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    2021, Vol. 12 Issue (1) : 61-71     DOI: 10.14336/AD.2020.1108
Orginal Article |
Metabolic Healthy Obesity, Vitamin D Status, and Risk of COVID-19
Shu Li1, Zhi Cao1, Hongxi Yang1,2, Yuan Zhang1, Fusheng Xu1, Yaogang Wang1,*
1School of Public Health, Tianjin Medical University, Tianjin, China.
2School of Public Health, Yale University, New Haven, CT, USA.
Download: PDF(1180 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Aging and obesity-related conditions seem to worsen the effect of Coronavirus Disease 2019 (COVID-19). This study assessed the possible roles of metabolic/obesity phenotypes and vitamin D status in increasing the greater severity of COVID-19. We studied 353,299 UK Biobank participants from England with a mean age of 67.7 years. Metabolic/obesity phenotypes were defined as a combination of metabolic components (hypertension, high cholesterol, and diabetes) and obesity. Multivariate logistic regression analysis was performed to test whether the addition of metabolic disorders and vitamin D insufficiency increased obesity associations with COVID-19 hospitalization, confirmed COVID-19, and severe COVID-19. Metabolically unhealthy obesity (MUHO) represented 12.3% of the total analytic samples, and 21.5%, 18.5%, and 19.8% of the included subpopulations with COVID-19 hospitalization, confirmed COVID-19, and severe COVID-19, respectively. Vitamin D insufficiency phenotypes represented 53.5% of the total analytic samples, and 59.5%, 61.7%, and 61.5% of the included subpopulations with COVID-19 hospitalization, confirmed COVID-19, and severe COVID-19, respectively. In multivariate logistic regression, MUHO and vitamin D insufficiency and their combination were significantly associated with COVID-19 illness severity (odds ratio [OR] for COVID-19 hospitalization = 2.33, 95% confidence interval [CI], 2.02-2.70; OR for confirmed COVID-19 = 2.06, 95% CI, 1.58-2.70; OR for severe COVID-19 = 2.06, 95% CI, 1.47-2.87). Elderly men were prone to have a higher risk of COVID-19 than women. Our findings showed that MUHO and vitamin D insufficiency are associated with a significantly increased risk of COVID-19 severity, especially for adults 65 years and older. Susceptible individuals should be aware of their conditions and avoid contact with new coronavirus.

Keywords Obesity      metabolic health      vitamin D      COVID-19     
Corresponding Authors: Wang Yaogang   
About author:

these authors contributed equally to this work.

Just Accepted Date: 13 November 2020   Issue Date: 11 January 2021
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Li Shu
Cao Zhi
Yang Hongxi
Zhang Yuan
Xu Fusheng
Wang Yaogang
Cite this article:   
Li Shu,Cao Zhi,Yang Hongxi, et al. Metabolic Healthy Obesity, Vitamin D Status, and Risk of COVID-19[J]. Aging and disease, 2021, 12(1): 61-71.
URL:  
