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Combined lifestyle factors, all-cause mortality and cardiovascular disease: a systematic review and meta-analysis of prospective cohort studies
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  1. Yan-Bo Zhang1,
  2. Xiong-Fei Pan1,2,
  3. Junxiang Chen1,
  4. Anlan Cao1,
  5. Lu Xia1,
  6. Yuge Zhang1,
  7. Jing Wang3,
  8. Huiqi Li1,
  9. Gang Liu4,
  10. An Pan1
  1. 1 Department of Epidemiology and Biostatistics, Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
  2. 2 Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
  3. 3 Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
  4. 4 Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
  1. Correspondence to An Pan, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; panan{at}hust.edu.cn

Abstract

Introduction Unhealthy lifestyles caused a huge disease burden. Adopting healthy lifestyles is the most cost-effective strategy for preventing non-communicable diseases. The aim was to perform a systematic review and meta-analysis to quantify the relationship of combined lifestyle factors (eg, cigarette smoking, alcohol consumption, physical activity, diet and overweight/obesity) with the risk of all-cause mortality, cardiovascular mortality and incident cardiovascular disease (CVD).

Methods PubMed and EMBASE were searched from inception to April 2019. Cohort studies investigating the association between the combination of at least three lifestyle factors and all-cause mortality, cardiovascular mortality or incidence of CVD were filtered by consensus among reviewers. Pairs of reviewers independently extracted data and evaluated study quality. Random-effects models were used to pool HRs. Heterogeneity and publication bias were tested.

Results In total, 142 studies were included. Compared with the participants with the least-healthy lifestyles, those with the healthiest lifestyles had lower risks of all-cause mortality (HR=0.45, 95% CI 0.41 to 0.48, 74 studies with 2 584 766 participants), cardiovascular mortality (HR=0.42, 95% CI 0.37 to 0.46, 41 studies with 1 743 530 participants), incident CVD (HR=0.38, 95% CI 0.29 to 0.51, 22 studies with 754 894 participants) and multiple subtypes of CVDs (HRs ranging from 0.29 to 0.45). The associations were largely significant and consistent among individuals from different continents, racial groups and socioeconomic backgrounds.

Conclusions Given the great health benefits, comprehensively tackling multiple lifestyle risk factors should be the cornerstone for reducing the global disease burden.

  • Cardiovascular disease
  • lifestyle
  • mortality
  • prevention
  • systematic reviews

https://doi.org/10.1136/jech-2020-214050

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    INTRODUCTION

    Lifestyle factors are often interrelated and associated with multiple non-communicable diseases (NCDs) including cardiovascular disease (CVD).1 It was estimated that unhealthy behaviours accounted for over 23 million deaths and 36.5% of disability-adjusted life-years in 2017 globally.2 Besides, adopting healthy lifestyle behaviours, including avoiding tobacco use and harmful alcohol consumption, as well as keeping a healthy diet, an optimal body weight and physically active, is the most cost-effective strategy for preventing NCDs.3 Hence, understanding the associations of combined lifestyle factors with mortality and the incidence of CVD is of vital importance for health policymaking and medical resource allocation.

    Many organisations have endorsed policies to reduce disease burden by diminishing unhealthy lifestyle factors.4–6 Besides, an increasing number of studies investigated the associations of combined lifestyle factors with the risk of incident CVD, CVD mortality and all-cause mortality. However, given a relatively small number of original studies, previous meta-analyses did not investigate whether these associations were consistent among individuals with different baseline characteristics, and did not comprehensively evaluate the evidence on the subtypes of CVD.7–12 Therefore, we conducted a systematic review and meta-analysis to evaluate the associations of combined lifestyle factors with total and subtypes of CVD mortality and morbidity as well as all-cause mortality, and whether these associations were consistent across individuals with different demographic characteristics.

    METHOD

    Data sources and searches

    The study followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Meta-Analysis of Observational Studies in the Epidemiology Group (MOOSE) guidelines.13 14 PubMed and EMBASE were searched from database inception to 26 April 2019. Reference lists of the included studies and relevant reviews were searched to identify additional publications. As the study was a section of a larger meta-analysis of the associations of combined lifestyle factors with mortality and major NCDs, the search terms included keywords in titles or abstracts and Medical Subject Heading terms related to ‘combined’, ‘lifestyle’, ‘cohort study’, ‘mortality’, ‘cardiovascular disease’, ‘diabetes’ and ‘cancer’. Detailed search strategies were reported previously.15 No language restriction was applied.

