(2024) 22:353
Connolly et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03577-8

BMC Medicine

RESEARCH ARTICLE

Open Access

Cruciferous vegetables lower blood pressurein adults with mildly elevated blood pressure
in a randomized, controlled, crossover trial:
the VEgetableS for vaScular hEaLth (VESSEL)
study
Emma L. Connolly1, Alex H. Liu1, Simone Radavelli‑Bagatini1, Armaghan Shafaei2, Mary C. Boyce3, Lisa G. Wood4,
Lyn McCahon1, Henrietta Koch5, Marc Sim1,6, Caroline R. Hill1, Benjamin H. Parmenter1, Nicola P. Bondonno1,7,
Amanda Devine1, Kevin D. Croft5, Richard Mithen8, Seng Khee Gan6,9, Carl J. Schultz6,10, Richard J. Woodman11,
Catherine P. Bondonno1,6, Joshua R. Lewis1,6,12, Jonathan M. Hodgson1,6 and Lauren C. Blekkenhorst1,6*

Abstract
Background Higher cruciferous vegetable intake is associated with lower cardiovascular disease risk in observational
studies. The pathways involved remain uncertain. We aimed to determine whether cruciferous vegetable intake(active) lowers 24-h brachial systolic blood pressure (SBP; primary outcome) compared to root and squash vegetables(control) in Australian adults with mildly elevated BP (SBP 120–160 mmHg inclusive).
Methods In this randomized, controlled, crossover trial, participants completed two 2-week dietary interventions
separated by a 2-week washout. Cruciferous vegetables were compared to root and squash vegetables (~ 300 g/day) consumed with lunch and dinner meals. Participants were blinded to which interventions were the active
and control. Adherence was assessed using food diaries and biomarkers (S-methyl cysteine sulfoxide (SMCSO, active)
and carotenoids (control)). Twenty-four-hour brachial ambulatory SBP and secondary outcomes were assessed preand post each intervention. Differences were tested using linear mixed effects regression.
Results Eighteen participants were recruited (median (IQR) age: 68 (66–70); female: n = 16/18; mean ± SD clinic
SBP: 135.9 ± 10.0 mmHg). For both interventions, 72% participants had 100% adherence (IQR: 96.4–100%). SMCSO
and carotenoids were significantly different between interventions (mean difference active vs. control SMCSO:
22.93 mg/mL, 95%CI 15.62, 30.23, P < 0.0001; carotenoids: − 0.974 mg/mL, 95%CI − 1.525, − 0.423, P = 0.001). Twentyfour-hour brachial SBP was significantly reduced following the active vs. control (mean difference − 2.5 mmHg,
95%CI − 4.2, − 0.9, P = 0.002; active pre: 126.8 ± 12.6 mmHg, post: 124.4 ± 11.8 mmHg; control pre: 125.5 ± 12.1 mmHg,
post: 124.8 ± 13.1 mmHg, n = 17), driven by daytime SBP (mean difference − 3.6 mmHg, 95%CI − 5.4, − 1.7, P < 0.001).
Serum triglycerides were significantly lower following the active vs. control (mean difference − 0.2 mmol/L,
95%CI − 0.4, − 0.0, P = 0.047).

*Correspondence:
Lauren C. Blekkenhorstl.blekkenhorst@ecu.edu.au
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/.

Connolly et al. BMC Medicine(2024) 22:353

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Conclusions Increased intake of cruciferous vegetables resulted in reduced SBP compared to root and squash
vegetables. Future research is needed to determine whether targeted recommendations for increasing cruciferous
vegetable intake benefits population health.
Trial registration Clinical trial registry ACTRN12619001294145. https://​www.​anzctr.​org.​au
Keywords Cruciferous, Brassica, Vegetables, Hypertension, Blood pressure, Older adults, Glucosinolates, Oxidative
stress, Inflammation, Cardiovascular disease, Randomized controlled trial

Background
Increasing vegetable intake is widely recommended toreduce cardiovascular disease (CVD) risk [1–3]. Historically researched for their anti-cancer properties, one
group of vegetables that have been proposed to havesuperior benefits on CVD are cruciferous vegetables (e.g.,arugula, bok choy, broccoli, Brussels sprouts, cabbage,
cauliflower, collard greens, horseradish, kale, radish, turnips, and watercress) [4–6]. Whilst these vegetables arecommonly consumed globally, cruciferous vegetables
typically make up a small proportion of total vegetableintake (5–24%) [7]. Cruciferous vegetable intake wasfound to be inversely associated with CVD risk in a dose–
response meta-analysis of prospective cohort studies [8].
Similar results were observed when objective markers of
cruciferous vegetable intake (i.e., urinary thiocyanate)
were considered [9]. More research is required to establish any causal pathways through which cruciferous vegetables benefit cardiovascular health.
Hypertension is the leading risk factor for CVD with
its prevalence increasing with age [10]. Glucosinolates
are found almost exclusively in cruciferous vegetables
and have been shown to lower blood pressure in animals,
but evidence in humans is limited [4]. These compounds
have been proposed to exhibit cardiovascular health benefits, such as reduced glycemic-related complications,improved endothelial function, and reduced formationand progression of atherosclerotic plaques [4]. Additionally, cruciferous vegetables also contain several othercomponents that likely influence blood pressure, such as
nitrate and vitamin K [11, 12].
Few intervention studies have been conducted inhumans to investigate the effects of cruciferous vegetables on risk factors for CVD, such as elevated blood pressure. As previously described in the published protocol[13], the primary objective of the VEgetableS for vaScular hEaLth (VESSEL) study was to determine if daily
consumption of cruciferous vegetables results in lower
24-h brachial systolic blood pressure (SBP) in middleaged and older adults with mildly elevated blood pressurecompared to root and squash vegetables. Our a priori
hypothesis was that daily consumption of cruciferous
vegetables, in comparison to root and squash vegetables,
would result in a greater reduction in ambulatory bloodpressure. Secondary objectives were to determine if cruciferous vegetables were superior in improving other brachial and arterial ambulatory blood pressures, arterial
stiffness, and circulating biomarkers of oxidative stressand inflammation and other CVD risk factors.

