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Aging and disease    2020, Vol. 11 Issue (3) : 668-678     DOI: 10.14336/AD.2020.0502
Review |
Comorbid Chronic Diseases are Strongly Correlated with Disease Severity among COVID-19 Patients: A Systematic Review and Meta-Analysis
Hong Liu1, Shiyan Chen2, Min Liu1, Hao Nie3, Hongyun Lu4,*
1Department of Nutrition, the Third Xiangya Hospital of Central South University, Changsha, China.
2Department of Endocrinology & Metabolism, the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
3Department of Geriatrics, the First Affiliated Hospital of Hunan Normal University, Changsha, China.
4Department of Endocrinology & Metabolism, Zhuhai Hospital Affiliated with Jinan University, Zhuhai People’s Hospital, Zhuhai, China.
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Abstract  

Coronavirus disease 2019 (COVID-19) has resulted in considerable morbidity and mortality worldwide since December 2019. In order to explore the effects of comorbid chronic diseases on clinical outcomes of COVID-19, a search was conducted in PubMed, Ovid MEDLINE, EMBASE, CDC, and NIH databases to April 25, 2020. A total of 24 peer-reviewed articles, including 10948 COVID-19 cases were selected. We found diabetes was present in 10.0%, coronary artery disease/cardiovascular disease (CAD/CVD) was in 8.0%, and hypertension was in 20.0%, which were much higher than that of chronic pulmonary disease (3.0%). Specifically, preexisting chronic conditions are strongly correlated with disease severity [Odds ratio (OR) 3.50, 95% CI 1.78 to 6.90], and being admitted to intensive care unit (ICU) (OR 3.36, 95% CI 1.67 to 6.76); in addition, compared to COVID-19 patients with no preexisting chronic diseases, COVID-19 patients who present with either diabetes, hypertension, CAD/CVD, or chronic pulmonary disease have a higher risk of developing severe disease, with an OR of 2.61 (95% CI 1.93 to 3.52), 2.84 (95% CI 2.22 to 3.63), 4.18 (95% CI 2.87 to 6.09) and 3.83 (95% CI 2.15 to 6.80), respectively. Surprisingly, we found no correlation between chronic conditions and increased risk of mortality (OR 2.09, 95% CI 0.26 to16.67). Taken together, cardio-metabolic diseases, such as diabetes, hypertension and CAD/CVD were more common than chronic pulmonary disease in COVID-19 patients, however, each comorbid disease was correlated with increased disease severity. After active treatment, increased risk of mortality in patients with preexisting chronic diseases may reduce.

Keywords coronavirus disease 2019 (COVID-19)      diabetes      cardiovascular diseases      hypertension      chronic pulmonary disease      meta-analysis     
Corresponding Authors: Lu Hongyun   
About author:

These authors contributed equally to this work.

Just Accepted Date: 07 May 2020   Issue Date: 13 May 2020
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Liu Hong,Chen Shiyan,Liu Min, et al. Comorbid Chronic Diseases are Strongly Correlated with Disease Severity among COVID-19 Patients: A Systematic Review and Meta-Analysis[J]. Aging and disease, 2020, 11(3): 668-678.
