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JAMA. Author manuscript; available in PMC 2018 March 15.
Published in final edited form as:
JAMA. 2017 March 07; 317(9): 912–924. doi:10.1001/jama.2017.0947.

Association Between Dietary Factors and Mortality From Heart
Disease, Stroke, and Type 2 Diabetes in the United States

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Renata Micha, RD, PhD, Jose L. Peñalvo, PhD, Frederick Cudhea, PhD, Fumiaki Imamura,
PhD, Colin D. Rehm, PhD, and Dariush Mozaffarian, MD, DrPH
Tufts Friedman School of Nutrition Science and Policy, Boston, Massachusetts (Micha, Peñalvo,
Cudhea, Mozaffarian); MRC Epidemiology Unit, University of Cambridge, Cambridge, England(Imamura); Office of Community and Population Health, Montefiore Medical Center, Bronx, NewYork (Rehm)

Abstract
IMPORTANCE—In the United States, national associations of individual dietary factors withspecific cardiometabolic diseases are not well established.
OBJECTIVE—To estimate associations of intake of 10 specific dietary factors with mortality due
to heart disease, stroke, and type 2 diabetes (cardiometabolic mortality) among US adults.

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DESIGN, SETTING, AND PARTICIPANTS—A comparative risk assessment model
incorporated data and corresponding uncertainty on population demographics and dietary habitsfrom National Health and Nutrition Examination Surveys (1999–2002: n = 8104; 2009–2012: n =8516); estimated associations of diet and disease from meta-analyses of prospective studies andclinical trials with validity analyses to assess potential bias; and estimated disease-specific nationalmortality from the National Center for Health Statistics.
EXPOSURES—Consumption of 10 foods/nutrients associated with cardiometabolic diseases:
fruits, vegetables, nuts/seeds, whole grains, unprocessed red meats, processed meats, sugarsweetened beverages (SSBs), polyunsaturated fats, seafood omega-3 fats, and sodium.

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Corresponding Author: Renata Micha, RD, PhD, Tufts Friedman School of Nutrition Science and Policy, 150 Harrison Ave, Boston,
MA 02111 (renata.micha@tufts.edu).
CME Quiz at jamanetworkcme.com
Author Contributions: Drs Micha and Mozaffarian had full access to all of the data in the study and take responsibility for the
integrity of the data and the accuracy of the data analysis.
Concept and design: Micha, Mozaffarian. Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Micha, Peñalvo, Cudhea, Rehm, Mozaffarian.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Micha, Peñalvo, Cudhea, Rehm.
Obtained funding: Micha, Mozaffarian.
Administrative, technical, or material support: Micha, Rehm.
Supervision: Micha, Mozaffarian.
Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts ofInterest. Drs Micha, Peñalvo, Cudhea, Rehm, and Mozaffarian report receipt of grants from the National Institutes of Health during the
conduct of the study. Dr Imamura reports receipt of support from the Medical Research Council Epidemiology Unit Core Support. Dr
Mozaffarian reports personal fees from Boston Heart Diagnostics, Haas Avocado board, AstraZeneca, GOED, DSM, Life Sciences
Research Organization, and UpToDate.

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MAIN OUTCOMES AND MEASURES—Estimated absolute and percentage mortality due toheart disease, stroke, and type 2 diabetes in 2012. Disease-specific and demographic-specific (age,sex, race, and education) mortality and trends between 2002 and 2012 were also evaluated.

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RESULTS—In 2012, 702 308 cardiometabolic deaths occurred in US adults, including 506 100
from heart disease (371 266 coronary heart disease, 35 019 hypertensive heart disease, and 99 815
other cardiovascular disease), 128 294 from stroke (16 125 ischemic, 32 591 hemorrhagic, and 79
578 other), and 67 914 from type 2 diabetes. Of these, an estimated 318 656 (95% uncertainty
interval [UI], 306 064–329 755; 45.4%) cardiometabolic deaths per year were associated with
suboptimal intakes—48.6% (95% UI, 46.2%–50.9%) of cardiometabolic deaths in men and 41.8%(95% UI, 39.3%–44.2%) in women; 64.2% (95% UI, 60.6%–67.9%) at younger ages (25–34
years) and 35.7% (95% UI, 33.1%–38.1%) at older ages (≥75 years); 53.1% (95% UI, 51.6%–
54.8%) among blacks, 50.0% (95% UI, 48.2%–51.8%) among Hispanics, and 42.8% (95% UI,
40.9%–44.5%) among whites; and 46.8% (95% UI, 44.9%–48.7%) among lower-, 45.7% (95%
UI, 44.2%–47.4%) among medium-, and 39.1% (95% UI, 37.2%–41.2%) among higher-educatedindividuals. The largest numbers of estimated diet-related cardiometabolic deaths were related tohigh sodium (66 508 deaths in 2012; 9.5% of all cardiometabolic deaths), low nuts/seeds (59 374;8.5%), high processed meats (57 766; 8.2%), low seafood omega-3 fats (54 626; 7.8%), lowvegetables (53 410; 7.6%), low fruits (52 547; 7.5%), and high SSBs (51 694; 7.4%). Between
2002 and 2012, population-adjusted US cardiometabolic deaths per year decreased by 26.5%. The
greatest decline was associated with insufficient polyunsaturated fats (−20.8% relative change[95% UI, −18.5% to −22.8%]), nuts/seeds (−18.0% [95% UI, −14.6% to −21.0%]), and excess
SSBs (−14.5% [95% UI, −12.0% to −16.9%]). The greatest increase was associated with
unprocessed red meats (+14.4% [95% UI, 9.1%–19.5%]).

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CONCLUSIONS AND RELEVANCE—Dietary factors were estimated to be associated with asubstantial proportion of deaths from heart disease, stroke, and type 2 diabetes. These results
should help identify priorities, guide public health planning, and inform strategies to alter dietary
habits and improve health.

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Dietary habits influence many risk factors for cardiometabolic health, including heartdisease, stroke, and type 2 diabetes, which collectively pose substantial health and economic
burdens.1 In both global2,3 and national4 modeling studies, the associations of suboptimal
diet with overall health have been estimated. Understanding the relations of individual
dietary components with cardiometabolic disease at the population level is essential toidentify priorities, guide public health planning, and inform strategies to alter these dietary
habits and improve health. In addition, the differences in these estimated health burdens byunderlying personal characteristics, such as age, sex, race/ethnicity, and education, arerelevant to consider more targeted approaches to reducing disparities.
For the United States, prior analyses have estimated the associations of suboptimal dietary
habits with cardiometabolic health overall4 or for a limited number of dietary factors (eg,sodium, sugar-sweetened beverages).5 The results for other individual dietary components,
as well as differences by age, sex, race/ethnicity, and socioeconomic status, are not wellestablished. The current investigation used a comparative risk assessment modelingdesign2,6,7 to estimate the cardiometabolic mortality related to suboptimal intakes of 10
dietary factors, individually and jointly, among US adults in 2012; to assess diet-associatedJAMA. Author manuscript; available in PMC 2018 March 15.

