Cases gender M/F Controls BMI, mean (SD) Cases BMI, mean (SD) VETI score NOS score
Demirbas et al. 2021[21] Turkey; NR 1/2019–12/2019 Case control HDL Total: 180 Total: 180 35.7 (7.39) 29.56 (9.8) 7.42 (7.48) 90/90 87/93 NR NR 2.8 (3.13) 8
D’arino et al. 2021[22] Italy; NR 1/2017–1/2021 Case control TG, TC, FBG, HDL, LDL Total: 316 Total: 839 NR 45.3 (15.5) NR 124/192 351/488 NR NR NR 7
Pietrzak et al. 2014[23] Italy; NR NR Case control BMI Total: 35 Total: 34 10.5 (2.3) 10.9 (2.0) 2.1 (0.8) 16/19 21/13 17.6 (2.8) 17.0 (2.1) NR 8
Nejad et al. 2013[14] Iran; NR NR Case control Diabetes Total: 77 Total: 86 NR 28.11 (12.5) NR NR 52/33 NR NR NR 7
Afkhami-Ardekani et al. 2014[24] Iran; NR 6 months Cross sectional Diabetes Total: 1100 Total: 1100 NR NR NR NR NR NR NR NR 8
Al Fahaad et al. 2015[25] Saudi Arabia; outpatient 1/2012–7/2012 Cross sectional Hypertension, diabetes NR Total: 101 NR NR NR NR 58/43 NR NR NR 5
Al Houssien et al. 2017[26] Saudi Arabia; NR 1/2016–9/2016 Case control Hypertension, diabetes, obesity Total: 61 Total: 61 40 (17) 45 (19) NR 17/44 17/44 NR NR NR 7
Al-Mutairi et al. 2011[27] Kuwait; NR 1/1005–12/2008 Prospective study Diabetes Total: 197 Total: 100 NR NR NR NR 95/102 NR NR NR 4
Anbar et al. 2016[28] Egypt; outpatient 11/2011–2012 Case control Hcy Total: 30 Total: 30 NR NR NR 12/18 13/17 NR NR NR 8
Aryanian et al. 2022[29] Iran; NR 4/2018–3/2019 Case control BMI, SBP, DBP, CRP, TG, TC, HDL, LDL Total: 40 Total: 40 37.50 (7.16) 37.95 (9.25) NR 21/19 17/23 24.42(2.71) 24.61(3.04) NR 8
Atas et al. 2015[30] Turkey; NR 2011–2013 Case control Hcy Total: 60 Total: 60 36.25 (7.8) 35.7 (11.2) NR 30/30 30/30 NR NR NR 8
Atas et al. 2017[31] Turkey; outpatient NR Case control Hypertension, Mts, hyperlipidemia, SBP, DBP, Waist circumference, TG, HDL, FBG Total: 65 Total: 63 40.3 (10.3) 40.1 (11.8) NR 31/34 30/33 NR NR NR 7
Azzazi et al. 2021[13] Egypt; NR 12/2018–1/2020 Case control TG, TC, HDL, LDL Total: 50 Total: 50 40.02 (14.561) NR NR NR 23/27 NR NR NR 7
Birlea et al. 2006[32] Romania; Community Residents 2002–2004 Cross-sectional Diabetes Total: 33 Total: 31 NR NR NR NR 10/21 NR NR NR 8
Birlea et al. 2008[33] Romania; Community Residents 1/2001–2/2006 Cross-sectional Diabetes NR Total: 51 NR NR NR NR NR NR NR NR 8
Butt et al. 2015[34] Lahore; outpatient NR Cross-sectional Diabetes NR Total: 100 NR NR NR NR 54/46 NR NR NR 2
Chen et al. 2016[35] China; NR NR Case control Hcy Total: 80 Total: 80 NR NR NR         NR 7
Demir et al. 2014[36] Turkey; NR NR Case control TG, TC, HDL, LDL, FBG, BMI, insulin, HOMA-IR Total: 30 Total: 30 32.40 (6.9) 28.66 (11.6) NR 15/15 15/15 23.84 (1.45) 23.22(1.65) NR 7
Demirbas et al. 2021[21] Turkey; NR 1/2019–12/2019 Case control HDL Total: 180 Total: 180 29.61 (9.66) 29.56 (9.8) 7.42 (7.48) 90/90 87/93 NR NR 2.8 (3.13) 8
Derakhshandeh-Rishehri et al. 2019[37] Iran; NR NR Case control BMI, TC Total: 110 Total: 100 23.22 (5.39) 20.71 (6.19) NR 62/33 38/68 23.31(5.86) 21.48(2.68) NR 8
El Tahlawi et al. 2020[38] Egypt; NR 9/2013–10/2014 Case control Hcy Total: 80 Total: 100 NR 34.96 (13.842) 6.20 (6.061) 20/60 20/80 NR NR NR 8
Farag et al. 2022[39] Egypt; outpatient NR Case control Mts, BMI, SBP, DBP, TG, HDL, FBG, TC Total: 45 Total: 45 32.89 (13.56) 35.51 (15.98) 4.69 (4.89) 27/28 25/20 23.89 (2.4) 29.53 (5.02) NR 7
Gönül et al. 2012[40] Turkey; NR NR Retrospective study Diabetes Hypertension NR Total: 93 NR 37.4 (17.05) NR NR 49/44 NR NR NR 3
Gopal et al. 2014[41] India; NR 1/2008–12/2008 Case-control study Diabetes Total: 100 Total: 150 NR NR NR 54/46 83/67 NR NR NR 6
Gopal et al. 2007[42] India; NR 4/2004–12/2004 Case-control study Diabetes Total: 100 Total: 150 NR NR NR NR NR NR NR NR 6
Gorial et al. 2021[43] Iraq; NR 9/2018–5/2019 Case-control study BMI, smoking Total: 63 Total: 63 39.9 (11.6) 38.7 (14.0) 13.3 (12.7) 30/33 34/29 30.6 (5.7) 27.6 (7,4) NR 7