http://www.aginganddisease.org/EN/10.14336/AD.2020.1108     OR
CharacteristicsTotal
(N= 353,299)
COVID-19 hospitalizationConfirmed COVID-19Severe COVID-19
No
(n=349,797)
Yes
(n=3,502)
P-valueNo
(n=352,217)
Yes
(n=1,082)
P-valueNo
(n=352,585)
Yes
(n=714)
P-value
Age (years)67.7 (8.1)67.7 (8.1)69.3 (8.5)<0.00167.7 (8.1)67 (9.3)0.00667.7 (8.1)68.3 (9)0.058
Age category<0.0010.957<0.001
< 65124,985 (35.4%)123,941 (35.4%)1,044 (29.8%)124,524 (35.3%)461 (42.6%)124,734 (35.4%)251 (35.1%)
≥ 65228,314 (64.6%)225,856 (64.6%)2,458 (70.2%)227,693 (64.7%)621 (57.4%)227,851 (64.6%)463 (64.9%)
Female192,001 (54.4%)190,322 (54.4%)1,679 (47.9%)<0.001191,475 (54.4%)526 (48.6%)<0.001191,681 (54.4%)320 (44.8%)<0.001
Townsend deprivation index<0.001<0.001<0.001
Q1 (lowest deprived)70,881 (20.1%)70,327 (20.1%)554 (15.8%)70,721 (20.1%)160 (14.8%)70,781 (20.1%)100 (14%)
Q270,800 (20%)70,161 (20.1%)639 (18.2%)70,621 (20.1%)179 (16.5%)70,684 (20.1%)116 (16.2%)
Q370,307 (19.9%)69,682 (19.9%)625 (17.9%)70,134 (19.9%)173 (16%)70,187 (19.9%)120 (16.8%)
Q470,762 (20%)70,062 (20%)700 (20%)70,533 (20%)229 (21.2%)70,610 (20%)152 (21.3%)
Q5 (highest deprived)70,549 (20%)69,565 (19.9%)984 (28.1%)70,208 (19.9%)341 (31.5%)70,323 (19.9%)226 (31.7%)
Ethnicity0.048<0.001<0.001
White334,181 (94.6%)330,901 (94.6%)3,280 (93.7%)333,235 (94.6%)946 (87.4%)333,556 (94.6%)625 (87.5%)
Black or black British5,997 (1.7%)5,919 (1.7%)78 (2.2%)5,940 (1.7%)57 (5.3%)5,965 (1.7%)32 (4.5%)
Asian or Asian British7,688 (2.2%)7,601 (2.2%)87 (2.5%)7,638 (2.2%)50 (4.6%)7,650 (2.2%)38 (5.3%)
Mixed5,433 (1.5%)5,376 (1.5%)57 (1.6%)5,404 (1.5%)29 (2.7%)5,414 (1.5%)19 (2.7%)
Employment<0.0010.0250.003
Working210,021 (59.4%)208,316 (59.6%)1,705 (48.7%)209,410 (59.4%)611 (56.5%)209,642 (59.5%)379 (53.1%)
Retired112,504 (31.8%)111,114 (31.8%)1,390 (39.7%)112,154 (31.8%)350 (32.4%)112,251 (31.8%)253 (35.4%)
Unemployed25,309 (7.2%)24,957 (7.1%)352 (10%)25,209 (7.2%)100 (9.2%)25,241 (7.2%)68 (9.5%)
Other5,465 (1.6%)5,410 (1.5%)55 (1.6%)5,444 (1.6%)21 (1.9%)5,451 (1.5%)14 (2%)
Qualifications<0.001<0.001<0.001
College degree116,025 (32.8%)115,100 (32.9%)925 (26.4%)115,759 (32.9%)266 (24.6%)115,857 (32.9%)168 (23.5%)
A levels/AS levels40,289 (11.4%)39,951 (11.4%)338 (9.7%)40,180 (11.4%)109 (10.1%)40,223 (11.4%)66 (9.3%)
O levels/GCESs77,335 (21.9%)76,619 (21.9%)716 (20.4%)77,132 (21.9%)203 (18.7%)77,200 (21.9%)135 (18.9%)
CSEs21,001 (5.9%)20,802 (6%)199 (5.7%)20,921 (5.9%)80 (7.4%)20,956 (5.9%)45 (6.3%)
NVQ or HND or HNC23,500 (6.7%)23,246 (6.6%)254 (7.3%)23,403 (6.6%)97 (9%)23,442 (6.6%)58 (8.1%)
Other professional qualifications18,275 (5.2%)18,081 (5.2%)194 (5.5%)18,212 (5.2%)63 (5.8%)18,232 (5.2%)43 (6%)
None of the above56,874 (16.1%)55,998 (16%)876 (25%)56,610 (16.1%)264 (24.4%)56,675 (16.1%)199 (27.9%)
Smoking status<0.001<0.001<0.001
Never196,688 (55.7%)195,053 (55.8%)1,635 (46.7%)196,148 (55.7%)540 (49.9%)196,357 (55.7%)331 (46.4%)