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mortality by disease subtypes (heart disease and subtypes, stroke and subtypes, and type 2
diabetes) and population subgroups (age, sex, race, and education); and to evaluate trends
between 2002 and 2012.

Methods
Study Design

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A comparative risk assessment model was used to estimate the numbers and proportions ofcardiometabolic deaths associated with suboptimal intakes of 10 dietary factors in the UnitedStates, both individually and in combination (eAppendix 1 in the Supplement). The model
incorporated separately derived data and corresponding uncertainty on (1) population
demographics and dietary habits by sex, age, race, and education from the National Healthand Nutrition Examination Survey (NHANES); (2) the estimated relationships of 10 foods
and nutrients with heart disease, stroke, or type 2 diabetes mortality, by age, from metaanalyses of prospective cohorts and randomized clinical trials, further evaluated by several
validity analyses; (3) the optimal population intake distributions of these dietary factors
based on observed intakes associated with lowest risk in observational studies; and (4)observed US disease-specific cardiometabolic deaths by sex, age, race, and education fromthe National Center for Health Statistics (NCHS). This modeling investigation was exempt
from human subjects review because it was based on published data and nationally
representative, deidentified data sets that included no personally identifiable information.
Identification of Relevant Dietary Factors

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The methods and results for review, identification, and assessment of evidence for etiologic
diet-disease relationships have been described (eAppendix 2 in the Supplement).8,9 Using
Bradford-Hill criteria and considering consistency with other criteria for assessing potential
causality of diet-disease relationships,10–12 probable or convincing evidence was identified
for associations of 17 dietary factors with coronary heart disease (CHD), stroke, type 2
diabetes, body mass index (BMI), or systolic blood pressure (SBP) (eTables 1–5 in the
Supplement). Of these, 10 were included in the present analysis (Table 1), excluding otherswith major overlap for estimating joint effects (eg, dietary fiber overlaps with whole grains,
fish overlaps with omega-3 fats). Several other dietary factors were evaluated and notincluded because of insufficient evidence for casual relationships, including
monounsaturated fats, vitamin D, magnesium, calcium, antioxidant vitamins, dairy products,
cocoa, coffee, and tea. Evidence for potential associations of diet with other conditions suchas cancer, osteoporosis, gallstones, inflammatory diseases, depression, cognitive function, or
micronutrient deficiency diseases was not evaluated.

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National Distributions of Dietary Intake and Demographics
Dietary intakes were estimated using nationally representative data from multiple NHANES
cycles, accounting for complex survey design and sampling weights,16 to be representativeof the US population aged 25 years or older (eTable 6 in the Supplement). As previously
described,17 intakes were assessed from up to 2 standardized 24-hour dietary recalls perperson, accounting for within-person variation (eTables 7–10 in the Supplement).18 Optimal
metrics and units for each dietary factor were characterized to be consistent with studies

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providing evidence on etiologic diet-disease relationships (Table 1; eTable 3 in the
Supplement).8 All dietary factors were adjusted for energy intake (using the residual
method18 or, for polyunsaturated fats, as percentage energy) to reduce measurement error
and account for potential differences in body size, lean mass, metabolic efficiency, andphysical activity.

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The means and standard deviations of intake of each dietary factor were estimated in
population strata by age (25–34, 35–44, 45–54, 55–64, 65–74, or ≥75 years), sex (male orfemale), race/ethnicity (non-Hispanic white, non-Hispanic black, Mexican American/other
Hispanic, or other race/mixed race), and education (less than high school diploma, highschool diploma/equivalent or some college, or 4-year college degree or greater). These
demographic characteristics were classified in NHANES based on self-report. Dietaryfactors were modeled based on the mean and standard deviations using gamma (rather thannormal) distributions, allowing for and incorporating skewed distributions. To maximize
power for subgroups, 2002 intakes were estimated by combining 1999–2000 and 2001–2002
cycles (the earliest with nationally representative 24-hour recalls; n = 8104; 48.2% men) and2012 intakes by combining 2009–2010 and 2011–2012 cycles (n = 8516; 47.6% men).
Estimated Diet-Disease Relationships

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Methods for reviewing and synthesizing evidence to estimate effect sizes (relative risks) for
associations between dietary factors and cardiometabolic end points have been described(eAppendix 2 in the Supplement).8,9 The present analysis incorporated evidence frompublished or de novo meta-analyses of prospective cohorts or randomized clinical trials
evaluating direct associations of dietary factors with CHD, stroke, or type 2 diabetes by age(Table 1; eTable 2 in the Supplement). We included additional BMI-mediated associations ofsugar-sweetened beverages (SSBs) by age and overweight/obesity status on deaths due toCHD, hypertensive heart disease, stroke, and type 2 diabetes and SBP-mediated associationsof dietary sodium by age, race, and hypertensive status on deaths due to heart disease,stroke, and type 2 diabetes (eTable 5 in the Supplement).

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These estimated effects can be used to model associations with cardiometabolic diseases if
bias from confounding (which might overestimate effects) or measurement error (whichmight underestimate effects) is limited. To reduce bias from confounding, all identified
observational studies in these meta-analyses used multivariable adjustment for other risk
factors. Measurement error was generally not addressed, although some studies used serial
measures of diet. In addition, associations of individual dietary factors with health may bedifferent from joint associations when consumed as diet patterns; eg, healthful dietaryfactors such as fruits, vegetables, and whole grains tend to positively correlate in diets while
inversely correlating with unhealthful dietary factors such as SSBs or processed meats. To
determine the extent to which the estimated multivariable-adjusted effect sizes might bebiased because of these limitations, 3 separate validity analyses were performed comparing
the estimated effect sizes for individual dietary components to (1) observed associations ofoverall dietary patterns with clinical end points in long-term observational studies; (2)effects of dietary patterns on cardiovascular risk factors (low-density lipoprotein cholesterol,
SBP) in randomized clinical feeding trials; and (3) effects of dietary patterns on hard end

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Page 5points in a large randomized clinical trial (eAppendix 2 and eTable 4 in the Supplement).

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9,19,20 Each of these validity analyses demonstrated that estimated effect sizes for individual

dietary components were very similar to what would be expected based on these other lines
of evidence.
Characterization of Optimal Intakes

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Optimal consumption levels for each dietary factor were characterized (Table 1) based on
observed levels associated with lowest disease risk in meta-analyses of clinical end points,
while further considering feasibility (observed national consumption levels in at least 2 to 3
countries around the world) and consistency with major dietary guidelines (eTable 3 in the
Supplement).8 The population distribution (ie, standard deviation) around each optimal
population mean was estimated from the optimal distributions of diet-related metabolic risk
factors in the Global Burden of Diseases study (10% of the mean).2 For each dietary factor,
the modeling assumed no additional health benefits beyond the optimal intake distribution
within each sex, age, race, and education stratum.
National Mortality, BMI, and SBP Distributions by Sex, Age, Race, and Education

