Higher intake of MUFA-Ps was associated with lower total mortality, and MUFA-As intake was associated with higher mortality. Significantly lower mortality risk was observed when saturated fatty acids, refined carbohydrates, or trans fats were replaced by MUFA-Ps, but not MUFA-As. These data suggest that other constituents in animal foods, such as saturated fatty acids, may confound the associations for MUFAs when they are primarily derived from animal products. More evidence is needed to elucidate the differential associations of MUFA-Ps and MUFA-As with mortality.

We followed 63 412 women from the NHS (Nurses’ Health Study; 1990–2012) and 29 966 men from the HPFS (Health Professionals Follow-Up Study; 1990–2012). MUFA-Ps and MUFA-As were calculated based on data collected through validated food frequency questionnaires administered every 4 years and updated food composition databases. During 1 896 864 person-years of follow-up, 20 672 deaths occurred. Total MUFAs and MUFA-Ps were inversely associated with total mortality after adjusting for potential confounders, whereas MUFA-As were associated with higher mortality. When MUFA-Ps were modeled to isocalorically replace other macronutrients, hazard ratios (HRs, 95% CIs) of total mortality were 0.84 (0.77–0.92; P <0.001) for replacing saturated fatty acids, 5% of energy); 0.86 (0.82–0.91; P <0.001) for replacing refined carbohydrates (5% energy); 0.91 (0.85–0.97; P <0.001) for replacing trans fats (2% energy), and 0.77 (0.71–0.82; P <0.001) for replacing MUFA-As (5% energy). For isocalorically replacing MUFA-As with MUFA-Ps, HRs (95% CIs) were 0.74 (0.64–0.86; P <0.001) for cardiovascular mortality; 0.73 (0.65–0.82; P <0.001) for cancer mortality, and 0.82 (0.73–0.91; P <0.001) for mortality because of other causes.

Dietary monounsaturated fatty acids (MUFAs) can come from both plant and animal sources with divergent nutrient profiles that may potentially obscure the associations of total MUFAs with chronic diseases.

Introduction

According to the World Health Organization, of the 56.4 million deaths worldwide in 2015, more than half (54%) were because of 10 top causes.1 Ischemic heart disease, stroke, and cancer remain leading causes of deaths in the United States and several other developed countries.1 Many premature deaths are preventable by adopting a healthy lifestyle, including smoking cessation, increasing physical activity, and improving diet quality.1

Editorial, see p 1154

In This Issue, see p 1141

Meet the First Author, see p 1142

Recommendations by international organizations and the 2015 USDA Dietary Guidelines for Americans have emphasized the importance of the quality of dietary fat rather than the quantity of fat for the primary prevention of chronic diseases.2 Specifically, the intake of plant oils and other fats from plant sources is encouraged whereas the intake of animal fats, and particularly those from red and processed meat and butter, is discouraged. Of the fatty acids rich in plant-based food sources, polyunsaturated fatty acids (PUFAs) were consistently associated with lower risk of cardiovascular disease (CVD) and mortality across observational studies and clinical trial,3–6 but the impact of monounsaturated fatty acids (MUFAs) on chronic disease risk and especially mortality is less clear.7,8

Existing studies about MUFA intake and mortality risk have largely reported inconsistent findings.3,5,9 One possible reason is that dietary MUFAs come from both plant and animal sources with divergent dietary components that may potentially obscure the associations for MUFAs and health outcomes. In a recent analysis, we found that MUFAs from plant-based foods (MUFA-Ps) were associated with a lower risk of coronary heart disease, whereas the opposite was observed for MUFAs from animal products (MUFA-As), suggesting that food sources may play an important role in the relation between MUFAs and human health.8 To our knowledge, potentially divergent associations of long-term intake of MUFA-Ps and MUFA-As with total and cause-specific mortality have never been evaluated. Moreover, no large cohort studies have examined the associations with cause-specific mortality when other nutrients are replaced by MUFAs from different sources.

In 2 large prospective cohorts of US men and women, we examined the hypothesis that the intake of MUFA-Ps is associated with lower total and cause-specific mortality whereas MUFA-A intake is not. In addition, we estimated the risk of total and cause-specific mortality when substituting MUFAs for saturated fatty acids (SFAs), refined carbohydrates, and trans fats, based on the current dietary guidelines that recommend replacing these nutrients with healthier alternatives.

Methods

The data that support the findings of this study are available from the corresponding author on reasonable request.

Study Design and Population

The NHS (Nurses’ Health Study) is a prospective cohort study of 121 700 female registered nurses aged 30 to 55 years at enrollment in 1976. The HPFS (Health Professionals Follow-up Study) is a prospective cohort study of 51 529 male health professionals aged 40 to 75 years at enrollment in 1986. In both cohorts, information about medical history, lifestyle, and health conditions has been collected by self-administered biennial questionnaires since baseline. Detailed information on the cohorts have been described in previous publications.10–12

For this analysis, we used 1990 as study baseline when olive oil consumption was first asked as part of a validated food frequency questionnaire (FFQ) administered in the cohorts. At baseline, 80 332 women and 38 842 men completed the FFQ. Participants were excluded if they reported physician-diagnosed cancer, diabetes mellitus, or CVD at study baseline; reported implausible energy intake (<600 or >3500 kcal/day in NHS, and <800 or >4200 kcal/d in HPFS); had missing values for age; or they answered the baseline questionnaire only. The final analyses included 63 412 women and 29 966 men. The institutional review boards of Brigham and Women’s Hospital and Harvard T.H. Chan School of Public Health approved the study protocol. The return of a completed questionnaire was considered as informed consent.