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Study[ref]aNOSDates (mm. yy)n
Age (years)Age ≥50 years (%)Symptoms (%)
Comorbidities(%)
AllMFfevercoughfatigue or myalgiashortness of breath or dyspneadiarrheadiabeteshypertensionCAD/CVDchronic pulmonary disease
Guan et al.,2020 [3]812.19-01.20109964045947.044.088.767.838.118.73.87.415.02.51.1
Chen et al.,2020 [4]701.20-01.2099673255.567.782.881.811.031.32.013.0b40.01
Huang et al.,2020 [18]612.19-01.2041301149.048.898.076.044.055.03.020.015.015.02.0
Liu et al.,2020 [8]712.19-01.20137617655.0b81.848.232.119.08.010.29.57.31.5
Shi et al.,2020 [22]612.19-01.2081423949.549.473.059.0b42.04.012.015.010.011
Song et al.,2020 [20]601.20-01.2051252649.047.196.047.031.014.010.06.010.02.02.0
Yang et al.,2020 [6]712.19-01.2052351759.755.098.077.011.563.5b17.0b10.08
Xu et al.,2020 [19]701.20-01.2062352741.0b77.081.052.0b8.02.08.0b2.0
Zhang et al.,2020 [15]801.20-02.20140716957.070.078.664.364.331.412.912.130.05.02.8
Wu et al.,2020 [16]701.20-02.2080394146.035.078.863.822.537.51.36.3b31.31.25
Hu et al.,2020 [14]601.20-02.202481632.537.520.88.38.3bb8.38.34.20.0
Huang et al.,2020 [17]712.19-01.2034142056.2b94.150.064.714.714.711.823.517.68.8
Yang et al.,2020 [13]801.20-02.20149816845.1b76.558.43.41.37.46.0b18.80.7
Wang et al.,2020 [5]701.20-01.20138756356.0b98.659.469.631.210.110.131.214.52.9
Xu et al.,2020 [21]701.20-02.2090395150.0b78.063.028.0b6.06.019.03.01.0
Li et al.,2020 [12]701.20-02.2083443945.5b86.778.318.110.88.47.86.01.26.0
Shi et al.,2020 [23]801.20-02.1041620521164b80.334.613.228.13.814.430.510.62.9
Bhatraju et al.,2020[24]602.20-03.202415964b50.088.0b88.0b58.0bb16.7
Feng et al.,2020 [25]701.20-02.2047627120553b81.956.511.622.910.310.323.78.04.6
Du et al.,2020 [26]812.19-02.20179978257.672.698.981.639.749.721.818.432.416.24.5
Liu et al.,2020 [27]612.19-01.2078393938b73.143.6bbb6.410.3b2.6
Grasselli et al.,2020 [28]802.20-03.20159113042876387.2bbbbb11.332.014.02.6
Richardson et al.,2020[29]803.20-04.205700343722636378.530.4bb17.3b31.853.016.98.4
Simonnet et al.,2020[30]702.20-04.201249034601.00bbbbb22.648.4bb
Total/Overall12.19-04.20109486764418452.4c
(32.5-64.0)d
Prevalencee59.079.061.032.031.07.010.020.08.03.0
95% CI49.0-68.065.0-92.054.0-69.021.0-43.025.0-
37.0
5.0-9.08.0-12.015.0-26.03.0-12.01.0-3.0
I2 (%)98.599.795.198.597.481.173.094.298.094.5
Table 1  Characteristics of the included studies and meta-analysis of the clinical symptoms and comorbid chronic diseases in patients with COVID-19.
Figure 1.  Systematic literature review process. The flow diagram describes the systematic review of the literature for the proportion of comorbid chronic diseases in patients with COVID-19.
Study[ref]anSymptoms(%)Comorbidities(%)

non-severeseverefevercoughfatigue or myalgiashortness of breath or dyspneadiarrheadiabeteshypertensionCAD/CVDchronic pulmonary disease

AllMFageAllMFagemildseveremildseveremildseveremildseveremildseveremildseveremildseveremildseveremildsevere
Guan et al.,2020 [3]92654038645.01731007352.088.191.967.370.537.839.915.137.63.55.85.716.213.523.71.85.80.63.5
Huang et al.,2020[18]2819949.01311249.096.0100.071.085.039.054.037.092.04.00.025.08.014.015.011.023.00.08.0
Yang et al.,2020 [6]2014651.932211164.6100.097.075.078.010.012.560.066.0bb10.022.0bb10.09.010.06.0
Xu et.al.,2020 [19]2916133933191445837379824558bb090.03.03.012.0bb03.0
Zhang et al.,2020[15]82384451.558332564.072.087.954.977.662.267.224.441.411.015.511.013.824.437.93.76.90.03.4
Wang et al.,2020 [5]102535151.036221466.098.0100.059.858.335.333.319.663.97.816.75.922.221.658.310.825.01.08.3
Li et al.,2020[12]58292941.925151053.786.288.070.796.017.220.03.428.08.68.00.028.05.28.00.04.01.716.0
Shi et al.,2020 [23]334161173608244387481.176.834.734.11218.327.231.74.51.21224.423.459.8629.31.88.5
Bhatraju etal.,2020[24]bbbb2415964.0b50.0b88.0bbb88.0bbb58.0bbbbb16.7
Feng et al.,2020 [25]bbbb476271205b085.9059.4012.6024.4011.0010.3023.708.004.6
Du et al.,2020 [26]15887715621101170.298.710083.566.736.761.944.985.719.638.117.128.628.561.910.857.15.10
Liu et al.,2020 [27]67323537117466bb44.846.4bbbbbb4.518.2918.2bb1.59.1
Grasselli et al.,2020[28]bbbb1591130428763bbbbbbbbbbb11.3b32b14b2.6
Simonnet et al.,2020[30]bbbb124903460bbbbbbbbbbb22.6b48.4bbbb
Total/Overall184098981743.8c
(37.0-60.0)d
2699196273760.9c
(45.0-74.0) d
Prevalencee90.099.064.071.032.037.027.055.08.08.0
95% CI86.0-95.098.0-100.052.0-75.060.0-81.021.0-44.024.0-50.017.0-36.039.0-71.04.0-11.04.0-13.0
Qf
I2 (%)95.594.095.491.795.694.294.495.981.292.5
Table 2  Characteristics of the included studies grouped by severe and non-severe cases and meta-analysis of the clinical symptoms and comorbid chronic diseases in patients with COVID-19.