    Study selection

    We included cohort studies investigating the association of combined lifestyle factors with the incidence of total or subtypes of CVD, total or subtypes of CVD mortality, or all-cause mortality. The lifestyle factors included but were not limited to cigarette smoking, alcohol consumption, physical activity/sedentary behaviour, diet, overweight/obesity, sleep duration/quality. Several studies also included metabolic factors (eg, the Life’s simple 7 (LS7) score defined by the American Heart Association included blood pressure, blood lipid level and blood glucose level, as well as smoking, physical activity, body weight and diet), which also reflected the overall lifestyle and remained in our main analysis. Online supplemental table A1 shows the components of three most common scores, defined as simple score (which gave equal weight to each lifestyle factor, eg, most studies assigned 1 or 0 to participants with or without a certain healthy behaviour),16 LS7 score17 18 and World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) score (which included physical activity, body weight, consumption of fast foods and other processed foods high in fat, starches or sugars, consumption of sugar-sweetened drinks, consumption of plant foods, consumption of animal foods, alcohol consumption, using supplements for cancer prevention, breastfeeding, and following recommendations after a cancer diagnosis).19 20 We did not select studies according to the characteristics of the participants. We also included studies conducted in specific occupational groups or patients with certain diseases.

    Supplemental material

    The exclusion criteria for this meta-analysis were as follows: (1) studies not investigating the combination of lifestyle factors or pre-decided outcomes, (2) ineligible publication types (such as reviews, protocols, cross-sectional studies and case–control studies) or not peer-reviewed publications (such as commentary, editorial and meeting abstracts), (3) studies focusing on an individual lifestyle factor or combinations of only two lifestyle factors, (4) studies with less than 1 year of follow-up, (5) studies aiming at formulating or validating prediction models, (6) duplicate publications or duplicate reporting from the same cohort and (7) studies not reporting the risk ratios with their CIs comparing the participants with the healthiest lifestyles versus those with the least-healthy lifestyles. We excluded conference abstracts from our analysis even if they reported the association of combined lifestyle factors with outcomes of interest. However, to avoid omitting any potential eligible studies, we searched online and contacted the authors to confirm whether the full texts of the conference abstracts had been accepted for publication.

    Y-BZ filtered all citations, and another group of researchers including JC, AC, LX, YZ, JW and HL also performed the study selection independently. Any differences were resolved by consensus, or by consulting with a third investigator (AP).

    Data extraction and quality assessment

    The following information was extracted: first author, cohort, country, follow-up duration, the definitions of the healthy lifestyle factors, the definitions and attainments of outcomes, the number of participants and events, effect size with its CI, age, sex composition, race and ethnicity, education level and health status. Study quality was evaluated according to the Newcastle-Ottawa Scale.21 Pairs of researchers independently conducted these procedures. Any differences were resolved by consensus, or by consulting with a senior investigator. We also made at least two attempts to contact the corresponding authors to obtain missing information.

    Data synthesis and analysis

    All analyses were performed by STATA software (version 13.0, StataCorp, College Station, Texas, USA). HR was used as an effect size for the pooled estimate, which was considered as interchangeable with relative risks and could be transformed from OR.22 The score systems of different studies varied; however, most studies classified participants into three to six groups based on the distribution of the lifestyle score in the study population. Hence, we pooled HRs comparing the participants in the highest versus the lowest score group, to represent the risk estimates comparing individuals with the healthiest versus the least-healthy lifestyles. We used random-effects models to synthesise data, which allow heterogeneity among different studies.

