Summary
Background
The improvement of life expectancy is one of the aims of the Healthy China 2030 blueprint. We aimed to investigate the extent to which healthy lifestyles are associated with life expectancy in Chinese adults.
Methods
We used the prospective China Kadoorie Biobank (CKB) study to examine the relative risk of mortality associated with individual and combined lifestyle factors (never smoking or quitting not for illness, no excessive alcohol use, being physically active, healthy eating habits, and healthy body shape). Participants with coronary heart disease, stroke, cancer, or missing values for body-mass index were excluded. For analysis of chronic respiratory diseases, participants with chronic obstructive pulmonary disease or asthma were excluded. We estimated the national prevalence of lifestyle factors using data from the China Nutrition and Health Surveillance (CNHS; 2015) and derived mortality rates from the Global Burden of Diseases, Injuries, and Risk Factors Study (2015). All three data sources were combined to estimate the life expectancy of individuals at age 30 years following different levels of lifestyle factors by using the life table method. The cause-specific decomposition of the life expectancy differences was analysed using Arriaga’s method.
Findings
After the exclusion of CKB participants with coronary heart disease, stroke, cancer, or missing BMI data at baseline, 487 209 were included in the primary analysis. Participants with COPD or asthma at baseline were additionally excluded for chronic respiratory disease-related analysis, leaving 451 233 participants with data available for analysis. Data from 171 127 adults aged 30–84 years from the CNHS 2015 were used to estimate the sex-specific and age-specific prevalence of lifestyle-related factors. There were 42 496 deaths documented over a median follow-up of 11·1 years (IQR 10·2–12·1) in CKB. The adjusted hazard ratios (aHRs) of participants adopting five versus 0–1 low-risk factors was 0·38 (95% CI 0·34–0·43) for all-cause mortality, aHR 0·37 (0·30–0·46) for cardiovascular disease mortality, aHR 0·47 (0·39–0·56) for cancer mortality, and aHR 0·30 (0·14–0·64) for chronic respiratory disease mortality. The life expectancy at age 30 years for individuals with 0–1 low-risk factors was on average 41·7 years (95% CI 41·5–42·0) for men and 47·3 years (46·6–48·0) for women. For individuals with all five low-risk factors, the life expectancy at age 30 was 50·5 years (95% CI 48·5–52·4) for men and 55·4 years (53·5–57·4) for women; meaning a difference of 8·8 years (95% CI 6·8–10·7) for men and 8·1 years (6·5–9·9) for women. The estimated extended life expectancy for men and women was mainly attributable to reduced death from cardiovascular disease (2·4 years [27% of the total extended life expectancy] for men and 3·7 years [46%] for women), cancer (2·6 years [30%] for men and 0·9 years [11%] for women), and chronic respiratory disease (0·6 years [7%] for men and 1·2 years [15%] for women).
Interpretation
Our findings suggest that increasing the adoption of these five healthy lifestyle factors through public health interventions could be associated with substantial gains in life expectancy in the Chinese population.
Funding
National Natural Science Foundation of China, National Key Research and Development Program of China, Kadoorie Charitable Foundation, UK Wellcome Trust.
Introduction
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The widespread prevalence of these risk factors has caused a great burden of disease (such as cardiovascular disease, cancer, and chronic respiratory diseases) worldwide,
with no exception to China.
Life expectancy as an absolute quantitative measure is more intuitive than indicators such as relative risk and absolute lifetime risk and has become a common metric for establishing public health priorities.
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Most of these studies were based on specific cohort populations; the results of such a study design only reflect the mortality of specific cohort populations over a follow-up period and caution should be maintained in generalising these results to national populations.
Evidence before this study
We searched PubMed, EMBASE, and Google Scholar for articles published from the inception of each database to Oct 31, 2021, using a combination of terms: (“life expectancy” OR “life span” OR “life time” OR “life years” OR “longevity”) AND (“lifestyle” OR “smoking” OR “tobacco use” OR “alcohol” OR “physical activity” OR “diet” OR “BMI” OR “overweight” OR “obesity”). No restrictions were applied to study type or language. Relevant studies were also found by checking reference lists of identified articles. Available studies that assessed the relationship between lifestyle and life expectancy were mainly done in high-income countries and based on specific cohort populations, limiting the generalisability of the results to other countries, where the factors that influence health might differ. The potential impact of healthy lifestyles on life expectancy at a population level in China remains unclear.
Added value of this study
The estimated life expectancy at age 30 years for individuals with five low-risk lifestyle factors was on average 8·8 years longer in men and 8·1 years longer in women than those with 0–1 low-risk factors. About two thirds of the extended life expectancy associated with adopting all five low-risk factors could be explained by the reduced death from cardiovascular disease, cancer, and chronic respiratory disease. To the best of our knowledge, this is the first study to quantify the association between combined lifestyle factors and life expectancy in China. The use of a large prospective cohort study of more than 500 000 Chinese people and a nationally representative survey of risk factors improved the representativeness of the findings for the national population.
