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      Frailty index and all-cause and cause-specific mortality in Chinese adults: a prospective cohort study

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      , BM a , , PhD a , , MSc c , , MSc d , , BM a , , PhD e , f , , DPhil e , f , , PhD e , f , , BM g , , BSc g , , PhD a , , Prof, FRCP e , , Prof, MD h , , Prof, DPhil e , , Prof, PhD a , b , c , * , , Prof, MPH a , China Kadoorie Biobank Collaborative Group
      The Lancet. Public Health
      Elsevier, Ltd

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          Summary

          Background

          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.

          Methods

          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.

          Findings

          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 (p interaction<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 (p interaction=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.

          Interpretation

          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.

          Funding

          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.

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          Most cited references29

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          Frailty: implications for clinical practice and public health

          Frailty is an emerging global health burden, with major implications for clinical practice and public health. The prevalence of frailty is expected to rise alongside rapid growth in the ageing population. The course of frailty is characterised by a decline in functioning across multiple physiological systems, accompanied by an increased vulnerability to stressors. Having frailty places a person at increased risk of adverse outcomes, including falls, hospitalisation, and mortality. Studies have shown a clear pattern of increased health-care costs and use associated with frailty. All older adults are at risk of developing frailty, although risk levels are substantially higher among those with comorbidities, low socioeconomic position, poor diet, and sedentary lifestyles. Lifestyle and clinical risk factors are potentially modifiable by specific interventions and preventive actions. The concept of frailty is increasingly being used in primary, acute, and specialist care. However, despite efforts over the past three decades, agreement on a standard instrument to identify frailty has not yet been achieved. In this Series paper, we provide an overview of the global impact and burden of frailty, the usefulness of the frailty concept in clinical practice, potential targets for frailty prevention, and directions that need to be explored in the future.
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            A standard procedure for creating a frailty index

            Background Frailty can be measured in relation to the accumulation of deficits using a frailty index. A frailty index can be developed from most ageing databases. Our objective is to systematically describe a standard procedure for constructing a frailty index. Methods This is a secondary analysis of the Yale Precipitating Events Project cohort study, based in New Haven CT. Non-disabled people aged 70 years or older (n = 754) were enrolled and re-contacted every 18 months. The database includes variables on function, cognition, co-morbidity, health attitudes and practices and physical performance measures. Data came from the baseline cohort and those available at the first 18-month follow-up assessment. Results Procedures for selecting health variables as candidate deficits were applied to yield 40 deficits. Recoding procedures were applied for categorical, ordinal and interval variables such that they could be mapped to the interval 0–1, where 0 = absence of a deficit, and 1= full expression of the deficit. These individual deficit scores were combined in an index, where 0= no deficit present, and 1= all 40 deficits present. The values of the index were well fit by a gamma distribution. Between the baseline and follow-up cohorts, the age-related slope of deficit accumulation increased from 0.020 (95% confidence interval, 0.014–0.026) to 0.026 (0.020–0.032). The 99% limit to deficit accumulation was 0.6 in the baseline cohort and 0.7 in the follow-up cohort. Multivariate Cox analysis showed the frailty index, age and sex to be significant predictors of mortality. Conclusion A systematic process for creating a frailty index, which relates deficit accumulation to the individual risk of death, showed reproducible properties in the Yale Precipitating Events Project cohort study. This method of quantifying frailty can aid our understanding of frailty-related health characteristics in older adults.
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              Biological Age Predictors

              The search for reliable indicators of biological age, rather than chronological age, has been ongoing for over three decades, and until recently, largely without success. Advances in the fields of molecular biology have increased the variety of potential candidate biomarkers that may be considered as biological age predictors. In this review, we summarize current state-of-the-art findings considering six potential types of biological age predictors: epigenetic clocks, telomere length, transcriptomic predictors, proteomic predictors, metabolomics-based predictors, and composite biomarker predictors. Promising developments consider multiple combinations of these various types of predictors, which may shed light on the aging process and provide further understanding of what contributes to healthy aging. Thus far, the most promising, new biological age predictor is the epigenetic clock; however its true value as a biomarker of aging requires longitudinal confirmation.
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                Author and article information

                Contributors
                Journal
                Lancet Public Health
                Lancet Public Health
                The Lancet. Public Health
                Elsevier, Ltd
                2468-2667
                30 November 2020
                December 2020
                30 November 2020
                : 5
                : 12
                : e650-e660
                Affiliations
                [a ]Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
                [b ]Peking University Institute of Environmental Medicine, Peking University, Beijing, China
                [c ]Peking University Health Science Center, and Key Laboratory of Molecular Cardiovascular Sciences, Ministry of Education, Peking University, Beijing, China
                [d ]Chinese Academy of Medical Sciences, Beijing, China
                [e ]Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
                [f ]Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
                [g ]Noncommunicable Diseases Prevention and Control Department, Maiji Centre for Disease Control and Prevention, Tianshui, China
                [h ]China National Center for Food Safety Risk Assessment, Beijing, China
                Author notes
                [* ]Correspondence to: Prof Jun Lv, Department of Epidemiology and Biostatistics, Peking University Health Science Center, Peking University, Beijing 100191, China lvjun@ 123456bjmu.edu.cn
                [†]

                Members of the China Kadoorie Biobank Collaborative Group are listed in appendix 2

                Article
                S2468-2667(20)30113-4
                10.1016/S2468-2667(20)30113-4
                7708389
                33271078
                c3da9677-97ef-494c-9fb1-40a3e1fee3b2
                © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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