Gupta et al. 2016[44] India; NR 11/2011–8/2012 Case-control study Hcy Total: 83 Total: 82 31.27 (0.87) 30.56 (0.85) 2.09 (0.09) 45/38 50/32 NR NR NR 7
Handa et al. 2003[45] India; NR 1990–1999 Retrospective study Diabetes NR Total: 625 NR NR NR NR NR NR NR NR 2
Handa et al. 1999[46] India; NR 2/1989–2/1993 Retrospective study Diabetes NR Total: 1436 NR NR NR NR 783/653 NR NR NR 2
Karadag et al. 2011[47] Turkey; NR NR Case-control study BMI, TC, HDL, LDL, waist circumference Total: 39 Total: 57 40.2 (11.1) 38.5 (14.2) NR 28/11 31/26 31.1 (17.8) 30.0 (15.6) NR 6
Mubki et al. 2017[48] Saudi Arabia; NR 7/2014–12/2015 Case-control study FBG Total: 89 Total: 115 30.73 (10.56) 29.53 (9.22) NR 37/52 51/64 NR NR NE 7
Nahidi et al. 2014[49] Iran; NR NR Case-control study Hcy Total: 40 Total: 50 NR NR NR NR 23/17 NR NR NR 6
Naik et al. 2010[50] India; NR 2 months Descriptive research Hypertension NR Total: 60 NR NR NR NR 25/35 NR NR NR 2
Namazi et al. 2020[19] Iran; outpatient 6/2019–9/2019 Case-control study Hypertension Total: 83 Total: 83 NR NR NR 50/33 50/33 NR NR NR 7
Namazi et al. 2021[18] Iran; NR 11/2016–2/2018 Case-control study TG, TC, HDL, LDL, FBG, BMI, SBP, DBP, waist circumference Total: 70 Total: 70 38.86 (11.4) 37.61 (12.27) 9.69 (7.07) NR NR 25.01 (3.32) 26.17 (4.91) NR 7
Narita et al. 2011[51] Japan; NR NR Descriptive research Diabetes NR Total: 133 NR 49.3 (19.8) 8.2 (8.6) NR 57/76 NR NR NR 2
P. K. Sinha et al. 2019[52] India; NR NR Case-control study TG, TC, HDL, LDL, FBG, BMI, SBP, DBP, waist circumference Total: 75 Total: 75 NR NR NR NR NR NR NR NR 6
Pietrzak et al. 2000[53] Lublin; NR NR Case-control study TG, TC, HDL, LDL, BMI, Ph Total: 19 Total: 11 10.05 (2.25) 10.91 (2.38) NR NR NR 17.51 (3.34) 17.62 (1.96) NR 5
Sallam et al. 2017[54] Egypt; outpatient NR Case-control study HOMA-IR, insulin, Mts, TG, TC, HDL, LDL, FBG, BMI, SBP, DBP, waist circumference Total: 89 Total: 102 34.53 (11.97) 32.73 (16.27) NR 33/56 40/62 30.75 (4.49) 27.51 (5.54) NR 7
Salman et al. 2016[55] Babylon-Iraq; outpatient 11/2015–3/2016 Case-control study Hcy Total: 44 Total: 44 27.56 (4.55) 27.82 (4.94) 10.73 (6.48) 26/18 25/19 NR NR NR 7
Shahzad et al. 2014[56] Iran; outpatient 2009–2010 Case-control study Diabetes Total: 70 Total: 70 NR NR NR NR NR NR NR NR 5
Shaker et al. 2008[57] Egypt; NR NR Case-control study Hcy Total: 26 Total: 26 33.04 (6.37) 31.4 (8.09) NR NR NR NR NR NR 6
Sharma et al. 2017[58] India; NR NR Case-control study Mts, TG, HDL, FBG, BMI, SBP, DBP, waist circumference, smoking, alcohol Total: 100 Total: 100 42.3 (11.5) 43.5 (10.5) NR 64/36 66/34 23.91 (3.66) 24.13 (3.75) NR 6
Sheth et al. 2014[59] USA; NR 1/1/2000–1/6/2010 Descriptive research Diabetes NR Total: 2441 NR NR NR NR 836/1137 NR NR NR 3
Singh et al. 2014[60] India; NR NR Case-control study BMI, TG, insulin Total: 30 Total: 35 NR NR NR NR NR 21.79 (2.51) 25.18 (4.59) NR 5
Singh et al. 2020[17] India; NR NR Case-control study CRP, Total: 30 Total: 35 NR NR NR NR NR NR NR NR 5
Singh et al. 2020[61] India; NR NR Case-control study BMI Total: 30 Total: 35 NR NR NR NR NR 21.79(2.51) 25.18 (4.59) NR 5
Singh et al. 2011[62] India; outpatient NR Case-control study Hcy Total: 30 Total: 30 NR NR NR NR 16/14 NR NR NR 5
Tanacan et al. 2020[63] Turkey; NR 11/2014–3/2016 Cross-sectional study Diabetes, hypertension, hyperlipidemia, obesity, smoking, alcohol, Mts, TG, HDL, LDL, FBG, BMI, SBP, DBP, CRP, waist circumference Total: 155 Total: 155 37.37 (12.60) 37.04 (12.07) NR 71/84 72/83 25.35 (3.71) 26.28 (4.71) NR 6
Taneja et al. 2016[64] India; NR NR Case-control study Hcy Total: 50 Total: 50 NR NR NR NR NR NR NR NR 5
Taneja et al. 2020[65] India; NR NR Cross-sectional study Hcy, TG, HDL, LDL, BMI, TC, waist circumference, smoking Total: 54 Total: 54 32.4 (9.7) 30.7 (11.3) 10.3 (5.8) 22/33 19/35 24.0 (2.5) 23.9 (2.6) NR 5
Topal et al. 2016[66] Turkey; NR 6/2013–5/2014 Descriptive research Diabetes NR Total: 100 NR NR NR NR NR NR NR NR 2
