Please wait a minute...
 Home  About the Journal Editorial Board Aims & Scope Peer Review Policy Subscription Contact us
Early Edition  //  Current Issue  //  Open Special Issues  //  Archives  //  Most Read  //  Most Downloaded  //  Most Cited
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.
Download: PDF(983 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    

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
E-mail this article
E-mail Alert
Articles by authors
Liu Hong
Chen Shiyan
Liu Min
Nie Hao
Lu Hongyun
Cite this article:   
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.
URL:     OR
Study[ref]aNOSDates (mm. yy)n
Age (years)Age ≥50 years (%)Symptoms (%)
AllMFfevercoughfatigue or myalgiashortness of breath or dyspneadiarrheadiabeteshypertensionCAD/CVDchronic pulmonary disease
Guan et al.,2020 [3]812.19-01.20109964045947.044.088.767.838.
Chen et al.,2020 [4]701.20-01.2099673255.567.782.881.811.
Huang et al.,2020 [18]612.19-01.2041301149.048.898.
Liu et al.,2020 [8]712.19-01.20137617655.0b81.848.
Shi et al.,2020 [22]612.19-01.2081423949.549.473.059.0b42.
Song et al.,2020 [20]601.20-01.2051252649.
Yang et al.,2020 [6]712.19-01.2052351759.755.
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.
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.
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.
Wang et al.,2020 [5]701.20-01.20138756356.0b98.659.469.631.
Xu et al.,2020 [21]701.20-02.2090395150.0b78.063.028.0b6.
Li et al.,2020 [12]701.20-02.2083443945.5b86.778.318.
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
95% CI49.0-68.065.0-92.054.0-69.021.0-43.025.0-
I2 (%)98.599.795.198.597.481.
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.

