# Hypertension May Reduce the Infection Risk but Increase the Severity of COVID-19: Based on the Current Data in China

Authors: Bo Li; Lu Zeng; Nengjun Sun; Yunhe Zhao; Faming Zhao; Hongjun Bian; Wei Yi; Jing Yang; Bin Li; Guohai Su
Journal: International Journal of Hypertension
 (2021)

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## Abstract

Increasing evidence has shown an unusual relationship between hypertension and COVID-19, which may not be as simple as previously thought. The purpose of our study was to determine the association of hypertension with the onset and development of COVID-19. A meta-analysis was performed to summarize the prevalence of hypertension in COVID-19 patients, as well as the usage of ACEIs/ARBs. Metaregression analyses were used to evaluate the association of hypertension with disease severity and mortality. PubMed and Google Scholar were searched for relevant studies. A total of 42 studies including 14138 patients were enrolled in the study. The proportion of hypertension in COVID-19 patients in China was 17.7% according to the enrolled studies, while it was 6.0% in a study containing 72314 confirmed cases, which are both much lower than in the general population. All of the data from the 11 provinces in China showed the same tendency. The proportions of hypertension were higher in severe/ICU patients and nonsurvivors than in nonsevere/ICU patients and survivors. The metaregression analyses suggested that both disease severity and risk of death were associated with the incidence of hypertension. A total of 27.6% of COVID-19 patients with hypertension received ACEI/ARB therapy. The proportion of deaths in COVID-19 patients with hypertension treated with ACEIs/ARBs was significantly lower than that in nonuse patients treated with ACEIs/ARBs. In conclusion, hypertension may reduce the infection risk of COVID-19 but increase the risk of developing worse clinical outcomes. The use of ACEIs/ARBs may benefit COVID-19 patients with hypertension.


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## Body

## 1. Introduction

With the publication of several studies concerning the epidemiological and clinical features of coronavirus disease 2019 (COVID-19), a growing amount of evidence is emerging about the relationship between cardiovascular complications and COVID-19. According to early published data in Wuhan, hypertension was associated with a high prevalence and an increased risk of morbidity and mortality.In Wang’s study, 31.2% of the 138 confirmed COVID-19 cases had hypertension [1]. Another study included 140 COVID-19 patients, of whom 42 patients (30%) had hypertension [2]. The prevalence of hypertension in fatal patients was extremely high (42.57%–60%) [3–5]. Thus, patients with hypertension seemed to be more likely to be infected with SARS-CoV-2 and face a greater risk of developing severe conditions [6, 7].However, emerging evidence shows the reported prevalence of hypertension is gradually decreasing. In our previous study, the 6 earliest studies about COVID-19 were summarized, and the results suggested that the proportion of hypertension in patients with COVID-19 was 17.1% [8]. A study that included 1099 patients with confirmed COVID-19 reported that 14.9% of patients had hypertension comorbidities [9]. In another study, among 44672 individuals with confirmed COVID-19, 2683 patients (12.8%) had hypertension [10]. Even more surprising was that in the largest study ever conducted in China thus far, including 72314 confirmed cases, only 6.0% of patients had hypertension [11]. Because the total number of infections in China was 84414 as of May 7th, the study was very representative of the total population. The incidence of 6.0% was much lower than that of the general population, which reported a 27.9% overall crude prevalence of hypertension according to the Summary of the 2018 report on cardiovascular diseases in China [12]. This information indicated that individuals with underlying hypertension seemed to be less susceptible to COVID-19. This discovery is very different from all others that have been made thus far. Thus, it is urgent to determine the relationship between hypertension and the virus.Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) are the main antihypertensive drugs recommended in the current guidelines. However, angiotensin-converting enzyme 2 (ACE2) has been identified as a receptor for SARS-CoV-2, which infects humans by binding to ACE2 in host respiratory epithelial cells [13]. Therefore, the application of ACEIs/ARBs to control blood pressure in patients with COVID-19 complicated with hypertension has been a matter of intense debate [14, 15]. Therefore, we will conduct an analysis on the incidence and prognosis of patients with COVID-19 complicated with hypertension, as well as the use of ACEIs/ARBs in infected patients and their impact on prognosis.


## 2. Methods

This meta-analysis was conducted following the Preferred Reporting Items for the Systematic Review and Meta-Analysis (PRISMA) statement [16].


### 2.1. Search Strategy and Selection Criteria

A literature search was conducted in MEDLINE via the PubMed database, Cochrane Library, Web of Science, EMBASE and EMBASE Classic databases, and Google Scholar from inception through April 23, 2020. The following terms (MeSH) were used: “2019-nCoV,” “COVID19 virus,” “2019 novel coronavirus,” “SARS-CoV-2,” and “hypertension.” The included articles met the following eligibility criteria: (1) RCTs or non-RCTs; (2) patients were diagnosed with 2019 novel coronavirus infection; (3) sample size ≥ 10 in each study population; and (4) comorbidities of hypertension and the outcome of hypertension were given. Studies not meeting these criteria, nonclinical studies, incompatible or repeated studies, case reports, and studies without complete data were excluded.