Yasar et al. 2012[67] Turkey; NR NR Case-control study Hcy Total: 40 Total: 40 25.42 (4.48) 27.77 (13.44) NR NR NR NR NR NR 6
Jalel Akrem et al. 2008[68] Tunisia; NR 9/2005–8/2006 Descriptive research Diabetes NR Total: 60 NR NR NR NR NR NR NR NR 4
Zhang et al. 2009[69] China; outpatient 12/2003–8/2007 Descriptive research Diabetes NR Total: 6199 NR 24.5 (14.6) NR NR 3276/2923 NR NR NR 4
Silverberg et al. 2013[70] New York; NR 6/2010–7/2011 Descriptive research Diabetes NR Total: 1541 NR 43.1 (13.4) NR NR 433/1080 NR NR NR 4
Seung Kyung et al. 1993[71] Korean; NR 3/1991–2/1993 Descriptive research Diabetes NR Total: 226 NR NR NR NR 103/123 NR NR NR 4
Martis et al. 2002[72] India; NR NR Descriptive research Diabetes NR Total: 100 NR NR NR NR 45/55 NR NR NR 4
Kubeyinje et al. 1996[73] Saudi Arabia; NR 5 years Descriptive research Diabetes NR Total: 167 NR NR NR NR NR NR NR NR 5
Korde et al. 2020[74] India; NR NR Cross-sectional study Diabetes Total: 80 Total: 80 NR NR NR NR NR NR NR NR 7
Jacyk et al. 1976[75] Poland; NR NR Descriptive research Diabetes NR Total: 30 NR NR NR NR NR NR NR NR 4
Gill et al. 2014[76] USA; NR 11 years Descriptive research Diabetes NR Total: 1098 NR NR NR NR 593/505 NR NR NR 5
Y. T. Chen et al. 2015[77] Taiwan; NR 1997–2011 Cross-sectional study Diabetes Total: 59,532 Total: 14,883 NR NR NR 25564/33968 6391/8492 NR NR NR 8
Dragoni et al. 2017[78] Italy; NR 3/2012–3/2015 Case-control study Smoking Total: 200 Total: 200 NR NR NR 108/92 108/92 NR NR NR 8
Balci et al. 2009[79] Turkey; NR NR Case-control study Hcy Total: 31 Total: 48 39.32 (13.15) 37.94 (16.27) 9.28 (9.32) 14/17 27/21 NR NR NR 7
Mustafa et al. 2023[80] Egypt; NR 12/2021–6/2022 Case-control study FBG, BMI, SBP, DBP, TC, TG, HDL, LDL, waist circumference, hyperlipidemia, Mts Total: 100 Total: 100 36.4 (5.6) 32.7 (13.0) 5.9 (6.35) 35/65 30/70 NR NR NR 8
EI-Hawary et al. 2022[81] Egypt; outpatient NR Case-control study BMI, waist circumference Total: 70 Total: 70 15 (16.30) 22 (14.82) 24 (44.44) 24/46 27/43 24.59 (7.26) 21.49 (8.81) NR 6
Kalayci 2023[82] Turkey; NR 1/2021–12/2021 Case-control study CRP Total: 78 Total: 78 35.8 (8.7) 33 (8.9) NR 32/46 30/48 NR NR NR 7
B. Li et al. 2023[83] China; NA 1/2010–10/2021 A cross-sectional study Smoking, alcohol Total: 428 Total: 107 48.9 (11.4) 48.8 (11.4) NR 256/172 64/43 NR NR NR 6
Kassab et al. 2023[84] Tunisia; NA 2020–2022 Case-control study BMI Total: 36 Total: 60 34.1 (13) 34.5 (10) 5.6 (4.3) 16/20 27/33 NR NR NR 5
Ibrahim et al. 2022[85] Egypt; NR NR Case-control study SBP, DBP, TG, HDL, waist circumference, BMI, FPG, Insulin resistance Total: 142 Total: 139 23 (17.78) 20 (17.04) NR 55/87 51/88 NR NR NR 7
Farajzadeh et al. 2022[86] Iran; NR 4/2018–8/2020 Case-control study Homocysteine Total: 30 Total: 30 6.16 (2.98) 6.90 (3.74) NR 15/15 14/16 NR NR NR 6
Choudhary et al. 2022[87] India; NR NR Case-control study Homocysteine Total: 30 Total: 70 NR NR NR NR NR NR NR NR 6
El-Hamd et al. 2023[88] Egypt; outpatients 8/2020–8/2021 Case-control study TC, TG, HDL, LDL, FPG, insulin, Insulin resistance, BMI Total: 45 Total: 45 37.87 (13.99) 43.16 (14.76) 6.03 (8.67) 23/22 21/24 25.99 (3.93) 26.71 (3.33) NR 7
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Abbreviations: Hcy, homocysteine; TC, total cholesterol; HDL, high-density lipoprotein; LDL, low-density lipoprotein; BMI, body mass index; TG, triglycerides; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBG, fasting blood glucose; CRP, C Reactive protein; Ph, phospholipids; Mts, metabolic syndrome; NR, not reported; VETI, vitiligo extent tensity index. A star scoring system was used to semiquantitatively assess study quality. In the selection and exposure categories, each numbered item was adjudged a maximum of four stars. A maximum of two stars was assigned for comparability. The NOS scores ranged from 0 to 9. We deemed studies achieving ≥7 stars as high-quality, those with 4–6 stars as medium-quality, and those with <4 stars as poor-quality.

