Impact of Healthy Lifestyle Factors on Life Expectancies in the US Population

Introduction

Clinical Perspective

What Is New?

  • A comprehensive analysis of the impact of adopting low-risk lifestyle factors on life expectancy in the US population is lacking.

  • Adherence to 5 low-risk lifestyle-related factors (never smoking, a healthy weight, regular physical activity, a healthy diet, and moderate alcohol consumption) could prolong life expectancy at age 50 years by 14.0 and 12.2 years for female and male US adults compared with individuals who adopted zero low-risk lifestyle factors.

What Are the Clinical Implications?

  • Americans could narrow the life-expectancy gap between the United States and other industrialized countries by adopting a healthier lifestyle.

  • Prevention should be a top priority for national health policy, and preventive care should be an indispensable part of the US healthcare system.

The United States is one of the wealthiest nations worldwide, but Americans have a shorter life expectancy compared with residents of almost all other high-income countries,1,2 ranking 31st in the world for life expectancy at birth in 2015.3 In 2014, with a total health expenditure per capita of $9402,4 the United States was ranked first in the world for health expenditure as a percent of gross domestic product (17.1%).4 However, the US healthcare system has focused primarily on drug discoveries and disease treatment rather than prevention. Chronic diseases such as cardiovascular disease (CVD) and cancer are the most common and costly of all health problems but are largely preventable.5 It has been widely acknowledged that unhealthy lifestyles are major risk factors for various chronic diseases and premature death.6

More than 2 decades ago, McGinnis and Foege7 and McGinnis and colleagues8 suggested that the nation’s major health policies should move to emphasize reducing unhealthy lifestyles. A meta-analysis9 of 15 studies including 531 804 participants from 17 countries with a mean follow-up of 13.24 years suggested that ≈60% of premature deaths could be attributed to unhealthy lifestyle factors, including smoking, excessive alcohol consumption, physical inactivity, poor diet, and obesity. A healthy lifestyle was associated with an estimated increase of 7.4 to 17.9 years in life expectancy in Japan,10 the United Kingdom,11 Canada,12 Denmark,13 Norway,13 and Germany.13,14 However, a comprehensive analysis of the impact of adopting low-risk lifestyle factors on life expectancy in the US population is lacking. Therefore, our aim was to evaluate the potential impact of individual and combined lifestyle factors on premature death and life expectancy in the US population.

Methods

The data, analytical methods, and study materials will be made available to other researchers from the corresponding authors on reasonable request for purposes of reproducing the results or replicating the procedure.

Overall Design

We first quantified the association between lifestyle-related low-risk factors and mortality on the basis of cohort data from the NHS (Nurses’ Health Study)15,16 and the HPFS (Health Professionals Follow-Up Study).17 Then, we used data from the NHANES (National Health and Nutrition Examination Surveys; 2013–2014) to estimate the distribution of the lifestyle-related factors among the US population.18 Furthermore, we derived the death rates of Americans from the CDC WONDER (Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research) database.19 Finally, we combined the results from those 3 sources to estimate the extended life expectancy associated with different categories of each individual lifestyle factor and a combination of low-risk lifestyle factors.

Study Population

The NHS began in 1976, when 121 700 female nurses 30 to 55 years of age responded to a questionnaire gathering medical, lifestyle, and other health-related information. In 1980, 92 468 nurses also responded to a validated food frequency questionnaire.15,16 The HPFS17 was established in 1986, when 51 529 male US health professionals (dentists, optometrists, osteopaths, podiatrists, pharmacists, and veterinarians) 40 to 75 years of age completed a mailed questionnaire about their medical history and lifestyle, including a food frequency questionnaire. We excluded participants with implausible energy intakes (women: <500 or >3500 kcal/d; men: <800 or >4200 kcal/d), with a body mass index (BMI) <18.5 kg/m2 at baseline, or with a missing value for BMI, physical activity, alcohol, or smoking. After these exclusions, 78 865 female and 44 354 male participants remained in the analysis at baseline. The NHS and HPFS were approved by the institutional review board of Brigham and Women’s Hospital in Boston; completion of the self-administered questionnaire was considered to imply informed consent.

We used the NHANES (2013–2014)18 to estimate the population distribution of lifestyle-related factors among American adults. The analytical population consisted of 2128 adults 50 to 80 years of age with complete information on diet, BMI, physical activity, alcohol use, and smoking status. We also excluded participants with BMIs of <18.5 kg/m2. The NHANES18 included a nationally representative sample of the US population. It was approved by the National Center for Health Statistics research ethics review board. Signed consents were obtained from all participants.