Implications of all the available evidence
Our findings suggest that fostering a healthy lifestyle through population-wide public health interventions could be associated with substantial gains in life expectancy in the Chinese population. The findings of the study could encourage the government to commit to promoting a healthy lifestyle, in order to achieve the goal of increasing the average life expectancy, as outlined in the blueprint of Healthy China 2030. Further investigations are also needed to explore the effect of other factors on life expectancy, such as environmental hazards.
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The effect of combined lifestyle behaviours on Chinese life expectancy remains unclear, and the evidence gaps need to be filled.
The blueprint of Healthy China 2030 set out the goal of increasing the average life expectancy of Chinese people at birth from 76·3 years in 2015 to 79 years in 2030. We aimed to evaluate the potential effects of individual and combined low-risk lifestyle factors on the life expectancy at age 30 years in the Chinese population.
Methods
Study design and participants
Briefly, 512 725 participants aged 30–79 years were recruited during 2004–08 from five urban and five rural areas geographically spread across China. Baseline survey and anthropometric measurements were undertaken by trained study staff. All participants signed informed consent forms. Ethical approval was obtained from the Ethics Review Committee of the Chinese Center for Disease Control and Prevention (CDC, Beijing, China) and the Oxford Tropical Research Ethics Committee, University of Oxford (Oxford, UK). In the present study, participants with coronary heart disease, stroke, or cancer at baseline were excluded, in addition to those with missing values for body-mass index (BMI). For analysis of chronic respiratory diseases, participants with chronic obstructive pulmonary disease (COPD) or asthma at baseline were excluded.
In the present study, data from adults aged 30–84 years from the CNHS 2015 were used to estimate the sex-specific and age-specific (every 5-years) prevalence of lifestyle-related factors. The Ethics Review Committee of the China CDC approved the survey. All participants had completed written informed consent forms.
Procedures
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Former drinkers were also excluded from the low-risk group to address the potential sick-quitter phenomenon (ie, cessation of alcohol consumption might result from disease onset and changes in health conditions).
However, such exclusion did not apply to the CNHS because its questionnaire did not ask about previous drinking habits. The low-risk group for physical activity included those who engaged in an age-specific (
For dietary habits, we created a simple diet score by considering the following criteria: eating fresh vegetables daily, eating fresh fruits daily, eating red meat 1–6 days per week, eating legumes ≥4 days per week, and eating fish ≥1 day per week. For each criterion met, one point was scored; otherwise, 0. Thus, the diet score ranged from 0 to 5, with a score of 4 to 5 classified as the low-risk group.
Both general and central adiposity indicators were considered for body shape, with BMI of 18·5–27·9 kg/m2 and waist circumference of
which emphasises prevention of extremely high or low weight and abdominal obesity.
A simple low-risk lifestyle score was derived according to the number of low-risk lifestyle factors, ranging from 0 to 5, with higher scores indicating a healthier lifestyle. The vital status of each participant in CKB was identified through the National Disease Surveillance Points system, supplemented with the annual active follow-up. The underlying causes of death were coded using the 10th revision of the International Classification of Diseases.
The primary outcomes were all-cause mortality and cause-specific mortality, including cardiovascular disease (I00–I99), cancer (C00–C97), and chronic respiratory disease (including COPD [J41–J44] and asthma [J45–J46]), assessed in all participants.
Statistical analysis
we estimated the cause-specific contributions to the life expectancy difference between participants adopting all five and 0–1 low-risk lifestyle factors to determine which cause-specific mortality differences were major contributors to the total change in life expectancy (appendix p 11).
The age-at-risk groups were 30–49, 50–59, 60–69, 70–79, and 80 years and older for men and 30–69, and 70 years and older for women, considering that few deaths occurred before the age of 70 and older than 80 years among women adopting 0–1 low-risk lifestyle factors (the reference group) in the CKB study. Considering the lag time between exposure and mortality outcome, we substituted the mortality data with the most recent data from 2019 (4-year lag).
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Subgroup analyses were done by the factors of residence (urban and rural), education level (no education and primary school, and middle school and higher), smoking status (men: never, former, and current; women: never and ever), body shape (underweight, neither general nor abdominal obesity, and either or both), and baseline disease status (neither hypertension nor diabetes, and either or both).
Considering the gradients in death risk according to different levels of each lifestyle factor, we further created an expanded low-risk lifestyle score. We graded the categories of each lifestyle factor from 1 (least healthy) to 5 (most healthy) according to the CKB findings of the association between lifestyle factors and all-cause mortality. The points across all five lifestyle factors were totalled, with the overall score ranging from 5 to 25.