Data extraction and quality assessment

Three reviewers independently extracted the data for each study using a predefined data extraction form. The extracted data included study design, number and characteristics of participants, indicators of inclusion, and disease duration. The Newcastle–Ottawa Scale was used to assess the study quality. Using this scale, case-control studies were categorized into selection, comparability, and exposure categories, whereas cohort studies were categorized into selection, comparability, and outcome categories. A maximum of one star can be awarded to each study for each item on the choice and exposure scales. Comparability can be awarded a maximum of two stars.

Data synthesis and analysis

The primary outcome was the comorbidity of metabolic syndrome, diabetes, hypertension, obesity, and hyperlipidemia in healthy controls and patients with vitiligo, and comorbidity rates were calculated accordingly. Simultaneously, the relationship between vitiligo and cardiovascular disease risk factors (such as hyperhomocysteinemia, HDL, and LDL) was analyzed. The I2 test was used to assess the degree of heterogeneity among studies. Considerable heterogeneity was identified for I2 values of > 50%. The global risk ratio (RR), standard error (SE), and mean difference (MD) were calculated using a random-effects model, and a sensitivity analysis was performed using the exclusion method. Fixed-effects models were used to examine studies with p > 0.05, I2 < 50%, or no significant within-study heterogeneity. A publication bias [14, 24, 26, 32,33, 41,42, 48, 63, 71, 77] analysis was performed for the primary outcome. The methods and results of this review followed the Epidemiological Group Guidelines, and the Checklists for Meta-Analysis of Observational Studies (Stata version 16.0; Stata Corp, College Station, TX, USA) were used for the meta-analysis.

Results

A total of 74 studies [13,14, 17–19, 21–79] involving 35362 participants with vitiligo were included. The details of the basic study characteristics are presented in Table 1. Based on the type of response variable, the meta-analysis consisted of two parts: The first part consisted of 46 studies [14, 19, 24–27, 31–34, 39, 41–43, 45,46, 48, 50,51, 54, 56, 58,59, 63, 65,66, 68–78, 80–88] with dichotomous metabolic syndrome, diabetes, obesity, hyperlipidemia, hypertension, smoking, and alcohol as response variables, and the second consisted of 36 studies [13, 17, 19, 21–23, 29–31, 35–39, 43,44, 47–49, 52–55, 57,58, 60–65, 67, 79, 83, 89] using continuous variables as response variables.

The main results showed that vitiligo had comorbidity with diabetes, obesity, hyperlipidemia, metabolic syndrome (Mts) and hypertension at rates of 6.0%, 38.5%, 43.0%, 28.3%, and 15.8%, respectively. The secondary outcomes indicated that the incidence of cardiovascular diseases in patients with vitiligo was associated with smoking, alcohol consumption, fasting blood glucose (FBG), insulin, total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), C-reactive protein (CRP), homeostasis model assessment of insulin resistance (HOMA-IR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and homocysteine (Hcy). In contrast, waist circumference, phospholipid levels, and body mass index (BMI) were not associated.

A publication bias analysis of the 11 papers was performed [14, 24, 26, 32,33, 41,42, 48, 63, 71, 77] with diabetes and vitiligo co-morbidities as the primary outcome. The analysis showed no bias, with Egger’s test coefficient = 0.651 (p = 0.301, which is >0.05) and Begg’s test p = 1.000, which is >0.05 (Table S1).

Primary outcomes

The comorbidity rates of patients with vitiligo were as follows: 28.3% of the patients had Mts [31, 39, 54, 58, 63, 80, 85](95% CI: 0.224, 0.345; p < 0.001; Table 2; Figure S21), 6.0% of the patients had diabetes [14, 24–27, 32–34, 41,42, 45,46, 48, 51, 56, 59, 63, 68–77] (95% CI: 0.041, 0.082; p < 0.001; Table 2; Figure S22), 38.5% were obese [26, 63], (95% CI: 0.089, 0.681; p = 0.011; Table 2; Figure S23), 15.8% had hypertension [5, 19, 25,26, 31, 63, 66], (95% CI: 0.061, 0.286; p = 0.001; Table 2; Figure S25), and 43.0% had hyperlipidemia [26, 31, 63, 80], (95% CI: 0.223, 0.650; p < 0.001; Table 2; Figure S24). Patients with vitiligo were more likely to have Mts [31, 39, 54, 58, 63, 80, 85] (RR: 1.894; 95% CI: 1.164–3.081; p < 0.05; Table 2; Figure S16), diabetes mellitus [14, 24, 26, 32,33, 41,42, 48, 63, 71, 77] (RR: 2.552; 95% CI: 1.990, 3.274; p < 0.001; Table 2; Figure S17), obesity [26, 63] (RR: 1.795; 95% CI: 1.168, 2.760; p = 0.008; Table 2; Figure S18), hyperlipidemia [26, 31, 63, 80] (RR: 1.380; 95% CI: 1.144, 1.664; p = 0.001; Table 2; Figure S19), and hypertension [19, 26, 31, 63] (RR: 1.330; 95% CI: 1.036, 1.707; p = 0.025; Table 2; Figure S20).

Table 2.

Comorbidity and comorbidity rates of patients with vitiligo and metabolic syndrome, diabetes, obesity, hypertension, and hyperlipidemia.

Comorbidity Number of studies Vitiligo
 Control
 RR 95% CI p value I^2
Events Total Events Total
Metabolic syndrome 7 203 704 113 696 1.894 [1.164, 3.081] 0.010 75.7%
Diabetes 11 183 17,003 89 61,547 2.552 [1.990, 3.274] 0.000 7.6%
Obesity 2 70 216 38 216 1.795 [1.168, 2.760] 0.008 39.7%
Hyperlipidemia 4 175 379 111 381 1.582 [1.088, 2.302] 0.016 67.5%
Hypertension 4 106 362 80 364 1.330 [1.036, 1.707] 0.025 9.3%
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Comorbidity rate Number of studies Events Total ES 95% CI p value I^2
Metabolic syndrome 7 203 704 0.283 [0.224, 0.345] 0.000 67.5%
Diabetes 27 443 32220 0.060 [0.041, 0.082] 0.000 97.4%
Obesity 2 70 216 0.385 [0.089, 0.681] 0.011 94.3%
Hyperlipidemia 4 175 379 0.430 [0.223, 0.650] 0.000 94.5%
Hypertension 7 119 623 0.158 [0.061, 0.286] 0.001 93.3%
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CI, confidence interval; RR, risk ratio; ES, standard error.