    Heterogeneity across studies was evaluated by I2 statistic.22 Pre-decided stratified analyses were conducted according to studies’ characteristics (study locations, mean/median follow-up durations and lifestyle score systems) and populations’ characteristics (average age, sex, race and ethnicity, education level and health status). Meta-regression was used to obtain p values for the difference between subgroups.22 Publication bias was evaluated by the fail-safe N statistic, Begg’s test and Egger’s test.22

    RESULTS

    Study selection and characteristics

    Based on the search strategy, we identified 82 230 unique citations and excluded 82 032 citations after screening for titles and abstracts. After reading the full text, 56 studies were excluded (online supplemental table A2 shows reasons). Finally, 87 studies (13 studies were only used for stratified analyses), 55 studies (14 were only used for stratified analyses), 25 studies (three were only used for stratified analyses) and 56 studies were respectively included for meta-analysis of all-cause mortality, CVD mortality, incident total CVD and subtypes of CVD (figure 1).

    Figure 1
    Figure 1

    Flow chart of study selection. There were 64 studies reporting two or more outcomes, so the total number of studies for different outcomes exceeded 142. RR, risk ratio.

    Among 94 studies used for the main analysis for all-cause mortality, CVD mortality and CVD (online supplemental tables A3–A5), 39 were from America, 36 from Europe, 15 from Asia, 2 from Oceania and 2 were global studies across several continents; 83 were from high-income countries. The mean baseline age ranged from less than 37.3 years to 81.3 years (median 55.9, IQR 11.8 years). The sample size ranged from 600 to 421 411. The mean/median follow-up duration ranged from 3.0 years to 33.9 years (median 10.3, IQR 7.3 years). Besides, several studies investigated coronary heart disease (CHD) mortality (10 studies), stroke mortality (4 studies) and the incidence of CHD (22 studies), stroke (18 studies), heart failure (9 studies), hypertension (6 studies), atrial fibrillation (2 studies) and peripheral artery disease (2 studies, online supplemental tables A6–A7). Newcastle-Ottawa Scale scores of all studies were no less than 5 (online supplemental table A8).

    Association between combined lifestyle factors and all-cause mortality

    The pooled HR comparing participants with the healthiest versus the least-healthy lifestyles for all-cause mortality was 0.45 (95% CI 0.41 to 0.48, I 2=91.0%, 74 studies with 2 584 766 participants and 304 130 deaths, figure 2 and online supplemental figure A1). The association was consistent in most stratified analyses. However, the associations seemed weaker in studies with shorter follow-up duration and in studies conducted among cancer survivors. Additionally, the association of the simple score (HR=0.41, 95% CI 0.37 to 0.45) with all-cause mortality was stronger than the LS7 score (HR=0.55, 95% CI 0.47 to 0.63) and WCRF/AICR score (HR=0.73, 95% CI 0.65 to 0.82). Also, the association of combined lifestyle factors with all-cause mortality was attenuated when the lifestyle score did not include smoking. The HRs were 0.45 (95% CI 0.38 to 0.54) for scores including all five factors versus 0.65 (95% CI 0.60 to 0.71) for scores not including smoking. P values for Egger’s test and Begg’s test were ≤0.05; however, the classic fail-safe N statistic indicated that additionally including 119 803 studies of null associations would make the pool result non-significant, which indicated that the influence of potential publication bias was mild (online supplemental table A9).

    Figure 2
    Figure 2

    Association of combined lifestyle factors with all-cause mortality. *Two studies were global studies that included participants from different continents. † Studies from several cohorts conducted stratified analyses, and thusly the total number of the studies from different groups exceeded 74. ‡ Several studies did not report the number of participants and deaths in each subgroup. AICR, American Institute for Cancer Research; CKD, chronic kidney disease; CVD, cardiovascular disease; LS7, Life’s Simple 7; NA, Not available; WCRF, World Cancer Research Fund.

    Association between combined lifestyle factors and CVD mortality

    The pooled HR comparing participants with the healthiest versus the least-healthy lifestyles was 0.42 (95% CI 0.37 to 0.46, I 2=73.9%, 41 studies with 1 743 530 participants) for total CVD mortality, 0.40 (95% CI 0.30 to 0.53, I 2=68.6) for CHD mortality and 0.38 (95% CI 0.27 to 0.53, I 2=51.2) for stroke mortality (figure 3 and online supplemental figures A2–A4). The associations were statistically significant and consistent in most stratified analyses. However, the association seemed stronger in the younger population and in studies using the simple score and LS7 score compared with those using the WCRF/AICR score. Again, the association was stronger in studies with smoking included in the score than the others. P values for Egger’s test were 0.001 for total CVD mortality and 0.04 for CHD mortality; however, the classic fail-safe N statistics were between 155 and 17 161 (online supplemental table A9).