### 2.2. Data Management and Quality Assessment

Clinical data, including age, sex, prevalence of hypertension, clinical outcome, and interventions for hypertension, were collected from the identified studies. The primary outcomes were to analyse the prevalence of hypertension and the impact of hypertension on the severity of the disease and mortality. The secondary outcomes were to analyse the use of ACEIs/ARBs in COVID-19 and the impact of ACEIs/ARBs on prognosis. Two reviewers assessed the eligibility of the included reports, extracted data with a standardized report form, and evaluated the quality of reports independently. All discrepancies were solved by discussion until a consensus was reached.


### 2.3. Statistical Analysis

The statistical analysis was performed using OpenMeta Analyst version 10.10 (https://www.cebm.brown.edu/open_meta) and Review Manager (RevMan) software version 5.3. Forest plots were used to illustrate the prevalence of hypertension in 2019-nCoV infection from the selected studies as well as the impact of 2019-nCoV infection and therapeutic interventions on hypertension. Heterogeneity among studies was assessed by I2 statistics. In the pooled analysis, a fixed-effects model was applied when the heterogeneity I2 was less than 50%; otherwise, a random-effects model was used [17, 18]. Metaregression analyses were used to evaluate the associations between disease severity, risk of death, and incidence of hypertension. Cochrane Collaboration’s tool was followed to assess the risk of bias.


## 2.1. Search Strategy and Selection Criteria

A literature search was conducted in MEDLINE via the PubMed database, Cochrane Library, Web of Science, EMBASE and EMBASE Classic databases, and Google Scholar from inception through April 23, 2020. The following terms (MeSH) were used: “2019-nCoV,” “COVID19 virus,” “2019 novel coronavirus,” “SARS-CoV-2,” and “hypertension.” The included articles met the following eligibility criteria: (1) RCTs or non-RCTs; (2) patients were diagnosed with 2019 novel coronavirus infection; (3) sample size ≥ 10 in each study population; and (4) comorbidities of hypertension and the outcome of hypertension were given. Studies not meeting these criteria, nonclinical studies, incompatible or repeated studies, case reports, and studies without complete data were excluded.


## 2.2. Data Management and Quality Assessment

Clinical data, including age, sex, prevalence of hypertension, clinical outcome, and interventions for hypertension, were collected from the identified studies. The primary outcomes were to analyse the prevalence of hypertension and the impact of hypertension on the severity of the disease and mortality. The secondary outcomes were to analyse the use of ACEIs/ARBs in COVID-19 and the impact of ACEIs/ARBs on prognosis. Two reviewers assessed the eligibility of the included reports, extracted data with a standardized report form, and evaluated the quality of reports independently. All discrepancies were solved by discussion until a consensus was reached.


## 2.3. Statistical Analysis

The statistical analysis was performed using OpenMeta Analyst version 10.10 (https://www.cebm.brown.edu/open_meta) and Review Manager (RevMan) software version 5.3. Forest plots were used to illustrate the prevalence of hypertension in 2019-nCoV infection from the selected studies as well as the impact of 2019-nCoV infection and therapeutic interventions on hypertension. Heterogeneity among studies was assessed by I2 statistics. In the pooled analysis, a fixed-effects model was applied when the heterogeneity I2 was less than 50%; otherwise, a random-effects model was used [17, 18]. Metaregression analyses were used to evaluate the associations between disease severity, risk of death, and incidence of hypertension. Cochrane Collaboration’s tool was followed to assess the risk of bias.