Graphs were plotted using R (version 4.0.3).
Role of the funding source
The funders had no role in the study design, data collection, data analysis, data interpretation, or writing of the report.
Results
Other medical histories relied on self-reported clinical diagnoses. In the present study, we used up to 171 127 adults aged 30–84 years from the CNHS 2015 to estimate the sex-specific and age-specific (every 5-years) prevalence of lifestyle-related factors (appendix p 13).
In CNHS, the mean age of the eligible 171 127 participants was 54·5 years (SD 12·3); 80 650 (47·1%) were men and 90 477 (52·9%) were women, and 101 707 (59·4%) resided in rural areas. Among the 67 798 participants used for lifestyle combination analysis, 46 559 (68·7%) adopted at least three low-risk lifestyle factors, 19 128 (28·2%) adopted four, and 2178 (3·2%) participants adopted all five low-risk lifestyle factors. The subset of participants used for lifestyle combination analysis shared similar characteristics with the total group of 171 127 eligible participants.
Table 1Multivariable-adjusted hazard ratios for all-cause and cause-specific mortality by individual lifestyle risk factors among 487 209 participants
Data from 487 209 participants. Multivariable models were adjusted for sex (men or women), education (no formal school, primary school, middle school, high school, college, or university or higher), marital status (married, widowed, divorced or separated, or never married), hip circumference (mm), family history of heart attack and stroke (presence, absence, or unknown; adjusted for analyses of all-cause and cardiovascular mortality), and family history of cancer (presence, absence, or unknown; adjusted for analyses of all-cause and cancer mortality). All five lifestyle factors were included simultaneously in the same model. HR=hazard ratio. BMI=body-mass index.
Table 2Multivariable-adjusted hazard ratios for mortality from chronic respiratory diseases by individual lifestyle risk factors among 451 233 participants
Data from 451 233 participants. Multivariable models were adjusted for sex (men or women), education (no formal school, primary school, middle school, high school, college, or university or higher), marital status (married, widowed, divorced or separated, or never married), hip circumference (mm), family history of heart attack and stroke (presence, absence, or unknown; adjusted for analyses of all-cause and cardiovascular mortality), and family history of cancer (presence, absence, or unknown; adjusted for analyses of all-cause and cancer mortality). All five lifestyle factors were included simultaneously in the same model. HR=hazard ratio. BMI=body-mass index.
Figure 1Multivariable-adjusted HRs and PARs% for all-cause and cause-specific mortality, by the number of low-risk lifestyle factors
Multivariable models were adjusted for sex (men or women), education (no formal school, primary school, middle school, high school, college, or university or higher), marital status (married, widowed, divorced or separated, or never married), hip circumference (mm), family histories of heart attack and stroke (presence, absence, or unknown; adjusted for analyses of all-cause and cardiovascular mortality), and family history of cancer (presence, absence, or unknown; adjusted for analyses of all-cause and cancer mortality). Low-risk lifestyle factors were defined as: never smoking or having stopped for reasons other than illness; less than daily drinking or drinking <30 g (men) and <15 g (women) of pure alcohol per day (former drinkers excluded); engaging in an age-specific (<50 years, 50–59 years, and ≥60 years) and sex-specific median or higher level of physical activity; scoring 4–5 for all food groups; having a BMI between 18·5 and 27·9 kg/m2 and a waist circumference <90 cm (men) and <85 cm (women). HR=hazard ratio. PAR%=population attributable risk percent.
Figure 2Projected gained or lost life expectancy at age 30 years, by individual lifestyle factors
Former alcohol drinkers were included in the heavy drinking category (≥60 g of pure alcohol per day). Data points shown without confidence intervals represent the reference group. BMI=body-mass index.
Figure 3Life expectancy and years of life gained by the number of low-risk lifestyle factors and attribution of the causes of death
(A) Estimated life expectancy at age 30 years by the number of low-risk lifestyle factors. (B) Gained age-specfic life expectancy from adopting low-risk lifestyle habits. (C) Estimated years of life gained from adopting five versus 0–1 low-risk lifestyle factors attributable to reduced death from cardiovascular disease, cancer, chronic respiratory disease, and other causes. Low-risk lifestyle factors were defined as: never smoking or having stopped for reasons other than illness; less than daily drinking or drinking <30 g (men) and <15 g (women) of pure alcohol per day (former drinkers excluded); engaging in an age-specific (<50 years, 50–59 years, and ≥60 years) and sex-specific median or higher level of physical activity; scoring 4–5 for all food groups; having a BMI between 18·5 and 27·9 kg/m2 and a waist circumference <90 cm (men) and <85 cm (women). BMI=body-mass index.