    Figure 3
    Figure 3

    Association of combined lifestyle factors with CVD mortality. * Studies from several cohorts conducted stratified analyses, and thusly the total number of the studies from different groups exceeded 41. † Several studies did not report the number of participants and deaths in each subgroup. AICR, American Institute for Cancer Research; CHD, coronary heart disease; CVD, cardiovascular disease; LS7, Life’s Simple 7; NA, not available; WCRF, World Cancer Research Fund.

    Association between combined lifestyle factors and the risk of CVD

    For the incidence of CVD, the HRs comparing participants with the healthiest versus the least-healthy lifestyles were 0.38 (95% CI 0.29 to 0.51, I 2=96.9, 22 studies with 754 894 participants) for total CVD, 0.31 (95% CI 0.24 to 0.40, I 2=93.0, 22 studies with 1 492 174 participants) for CHD, 0.45 (95% CI 0.37 to 0.54, I 2=80.0, 17 studies with 1 441 107 participants) for stroke, 0.29 (95% CI 0.24 to 0.35, I 2=80.3) for heart failure, 0.35 (95% CI 0.28 to 0.45, I 2=94.8) for hypertension, 0.44 (95% CI 0.31 to 0.61, I 2=50.2) for atrial fibrillation and 0.33 (95% CI 0.19 to 0.56, I 2=0) for peripheral artery disease (figures 4–5, online supplemental tables A10–A11and online supplemental figures A5–A11). The associations were statistically significant and consistent in most stratified analyses, except that the association of the LS7 score (HR=0.24, 95% CI 0.16 to 0.34) with total CVD was stronger than the simple score (HR=0.52, 95% CI 0.42 to 0.66). P value for Egger’s test was 0.04 for incident CHD; however, the classic fail-safe N statistics were between 2423 and 6550 (online supplemental table A9).

    Figure 4
    Figure 4

    Association of combined lifestyle factors with the risk of total CVD, CHD and stroke. *The number of the incident CVD cases was not reported in Foraker et al (2016).23 † Studies from several cohorts conducted stratified analyses, and thus the total number of the studies from different groups exceeded the number of studies used in the main analysis. ‡ Several studies did not report the number of participants and deaths in each subgroup. CHD, coronary heart disease; CVD, cardiovascular disease; HR, hazard ratio; LS7, Life’s Simple 7; NA, not available.

    Figure 5
    Figure 5

    Association of combined lifestyle factors with the risk of atrial fibrillation, heart failure, hypertension and peripheral artery disease.

    DISCUSSION

    This systematic review and meta-analysis of cohort studies suggest that an overall healthy lifestyle was associated with a considerably lower risk of all-cause mortality, CVD mortality and incident CVD. Compared with the participants with the least-healthy lifestyles, those with the healthiest lifestyles had 55%, 58% and 62% lower risks of all-cause mortality, CVD mortality and incident CVD, respectively. Besides, adopting the healthiest lifestyles would have a 55–71% lower risk of fatal/total stroke, atrial fibrillation, hypertension, peripheral artery disease, fatal/total CHD and heart failure. The associations were largely consistent among populations from different continents, racial groups and socioeconomic backgrounds.

    A meta-analysis published in 2012 (15 studies with 531 804 participants) reported that a combination of at least four healthy behaviours was associated with a 66% reduction in all-cause mortality, which was similar to our result but we included 74 studies with over 2.5 million participants.11 Another meta-analysis concluded that adopting the healthiest behavioural pattern was associated with a 60–69% reduced risk for incident CVD, stroke and heart failure.12 However, the authors pooled all CVD subtypes together in the CVD analysis, while we only included studies using total CVD as the outcome, and results for subtypes were analysed separately. Additionally, we included more studies and additionally reported results for incident CHD, hypertension, atrial fibrillation and peripheral artery disease.