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National disease-specific deaths in each stratum for 2002 and 2012 were obtained from theNCHS, which includes the entire US population (https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm). Deaths were excluded for foreign residents (individuals dying in the
United States but whose place of residence is outside the United States), ages 25 years or
younger, missing age information (2012: 0.005%; 2002: 0.006%), or, in education-stratified
analyses, missing education information (2012: 2.1%; 2002: 6.2%). Diet-related
cardiometabolic diseases were defined using International Statistical Classification of
Diseases and Related Health Problems, Tenth Revision, including heart disease (the sum of
CHD, hypertensive heart disease, and other cardiovascular disease), stroke (the sum ofischemic, hemorrhagic, and other stroke), and type 2 diabetes (Table 2; eTables 11–12 in the
Supplement). Events were characterized by age, sex, race/ethnicity, and education as
described above to match dietary strata. For associations mediated by BMI (SSBs),
including with CHD, hypertensive heart disease, stroke, and type 2 diabetes, and by bloodpressure (sodium), including with CHD, hypertensive heart disease, other cardiovasculardisease, and stroke, the stratum-specific distributions (means and standard deviations) of
BMI (based on measured heights and weights) and SBP (from certified examiners, using themean of 3 measurements or 4 if necessary) in 2002 and 2012 were estimated from the 1999–
2002 and 2009–2012 NHANES cycles, respectively. Hypertension was defined as systolicblood pressure of at least 140 mm Hg, diastolic blood pressure of at least 90 mm Hg, or useof antihypertensive drugs.23

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Cardiometabolic Disease Burdens Attributable to Key Dietary Targets
All data inputs were combined in a comparative risk assessment model to estimate theabsolute number and percentage of overall cardiometabolic deaths associated with
suboptimal intake of each dietary factor. This framework21 incorporated each stratumspecific input and its uncertainty (except for uncertainty in baseline number of deaths, notreported by the NCHS) to estimate associated mortality by age and sex; by age, sex, andrace; and by age, sex, and education. Stratification by all 4 demographic factors was not
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performed because of low sample size and unstable estimates in some strata. The main
outcomes were the estimated absolute number and percentage of cardiometabolic mortality
related to suboptimal intakes of 10 dietary factors, individually and jointly, in 2012. We also
evaluated disease-specific and demographic-specific (age, sex, race, and education)mortality and trends between 2002 and 2012.

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For each stratum, the model calculated the percentage of disease-specific mortality
associated with each dietary factor by comparing the present distribution of consumption
with the optimal distribution using the continuous population-attributable fraction (PAF)
formula (eAppendix 1 in the Supplement).21 This PAF was multiplied by the actual numberof disease-specific deaths in that stratum of the US population to estimate the absolute
number of disease-specific deaths in that stratum related to the dietary factor. The joint
associations of all 10 dietary factors was estimated by proportional multiplication of eachstratum-specific PAF (eAppendix 1). For comparing trends between 2002 and 2012, theestimated absolute (2012–2002) and relative (2012–2002/2002×100) associated mortality
rates in 2002 were age- and sex-standardized to 2012 age-sex distributions.

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Uncertainty was quantified using multiway probabilistic Monte Carlo simulations, jointly
incorporating stratum-specific uncertainties in dietary exposure distributions, diet-disease
relative risk estimates, and, for sodium, prevalence of hypertension and proportion of nonHispanic blacks. Corresponding 95% uncertainty intervals (UIs) were derived from the 2.5th
and 97.5th percentiles of 1000 estimated models. Different outcomes were evaluated without
adjustment for multiple comparisons, so the UI bounds for each finding should beinterpreted in that context. These analyses represent the estimated total cardiometabolic
mortality associated with each dietary factor, including any mediated relationships throughmajor cardiovascular risk factors (eg, the estimated mortality from low fruit or vegetableconsumption would include any association mediated by their effects on lowering of bloodpressure and blood cholesterol). Except for SSBs, additional potential relationships of
dietary habits with obesity were not considered, which could underestimate total diet-related
cardiometabolic mortality. All analyses were performed using R statistical software, version
3.1.0.

Results

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In both 2002 and 2012, national intakes of each dietary factor were suboptimal (Table 1;eTables 7–10 in the Supplement). In 2012, a total of 702 308 cardiometabolic deaths
occurred in US adults, including 506 100 due to heart disease (including 371 266 due toCHD, 35 019 due to hypertensive heart disease, and 99 815 due to other cardiovasculardisease), 128 294 from stroke (16 125 ischemic, 32 591 hemorrhagic, and 79 578 other), and
67 914 from type 2 diabetes (eTable 11 in the Supplement).
Estimated Cardiometabolic Mortality Attributed to Diet
When all 10 dietary factors were evaluated in combination, they were associated with 318
656 estimated cardiometabolic deaths, or nearly 1 in 2 (45.4%) of all US cardiometabolic
deaths in 2012. Among individual factors, largest numbers of estimated diet-related
cardiometabolic deaths were related to high sodium (66 508 estimated cardiometabolic

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deaths [9.5% of all cardiometabolic deaths]), low nuts/seeds (59 374 [8.5%]), high processedmeats (57 766 [8.2%]), low seafood omega-3 fats (54 626 [7.8%]), low vegetables (53 410[7.6%]), low fruits (52 547 [7.5%]), and high SSBs (51 694 [7.4%]) compared with optimal
consumption levels (Table 2; eFigure 1 in the Supplement). Lowest estimated mortality
burdens were associated with low polyunsaturated fats (16 025 [2.3%]) and highunprocessed red meats (2869 [0.4%]).

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Among cardiometabolic diseases, the largest numbers of deaths due to CHD were associatedwith low nuts/seeds (54 591 [14.7% of CHD deaths]), low seafood omega-3 fats (54 626[14.7%]), high processed meats (45 637 [12.3%]), high SSBs (39 937 [10.8%]), and highsodium (37 744 [10.2%]); due to total stroke, to low vegetables (28 039 [21.9%]), low fruits(28 741 [22.4%]), and high sodium (13 787 [10.7%]); due to hypertensive heart disease, tohigh sodium (7505 [21.4%]); and due to type 2 diabetes, to high processed meats (11 900[17.5%]), low whole grains (11 639 [17.1%]), and high SSBs (10 043 [14.8%]) (Table 2).
Findings by Sex, Age, Race, and Education

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Estimated cardiometabolic mortality associated with each dietary factor was modestly higher
in men than in women, primarily because of generally unhealthier dietary habits in men(Figure 1; eFigure 5 and eTable 14 in the Supplement). The largest sex differences were seenfor processed meats (10.8% of all cardiometabolic deaths in men and 5.4% in women;
difference, +5.4%; 95% UI, 2.3%–8.3%) and SSBs (9.3% vs 5.3%; difference, +3.9%; 95%
UI, 2.3%–5.4%). In men, the top 5 estimated dietary factors associated with cardiometabolic
deaths were excess processed meats (38 632 deaths [10.8% of all cardiometabolic deaths]),
sodium (35 777 [10.0%]), SSBs (33 314 [9.3%]); and insufficient nuts/seeds (31 587[8.8%]) and seafood omega-3 fats (31 545 [8.8%]). In women, these were excess sodium (30281 [8.8%]) and insufficient nuts/seeds (27 721 [8.1%]), vegetables (25 592 [7.4%]), fruits(24 449 [7.1%]), and omega-3 fats (23 032 [6.7%]). Jointly, suboptimal diet was related to
48.6% of estimated cardiometabolic deaths in men and 41.8% in women in 2012 (absolutedifference, +6.9%; 95% UI, 3.3%–10.1%) (Figure 2).