Secondary outcomes

Diabetes

Eleven studies were included to evaluate FBG levels [19, 31, 36, 39, 48, 52, 54, 58, 63, 80, 85, 88] using a fixed-effects model, which showed that patients with vitiligo had significantly higher FBG levels than those of controls (MD: 0.389; 95% CI: 0.301, 0.477; p < 0.001; Table 3; Figure S1). In addition, a meta-analysis of five studies [36, 54, 61, 85, 88] showed a significant difference in the serum insulin levels between the vitiligo and control groups (MD: 0.653; 95% CI: 0.318, 0.988; p < 0.001; Table 3; Figure S2). Meanwhile, a significant effect on HOMA-IR was found between the vitiligo and control groups, which were modeled using the random effects from four of the eleven studies [36, 54, 85, 88] (MD: 0.674; 95% CI: 0.278, 1.070; p = 0.001; Table 3; Figure S3).

Table 3.

Cardiovascular risk factors in patients with vitiligo.

Cardiovascular risk factors Number of studies N
(vitiligo)		 N
(control)		 MD 95% CI p value I^2
Diabetes              
 FBG 12 1039 1005 0.389 [0.301, 0.477] 0.000 33.8%
 Insulin 5 351 336 0.653 [0.318, 0.988] 0.000 75.2%
 HOMA-IR 4 316 306 0.674 [0.278, 1.070] 0.001 80.4%
Obesity              
 Waist circumference 12 1020 989 −0.014 [−0.233, 0.205] 0.900 83.1%
 Non-segmental vitiligo 3 274 279 −0.118 [−1.031, 0.795] 0.800 95.9%
 Segmental vitiligo 3 53 279 −0.792 [−1.675, −0.090] 0.079 83.6%
 BMI 19 1313 1269 0.057 [−0.146, 0.260] 0.582 84.2%
 Non-segmental vitiligo 3 274 279 −0.168 [−0.999, 0.662] 0.691 95.1%
 Segmental vitiligo 3 53 279 −0.405 [−1.001, 0.190] 0.182 64.7%
Hypertension              
 SBP 10 889 881 0.213 [0.023, 0.404] 0.028 74.5%
 Non-segmental vitiligo 2 239 244 0.176 [−0.711, 1.062] 0.698 95.6%
 Segmental vitiligo 2 18 244 0.132 [−0.347, 0.611] 0.590 8.9%
 DBP 10 889 881 0.259 [0.062, 0.456] 0.010 76.0%
 Non-segmental vitiligo 2 239 244 0.599 [0.417, 0.782] 0.000 0.0%
 Segmental vitiligo 2 18 244 0.348 [−0.132, 0.828] 0.156 4.5%
Hyperlipidemia              
 TC 12 681 682 0.665 [0.307, 1.022] 0.000 89.8%
 TG 18 1987 1460 0.299 [0.140, 0.458] 0.000 76.9%
 Non-segmental vitiligo 2 239 244 0.065 [−0.111, 0.241] 0.471 0.0%
 Segmental vitiligo 2 18 244 −0.221 [−0.700, 0.257] 0.365 0.0%
 LDL 13 766 757 0.594 [0.306, 0.882] 0.000 85.9%
 HDL 18 1350 1333 −0.373 [−0.623, −0.123] 0.003 89.8%
 Non-segmental vitiligo 2 239 244 0.174 [−0.215, 0.563] 0.380 77.5%
 Segmental vitiligo 2 18 244 1.320 [−1.204, 3.843] 0.305 95.7%
 Phospholipids 2 45 54 0.049 [−0.350, 0.448] 0.810 0.0%
Other risk factors              
 Hcy 14 754 678 0.692 [0.352, 1.032] 0.000 89.3%
 CRP 4 308 303 0.837 [0.320, 1.354] 0.002 87.5%
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Lifestyle Number of studies Vitiligo
 Control
 RR 95% CI p value I^2
Events Total Events Total
Smoking 6 239 659 230 1000 1.376 [1.096, 1.726] 0.006 53.3%
Alcohol 3 98 362 96 683 1.457 [1.127, 1.885] 0.004 0.0%
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CI, confidence interval; RR, risk ratio; Hcy, homocysteine; TC, total cholesterol; HDL, high-density lipoprotein; LDL, low-density lipoprotein; BMI, body mass index; TG, triglycerides; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBG, fasting blood glucose; CRP, C-reactive protein; Ph, phospholipids; HOMA-IR, homeostatic model assessment of insulin resistance.

Obesity

A meta-analysis of waist circumference [17, 19, 31, 47, 52, 54, 58, 63, 65, 80,81, 85] of patients with vitiligo and controls revealed high heterogeneity (I2 = 83.1%, p < 0.001). We observed no significant difference between the waist circumferences of patients with vitiligo and those of controls in twelve studies, with a pooled MD of −0.014 (95% CI: −0.233, 0.205, p = 0.900; Table 3; Figure S4) using a random-effects model. Three of these studies [54, 63, 81] showed no difference between the waist circumferences of the patients with nonsegmental vitiligo and those of the controls (MD: −0.118; 95% CI: −1.031, 0.795; p = 0.800; Table 3; Figure S4) and, similarly, no difference between the waist circumferences of patients with segmental vitiligo and those of controls (MD: −0.792; 95% CI: −1.675, −0.090; p = 0.079; Table 3; Figure S4).