## 3. Results

### 3.1. Selected Studies and Baseline Characteristics

We searched all the databases and websites using the search words described above, and 21052 total records were retrieved. Among the articles, 2987 records were excluded because of duplication. In addition, 17943 records were excluded because they were animal trials, letters, review articles, case reports, or repetitive content. The remaining 124 studies were carefully evaluated in detail. Finally, 93 studies were excluded because the participants of the trials did not meet the criteria we set. Then, 11 studies published in the preprint platform were added. Finally, a total of 42 articles were selected for this study (Table1 and Figure 1).Table 1
Number, age, sex, and hypertension of patients of the included studies.
References
Date
Number of patients
Area
Age
Sex (male, %)
Hypertension (%)
Chaolin Huang
As of 2020.1.2
41
Wuhan
49
73.2
14.6
Youbin Liu
2020.1.10–2020.2.24
291
Guangzhou
48.1
45.7
18.6
Jie Li
2020.1.22–2020.2.10
17
Dazhou
45
52.9
5.9
Guyi Wang
As of 2020.2.20
242
Changsha
45
49.2
14.9
Yafei Wang
2020.1.1–2020.2.10
110
Wuhan
—
43.6
20.9
Xiaowei Xu
2020.1.10–2020.1.26
62
7 hospitals in Zhejiang
41
56.5
8.1
Wanbo Zhu
2020.1.24–2020.2.20
32
Hefei
46
46.9
21.9
Xu Chen
2020.1.23–2020.2.14
291
Changsha and Loudi
46
49.8
13.4
Qingxian Cai
2020.1.11–2020.2.6
298
Shenzhen
47
50
12.8
Dawei Wang
2020.1.1–2020.1.28
138
Wuhan
56
54.3
31.2
W. Guan
2019.12.11–2020.1.29
1099
552 hospitals
47
58.1
15.0
Fei Zhou
2019.12.29–2020.1.31
191
Wuhan
56
62.3
30.4
Guoqing Qian
2020.1.20–2020.2.11
91
7 hospitals in Zhejiang
50
40.7
16.5
Guqin Zhang
2020.1.2–2020.2.10
221
Wuhan
55
48.9
24.4
Pengfei Cui
2020.1.28–2020.2.18
35
Wuhan
61.5
0
34.3
Yu Lei
2020.1.4–2020.2.28
67
Daofu
39.3
58.2
11.9
Lei Liu
2020.1.20–2020.2.3
51
Chongqing
45
62.7
7.8
Lei Wang
2020.1.21–2020.2.5
18
Zhengzhou
39
55.6
27.8
Penghui Yang
2019.12.27–2020.2.18
55
Beijing
44
60
20
Jinjin Zhang
2020.1.16–2020.2.3
140
Wuhan
57
50.7
30
Jie Liu
2020.1.16–2020.2.15
64
Wuhan
35
35.9
4.7
Min Cao
2020.1.20–2020.2.15
198
Shanghai
50.1
51.0
21.2
Chengfeng Qiu
2020.1.22–2020.2.12
104
Huaihua
43
47.1
14.4
Rui Huang
2020.1.22–2020.2.10
221
10 hospitals in Jiangsu
45
57.0
14.5
Zhijun Xie
2020.1.22–2020.2.15
60
Hangzhou
45
45
15
Fengqin Zhang
2020.1.30–2020.2.15
81
Jinzhou
—
55.6
13.6
Songqiao Liu
2020.1.10–2020.2.18
620
24 hospitals in Jiangsu
44.48
52.6
15.5
Weijie Guan
2019.12.11–2020.1.31
1590
575 hospitals
48.9
57.3
17.0
Peng Zhang
2019.12.31–2020.2.20
3430
9 hospitals in Hubei
—
32.9
32.9
Juan Meng
2020.1.11–2020.2.23
417
Shenzhen
64.5
—
12.2
Sijiao Wang
2020.1.22–2020.2.16
165
Fuzhou
44
55.8
14.5
Shijiao Yan
2020.1.22–2020.3.13
168
Haikou
51
48.2
14.3
Xiufeng Jiang
2020.1.23–2020.2.16
55
Wuxi
45
49.1
30.9
Yu Shi
2020.1.16–2020.2.17
487
5 hospitals in Zhejiang
46
53.2
20.3
Huisi He
2020.2.8–2020.3.16
94
Wuhan
69.2
57.4
59.6
Caizheng Yu
2020.1.14–2020.2.28
1663
Wuhan
64
50.4
20.9
Lin Fu
2020.1.1–2020.1.30
200
Wuhan
—
49.5
50.5
Zhenhua Zeng
2020.1.5–2020.3.8
274
Wuhan
60
54.7
27.4
Xin Chen
2020.2.11–2020.2.29
208
Xiaogan
50.5
51.4
19.7
Xingwei He
2020.2.3–2020.2.24
54
Wuhan
68
63.0
44.4
Bo Hu
2020.1.8–2020.2.9
50
Wuhan
62
68
36
Suxin Wan
2020.1.23–2020.2.8
135
Chongqing
47
53.3
9.6
Yimei Yin
As of 2020.2.15
112
Wuhan
66
68.8
43.8
Zhongbao Zuo
2020.1.20–2020.2.28
70
Hangzhou
43
41.4
12.9
Wei Chen
2020.1.19–2020.2.7
74
Nanjing
48.1
58.1
13.5
Jiaxi Chen
2020.1.22–2020.2.26
137
Taizhou
—
52.6
12.4
Wentao Xu
2020.1.10–2020.2.18
87
Suzhou
—
52.9
6.9
Yingxia Liu
2020.1.11–2020.2.5;
78
Shenzhen
—
—
100
2020.1.12–2020.2.9;
Wuhan
2019.12.27–2020.2.27
Beijing
Kun Wang
2020.1.7–2020.2.11
305
Wuhan
47.8
46.6
14.8
Fei Zhou
2019.12.29–2020.1.31
191
Wuhan
56
62.3
30.4Figure 1
Flow diagram of the study selection process.


