Intake of glucosinolates and risk of coronary heart disease in three large prospective cohorts of US men and women

Introduction

The American Heart Association guidelines underscore the importance of increasing consumption of vegetables for the prevention of heart disease and other chronic conditions, and a variety of vegetables in a healthy diet has been recently emphasized by the US Department of Agriculture Dietary Guidelines for Americans as well.^1,2 Growing evidence indicates that specific types of vegetables may have distinct effects on the risk for coronary heart disease (CHD), which may be due to the heterogeneous composition of nutrients and other constituents in vegetables.^3,4

Glucosinolates are a class of secondary plant metabolites that are particularly rich in cruciferous vegetables.^5,6 Dietary glucosinolates can be hydrolyzed to biologically active compounds, such as isothiocyanates (ITCs), which are able to modulate cellular redox status and protect against carcinogenesis in animal experiements.^6,7 Emerging evidence from experimental studies has shown that glucosinolate metabolites can reduce oxidative stress, inflammation, endothelial dysfunction, and cardiomyocyte death,^8–10 indicating that these compounds may also have beneficial effects on the cardiovascular system. Despite the evidence from basic science research, human data regarding glucosinolates intake and CHD risk are limited. A couple of epidemiologic studies investigated cruciferous vegetable intake in relation to risk of CHD, and mixed results were observed.^11,12 Furthermore, existing evidence from relatively small clinical trials regarding the effects of glucosinolates or glucosinolate-rich foods on the development of coronary intermediate endpoints remains limited and inconclusive.^13,14

In the current investigation, we aimed to evaluate the hypothesis that higher glucosinolate intake is associated with lower risk of CHD. To test this hypothesis, we prospectively examined dietary glucosinolate intake, as well as major dietary glucosinolate sources, in relation to the risk of CHD.

Subjects and methods

Study population

Participants in this analysis were US men and women from three prospective cohort studies: Nurses’ Health Study (NHS; n=121,700 female registered nurses enrolled in 1976), NHSII (n=116,686 younger female registered nurses enrolled in 1989), and Health Professionals Follow-Up Study (HPFS; n=51,529 male health professionals enrolled in 1986). Detailed descriptions of the cohorts are provided elsewhere.¹⁵ For this analysis, we excluded participants who had diagnoses of cardiovascular disease (CVD) or cancer at baseline, left 70 or more items blank on the food frequency questionnaire (FFQ), reported implausible energy intake (<3,347 or >17,573 kJ/day for men and <2,510 or >14,644 kJ/day for women), did not complete the baseline FFQ or questions of cruciferous vegetables intake, or who only returned the baseline questionnaire. After exclusions, a total of 210,574 participants (74,241 in NHS, 94,163 in NHSII, and 42,170 in HPFS) were included in the current analysis. The study protocol was approved by the institutional review boards of Brigham and Women’s Hospital and Harvard T.H. Chan School of Public Health. The completion of the self-administered questionnaire was considered to imply written informed consent.

Assessment of glucosinolate intake

In each cohort, the intake of glucosinolates was assessed using validated FFQs every 2–4 years. The FFQs inquired about the consumption of selected foods (with a prespecified serving size) during the past year with nine categories of intake frequency. The Harvard University Food Composition Database was primarily used to calculate the nutrient values, complemented by published data.¹⁶ Intake of individual glucosinolates was calculated by multiplying the glucosinolate levels in a prespecified portion size with the consumption frequency for each contributing food item and then summing the intake levels across all contributing food items. Intake of total and subgroups of glucosinolates was derived by summing up individual glucosinolates in each category. Glucosinolate intakes were energy-adjusted using the residual method. Reasonable validity and reproducibility of the assessments of food sources of glucosinolates, including broccoli, cabbage, and Brussels sprouts, have been demonstrated in validation studies.^17–19