Data Collection

Diet in the NHS and HPFS was assessed every 4 years with a validated food frequency questionnaire asking the frequency, on average, a participant had consumed a particular amount of a specific type of food during the previous year.15,16 Physical activity levels were investigated with a validated questionnaire and updated every 2 years.20 Body weight and smoking habits were self-reported and updated every 2 years. Alcohol consumption was also collected by the food frequency questionnaire. Biennial questionnaires were used to collect information on potential confounders such as age, ethnicity, multivitamin use, regular aspirin use, postmenopausal hormone use (NHS only), and the presence or absence of a family history of diabetes mellitus, cancer, or myocardial infarction.

Dietary data in the NHANES18 were collected by an interviewer-administered, computer-assisted, 24-hour dietary recall, which was an in-depth interview conducted by a trained interviewer who solicited detailed information about everything that the participant ate and drank in the prior 24 hours. Body weight and height were measured in a mobile examination center with standardized techniques and equipment. Smoking status was self-reported and included questions about numbers of cigarettes, pipes, or cigars smoked per day and whether the participant had smoked at least 100 cigarettes in his or her lifetime. Participants also reported duration of moderate and vigorous physical activity during leisure time and at work. Usual alcohol intakes were recorded by two 24-hour dietary recalls.18

Low-Risk Lifestyle Score

We included 5 lifestyle-related factors: diet, smoking, physical activity, alcohol consumption, and BMI. Because this study was focused on modifiable lifestyle factors, we did not include clinical risk factors such as hypertension, hypercholesterolemia, or medication use in the score.

Diet quality in the NHS, HPFS, and NHANES was assessed with the Alternate Healthy Eating Index score (Methods in the online-only Data Supplement), which is strongly associated with the onset of cardiometabolic disease in the general population.21–23 We defined a healthy diet as a diet score in the top 40% of each cohort distribution. For smoking, we defined low risk as never smoking. For physical activity, we classified low risk as >30 min/d of moderate or vigorous activities (including brisk walking) that require the expenditure of at least 3 metabolic equivalents per hour. We defined low-risk alcohol consumption as moderate alcohol consumption, for example, 5 to 15 g/d for women and 5 to 30 g/d for men. BMI was calculated as self-reported weight (kilograms) divided by height (meters squared). Low-risk body weight was defined as BMI in the range of 18.5 to 24.9 kg/m2.

For each low-risk factor, the participant received a score of 1 if he or she met the criterion for low risk. If the participant did not meet the criterion, he or she was classified as high risk for that factor and received a score of 0. The sum of these 5 scores provided a total number of low-risk factors of 0, 1, 2, 3, 4, or 5, with higher scores indicating a healthier lifestyle.

Ascertainment of Deaths

In the NHS and HPFS, deaths were identified from state vital statistics records, the National Death Index, reports by the families, and the postal system.24 The follow-up for death in both cohorts was at least 98% complete. A physician reviewed death certificates or medical records to classify the cause of death according to International Classification of Diseases, Eighth Revision in the NHS (International Classification of Diseases, Ninth Revision in the HPFS).

We also derived the population all-cause, cardiovascular (I00–I99), and cancer mortality (C00–D48) rates for 2014 by sex and single-year ages ranging from 50 to 84 years from the CDC WONDER database of the US population.19 Because the database provides mortality rates only up to age of 84, we estimated the all-cause and cause-specific mortality rates in single years of age from 85 to 105 years by extrapolation based on a Poisson regression model with both linear and quadratic terms for the midpoints of single-year age groups minus age of 49.5 years (Methods and Figure I in the online-only Data Supplement).

Statistical Analysis

Participants contributed person-time from the return of the baseline questionnaire (NHS, 1980; HPFS, 1986) until the date of death or the end of the follow-up period (June 30, 2014, for NHS and January 30, 2014, for HPFS), whichever came first. We used Cox proportional hazard models to calculate the adjusted hazard ratios (HRs) of all-cause, cancer, and cardiovascular mortality with their 95% confidence intervals (CIs) across categories of each individual factor and joint classification of number of low-risk factors (0, 1, 2, 3, 4, or 5).

