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      Healthy lifestyle and life expectancy free of cancer, cardiovascular disease, and type 2 diabetes: prospective cohort study

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          Abstract

          Objective

          To examine how a healthy lifestyle is related to life expectancy that is free from major chronic diseases.

          Design

          Prospective cohort study.

          Setting and participants

          The Nurses’ Health Study (1980-2014; n=73 196) and the Health Professionals Follow-Up Study (1986-2014; n=38 366).

          Main exposures

          Five low risk lifestyle factors: never smoking, body mass index 18.5-24.9, moderate to vigorous physical activity (≥30 minutes/day), moderate alcohol intake (women: 5-15 g/day; men 5-30 g/day), and a higher diet quality score (upper 40%).

          Main outcome

          Life expectancy free of diabetes, cardiovascular diseases, and cancer.

          Results

          The life expectancy free of diabetes, cardiovascular diseases, and cancer at age 50 was 23.7 years (95% confidence interval 22.6 to 24.7) for women who adopted no low risk lifestyle factors, in contrast to 34.4 years (33.1 to 35.5) for women who adopted four or five low risk factors. At age 50, the life expectancy free of any of these chronic diseases was 23.5 (22.3 to 24.7) years among men who adopted no low risk lifestyle factors and 31.1 (29.5 to 32.5) years in men who adopted four or five low risk lifestyle factors. For current male smokers who smoked heavily (≥15 cigarettes/day) or obese men and women (body mass index ≥30), their disease-free life expectancies accounted for the lowest proportion (≤75%) of total life expectancy at age 50.

          Conclusion

          Adherence to a healthy lifestyle at mid-life is associated with a longer life expectancy free of major chronic diseases.

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          Most cited references23

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          Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970–2016: a systematic analysis for the Global Burden of Disease Study 2016

          Summary Background Detailed assessments of mortality patterns, particularly age-specific mortality, represent a crucial input that enables health systems to target interventions to specific populations. Understanding how all-cause mortality has changed with respect to development status can identify exemplars for best practice. To accomplish this, the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) estimated age-specific and sex-specific all-cause mortality between 1970 and 2016 for 195 countries and territories and at the subnational level for the five countries with a population greater than 200 million in 2016. Methods We have evaluated how well civil registration systems captured deaths using a set of demographic methods called death distribution methods for adults and from consideration of survey and census data for children younger than 5 years. We generated an overall assessment of completeness of registration of deaths by dividing registered deaths in each location-year by our estimate of all-age deaths generated from our overall estimation process. For 163 locations, including subnational units in countries with a population greater than 200 million with complete vital registration (VR) systems, our estimates were largely driven by the observed data, with corrections for small fluctuations in numbers and estimation for recent years where there were lags in data reporting (lags were variable by location, generally between 1 year and 6 years). For other locations, we took advantage of different data sources available to measure under-5 mortality rates (U5MR) using complete birth histories, summary birth histories, and incomplete VR with adjustments; we measured adult mortality rate (the probability of death in individuals aged 15–60 years) using adjusted incomplete VR, sibling histories, and household death recall. We used the U5MR and adult mortality rate, together with crude death rate due to HIV in the GBD model life table system, to estimate age-specific and sex-specific death rates for each location-year. Using various international databases, we identified fatal discontinuities, which we defined as increases in the death rate of more than one death per million, resulting from conflict and terrorism, natural disasters, major transport or technological accidents, and a subset of epidemic infectious diseases; these were added to estimates in the relevant years. In 47 countries with an identified peak adult prevalence for HIV/AIDS of more than 0·5% and where VR systems were less than 65% complete, we informed our estimates of age-sex-specific mortality using the Estimation and Projection Package (EPP)-Spectrum model fitted to national HIV/AIDS prevalence surveys and antenatal clinic serosurveillance systems. We estimated stillbirths, early neonatal, late neonatal, and childhood mortality using both survey and VR data in spatiotemporal Gaussian process regression models. We estimated abridged life tables for all location-years using age-specific death rates. We grouped locations into development quintiles based on the Socio-demographic Index (SDI) and analysed mortality trends by quintile. Using spline regression, we estimated the expected mortality rate for each age-sex group as a function of SDI. We identified countries with higher life expectancy than expected by comparing observed life expectancy to anticipated life expectancy on the basis of development status alone. Findings Completeness in the registration of deaths increased from 28% in 1970 to a peak of 45% in 2013; completeness was lower after 2013 because of lags in reporting. Total deaths in children younger than 5 years decreased from 1970 to 2016, and slower decreases occurred at ages 5–24 years. By contrast, numbers of adult deaths increased in each 5-year age bracket above the age of 25 years. The distribution of annualised rates of change in age-specific mortality rate differed over the period 2000 to 2016 compared with earlier decades: increasing annualised rates of change were less frequent, although rising annualised rates of change still occurred in some locations, particularly for adolescent and younger adult age groups. Rates of stillbirths and under-5 mortality both decreased globally from 1970. Evidence for global convergence of death rates was mixed; although the absolute difference between age-standardised death rates narrowed between countries at the lowest and highest levels of SDI, the ratio of these death rates—a measure of relative inequality—increased slightly. There was a strong shift between 1970 and 2016 toward higher life expectancy, most noticeably at higher levels of SDI. Among countries with populations greater than 1 million in 2016, life expectancy at birth was highest for women in Japan, at 86·9 years (95% UI 86·7–87·2), and for men in Singapore, at 81·3 years (78·8–83·7) in 2016. Male life expectancy was generally lower than female life expectancy between 1970 and 2016, and the gap between male and female life expectancy increased with progression to higher levels of SDI. Some countries with exceptional health performance in 1990 in terms of the difference in observed to expected life expectancy at birth had slower progress on the same measure in 2016. Interpretation Globally, mortality rates have decreased across all age groups over the past five decades, with the largest improvements occurring among children younger than 5 years. However, at the national level, considerable heterogeneity remains in terms of both level and rate of changes in age-specific mortality; increases in mortality for certain age groups occurred in some locations. We found evidence that the absolute gap between countries in age-specific death rates has declined, although the relative gap for some age-sex groups increased. Countries that now lead in terms of having higher observed life expectancy than that expected on the basis of development alone, or locations that have either increased this advantage or rapidly decreased the deficit from expected levels, could provide insight into the means to accelerate progress in nations where progress has stalled. Funding Bill & Melinda Gates Foundation, and the National Institute on Aging and the National Institute of Mental Health of the National Institutes of Health.
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            Dietary fat and coronary heart disease: a comparison of approaches for adjusting for total energy intake and modeling repeated dietary measurements.