Assessment of covariates

In all three cohorts, information on age, body weight, medical history, smoking status, physical activity, parental history of myocardial infarction (MI) before age 65 years, medical history, menopausal status and use of hormone therapy (women only), and medication use was collected and updated in biennial validated questionnaires. Alcohol intake was assessed and updated by validated FFQs. Detailed descriptions on the validity and reproducibility of these assessments have been published elsewhere.^20–22 We calculated an Alternative Healthy Eating Index (AHEI) score to quantify the overall diet quality of the participants.²³ The AHEI score summarizes the intake of 11 foods or nutrients that are most predictive of chronic diseases: vegetables, fruits, whole grains, nuts and legumes, long-chain n-3 fats, polyunsaturated fats, sugar-sweetened beverages and fruit juice, red and processed meat, trans fat, sodium, and alcohol. Individual food/nutrient items were scored from 0 (worst) to 10 (best) based on prespecified criteria,²⁴ with a higher score received for higher intake of healthy foods/nutrients (i.e., vegetables, fruits, whole grains, nuts and legumes, long-chain n-3 fats, polyunsaturated fats), lower intake of less healthy components (i.e., sugar-sweetened beverages and fruit juice, red and processed meat, trans fat, and sodium), or moderate intake of alcohol. A total AHEI score has the possible range from 0 (lowest quality) to 110 (highest quality). For the current analysis, we excluded cruciferous vegetables when calculating the AHEI score.

Ascertainment of endpoint

The primary endpoints for this study were incident CHD (defined as nonfatal MI and fatal CHD). Participants who reported a new diagnosis of MI on a biennial follow-up questionnaire were asked for permission to review their medical records. Medical records were reviewed by the study physicians blinded to the exposure status of the patients. Nonfatal CHD cases were confirmed according to the World Health Organization criteria, which require typical symptoms plus either diagnostic electrocardiographic changes or elevated cardiac enzyme concentrations.²⁵ Fatal CHD was identified by reports from next of kin, postal authorities, or by searching the National Death Index. Fatal CHD was confirmed through reviewing death certificates, hospital records, or autopsy reports if CHD was listed as the cause of death and if evidence of previous CHD was available from medical records. When CHD was listed as the underlying cause on the death certificate but no prior knowledge of CHD was indicated and medical records concerning the death were unavailable, we designated such cases as probable fatal CHD cases.²⁶ Because the exclusion of probable CHD cases did not alter the results, we included both confirmed and probable cases in our study to maximize statistical power.

Statistical analysis

We calculated person-years of follow-up from the return date of the baseline questionnaire to the date of CHD diagnosis, death, or the end of follow-up (NHS: 30 June, 2012; NHSII: 30 June, 2013; and HPFS: 31 January, 2012), whichever came first. To better represent long-term habitual intake and to reduce random within-person variation, we used the cumulative average of food intakes from all FFQs from baseline through the end of follow-up.²⁷ We stopped updating diet after participants reported a diagnosis of angina, coronary artery bypass graft, diabetes, or cancer, because of possible changes of usual diet after occurrence of these conditions. The hazard ratios (HRs) and 95% CIs of incident CHD were estimated for glucosinolate intake by using time-dependent Cox proportional hazards regression after pooling data from three cohorts. The analysis was stratified jointly by age (years) and calendar year, and adjusted for ethnicity (Caucasian, African American, Asian, and other ethnicity), body mass index (BMI, <23.0, 23.0–24.9, 25.0–29.9, 30.0–34.9, ≥35 kg/m², or missing), smoking status (never, former, current [1–14, 15–24, or ≥25 cigarettes/day], or missing), alcohol intake (0, 0.1–4.9, 5.0–14.9, and ≥15.0 g/day for women; 0, 0.1–4.9, 5.0–29.9, and ≥30.0 g/day for men; or missing), physical activity (<3.0, 3.0–8.9, 9.0–17.9, 18.0–26.9, ≥27.0 metabolic equivalents of task-hours/week, or missing), menopausal status and postmenopausal hormone use (premenopause, postmenopause [never, former, or current hormone use], or missing), oral contraceptive use (yes, no, or missing, NHSII only), family history of heart disease (yes/no), multivitamin use (yes/no), hypertension (yes/no), hypercholesterolemia (yes/no), total energy intake (kcal/day), and modified AHEI score (quintiles). A test for linear trend was conducted by assigning the median value to each category and modeling this value as a continuous variable. We used restricted cubic spline regressions with four knots to examine the dose–response relationships between glucosinolate intake and the risk of CHD. We evaluated the potential effect modification by race, age, BMI, the modified AHEI score, physical activity, smoking status, and alcohol consumption using the likelihood ratio test by comparing models with main effects and interaction terms with models containing the main effects only. We also examined the associations of major glucosinolate subgroups and individual glucosinolates, separately, on the risk of CHD. To test the robustness of our findings, we conducted four sensitivity analyses: 1) adjusting for individual dietary variables instead of the modified AHEI score; 2) using only baseline dietary variables; 3) continuing updating dietary information after participant reported a diagnosis of cancer or diabetes; and 4) placing a 4- or 8-year lag between the assessments of glucosinolate intake and CHD ascertainment. Statistical analyses were performed using SAS statistical software, version 9.3 (SAS Institute Inc.). All P-values presented were two-sided, with statistical significance defined as P<0.05.