Because lifestyle factors may affect mortality risk over an extended period of time, to best represent long-term effects, we calculated cumulative average levels of lifestyle factors using the latest 2 repeated measurements for our primary analysis of diet, physical activity, and alcohol consumption. For example, in the NHS, mortality cases that occurred between 1980 and 1982 were examined in relation to physical activity on the basis of data collected on the 1980 questionnaire, the average of the 1980 and 1982 physical activity measurements was used to assess risk of mortality in the 1982 to 1984 follow-up period, the average of the 1982 and 1984 physical activity measurements was used to assess risk of mortality in the 1984 to 1986 follow-up period, and so forth. For dietary Alternate Healthy Eating Index score and alcohol use, the average was calculated on the basis of 4-year repeated measurements. Smoking status was estimated from both smoking history and most recent status updated every other year and classified into 5 categories: never, past, and current smoking of 1 to 14, 15 to 24, and ≥25 cigarettes per day. To minimize the reverse causality bias resulting from weight loss caused by preexisting illness, we applied the lifelong maximum BMI.25 For example, we applied the maximum value of BMI at age 18 years and BMI in 1980 to predict mortality between 1980 and 1982 and the maximum value of BMI at age 18 years, BMI in 1980, and BMI in 1982 to predict mortality between 1982 and 1984, and so forth. The same analytical strategy was applied to the HPFS. If data on low-risk factors were missing at a given time point, the last observation was carried forward. The following covariates were included in the multivariable model: age, ethnicity, current multivitamin use, current aspirin use, menopausal status and hormone use (women only), and family history of diabetes mellitus, myocardial infarction, or cancer. We applied a competing-risk regression model for cause-specific mortality by including lifestyle factors as exposure and other risk factors as unconstrained covariates, allowing the effects of the covariates to vary across cause-specific mortality.26

We calculated the hypothetical population-attributable risk, an estimation of the percentage of premature mortality in the study population that theoretically would not have occurred if all people had been in the low-risk category, assuming that the observed associations represent causal effects. For these analyses, we used a single binary categorical variable (with all 5 low-risk factors) and compared participants in the low-risk category with the rest of the population (without all 5 low-risk factors or with any high-risk factor) to calculate the HRs. We combined these HRs with the prevalence of the low-risk category among American adults based on NHANES data to estimate the population-attributable risk.27

To calculate the life expectancy of participants following different levels of healthy lifestyles, we used life tables. We built the life table starting at age 50 years and ending at age 105 years with the following 3 estimates to calculate the cumulative survival from 50 years onward: (1) sex- and age-specific HRs of mortality associated with numbers of low-risk lifestyles derived from the NHS and HPFS; (2) sex- and age-specific population mortality rate of all causes, cardiovascular mortality (I00–I99), and cancer mortality (C00–D48) from the US CDC WONDER database19; and (3) age- and sex-specific population prevalence of the number of low-risk lifestyles derived from the NHANES.18 We fitted multivariable-adjusted Cox regression models for each sex separately to calculate the age-specific HRs for mortality by the number of low-risk factors compared with zero low-risk factors. The model specification included linear and quadratic terms for the age variable (every 5 years up to 85 years) and the interactions between the number of low-risk factors and linear and quadratic terms of the age variable. The age-specific HRs for mortality were obtained as linear combinations of the relevant estimated coefficients, with age fixed at values corresponding to midpoints of 5-year age groups from age 50 to 85 years. The HR of age >85 years was assumed to be the same as that in the 85-year age group. Then we applied the age- and sex-specific HRs to estimate the life expectancy at different ages by the number of low-risk lifestyle factors (online-only Data Supplement).

In the sensitivity analysis, we applied the sex-specific HRs (adjusted for age only) for all-cause and cause-specific mortality to test the robustness of our findings. To address the potential aging effect on the association between lifestyle and mortality, we conducted a sensitivity analysis limited to NHS and HPFS participants <75 years of age. We conducted 3 stratified analyses: 1 analysis stratified by smoking status, another stratified by BMI status to estimate the joint effect of other 4 lifestyle factors, and the third stratified by baseline disease status (with or without elevated cholesterol, hypertension, or diabetes mellitus). To address the concern about the potential adverse effects of moderate alcohol intake, we created a healthy lifestyle score that was based on the other 4 low-risk factors without alcohol.

Because the binary variables could not account for the gradient in mortality risk with more extreme levels of these lifestyle factors, we conducted a third sensitivity analysis in which we calculated an expanded low-risk score on the basis of the associations between each lifestyle factor and mortality in the cohorts. We assigned scores of 1 (least healthy) to 5 (most healthy) to the categories of the lifestyle factors and summed the points across all 5 factors (score range, 5–25 points). For this analysis, the healthiest group was defined as never smoking, BMI between 18.5 and 22.9 kg/m2, moderate alcohol intake (5–14.9 g/d), moderate or vigorous activity duration of ≥6 h/wk, and the highest quintile of the Alternate Healthy Eating Index diet score.