Dietary Assessment

Dietary intake was measured using the FFQ with >130 items administered every 4 years to assess and update the habitual diet. The questionnaire inquires how often, on average, participants had consumed specific foods, as well as the types of fats, oils, and brand or type of margarines used for cooking and added at the table in the preceding year. Nutrient intakes were calculated based on the US Department of Agriculture and Harvard University Food Composition Database, which is updated over time to reflect potential changes in the nutrient profile of food items and to incorporate new items.13 We periodically analyzed the fatty acid composition of commonly consumed foods using gas chromatography during the follow-up period to account for changes in food processing. The nutrient database separated trans fats with one double bond from cis-MUFAs, which are the main exposures of the present study. MUFA-As were the sum of MUFAs from animal foods, such as animal fats for cooking, dairy products, eggs, poultry, processed and unprocessed red meats, and fish; MUFA-Ps were calculated based on plant-based foods, such as plant-based cooking oils, salad dressing, margarines, bread and cereals, fruits, vegetables, legumes, nuts, and seeds. For mixed food items, ingredients were identified according to manufacturer product labels or cookbooks for home-prepared items. We derived refined carbohydrates as the sum of added sugar and carbohydrates from refined grains (such as pasta, bread, white rice, pizza, and English muffins, among others).

The cumulative average of food intake from all available FFQs was calculated to better represent long-term diet and to minimize within-person variation.14 To minimize the possibility of reverse causation bias; we stopped updating diet information after participants reported a diagnosis of stroke, heart disease, angina, diabetes mellitus, or cancer. We replaced missing values of MUFAs during follow-up with valid cumulative averages of prior assessments. Intakes of different dietary fats estimated by FFQs were validated at baseline and during follow-up.12,15,16 In the most recent validation study of the NHS, deattenuated Spearman rank correlation coefficients (rs) of energy-adjusted nutrient data from FFQ and multiple 7-day dietary records were between 0.58 and 0.65 (both P<0.001) for total MUFAs and oleic acid, respectively.17

Ascertainment of Deaths

Deaths were identified through search of the vital records of states and of the National Death Index. This search was supplemented by reports from next of kin and postal authorities. Using these methods, we were able to ascertain >98% of the deaths in the cohorts.18 A physician who was blinded to data on food consumption data and other risk factors reviewed death certificates, medical records, or autopsy reports to classify the cause of deaths according to the International Classification of Diseases, Eighth and Ninth Revisions. Deaths were grouped into 5 major groups (CVD, cancer, respiratory disease, neurodegenerative disease, and all other causes, including suicide, injury, infections, diabetes mellitus, kidney disease, etc; Online Table I).

Statistical Analysis

Because the consumption of MUFA-Ps and MUFA-As changed during follow-up,8 we presented participants’ characteristics according to MUFA quintiles and the correlations among dietary fats at the midpoint of follow-up in 2002. Macronutrients were analyzed as percentages of energy by dividing the energy from specific macronutrients by total energy intake. We calculated each individual’s person-time from the return date of baseline questionnaire to the date of death, or the end of follow-up (June 2012 in NHS and January 2012 in HPFS), whichever came first. We used Cox proportional hazards regression models to estimate hazard ratios (HRs) and 95% CIs of total and cause-specific mortality in each cohort with follow-up duration as the timescale. Multivariate models were stratified jointly by age in months and calendar year to better control for confounding by age, calendar time, and any possible 2-way interactions between them. Multivariate models were adjusted for covariates that were updated in follow-up questionnaires. The following covariates were considered: ethnicity, smoking status, alcohol intake, family history of myocardial infarction, family history of diabetes mellitus, family history of cancer, menopausal status and postmenopausal hormone use (NHS only), physical activity, current aspirin use, multivitamin use, baseline hypertension, baseline hypercholesterolemia, body mass index, calorie intake, energy from trans fats, energy from SFAs, fruit and vegetables, and coffee intake (in quintiles). When modeling MUFA-Ps, we further included MUFA-As as a covariate, and vice versa. We calculated P values for trend with the use of the Wald test of a score variable based on the median of MUFAs in each category as a continuous variable. The proportional hazards assumption was tested by fitting a model that included interaction terms between MUFAs and duration of follow-up and by using a likelihood ratio test to examine the significance of the interaction terms. The assumption was unlikely to be violated (P>0.05 for all tests).

We estimated the risk of total and cause-specific mortality when energy from SFAs was replaced by MUFA-Ps in an isocaloric energy density model that included total energy, energy from carbohydrates, energy from protein, and energy from other fats (PUFAs, trans fats, and MUFA-As). By leaving SFAs out of the model, regression coefficients for MUFA-Ps can be interpreted as the estimated effect of isocalorically substituting MUFA-Ps for SFAs although holding other fats and total energy constant. Similar isocaloric substitution analyses were conducted for MUFA-As and for substituting MUFA fractions for trans fats and refined carbohydrates. In light of strong correlations of SFAs and MUFA-As and of PUFAs and MUFA-Ps, we further conducted substitution analyses replacing SFAs+MUFA-As with PUFAs+MUFA-Ps.