            Previous cohort studies of fat intake and risk of coronary heart disease (CHD) have been inconsistent, probably due in part to methodological differences and various limitations, including inadequate dietary assessment and incomplete adjustment for total energy intake. The authors analyzed repeated assessment of diet from the Nurses' Health Study to examine the associations between intakes of four major types of fat (saturated, monounsaturated, polyunsaturated, and trans fats) and risk of CHD during 14 years of follow-up (1980-1994) by using alternative methods for energy adjustment. In particular, the authors compared four risk models for energy adjustment: the standard multivariate model, the energy-partition model, the nutrient residual model, and the multivariate nutrient density model. Within each model, the authors compared four different approaches for analyzing repeated dietary measurements: baseline diet only, the most recent diet, and two different algorithms for calculating cumulative average diets. The substantive results were consistent across all models; that is, higher intakes of saturated and trans fats were associated with increased risk of CHD, while higher intakes of monounsaturated and polyunsaturated fats were associated with reduced risk. When nutrients were considered as continuous variables, the four energy-adjustment methods yielded similar associationS. However, the interpretation of the relative risks differed across models. In addition, within each model, the methods using the cumulative averages in general yielded stronger associations than did those using either only baseline diet or the most recent diet. When the nutrients were categorized according to quintiles, the residual and the nutrient density models, which gave similar results, yielded statistically more significant tests for linear trend than did the standard and the partition models.
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              The combined effects of healthy lifestyle behaviors on all cause mortality: a systematic review and meta-analysis.

              Lifestyle factors are related to mortality. Although much is known about the impact of single factors, the current evidence about the combined effects of lifestyle behaviors on mortality has not yet been systematically compiled. We searched Medline, Embase, Global Health, and Somed up to February 2012. Prospective studies were selected if they reported the combined effects of at least three of five lifestyle factors (obesity, alcohol consumption, smoking, diet, and physical activity). The mean effect sizes that certain numbers of combined lifestyle factors have on mortality were compared to the group with the least number of healthy lifestyle factors by meta-analysis. Sensitivity analyses were conducted to explore the robustness of the results. 21 studies (18 cohorts) met the inclusion criteria of which 15 were included in the meta-analysis that comprised 531,804 people with a mean follow-up of 13.24 years. The relative risks decreased proportionate to a higher number of healthy lifestyle factors for all cause mortality. A combination of at least four healthy lifestyle factors is associated with a reduction of the all cause mortality risk by 66% (95% confidence interval 58%-73%). Adherence to a healthy lifestyle is associated with a lower risk of mortality. Copyright © 2012. Published by Elsevier Inc.
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                Author and article information

                Contributors
                Role: senior research scientist
                Role: senior researcher
                Role: research scientist
                Role: assistant professor
                Role: professor
                Role: research associate
                Role: assistant professor
                Role: professor
                Role: instructor in medicine
                Role: associate professor
                Role: postdoctoral research fellow
                Role: associate professor
                Role: professor
                Role: professor
                Role: professor
                Role: professor
                Role: professor
                Role: professor
                Journal
                BMJ
                BMJ
                BMJ-US
                bmj
                The BMJ
                BMJ Publishing Group Ltd.
                0959-8138
                1756-1833
                2020
                8 January 2020
                : 368
                : l6669
                Affiliations
                [1 ]Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
                [2 ]Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
                [3 ]Faculty of Sports and Nutrition, ACHIEVE - Centre of Applied Research, Faculty of Health, Amsterdam University of Applied Sciences, Amsterdam, Netherlands
                [4 ]Department of Internal Medicine, Rush University Medical Center, Chicago, IL, USA
                [5 ]Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
                [6 ]Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
                [7 ]Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
                [8 ]Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA
                [9 ]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
                [10 ]Division of General Internal Medicine, Division of Global Health Equity, Department of Medicine, Brigham and Women's Hospital, Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
                [11 ]Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
                [12 ]Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA
                [13 ]Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
                Author notes
                Correspondence to: FB Hu fhu@ 123456hsph.harvard.edu
                Author information
                https://orcid.org/0000-0002-0412-2748
                Article
                liy050725
                10.1136/bmj.l6669
                7190036
                31915124
                79501722-ccd8-453a-b99f-2a28d8fb1a54
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 14 October 2019
                Categories
                Research

                Medicine
                Medicine

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