We used SAS version 9.3 (SAS Institute Inc, Cary, NC) to analyze the data. Statistical significance was set at a 2-tailed value of P<0.05. We used Monte Carlo simulation (parametric bootstrapping) with 10 000 runs to calculate the CIs of the life expectancy estimation with @RISK 7.5 (Palisade Corp, Ithaca, NY).

Results

At baseline, participants with a higher number of low-risk lifestyle factors were slightly younger, more likely to use aspirin, and less likely to use multivitamin supplements (Table 1). During a median of 33.9 years of follow-up of women and 27.2 years of follow-up of men, 42 167 deaths were recorded (13 953 deaths resulting from cancer and 10 689 deaths caused by CVD).

Table 1. Participant Characteristics* at Baseline According to the Number of Low-Risk Lifestyle Factors

Low-Risk Lifestyle Factors, n†
0 1 2 3 4 5
NHS (1980)
 n (%) 5216 (6.6) 192 00 (24.3) 26 790 (34.0) 19 563 (24.8) 7179 (9.1) 917 (1.2)
 Age, y 47.2 (6.9) 46.7 (7.1) 46.1 (7.2) 45.8 (7.3) 45.7 (7.3) 45.7 (7.3)
 BMI, kg/m2 29.8 (4.5) 26.6 (5.0) 24.5 (4.1) 23.1 (3.0) 22.3 (1.9) 22.1 (1.6)
 Alternate Healthy Eating Index score 26.7 (3.4) 28.5 (5.0) 30.6 (6.0) 33.3 (6.2) 35.9 (5.5) 37.5 (4.3)
 Physical activity, h/wk 1.7 (1.2) 2.4 (2.1) 3.6 (2.8) 5.1 (2.9) 6.5 (2.1) 7.1 (1.2)
 Alcohol consumption, g/d 5.6 (12.6) 6.2 (12.4) 6.3 (10.8) 6.5 (9.1) 7.1 (6.8) 9.5 (2.8)
 Past smoking, % 48.5 33.1 27.7 22.9 15.7 0.0
 Current smoking, % 51.5 41.9 28.8 18.2 9.8 0.0
 White, % 97.9 97.7 97.6 97.4 97.4 97.8
 Multivitamin use, % 26.8 30.2 33.7 38.0 39.7 42.2
 Regular aspirin use, % 50.6 48.1 46.8 46.5 44.4 47.5
 Family history of diabetes mellitus, % 34.3 30.8 28.3 26.2 25.0 25.1
 Family history of cancer, % 13.0 13.3 14.1 14.1 14.7 14.1
 Family history of myocardial infarction, % 27.3 25.6 24.6 24.1 24.0 23.5
HPFS (1986)
 n (%) 4388 (9.9) 12133 (27.4) 14151 (31.9) 9337 (21.1) 3680 (8.3) 665 (1.5)
 Age, y 55.0 (9.6) 54.1 (9.6) 53.6 (9.8) 53.7 (9.8) 53.2 (9.9) 53.0 (9.4)
 BMI, kg/m2 28.2 (3.2) 27.1 (3.4) 25.8 (3.3) 24.7 (2.8) 23.8 (2.0) 23.2 (1.2)
 Alternate Healthy Eating Index score 39.5 (6.7) 42.9 (9.5) 47.2 (10.7) 51.6 (10.4) 55.8 (8.9) 58.6 (6.8)
 Physical activity, h/wk 0.7 (0.9) 1.4 (2.5) 2.5 (3.6) 4.3 (5.4) 6.2 (5.4) 7.9 (5.5)
 Alcohol consumption, g/d 16.3 (23.7) 11.6 (17.7) 10.3 (13.7) 10.5 (11.2) 10.7 (8.7) 12.6 (5.7)
 Past smoking, % 76.6 54.2 41.9 30.2 18.1 0.0
 Current smoking, % 23.4 14.9 7.8 3.3 1.5 0.0
 White, % 94.5 94.2 93.8 94.0 94.5 97.0
 Multivitamin use, % 57.0 58.7 61.2 64.1 68.4 66.9
 Regular aspirin use, % 31.7 31.7 29.6 30.0 27.7 26.7
 Family history of diabetes mellitus, % 22.1 22.9 20.9 19.9 19.9 21.8
 Family history of cancer, % 32.5 33.1 34.4 35.1 35.2 37.1
 Family history of myocardial infarction, % 34.4 33.7 33.3 34.0 32.6 33.6