We performed sensitivity analyses to examine the robustness of findings by (1) continuing updating diet after the diagnosis of intermediate outcomes and (2) further adjusting for modified Alternate Healthy Eating Index, to explore whether findings may be explained by underlying dietary pattern. Analyses were conducted in the 2 cohorts separately, and then results were pooled with the use of an inverse variance–weighted meta-analysis using a fixed-effect model. Analyses were performed with the SAS statistical package (version 9.4, SAS Institute). Statistical tests were 2 sided, and P values of <0.05 were considered to indicate statistical significance.

Results

During 22 years of follow-up, we documented 20 672 deaths (12 774 in NHS and 7898 in HPFS) in 1 896 864 person-years. Participants’ characteristics according to MUFA-P and MUFA-A quintiles at the midpoint of follow-up (2002) are shown in Table 1. Compared with participants with lower MUFA-P intake, those in the highest quintile were younger, less likely to have hypertension, more likely to take aspirin and multivitamins, and had a higher Alternate Healthy Eating Index score. Participants with higher MUFA-A intake were younger, more likely to smoke, and less likely to exercise. They also had higher body mass index, a lower intake of fruits and vegetables, and a lower Alternate Healthy Eating Index score (Table 1).

Table 1. Age-Standardized Characteristics According to MUFA Intake at the Midpoint of Follow-Up (2002) Variable* NHS HPFS MUFA-P Q1 Q3 Q5 Q1 Q3 Q5 N 12 007 12 008 12 007 5497 5497 5497 Total MUFAs, % energy 9.4±2.0 11.6±1.6 14.4±2.1 9.8±2.3 12.0±1.8 14.4±2.1 MUFA-Ps, % energy 3.7±0.6 5.9±0.3 9.2±1.7 4.0±0.7 6.2±0.3 9.2±1.5 MUFA-As, % energy 5.4±1.8 5.5±1.5 4.9±1.5 5.6±2.1 5.6±1.8 5.0±1.7 Age, y 68.9±7.1 67.4±7.1 67.0±6.9 68.4±9.0 66.9±8.9 67.7±8.7 White, % 97 98 98 94 96 97 Alcohol intake, g/d 5.7±11.5 5.6±10.1 7.2±11.2 13.3±17.5 12.5±15.6 12.4±16.0 Current smoking, % 8 8 8 4 4 4 Physical activity, METs/wk 18.4±24.4 16.7±20.9 18.1±21.4 35.7±40.9 34.9±41.7 35.5±40.1 BMI, kg/m2 26.7±5.4 26.6±5.3 26.1±5.1 23.4±8.8 24.2±7.9 23.9±7.9 Hypertension at midpoint (2002) 55 53 49 48 45 42 Hypercholesterolemia at midpoint (2002) 63 63 63 53 55 53 Family history of myocardial infarction, % 38 38 37 33 31 32 Family history of diabetes mellitus, % 25 25 24 21 21 20 Family history of cancer, % 44 46 45 27 27 28 Multivitamin use, % 60 62 62 55 58 57 Current use of aspirin, % 32 34 34 45 48 47 Any use of postmenopausal hormone, % 67 71 72 Total energy, kcal 1614±423 1757±441 1811±470 1843±506 2010±534 2095±572 Total fats, % energy 24.3±4.9 28.9±4.2 32.9±4.8 24.8±5.6 29.5±4.6 33.3±4.9 PUFAs, % energy 4.6±0.8 5.7±0.9 6.6±1.4 4.7±0.9 5.8±0.9 6.9±1.4 n-3 PUFAs, % energy 0.6±0.2 0.6±0.1 0.7±0.2 0.6±0.2 0.7±0.2 0.7±0.3 n-6 PUFAs, % energy 3.9±0.7 5.0±0.8 5.8±1.3 4.1±1.0 5.2±1.0 6.2±1.6 SFAs, % energy 9.1±2.5 10.1±2.1 10.3±2.3 9.0±2.7 10.0±2.3 10.3±2.3 Trans fats, % energy 1.2±0.3 1.6±0.4 1.7±0.6 1.2±0.4 1.6±0.5 1.8±0.7 Proteins, % energy 19.4±3.0 17.9±2.4 16.8±2.4 19.1±3.1 17.7±2.4 16.5±2.4 Fruit and vegetables, serving/d 5.9±2.4 5.5±2.2 5.6±2.3 6.3±2.9 5.9±2.5 5.8±2.6 Coffee intake, servings/d 2.0±1.4 2.2±1.4 2.2±1.5 1.8±1.6 1.9±1.5 1.9±1.6 Modified AHEI† 31.6±7.1 31.3±7.0 34.6±7.5 31.6±8.3 30.9±7.7 34.0±8.2 MUFA-A Q1 Q3 Q5 Q1 Q3 Q5 N 12 007 12 007 12 007 5497 5497 5497 Total MUFAs, % energy 9.9±2.5 11.6±1.9 13.7±1.9 9.8±2.5 12.0±1.8 14.3±1.8 MUFA-Ps, % energy 6.5±2.5 6.2±1.9 5.8±1.8 6.7±2.4 6.5±1.8 6.0±1.6 MUFA-As, % energy 3.2±0.7 5.3±0.2 7.6±1.0 3.0±0.8 5.4±0.3 8.2±1.1 Age, y 69.4±7.1 67.5±7.1 66.4±6.9 68.1±9.0 67.4±9.0 67.3±8.8 White, % 97 98 98 95 96 96 Alcohol intake, g/d 6.2±10.8 6.3±10.8 5.4±10.2 12.2±16.2 13.4±16.8 11.2±14.7 Current smoking, % 4 7 13 1 4 6 Physical activity, METs/wk 23.3±26.0 17.1±22.2 13.3±18.4 42.7±45.4 33.9±38.7 30.7±41.5 BMI, kg/m2 25.0±4.5 26.6±5.0 27.8±5.9 22.9±7.2 24.4±7.6 24.2±9.3 Hypertension at midpoint (2002) 48 53 56 41 44 47 Hypercholesterolemia at midpoint (2002) 67 64 58 57 54 48 Family history of myocardial infarction, % 38 38 36 35 31 28 Family history of diabetes mellitus, % 24 24 26 20 21 21 Family history of cancer, % 45 46 45 27 28 28 Multivitamin use, % 68 63 53 63 58 49 Current use of aspirin, % 35 35 31 51 48 43 Any use of postmenopausal hormone, % 72 71 65 Total energy, kcal 1714±444 1754±445 1729±476 1923±526 1997±540 2057±590 Total fats, % energy 23.7±4.6 28.7±3.8 33.9±4.2 23.6±5.0 29.3±3.8 35.0±4.1 PUFAs, % energy 5.4±1.3 5.7±1.2 5.8±1.2 5.6±1.4 5.8±1.1 6.0±1.2 n-3 PUFAs, % energy 0.6±0.2 0.6±0.2 0.6±0.2 0.7±0.2 0.7±0.2 0.6±0.2 n-6 PUFAs, % energy 4.7±1.2 5.0±1.1 5.1±1.1 5.0±1.5 5.2±1.3 5.2±1.3 SFAs, % energy 7.3±1.4 9.8±1.2 12.6±1.8 7.0±1.5 9.9±1.3 12.8±1.8 Trans fats, % energy 1.2±0.4 1.5±0.4 1.8±0.5 1.2±0.5 1.6±0.5 1.9±0.5 Proteins, % energy 17.2±2.7 18.0±2.6 18.9±2.7 16.9±2.7 17.6±2.6 18.8±2.7 Fruit and vegetables, serving/d 6.7±2.6 5.6±2.1 4.7±2.0 7.3±3.0 5.8±2.4 4.9±2.1 Coffee intake, serving/d 1.9±1.4 2.2±1.4 2.3±1.5 1.6±1.4 1.9±1.5 2.2±1.7 Modified AHEI† 36.7±7.0 31.9±6.5 28.7±6.7 38.3±7.4 31.0±7.0 26.8±6.7

