Smoking and COVID-19 outcomes: an observational and Mendelian randomisation study using the UK Biobank cohort

被引:125
作者
Clift, Ashley K. [1 ,2 ]
von Ende, Adam [3 ]
San Tan, Pui [1 ]
Sallis, Hannah M. [4 ,5 ,6 ,7 ]
Lindson, Nicola [1 ]
Coupland, Carol A. C. [1 ,8 ]
Munafo, Marcus R. [4 ,5 ,6 ,7 ]
Aveyard, Paul [1 ]
Hippisley-Cox, Julia [1 ]
Hopewell, Jemma C. [3 ]
机构
[1] Univ Oxford, Nuffield Dept Primary Care Hlth Sci, Oxford, England
[2] Univ Oxford, Canc Res UK Oxford Ctr, Dept Oncol, Oxford, England
[3] Univ Oxford, Nuffield Dept Populat Hlth, Clin Trial Serv Unit, Oxford, England
[4] Univ Bristol, MRC Integrat Epidemiol Unit, Bristol, Avon, England
[5] Univ Bristol, Sch Psychol Sci, Bristol, Avon, England
[6] Univ Hosp Bristol NHS Fdn Trust, NIHR Bristol Biomed Res Ctr, Bristol, Avon, England
[7] Univ Bristol, Bristol, Avon, England
[8] Univ Nottingham, Div Primary Care, Nottingham, England
基金
英国医学研究理事会; 欧洲研究理事会;
关键词
COVID-19; clinical epidemiology; tobacco control; BIAS;
D O I
10.1136/thoraxjnl-2021-217080
中图分类号
R56 [呼吸系及胸部疾病];
学科分类号
摘要
Background Conflicting evidence has emerged regarding the relevance of smoking on risk of COVID-19 and its severity. Methods We undertook large-scale observational and Mendelian randomisation (MR) analyses using UK Biobank. Most recent smoking status was determined from primary care records (70.8%) and UK Biobank questionnaire data (29.2%). COVID-19 outcomes were derived from Public Health England SARS-CoV-2 testing data, hospital admissions data, and death certificates (until 18 August 2020). Logistic regression was used to estimate associations between smoking status and confirmed SARS-CoV-2 infection, COVID-19-related hospitalisation, and COVID-19-related death. Inverse variance-weighted MR analyses using established genetic instruments for smoking initiation and smoking heaviness were undertaken (reported per SD increase). Results There were 421 469 eligible participants, 1649 confirmed infections, 968 COVID-19-related hospitalisations and 444 COVID-19-related deaths. Compared with never-smokers, current smokers had higher risks of hospitalisation (OR 1.80, 95% CI 1.26 to 2.29) and mortality (smoking 1-9/day: OR 2.14, 95% CI 0.87 to 5.24; 10-19/day: OR 5.91, 95% CI 3.66 to 9.54; 20+/day: OR 6.11, 95% CI 3.59 to 10.42). In MR analyses of 281 105 White British participants, genetically predicted propensity to initiate smoking was associated with higher risks of infection (OR 1.45, 95% CI 1.10 to 1.91) and hospitalisation (OR 1.60, 95% CI 1.13 to 2.27). Genetically predicted higher number of cigarettes smoked per day was associated with higher risks of all outcomes (infection OR 2.51, 95% CI 1.20 to 5.24; hospitalisation OR 5.08, 95% CI 2.04 to 12.66; and death OR 10.02, 95% CI 2.53 to 39.72). Interpretation Congruent results from two analytical approaches support a causal effect of smoking on risk of severe COVID-19.
引用
收藏
页码:65 / 73
页数:9
相关论文
共 36 条
[1]   Dynamic linkage of COVID-19 test results between Public Health England's Second Generation Surveillance System and UK Biobank [J].
Armstrong, Jacob ;
Rudkin, Justine K. ;
Allen, Naomi ;
Crook, Derrick W. ;
Wilson, Daniel J. ;
Wyllie, David H. ;
O'Connell, Anne Marie .
MICROBIAL GENOMICS, 2020, 6 (07) :1-9
[2]   Comparison of risk factor associations in UK Biobank against representative, general population based studies with conventional response rates: prospective cohort study and individual participant meta-analysis [J].
Batty, G. David ;
Gale, Catharine R. ;
Kivimaki, Mika ;
Deary, Ian J. ;
Bell, Steven .
BMJ-BRITISH MEDICAL JOURNAL, 2020, 368
[3]   The relationship between cigarette smoking and impulsivity: A review of personality, behavioral, and neurobiological assessment [J].
Bloom, Erika Litvin ;
Matsko, Stephen V. ;
Cimino, Cynthia R. .
ADDICTION RESEARCH & THEORY, 2014, 22 (05) :386-397
[4]  
BOWDEN J, 2016, INT J EPIDEMIOL, V45
[5]   Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator [J].
Bowden, Jack ;
Smith, George Davey ;
Haycock, Philip C. ;
Burgess, Stephen .
GENETIC EPIDEMIOLOGY, 2016, 40 (04) :304-314
[6]   Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression [J].
Bowden, Jack ;
Smith, George Davey ;
Burgess, Stephen .
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, 2015, 44 (02) :512-525
[7]   Bias due to participant overlap in two-sample Mendelian randomization [J].
Burgess, Stephen ;
Davies, Neil M. ;
Thompson, Simon G. .
GENETIC EPIDEMIOLOGY, 2016, 40 (07) :597-608
[8]   The UK Biobank resource with deep phenotyping and genomic data [J].
Bycroft, Clare ;
Freeman, Colin ;
Petkova, Desislava ;
Band, Gavin ;
Elliott, Lloyd T. ;
Sharp, Kevin ;
Motyer, Allan ;
Vukcevic, Damjan ;
Delaneau, Olivier ;
O'Connell, Jared ;
Cortes, Adrian ;
Welsh, Samantha ;
Young, Alan ;
Effingham, Mark ;
McVean, Gil ;
Leslie, Stephen ;
Allen, Naomi ;
Donnelly, Peter ;
Marchini, Jonathan .
NATURE, 2018, 562 (7726) :203-+
[9]   Risk factors for SARS-CoV-2 among patients in the Oxford Royal College of General Practitioners Research and Surveillance Centre primary care network: a cross-sectional study [J].
de Lusignan, Simon ;
Dorward, Jienchi ;
Correa, Ana ;
Jones, Nicholas ;
Akinyemi, Oluwafunmi ;
Amirthalingam, Gayatri ;
Andrews, Nick ;
Byford, Rachel ;
Dabrera, Gavin ;
Elliot, Alex ;
Ellis, Joanna ;
Ferreira, Filipa ;
Bernal, Jamie Lopez ;
Okusi, Cecilia ;
Ramsay, Mary ;
Sherlock, Julian ;
Smith, Gillian ;
Williams, John ;
Howsam, Gary ;
Zambon, Maria ;
Joy, Mark ;
Hobbs, F. D. Richard .
LANCET INFECTIOUS DISEASES, 2020, 20 (09) :1034-1042
[10]   COVID-19 mortality in the UK Biobank cohort: revisiting and evaluating risk factors [J].
Elliott, Joshua ;
Bodinier, Barbara ;
Whitaker, Matthew ;
Delpierre, Cyrille ;
Vermeulen, Roel ;
Tzoulaki, Ioanna ;
Elliott, Paul ;
Chadeau-Hyam, Marc .
EUROPEAN JOURNAL OF EPIDEMIOLOGY, 2021, 36 (03) :299-309