Each individual component of a healthy lifestyle showed a significant association with risk of total mortality, cancer mortality, and CVD mortality (Table 2). A combination of 5 low-risk lifestyle factors was associated with an HR of 0.26 (95% CI, 0.22–0.31) for all-cause mortality, 0.35 (95% CI, 0.27–0.45) for cancer mortality, and 0.18 (95% CI, 0.12–0.26) for CVD mortality compared with participants with zero low-risk factors. The population-attributable risk of nonadherence to 5 low-risk lifestyle factors was 60.7% (95% CI, 53.6–66.7) for all-cause mortality, 51.7% (95% CI, 37.1–62.9%) for cancer mortality, and 71.7% (95% CI, 58.1–81.0) for cardiovascular mortality. We observed a similar association between the low-risk lifestyle factors and mortality before 75 years of age (Table I in the online-only Data Supplement). The low-risk lifestyle factors were associated with lower risk of cause-specific mortality in women and men similarly (Figure II in the online-only Data Supplement).

Table 2. HRs (95% CIs) of Total and Cause-Specific Mortality According to Individual Lifestyle Risk Factors*

Person-Years Deaths Resulting From Any Cause Cancer Deaths CVD Deaths
Cases HR (95% CI) Cases HR (95% CI) Cases HR (95% CI)
Body mass index, kg/m2
 18.5–22.9 624 140 5337 1.06 (1.02–1.09) 1868 0.96 (0.91–1.02) 1077 1.02 (0.94–1.10)
 23–24.9 677 848 7289 1.0 (Referent) 2588 1.0 (Referent) 1716 1.0 (Referent)
 25–29.9 1 381 081 17 903 1.05 (1.02–1.08) 5935 1.01 (0.96–1.06) 4738 1.16 (1.10–1.23)
 30–34.9 518 621 7427 1.25 (1.21–1.29) 2371 1.12 (1.05–1.18) 2006 1.66 (1.56–1.78)
 ≥35 250 013 4211 1.67 (1.61–1.74) 1191 1.24 (1.16–1.33) 1152 2.58 (2.39–2.79)
Cigarette smoking
 Never 1 508 401 13 694 1.0 (Referent) 4324 1.0 (Referent) 3390 1.0 (Referent)
 Past 1505 488 23 155 1.41 (1.38–1.44) 7526 1.50 (1.44–1.56) 6045 1.38 (1.32–1.44)
 Current 1–14/d 174 422 2458 2.02 (1.93–2.10) 873 2.00 (1.86–2.15) 596 2.08 (1.91–2.27)
 Current 15–24/d 163 678 1756 2.33 (2.21–2.45) 729 2.28 (2.11–2.48) 428 2.62 (2.37–2.91)
 Current ≥25/d 99 716 1104 2.87 (2.70–3.06) 501 2.97 (2.70–3.27) 230 2.78 (2.43–3.19)
Alcohol consumption, g/d
 0 1 037 840 16 611 1.27 (1.24–1.30) 4671 1.03 (0.98–1.08) 4263 1.49 (1.41–1.57)
 1–4.9 1 087 210 10 454 1.03 (1.00–1.06) 3841 0.98 (0.93–1.03) 2632 1.13 (1.07–1.20)
 5–14.9 773 186 8041 1.0 (Referent) 2953 1.0 (Referent) 2007 1.0 (Referent)
 15–29.9 345 034 4009 0.99 (0.96–1.03) 1417 0.99 (0.93–1.06) 1017 0.97 (0.90–1.05)
 ≥30 208 434 3052 1.25 (1.19–1.30) 1071 1.21 (1.13–1.30) 770 1.17 (1.08–1.27)
Physical activity, h/wk
 0–0.4 1 089 120 24 254 1.0 (Referent) 6997 1.0 (Referent) 6177 1.0 (Referent)
 0.5–1.9 921 192 8239 0.65 (0.63–0.66) 3044 0.71 (0.68–0.75) 2159 0.69 (0.66–0.73)
 2.0–3.4 515 731 3751 0.56 (0.54–0.58) 1491 0.66 (0.62–0.70) 930 0.54 (0.50–0.57)
 3.5–5.4 369 688 2524 0.50 (0.48–0.52) 1023 0.60 (0.56–0.64) 590 0.44 (0.40–0.48)
 ≥5.5 555 972 3399 0.44 (0.43–0.46) 1398 0.55 (0.52–0.58) 833 0.39 (0.37–0.43)
Alternate Healthy Eating Index score
 Fifth 1 736 051 11 125 1.0 (Referent) 3438 1.0 (Referent) 2588 1.0 (Referent)
 Fifth 2 701 947 9228 0.86 (0.83–0.88) 2983 0.89 (0.85–0.93) 2306 0.89 (0.84–0.94)
 Fifth 3 689 795 8082 0.77 (0.75–0.79) 2677 0.81 (0.77–0.85) 2073 0.81 (0.76–0.86)
 Fifth 4 672 973 7250 0.70 (0.68–0.72) 2511 0.76 (0.72–0.80) 1954 0.75 (0.71–0.80)
 Fifth 5 650 937 6482 0.63 (0.61–0.65) 2344 0.70 (0.67–0.74) 1768 0.67 (0.63–0.71)
No. of 5 low-risk factors†
 0 458 169 9286 1.0 (Referent) 2785 1.0 (Referent) 2430 1.0 (Referent)
 1 1 101 853 16 329 0.79 (0.77–0.81) 5227 0.83 (0.79–0.87) 4143 0.75 (0.71–0.79)
 2 1 053 250 10 908 0.61 (0.59–0.62) 3821 0.68 (0.65–0.71) 2719 0.54 (0.51–0.57)
 3 596 784 4408 0.47 (0.45–0.49) 1607 0.53 (0.50–0.57) 1101 0.40 (0.38–0.43)
 4 208 683 1113 0.35 (0.33–0.37) 458 0.44 (0.40–0.49) 270 0.28 (0.25–0.32)
 5 32 964 123 0.26 (0.22–0.31) 55 0.35 (0.27–0.45) 26 0.18 (0.12–0.26)
For not having all 5 low-risk factors vs all others
 HR of 5 vs. <5 low-risk factors 0.39 (0.33–0.46) 0.48 (0.37–0.63) 0.28 (0.19–0.42)
 PAR, %‡ 60.7 (53.6–66.7) 51.7 (37.1–62.9) 71.7 (58.1–81.0)