Major MUFA-P sources included olive oil, nuts, salad dressing, fried foods, baked products (chocolate chip cookies and homemade/ready-made pie), margarine, milk chocolate, and avocado.8 MUFA-As came mainly from red (beef and pork) and processed meats (41%–42%), dairy products, butter, poultry, eggs, and fish. In both cohorts, mean percentage of energy from MUFA-Ps increased from 5.8% to 6.3% to 7.9% during the follow-up, whereas MUFA-As decreased from 5.4% to 5.5% to 4.2%–4.4%.8

Intakes of MUFA-Ps and MUFA-As were weakly, inversely correlated (r=−0.07 for NHS and −0.10 HPFS). MUFA-Ps were positively correlated with total PUFAs and n-6 PUFAs (r≥0.59, P<0.001). MUFA-As were weakly correlated with total PUFAs and n-6 PUFAs (r≤0.16, P<0.001). SFA intake was strongly correlated with MUFA-As (r≥0.82, P<0.001; Online Table II).

Age-adjusted and multivariate-adjusted analyses showed a consistent, significant, inverse association between MUFA-Ps and total mortality, and a positive association between MUFA-As and total mortality (Table 2). The pooled multivariate-adjusted HRs (95% CIs) for participants in the highest quintile of MUFA-Ps and MUFA-As, as compared with those in the lowest quintile, were: 0.84 (0.80–0.89; P trend <0.001) and 1.16 (1.08–1.24; P trend <0.001), respectively (Table 2). In the model without SFAs, total MUFAs were not associated with total mortality. After adjustment for SFAs, which were highly correlated with MUFA-As, the HR (95% CIs) of total mortality comparing extreme quintiles of total MUFAs was significant at 0.84 (0.79–0.89; P trend <0.001).

