The fraily index is a useful proxy measure of accelerated biological ageing and in estimating all-cause and cause-specific mortality in older individuals in European and US populations. However, the predictive value of the frailty index in other populations outside of Europe and the USA and in adults younger than 50 years is unknown. We aimed to examine the association between the frailty index and mortality in a population of Chinese adults. In this prospective cohort study, we used data from the China Kadoorie Biobank. We included adults aged 30-79 years from ten areas (five urban areas and five rural areas) of China who had no missing values for the items that made up the frailty index. We did not exclude participants on the basis of baseline morbidity status. We calculated the follow-up person-years from the baseline date to either the date of death, loss to follow-up, or Dec 31, 2017, whichever came first, through linkage with the registries of China's Disease Surveillance Points system and local residential records. Active follow-up visits to local communities were done annually for participants who were not linked to any established registries. Causes of death from official death certificates were supplemented, if necessary, by reviewing medical records or doing standard verbal autopsy procedures. The frailty index was calculated using 28 baseline variables, all of which were health status deficits measured by use of questionnaires and physical examination. We defined three categories of frailty status: robust (frailty index ≤0·10), prefrail (frailty index >0·10 to <0·25), and frail (frailty index ≥0·25). The primary outcomes were all-cause mortality and cause-specific mortality in Chinese adults aged 30-79 years. We used a Cox proportional hazards model to estimate the associations between the frailty index and all-cause and cause-specific mortality, adjusting for chronological age, education, and lifestyle factors. 512 723 participants, recruited between June 25, 2004, and July 15, 2008, were followed up for a median of 10·8 years (IQR 10·2-13·1; total follow-up 5 551 974 person-years). 291 954 (56·9%) people were categorised as robust, 205 075 (40·0%) people were categorised as prefrail, and 15 694 (3·1%) people were categorised as frail. Women aged between 45 years and 79 years had a higher mean frailty index and a higher prevalence of frailty than did men. During follow-up, 49 371 deaths were recorded. After adjustment for established and potential risk factors for death, each 0·1 increment in the frailty index was associated with a higher risk of all-cause mortality (hazard ratio [HR] 1·68, 95% CI 1·66-1·71). Such associations were stronger among younger adults than among older adults (pinteraction<0·0001), with HRs per 0·1 increment of the frailty index of 1·95 (95% CI 1·87-2·03) for those younger than 50 years, 1·80 (1·76-1·83) for those aged 50-64 years, and 1·56 (1·53-1·59) for those 65 years and older. After adjustments, there was no difference between the sexes in the association between the frailty index and all-cause mortality (pinteraction=0·75). For each 0·1 increment of the frailty index, the corresponding HRs for risk of death were 1·89 (95% CI 1·83-1·94) from ischaemic heart disease, 1·84 (1·79-1·89) from cerebrovascular disease, 1·19 (1·16-1·22) from cancer, 2·54 (2·45-2·63) from respiratory disease, 1·78 (1·59-2·00) from infection, and 1·78 (1·73-1·83) from all other causes. The frailty index is associated with all-cause and cause-specific mortality independent of chronological age in younger and older Chinese adults. The identification of younger adults with accelerated ageing by use of surrogate measures could be useful for the prevention of premature death and the extension of healthy active life expectancy. The National Natural Science Foundation of China, the National Key R&D Program of China, the Chinese Ministry of Science and Technology, the Kadoorie Charitable Foundation, and the Wellcome Trust.

Aims : The potential difference in the impacts of lifestyle factors (LFs) on progression from healthy to first cardiometabolic disease (FCMD), subsequently to cardiometabolic multimorbidity (CMM), and further to death is unclear.Methods and results : We used data from the China Kadoorie Biobank of 461 047 adults aged 30–79 free of heart disease, stroke, and diabetes at baseline. Cardiometabolic multimorbidity was defined as the coexistence of two or three CMDs, including ischaemic heart disease (IHD), stroke, and type 2 diabetes (T2D). We used multi-state model to analyse the impacts of high-risk LFs (current smoking or quitting because of illness, current excessive alcohol drinking or quitting, poor diet, physical inactivity, and unhealthy body shape) on the progression of CMD. During a median follow-up of 11.2 years, 87 687 participants developed at least one CMD, 14 164 developed CMM, and 17 541 died afterwards. Five high-risk LFs played crucial but different roles in all transitions from healthy to FCMD, to CMM, and then to death. The hazard ratios (95% confidence intervals) per one-factor increase were 1.20 (1.19, 1.21) and 1.14 (1.11, 1.16) for transitions from healthy to FCMD, and from FCMD to CMM, and 1.21 (1.19, 1.23), 1.12 (1.10, 1.15), and 1.10 (1.06, 1.15) for mortality risk from healthy, FCMD, and CMM, respectively. When we further divided FCMDs into IHD, ischaemic stroke, haemorrhagic stroke, and T2D, we found that LFs played different roles in disease-specific transitions even within the same transition stage.Conclusion : Assuming causality exists, our findings emphasize the significance of integrating comprehensive lifestyle interventions into both health management and CMD management.