### 3.2. Primary Outcomes

Forty-two studies, including 14138 COVID-19 patients with hypertension, were collected in the study. Meta-analysis of the identified studies showed that the prevalence of hypertension was 17.7% (95% CI: 15.1–20.4%) (Figure2(a)). There was significant heterogeneity (Cochran’s Q) in the estimates of hypertension among the identified studies with an I2 index of 93.3%. Next, 26 studies including 4458 patients outside of Hubei were also analysed, showing that the prevalence of hypertension was 14.3% (95% CI: 13.3–15.3%) (Figure 2(b)). Then, 14 studies including 6991 patients in Hubei Province were analysed, and the data showed that the prevalence of hypertension was 24.7% (95% CI: 19.4–30.1%) (Figure 2(c)).Figure 2
Meta-analysis for the proportion of hypertension in COVID-19 cases. Weights were calculated from binary random-effects model analysis. Values represent the proportion of hypertension in COVID-19 patients and 95% CI. Heterogeneity analysis was carried out using the Q test among the studies variation (I2 index). (a) The proportion of hypertension in data from all of China. (b) The proportion of hypertension outside Hubei. (c) The proportion of hypertension in Hubei.
(a)
(b)
(c)Furthermore, we compared the prevalence of hypertension in COVID-19 patients with data from the China Hypertension Survey, 2012–2015 [19]. The results demonstrated the differences in all 11 provinces outside of Hubei whose data could be currently obtained (Figure 3) as follows: Beijing (35.9% vs. 20.5%) [20, 21], Shanghai (29.1% vs. 21.2%) [22], Zhejiang (23.2% vs. 13.4%) [23–25], Hunan (15.6% vs. 14.1%) [26–28], Guangdong (27.3% vs. 12.8%) [29], Chongqing (20.6% vs. 7.8%) [30], Sichuan (23.6% vs. 5.88%) [31], Hainan (20.3% vs. 14.3%) [32], Fujian (23.9% vs. 14.5%) [33], Jiangsu (25.3% vs. 7.0%) [34], and Tibet (25.0% vs. 11.9%) [35] (Figure 3).Figure 3
Comparison of the incidence rates of hypertension in COVID-19 patients with that from the China Hypertension Survey, 2012–2015, in 11 provinces and all of China.Then, the differences in the prevalence of hypertension between severe/intensive care unit (ICU) and nonsevere/ICU patients and nonsurvivors and survivors were compared. There was significant statistical heterogeneity in the test results (severe/ICU and nonsevere/ICU patients:I2 = 82%; nonsurvivors and survivors: I2 = 85%). Therefore, a random-effects model was selected for further analysis. Studies with a total of 8004 patients showed that hypertension accounted for 28.0% of severe/ICU cases, but only 15.7% of nonsevere/ICU cases. The proportion of hypertension was significantly higher in severe/ICU patients than in nonsevere/ICU patients (RR = 1.91, 95% CI (1.48, 2.48), Z = 4.90, P<0.00001) (Figure 4(a)). For nonsurvivors and survivors with hypertension (with a total number of 2870 patients), the heterogeneity test showed that I2 = 85%, and a random effects model was used. Hypertension was present in 52.1% of nonsurvivors but 20.8% of survivors. The results indicated a much higher proportion of hypertension in nonsurvivors than in survivors, with statistical significance (RR = 1.71, 95% CI (1.19, 2.46), Z = 2.91, P=0.004) (Figure 4(b)). In metaregression analyses of the included studies, disease severity and risk of death were both associated with the incidence of hypertension (β = 0.010, P=0.032 and β = 0.009, P<0.001, respectively) (Figures 5(a) and 5(b)).Figure 4
(a) Forest plots depict the comparison of the incidences of hypertension in severe/ICU and nonsevere/ICU patients. (b) Forest plots depict the comparison of the incidences of hypertension in nonsurvivors and survivors. Forest plots depict the comparison of the incidences of cardiac injury in ICU/severe and non-ICU/severe patients.
(a)
(b)Figure 5
(a) Bubble plots for the association of hypertension with severe/ICU rates in COVID-19 cases. (b) Bubble plots for the association of hypertension with mortality in COVID-19 cases.
(a)
(b)