Table 2. Hazard Ratios (95% CIs) of Total and Cause-Specific Mortality According to MUFA Intake in NHS and HPFS Pooled Cases/Person-Years Quintiles of MUFA Intake (% Energy) P for Trend Q1 Q2 Q3 Q4 Q5 Total mortality 4574/379 071 4037/379 760 3939/379 654 4030/379 389 4092/378 630 Total MUFAs Model 1 1 0.99 (0.95–1.03) 1.06 (1.01–1.10) 1.13 (1.08–1.18) 1.21 (1.16–1.27) <0.001 Model 2 1 0.99 (0.95–1.04) 1.01 (0.97–1.06) 1.04 (0.99–1.09) 1.00 (0.95–1.06) 0.50 Model 3 1 0.93 (0.89–0.98) 0.92 (0.88–0.97) 0.91 (0.87–0.96) 0.84 (0.79–0.89) <0.001 5327/377 503 4334/379 286 3808/379 871 3752/379 897 3451/380 306 MUFA-Ps Model 1 1 0.89 (0.85–0.92) 0.83 (0.79–0.86) 0.83 (0.79–0.86) 0.77 (0.73–0.80) <0.001 Model 2 1 0.94 (0.90–0.98) 0.88 (0.84–0.92) 0.86 (0.82–0.90) 0.79 (0.76–0.83) <0.001 Model 3 1 0.95 (0.91–1.00) 0.90 (0.86–0.95) 0.90 (0.86–0.94) 0.84 (0.80–0.89) <0.001 3873/379 974 3812/379 939 3940/379 823 4139/379 262 4908/377 885 MUFA-As Model 1 1 1.10 (1.05–1.15) 1.20 (1.15–1.26) 1.35 (1.29–1.41) 1.67 (1.60–1.75) <0.001 Model 2 1 1.10 (1.05–1.15) 1.15 (1.10–1.21) 1.21 (1.16–1.27) 1.32 (1.26–1.39) <0.001 Model 3 1 1.05 (1.00–1.11) 1.08 (1.03–1.14) 1.11 (1.05–1.18) 1.16 (1.08–1.24) <0.001 Cardiovascular mortality 1033/379 071 885/379 760 887/379 654 889/379 389 894/378 630 Total MUFAs Model 1 1 0.97 (0.88–1.06) 1.09 (0.99–1.19) 1.10 (1.01–1.21) 1.22 (1.12–1.34) <0.001 Model 2 1 1.01 (0.92–1.11) 1.12 (1.01–1.24) 1.15 (1.04–1.28) 1.16 (1.04–1.30) <0.01 Model 3 1 0.94 (0.85–1.04) 1.01 (0.91–1.12) 1.00 (0.89–1.13) 0.96 (0.84–1.09) 0.79 1129/377 503 955/379 286 831/379 823 780/379 897 793/380 306 MUFA-Ps Model 1 1 0.87 (0.79–0.94) 0.82 (0.75–0.90) 0.77 (0.70–0.84) 0.79 (0.72–0.87) <0.001 Model 2 1 0.94 (0.86–1.02) 0.91 (0.83–1.00) 0.85 (0.77–0.94) 0.89 (0.80–0.99) <0.01 Model 3 1 0.96 (0.88–1.05) 0.94 (0.85–1.04) 0.89 (0.81–0.99) 0.96 (0.86–1.07) 0.31 886/379 974 831/379 286 877/379 823 930/379 262 1064/377 885 MUFA-As Model 1 1 1.07 (0.97–1.18) 1.19 (1.08–1.31) 1.37 (1.24–1.50) 1.62 (1.48–1.78) <0.001 Model 2 1 1.04 (0.94–1.16) 1.11 (0.99–1.24) 1.18 (1.05–1.33) 1.20 (1.04–1.38) <0.01 Model 3 1 1.03 (0.93–1.14) 1.09 (0.97–1.22) 1.15 (1.01–1.30) 1.16 (1.00–1.35) 0.03 Cancer mortality 1471/380 877 1410/381 180 1376/381 046 1499/380 710 1547/380 248 Total MUFAs Model 1 1 1.04 (0.96–1.12) 1.08 (1.00–1.16) 1.21 (1.13–1.31) 1.29 (1.20, 1.39) <0.001 Model 2 1 1.06 (0.98–1.14) 1.06 (0.98–1.15) 1.16 (1.07–1.26) 1.13 (1.04, 1.23) <0.01 Model 3 1 1.01 (0.94–1.10) 0.99 (0.91–1.08) 1.05 (0.96–1.15) 0.99 (0.90, 1.10) 0.91 1735/3 379 452 1465/380 886 1392/381 219 1370/381 159 1370/381 342 MUFA-Ps Model 1 1 0.90 (0.84–0.97) 0.89 (0.83–0.95) 0.89 (0.82–0.95) 0.86 (0.80–0.92) <0.001 Model 2 1 0.95 (0.88–1.02) 0.94 (0.87–1.02) 0.93 (0.86–1.00) 0.89 (0.82–0.96) <0.01 Model 3 1 0.97 (0.90–1.04) 0.98 (0.91–1.06) 0.98 (0.90–1.06) 0.96 (0.88–1.05) 0.43 1326/381 444 1340/381 296 1358/381 176 1490/380 643 1784/379 500 MUFA-As Model 1 1 1.09 (1.01–1.17) 1.15 (1.07–1.24) 1.32 (1.23–1.43) 1.63 (1.51–1.75) <0.001 Model 2 1 1.09 (1.00–1.18) 1.11 (1.02–1.21) 1.21 (1.10–1.33) 1.32 (1.18–1.47) <0.001 Model 3 1 1.08 (0.99–1.17) 1.10 (1.00–1.20) 1.20 (1.09–1.32) 1.29 (1.15–1.45) <0.001 Noncardiovascular and noncancer mortality 2073/381 379 1741/381 855 1673/381 802 1640/381 578 1647/381 297 Total MUFAs Model 1 1 0.97 (0.91–1.03) 1.03 (0.96–1.10) 1.06 (0.99–1.13) 1.14 (1.07–1.22) <0.001 Model 2 1 0.95 (0.89–1.02) 0.94 (0.88–1.01) 0.91 (0.85–0.98) 0.86 (0.79–0.93) <0.001 Model 3 1 0.88 (0.83–0.95) 0.84 (0.78–0.90) 0.78 (0.72–0.85) 0.70 (0.63–0.77) <0.001 2360/380 294 1913/381 513 1583/381 840 1599/381 930 1319/382 254 MUFA-Ps Model 1 1 0.89 (0.84–0.95) 0.80 (0.75–0.85) 0.82 (0.77–0.88) 0.69 (0.65–0.74) <0.001 Model 2 1 0.95 (0.89–1.01) 0.84 (0.79–0.90) 0.84 (0.79–0.91) 0.71 (0.66–0.76) <0.001 Model 3 1 0.95 (0.89–1.01) 0.84 (0.78–0.90) 0.84 (0.78–0.91) 0.71 (0.65–0.77) <0.001 1660/381 966 1640/381 940 1404/381 887 1713/381 561 2057/380 556 MUFA-As Model 1 1 1.12 (1.04–1.20) 1.25 (1.16–1.33) 1.34 (1.25–1.43) 1.70 (1.59–1.81) <0.001 Model 2 1 1.09 (1.01–1.17) 1.14 (1.06–1.24) 1.13 (1.04–1.23) 1.24 (1.12–1.37) <0.001 Model 3 1 1.05 (1.02–1.07) 1.03 (0.96–1.11) 1.05 (0.97–1.14) 1.01 (0.92–1.10) 0.05