We observed a modest difference in HRs across age groups (Figure 1A). Using these age- and sex-specific HRs, we estimated that the life expectancy at age 50 years was 29.0 years (95% CI, 28.3–29.8) for women and 25.5 years (95% CI, 24.7–26.2) for men who adopted zero low-risk lifestyle factors. In contrast, for those who adopted all 5 low-risk factors, we projected a life expectancy at age 50 years of 43.1 years (95% CI, 41.3–44.9) for women and 37.6 years (95% CI, 35.8–39.4) for men (Figure 1B). Equivalently, women with 5 low-risk lifestyle factors could gain 14.0 years (95% CI, 11.8–16.8) of life expectancy on average, and men could gain 12.2 years (95% CI, 10.1–14.2) of life expectancy compared with those with zero low-risk lifestyle factors (Figure 1C). The preceding inferences were similar in sensitivity analyses using sex-specific HRs adjusted for age (Figure IIIA and IIIB in the online-only Data Supplement). Among women, on average, ≈30.8% of the gained life expectancy at age 50 years from adopting 5 versus zero low-risk lifestyle factors was attributable to reduced CVD death and the remainder to lower cancer (21.2%) or other causes (48.0%) of mortality. For men, the corresponding percentage was 34.1%, 22.8%, and 43.1%, respectively (Figure IIIC in the online-only Data Supplement). We observed a consistent dose-response relationship between the increasing number of low-risk factors and gained life expectancy among both smokers and nonsmokers (Figure IV in the online-only Data Supplement), among both normal-weight and overweight adults (Figure V in the online-only Data Supplement), and among individuals with and without chronic conditions at baseline (Figure VI in the online-only Data Supplement).

Figure 1.

Figure 1. Life expectancy estimated from the overall mortality rate of Americans (Centers for Disease Control and Prevention [CDC] report), the prevalence of lifestyle factors using NHANES (National Health and Nutrition Examination Surveys) data 2013 to 2014, and age- and sex-specific hazard ratios. A, Hazard ratio; B, life expectancy at age 50 years; C, life expectancy by age. Low-risk lifestyle factors included cigarette smoking (never smoking), physically active (≥3.5 h/wk of moderate to vigorous intensity activity), high diet quality (upper 40% of Alternate Healthy Eating Index), moderate alcohol intake of 5 to 15 g/d (female) or 5 to 30 g/d (male), and normal weight (body mass index <25 kg/m2). Estimates of cumulative survival from 50 years of age onward among the 5 lifestyle risk factor groups were calculated by applying the following: (1) all-cause and cause-specific mortality rates were obtained from the US CDC WONDER database; (2) distribution of different numbers of low-risk lifestyles was based on the US NHANES 2013 to 2014; and (3) multivariate-adjusted hazard ratios (sex- and age-specific) for all-cause mortality associated with the 5 low-risk lifestyles compared with those without any low-risk lifestyle factors, adjusted for ethnicity, current multivitamin use, current aspirin use, family history of diabetes mellitus, myocardial infarction, or cancer, and menopausal status and hormone use (women only), were based on data from the NHS (Nurses’ Health Study) and HPFS (Health Professionals Follow-up Study). CDC WONDER indicates Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research; and Ref, reference.