### 3.3. Secondary Outcomes

Finally, we collected data from COVID-19 patients with hypertension according to the use of ACEIs/ARBs. Four studies (including 1300 patients) showed that 27.6% (95% CI: 15.8–39.4%) of COVID-19 patients with hypertension received ACEI/ARB therapy (Figure6(a)). Then, we compared the differences in the severe/ICU rate and mortality of COVID-19 patients with hypertension between the ACEI/ARB group and the non-ACEI/ARB group. Meta-analysis showed that the proportion of deaths in COVID-19 patients with hypertension treated with ACEIs/ARBs was significantly lower than that in nonuse patients treated with ACEIs/ARBs (RR = 0.41, 95% CI (0.21, 0.81), Z = 2.60, P=0.009) (Figure 6(b)). The results also showed a slightly lower proportion of severe/ICU among patients treated with ACEIs/ARBs than in patients not treated with ACEIs/ARBs, but the difference was not statistically significant (RR = 0.77, 95% CI (0.30, 1.97), Z = 0.54, P=0.59) (Figure 6(c)).Figure 6
(a) Prevalence of usage of ACEIs/ARBs in COVID-19 patients with hypertension. (b) Comparison of mortality in ACEI/ARB and non-ACEI/ARB patients. (c) Comparison of the incidences of the severe/ICU rate in ACEI/ARB and non-ACEI/ARB patients.
(a)
(b)
(c)


## 3.1. Selected Studies and Baseline Characteristics

We searched all the databases and websites using the search words described above, and 21052 total records were retrieved. Among the articles, 2987 records were excluded because of duplication. In addition, 17943 records were excluded because they were animal trials, letters, review articles, case reports, or repetitive content. The remaining 124 studies were carefully evaluated in detail. Finally, 93 studies were excluded because the participants of the trials did not meet the criteria we set. Then, 11 studies published in the preprint platform were added. Finally, a total of 42 articles were selected for this study (Table1 and Figure 1).Table 1
Number, age, sex, and hypertension of patients of the included studies.
References
Date
Number of patients
Area
Age
Sex (male, %)
Hypertension (%)
Chaolin Huang
As of 2020.1.2
41
Wuhan
49
73.2
14.6
Youbin Liu
2020.1.10–2020.2.24
291
Guangzhou
48.1
45.7
18.6
Jie Li
2020.1.22–2020.2.10
17
Dazhou
45
52.9
5.9
Guyi Wang
As of 2020.2.20
242
Changsha
45
49.2
14.9
Yafei Wang
2020.1.1–2020.2.10
110
Wuhan
—
43.6
20.9
Xiaowei Xu
2020.1.10–2020.1.26
62
7 hospitals in Zhejiang
41
56.5
8.1
Wanbo Zhu
2020.1.24–2020.2.20
32
Hefei
46
46.9
21.9
Xu Chen
2020.1.23–2020.2.14