Results

During 4,824,001 person-years of follow-up, we documented 8,010 incident cases of nonfatal MI or fatal CHD. Stratified incidence density of CHD according to total glucosinolate intake by various characteristics of participants is shown in Table S1. Table 1 presents the age-standardized baseline characteristics of the study population by glucosinolate intake. In all three cohorts, participants with higher glucosinolate intake were older and more physically active and had a higher modified AHEI score. They consumed less red meat and more fruits and vegetables. Higher glucosinolate intake was associated with lower trans fat intake and a higher polyunsaturated fat-to-saturated fat ratio.

Table 1 Baseline characteristics of the participants according to total glucosinolate intake in the NHS, NHSII, and HPFSa Notes: aValues were standardized to the age distribution of the study population. bData are mean unless otherwise indicated. cValues were not age adjusted. Abbreviations: AHEI, Alternative Healthy Eating Index; BMI, body mass index; HPFS, the Health Professionals Follow-Up Study; MET, metabolic equivalents of task; NHS, Nurses’ Health Study.

In the three cohorts, higher intake of total glucosinolates was consistently associated with a higher risk of CHD after adjustment for demographic, lifestyle, and dietary risk factors (Table 2). In pooled multivariable analyses, an increased intake of total glucosinolate was significantly associated with a slightly higher CHD risk. The multivariable-adjusted HR (95% CI) of CHD comparing participants in the highest vs lowest quintiles was 1.09 (95% CI: 1.01, 1.17; P_trend<0.001).

Table 2 Hazard ratio (95% CI) of coronary heart disease according to quintiles of total glucosinolate intake Notes: aEstimates are calculated in Cox proportional hazards models. Model 1, adjusted for age (years). bModel 2, further adjusted for ethnicity (Caucasian, African American, Asian, and other ethnicity), family history of myocardial infarction (yes/no), hypertension (yes/no), hypercholesterolemia (yes/no), smoking status (never, former, current [1–14, 15–24, or ≥25 cigarettes/day], or missing), alcohol intake (0, 0.1–4.9, 5.0–14.9, and ≥15.0 g/day for women, 0, 0.1–4.9, 5.0–29.9, and ≥30.0 g/day for men, or missing), physical activity (<3.0, 3.0–8.9, 9.0–17.9, 18.0–26.9, ≥27.0 MET-hours/week, or missing), menopausal status and postmenopausal hormone use (premenopause, postmenopause [never, former, or current hormone use], or missing, for women), oral contraceptive use (yes, no, or missing, for NHSII), multivitamin use (yes/no), BMI (<23, 23–24.9, 25–29.9, 30–34.9, ≥35 kg/m2, or missing), and total energy intake (kcal/day) based on model 1. cModel 3, further adjusted for modified Alternative Healthy Eating Index score (in quintiles), based on model 2. dResults from each cohort were pooled using fixed-effects model. Abbreviations: BMI, body mass index; HPFS, Health Professionals Follow-up Study; MET, metabolic equivalents of task; NHS, Nurses’ Health Study.

Spline regression analyses showed that the association between total glucosinolate intake and risk of CHD was likely to be linear (P_linearity<0.001 and P_curvature=0.70; Figure S1). For each SD increment of glucosinolate intake, the risk of CHD increased by 3% (95% CI: 1%, 5%; P=0.01).

We did not detect statistically significant interactions of total glucosinolate intake with ethnicity, age, BMI, the modified AHEI score, physical activity, smoking status, or alcohol consumption in relation to CHD risk (all P_interaction >0.10; Table S2). The association of glucosinolate intake with CHD appeared to be stronger among white participants (HR: 1.09; 95% CI: 1.01, 1.17; P_trend=0.001) than among non-white participants (HR: 0.95; 95% CI: 0.62, 1.46; P_trend=0.34), comparing extreme quintiles.