Previous122,615 (34.7%)121,222 (34.6%)1,393 (39.8%)122,198 (34.7%)417 (38.5%)122,319 (34.7%)296 (41.4%)
Current33,996 (9.6%)33,522 (9.6%)474 (13.5%)33,871 (9.6%)125 (11.6%)33,909 (9.6%)87 (12.2%)
BMI (kg/m2)27.4 (4.7)27.4 (4.7)28.6 (5.2)<0.00127.4 (4.7)28.7 (5.3)<0.00127.4 (4.7)28.8 (5.2)<0.001
BMI category<0.001<0.001<0.001
Normal weight (18.5-24.9)116,757 (33%)115,875 (33.1%)882 (25.2%)116,488 (33.1%)269 (24.9%)116,590 (33.1%)167 (23.4%)
Overweight (25.0-29.9)151,555 (42.9%)150,088 (42.9%)1,467 (41.9%)151,086 (42.9%)469 (43.3%)151,240 (42.9%)315 (44.1%)
Obese (≥ 30.0)84,987 (24.1%)83,834 (24%)1,153 (32.9%)84,643 (24%)344 (31.8%)84,755 (24%)232 (32.5%)
Hypertension96,247 (27.2%)94,883 (27.1%)1,364 (39%)<0.00195,871 (27.2%)376 (34.8%)<0.00195,979 (27.2%)268 (37.5%)<0.001
Hypercholesterolemia64,375 (18.2%)63,378 (18.1%)997 (28.5%)<0.00164,114 (18.2%)261 (24.1%)<0.00164,176 (18.2%)199 (27.9%)<0.001
Diabetes16,585 (4.7%)16,237 (4.6%)348 (9.9%)<0.00116,490 (4.7%)95 (8.8%)<0.00116,514 (4.7%)71 (9.9%)<0.001
Metabolic/obesity phenotypes<0.001<0.001<0.001
MHNW93,130 (26.4%)92,486 (26.4%)644 (18.4%)92,935 (26.4%)195 (18%)93,015 (26.4%)115 (16.1%)
MHOW98,460 (27.9%)97,643 (27.9%)817 (23.3%)98,172 (27.9%)288 (26.6%)98,281 (27.9%)179 (25.1%)
MHO41,480 (11.7%)41,080 (11.8%)400 (11.4%)41,336 (11.7%)144 (13.3%)41,389 (11.7%)91 (12.7%)
MUHNW23,627 (6.7%)23,389 (6.7%)238 (6.8%)23,553 (6.7%)74 (6.8%)23,575 (6.7%)52 (7.3%)
MUHOW53,095 (15%)52,445 (15%)650 (18.6%)52,914 (15%)181 (16.7%)52,959 (15%)136 (19%)
MUHO43,507 (12.3%)42,754 (12.2%)753 (21.5%)43,307 (12.3%)200 (18.5%)43,366 (12.3%)141 (19.8%)
Vitamin D concentration (nmol/L)49.6 (21)49.6 (21)47 (21.4)<0.00149.6 (21)46.2 (21.4)<0.00149.6 (21)46.3 (22.1)<0.001
Vitamin D deficiency (< 25 nmol/L)42,853 (12.1%)42,284 (12.1%)569 (16.3%)<0.00142,676 (12.1%)177 (16.4%)<0.00142,726 (12.1%)127 (17.8%)<0.001
Vitamin D insufficiency (< 50 nmol/L)188,888 (53.5%)186,804 (53.4%)2,084 (59.5%)<0.001188,221 (53.4%)667 (61.7%)<0.001188,449 (53.5%)439 (61.5%)<0.001
Table 1  Basic characteristics.
Figure 1.  Association between (A) vitamin D insufficiency and (B) vitamin D deficiency (as exposure variable) and reported COVID-19 outcomes (as outcome) by subgroup analysis. The analysis used univariate logistic regression models. Abbreviations: COVID-19, Coronavirus Disease 2019; BMI, body mass index; CI, confidence interval; MHNW, metabolically healthy normal weight; MHO, metabolically healthy obesity; MHOW, metabolically healthy overweight; MUHNW, metabolically unhealthy normal weight; MUHO, metabolically unhealthy obesity; MUHOW, metabolically unhealthy overweight; TDI, Townsend deprivation index.
Model adjustedCOVID-19 hospitalizationConfirmed COVID-19Severe COVID-19
OR (95% CI)Difference in OR, %OR (95% CI)Difference in OR, %OR (95% CI)Difference in OR, %
Non metabolic & vitamin D status adjusted1.56 (1.42, 1.70)Ref.1.48 (1.25, 1.74)Ref.1.53 (1.25, 1.88)Ref.