    Four meta-analyses focused on LS7 and found ideal cardiovascular health profile was associated with a 46% lower risk for all-cause mortality (six studies), 70% for CVD mortality (six studies), 77% for incident CVD (four studies), 67% for incident stroke (five studies) and 79% for incident CHD (two studies), compared with poor cardiovascular health profile.7–10 We included approximately twice more studies and further compared the lifestyle scores with vs without metabolic factors. As expected, the LS7 score was strongly related to incident CVD given that blood pressure, lipid and glucose levels were powerful predictors for incident CVD. However, the risk reductions for all-cause and CVD mortality related to LS7 were similar or even weaker compared with the simple score, indicating that more emphases should be given to the upstream lifestyle factors, in addition to the intermediate metabolic changes, for the prevention of premature deaths.

    Evidence from randomised controlled trials regarding the effects of comprehensive lifestyle intervention on premature death and CVD is limited. The Da Qing Diabetes Prevention Outcome Study (577 Chinese adults with impaired glucose tolerance) found that participants receiving dietary and/or exercise interventions for 6 years had 26%, 33% and 26% lower risks of CVD, CVD mortality and all-cause mortality after a 30-year follow-up, respectively.24 Two studies reported that lifestyle interventions could reduce 20% and 38% risks of primary CVD outcomes and stroke in diabetic individuals, respectively.25 26 However, studies applying lifestyle counselling reported inconsistent conclusions.27–29 In patients with CVD, lifestyle interventions could reduce 48–81% risk of cardiovascular events,30–32 but the effect on deaths remained controversial.33–35 Generally, randomised controlled trials were conducted in relatively small groups of individuals or diseased populations and were followed up for relatively short periods. Besides, it was difficult for participants to follow the structured lifestyle, and the intervention period was short. Hence, high-quality evidence from cohort studies is essential for understanding the protective effects of healthy lifestyles.

    To the best of our knowledge, this study is the first systematic review conducting stratified analyses according to the populations’ characteristics to understand the relations of combined lifestyle factors with the risk of incident CVD, CVD mortality and all-cause mortality, which may have important public health implications. Considering that socioeconomic factors could be upstream determinants of lifestyles, individuals with different characteristics may perceive and choose healthy lifestyles differently.4 However, our stratified analyses showed the associations were largely consistent across different regions, economic levels, races and ethnicities, sexes, and education levels. Notably, the stratified analyses showed that the association was stronger in studies with longer follow-up or among younger participants, indicating larger benefits could be obtained if people adopt healthy lifestyles at an early age and follow for a long time.

    The associations between different lifestyle factors and the outcomes are varied. For instance, smoking showed a stronger association with all-cause and CVD mortality than other factors.36 37 Accordingly, our stratified analysis showed that the associations were stronger in studies with smoking as a component of lifestyle score. Therefore, avoiding smoking should be prioritised when we make lifestyle-related recommendations or policies to prevent premature death.

    This study also raised an important clinical issue of whether patients with certain diseases could also benefit from healthy lifestyles. Among individuals with CVD, associations between healthy lifestyles and all-cause mortality or CVD mortality were similar to those among the general population. The finding supports the recommendations from several organisations that lifestyle modification should be the cornerstone for the management of CVD.5 6 However, the association between healthy lifestyles and all-cause mortality was weaker among cancer survivors, which might be because treatment is also an important predictor of prognosis among cancer survivors and thus the impact of lifestyle becomes relatively weaker. Nonetheless, the risk reductions were still substantial, indicating that lifestyle modifications are still meaningful and should be recommended for cancer survivors. With limited large randomised controlled trials investigating the effect of lifestyle intervention on mortality among individuals with cancer or CVD, evidence from high-quality cohort studies is urgently needed for formulating clinical guidelines in the diseased populations.

    Based on a thorough search strategy and the standard procedures of PRISMA and MOOSE guidelines,13 14 this study is the most comprehensive and up-to-date systematic review and meta-analysis to summarise the associations of combined lifestyle factors with all-cause mortality, CVD mortality and the risk of CVD. We had sufficient power to conduct many stratified analyses, and the results were largely consistent. These analyses could provide new clinical and public health viewpoints. However, several limitations should also be acknowledged. First, most studies were conducted in high-income countries and whites. Hence, more evidence from other populations is still needed. Second, the constructions of lifestyle scores varied across studies, which could generate potential heterogeneity. However, we only pooled risk ratios comparing the extreme groups, and most studies grouped participants into three to six groups according to the distribution of lifestyle scores. Besides, we also conducted stratified analyses according to score systems to explore the sources of heterogeneity. Third, there were possibilities of publication bias, and limited studies were available for incident atrial fibrillation, heart failure, hypertension and peripheral artery disease. Thus, the results should be interpreted cautiously. Fourth, some original studies did not fully control for socioeconomic status, psychological characteristics, comorbidities and medical treatment adherence at baseline, and thus residual cofounding in original studies might bias the results.