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By age, in 25- to 64-year-olds, excess SSBs and processed meats were the top estimated dietfactors associated with cardiometabolic mortality; in 65-year-olds and older, these were
excess sodium and insufficient nuts/seeds and vegetables (eFigure 2, eFigure 5, and eTable
14 in the Supplement). Overall, suboptimal diet was associated with 64.2% of all estimatedcardiometabolic deaths in 25- to 34-year-olds and 35.7% in 75-year-olds and older (absolutedifference, −28.6%; 95% UI, −32.9% to −24.0%) (Figure 2). The highest estimatedproportional deaths at youngest ages (<44 years) were associated with SSBs followed byprocessed meat, fruits, nuts/seeds, and vegetables; at middle age (45–54 years), with SSBs,
processed meat, nuts/seeds, and seafood omega-3 fats; and at oldest age (≥65 years), withsodium. For example, estimated proportions of SSB-related deaths were much higher at age
25–34 years (26.8%) and 35–44 years (28.9%) than at age ≥75 years (3.5%). Estimatedproportions of deaths related to processed meat and nuts/seeds were higher at age 45–54
years (16.8% and 15.7%, respectively) than at age ≥75 years (4.9% and 6.8%).
By race/ethnicity, estimated proportional diet-related mortality was higher among blacks or
Hispanics for most dietary factors assessed (Figure 2; eFigure 3, eFigure 5, eFigure 6, and
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eTable 14 in the Supplement). For example, estimated cardiometabolic mortality associatedwith SSBs was nearly twice as high in blacks (12.6%; the leading factor) vs whites (6.4%),
and from low nuts/seeds, higher in Hispanics (11.7%; the leading factor) vs whites (7.9%).
One exception was omega-3 fat–associated proportional mortality, which was higher inwhites (8.0%). Relative rankings of cardiometabolic mortality related to different dietaryfactors were otherwise generally similar by race/ethnicity. Overall, suboptimal diet was
associated with 53.1% of total estimated cardiometabolic deaths among blacks, 50.0%
among Hispanics, and 42.8% among whites (absolute differences, +10.5% [95% UI, 8.0%–
12.7%] for blacks vs whites and +7.2% [95% UI, 4.8%–9.8%] for Hispanics vs whites).

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Estimated proportional diet-related cardiometabolic mortality was generally higher amongindividuals with low or medium education compared with high education (Figure 2; eFigure
4, eFigure 5, eFigure 7, and eTable 14 in the Supplement). This was most notable for nuts/
seeds (in low vs high education, 10.7% vs 6.2% of cardiometabolic deaths), SSBs (8.4% vs4.5%), and fruits (8.5% vs 6.4%). Overall, suboptimal diet was associated with 46.8% ofcardiometabolic deaths for lower-, 45.7% for medium-, and 39.1% for higher-educatedadults (absolute differences, +7.7% [95% UI, 4.9%–10.4%] for low vs high and +6.7% [95%UI, 4.1%–9.0%] for medium vs high).
Trends Between 2002 and 2012

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Between 2002 and 2012, the total number of population-adjusted US cardiometabolic deaths
per year decreased by 26.5%. Improvements were seen in national intakes of some factors,
including polyunsaturated fats, nuts/seeds, SSBs, whole grains, and fruits (eFigure 8 in the
Supplement). Thus, absolute numbers of diet-related cardiometabolic deaths decreased forall dietary factors (eTable 13 in the Supplement). As a percentage of annual cardiometabolic
deaths, which accounts for underlying trends in absolute death rates, estimated dietassociated mortality declined for polyunsaturated fats (−20.8% smaller proportion of deaths;
95% UI, −18.5% to −22.8%), nuts/seeds (−18.0%; 95% UI, −14.6% to −21.0%), and SSBs(−14.5%; 95% UI, −12.0% to −16.9%); remained relatively stable for whole grains, fruits,
vegetables, seafood omega-3 fats, and processed meats; and increased for sodium (+5.8%;
95% UI, 2.9%–8.8%) and unprocessed red meats (+14.4%; 95% UI, 9.1%–19.5%) (Figure3). In 2002, excess SSB intake was the third leading risk factor for diet-associatedcardiometabolic death among these 10 dietary factors, with an estimated 73 162 associateddeaths, or 8.6% of all cardiometabolic deaths (see eTables 5, 8, and 12 for 2002 inputs andeFigures 9–16 in the Supplement for 2002 results overall and by population subgroups). In
comparison, by 2012, SSBs had declined to the seventh cause of diet-associated deaths.

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Proportional trends in cardiometabolic mortality associated with dietary factors weregenerally similar by sex and age (eFigures 5, 14, and 17 in the Supplement). Trends by race
were also consistent with overall results, with some exceptions. For instance, the percentage
of cardiometabolic deaths associated with insufficient nuts/seeds declined in whites (from10.0% to 7.9%; −21.8% [95% UI, −35.8% to −3.4%]) but not in blacks or Hispanics, while
the percentage of cardiometabolic deaths associated with insufficient whole grains declinedin Hispanics (from 12.9% to 7.6%; −41.2% [95% UI, −49.8% to −28.8%]) but not in whitesor blacks, yet Hispanics started at higher levels and declined to more similar associatedJAMA. Author manuscript; available in PMC 2018 March 15.

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burdens by 2012. Trends in diet-associated cardiometabolic deaths were also generally
similar by education, except that the percentage of cardiometabolic deaths associated with
low nuts/seeds declined in adults with high (8.7% to 6.2%; −29.7% [95% UI, −36.0% to−23.3%]) but not low (10.9% to 10.7%; −3.0% [95% UI, −8.4% to 6.3%]) education; andwith SSBs declined more among adults with high (5.9% vs 4.5%; −23.9% [95% UI, −29.5%
to −17.9%]) compared with low (9.2% vs 8.4%; −8.3% [95% UI, −12.6% to −4.0%])
education.

Discussion

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Based on a comparative risk assessment model and nationally representative data, anestimated 45.4% of all cardiometabolic deaths (n=318 656 due to heart disease, stroke, andtype 2 diabetes) were associated with suboptimal intakes of 10 dietary factors in 2012. Bysex, larger diet-related proportional mortality was estimated in men than in women,
consistent with generally unhealthier dietary habits in men. Suboptimal diet was also
associated with larger proportional mortality at younger vs older ages, among blacks andHispanics vs whites, and among individuals with low and medium education vs higheducation.