MUFA-Ps were associated with significantly lower cardiovascular and cancer mortality after multivariate adjustments of covariates, although these associations were attenuated to nonsignificant when further adjusting for intake of MUFA-As and SFAs. In contrast, MUFA-As were associated with 16% and 29% higher risk of cardiovascular and cancer mortality, respectively, after adjustment for covariates and MUFA-Ps (Table 2). MUFA-Ps were inversely associated with mortality because of other causes whereas MUFA-As were not associated to these deaths after mutual adjustments. Cohort-specific HRs and 95% CIs of total and cause-specific mortality according to MUFA intake are presented in Online Table III. Furthermore, we observed inverse associations between MUFA-Ps and neurodegenerative and respiratory deaths (Online Table IV). After adjusting for potential confounders including MUFA-As, the HRs (95% CIs) comparing extreme quintiles of MUFA-Ps were 0.75 (0.63–0.89; P trend <0.001) for neurodegenerative disease mortality and 0.65 (0.54–0.78; P trend <0.001) for respiratory disease mortality.

The Figure shows the pooled substitution analyses for total, CVD, cancer, and non-CVD and noncancer deaths. For MUFA-Ps, pooled HRs (95% CIs) of total mortality were 0.84 (0.77–0.92; P trend <0.001) when replacing 5% energy of SFAs; 0.86 (0.82–0.91; P trend <0.01) when replacing 5% energy of refined carbohydrates; and 0.91 (0.85–0.97; P trend =0.003) when replacing 2% energy of trans fats. The relative risk of total mortality was 20% lower when 5% energy from MUFA-Ps isocalorically replaced SFAs and MUFA-As (0.80 [0.77–0.84]; P trend <0.001). A 15% to 27% lower risk of CVD, cancer, and other deaths was observed when replacing MUFA-As or SFAs+MUFA-As with MUFA-Ps. Substituting MUFA-Ps for other nutrients was not significantly associated with CVD and cancer mortality. Cohort-specific and pooled HRs and 95% CIs for substitution analysis are shown in Online Table V.

Figure. Risk of total and cause-specific mortality for substitution analysis replacing other nutrients with monounsaturated fatty acids (MUFAs). Hazard ratios were adjusted for age, ethnicity (white, and other ethnicity) smoking status (never, former, current [1–14, 15–24, or ≥25 cigarettes/d], alcohol intake [grams/d: 0, 0.1–4.9, 5.0–14.9, and >15.0 in women; 0, 0.1–4.9, 5.0–29.9, and >30.0 in men] family history of myocardial infarction (yes/no), family history of diabetes mellitus (yes/no), family history of cancer (yes/no), menopausal status and postmenopausal hormone use (premenopause, post-menopause [never, former, or current hormone use] for women), physical activity (<3, 3.0–8.9, 9.0–17.9, 18.0–26.9, ≥27.0 METs/wk), current aspirin use (yes/no), multivitamin use (yes/no), baseline hypertension, baseline hypercholesterolemia, BMI (<23, 23–24.9, 25–29.9, 30–34.9, >35 kg/m2), intakes of total energy, fruits and vegetables, and coffee intake (in quintiles); For refined carbohydrate substitution, models were further adjusted for energy from protein, whole grain carbohydrates, trans fats, PUFAs, and SFAs; For trans fats substitution, models were further adjusted for total fats, PUFAs, and SFAs; For SFA substitution, models were further adjusted for total fats, trans fats, and PUFAs; All MUFA-Ps models were further adjusted for MUFA-As, and vice versa. †Study estimates from 2 cohorts were pooled using a fixed effects model. CVD indicates cardiovascular disease; MUFA-A, MUFA from animal sources; MUFA-P, MUFA from plant sources; NHS, Nurses’ Health Study; and SFA, saturated fatty acids.

The results for the substitution models remained largely unchanged when we continuously updated the diet regardless of the development of intermediate outcomes (Online Table VI) or when the models were adjusted for the Alternate Healthy Eating Index score (Online Table VII).