    CONCLUSION

    Adopting healthy lifestyles was associated with substantially lower risks of all-cause mortality, CVD mortality and incident CVD. The results were generally consistent among populations from different continents, racial groups and socioeconomic backgrounds. Given that the proportion of individuals adopting the healthiest lifestyles is low globally, all countries and regions should give high priority to the promotion of healthy lifestyles. Governments and other organisations should formulate policies and guidelines tailored to the preference of the locals to facilitate their adopting healthy lifestyles. Health workers should instruct patients, especially those with CVD, high-risk individuals and general populations to adopt healthy lifestyles for comprehensive prevention for CVD and premature death. Future studies should focus on non-high-income countries and non-white ethnicity, as well as the associations between combined lifestyle factors and the risk of subtypes of CVD.

    What is already known on this subject

    • Single healthy lifestyle factors are associated with lower risks of all-cause and cardiovascular mortality as well as incident cardiovascular disease.

    • Lifestyle factors are often interrelated and associated with multiple non-communicable diseases, and thus, investigating the combined effects of multiple lifestyle factors, which could reflect the benefits of overall healthy lifestyles, might be more appropriate to account for interactions between lifestyle factors.

    What this study adds

    • In this systematic review and meta-analysis, compared with the participants with the least-healthy lifestyles, those with the healthiest lifestyles had 55%, 58% and 62% lower risks of all-cause mortality, cardiovascular mortality, and incident cardiovascular disease, respectively, as well as 55–71% lower risks of multiple subtypes of cardiovascular disease including fatal/total stroke, atrial fibrillation, hypertension, peripheral artery disease, fatal/total coronary heart disease and heart failure.

    • The associations were largely consistent among populations from different continents, racial groups and socioeconomic backgrounds, and adopting healthy lifestyles could also benefit individuals with cardiovascular disease or cancer.

    • Our findings indicated that comprehensively tackling multiple lifestyle risk factors should be the cornerstone for reducing the global disease burden.

    Acknowledgments

    The authors would like to thank Abdonas Tamosiunas, Aferdita Spahillari, Ana C Ricardo, Annie Britton, Chuanhui Dong, Debora Rizzuto, Erin L Van Blarigan, Gerben Hulsegge, James R Cerhan, Jesús Díaz Gutiérrez, Jian-Min Yuan, Lisa Iversen, Matthew Pase, Miguel Ángel Martínez-González, Ralph L Sacco, Randi Foraker, Sameera Talegawkar, Shouling Wu, Yichen Jin, Yuming Guo and Zhensheng Wang for providing extra information or analyses.

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    Footnotes

    • Contributors Y-BZ, X-FP and AP designed the study question. Y-BZ, JC, AC, LX, YZ, JW and HL conducted the literature screening, extracted the data and assessed the risk of bias of included studies. Y-BZ and JC conducted the analyses. Y-BZ wrote the first draft of the paper. AP took responsibility for the contents of the article. Y-BZ, GL and AP interpreted the data. All authors critically reviewed the manuscript and approved submission of the final manuscript.

    • Funding This work was supported by the National Key Research and Development Program of China (2017YFC0907504), National Nature Science Foundation of China (81930124), Hubei Province Science Fund for Distinguished Young Scholars (2018CFA033), China Postdoctoral Science Foundation (176596) and International Postdoctoral Exchange Fellowship of the China Postdoctoral Council (20180062).

    • Disclaimer The study sponsors had no role in study design; collection, analysis and interpretation of data; writing the report and the decision to submit the report for publication.

    • Competing interests None declared.

    • Patient consent for publication Not required.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement Data are available upon reasonable request.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.