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Among individual dietary components, the largest estimated mortality was associated with
suboptimal sodium (9.5%) followed by nuts/seeds, processed meats, seafood omega-3 fats,vegetables, fruits, SSBs, and whole grains (each between 5.9%–8.5%), and, last,polyunsaturated fats (2.3%) and unprocessed red meats (0.4%). Estimated deaths related toprocessed meats and SSBs were higher among men than women. By age, SSBs were theleading estimated factor associated with cardiometabolic mortality between ages 25 and 64
years and sodium at age 65 years or older. Disparities were evident by race, especially forexcess SSBs among blacks and insufficient nuts/seeds among Hispanics, and by education,
especially for low nuts/seeds and fruits and excess SSBs among less-educated adults.
Income-related disparities in current levels and trends over time of national consumption ofnuts/seeds, fruits, and SSBs have been reported,17 which likely contribute to the disparities
in diet-associated mortality by race and education identified in the present investigation.
Between 2002 and 2012, several improvements were identified. Even accounting forunderlying declines in total cardiometabolic mortality, fewer diet-associated proportional
deaths were related to excess SSBs and insufficient polyunsaturated fats and nuts/seeds.
Improvements were not uniform. For example, less-educated individuals experienced nosignificant declines in cardiometabolic deaths associated with low nuts/seeds and smaller
declines in cardiometabolic deaths associated with SSBs.

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Nationally, estimated cardiometabolic deaths related to insufficient healthier foods/nutrientsremained at least as substantial as those related to excess unhealthful foods/nutrients. These
results inform strategies for prevention to reduce the health and economic burdens ofcardiometabolic diseases in the United States. For example, positive messaging to patients,
the public, and industry can emphasize maximizing the good (rather than simply reducing
the harmful) food choices and products. Within the health system, changes to clinician
education, multidisciplinary care teams, electronic health records, quality guidelines, and

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reimbursement standards can each facilitate lifestyle counseling and behavior change.1,23 At
local or national levels, strategies with evidence for effectiveness include multicomponent
school and workplace programs focused on healthier eating, economic incentives (eg,subsidies) for more healthful foods or taxation of less healthful foods, incentivized or
mandated product reformulation (eg, to reduce additives such as sodium and trans fats), andrestrictions on advertising of unhealthy foods to children.24,25 For example, the US Foodand Drug Administration recently announced voluntary sodium reduction targets for the
food industry,26 while in the 2016 elections, SSB taxes were passed in all 4 cities with thismeasure on the ballot.27 Compared with education alone, such “upstream” strategies could
also reduce disparities. For example, disparities in diet-related cardiometabolic deaths
identified in our investigation might be partly addressed by the Supplemental Nutrition
Assistance Program (SNAP), which serves 44 million low-income individuals in the UnitedStates; for example, by expanding the SNAP Food Insecurity Nutrition Incentive program28
to provide wider incentives for purchasing fruits and vegetables as well as nuts/seeds andadding restrictions or disincentives for unhealthier products such as SSBs, processed meats,and high-sodium foods.29,30 Gaps in knowledge remain regarding cost-effectiveness, equity
assurance, and political feasibility of dietary policies in different settings and withindifferent subgroups; our results highlight the need for government and other stakeholders toprioritize implementation and evaluation of such strategies.

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Among unhealthful foods/nutrients, the present findings suggest that sodium is a key target.
Population-wide salt reduction policies that include a strong government role to educate thepublic and engage industry to gradually reduce salt content in processed foods (for example,
as implemented in the United Kingdom and Turkey) appear to be effective, equitable, andhighly cost-effective or even cost-saving.31,32 Such population approaches can also
minimize challenges of public taste preferences, placing all companies on a level playing
field and allowing the population’s taste receptors for salt to gradually upregulate,preventing any major perception of changes in taste.33 Functional benefits of salt in foods,
such as for texture or food safety, must also be further addressed by advances in foodprocessing.33

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The decline in SSB-associated proportional mortality between 2002 and 2012 is promising.
The current results suggest that continuing programs to reduce SSBs are important,especially among younger adults, blacks, Hispanics, and individuals in the United Stateswith lower educational attainment. The price responsivity of SSBs34–36 makes tax strategies,
already implemented in Mexico, the United Kingdom, and several US cities, an effective
option. For example, evidence from Mexico suggests that a national SSB tax reduces overall
consumption and with greatest benefits among those of lower socioeconomic status,35
reducing disparities. Whether these taxes ultimately improve health outcomes remains
unknown.
This study extends and is consistent with a number of prior US analyses of diet-related
cardiometabolic mortality have been performed. In an analysis from the Global Burden of
Diseases Study, 26% of US deaths from all causes in 2010 were estimated to be related tojoint suboptimal intakes of 14 dietary factors4; with approximately 40% of these deaths due
to cardiometabolic diseases.4 In earlier analysis5 evaluating 5 dietary factors in 2005, excess

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sodium was estimated to be associated with 102 000 cardiovascular deaths, followed by low
omega-3 fats (84 000 deaths), high trans fats (82 000 deaths), low fruits and vegetables (58000 deaths), and low polyunsaturated fats (15 000 deaths). In global analyses of mortality
related to excess sodium7 and SSBs15 in 2010, about 58 000 US cardiovascular deaths were
estimated to be associated with sodium and 24 000 US cardiometabolic deaths with SSBs.
The present investigation builds on and expands these prior reports by using direct national
data on dietary intakes and mortality from NHANES and the NCHS rather than values
imputed from these sources plus other global data4,5,7,15; gamma distributions for dietaryfactors, particularly relevant for skewed intakes such as for SSBs, nuts/seeds, and processedmeats; and updated estimates of relative risks of dietary factors on disease risk.

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Our investigation has several strengths. The modeling study design incorporated separately
derived measures of demographics, dietary habits, optimal dietary intakes, disease rates, andestimated diet-disease relationships. This approach, which derived and estimatedcardiometabolic mortality associated with dietary intakes based on external evidence, should
be differentiated from an ecologic study design, which would assess cross-sectionalcorrelations within a national data set. Relative risks were based on multivariable-adjusted
meta-analyses of different dietary factors and cardiometabolic end points, with supportive
validity analyses from both long-term cohorts and randomized clinical trials of dietary
patterns. The modeling framework incorporated stratum-specific data, including by sex, age,
race, and education, wherever relevant and available, increasing validity of our estimates andability to evaluate disparities. Uncertainty was incorporated and quantified in the model
inputs using probabilistic sensitivity analyses, allowing estimation of the bounds of plausible
effects. Nationally representative data sets on dietary habits, cardiovascular risk factors, anddeath rates provide generalizability to the US adult population.