Circulating plasma proteins play key roles in human health and can potentially be used to measure biological age, allowing risk prediction for age-related diseases, multimorbidity and mortality. Here we developed a proteomic age clock in the UK Biobank (n = 45,441) using a proteomic platform comprising 2,897 plasma proteins and explored its utility to predict major disease morbidity and mortality in diverse populations. We identified 204 proteins that accurately predict chronological age (Pearson r = 0.94) and found that proteomic aging was associated with the incidence of 18 major chronic diseases (including diseases of the heart, liver, kidney and lung, diabetes, neurodegeneration and cancer), as well as with multimorbidity and all-cause mortality risk. Proteomic aging was also associated with age-related measures of biological, physical and cognitive function, including telomere length, frailty index and reaction time. Proteins contributing most substantially to the proteomic age clock are involved in numerous biological functions, including extracellular matrix interactions, immune response and inflammation, hormone regulation and reproduction, neuronal structure and function and development and differentiation. In a validation study involving biobanks in China (n = 3,977) and Finland (n = 1,990), the proteomic age clock showed similar age prediction accuracy (Pearson r = 0.92 and r = 0.94, respectively) compared to its performance in the UK Biobank. Our results demonstrate that proteomic aging involves proteins spanning multiple functional categories and can be used to predict age-related functional status, multimorbidity and mortality risk across geographically and genetically diverse populations.

Adherence to a combination of healthy lifestyle factors has been related to a considerable reduction of cardiovascular risk in white populations; however, little is known whether such associations persist in nonwhite populations like the Asian population. This study aimed to examine the associations of a combination of modifiable, healthy lifestyle factors with the risks of ischemic cardiovascular diseases and estimate the proportion of diseases that could potentially be prevented by adherence to these healthy lifestyle patterns. This study examined the associations of 6 lifestyle factors with ischemic heart disease and ischemic stroke (IS) in the China Kadoorie Biobank of 461,211 participants 30 to 79 years of age who did not have cardiovascular diseases, cancer, or diabetes at baseline. Low-risk lifestyle factors were defined as nonsmoking status or having stopped smoking for reasons other than illness, alcohol consumption of <30 g/day, a median or higher level of physical activity, a diet rich in vegetables and fruits and limited in red meat, a body mass index of 18.5 to 23.9 kg/m2, and a waist-to-hip ratio <0.90 for men and <0.85 for women. During a median of 7.2 years (3.3 million person-years) of follow-up, this study documented 3,331 incident major coronary events (MCE) and 19,348 incident ISs. In multivariable-adjusted analyses, current nonsmoking status, light to moderate alcohol consumption, high physical activity, a diet rich in vegetables and fruits and limited in red meat, and low adiposity were independently associated with reduced risks of MCE and IS. Compared with participants without any low-risk factors, the hazard ratio for participants with ≥4 low-risk factors was 0.42 (95% confidence interval: 0.34 to 0.52) for MCE and 0.61 (95% confidence interval: 0.56 to 0.66) for IS. Approximately 67.9% (95% confidence interval: 46.5% to 81.9%) of the MCE and 39.1% (95% confidence interval: 26.4% to 50.4%) of the IS cases were attributable to poor adherence to healthy lifestyle. Adherence to healthy lifestyle may substantially lower the burden of cardiovascular diseases in Chinese.

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. 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. 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). 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. National Natural Science Foundation of China, National Key Research and Development Program of China, Kadoorie Charitable Foundation, UK Wellcome Trust.

Background: Adherence to a healthy lifestyle is associated with substantially lower risks of mortality from all causes, cardiovascular diseases, and cancer in white populations. However, little is known about the health benefits among non-white populations. Also, no previous studies have focused on respiratory disease mortality in both white and non-white populations. We assessed the relationships between a combination of healthy lifestyle factors and multiple death outcomes in Chinese adults. Methods: This study included 487,198 adults aged 30–79 years from the China Kadoorie Biobank without heart disease, stroke, and cancer at study enrolment. We defined five healthy lifestyle factors as never smoking or smoking cessation not due to illness; non-daily drinking or moderate alcohol drinking; median or higher level of physical activity; a diet rich in vegetables, fruits, legumes and fish, and limited in red meat; a body mass index of 18.5 to 27.9 kg/m² and a waist circumference < 90 cm (men)/85 cm (women). Cox regression was used to produce adjusted hazard ratios (HRs) relating these healthy lifestyle factors to all-cause and cause-specific mortality. Results: During a median follow-up of 10.2 years (IQR 9.2–11.1), we documented 37,845 deaths. After multivariable adjustment, the number of healthy lifestyle factors exhibited almost inverse linear relationships with the risks of all-cause and cause-specific mortality. Compared with participants without any healthy factors, the hazard ratio of participants with five healthy factors was 0.32 [95% confidence interval (CI): 0.28, 0.37] for all-cause mortality. The corresponding HRs in specific cause of death were 0.42 (95% CI: 0.26, 0.67) for ischaemic heart disease, 0.21 (95% CI: 0.09, 0.49) for ischaemic stroke, 0.37 (95% CI: 0.22, 0.60) for haemorrhage stroke, 0.36 (95% CI: 0.29, 0.45) for cancer, 0.26 (95% CI: 0.14, 0.48) for respiratory diseases, and 0.29 (95% CI: 0.22, 0.39) for other causes. Theoretically, 38.5% (95% CI: 33.0, 43.8%) of all-cause mortality was attributable to nonadherence to a healthy lifestyle, and the proportions of preventable deaths through lifestyle modification ranged from 26.9 to 47.9% for cause-specific mortality. Conclusions: Adherence to a healthy lifestyle was associated with substantially lower risks of all-cause, cardiovascular, respiratory, and cancer mortality in Chinese adults. Promotion of a healthy lifestyle may considerably reduce the burden of non-communicable diseases in China.