291
Changsha and Loudi
46
49.8
13.4
Qingxian Cai
2020.1.11–2020.2.6
298
Shenzhen
47
50
12.8
Dawei Wang
2020.1.1–2020.1.28
138
Wuhan
56
54.3
31.2
W. Guan
2019.12.11–2020.1.29
1099
552 hospitals
47
58.1
15.0
Fei Zhou
2019.12.29–2020.1.31
191
Wuhan
56
62.3
30.4
Guoqing Qian
2020.1.20–2020.2.11
91
7 hospitals in Zhejiang
50
40.7
16.5
Guqin Zhang
2020.1.2–2020.2.10
221
Wuhan
55
48.9
24.4
Pengfei Cui
2020.1.28–2020.2.18
35
Wuhan
61.5
0
34.3
Yu Lei
2020.1.4–2020.2.28
67
Daofu
39.3
58.2
11.9
Lei Liu
2020.1.20–2020.2.3
51
Chongqing
45
62.7
7.8
Lei Wang
2020.1.21–2020.2.5
18
Zhengzhou
39
55.6
27.8
Penghui Yang
2019.12.27–2020.2.18
55
Beijing
44
60
20
Jinjin Zhang
2020.1.16–2020.2.3
140
Wuhan
57
50.7
30
Jie Liu
2020.1.16–2020.2.15
64
Wuhan
35
35.9
4.7
Min Cao
2020.1.20–2020.2.15
198
Shanghai
50.1
51.0
21.2
Chengfeng Qiu
2020.1.22–2020.2.12
104
Huaihua
43
47.1
14.4
Rui Huang
2020.1.22–2020.2.10
221
10 hospitals in Jiangsu
45
57.0
14.5
Zhijun Xie
2020.1.22–2020.2.15
60
Hangzhou
45
45
15
Fengqin Zhang
2020.1.30–2020.2.15
81
Jinzhou
—
55.6
13.6
Songqiao Liu
2020.1.10–2020.2.18
620
24 hospitals in Jiangsu
44.48
52.6
15.5
Weijie Guan
2019.12.11–2020.1.31
1590
575 hospitals
48.9
57.3
17.0
Peng Zhang
2019.12.31–2020.2.20
3430
9 hospitals in Hubei
—
32.9
32.9
Juan Meng
2020.1.11–2020.2.23
417
Shenzhen
64.5
—
12.2
Sijiao Wang
2020.1.22–2020.2.16
165
Fuzhou
44
55.8
14.5
Shijiao Yan
2020.1.22–2020.3.13
168
Haikou
51
48.2
14.3
Xiufeng Jiang
2020.1.23–2020.2.16
55
Wuxi
45
49.1
30.9
Yu Shi
2020.1.16–2020.2.17
487
5 hospitals in Zhejiang
46
53.2
20.3
Huisi He
2020.2.8–2020.3.16
94
Wuhan
69.2
57.4
59.6
Caizheng Yu
2020.1.14–2020.2.28
1663
Wuhan
64
50.4
20.9
Lin Fu
2020.1.1–2020.1.30
200
Wuhan
—
49.5
50.5
Zhenhua Zeng
2020.1.5–2020.3.8
274
Wuhan
60
54.7
27.4
Xin Chen
2020.2.11–2020.2.29
208
Xiaogan
50.5
51.4
19.7
Xingwei He
2020.2.3–2020.2.24
54
Wuhan
68
63.0
44.4
Bo Hu
2020.1.8–2020.2.9
50
Wuhan
62
68
36
Suxin Wan
2020.1.23–2020.2.8
135
Chongqing
47
53.3
9.6
Yimei Yin
As of 2020.2.15
112
Wuhan
66
68.8
43.8
Zhongbao Zuo
2020.1.20–2020.2.28
70
Hangzhou
43
41.4
12.9
Wei Chen
2020.1.19–2020.2.7
74
Nanjing
48.1
58.1
13.5
Jiaxi Chen
2020.1.22–2020.2.26
137
Taizhou
—
52.6
12.4
Wentao Xu
2020.1.10–2020.2.18
87
Suzhou
—
52.9
6.9
Yingxia Liu
2020.1.11–2020.2.5;
78
Shenzhen
—
—
100
2020.1.12–2020.2.9;
Wuhan
2019.12.27–2020.2.27
Beijing
Kun Wang
2020.1.7–2020.2.11
305
Wuhan
47.8
46.6
14.8
Fei Zhou
2019.12.29–2020.1.31
191
Wuhan
56
62.3
30.4Figure 1
Flow diagram of the study selection process.