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Potential limitations should be considered. Although every effort was made to maximize
validity, minimize bias, and incorporate heterogeneity and uncertainty, the study’s
comparative risk assessment model does not prove that changes in these dietary habits
reduce disease risk. Causality is different from identifying associations. Estimated relative
risks of individual dietary components could be limited by measurement error (typicallycausing underestimation of effects) and residual confounding (typically causing
overestimation of effects). For any person, the contribution to health of each dietarycomponent may be modified by other factors such as other dietary habits as well as age, sex,
activity, adiposity, and genetics. Thus, as with any medical or public health intervention, ourfindings should be considered as estimates of the average population relationships. Dietary
habits are intercorrelated, increasing complexity of estimating associations. Yet separate
validity analyses of dietary pattern studies, including from interventional studies, suggested
that the estimated relative risks for both individual components and their joint associations
were reasonable. We limited our investigation to dietary factors with the strongest evidence,
not including many other dietary factors that may influence cardiometabolic health, andexcept for SSBs did not incorporate additional potential effects of these dietary factors on
obesity, which could underestimate the full health associations of poor diet.
Dietary habits were based on self-reported 24-hour recalls, which have known measurement
errors for individual people; and sodium intake is best assessed by multiple 24-hour urine

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collections, not available in NHANES. Yet our analysis used stratum-specific mean intakes,
which are measured reasonably well by 24-hour recalls; potential measurement error was
further reduced by energy adjustment and distributions were corrected for within-person
variation. Optimal intake levels for each dietary factor are not conclusively established andcould be modestly lower or higher. If benefits continue beyond our optimal levels, thereported associated mortality may be underestimated. National cause-of-death data are prone
to error; these findings should be considered the best available national data rather thanperfect clinical determination of mortality burdens. National Center for Health Statistics data
do not differentiate by type of diabetes, so our estimates may include a small number ofdeaths due to type 1 diabetes. Like most disease models, comparative risk assessment may
underestimate mortality for risk factors with prolonged lag effects. For example, SSB intake
is much higher in children and young adults, in whom measurable mortality would be low
compared with older adults. Therefore, the full lifetime disease associations of suboptimal
dietary habits at younger ages may be underestimated. Model estimates do not makeassumptions about feasibility of interventions to improve diet, which must consider cost,production, distribution, cultural preferences, disparities, potential industry cooperation or
opposition, and political feasibility.37 Thus, these findings should be considered estimates ofnational cardiometabolic mortality related to suboptimal intakes of these 10 dietary factors,
and potential effects of specific interventions should be evaluated in future studies.

Conclusions
Dietary factors were estimated to be associated with a substantial proportion of deaths fromheart disease, stroke, and type 2 diabetes. These results should help identify priorities, guide
public health planning, and inform strategies to alter dietary habits and improve health.

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Supplementary Material
Refer to Web version on PubMed Central for supplementary material.

Acknowledgments
Funding/Support: This research was supported by the National Institutes of Health and the National Heart, Lung,
and Blood Institute (grant R01 HL130735 to Dr Micha and grant R01 HL115189 to Dr Mozaffarian). Dr Peñalvo
was partly supported by a Bunge Fellowship in Global Nutrition.
Role of the Funder/Sponsor: The funding agencies did not contribute to the design or conduct of the study;
collection, management, analysis, or interpretation of the data; the preparation, review, or approval of themanuscript; or the decision to submit the manuscript for publication.

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37. Afshin, A., Micha, R., Khatibzadeh, S., Schmidt, L., Mozaffarian, D. Dietary policies to reduce
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Key Points
Question
What is the estimated mortality due to heart disease, stroke, or type 2 diabetes(cardiometabolic deaths) associated with suboptimal intakes of 10 dietary factors in the
United States?
Findings
In 2012, suboptimal intake of dietary factors was associated with an estimated 318 656
cardiometabolic deaths, representing 45.4% of cardiometabolic deaths. The highest
proportions of cardiometabolic deaths were estimated to be related to excess sodium
intake, insufficient intake of nuts/seeds, high intake of processed meats, and low intake ofseafood omega-3 fats.

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Meaning
Suboptimal intake of specific foods and nutrients was associated with a substantial
proportion of deaths due to heart disease, stroke, or type 2 diabetes.

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Figure 1. Absolute and Proportional Cardiometabolic Disease Mortality Associated With
Suboptimal Dietary Habits Among US Men and Women in 2012

The bars represent the estimated absolute number (top panel) and percentage (bottom panel)of cardiometabolic deaths related to 10 dietary factors compared with optimal intakes. The
dietary factors are listed in rank order of total mortality in men and women combined. Error
bars indicate 95% uncertainty intervals. CHD indicates coronary heart disease; CVD,
cardiovascular disease; PUFA, polyunsaturated fat.

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Figure 2. Absolute and Proportional Cardiometabolic Disease Mortality Associated With Overall
Suboptimal Diet in the United States in 2012 by Population Subgroups

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The bars represent the estimated absolute number (left panel) and percentage (right panel) of
cardiometabolic deaths jointly related to suboptimal intakes of 10 dietary factors. The 10
factors were low intakes of fruits, vegetables, nuts/seeds, whole grains, seafood omega-3
fats, and polyunsaturated fats (replacing saturated fats) and high intakes of sodium,
unprocessed red meats, processed meats, and sugar-sweetened beverages (see Table 1 fordetails). Error bars indicate 95% uncertainty intervals. CHD indicates coronary heartdisease; CVD, cardiovascular disease.

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Figure 3. Change in Proportional Cardiometabolic Disease Mortality in the United States
Between 2002 and 2012

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The bars represent the estimated relative changes in percentage of cardiometabolic deaths
associated with 10 dietary factors between 2002 and 2012 compared with optimal intakes.
These percentage changes correspond to (2012mortality−2002mortality)/2002mortality × 100.
Error bars indicate 95% uncertainty intervals.

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179.8 (98.4)/136.5 (50.1–259.7)

7.3 (11.8)/0 (0–4.1)

15.3 (15.6)/0.7 (0–19.5)

50.5 (22.6)/8.7 (0–84.2)

29.9 (17.8)/0 (0–43.5)

1.52 (1.53)/0.6 (0–2.4)

7.0 (1.4)/6.4 (4.7–8.6)

117 (85)/27.5 (0–72)

3400 (552)/3261 (2636–3963)

Vegetables including legumes, g/dNuts/seeds, g/d

Whole grains, g/d

Red meats, unprocessed, g/d

Processed meats, g/d

SSBs, 8-oz servings/d

PUFAs, % energy replacing
carbohydrates or saturated fatsg

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Seafood omega-3 fats, mg/d

Sodium, mg/d

3480 (605)/3355 (2874–3933)

100 (69)/37.9 (15.1–88.5)

7.7 (1.5)/7.4 (5.9–9.2)

1.14 (1.44)/0.4 (0–1.7)

30.8 (19.0)/17.6 (0–47.6)

47.4 (23.6)/34.4 (0–74)

21.2 (18.7)/12.4 (0–32.1)

11.7 (21.1)/1.5 (0–12.6)

182.5 (106.2)/154.0 (83.6–243.2)

Per 2300 mg/d

Increased SBP, white, normotensivei

2000

Per 100 mg/d

Decreased CHD

250h3.74 mm Hg (2.30–5.17)

0.82 (0.75–0.90)

0.88 (0.83–0.94)

1.27 (1.11–1.46)

Increased diabetes (BMI adjusted)f
Per 5% energy/d1.26 (1.15–1.37)

Increased CHD (BMI adjusted)f

Decreased CHD

0.23 (0.14–0.32)

Increased BMI (baseline BMI ≥25)f

0.10 (0.05–0.15)

Per 8 oz/dIncreased BMI (baseline BMI <25)f

1.47 (1.14–1.88)

1.24 (1.04–1.47)

1.65 (1.30–2.08)

Per 50 g/dIncreased diabetes

Increased CHD

Per 100 g/d

0.86 (0.80–0.92)

Decreased diabetes
Increased diabetes

0.90 (0.83–0.97)

Decreased hemorrhagic stroke

0.96 (0.93–0.99)
0.90 (0.83–0.97)

