Modifiable lifestyle factors and the risk of post-COVID-19 multisystem sequelae, hospitalization, and death – Nature.com

Data sources and study cohorts

UK Biobank is a large-scale population-based prospective cohort study with deep phenotyping and genomic data, as detailed elsewhere51. Briefly, between 2006 and 2010, over 500,000 individuals aged 4069 years were recruited from 22 assessment centers across the United Kingdom at baseline, with collection of socio-demographic, lifestyle and health-related factors, a range of physical measures, and blood samples51. Follow-up information is obtained by linking health and medical records, including national primary and secondary care, disease and mortality registries52, with validated reliability, accuracy and completeness53. To identify cases of SARS-CoV-2 infection, polymerase chain reaction (PCR)-based test results were obtained by linking all participants to the Public Health Englands Second Generation Surveillance System, with dates of specimen collection and healthcare settings of testing54. Outbreak dynamics were validated to be broadly similar between UK Biobank participants and the general population of England54.

In this study, we included participants who were alive by March 1, 2020 and had a positive SARS-CoV-2 PCR test result between March 1, 2020 (date of the first recorded case in the UK Biobank), and March 1, 2022, with the date of first infection considered as index date (T0). For those diagnosed with COVID-19 in hospital, we defined T0 as the date of hospital admission minus a random number of 7 days. The major prevalent variants during the study period included wildtype, Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (B.1.1.529 BA.1). The calendar periods of dominant variants in the UK were based on pandemic data from the Office for National Statistics26. Participants with missing data on study exposures at baseline were excluded. We addressed missing data on covariates using the following approaches: (1) participants with missing values in age and sex (<0.1%) were excluded. (2) participants with missing values in ethnicity were classified as other ethnic groups. (3) participants with missing values in education level (0.9%) were classified as category I, which includes none of the above and prefer not to answer. (4) missing values in IMD (13.8%) were imputed with the mean value of the entire UK Biobank cohort. All participants included in this study provided written informed consent at recruitment. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines and received ethical approval from the UKBB ethics advisory committee. Study design, cohort construction, and timeline are provided in Supplementary Fig.1. All participants provided written informed consent at the UK Biobank cohort recruitment. This study received ethical approval from UK Biobank Ethics Advisory Committee (EAC) and was performed under the application of 65397.

Ten prespecified potentially modifiable lifestyle factors were assessed, including smoking, alcohol consumption, body mass index (BMI), physical activity, sedentary time, sleep duration, intake of fruit and vegetable, intake of oily fish, intake of red meat, and intake of processed meat. Selection and categorization of lifestyle factors was based on literature review, previous knowledge, and UK national health service guidelines55,56. Multiple lifestyle factors were measured by validated questionnaire for all participants at baseline recruitment. Detailed definitions on measurement and classification of lifestyle factors are provided in Supplementary Table1. Briefly, healthy lifestyle components including past or never smoker, moderate alcohol intake (4 times week), BMI<30kg/m2, at least 150min of moderate or 75min of vigorous physical activity per week, less sedentary time (<4h per day), healthy sleep duration (79h per day), adequate intake of fruit and vegetables (400g/day), adequate oily fish intake (1 portion/week), moderate intake of red meat (4 portion week) and processed meat (4 portion week) were defined, in accordance with previous evidence or UK national health service guidelines55,56.

A binary variable was created for each of the 10 factors, with 1 point assigned for those meeting the healthy criteria and 0 otherwise. A composite lifestyle score was then calculated for each participant by summing the total number of healthy lifestyle factors, ranging from 0 to 10. Based on the composite score, participants were classified into three lifestyle categories: unfavorable (05), intermediate (67), and favorable (810). The lifestyle score was also used as a continuous variable of number of healthy lifestyle factors. Similar methods of defining lifestyle score have been used in the same UK Biobank cohort57 as well as external cohorts16,28. Distributions of lifestyle score and categories are provided in Supplementary Table2.

The median [IQR] duration between baseline assessment of lifestyle factors and the date of infection was 12.5 [11.813.3] years. Part of participants took part in up to two further touchscreen interviews with lifestyle and health-related factors similarly measured. There were generally stable responses to lifestyle factors between baseline assessment and the latest repeat assessment (median time difference from baseline, 8 years) as shown in Supplementary Fig.2. 34.9% of participants with an unfavorable lifestyle, 48.6% with an intermediate lifestyle, and 73.7% with a favorable lifestyle at baseline remained in the same corresponding lifestyle category at the latest repeat assessment following a median of 8 years. Overall, the proportion of stable lifestyle categories is 60.6%.

The outcomes after COVID-19 were prespecified, including a set of multisystem sequelae, death, and hospital admission following the SARS-CoV-2 infection. The multisystem sequelae were selected and defined based on previous evidence of the long COVID, including 75 systemic diseases or symptoms in 10 organ systems: cardiovascular46, coagulation and hematologic46, metabolic and endocrine44, gastrointestinal48, kidney43, mental health45, musculoskeletal47, neurologic47, and respiratory disorders10,13,14, and general symptoms of fatigue and malaise3,4,42,49. Detailed definitions of multisystem sequelae are listed in the Supplementary Table3. Outcomes were identified as follows: individual sequela from the hospital inpatient ICD-10 (International Classification of Diseases 10th Revision) diagnosis codes, deaths from the records of national death registry, and hospital admission from hospital inpatient data from the Hospital Episode Statistics. Incident outcomes were assessed in participants with no history of the related outcome within one year before the date of the first infection.