A meta-analysis of 19 studies [19, 29, 36,37, 39, 43, 47, 52–54, 58, 63, 65, 77, 80,81, 84,85, 88] showed no significant difference in the BMI between patients with vitiligo and the controls (MD: 0.057;95% CI: −0.146, 0.260; p = 0.582; Table 3; Figure S5), two studies [54, 63] reflected no significant difference in the BMI between patients with segmental (MD: −0.385;95% CI: −1.553, 0.783; p = 0.518; Table 3; Figure S5) and non-segmental vitiligo (MD: −0.133;95% CI: −1.327, 1.062; p = 0.828; Table 3; Figure S5) and the controls.

Hypertension

Ten studies [19, 29, 31, 39, 52, 54, 58, 63, 80, 85] analyzed the SBP and DBP of patients with vitiligo and the control groups; the meta-analysis showed a significant difference in the SBP (MD: 0.213; 95% CI: 0.023, 0.404; p = 0.028; Table 3; Figure S6) and DBP (MD: 0.259; 95% CI: 0.062, 0.456; p = 0.05510; Table 3; Figure S7); the meta-analysis of two of the studies [54, 63] indicated no significant difference in the SBP between patients with non-segmental (MD: 0.176; 95% CI: −0.711, 1.062; p = 0.698; Table 3; Figure S6) and segmental vitiligo (MD: 0.132; 95% CI: −0.347, 0.611; p = 0.590; Table 3; Figure S6) and the controls, and no significant difference in DBP was observed between patients with segmental vitiligo (MD: 0.348; 95% CI: −0.132, 0.828; p = 0.156; Table 3; Figure S7) and the controls. However, significant differences in DBP were noted between patients with nonsegmental vitiligo (MD: 0.599; 95% CI, 0.417–0.782; p < 0.001; Table 3; Figure S7) and the controls.

Hyperlipidemia

Twelve studies [13, 19, 23, 29, 37, 39, 47, 52,53, 65, 80, 88] compared serum TC levels between patients with vitiligo and controls; the meta-analysis revealed that serum TC levels were significantly higher in patients with vitiligo than in the controls (MD: 0.665; 95% CI: 0.307, 1.022; p < 0.001; Table 3; Figure S8).

A meta-analysis of 18 studies [13, 19, 22,23, 29, 31, 36, 39, 52–54, 58, 60, 63, 65, 80, 85, 88] indicated that the serum TG levels of patients with vitiligo were significantly higher than those of the controls (MD: 0.299; 95% CI: 0.140, 0.458; p < 0.001; Table 3; Figure S9). Two studies [54, 63] analyzed the serum TG levels of patients with segmental and nonsegmental vitiligo and controls. The results of the meta-analysis showed no significant difference in the serum TG levels between patients with segmental vitiligo and the controls (MD: −0.221; 95% CI: −0.700, 0.257; p = 0.365; Table 3; Figure S9). Moreover, the serum TG levels of the patients with non-segmental vitiligo were not significantly different from those of the control group (MD: 0.065; 95% CI: −0.111, 0.241; p = 0.471; Table 3; Figure S9).

A meta-analysis of 13 studies [13, 19, 23, 29, 36, 39, 47, 52,53, 63, 65, 80, 88] showed that the serum LDL levels of patients with vitiligo were significantly higher than those of the controls (MD: 0.594; 95% CI: 0.306–0.882; p < 0.001; Table 3; Figure S10). A meta-analysis of 18 studies [13, 19, 21, 23, 29, 31, 36, 39, 47, 52–54, 58, 63, 65, 80, 85, 88] showed that patients with vitiligo had lower serum HDL levels than those of the controls (MD: −0.373; 95% CI: −0.623, −0.123; p = 0.003; Table 3; Figure S11). A meta-analysis of two of these studies [54, 63] showed that the serum HDL levels of patients with segmental (MD: 1.320; 95% CI: −1.204, 3.843; p = 0.305; Table 3; Figure S11) and non-segmental (MD: 0.174; 95% CI: −0.215, 0.563; p = 0.380; Table 3; Figure S11) vitiligo were not significantly different from those of the controls.

A meta-analysis of two studies [23, 53] showed no significant difference in serum phospholipid levels between patients with vitiligo and the controls (MD: 0.049; 95% CI: −0.350, 0.448; p = 0.810; Table 3; Figure S26).

Lifestyle

Six studies [43, 58, 63, 65, 78, 83] compared the smoking habits of patients with vitiligo and control groups. The results of the meta-analysis indicated that the number of smokers in the vitiligo group was higher than that in the controls (RR: 1.376; 95% CI: 1.096, 1.726; p = 0.006; Table 3; Figure S14).

Simultaneously, we conducted a meta-analysis of three studies [54, 63, 83], which indicated that the number of vitiligo patients with alcohol addiction was significantly higher than that of the controls. (RR: 1.457; 95% CI: 1.127, 1.885; p = 0.004; Table 3; Figure S15).

Other risk factors

A meta-analysis of 14 studies [30, 35, 38, 44, 49, 55, 57, 62, 64,65, 67, 79, 86,87] analyzing the serum Hcy levels of patients with vitiligo and controls indicated that patients with vitiligo had significantly higher serum Hcy levels than those of the controls (MD: 0.692; 95% CI: 0.352, 1.032; p < 0.001; Table 3; Figure S12).

A meta-analysis of four studies [29, 52, 63, 82] showed that the serum CRP levels of patients with vitiligo were significantly higher than those of the controls (MD: 0.837; 95% CI: 0.320, 1.354; p = 0.002; Table 3; Figure S13).

Discussion

Vitiligo is a skin disease affecting people worldwide. Its pathogenesis is unclear; however, it is believed to be related to several factors, such as heredity, immunity, and oxidative stress [9–11]. Previous studies have reported a relationship between vitiligo and cardiovascular diseases [16–18]. In contrast, a case-control study conducted in Spain provided conflicting results [20]. Therefore, since the relationship between vitiligo and cardiovascular diseases is unclear and controversial among clinicians, a meta-analysis is needed to examine the association between vitiligo and cardiovascular diseases.