## 3.2. Primary Outcomes

Forty-two studies, including 14138 COVID-19 patients with hypertension, were collected in the study. Meta-analysis of the identified studies showed that the prevalence of hypertension was 17.7% (95% CI: 15.1–20.4%) (Figure2(a)). There was significant heterogeneity (Cochran’s Q) in the estimates of hypertension among the identified studies with an I2 index of 93.3%. Next, 26 studies including 4458 patients outside of Hubei were also analysed, showing that the prevalence of hypertension was 14.3% (95% CI: 13.3–15.3%) (Figure 2(b)). Then, 14 studies including 6991 patients in Hubei Province were analysed, and the data showed that the prevalence of hypertension was 24.7% (95% CI: 19.4–30.1%) (Figure 2(c)).Figure 2
Meta-analysis for the proportion of hypertension in COVID-19 cases. Weights were calculated from binary random-effects model analysis. Values represent the proportion of hypertension in COVID-19 patients and 95% CI. Heterogeneity analysis was carried out using the Q test among the studies variation (I2 index). (a) The proportion of hypertension in data from all of China. (b) The proportion of hypertension outside Hubei. (c) The proportion of hypertension in Hubei.
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(c)Furthermore, we compared the prevalence of hypertension in COVID-19 patients with data from the China Hypertension Survey, 2012–2015 [19]. The results demonstrated the differences in all 11 provinces outside of Hubei whose data could be currently obtained (Figure 3) as follows: Beijing (35.9% vs. 20.5%) [20, 21], Shanghai (29.1% vs. 21.2%) [22], Zhejiang (23.2% vs. 13.4%) [23–25], Hunan (15.6% vs. 14.1%) [26–28], Guangdong (27.3% vs. 12.8%) [29], Chongqing (20.6% vs. 7.8%) [30], Sichuan (23.6% vs. 5.88%) [31], Hainan (20.3% vs. 14.3%) [32], Fujian (23.9% vs. 14.5%) [33], Jiangsu (25.3% vs. 7.0%) [34], and Tibet (25.0% vs. 11.9%) [35] (Figure 3).Figure 3
Comparison of the incidence rates of hypertension in COVID-19 patients with that from the China Hypertension Survey, 2012–2015, in 11 provinces and all of China.Then, the differences in the prevalence of hypertension between severe/intensive care unit (ICU) and nonsevere/ICU patients and nonsurvivors and survivors were compared. There was significant statistical heterogeneity in the test results (severe/ICU and nonsevere/ICU patients:I2 = 82%; nonsurvivors and survivors: I2 = 85%). Therefore, a random-effects model was selected for further analysis. Studies with a total of 8004 patients showed that hypertension accounted for 28.0% of severe/ICU cases, but only 15.7% of nonsevere/ICU cases. The proportion of hypertension was significantly higher in severe/ICU patients than in nonsevere/ICU patients (RR = 1.91, 95% CI (1.48, 2.48), Z = 4.90, P<0.00001) (Figure 4(a)). For nonsurvivors and survivors with hypertension (with a total number of 2870 patients), the heterogeneity test showed that I2 = 85%, and a random effects model was used. Hypertension was present in 52.1% of nonsurvivors but 20.8% of survivors. The results indicated a much higher proportion of hypertension in nonsurvivors than in survivors, with statistical significance (RR = 1.71, 95% CI (1.19, 2.46), Z = 2.91, P=0.004) (Figure 4(b)). In metaregression analyses of the included studies, disease severity and risk of death were both associated with the incidence of hypertension (β = 0.010, P=0.032 and β = 0.009, P<0.001, respectively) (Figures 5(a) and 5(b)).Figure 4
(a) Forest plots depict the comparison of the incidences of hypertension in severe/ICU and nonsevere/ICU patients. (b) Forest plots depict the comparison of the incidences of hypertension in nonsurvivors and survivors. Forest plots depict the comparison of the incidences of cardiac injury in ICU/severe and non-ICU/severe patients.
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(b)Figure 5
(a) Bubble plots for the association of hypertension with severe/ICU rates in COVID-19 cases. (b) Bubble plots for the association of hypertension with mortality in COVID-19 cases.
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## 3.3. Secondary Outcomes

Finally, we collected data from COVID-19 patients with hypertension according to the use of ACEIs/ARBs. Four studies (including 1300 patients) showed that 27.6% (95% CI: 15.8–39.4%) of COVID-19 patients with hypertension received ACEI/ARB therapy (Figure6(a)). Then, we compared the differences in the severe/ICU rate and mortality of COVID-19 patients with hypertension between the ACEI/ARB group and the non-ACEI/ARB group. Meta-analysis showed that the proportion of deaths in COVID-19 patients with hypertension treated with ACEIs/ARBs was significantly lower than that in nonuse patients treated with ACEIs/ARBs (RR = 0.41, 95% CI (0.21, 0.81), Z = 2.60, P=0.009) (Figure 6(b)). The results also showed a slightly lower proportion of severe/ICU among patients treated with ACEIs/ARBs than in patients not treated with ACEIs/ARBs, but the difference was not statistically significant (RR = 0.77, 95% CI (0.30, 1.97), Z = 0.54, P=0.59) (Figure 6(c)).Figure 6
(a) Prevalence of usage of ACEIs/ARBs in COVID-19 patients with hypertension. (b) Comparison of mortality in ACEI/ARB and non-ACEI/ARB patients. (c) Comparison of the incidences of the severe/ICU rate in ACEI/ARB and non-ACEI/ARB patients.
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## 4. Discussion