In the sensitivity analyses, adjustment for other major dietary factors instead of the modified AHEI score slightly attenuated the HR (95% CI) per SD increment of glucosinolate intake for CHD to 1.02 (0.99, 1.04) (P=0.15). When we continued updating dietary variables throughout follow-up even after a diagnosis of cancer or diabetes, the associations did not change materially (HR per SD change: 1.03; 95% CI: 1.00, 1.05; P=0.02). Use of baseline glucosinolate intake instead of the cumulative average yielded similar results (HR per SD change: 1.03; 95% CI: 1.00, 1.05; P=0.01). Placing a 4-year (HR per SD change: 1.03; 95% CI: 1.01, 1.05; P=0.01) or an 8-year lag (HR per SD change: 1.03; 95% CI: 1.00, 1.05; P=0.03) also did not change the associations of CHD (Table S3).

Trends toward increased CHD risk were observed for all three glucosinolate subgroups. Multivariable HRs (95% CIs) for CHD comparing the highest vs lowest quintiles of glucosinolates were 1.10 (1.02, 1.18), 1.04 (0.97, 1.12), and 1.16 (1.08, 1.24) for aliphatic glucosinolate, indolylglucosinolate, and aromatic glucosinolate, respectively (Table 3). Each SD increment of these glucosinolate subgroup intakes was associated with a 3%, 2%, and 3% greater risk of CHD, respectively. In the analyses of individual glucosinolates, a positive trend was also observed for glucobrassicin, sinigrin, and glucoiberin, although only the associations for sinigrin and glucoiberin achieved statistical significance (Table S4).

Table 3 HR (95% CI) of coronary heart disease according to quintiles of glucosinolate subgroupsa Notes: aEstimates are calculated in Cox proportional hazards models, adjusted for age (years), ethnicity (Caucasian, African American, Asian, and other ethnicity), family history of myocardial infarction (yes/no), hypertension (yes/no), hypercholesterolemia (yes/no), smoking status (never, former, current [1–14, 15–24, or ≥25 cigarettes/day], or missing), alcohol intake (0, 0.1–4.9, 5.0–14.9, and ≥15.0 g/day for women, 0, 0.1–4.9, 5.0–29.9, and ≥30.0 g/day for men, or missing), physical activity (<3.0, 3.0–8.9, 9.0–17.9, 18.0–26.9, ≥27.0 MET-hours/week, or missing), menopausal status and postmenopausal hormone use (premenopause, postmenopause [never, former, or current hormone use], or missing, for women), oral contraceptive use (yes, no, or missing, for NHSII), multivitamin use (yes/no), BMI (<23, 23–24.9, 25–29.9, 30–34.9, ≥35 kg/m2, or missing), and total energy intake (kcal/day), and the modified Alternate Healthy Eating Index score (quintiles). bResults from each cohort were pooled using fixed-effects model. Abbreviations: BMI, body mass index; HPFS, Health Professionals Follow-Up Study; HR, hazard ratio; MET, metabolic equivalents of task; NHS, Nurses’ Health Study.

Higher cruciferous vegetable consumption was non-significantly associated with an increased risk of CHD. Compared with less than one serving cruciferous vegetable/week, the multivariable-adjusted HR was 1.04 (95% CI: 0.95, 1.14) for more than one serving/day of total cruciferous vegetables (Table 4). For individual cruciferous vegetables, significant associations were observed for Brussels sprouts (HR: 1.16; 95% CI: 1.08, 1.26; P<0.001) and cabbage (HR: 1.09; 95% CI: 1.02, 1.17; P=0.009; Table S5). Each two servings/week increment of Brussels sprouts and cabbage intake was associated with a 13% (95% CI: 5%, 21%) and 2% (95% CI: 0%, 3%) higher risk of CHD, respectively.