Per 50 g/d

0.96 (0.94–0.98)

0.91 (0.87–0.94)

Decreased ischemic stroke

Decreased CHD

Decreased diabetes

Per 1 oz/wk

0.80 (0.67–0.96)

Decreased CHD

0.80 (0.70–0.92)

0.94 (0.91–0.97)

Decreased hemorrhagic stroke

Per 100 g/d

Decreased ischemic stroke

Decreased CHD

0.69 (0.56–0.84)

Decreased hemorrhagic stroke

0.93 (0.89–0.97)
0.86 (0.80–0.92)

Per 100 g/d

Decreased ischemic stroke

Decreased CHD

11

No intakeNo intake14.3 (1 100–g serving/wk)

125 (2.5 50–g servings/d)

20.2 (5 1–oz servings/wk)

400

300

96.4 (89.1)/14.2 (0–141.8)

Fruits excluding fruit juices, g/d

115.0 (107.5)/71.2 (3.5–174.9)

Age 50 ye

Cardiometabolic Outcome

Unit of RRd

RR (95% Cl)
Optimal Consumption, MeancConsumption in 1999–2002b

Consumption in 2009–2012b

Mean (SD)/Median (IQR)

Dietary Targetsa

5.84 mm Hg (4.01–7.66)

0.87 (0.82–0.93)

0.92 (0.88–0.96)

1.18 (1.07–1.29)

1.17 (1.10–1.24)

0.23 (0.14–0.32)

0.10 (0.05–0.15)

1.41 (1.20–1.65)

1.30 (1.09–1.54)

1.16 (1.03–1.30)

0.90 (0.86–0.94)

0.93 (0.88–0.98)

0.93 (0.88–0.98)

0.97 (0.95–0.99)

0.97 (0.96–0.99)

0.93 (0.91–0.96)

0.86 (0.76–0.97)

0.86 (0.78–0.94)

0.96 (0.94–0.98)

0.77 (0.67–0.89)

0.90 (0.86–0.94)

0.95 (0.92–0.98)

Age 70 ye

Dietary Factors in the Analysis, Consumption Levels Among Adults Aged ≥ 25 Years in 1999–2002 and 2009–2012, Optimal Consumption Levels, Related Cardiometabolic Disease Outcomes, and Relative
Risks at Ages 50 and 70 Years

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Table 1
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Optimal Consumption, Meanc

1.87 mm Hg (0.12–3.63)

Increased SBP, additional effect among
patients with hypertensioni

Age 50 ye
2.49 mm Hg (0.13–4.85)

Unit of RRd

Increased SBP, additional effect among black
patientsi

Cardiometabolic Outcome

1.87 mm Hg (0.12–3.63)

2.49 mm Hg (0.13–4.85)

Age 70 ye

For SSBs and BMI, data are change in BMI calculated as weight in kilograms divided by height in meters squared; for sodium and SBP,change in SBP in mm Hg. Effect sizes correspond to associations between increased consumption of each dietary factor and cardio metabolic

Based on published or de novo dose-response meta-analyses of prospective cohorts or randomized clinical trials (eAppendix 2 and eTables 2 and 5 in the Supplement).

A linear reduction in risk was modeled until 250 mg/d, with no additional benefit thereafter.

hypertensiveindividuals,weredeterminedfromrandomizedclinicaltrials(eAppendix1andeTable5intheSupplement).7

Evidence suggests that sodium is associated with increased mortality due to heart disease and stroke through effects on SBP.7,14 Effects of sodium on SBP, including evidence for larger effects among older adults, blacks, andihEstimated RRs are similar for PUFAs replacing carbohydrates (49.4% of US calories in 2012) or saturated fats (10.7% of US calories in 2012); the former is reported herein. For comparison, in sensitivity analyses we also evaluated estimated mortality associated with excess
saturated fats in place of PUFAs.

g

Evidence suggests that SSBs are associated with increased cardiometabolic risk through relationships with BMI plus additional BMI-independent relationships of SSBs with CHD and type 2 diabetes (eAppendix 1 and eTable 5 in the Supplement).15

frisk. Analyses used age-specific RRs (eAppendix 2 and eTable 5 in the Supplement).5,14edBased on observed levels at which the lowest disease risk occurs, with further considerations of feasibility and consistency with major guidelines (eTable 3 in the Supplement).13c

Based on the National Health and Nutrition Examination Survey (see Methods and eTables 7–10 in the Supplement).

bin the Supplement).9 Of these, beans/legumes were summed with vegetables; dietary fiber, glycemic load, and potassium were not included because of overlap with major food sources (eg, fruits, vegetables, legumes, whole grains), fish/seafood because of overlap with seafood
omega-3 fats, yogurt because of lack of data on generalized least-squares dose-response relationships, and trans fats because of limited national data on intakes as well as rapidly declining US levels due to policy interventions.

From 17 dietary factors with probable or convincing evidence, based on criteria for assessing causality,10–12 for etiologic relationships with cardiometabolic outcomes including coronary heart disease (CHD), stroke, type 2 diabetes, body mass index (BMI), or SBP (eTable 1aAbbreviations: IQR, interquartile range; PUFA, polyunsaturated fat; RR, relative risk; SBP, systolic blood pressure; SSB, sugar-sweetened beverage.

Consumption in 2009–2012b

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Consumption in 1999–2002b

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Dietary Targetsa

RR (95% Cl)

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Mean (SD)/Median (IQR)

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Table 2

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Cardiometabolic Deaths Among US Adults Aged ≥25 Years Associated With Suboptimal Dietary Habits in2012
Cardiometabolic Disease by Dietary Factor (Suboptimal
Intake Level)a

Associated Deaths/y, No. (95% UI)b

Total Disease-Specific Deaths/y,%bHeart disease

223 960 (211 689–234 444)

44.3 (41.8–46.3)

CHD

197 981 (187 580–207 070)

53.3 (50.5–55.8)

Hypertensive heart disease7958 (7158–8792)

22.7 (20.4–25.1)

Other CVDd

7449 (6858–8109)

7.5 (6.9–8.1)

66 547 (62 799–69 915)

51.9 (48.9–54.5)

Overall Suboptimal Dietc

StrokeIschemic

Author Manuscript

7170 (6412–7779)

44.5 (39.8–48.2)

Hemorrhagic

19 863 (18 301–21 265)60.9 (56.2–65.2)

Other

39 289 (35 902–42 399)

49.4 (45.1–53.3)

Diabetes

32 732 (30 803–34 568)

48.2 (45.4–50.9)

318 656 (306 064–329 755)

45.4 (43.6–47)

CHD

23 865 (18 658–28 884)

6.4 (5.0–7.8)

Stroke28 741 (25 609–31 682)

22.4 (20–24.7)

Total cardiometabolic disease
Fruits (<300 g/d)

Ischemic

1920 (1602–2267)

11.9 (9.9–14.1)

Hemorrhagic

10 317 (8661–11 823)

31.7 (26.6–36.3)

Other

16 470 (13 671–19 119)20.7 (17.2–24)

52 547 (46 557–58 706)

7.5 (6.6–8.4)