Few articles have described the direct relationship between vitiligo and cardiovascular diseases (such as acute coronary syndrome and coronary heart disease). Therefore, this meta-analysis focused on the risk factors for cardiovascular diseases in patients with vitiligo. Cardiovascular disease accounts for approximately one-third of all deaths worldwide, and its prevalence continues to increase as the population ages [90]. It is a disease with multiple contributing factors; therefore, it is difficult to identify the specific factors in isolation. The main factors associated with the present study were obesity, dyslipidemia, high blood pressure, lifestyle (drinking and smoking), Hcy and CRP levels, and hyperglycemia [91,92].

Previous studies have reported a correlation between vitiligo and its common comorbid atopic and autoimmune diseases, such as diabetes, autoimmune thyroid diseases, psoriasis, pernicious anemia, Addison’s disease, and systemic lupus erythematosus [12–14, 23–27, 32–34, 40–42, 45,46, 51, 56, 59, 63, 66, 68–77]. In addition, our study showed that the comorbidities of vitiligo include diabetes, which is consistent with previous findings.

As expected, patients with vitiligo are at an increased risk of metabolic syndrome [17,18, 22, 29, 31, 39, 52, 54,55, 58, 60, 63], and significant differences were observed in the prevalence of metabolic syndrome between patients with vitiligo and the controls. We did not observe any significant differences in the waist circumference or BMI between the vitiligo and control groups. The results of a meta-analysis conducted in 2022 in China [89] and a case-control study in Africa [54] were contrary to our results; however, those of another meta-analysis conducted in 2022 in Taiwan and China [93] and three case-control studies in Asia [31, 39, 58, 63] were consistent with our results. Differences in ethnicity may be responsible for this discrepancy in metabolic syndrome. It has also been suggested that vitiligo and insulin resistance are components of metabolic syndrome [94]. Waist circumference and BMI did not significantly differ in patients with vitiligo compared to those in controls; therefore, obesity cannot be considered a risk factor for cardiovascular disease in patients with vitiligo. Given that the prevalence of obesity in patients with vitiligo is approximately 38.5%, dermatologists and other physicians are advised to be vigilant in clinical situations. Hypertension is the most common potential risk factor for cardiovascular disease [95], and this study showed that patients with vitiligo have significantly higher SBP and DBP than controls.

TC, TG, HDL, LDL, FBG, and insulin levels are considered uncontroversial risk factors for cardiovascular disease. The TC level does not exactly reflect that of HDL and LDL cholesterol and therefore cannot be used as a separate indicator of the magnitude of the risk of atherosclerosis. The contribution of TG to cardiovascular disease may lead to low-grade inflammation, foam cell formation, and atherosclerotic plaque formation [96–99]. HDL has the ability to promote reverse cholesterol transport and protect LDL from oxidative modifications. The lysolemic lipid sphingosine-1-phosphate (S1P), present in HDL, can promote vasodilation and angiogenesis and prevent ischemia/reperfusion injury, and the expression of anti-inflammatory cytokines can be induced by HDL. These antioxidant and anti-inflammatory properties of HDL can antagonize the inflammatory process underlying atherosclerosis [100–104]. LDL transports cholesterol to peripheral tissues and, together with very-low-density lipoprotein and celiac remnant particles, accumulates in dysfunctional subendothelial vessel walls. Oxidative stress induces the oxidative modification of LDL particles and accumulation of oxidized LDL in macrophages, triggering a pro-inflammatory macrophage response, excessive macrophage apoptosis, and endothelial cell activation, leading to persistent vascular inflammation in atherosclerotic lesions [105–107]. Hyperglycemia can promote vascular complications through multiple mechanisms, such as the formation of late glycation end-products, and increased oxidative stress can lead to macrovascular and microvascular complications. Myocardial microangiopathy can lead to scattered myocardial fibrosis and necrosis. Studies have shown that FBG levels contributes to subclinical myocardial injuries. In addition, a sustained but mild inflammatory response induced by hyperglycemia leads to further enhancement of oxidative stress and further release of inflammatory factors, and both inflammation-induced microvascular and macrovascular disease can further increase high-sensitivity cardiac troponin T levels, leading to subclinical myocardial injury [108,109]. A hyperinsulinemia environment leads to insulin resistance; however, the additional cellular response to high insulin levels is not attenuated by insulin resistance. Hyperinsulinemia and insulin resistance trigger cardiovascular disease by inducing endothelial dysfunction, inhibiting endothelial nitric oxide synthase, and activating and promoting calcium ion influx into smooth muscle cells, thereby enhancing vascular tone and renal tubular reabsorption of sodium ions, causing macrophages to adhere to the vessel wall and forming arterial lesions [110]. Furthermore, our study overturned the findings of previous studies and showed significant differences in the TC, TG, HDL, LDL, FBG, HOMA-IR and insulin levels between patients with vitiligo and the controls.

Hcy and CRP are independent risk factors for high cardiovascular disease risk [111–115]. Previous studies have shown that Hcy is involved in the pathogenesis of atherosclerosis by stimulating CRP production and triggering inflammatory responses in vascular smooth muscle cells [116]. CRP is present in most atherosclerotic plaques and all acute myocardial infarction lesions and accumulates during the development of atherosclerosis. Endothelial nitric oxide production is inhibited by CRP, the expression of endothelial cell adhesion molecules is promoted by CRP, monocyte recruitment to atheromatous plaques is facilitated, and modified LDL leads to plaque instability. Platelet activation and thrombus growth are also induced by CRP [117,118]. We found that the serum CRP levels in patients with vitiligo were higher than those in the control group, thus providing a basis for the relationship between vitiligo and cardiovascular diseases. However, conflicting reports on the correlation between vitiligo and serum Hcy levels exist [55]. Our study demonstrated that serum Hcy levels were significantly higher in patients with vitiligo than in the controls, suggesting the clinical significance of serum Hcy levels in preventing cardiovascular events in patients with vitiligo. Unfortunately, our study lacked the data to clarify the relationship between vitiligo and serum homocysteine levels.