Table 4 HR (95% CI) of coronary heart disease according to consumption of total cruciferous vegetablesa Notes: aTotal cruciferous vegetables included broccoli, cabbage, cauliflower, Brussels sprouts, kale, mustard, and chard greens. bEstimates are calculated in Cox proportional hazards models. Adjusted for age (years), ethnicity (Caucasian, African American, Asian, and other ethnicity), family history of myocardial infarction(yes/no), smoking status (never, former, current [1–14, 15–24, or ≥25 cigarettes/day], or missing), alcohol intake (0, 0.1–4.9, 5.0–14.9, and >15.0 g/day in women, 0, 0.1–4.9, 5.0–29.9, and >30.0 g/day in men, or missing), hypertension (yes/no), hypercholesterolemia (yes/no), (never, former, current [1–14, 15–24, or ≥25 cigarettes/day], or missing), alcohol intake (0, 0.1–4.9, 5.0–14.9, and ≥15.0 g/day for women, 0, 0.1–4.9, 5.0–29.9, and ≥30.0 g/day for men, or missing), physical activity (<3.0, 3.0–8.9, 9.0–17.9, 18.0–26.9, ≥27.0 MET-hours/week, or missing), menopausal status and postmenopausal hormone use (premenopause, postmenopause [never, former, or current hormone use], or missing, for women), oral contraceptive use (yes, no, or missing, for NHSII), multivitamin use (yes/no), BMI (<23, 23–24.9, 25–29.9, 30–34.9, ≥35 kg/m2, or missing), and total energy intake (kcal/day), and the modified Alternate Healthy Eating Index score (quintiles). cResults from each cohort were pooled using fixed-effects model. Abbreviations: BMI, body mass index; HPFS, Health Professionals Follow-Up Study; HR, hazard ratio; MET, metabolic equivalents of task; NHS, Nurses’ Health Study.

Discussion

In three cohorts of US men and women, we found weak to modest positive associations between intake of total and individual glucosinolates and incident CHD. This association was independent of established dietary and non-dietary CVD risk factors, and largely persisted among participants with various risk profiles. Increased consumption of food sources of glucosinolates, particularly Brussels sprouts and cabbage, was also associated with a higher risk of CHD.

The glucosinolates–myrosinase system is known as “mustard oil bomb” and used by Brassicales as a defense system against the aggressions of pathogens. Upon rupture of cellular membranes, active myrosinase comes in contact with glucosinolates, hydrolyzes the glucosinolates, and subsequently produces highly reactive metabolites that serve as a defense for the plants.²⁸ Mastication of fresh or lightly cooked Brassica vegetables with active myrosinase and metabolism by human gut microbiota when the myrosinase is inactivated are the two primary sources of exposure to ITCs and other metabolites.²⁹ Abundant evidence from experimental studies has illustrated that ITCs and other metabolites of glucosinolates may exhibit anticarcinogenic, anti-inflammatory, and antioxidant effects.^30,31 These bioactive compounds have been shown to induce Phase II and antioxidant gene expression through activation of nuclear factor erythroid-2-related factor 2-regulated transcription.³² ITCs could also modulate cytokine production and inhibit the lipopolysaccharide-stimulated inflammatory response in human monocytes.³³ These lines of evidence constitute the knowledge base for us to hypothesize that glucosinolate intake is associated with a lower CHD risk. However, the findings of the current investigation are contradictory to our initial hypothesis.

Indeed, emerging evidence suggests that the health effects of ITCs and other glucosinolates can be complex. Administration of glucosinolates and their degradation products induces the activities of certain Phase I enzymes with an influence on the metabolism of xenobiotics and on the generation of reactive oxygen species (ROS) in vitro.³⁴ Cumulatively, elevated ROS production may accelerate decline in cardiomyocyte function and progression to CHD.^35,36 Such pro-oxidant activity of ITCs is one of the mechanisms underlying ITCs’ potentially anticarcinogenic role, because the variation of the intracellular redox status triggers apoptosis and other defensive mechanisms.³⁷ ITCs may also undergo oxidative desulfuration to produce the corresponding isocyanate by cytochrome P450 enzymes.³⁸ Moreover, glucosinolate hydrolysis products could rapidly accumulate in the cytoplasm of the cells, bind to glutathione and other cellular thiols, and react with the SH groups, which leads to intracellular glutathione depletion and subsequent ROS generation.^39,40 In addition to depleting glutathione and other thiols, the breakdown products of glucosinolates can also enhance the cellular concentration of ROS and oxidative stress by inducing rapid loss of transmembrane potential, mitochondrial damage, and loss of cytochrome c.^41,42 Glutathione depletion was also found to significantly accelerate ITC-triggered apoptosis through a mitochondrial redox-sensitive mechanism.^42,43 As other products of glucosinolate hydrolysis, nitriles also have a potential to induce cytotoxicity and genotoxicity.⁴⁴