Figure 2.  The proportions of comorbid chronic diseases in patients with COVID-19. Forest plot showing the proportion of comorbid diabetes (A), coronary artery disease/cardiovascular disease (CAD/CVD) (B), hypertension (C), and chronic pulmonary disease (D) in SARS-CoV-2-infected patients. Weights were calculated from random-effects model analyses. The size of the squares reflects the relative weight of each study in the meta-analysis. Inserts within each panel show the total number of subjects analyzed (n) and prevalence (%) of the comorbid diseases (%), together with heterogeneity analysis carried out using the Q test and the among-studies variation (I2 index).
Figure 3.  Correlation between comorbid chronic diseases and severe COVID-19 in SARS-CoV-2 infected patients. Forest plot showing the effects of comorbid diabetes (A), hypertension (B), CAD/CVD (C), and chronic pulmonary disease (D) on the risk of severe COVID-19 in SARS-CoV-2-infected patients. In this figures, the horizontal lines indicate the lower and upper limits of the 95% CI, and the size of the squares reflects the relative weight of each study in the meta-analysis. Weights were calculated from fixed-effects model analyses. Heterogeneity analysis was carried out using Q test and among-studies variation (I2 index).
[1] The L (2020). Emerging understandings of 2019-nCoV. Lancet, 395:311.
[2] Lu H, Stratton CW, Tang YW (2020). Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle. J Med Virol, 92:401-402.
[3] Guan W-J, Ni Z-Y, Hu Y, Liang W-H, Ou C-Q, He J-X, et al. (2020). Clinical Characteristics of Coronavirus Disease 2019 in China. New Engl J Med, in press.
[4] Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. (2020). Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet, 395:507-513.
[5] Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. (2020). Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA, in press.
[6] Yang X, Yu Y, Xu J, Shu H, Xia Ja, Liu H, et al. (2020). Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Resp Med, in press.
[7] Tian S, Hu N, Lou J, Chen K, Kang X, Xiang Z, et al. (2020). Characteristics of COVID-19 infection in Beijing. J Infection, in press.
[8] Liu K, Fang Y-Y, Deng Y, Liu W, Wang M-F, Ma J-P, et al. (2020). Clinical characteristics of novel coronavirus cases in tertiary hospitals in Hubei Province. Chin Med J, in press.
[9] Ren LL, Wang YM, Wu ZQ, Xiang ZC, Guo L, Xu T, et al. (2020). Identification of a novel coronavirus causing severe pneumonia in human: a descriptive study. Chin Med J (Engl), in press.
[10] Chen H, Guo J, Wang C, Luo F, Yu X, Zhang W, et al. (2020). Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women: a retrospective review of medical records. Lancet, in press.
[11] Liu Y, Yang Y, Zhang C, Huang F, Wang F, Yuan J, et al. (2020). Clinical and biochemical indexes from 2019-nCoV infected patients linked to viral loads and lung injury. Sci China Life Sci, in press.
[12] Li K, Wu J, Wu F, Guo D, Chen L, Fang Z, et al. (2020). The Clinical and Chest CT Features Associated with Severe and Critical COVID-19 Pneumonia. Invest Radiol, in press.