The role of lifestyle in the relationship between vitiligo and cardiovascular disease is intriguing. Here, we discuss only smoking and drinking habits. Smoking is a risk factor for cardiovascular disease. The key processes of smoking-induced atherosclerosis are endothelial dysfunction and damage, resulting in increased and oxidized atherogenic lipids and decreased HDL levels, inducing inflammation and contributing to a pro-coagulant state in the circulation [119,120]. Enomoto et al. [121] demonstrated that smoking is a risk factor for vitiligo on the hands, and fingertip vitiligo may be associated with direct exposure to cigarette smoke. However, the relationship between alcohol consumption and cardiovascular disease remains complex and controversial [122,123].

Interestingly, a recent cohort study showed that both light and heavy alcohol consumption is associated with an increased risk of cardiovascular disease [124,125]. However, the underlying mechanism remains unclear and may be associated with an increased risk of hypertension associated with alcohol consumption. Our research showed that the number of smokers and alcoholics among patients with vitiligo was significantly higher than in the control group. Therefore, clinicians should consider in the lifestyles of patients with vitiligo. Future studies should explore the relationship between alcohol consumption and vitiligo and the relationship between alcohol consumption and cardiovascular diseases in patients with vitiligo.

Limitations

This study had several limitations. First, data on cardiovascular disease risk factors in patients with segmental and non-segmental vitiligo are sparse, and the evidence is insufficient. Second, this study was highly heterogeneous, which may be due to the different regimens, doses, durations, center settings, and population registries. Third, most studies were conducted in Eastern countries; therefore, the extrapolation of these results to Western populations is questionable. Fourth, several studies had a serious source of bias. Fifth, the effect in many occasions was assessed by very few studies; thus, the evidence to support this finding is low.

Conclusions

Our study identified cardiovascular disease risk factors in patients with vitiligo, including smoking; alcohol consumption; high serum FBG, CRP, TC, TG, LDL, insulin, and Hcy levels; and low serum HDL levels (Figure 2). Cardiovascular risk factors in patients with vitiligo were not associated with BMI, waist circumference, systolic and diastolic blood pressure, or phospholipid levels. Moreover, patients with vitiligo are prone to complications, such as diabetes, obesity, hyperlipidemia, and hypertension. Therefore, although an inevitable relationship exists between vitiligo and cardiovascular diseases, further research is required to evaluate the causal relationship between them and to identify the cardiovascular diseases more closely related to vitiligo.

Figure 2.

Figure 2.

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Cardiovascular risk factors in patients with vitiligo.

Vitiligo patients have high serum fasting glucose, insulin, total cholesterol, triglycerides, low-density lipoproteins, C-reactive protein, and homocysteine levels; smoking and alcohol consumption increase the risk of cardiovascular disease in patients with vitiligo.

Supplementary Material

Supplemental Material
IANN_A_2326297_SM1454.zip (2.6MB, zip)

Funding Statement

This work was supported by the Shanghai Pujiang Program (grant 33 no. 2020PJD067), National Natural Science Foundation of Shanghai (Grant No. 19ZR1458700), Science and Technology Commission of Baoshan District, Shanghai Medical Health Project (Grant No. 21-E-33), Key Discipline Construction Project of Shanghai’s Three Year Action Plan for Strengthening the Construction of Public Health System (Grant No. GWVI-11.1-24); High-level Chinese Medicine Key Discipline Construction Project (Integrative Chinese and Western Medicine Clinic) of National Administration of TCM (Grant No. zyyzdxk-2023065); Shanghai Three-Year Action Plan to Further Accelerate the Inheritance and Innovative Development of Chinese Medicine (2021–2023) (Grant No. ZY(2021-2023)-0302), New round (2023-2025) Baoshan Medical key (specialized) Department "Vitiligo, hair loss integrated Traditional Chinese and Western medicine characteristic Treatment Clinic"(Grant No. BSZK-2023-A15), Li Bin Shanghai famous Chinese medicine studio grassroots workstation(Grant No. JCGZZ-2023078), 2023 Shanghai Traditional Chinese Medicine Specialty Capacity Construction "Traditional Chinese Medicine Dermatology"(Grant No. SQZBZK-23-25)

Authors’ contributions

Xin Liang contributed to data curation, methodology, writing of the original draft, and revision of the paper for important intellectual content. Fei Guo contributed to data curation, methodology, writing of the original draft, and revision of the paper for important intellectual content. Miao Zhang contributed to data curation and revision of the manuscript for important intellectual content. Chunxiao Wang contributed to data analysis and revision of the paper for important intellectual content. Naixuan Lin contributed to the validation and revision of the paper for important intellectual content. Li Liu contributed to the investigation and revision of the paper for important intellectual content. Yan Chen contributed to resources and revision of the paper for important intellectual content. Fang Liu contributed to data analysis, drafting of the paper, and revision of the paper for important intellectual content. Yuhua Du contributed to the visualization and revision of the manuscript for important intellectual content. Lei Li contributed to the conception and design of the study and approved the final version to be published. Xin Li contributed to the conceptualization, methodology, writing, reviewing, editing, and revision of the paper for important intellectual content. All authors approved the final manuscript.

Disclosure statement

The authors declare no conflict of interest regarding the publication of this paper.

Data availability statement

All data generated or analyzed during this study are included in this published article (and its Supplementary Information Files).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Material
IANN_A_2326297_SM1454.zip (2.6MB, zip)

Data Availability Statement

All data generated or analyzed during this study are included in this published article (and its Supplementary Information Files).

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