Metabolic status adjusted1.44 (1.31, 1.58)7.71.39 (1.18, 1.65)6.11.43 (1.16, 1.76)6.5
Vitamin D status adjusted1.50 (1.36, 1.64)3.81.43 (1.22, 1.69)3.41.49 (1.21, 1.83)2.6
Both metabolic & vitamin D status adjusted1.38 (1.26, 1.52)11.51.35 (1.14, 1.60)8.81.39 (1.12, 1.71)9.2
Table 2  Comparison of multivariable ORs for obesity vs. normal-weight level of BMI for COVID-19, without and with adjustment for metabolic or vitamin D status.
CharacteristicsVitamin D deficiency (< 25 nmol/L)Vitamin D insufficiency (< 50 nmol/L)
NoYesNoYes
OR (95% CI)POR (95% CI)POR (95% CI)POR (95% CI)P
COVID-19 hospitalization
MHNW1 (Ref.)1.24 (0.98, 1.57)0.0711 (Ref.)1.24 (1.06, 1.45)0.007
MHOW1.14 (1.02, 1.27)0.0241.27 (1.03, 1.58)0.0271.20 (1.03, 1.40)0.0211.30 (1.12, 1.50)<0.001
MHO1.22 (1.06, 1.40)0.0061.77 (1.42, 2.22)<0.0011.23 (0.99, 1.52)0.0581.53 (1.30, 1.81)<0.001
MUHNW1.21 (1.02, 1.42)0.0241.52 (1.06, 2.18)0.0241.21 (0.98, 1.51)0.0791.49 (1.20, 1.84)<0.001
MUHOW1.36 (1.21, 1.54)<0.0011.97 (1.59, 2.46)<0.0011.33 (1.13, 1.58)0.0011.76 (1.50, 2.05)<0.001
MUHO1.91 (1.69, 2.15)<0.0012.46 (2.05, 2.94)<0.0011.88 (1.58, 2.24)<0.0012.33 (2.02, 2.70)<0.001
Confirmed COVID-19
MHNW1 (Ref.)0.93 (0.60, 1.44)0.7341 (Ref.)1.11 (0.84, 1.47)0.474
MHOW1.31 (1.08, 1.59)0.0071.09 (0.75, 1.59)0.6571.34 (1.02, 1.75)0.0341.38 (1.07, 1.79)0.015
MHO1.42 (1.12, 1.80)0.0041.34 (0.89, 2.01)0.1671.02 (0.68, 1.52)0.9371.73 (1.31, 2.29)<0.001
MUHNW1.31 (0.97, 1.77)0.0782.21 (1.29, 3.78)0.0041.33 (0.89, 1.98)0.1681.71 (1.18, 2.48)0.005
MUHOW1.41 (1.12, 1.77)0.0031.72 (1.15, 2.57)0.0091.34 (0.98, 1.84)0.0651.70 (1.28, 2.26)<0.001
MUHO1.68 (1.34, 2.11)<0.0012.34 (1.69, 3.23)<0.0011.63 (1.16, 2.28)0.0042.06 (1.58, 2.70)<0.001
Severe COVID-19
MHNW1 (Ref.)1.05 (0.61, 1.81)0.8661 (Ref.)1.00 (0.69, 1.45)0.996
MHOW1.32 (1.03, 1.70)0.0311.35 (0.86, 2.12)0.1961.27 (0.90, 1.79)0.1731.36 (0.98, 1.88)0.067
MHO1.42 (1.04, 1.93)0.0271.90 (1.19, 3.03)0.0070.99 (0.60, 1.65)0.9761.75 (1.24, 2.49)0.002
MUHNW1.38 (0.96, 2.00)0.0852.43 (1.29, 4.59)0.0061.37 (0.85, 2.22)0.1951.65 (1.04, 2.60)0.033
MUHOW1.58 (1.19, 2.09)0.0011.86 (1.15, 3.02)0.0121.35 (0.92, 1.97)0.1251.82 (1.29, 2.57)0.001
MUHO1.82 (1.37, 2.41)<0.0012.48 (1.66, 3.70)<0.0011.68 (1.12, 2.52)0.0122.06 (1.47, 2.87)<0.001
Table 3  Joint associations of metabolic/obesity phenotypes and vitamin D status with COVID-19 outcomes.