In a sensitivity analysis using a low-risk score without moderate alcohol intake, the projected life expectancy at age 50 years was on average 11.4 years (95% CI, 9.5–13.3) longer among female Americans with 4 low-risk factors compared with those with zero low-risk factors; for men, the difference was 10.0 years (95% CI, 9.2–10.9; Figure VII in the online-only Data Supplement).

We also estimated the gained life expectancy related to each of the lifestyle factors. As expected, increased exercise, not smoking or a reduced amount of smoking if a smoker, a healthy dietary pattern, moderate alcohol intake, and optimal body weight were all associated with longer life expectancy (Figure 2). The estimate based on the expanded low-risk score indicated a 20.5-year difference in life expectancy at age 50 years in women (19.6 years among men) who adhered to the highest expanded lifestyle score compared with the lowest expanded score (Figure VIII in the online-only Data Supplement).

Figure 2.

Figure 2. Projected gained or lost life expectancy according to individual low-risk lifestyle factors. A, Physical activity; B, smoking; C, diet; D, alcohol; E, body mass index. Estimates of cumulative survival from 50 years of age onward among different levels of each lifestyle factor were calculated by applying the following: (1) all-cause and cause-specific mortality rates were obtained from the US CDC WONDER database; (2) distributions of different groups of each lifestyle factor were based on the US NHANES (National Health and Nutrition Examination Surveys) 2013 to 2014; (3) multivariate-adjusted hazard ratios (sex-specific) for all-cause and cause-specific mortality associated with each lifestyle factor adjusted for ethnicity; current multivitamin use; current aspirin use; family history of diabetes mellitus, myocardial infarction, or cancer; and menopausal status and hormone use (women only) were based on data from the NHS (Nurses’ Health Study) and HPFS (Health Professionals Follow-up Study). AHEI indicates Alternate Healthy Eating Index; BMI, body mass index; CDC WONDER, Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research; cigs, cigarettes; Q, quartile; and Ref, referent.

Discussion

We estimated that adherence to 5 low-risk lifestyle-related factors could prolong life expectancy at age 50 years by 14.0 and 12.2 years for female and male US adults, respectively, compared with individuals who adopted zero low-risk lifestyle factors. These estimates suggest that Americans could narrow the life-expectancy gap between the United States and other industrialized countries by adopting a healthier lifestyle. In 2014, the life expectancy for American adults at age 50 years was 33.3 years for women and 29.8 years for men.28 We estimated that the life expectancies were 29.0 years for women and 25.5 years for men if they had zero low-risk factors but could be extended to 43.1 years for women and 37.6 years for men if they adopted all 5 low-risk factors. However, in US adults, adherence to a low-risk lifestyle pattern has decreased during the last 3 decades, from 15% in 1988 to 1992 to 8% in 2001 to 2006,29 driven primarily by the increasing prevalence of obesity.

The life expectancy of Americans increased from 62.9 years in 1940 to 76.8 years in 2000 and 78.8 years in 2014.28 This increase could be the result of a number of factors such as improvements in living standards, improved medical treatment, substantial reduction in smoking,30 and a modest improvement in diet quality.23 However, some unhealthy lifestyle factors may have counterbalanced the gain in life expectancy, particularly the increasing obesity epidemic30,31 and decreasing physical activity levels.32 In our study, three fourths of premature CVD deaths and half of premature cancer deaths in the United States could be attributed to lack of adherence to a low-risk lifestyle. There is still much potential for improvement in health and life expectancy, which depends not only on an individual’s efforts but also on the food, physical, and policy environments.33,34 A recent study found that low-income residents in relatively wealthy areas such as New York and San Francisco had significantly longer life expectancies than those in poorer regions such as Gary, IN, and Detroit.35 This phenomenon suggests that the living environment contributes to life expectancy beyond socioeconomic status. For instance, residents in affluent cities have more access to public health services and less exposure to smoking because of the more restricted policies on smoking in public.35 Studies36 have linked healthy eating and exercise habits with built, social, and socioeconomic environment assets (access to parks, social ties, affluence) and unhealthy behaviors with built environment inhibitors (access to fast food outlets), suggesting that supporting environments for health lifestyle should be 1 part of the promotion of longevity for the US population. Prevention should be a top priority for national health policy, and preventive care should be an indispensable part of the healthcare system.