CHD

25 443 (20 252–30 895)

6.9 (5.5–8.3)

Stroke28 039 (23 525–31 941)

21.9 (18.3–24.9)

3466 (2567–4208)

21.5 (15.9–26.1)

Total cardiometabolic disease

Author Manuscript

Vegetables (<400 g/d)

IschemicHemorrhagic

8041 (5987–9897)

24.7 (18.4–30.4)

16 584 (12 721–20 123)

20.8 (16–25.3)

53 410 (46 290–60 398)

7.6 (6.6–8.6)

CHD

54 591 (46 447–63 554)

14.7 (12.5–17.1)

Diabetes

4732 (3763–5715)

7.0 (5.5–8.4)

59 374 (51 211–68 422)

8.5 (7.3–9.7)

Other
Total cardiometabolic disease
Nuts/Seeds (<20.2 g/d)

Total cardiometabolic disease
Whole Grains (<125 g/d)

Author Manuscript

CHD

16 169 (11 749–20 833)

4.4 (3.2–5.6)

Stroke13 449 (11 539–15 160)

10.5 (9–11.8)

Ischemic

1618 (1205–2072)

10 (7.5–12.8)

Hemorrhagic4024 (3172–4810)

12.3 (9.7–14.8)

Other7774 (6211–9233)

9.8 (7.8–11.6)

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Cardiometabolic Disease by Dietary Factor (Suboptimal
Intake Level)a

Author Manuscript

Associated Deaths/y, No. (95% UI)b

Total Disease-Specific Deaths/y,%b

Diabetes

11 639 (10 102–13 143)

17.1 (14.9–19.4)

Total cardiometabolic disease

41 311 (36 141–46 360)

5.9 (5.1–6.6)

Diabetes

2869 (2091–3694)

4.2 (3.1–5.4)

Total cardiometabolic disease2869 (2091–3694)

0.4 (0.3–0.5)

CHD

45 637 (35 048–56 391)

12.3 (9.4–15.2)

Diabetes

11 900 (10 070–13 833)

17.5 (14.8–20.4)

Total cardiometabolic disease

57 766 (47 220–68 866)

8.2 (6.7–9.8)

Heart disease

40 552 (35 643–45 841)

8.0 (7.0–9.1)

CHD

39 937 (34 992–45 204)10.8 (9.4–12.2)

616 (433–830)

1.8 (1.2–2.4)

916 (809–1028)

0.7 (0.6–0.8)

82 (69–98)

0.5 (0.4–0.6)

Hemorrhagic

405 (342–479)

1.2 (1.1–1.5)

Other

426 (345–512)

0.5 (0.4–0.6)

Diabetes

10 043 (8419–11 979)

14.8 (12.4–17.6)

Total cardiometabolic disease

51 694 (46 363–57 156)

7.4 (6.6–8.1)

CHD

16 025 (13 280–18 925)

4.3 (3.6–5.1)

Total cardiometabolic disease

16 025 (13 280–18 925)

2.3 (1.9–2.7)

CHD

54 626 (45 541–65 053)

14.7 (12.3–17.5)

Total cardiometabolic disease

54 626 (45 541–65 053)

7.8 (6.5–9.3)

Heart disease

52 711 (44 681–60 826)

10.4 (8.8–12)

CHD

37 744 (29 879–45 697)

10.2 (8.0–12.3)

Hypertensive heart disease

7505 (6627–8325)

21.4 (18.9–23.8)

Other CVDd

7439 (6859–8105)

7.5 (6.9–8.1)

Red Meats, Unprocessed (>14.3 g/d)

Processed Meats (>0 g/d)

Sugar-Sweetened Beverages (>0 g/d)

Author Manuscript

Hypertensive heart disease
StrokeIschemic

PUFAs Replacing Carbohydrates or Saturated Fats (<11%Energy/d)eAuthor Manuscript

Seafood omega-3 Fats (<250 mg/d)

Sodium (>2000 mg/d)f

StrokeAuthor Manuscript

13 787 (12 018–15 870)

10.7 (9.4–12.4)

Ischemic

1629 (1349–1928)

10.1 (8.4–12)

Hemorrhagic

4011 (3306–4780)

12.3 (10.1–14.7)

Other8131 (6580–10 023)

10.2 (8.3–12.6)

66 508 (58 500–74 840)

9.5 (8.3–10.7)

Total cardiometabolic disease

Abbreviations: CHD, coronary heart disease; CVD, cardiovascular disease; PUFA, polyunsaturated fat; UI, uncertainty interval.

aBased on the National Center for Health Statistics, International Statistical Classification of Diseases and Related Health Problems, Tenth
Revision (eTable 11 in the Supplement). Cardiometabolic diseases included heart disease (the sum of CHD, hypertensive heart disease, and other

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Author Manuscript

CVD, including rheumatic heart disease, cardiomyopathy and myocarditis, atrial fibrillation and flutter, aortic aneurysm, peripheral vasculardisease, endocarditis, and other cardiovascular and circulatory diseases), stroke (including ischemic, hemorrhagic, and other stroke [unclassified
stroke or sequelae of stroke not specified as hemorrhage or infarction]), and type 2 diabetes. Diabetes deaths are those coded as proximally due todiabetes; diabetes is also separately a risk factor for CVD deaths along with other risk factors such as smoking, high blood pressure, highcholesterol, obesity, physical inactivity, etc.

b

Calculated from a total of 702 308 total US cardiometabolic deaths in 2012. Because each value represents central estimates from 1000 Monte
Carlo simulations (see Methods section of text and eAppendix 1 in the Supplement), the subtype estimates may not sum perfectly to the total
number. Deaths due to hypertensive heart disease were estimated from relationships with blood pressure (sodium) or body mass index (sugarsweetened beverages) and deaths due to other CVD from relationships with blood pressure (sodium) (eTable 5 in the Supplement).

cBased on the joint (multiplicative) population attributable fraction21 for the factors in this Table. For this, we used PUFAs replacing saturated fatsrather than carbohydrates to be more conservative when estimating joint associations and excluded CHD estimates for processed meat because thisrelationships may be mainly driven by sodium content,22 already separately included.

d
For findings for subtypes of other CVD deaths, see eTable 14 in the Supplement.
e

Associated mortality was similar for insufficient PUFAs in place of saturated fats alone: 14 382 CHD deaths (95% UI, 11 732-17 079),
representing 3.9% of CHD deaths (95% UI, 3.2%–4.6%) and 2.0% of total cardiometabolic disease deaths (95% UI, 1.7%–2.4%). In sensitivity
analyses, we also modeled mortality associated with excess saturated fats (>10%) in place of PUFAs: 4244 CHD deaths (95% UI, 3366-5278),
representing 1.1% of CHD deaths (95% UI, 0.9%–1.4%) and 0.6% of total cardiometabolic disease deaths (95% UI, 0.5%–0.8%).

Author ManuscriptfBased on effects of sodium on systolic blood pressure in randomized trials, including by age, race, and hypertension status (eAppendix 1 andeTable 5 in the Supplement) and associations of blood pressure with heart disease and stroke by age (eTable 5 in the Supplement).7,14

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