Discussion

In the present prospective investigation among men and women in 2 large US cohorts, we observed that the association of MUFA intake with mortality was determined by food sources of these fatty acids. Higher intake of MUFA-Ps was associated with lower total mortality, whereas the opposite was true for higher intake of MUFA-As. Moreover, total mortality was 14% to 28% lower when SFAs, refined carbohydrates, or trans fats were isocalorically replaced by MUFA-Ps. Substituting MUFA-Ps for MUFA-As and SFAs combined was also associated with lower total and cause-specific mortality. To our knowledge, this is the first prospective study that examined MUFAs from plant and animal sources separately in relation to total and cause-specific mortality.

Previous data on the association between MUFA intake and mortality have been inconsistent. In some studies, nonsignificant associations were observed, although others showed positive associations.3,9,19 In a recent meta-analysis of 17 prospective cohort studies, Schwingshackl et al9 found that MUFA intake was associated with 11% lower risk of all-cause mortality and 12% lower risk of CVD mortality. However, substantial between-study heterogeneity was observed, partly because of the inconsistent adjustment of covariates among individual studies.9 The other possible reason might be that MUFAs have diverse food sources, some of which may contain high amounts of unhealthful nutrients, such as SFAs or cholesterol in meats, dairy products, and partially hydrogenated oils, that may confound the associations for total MUFAs. In the NHS and HPFS, in the earlier FFQs, total MUFAs were strongly correlated with SFAs (r=0.8), which have been associated with higher mortality in previous analyses.5 Strong correlations of MUFAs with SFAs could likely explain the lack of associations observed between MUFAs and all-cause mortality when SFAs were not included in the model.

Our study findings generated novel evidence suggesting that MUFAs from plant and animal sources are differentially associated with total and cause-specific mortality. Existing studies that addressed MUFAs from different food sources in relation to mortality are sparse. In an ecological study from the Seven Countries Study, all-cause mortality rates were inversely correlated with the ratios of MUFAs/SFAs and (MUFAs+PUFAs)/(SFAs+trans fats), as well as plant oils,20 but this study did not explicitly examined MUFA-As and MUFA-Ps. In contrast, evidence is abundant for some major food sources of MUFAs. In our cohorts, olive oil, nuts, salad dressing, and fried foods were major sources of MUFA-Ps, whereas red and processed meats, dairy products, butter, and poultry were leading contributors of MUFA-As.8 The meta-analysis by Schwingshackl et al9 showed that higher intake of olive oil was associated with a 23% lower risk of all-cause mortality. In addition, higher intake of nuts was associated with a lower risk of all-cause and cause-specific mortality in the NHS and HPFS cohorts21 and in a meta-analysis that included 20 prospective cohort studies.22 Specifically, this meta-analysis showed that per 28 g increase in nut consumption was associated with 22% (95% CI, 16%–28%) lower risk of all-cause mortality risk. In contrast, higher intake of red meat and processed meat has been associated with higher risk of mortality in prospective cohort studies.23,24

Specification of an explicit comparison is the cornerstone of isocaloric nutritional substitution analysis, which evaluates the effects of adding or subtracting a calorie-bearing macronutrient by changing intake of other macronutrients correspondingly although holding the total energy intake constant. In the NHS and HPFS cohorts, we previously reported that replacing 5% of energy from SFAs with equivalent energy from PUFAs and MUFAs was associated with 27% and 13% lower total mortality, respectively.5 In addition, the risk of coronary heart disease was significantly lower when SFAs, refined carbohydrates, or trans fats were isocalorically replaced by MUFA-Ps but not MUFA-As in our recent analysis.8 Findings from the present analysis also showed significantly lower CVD mortality when MUFA-Ps replaced MUFA-As and MUFA-As+SFAs, but not SFAs or refined carbohydrates.

Moreover, we also observed lower mortality of cancer and non-CVD and noncancer causes when MUFA-Ps replaced MUFA-As and MUFA-As+SFAs. Existing data on specific types of dietary fats and non-CVD mortality are sparse. One prospective study showed an inverse association between MUFAs and breast cancer incidence in women aged 50 years or more, whereas other studies reported nonsignificant associations.25 Some studies have suggested that olive oil could be beneficial in the prevention of certain cancers, such as breast cancer.26 The consumption of nuts, an important source of MUFAs, has also been inversely related to the incidence of colorectal cancer, endometrial cancer, pancreatic cancer, and total cancer.27 Nut consumption was not associated with a lower risk of prostate cancer incidence and mortality,27,28 although frequent nut consumption was associated with better survival among prostate cancer patients.28 In addition, several lines of evidence suggested that high intakes of MUFAs and PUFAs were associated with slower cognitive decline.29 An analysis in the Rotterdam Study that prospectively followed 5289 participants ≥55 years old showed that the intakes of total fats, cis-MUFAs, and PUFAs were significantly associated with a lower risk of Parkinson disease.30

The cardiovascular effects of different fatty acids have been extensively examined. Results from clinical trials showed that higher MUFA intake improves blood lipid profile,31 decreases blood pressure,32 and modulates insulin resistance and glycemic control.33 In a meta-analysis of RCTs comparing high- versus low-MUFA diets in patients with abnormal glucose metabolism, high MUFA intake was associated with lower HbA1c (hemoglobin A1c), but other parameters of insulin resistance were unaffected.32 However, whether these effects can be entirely ascribed to MUFA-Ps deserves further investigation. Nevertheless, controlled feeding studies that examined plant oils rich in MUFAs, including olive oil, high-oleic-acid sunflower oil, high-oleic acid canola oil and nuts, have consistently demonstrated beneficial effects of higher intake of these oils on reducing cardiovascular risk.6,33,34 These findings may underlie the associations that we observed between MUFA-P intake and mortality risk. The observed inverse associations between plant food sources of MUFAs and mortality can also be explained by the potential synergic effects with other bioactive components such as polyphenols, dietary fiber, vitamins, and minerals.35,36 Meanwhile, recent clinical trials comparing plant oils that differed in the composition of fatty acids only showed that MUFAs significantly improved blood lipid profile and reduced central obesity, independent of the other components in the plant oils.37–39