[13] Yang W, Cao Q, Qin L, Wang X, Cheng Z, Pan A, et al. (2020). Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID-19): A multi-center study in Wenzhou city, Zhejiang, China. J Infection, in press.
[14] Hu Z, Song C, Xu C, Jin G, Chen Y, Xu X, et al. (2020). Clinical characteristics of 24 asymptomatic infections with COVID-19 screened among close contacts in Nanjing, China. Sci China Life Sci, in press.
[15] Zhang J-J, Dong X, Cao Y-Y, Yuan Y-D, Yang Y-B, Yan Y-Q, et al. (2020). Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China. Allergy, in press.
[16] Wu J, Liu J, Zhao X, Liu C, Wang W, Wang D, et al. (2020). Clinical Characteristics of Imported Cases of COVID-19 in Jiangsu Province: A Multicenter Descriptive Study. Clin Infec Dis, in press.
[17] Huang Y, Tu M, Wang S, Chen S, Zhou W, Chen D, et al. (2020). Clinical characteristics of laboratory confirmed positive cases of SARS-CoV-2 infection in Wuhan, China: A retrospective single center analysis. Travel Med Infect Dis, 27:101606.
[18] Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet (London, England), 395:497-506.
[19] Xu XW, Wu XX, Jiang XG, Xu KJ, Ying LJ, Ma CL, et al. (2020). Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series. BMJ, 368:m606.
[20] Song F, Shi N, Shan F, Zhang Z, Shen J, Lu H, et al. (2020). Emerging 2019 Novel Coronavirus (2019-nCoV) Pneumonia. Radiol, 295:210-217.
[21] Xu X, Yu C, Qu J, Zhang L, Jiang S, Huang D, et al. (2020). Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2. Eur J Nucl Med Mol Imaging. in press.
[22] Shi H, Han X, Jiang N, Cao Y, Alwalid O, Gu J, et al. (2020). Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infec Dis, in press.
[23] Shi S, Qin M, Shen B, Cai Y, Liu T, Yang F, et al. (2020). Association of Cardiac Injury With Mortality in Hospitalized Patients With COVID-19 in Wuhan, China. JAMA Cardiol, in press.
[24] Bhatraju PK, Ghassemieh BJ, Nichols M, Kim R, Jerome KR, Nalla AK, et al. (2020). Covid-19 in Critically Ill Patients in the Seattle Region - Case Series. New Engl J Med, in press.
[25] Feng Y, Ling Y, Bai T, Xie YS, Huang J, Li J, et al. (2020). COVID-19 with different severity: A multi-center study of clinical features. American Thoracic Society, in press.
[26] Du RH, Liang LR, Yang CQ, Wang W, Cao TZ, Li M, et al. (2020). Predictors of mortality for patients with COVID-19 pneumonia caused by SARS-CoV-2: a prospective cohort study. Eur Respir J, in press.
[27] Liu W, Tao ZW, Wang L, Yuan ML, Liu K, Zhou L, et al. (2020). Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus disease. Chinese Med J, in press.
[28] Grasselli Giacomo, Zangrillo Alberto, Zanella Alberto, Antonelli Massimo, Cabrini Luca, Castelli Antonio, et al. (2020). Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy. JAMA, in press.
[29] Richardson Safiya, Hisch Jamie, Narasimhan Mangala, Crawford James, McGinn Thomas, Davidson Karina, et al. (2020). Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA, in press.
[30] Simonnet Arthur, Chetboun Mikael, Poissy Julien, Raverdy Violeta, Noulette Jerome, Duhamel Alain, et al. (2020). High prevalence of obesity in severe acute respiratory syndrome coronavirus-2 (SARS-CoV2) requiring invasive mechanical ventilation. Obesity (Silver Spring), in press.
[31] Melsen WG, Bootsma MCJ, Rovers MM, Bonten MJM (2014). The effects of clinical and statistical heterogeneity on the predictive values of results from meta-analyses. Clin Microbiolo Infec, 20:123-129.
[32] Jiang F, Deng L, Zhang L, Cai Y, Cheung CW, Xia Z (2020). Review of the Clinical Characteristics of Coronavirus Disease 2019 (COVID-19). J Gen Intern Med, in press.