A growing number of studies on the epidemiological and clinical characteristics of COVID-19 suggest a relationship between cardiovascular complications and COVID-19. Hypertension is one of the most important complications in patients with COVID-19. According to earlier data in Wuhan, there was a high proportion of hypertension among confirmed cases, reaching more than 30%. However, in a study covering 72314 confirmed cases, only 6.0% of patients had hypertension [11]. In the present study, we collected all of the published data involving provinces outside Hubei. We compared the prevalence of hypertension in COVID-19 patients with the data from the China Hypertension Survey, 2012–2015 [19]. The results demonstrated the differences in all 11 provinces whose data could be currently obtained. All of the incidences of hypertension in COVID-19 patients were lower than those in the general population. We do not believe this is just a coincidence. We hypothesized that hypertension might reduce the risk of SARS-CoV-2 infection.However, in Hubei Province, the result was the opposite. One possible reason why the incidence of hypertension in COVID-19 patients was higher than that of the general population in Hubei was that many people with mild illnesses might have gone undiagnosed in the face of a collapsed health care system, which would reduce the accuracy of the data. However, when the healthcare system is healthy, milder and even asymptomatic patients can be diagnosed, which might result in a lower prevalence of hypertension outside of Hubei.If the above hypothesis and interpretation can be accepted, there must be a premise as follows: the incidence of hypertension in mild patients is low, but it is high in severe patients. To test this premise, we analysed the prevalence of hypertension between severe and nonsevere patients and nonsurvivors and survivors. We found that patients with hypertension were more likely to develop severe cases or be nonsurvivors after SARS-CoV-2 infection. The total proportion of hypertension in severe/ICU patients was approximately 1.78-fold that in nonsevere/ICU patients, while in nonsurvivors, it was 2.50-fold that in survivors, both of which showed significant differences. In metaregression analyses in patients with COVID-19, the disease severity and risk of death were both statistically associated with the incidence of hypertension.Although the underlying mechanism is unknown, further investigation of the expression and activity of ACE2 is worthwhile. ACE2 is a carboxypeptidase that can hydrolyse Ang I to Ang-(1–9) and Ang II to Ang-(1–7) without being inhibited by selective ACE inhibitors. ACE2 has more favourable kinetics for the hydrolysis of Ang II than Ang I. [36] A previous study suggested that the ACE2 expression level was significantly downregulated in the kidneys of three hypertensive rat strains [37]. In a clinical study, hypertension status was also confirmed as an independent confounding determinant of the ACE to ACE2 ratio, leading to the relative downregulation of ACE2 [38]. SARS-CoV-2 binds to its target cells through ACE2, which is expressed in epithelial cells, type 2 pneumocytes, and macrophages in the lungs. Studies have speculated that high expression of ACE2 in patients with hypertension might facilitate SARS-CoV-2 entry into targeted cells in the respiratory system [13]. One of the main reasons why hypertension could reduce SARS-CoV-2 infection might be the loss of ACE2 in hypertensive subjects. ACE2 has a strong cardiovascular protective effect, which could also explain why patients with hypertension had a worse prognosis once they were infected with the virus. The downregulated expression of ACE2 after infection with SARS-CoV leads to excessive activation of the RAS [39], which activates peripheral sympathetic neuron release of catecholamine mediated by activation of multiple signalling pathways causing vasoconstriction and bronchial contraction [40]. It has been shown that the binding of coronavirus to ACE2 leads to the downregulation of ACE2, which in turn causes an ACE/ACE2 imbalance and an excessive production of angiotensin II by the related ACE enzyme. Excessive activation of the RAS also promotes inflammation, causes cytokine storms [41], generates ROS by activating the NADH/NADPH oxidase system [42], and induces cell apoptosis, thus promoting the progression of coronavirus-related lung injury [43].ACEIs and ARBs are the major RAS inhibitors commonly used in clinical practice and the main antihypertensive drugs recommended in current guidelines. Although ACE is not directly inhibited by ACE inhibitors, ACE2 is affected by chronic treatment with these drugs, which leads to an increase in ACE2 expression in several tissues [44]. Interestingly, this feature is also shared by another drug, angiotensin receptor-1 blocker (ARB). According to the circulating level of the ACE2 product angiotensin 1–7, long-term administration can also increase the expression level and activity of ACE2 [45]. The use of ACEIs/ARBs in COVID-19 patients with hypertension has caused great controversy. We performed a meta-analysis on the application of ACEIs/ARBs in patients with COVID-19 and hypertension and found that 27.6% (95% CI: 15.8–39.4%) of patients received ACEI/ARB therapy. The proportion of nonsurvivors or severe patients with hypertension and COVID-19 treated with ACEIs/ARBs was lower than that of survivors or nonsevere patients. The use of ACEIs/ARBs upregulated the expression of ACE2, which might increase susceptibility to COVID-19 and in turn reduce the severity of the disease. In Zhang’s study, among hospitalized COVID-19 patients with hypertension, the use of ACEIs/ARBs was associated with a lower risk of all-cause mortality. The upregulation of ACE2 induced by long-term intake of AT1R and ACE inhibitors may play a protective role through the following two mechanisms, first, by blocking the increase of angiotensin 1–7 and, second, by reducing the production of angiotensin II [46].There were several limitations in the present study. First, there was a difference in the proportion of hypertension among the included cohorts, which may account for the heterogeneity. Occasionally, a small number of samples were reused, which might lead to bias; however, they cannot be excluded crudely in order to avoid bias caused by incomplete data inclusion. Second, the differences in sample size and study design in different studies may be one of the reasons for the heterogeneity. Third, in different studies, severe patients are defined according to different criteria. To simplify the conclusions and improve readability, we combined the severe patients and ICU patients into one category for analysis. In addition, confounding factors (such as sex, smoking, drinking, and history of other comorbidities) in studies may have impacted the outcomes of the patients.



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*Source: 6594863-2021-12-20.xml*