The potentially complicated biological effects of glucosinolates and their metabolites are also suggested by mixed evidence from human trials.^13,14,45,46 In a 12-week intervention study among participants with elevated risk of developing CVD, supplementation with 400 g high-glucosinolate broccoli per week led to significant reduction of plasma low-density lipoprotein-C level.¹³ In a randomized double-blind clinical trial among diabetes patients, Mirmiran et al observed beneficial effects of 10 g/day broccoli sprouts powder on serum interleukin-6 and C-reactive protein levels, but not on tumor necrosis factor α.⁴⁵ In contrast, among individuals with moderate risk for the development of CVD, supplementation with broccoli did not exert significant changes in CVD risk markers.⁴⁶ In patients with established hypertension, a 4-week treatment with dried broccoli sprouts did not exert any significant effect on serum cholesterol levels and endothelial function measured by flow-mediated dilation.¹⁴

To our knowledge, the current study is the first prospective investigation that assessed the relationship between dietary glucosinolates and CHD risk. Of note, previous studies that focused on cruciferous vegetable intake in relation to CHD risk overall demonstrated no associations between the consumption of these vegetables and CHD risk.^11,12,47 In the prospective Danish Diet, Cancer and Health cohort study, increasing consumption of total vegetables and cruciferous vegetables was not significantly associated with the risk of acute coronary syndrome after multivariable adjustment.¹¹ Similarly, Genkinger et al also found no inverse association between dietary intake of cruciferous vegetables and CVD mortality in a community-based prospective cohort study.⁴⁷

The strengths of this study include the prospective design, the large sample size, long follow-up durations, detailed and repeated dietary and lifestyle assessments, and high rates of follow-up. There are several potential limitations that also need to be considered. First, some measurement errors and misclassification in the assessment of food consumption are inevitable, although the FFQs used in these cohorts have been validated against multiple diet records and demonstrated reasonable reproducibility and validity. Because of the prospective study design, misclassification of glucosinolate intake was unlikely to be correlated with study outcome ascertainment and, therefore, more likely to attenuate associations toward the null. Second, although we controlled for a large number of potential dietary and lifestyle factors in multivariate models, it is possible that residual and unmeasured confounding may still remain. Third, several factors, such as cooking methods, storage time, and temperature, can determine the activities of myrosinase and subsequently influence the bioavailability of glucosinolates and the production of breakdown products. Although the urinary excretion of ITCs was correlated significantly with cruciferous vegetable or glucosinolate consumption, potentially large between-individual variability in the production of ITCs upon the intake of the same food sources may render our observations less extrapolatable to ITCs.^48–50 Future studies should examine circulating levels of ITCs in relation to chronic disease risk to provide evidence complementary to research on glucosinolate intake. Fourth, a plant-based diet may contain a variety of secondary plant metabolites, including glucosinolates, polyphenols, and other phytonutrients. It is very likely that these phytochemicals may have additive or synergistic effects on modulating human health beyond the effects of a specific group of phytochemicals, although the current analysis was unable to explore this possibility which would require a larger study population for detecting interactions between dietary components. Finally, participants in our study are mostly health care professionals of European ancestry. Our ethnicity-stratified analysis implied that the positive association between glucosinolate intake and CHD risk was primarily observed in white participants, whereas the association was entirely absent in minorities. It is likely that the effects of glucosinolate intake may be modulated by variabilities in genes operating in the complex biological pathways of glucosinolates and their products. For example, the association between low cruciferous vegetable intake and breast cancer risk appeared to be somewhat more pronounced among Chinese women with the Val/Val genotype in GSTP1 gene,⁵¹ which encodes glutathione S-transferases, enzymes involved in the biological effects of ITCs.⁵² Despite this plausibility, we cannot exclude the role of chance in this finding of interaction by ethnicity. Nonetheless, caution must be taken when extrapolating the current findings to other ethnic groups.