Discussion
Our results suggest that adherence to each of the five low-risk lifestyle factors, namely never smoking or quitting for reasons other than illness, no excessive alcohol use, being physically active, healthy eating habits, and a BMI between 18·5 and 27·9 kg/m2 without abdominal obesity, was associated with longer life expectancy for Chinese adults. The estimated life expectancy at age 30 years for individuals with all five low-risk factors was on average 8·8 years longer in men and 8·1 years longer in women than those with 0–1 low-risk factors. The estimated improved life expectancy for men and women was mostly attributable to reduced death from cardiovascular disease, cancer, and chronic respiratory disease.
In the present study, the estimated life expectancy at age 30 for individuals with 0–1 low-risk lifestyle factors was 41·7 years for men and 47·3 years for women. However, adopting all five low-risk lifestyle factors was associated with an improved life expectancy at age 30, reaching 50·5 years for men and 55·4 years for women. The Singapore Chinese Health Study—which had a median of 20·6 years of follow-up data—showed that the differences in life expectancy when comparing individuals with 4–5 low-risk lifestyle factors with those with zero low-risk lifestyle factors at age 50 years were 6·6 years for men and 8·1 years for women.
In the present study, the corresponding estimates of gained life-years at 50 years were 7·7 years for men and 7·6 years for women, similar to the estimates from the aforementioned study, but with a smaller sex difference.
and 12·2 years (for men) and 14 years (for women) at age 50 years for Americans,
and 18·5 years (for men) and 15·7 years (for women) at age 40 years for the EPIC-Heidelberg cohort population from Germany.
By contrast, the estimates of gained life-years in our study were lower than that of the three aforementioned studies. This inconsistency might be explained by the differences between populations in the definitions and components of a healthy lifestyle and their prevalence.
Additionally, in developing countries, potential environmental hazards in the home, work, and broader outdoor environment, such as ambient and household air pollution, and chemical contamination of food and water, could increase the burden of diseases.
Therefore, the relative impact of a healthy lifestyle alone on life expectancy might be slightly diminished in developing countries.
In the cause-specific decomposition analysis of the life expectancy differences, we observed that compared with individuals with 0–1 low-risk lifestyle factors, about two thirds of the increased life expectancy from adopting all five low-risk factors could be explained by the reduced death from cardiovascular disease, cancer, and chronic respiratory disease, all representing the leading causes of death in the Chinese population. A larger proportion (72%) of the gained life expectancy between individuals with all five low-risk lifestyle factors and those with 0–1 low-risk lifestyle factors in women was attributable to the reduced death from cardiovascular disease, cancer, and chronic respiratory disease than that in men (64%). Additionally, the major contributors to the life expectancy difference were from cardiovascular disease and other causes among women and from cancer and other causes among men. This difference might be related to the sex differences in the relative risks of lifestyle risk factors for various outcomes, disease burden patterns, and prevalence of lifestyle risk factors.
We defined the low-risk group according to total physical activity, and being physically active was associated with an increase in life expectancy at age 30 by more than 4 years. We suggest that this alternative definition of physical activity is valid in the Chinese population. Regarding adiposity measures, by contrast to previous studies that only included BMI,
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this study used both BMI and waist circumference. A recent meta-analysis of 72 prospective studies suggests that the measures of central adiposity could be used with BMI as an auxiliary indicator to determine the risk of premature death.
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The present study expands on previous findings and supports the benefits of starting a healthy lifestyle early at a young age.
Third, we dichotomised each lifestyle factor and counted the number of low-risk lifestyle factors, ignoring the difference in the magnitude of association between various lifestyle factors and death. However, two previous studies compared the analyses using weighted lifestyle scores with non-weighted scores, and no significant differences were observed.
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Fourth, the definitions of low-risk lifestyle factors might not be entirely consistent between the CKB and CNHS due to subtle differences in the questionnaires. Nevertheless, slight changes in the prevalence of lifestyle factors would not substantially affect the results of our study under different simulation scenarios. Other limitations include the observational nature of the study precluding causal inference, the CKB cohort not being fully representative of the Chinese population, and the neglect of potential secular changes in health risk factors or clinical advances in the future.
Public health interventions that improve adoption of healthy lifestyles should be one of the priorities for implementing the Healthy China 2030 agenda.
QS and DY are joint first authors. JL and LL conceived and designed the study, and contributed to the interpretation of the results and critical revision of the manuscript for valuable intellectual content. LL, ZC, and JC, as members of the CKB steering committee, designed and supervised the conduct of the study, obtained funding, and together with CY, YG, PP, LY, YC, HD, XY, SS, and YW, acquired the CKB data. DY, LZ, and WZ designed and supervised the conduct of the CNHS. QS, DY, and JF accessed, verified, and analysed the data. QS drafted the manuscript. The corresponding authors attest that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. JL, LL, and LZ are the guarantors. All authors had access to the data, have read and approved the final manuscript, and accept responsibility for the decision to submit for publication.