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

Guan et al.,2020 [3]92654038645.01731007352.088.191.967.370.537.839.915.
Huang et al.,2020[18]2819949.01311249.096.0100.
Yang et al.,2020 [6]2014651.932211164.6100.
Xu,2020 [19]2916133933191445837379824558bb090.
Zhang et al.,2020[15]82384451.558332564.072.087.954.977.662.267.224.441.411.015.511.013.824.437.
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.
Shi et al.,2020 [23]334161173608244387481.176.834.734.11218.327.
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
(45.0-74.0) d
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
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.
[1] Chin-Hsiao Tseng. Dementia Risk in Type 2 Diabetes Patients: Acarbose Use and Its Joint Effects with Metformin and Pioglitazone[J]. Aging and disease, 2020, 11(3): 658-667.
[2] Xiaotian Dong, Mengyan Wang, Shuangchun Liu, Jiaqi Zhu, Yanping Xu, Hongcui Cao, Lanjuan Li. Immune Characteristics of Patients with Coronavirus Disease 2019 (COVID-19)[J]. Aging and disease, 2020, 11(3): 642-648.
[3] Yazhen Zhang, Wenyi Chen, Bing Feng, Hongcui Cao. The Clinical Efficacy and Safety of Stem Cell Therapy for Diabetes Mellitus: A Systematic Review and Meta-Analysis[J]. Aging and disease, 2020, 11(1): 141-153.
[4] Yanqing Wu, Libing Ye, Yuan Yuan, Ting Jiang, Xin Guo, Zhouguang Wang, Ke Xu, Zeping Xu, Yanlong Liu, Xingfeng Zhong, Junmin Ye, Hongyu Zhang, Xiaokun Li, Jian Xiao. Autophagy Activation is Associated with Neuroprotection in Diabetes-associated Cognitive Decline[J]. Aging and disease, 2019, 10(6): 1233-1245.
[5] Chia-Ter Chao, Jui Wang, Jenq-Wen Huang, Ding-Cheng Chan, Kuo-Liong Chien. Frailty Predicts an Increased Risk of End-Stage Renal Disease with Risk Competition by Mortality among 165,461 Diabetic Kidney Disease Patients[J]. Aging and disease, 2019, 10(6): 1270-1281.
[6] Le Gao, Shuqing Yu, Andrea Cipriani, Shanshan Wu, Yi Huang, Zilu Zhang, Jun Yang, Yixin Sun, Zhirong Yang, Sanbao Chai, Yuan Zhang, Linong Ji, Siyan Zhan, Feng Sun. Neurological Manifestation of Incretin-Based Therapies in Patients with Type 2 Diabetes: A Systematic Review and Network Meta-Analysis[J]. Aging and disease, 2019, 10(6): 1311-1319.
[7] Wenjun Li, Kiran Chaudhari, Ritu Shetty, Ali Winters, Xiaofei Gao, Zeping Hu, Woo-Ping Ge, Nathalie Sumien, Michael Forster, Ran Liu, Shao-Hua Yang. Metformin Alters Locomotor and Cognitive Function and Brain Metabolism in Normoglycemic Mice[J]. Aging and disease, 2019, 10(5): 949-963.
[8] Tangzhiming Li, Lihuang Zha, Hui Luo, Suqi Li, Lin Zhao, Jingni He, Xiaohui Li, Qiangqiang Qi, Yuwei Liu, Zaixin Yu. Galectin-3 Mediates Endothelial-to-Mesenchymal Transition in Pulmonary Arterial Hypertension[J]. Aging and disease, 2019, 10(4): 731-745.
[9] Fei Han, Xiaochen Li, Juhong Yang, Haiyi Liu, Yi Zhang, Xiaoyun Yang, Shaohua Yang, Bai Chang, Liming Chen, Baocheng Chang. Salsalate Prevents β-Cell Dedifferentiation in OLETF Rats with Type 2 Diabetes through Notch1 Pathway[J]. Aging and disease, 2019, 10(4): 719-730.
[10] Raquel Maeso-Díaz, Martí Ortega-Ribera, Erica Lafoz, Juan José Lozano, Anna Baiges, Rubén Francés, Agustín Albillos, Carmen Peralta, Juan Carlos García-Pagán, Jaime Bosch, Victoria C Cogger, Jordi Gracia-Sancho. Aging Influences Hepatic Microvascular Biology and Liver Fibrosis in Advanced Chronic Liver Disease[J]. Aging and disease, 2019, 10(4): 684-698.
[11] Navneet Kumar Dubey, Hong-Jian Wei, Sung-Hsun Yu, David F. Williams, Joseph R. Wang, Yue-Hua Deng, Feng-Chou Tsai, Peter D. Wang, Win-Ping Deng. Adipose-derived Stem Cells Attenuates Diabetic Osteoarthritis via Inhibition of Glycation-mediated Inflammatory Cascade[J]. Aging and disease, 2019, 10(3): 483-496.
[12] Rongrong Han, Zeyue Liu, Nannan Sun, Shu Liu, Lanlan Li, Yan Shen, Jianbo Xiu, Qi Xu. BDNF Alleviates Neuroinflammation in the Hippocampus of Type 1 Diabetic Mice via Blocking the Aberrant HMGB1/RAGE/NF-κB Pathway[J]. Aging and disease, 2019, 10(3): 611-625.
[13] Feng-Juan Li, Cheng-Long Zhang, Xiu-Ju Luo, Jun Peng, Tian-Lun Yang. Involvement of the MiR-181b-5p/HMGB1 Pathway in Ang II-induced Phenotypic Transformation of Smooth Muscle Cells in Hypertension[J]. Aging and disease, 2019, 10(2): 231-248.
[14] Tseng Chin-Hsiao. Metformin and the Risk of Dementia in Type 2 Diabetes Patients[J]. Aging and disease, 2019, 10(1): 37-48.
[15] Changhong Ren, Hang Wu, Dongjie Li, Yong Yang, Yuan Gao, Yunneng Jizhang, Dachuan Liu, Xunming Ji, Xuxiang Zhang. Remote Ischemic Conditioning Protects Diabetic Retinopathy in Streptozotocin-induced Diabetic Rats via Anti-Inflammation and Antioxidation[J]. Aging and disease, 2018, 9(6): 1122-1133.
Full text



Copyright © 2014 Aging and Disease, All Rights Reserved.
Address: Aging and Disease Editorial Office 3400 Camp Bowie Boulevard Fort Worth, TX76106 USA
Fax: (817) 735-0408 E-mail:
Powered by Beijing Magtech Co. Ltd