Figure 2.  Associations of metabolic/obesity phenotypes and vitamin D status with COVID-19 outcomes stratified by (A) sex and (B) age subgroups. The models adjusted for age, Townsend deprivation index, qualifications, employment, ethnicity, and smoking status. Abbreviations: COVID-19, Coronavirus Disease 2019; MHNW, metabolically healthy normal weight; MHO, metabolically healthy obesity; MHOW, metabolically healthy overweight; MUHNW, metabolically unhealthy normal weight; MUHO, metabolically unhealthy obesity; MUHOW, metabolically unhealthy overweight.
[1] Hauser A, Counotte MJ, Margossian CC, Konstantinoudis G, Low N, Althaus CL, et al. (2020). Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe. PLoS Med, 17:e1003189.
[2] Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, et al. (2020). Factors associated with COVID-19-related death using OpenSAFELY. Nature, 584:430-436.
[3] Liu H, Chen S, Liu M, Nie H, Lu H (2020). Comorbid Chronic Diseases are Strongly Correlated with Disease Severity among COVID-19 Patients: A Systematic Review and Meta-Analysis. Aging Dis, 11:668-678.
[4] Stefan N, Birkenfeld AL, Schulze MB, Ludwig DS (2020). Obesity and impaired metabolic health in patients with COVID-19. Nat Rev Endocrinol, 16:341-342.
[5] Bornstein SR, Rubino F, Khunti K, Mingrone G, Hopkins D, Birkenfeld AL, et al. (2020). Practical recommendations for the management of diabetes in patients with COVID-19. Lancet Diabetes Endocrinol, 8:546-550.
[6] Remuzzi A, Remuzzi G (2020). COVID-19 and Italy: what next? Lancet, 395:1225-1228.
[7] Mitchell F (2020). Vitamin-D and COVID-19: do deficient risk a poorer outcome? Lancet Diabetes Endocrinol, 8:570.
[8] Gilbert CR, Arum SM, Smith CM (2009). Vitamin D deficiency and chronic lung disease. Can Respir J, 16:75-80.
[9] Liu J, Dong YQ, Yin J, Yao J, Shen J, Sheng GJ, et al. (2019). Meta-analysis of vitamin D and lung function in patients with asthma. Respir Res, 20:161.
[10] Guan WJ, Liang WH, Zhao Y, Liang HR, Chen ZS, Li YM, et al. (2020). Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis. Eur Respir J, 55.
[11] Jeong IK, Yoon KH, Lee MK (2020). Diabetes and COVID-19: Global and regional perspectives. Diabetes Res Clin Pract, 166:108303.
[12] Ollier W, Sprosen T, Peakman T (2005). UK Biobank: from concept to reality. Pharmacogenomics, 6:639-646.
[13] Eckel N, Li Y, Kuxhaus O, Stefan N, Hu FB, Schulze MB (2018). Transition from metabolic healthy to unhealthy phenotypes and association with cardiovascular disease risk across BMI categories in 90 257 women (the Nurses' Health Study): 30 year follow-up from a prospective cohort study. Lancet Diabetes Endocrinol, 6:714-724.
[14] Zhu Z, Hasegawa K, Ma B, Fujiogi M, Camargo CAJr, Liang L (2020). Association of asthma and its genetic predisposition with the risk of severe COVID-19. J Allergy Clin Immunol, 146:327-329 e324.
[15] Stefan N, Haring HU, Schulze MB (2018). Metabolically healthy obesity: the low-hanging fruit in obesity treatment? Lancet Diabetes Endocrinol, 6:249-258.
[16] Stefan N (2020). Causes, consequences, and treatment of metabolically unhealthy fat distribution. Lancet Diabetes Endocrinol, 8:616-627.
[17] Catapano AL, Graham I, De Backer G, Wiklund O, Chapman MJ, Drexel H, et al. (2016). 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias. Eur Heart J, 37:2999-3058.
[18] Bornstein SR, Dalan R, Hopkins D, Mingrone G, Boehm BO (2020). Endocrine and metabolic link to coronavirus infection. Nat Rev Endocrinol, 16:297-298.
[19] Petersen A, Bressem K, Albrecht J, Thiess HM, Vahldiek J, Hamm B, et al. (2020). The role of visceral adiposity in the severity of COVID-19: Highlights from a unicenter cross-sectional pilot study in Germany. Metabolism, 110:154317.