[33] de Wit E, van Doremalen N, Falzarano D, Munster VJ (2016). SARS and MERS: recent insights into emerging coronaviruses. Nat Rev Microbiol, 14:523-534.
[34] Wan Y, Li J, Shen L, Zou Y, Hou L, Zhu L, et al. (2020). Enteric involvement in hospitalised patients with COVID-19 outside Wuhan. Lancet Gastroenterol Hepatol, in press.
[35] Mertz D, Kim TH, Johnstone J, Lam P-P, Science M, Kuster SP, et al. (2013). Populations at risk for severe or complicated influenza illness: systematic review and meta-analysis. BMJ, 347: f5061.
[36] Lewington S, Lacey B, Clarke R, Guo Y, Kong XL, Yang L, et al. (2016). The burden of hypertension and association risk for cardiovascular mortality in China. JAMA Intern Med. 176:524-532.
[37] Wang B, Li RB, Lu Z, Huang Y (2020). Does comorbidity increase the risk of patients with COVID-19: evidence from meta-analysis. Aging, in press.
[38] Badawi A, Ryoo SG (2016). Prevalence of comorbidities in the Middle East respiratory syndrome coronavirus (MERS-CoV): a systematic review and meta-analysis. Int J Infect Dis, 49:129-133.
[39] Suryaprasad A, Redd JT, Hancock K, Branch A, Steward-Clark E, Katz JM, et al. (2013). Severe acute respiratory infections caused by 2009 pandemic influenza A (H1N1) among American Indians--southwestern United States, May 1-July 21, 2009. Influenza Other Respi Viruses, 7:1361-1369.
[40] Kusznierz G, Uboldi A, Sosa G, Torales S, Colombo J, Moyano C, et al. (2013). Clinical features of the hospitalized patients with 2009 pandemic influenza A (H1N1) in Santa Fe, Argentina. Influenza Other Respi Viruses, 7:410-417.
[41] Htun NSN, Odermatt P, Eze IC, Boillat-Blanco N, D'Acremont V, Probst-Hensch N (2015). Is diabetes a risk factor for a severe clinical presentation of dengue?--review and meta-analysis. PLoS Negl Trop Dis, 9:e0003741.
[42] Limonta D, Torres G, Capó V, Guzmán MG (2008). Apoptosis, vascular leakage and increased risk of severe dengue in a type 2 diabetes mellitus patient. Diab Vasc Dis Res, 5:213-214.
[43] Dharmashankar K, Widlansky ME (2010). Vascular endothelial function and hypertension: insights and directions. Curr Hypertens Rep, 12:448-455.
[44] Magen E, Feldman A, Cohen Z, Alon DB, Linov L, Mishal J, et al. (2010). Potential link between C3a, C3b and endothelial progenitor cells in resistant hypertension. Ame J Med Sci, 339:415-419.
[45] Didion SP, Kinzenbaw DA, Schrader LI, Chu Y, Faraci FM (2009). Endogenous interleukin-10 inhibits angiotensin II-induced vascular dysfunction. Hypertension, 54:619-624.
[46] Booth CM, Matukas LM, Tomlinson GA, Rachlis AR, Rose DB, Dwosh HA, et al. (2003). Clinical features and short-term outcomes of 144 patients with SARS in the greater Toronto area. JAMA, 289:2801-2809.
[47] Yang JK, Feng Y, Yuan MY, Yuan SY, Fu HJ, Wu BY, et al. (2006). Plasma glucose levels and diabetes are independent predictors for mortality and morbidity in patients with SARS. Diabet Med, 23:623-628.
[48] Allard R, Leclerc P, Tremblay C, Tannenbaum T-N (2010). Diabetes and the severity of pandemic influenza A (H1N1) infection. Diabetes Care, 33:1491-1493.
[49] van den Brand JM, Smits SL, Haagmans BL (2015). Pathogenesis of Middle East respiratory syndrome coronavirus. J Pathol, 235:175-184.
[50] Yang J-K, Lin S-S, Ji X-J, Guo L-M (2010). Binding of SARS coronavirus to its receptor damages islets and causes acute diabetes. Acta Diabetol, 47:193-199.
[51] Wu F, Zhao S, Yu B, Chen Y-M, Wang W, Song Z-G, et al. (2020). A new coronavirus associated with human respiratory disease in China. Nature, 579:265-269.
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