Observational studies have suggested that higher consumption of red meat and processed meat, sources of MUFA-As and SFAs, is associated with a higher risk of developing type 2 diabetes mellitus,40 CVD,41 and certain cancers.42,43 In addition, many controlled feeding studies have shown that dietary cholesterol and SFAs increase total and LDL (low-density lipoprotein) concentrations, especially when compared with unsaturated fatty acids.34 Because dietary cis-MUFAs, including those from animal sources, are mostly oleic acid, the positive associations of MUFA-As with total and CVD mortality are likely explained by confounding of other components in animal foods, especially SFAs. Importantly, our results indicated that replacement of the combination of MUFA-As and SFAs by MUFA-Ps was significantly associated with lower risk of total, CVD, cancer and non-CVD and noncancer mortality. Given that MUFA-As and SFAs cannot be easily separated in the diet, they shall be replaced together by MUFAs from plant foods as a preferable source of fats.

Although the intake of cis-MUFAs has been associated with beneficial effects on health, there are still no consistent dietary recommendations about MUFAs. However, most dietary guidelines recommend higher intake of healthy plant foods, including mainly unsaturated plant oils, nuts and seeds, which are high in MUFAs and PUFAs, and lower intake of animal foods to prevent chronic diseases.2 Our results provide further epidemiological evidence supporting the recommendation of increasing the intake of unsaturated fats from plant-based food sources instead of fats from animal food sources, as well as replacing SFAs with unsaturated fatty acids. This evidence may also assist individuals in identifying healthy dietary choices for reducing animal fat intake.

The present study has several strengths including using data from 2 large, longitudinal cohorts with long follow-up and repeated measurements of diet. As in any observational study, the possibility of residual or unmeasured confounding cannot be excluded. Although we adjusted for many dietary factors in our analysis, confounding by other dietary components in the same food sources of MUFAs cannot be fully ruled out. In addition, synergistic effects of MUFAs with other nutrients are also possible,8 although larger sample size is needed to examine such interactions. Our study population was relatively homogeneous (predominantly white health professionals), and thus caution shall be exercised when extrapolating our findings to other populations with different demographic characteristics. However, there is no reason to believe that the biological mechanisms would differ in other populations. We cannot entirely rule out reverse causation bias, because people with chronic diseases might change their habitual diet. However, we excluded participants with known major chronic diseases at baseline, used cumulative averages of diet to reduce short-term variability, and stopped updating diet after the development of certain major chronic diseases.

In conclusion, we found divergent associations between MUFA-Ps and MUFA-As with total and cause-specific mortality. Higher MUFA-P intake was associated with lower mortality whereas MUFA-A intake was associated with higher mortality. Significantly lower mortality was observed when SFAs, trans fats, or refined carbohydrates were replaced by MUFA-Ps. Overall, these data support current dietary recommendations to replace animal fats with unsaturated plant oils for the prevention of chronic diseases and premature deaths.

Nonstandard Abbreviations and Acronyms CVD cardiovascular disease FFQ food frequency questionnaire HPFS Health Professionals Follow-up Study HR hazard ratio LDL low-density lipoprotein MUFA monounsaturated fatty acids MUFA-As monounsaturated fatty acids from animal sources MUFA-Ps monounsaturated fatty acids from plant sources NHS Nurses’ Health Study PUFA polyunsaturated fatty acids SFA saturated fatty acids

Acknowledgments

We thank the participants and staff of the Nurses’ Health Study and Health Professionals Follow-up Study for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data. F.B. Hu and Q. Sun designed the study. M. Guasch-Ferré and G. Zong analyzed data. M. Guasch-Ferré and G. Zong wrote the first draft of the article. All authors contributed to the interpretation of data, critical revision of the article, and had final approval of the submitted and published version. M. Guasch-Ferré, G. Zong and Q. Sun 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.

Sources of Funding This study was supported by research grants UM1 CA186107, P01 CA87969, U01 CA167552, P30 DK046200, HL034594, HL088521, HL35464, and HL60712 from the National Institutes of Health. M. Guasch-Ferré is supported by American Diabetes Association grant No. 1-18-PMF-029. G. Zong is supported by the National Key Research and Development Program of China (Project No. 2018YFC604404) and the Key deployment project of Chinese Academy of Sciences (ZDBS-SSW-DQC-01). G. Zong is supported by 100 Talents Program of The Chinese Academy of Sciences.

Disclosures G. Zong is supported by a postdoctoral fellowship funded by Unilever R&D. Peter L. Zock and A.J. Wanders are employees of Unilever Research and Development. Unilever is a producer of food consumer products. F.B. Hu has received research support from California Walnut Commission and honoraria for lectures from Metagenics and Standard Process and honoraria from Diet Quality Photo Navigation, outside the submitted work. Q. Sun reports receiving ad hoc consulting fees from Emavant Solutions GmbH. The other authors report no conflicts.

Footnotes