[20] Apicella M, Campopiano MC, Mantuano M, Mazoni L, Coppelli A, Del Prato S (2020). COVID-19 in people with diabetes: understanding the reasons for worse outcomes. Lancet Diabetes Endocrinol, 8:782-792.
[21] Mauvais-Jarvis F (2020). Aging, Male Sex, Obesity, and Metabolic Inflammation Create the Perfect Storm for COVID-19. Diabetes, 69:1857-1863.
[22] Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. (2014). Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet, 384:766-781.
[23] Martineau AR, Jolliffe DA, Hooper RL, Greenberg L, Aloia JF, Bergman P, et al. (2017). Vitamin D supplementation to prevent acute respiratory tract infections: systematic review and meta-analysis of individual participant data. BMJ, 356:i6583.
[24] Hastie CE, Mackay DF, Ho F, Celis-Morales CA, Katikireddi SV, Niedzwiedz CL, et al. (2020). Vitamin D concentrations and COVID-19 infection in UK Biobank. Diabetes Metab Syndr, 14:561-565.
[25] Wimalawansa SJ (2018). Associations of vitamin D with insulin resistance, obesity, type 2 diabetes, and metabolic syndrome. J Steroid Biochem Mol Biol, 175:177-189.
[26] Zheng Z, Peng F, Xu B, Zhao J, Liu H, Peng J, et al. (2020). Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis. J Infect, 81:e16-e25.
[27] Jin JM, Bai P, He W, Wu F, Liu XF, Han DM, et al. (2020). Gender Differences in Patients With COVID-19: Focus on Severity and Mortality. Front Public Health, 8:152.
[28] Galbadage T, Peterson BM, Awada J, Buck AS, Ramirez DA, Wilson J, et al. (2020). Systematic Review and Meta-Analysis of Sex-Specific COVID-19 Clinical Outcomes. Front Med (Lausanne), 7:348.
[29] Docherty AB, Harrison EM, Green CA, Hardwick HE, Pius R, Norman L, et al. (2020). Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study. BMJ, 369:m1985.
[30] Channappanavar R, Fett C, Mack M, Ten Eyck PP, Meyerholz DK, Perlman S (2017). Sex-Based Differences in Susceptibility to Severe Acute Respiratory Syndrome Coronavirus Infection. J Immunol, 198:4046-4053.
[31] Karlberg J, Chong DS, Lai WY (2004). Do men have a higher case fatality rate of severe acute respiratory syndrome than women do? Am J Epidemiol, 159:229-231.
[32] Alghamdi IG, HussainII, Almalki SS, Alghamdi MS, Alghamdi MM, El-Sheemy MA (2014). The pattern of Middle East respiratory syndrome coronavirus in Saudi Arabia: a descriptive epidemiological analysis of data from the Saudi Ministry of Health. Int J Gen Med, 7:417-423.
[33] La Vignera S, Cannarella R, Condorelli RA, Torre F, Aversa A, Calogero AE (2020). Sex-Specific SARS-CoV-2 Mortality: Among Hormone-Modulated ACE2 Expression, Risk of Venous Thromboembolism and Hypovitaminosis D. Int J Mol Sci, 21.
[34] Bourgonje AR, Abdulle AE, Timens W, Hillebrands JL, Navis GJ, Gordijn SJ, et al. (2020). Angiotensin-converting enzyme 2 (ACE2), SARS-CoV-2 and the pathophysiology of coronavirus disease 2019 (COVID-19). J Pathol, 251:228-248.
[35] Cai H (2020). Sex difference and smoking predisposition in patients with COVID-19. Lancet Respir Med, 8:e20.
[36] Sharma G, Volgman AS, Michos ED (2020). Sex Differences in Mortality From COVID-19 Pandemic: Are Men Vulnerable and Women Protected? JACC Case Rep, 2:1407-1410.
[1] Supplementary data Download
[1] Fangfang Zhao,Ziping Han,Rongliang Wang,Yumin Luo. Neurological Manifestations of COVID-19: Causality or Coincidence?[J]. Aging and disease, 2021, 12(1): 27-35.
[2] Sorin Hostiuc,Ionut Negoi,Oana Maria-Isailă,Ioana Diaconescu,Mihaela Hostiuc,Eduard Drima. Age in the Time of COVID-19: An Ethical Analysis[J]. Aging and disease, 2021, 12(1): 7-13.