In conclusion, our data do not support the hypothesis that a higher glucosinolate intake decreases the risk of CHD. In contrast, our findings suggest that a higher glucosinolate intake may be associated with a small increment of CHD risk among US men and women. Given the observational nature of the current analysis and the complex metabolism and biology of glucosinolates, future studies are warranted to replicate these findings and elucidate the mechanistic pathways linking glucosinolates, ITCs, and cardiovascular health.

Key points

Question: What is the association of dietary glucosinolate intake with incident coronary heart disease (CHD) in US adults?

Findings: In three cohorts of US men and women, the intake of total and subtypes of glucosinolates, as well as cruciferous vegetables, was associated with a slightly increased risk of developing CHD. These associations were independent of established and potential confounders of CHD and persistent in various sensitivity analyses.

Meaning: Our findings highlight the potentially complicated biological effects of glucosinolate intake on human health.

Acknowledgments

This work was supported by the National Institutes of Health (CA186107, HL034594, CA176726, CA167552, and HL35464). The study sponsor had no role in the design and conduct of the study; the collection, management, analysis, or interpretation of the data; the preparation, review, or approval of the paper; or the decision to submit the paper for publication.

Author contributions

QS and LM participated in project conception and development of research methods; QS, FBH, WCW, EBR, KMR, EBR, and JEM obtained funding and provided oversight; LM, GL, GZ, and QS analyzed data and performed the analysis; LM drafted the paper. All authors contributed toward data analysis, drafting and revising the paper and agree to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this work.

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Supplementary materials

Table S1 Stratified incidence density of coronary heart disease according to total glucosinolate intake by various characteristics of participants Abbreviations: AHEI, Alternative Healthy Eating Index; BMI, body mass index; MET, metabolic equivalents of task.

Table S2 Stratified hazard ratio (95% CI) of coronary heart disease according to total glucosinolate intake by various characteristics of participantsa Notes: aEstimates are calculated in Cox proportional hazards models, adjusted for age (years), ethnicity (Caucasian, African American, Asian, and other ethnicity), family history of myocardial infarction (yes/no), hypertension (yes/no), hypercholesterolemia (yes/no), smoking status (never, former, current [1–14, 15–24, or ≥25 cigarettes/day], or missing), alcohol intake (0, 0.1–4.9, 5.0–14.9, and ≥15.0 g/day for women, 0, 0.1–4.9, 5.0–29.9, and ≥30.0 g/day for men, or missing), physical activity (<3.0, 3.0–8.9, 9.0–17.9, 18.0–26.9, ≥27.0 MET-hours/week, or missing), menopausal status and postmenopausal hormone use (premenopause, postmenopause [never, former, or current hormone use], or missing, for women), oral contraceptive use (yes, no, or missing, for Nurses’ Health Study II), multivitamin use (yes/no), BMI (<23, 23–24.9, 25–29.9, 30–34.9, ≥35 kg/m2, or missing), and total energy intake (kcal/day), and the modified AHEI score (quintiles). bPinteraction was calculated using the likelihood ratio test. Abbreviations: AHEI, Alternative Healthy Eating Index; BMI, body mass index; MET, metabolic equivalents of task.

Table S3 Sensitivity analyses for the association between total glucosinolate intake and coronary heart disease in three cohortsa Notes: aEstimates are calculated in Cox proportional hazards models, adjusted for age (years), ethnicity (Caucasian, African American, Asian, and other ethnicity), family history of myocardial infarction (yes/no), hypertension (yes/no), hypercholesterolemia (yes/no), smoking status (never, former, current [1–14, 15–24, or ≥25 cigarettes/day], or missing), alcohol intake (0, 0.1–4.9, 5.0–14.9, and ≥15.0 g/day for women, 0, 0.1–4.9, 5.0–29.9, and ≥30.0 g/day for men, or missing), physical activity (<3.0, 3.0–8.9, 9.0–17.9, 18.0–26.9, ≥27.0 MET-hours/week, or missing), menopausal status and postmenopausal hormone use (premenopause, postmenopause [never, former, or current hormone use], or missing, for women), oral contraceptive use (yes, no, or missing, for Nurses’ Health Study II), multivitamin use (yes/no), BMI (<23, 23–24.9, 25–29.9, 30–34.9, ≥35 kg/m2, or missing), and total energy intake (kcal/day), and the modified AHEI score (quintiles). Abbreviations: AHEI, Alternative Healthy Eating Index; BMI, body mass index; MET, metabolic equivalents of task.