As SARS-CoV-2 infection has been associated with both multisystem manifestations during its acute phase and with sequelae during its post-acute phase7,49, we conducted analyses stratified by phase of infection. We reported risk of each outcome during the acute phase (T0 to T0+30d), post-acute phase (T0+30d to T0+210d), and overall period following infection (T0 to T0+210d) to reflect the full spectrum of post-COVID conditions. The end of follow-up for the overall cohort was September 30, 2022, with the maximum follow-up period censored to 210 days.

We prespecified a list of covariates for adjustment or stratification based on literature review and prior knowledge: socio-demographic characteristics including age, sex, education level (mapped to the international standard for classification of education), index of multiple deprivation (IMD, a summary measure of crime, education, employment, health, housing, income, and living environment)58, and race and ethnicity; and infection related factors including healthcare settings of the testing (community/outpatient vs inpatient setting as proxy of severity of infection), COVID-19 vaccination status, and SARS-CoV-2 variants.

Baseline characteristics of the overall cohort of participants with SARS-CoV-2 infection and by composite healthy lifestyle categories were reported as mean and standard deviation or frequency and percentage, when appropriate. Multivariable cox proportional hazard (PH) model was used to assess the association between composite healthy lifestyle and risk of multisystem sequelae (composite or by organ systems), death, and hospital admission, with adjustment for age, sex, ethnicity, education level, and IMD. PH assumption across lifestyle categories was tested by Schoenfeld residuals with no violations observed for outcomes. Hazard ratio (HR) and absolute risk reduction (ARR, difference in incidence rate between lifestyle groups per 100 persons during the corresponding follow-up period) were estimated from the Cox model. We also assessed the association between individual lifestyle factor instead of composite categories (each component as a categorical variable with or without mutual adjustment for others, or the number of factors as continuous variables) and risk of outcomes.

We conducted causal mediation analysis59,60 to quantify the extent to which the habitual healthy lifestyle may affect COVID-19 sequelae through the potential pathway of relevant pre-infection medical conditions (mediator), with the proportion of direct and indirect effects estimated by quasi-Bayesian Monte Carlo methods with 1000 simulations for each. Detailed modeling procedures and a directed acyclic graph are provided in Supplementary Methods.

We examined the association between composite healthy lifestyle and the overall risk of multisystem sequelae in prespecified clinical subgroups by demographic and infection-related factors. The demographic factors included age (65 and >65 years), sex (male and female), and ethnicity (White and other ethnic groups). As the risk profile of COVID sequelae was related to vaccination and severity of infection, and may change with the evolving pandemic, infection-related factors including vaccine status (no or one-dose partial vaccination and two-dose full vaccination), test setting (inpatient and outpatient or community), dominant variants during the study period (wildtype, Alpha, Delta, and Omicron BA.1) were assessed. Multiplicative interactions between the composite healthy lifestyle and the stratification variables were tested, with P-value reported.

We conducted multiple sensitivity analyses to assess the robustness of primary findings. First, to reflect the multisystem and potentially comorbid nature of COVID sequelae, accounting for both the number of sequelae by an individual and the relative health impact of each sequela. Weights based on Global Burden of Disease study data and methodologies for general diseases and long COVID were assigned to each sequela (Supplementary Table1)61,62. The weighted score was calculated for each participant by summing the weights of all incident sequelae during the follow-up period. Zero inflated Poisson regression was then used to calculate relative risk (RR), with follow-up time set as the offset of the model and adjustment for covariates. Second, to further account for potential reverse causality and more accurately define incident cases, extending the washout period for outcomes from one year to two years. Third, defining events of post-acute sequelae 90 days after infection (follow-up period T0+90d to T0+210d), instead of 30 days in the main analyses. The adjustment was made as there is no uniform definition for long COVID, which is currently described as conditions occurring 3090 days after infection in existing guidelines27. Fourth, restricting the identification of outcomes to the first three ICD diagnoses, which are the main causes for each hospital admission. Fifth, reconstructing a composite lifestyle index without BMI and assessed its association with outcomes. Finally, we conducted quantitative sensitivity analysis to adjust for changes in lifestyle factors over time since the baseline assessment. We used odds ratios to quantify associations and assumed a sensitivity and specificity of 90% for each lifestyle component (Supplementary Methods).

As a healthy lifestyle is associated with a lower risk of chronic diseases and mortality among the general population predated pandemic, we conduct exploratory analysis to compare the effects of healthy lifestyle on adverse outcomes following COVID-19 with the effects among participants without infection. A random index date was assigned to the participants without infection based on the distribution of T0 among those with infection, and we repeated the main analyses with the maximum follow-up period censored to 210 days.

Statistical significance was determined by a 95% confidence interval (CI) that excluded 1 for ratios and 0 for rate differences. All analyses and data visualizations were conducted using R statistical software (version 4.2.2).

Further information on research design is available in theNature Portfolio Reporting Summary linked to this article.

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Modifiable lifestyle factors and the risk of post-COVID-19 multisystem sequelae, hospitalization, and death - Nature.com