[3] Diogo de Moraes,Brunno Vivone Buquete Paiva,Sarah Santiloni Cury,Raissa Guimarães Ludwig,João Pessoa Araújo Junior,Marcelo Alves da Silva Mori,Robson Francisco Carvalho. Prediction of SARS-CoV Interaction with Host Proteins during Lung Aging Reveals a Potential Role for TRIB3 in COVID-19[J]. Aging and disease, 2021, 12(1): 42-49.
[4] Yu Peng,Hongxun Tao,Senthil Kumaran Satyanarayanan,Kunlin Jin,Huanxing Su. A Comprehensive Summary of the Knowledge on COVID-19 Treatment[J]. Aging and disease, 2021, 12(1): 155-191.
[5] Ann Liebert,Brian Bicknell,Wayne Markman,Hosen Kiat. A Potential Role for Photobiomodulation Therapy in Disease Treatment and Prevention in the Era of COVID-19[J]. Aging and disease, 2020, 11(6): 1352-1362.
[6] Ya Yang,Yalei Zhao,Fen Zhang,Lingjian Zhang,Lanjuan Li. COVID-19 in Elderly Adults: Clinical Features, Molecular Mechanisms, and Proposed Strategies[J]. Aging and disease, 2020, 11(6): 1481-1495.
[7] Carly Welch,Carolyn Greig,Tahir Masud,Daisy Wilson,Thomas A Jackson. COVID-19 and Acute Sarcopenia[J]. Aging and disease, 2020, 11(6): 1345-1351.
[8] Pietro Gentile,Aris Sterodimas,Jacopo Pizzicannella,Claudio Calabrese,Simone Garcovich. Research progress on Mesenchymal Stem Cells (MSCs), Adipose-Derived Mesenchymal Stem Cells (AD-MSCs), Drugs, and Vaccines in Inhibiting COVID-19 Disease[J]. Aging and disease, 2020, 11(5): 1191-1201.
[9] Duygu Koyuncu Irmak,Hakan Darıcı,Erdal Karaöz. Stem Cell Based Therapy Option in COVID-19: Is It Really Promising?[J]. Aging and disease, 2020, 11(5): 1174-1191.
[10] Undurti N Das. Bioactive Lipids as Mediators of the Beneficial Action(s) of Mesenchymal Stem Cells in COVID-19[J]. Aging and disease, 2020, 11(4): 746-755.
[11] Ting Wu,Zhihong Zuo,Shuntong Kang,Liping Jiang,Xuan Luo,Zanxian Xia,Jing Liu,Xiaojuan Xiao,Mao Ye,Meichun Deng. Multi-organ Dysfunction in Patients with COVID-19: A Systematic Review and Meta-analysis[J]. Aging and disease, 2020, 11(4): 874-894.
[12] Pedro C Lara,David Macías-Verde,Javier Burgos-Burgos. Age-induced NLRP3 Inflammasome Over-activation Increases Lethality of SARS-CoV-2 Pneumonia in Elderly Patients[J]. Aging and disease, 2020, 11(4): 756-762.
[13] Michael D Schwartz,Stephen G Emerson,Jennifer Punt,Willow D Goff. Decreased Naïve T-cell Production Leading to Cytokine Storm as Cause of Increased COVID-19 Severity with Comorbidities[J]. Aging and disease, 2020, 11(4): 742-745.
[14] Feng He, Yibo Quan, Ming Lei, Riguang Liu, Shuguang Qin, Jun Zeng, Ziwen Zhao, Na Yu, Liuping Yang, Jie Cao. Clinical features and risk factors for ICU admission in COVID-19 patients with cardiovascular diseases[J]. Aging and disease, 2020, 11(4): 763-769.
[15] Agnieszka Neumann-Podczaska,Salwan R Al-Saad,Lukasz M Karbowski,Michal Chojnicki,Slawomir Tobis,Katarzyna Wieczorowska-Tobis. COVID 19 - Clinical Picture in the Elderly Population: A Qualitative Systematic Review[J]. Aging and disease, 2020, 11(4): 988-1008.
Viewed
Full text


Abstract

Cited

  Shared   
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: editorial@aginganddisease.org
Powered by Beijing Magtech Co. Ltd