Our estimation of gained life expectancy by adopting a low-risk lifestyle was broadly consistent with previous studies. A healthy lifestyle was associated with an estimated greater life expectancy of 8.3 years (women) and 10.3 years (men) in Japan,10 17.9 years in Canada,12 and 13.9 years (women) and 17.0 years (men) in Germany,14 as well as 14 years’ difference in chronological age in the United Kingdom.11 Data from 3 European cohorts from Denmark, Germany, and Norway13 suggested that men and women 50 years of age who had a favorable lifestyle would live 7.4 to 15.7 years longer than those with an unfavorable lifestyle. These estimates were somewhat different because of different definitions of a low-risk lifestyle and study population characteristics.10,12–14

We observed that a healthy diet pattern, moderate alcohol consumption, nonsmoking status, a normal weight, and regular physical activity were each associated with a low risk of premature mortality. Smoking is a strong independent risk factor of cancer, diabetes mellitus, CVDs, and mortality potentially through inducing oxidative stress and chronic inflammation, and smoking cessation has been associated with a reduction of these excess risks.37–39 A healthy dietary pattern and its major food components have been associated with lower risk of morbidities and mortality of diabetes mellitus, CVD, cancer, and neurodegenerative disease,40 and its potential health benefits have been replicated in clinical trials.41 Physical activity and weight control significantly reduced the risk of diabetes mellitus, cardiovascular risk factors, and breast cancer.42–44 Although no long-term trial of alcohol consumption on chronic disease risk has been conducted, cardiovascular benefits of moderate alcohol consumption have been consistently observed in large cohort studies.45 Results of our sensitivity analysis further indicated that combinations of the healthy lifestyle factors were particularly powerful: the larger the number of low-risk lifestyle factors, the longer the potential prolonged life expectancy, regardless of the combined factors.5

A major strength of this study is the long follow-up of 2 large cohorts with detailed and repeated measurements of diet and lifestyle and low rates of loss to follow-up. Another important strength is the combination of the cohort estimates with a nationally representative study, the NHANES, which improved the generalizability of our findings. Although the HRs between lifestyle factors and mortality were estimated from only our cohort data, they were similar to those published in other populations.9–14 Because our cohorts included mostly white health professionals, we could not specifically examine the overall impact of lifestyle adherence among different ethnic subgroups; further studies are warranted to examine the impact of lifestyle factors in other ethnic and racial groups.

The current study has several limitations. First, diet and lifestyle factors were self-reported; thus, measurement errors are inevitable. However, the use of repeated measures of these variables could reduce measurement errors and represent long-term diet and lifestyle. Second, we counted the number of lifestyle factors on the basis of the dichotomized value of each lifestyle factor, although the lifestyle factors were differentially associated with mortality. However, our analysis based on an expanded score considered different levels of each risk factor and yielded similar results. Third, we did not fully consider the baseline comorbid conditions and background medical therapies. Although our stratification analysis by baseline chronic conditions of diabetes mellitus, hypertension, and elevated cholesterol provided some support for the hypothesis that adopting a healthy lifestyle is important for both healthy individuals and those with existing chronic conditions, further studies among individuals with diagnosed cancer and CVDs are warranted.

Conclusions

We estimate that adherence to a low-risk lifestyle could prolong life expectancy at age 50 years by 14.0 and 12.2 years in female and male US adults compared with individuals without any of the low-risk lifestyle factors. Our findings suggest that the gap in life expectancy between the United States and other developed countries could be narrowed by improving lifestyle factors.

Acknowledgments

The authors thank the participants and staff of the NHS and the HPFS who contributed data for their valuable contributions, as well as the following state cancer registries for their help: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Nebraska, New Hampshire, New Jersey, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, South Carolina, Tennessee, Texas, Virginia, Washington, and Wyoming. The authors assume full responsibility for analyses and interpretation of these data.

Footnotes

*Drs Li and Pan contributed equally.

Sources of Funding, see page 354

The online-only Data Supplement, podcast, and transcript are available with this article at https://www.ahajournals.org/journal/circ/doi/suppl/10.1161/circulationaha.117.032047.

https://www.ahajournals.org/journal/circ

Frank B. Hu, MD, PhD, Departments of Nutrition and Epidemiology, or Yanping Li, MD, PhD, Department of Nutrition, Harvard T.H. Chan School of Public Health, 655 Huntington Ave, Boston, MA 02115. E-mail frank.[email protected]harvard.edu or [email protected]harvard.edu

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