Table S4 HR (95% CI) of coronary heart disease according to quintiles of main individual glucosinolatesa Notes: aEstimates are calculated in Cox proportional hazards models, adjusted for age (years), ethnicity (Caucasian, African American, Asian, and other ethnicity), family history of myocardial infarction (yes/no), hypertension (yes/no), hypercholesterolemia (yes/no), smoking status (never, former, current [1–14, 15–24, or ≥25 cigarettes/day], or missing), alcohol intake (0, 0.1–4.9, 5.0–14.9, and ≥15.0 g/day for women, 0, 0.1–4.9, 5.0–29.9, and ≥30.0 g/day for men, or missing), physical activity (<3.0, 3.0–8.9, 9.0–17.9, 18.0–26.9, ≥27.0 MET-hours/week, or missing), menopausal status and postmenopausal hormone use (premenopause, postmenopause [never, former, or current hormone use], or missing, for women), oral contraceptive use (yes, no, or missing, for NHSII), multivitamin use (yes/no), BMI (<23, 23–24.9, 25–29.9, 30–34.9, ≥35 kg/m2, or missing), and total energy intake (kcal/day), and the modified Alternate Healthy Eating Index score (quintiles). bResults from each cohort were pooled using fixed-effects model. Abbreviations: BMI, body mass index; HPFS, Health Professionals Follow-Up Study; HR, hazard ratio; MET, metabolic equivalents of task; NHS, Nurses’ Health Study.

Table S5 Hazard ratio (95% CI) of coronary heart disease according to consumption levels of individual cruciferous vegetablesa Notes: aEstimates are calculated in Cox proportional hazards models, adjusted for age (years), ethnicity (Caucasian, African American, Asian, and other ethnicity), family history of myocardial infarction (yes/no), hypertension (yes/no), hypercholesterolemia (yes/no), smoking status (never, former, current [1–14, 15–24, or ≥25 cigarettes/day], or missing), alcohol intake (0, 0.1–4.9, 5.0–14.9, and ≥15.0 g/day for women, 0, 0.1–4.9, 5.0–29.9, and ≥30.0 g/day for men, or missing), physical activity (<3.0, 3.0–8.9, 9.0–17.9, 18.0–26.9, ≥27.0 MET-hours/week, or missing), menopausal status and postmenopausal hormone use (premenopause, postmenopause [never, former, or current hormone use], or missing, for women), oral contraceptive use (yes, no, or missing, for NHSII), multivitamin use (yes/no), BMI (<23, 23–24.9, 25–29.9, 30–34.9, ≥35 kg/m2, or missing), and total energy intake (kcal/day), and the modified Alternative Healthy Eating Index score (quintiles). bResults from each cohort were pooled using fixed-effects model. Abbreviations: BMI, body mass index; HPFS, Health Professionals Follow-Up Study; MET, metabolic equivalents of task; NHS, Nurses’ Health Study.

Figure S1 Restricted cubic spline analysis of the association between total glucosinolate intake (mg/day) and coronary heart disease. Notes: Estimates are calculated in Cox proportional hazards models, adjusted for age (years), ethnicity (Caucasian, African American, Asian, and other ethnicity), family history of myocardial infarction (yes/no), hypertension (yes/no), hypercholesterolemia (yes/no), smoking status (never, former, current [1–14, 15–24, or ≥25 cigarettes/day], or missing), alcohol intake (0, 0.1–4.9, 5.0–14.9, and ≥15.0 g/day for women, 0, 0.1–4.9, 5.0–29.9, and ≥30.0 g/day for men, or missing), physical activity (<3.0, 3.0–8.9, 9.0–17.9, 18.0–26.9, ≥27.0 MET-hours/week, or missing), menopausal status and postmenopausal hormone use (premenopause, postmenopause [never, former, or current hormone use], or missing, for women), oral contraceptive use (yes, no, or missing, for Nurses’ Health Study II), multivitamin use (yes/no), BMI (<23, 23–24.9, 25–29.9, 30–34.9, ≥35 kg/m2, or missing) and total energy intake (kcal/day), and the modified Alternative Healthy Eating Index score (quintiles). Solid line is point estimate, and dashed lines are 95% CIs. Abbreviations: BMI, body mass index; MET, metabolic equivalents of task.