Methods
EthicsThe Edith Cowan University Human Research Ethics
Committee granted ethics approval for the VESSEL Study(2019–00356-BLEKKENHORST) and the trial was registered at www.​anzctr.​org.​au (ACTRN12619001294145).
Written informed consent was obtained from all participants. Procedures were followed in accordance withinstitutional guidelines.
Study design

The VESSEL study was a randomized, controlled, crossover trial with two 2-week dietary intervention periods,
as previously described in the protocol [13]. Intervention periods were separated by a 2-week washout (Fig. 1).
The study was conducted at the Royal Perth Hospital
Research Foundation, Perth, Australia.
Participants

Six newspaper advertisements were placed at varying
intervals between August 2019 and March 2021 to recruit
men and women aged 50 to 75 years with mild to moderately elevated blood pressure (SBP 120–160 mmHg,
inclusive, and diastolic (DBP) < 100 mmHg) from thegeneral population of Perth, Australia. Detailed inclusion and exclusion criteria are shown in Additional File 1:
Table S1 [2, 14].
Screening blood pressure was assessed using a CARESCAPE Dinamap v100 Vital Signs Monitor (GE Healthcare, Buckinghamshire, UK). After resting in a supine
position for 5 min, five blood pressure and heart ratemeasurements were taken at 1-min intervals. The firstmeasurement was excluded, and the next four readings
were averaged to calculate mean resting blood pressure.
Randomization

Using computer-generated random numbers, eligible participants were randomly assigned to one of twoConnolly et al. BMC Medicine(2024) 22:353

Page 3 of 15

Fig. 1 Overview of study designintervention sequence orders (1:1 allocation). The intervention sequence orders were placed in opaque sealed
envelopes by a study investigator and opened in consecutive order as participants were enrolled in the study.
Dietary intervention

Participants completed two 2-week dietary interventions
in random order, as follows:
1. Active: four serves (~ 300 g/day) of cruciferous vegetables (broccoli, kale, cauliflower, and cabbage) consumed as two soups: one at lunch and one at dinner(~ 600 mL soup/day, ~ 600 kJ/day).
2. Control: four serves (~ 300 g/day) of root and squash
vegetables (potato, sweet potato, carrot, and pumpkin) consumed as two soups: one at lunch and one atdinner (~ 600 mL soup/day, ~ 600 kJ/day).
All soups were prepared at Edith Cowan University,
Joondalup Campus, Perth, Australia, using standardized recipes, as detailed in the protocol [13]. Vegetables
were chopped and boiled prior to blending into a soup
and were immediately frozen at − 18 °C for storage. Soup
was generally consumed within the week, but could be
stored for up to 6 weeks at − 18 °C. The active soup contained 40% broccoli, 25% cauliflower, 25% cabbage, and
10% kale, and the control soup contained 40% potato,
30% pumpkin, 20% carrot, and 10% sweet potato. Rootand squash vegetables were chosen as the control intervention as these vegetables are commonly consumed inAustralia [15]. The macronutrient content of the soups
was closely matched, as previously reported in the protocol [13]. Participants were instructed not to add salt totheir soups and were blinded to which interventions were
the active and control.
Standard lunch and dinner meals were provided
throughout both interventions to minimize background
diet variation amongst participants. These meals provided approximately 1–4 serves (75–450 g) of vegetables
per day, excluding the additional vegetable serves provided in the soups. Cruciferous vegetables were avoided
when selecting these meals by checking the listed ingredients. Participants were able to select meals based on
personal preference and meal orders were duplicated foreach intervention to limit variation in the diet between
intervention periods. All meals for each participant wereordered and stored at − 18 °C at the beginning of thestudy period to mitigate potential disruptions to stock
availability due to the COVID-19 pandemic.
Participants were instructed to consume their usual
breakfast foods and snacks but were asked to avoid
consuming any snacks in the 2-h window after soup
was consumed. Participants were required to complete food diaries, including the timing of all meals
and snacks. Participant food diaries were checked by adietitian (ELC) and Foodworks software using the AusBrands 2019 and AusFoods 2019 databases was used for
dietary assessment (FoodWorks 10 Professional, v10.0.
Brisbane: Xyris Pty Ltd, 2019). Intervention adherence
was assessed using self-reported (i.e., food diaries) andobjective biomarkers of intake (i.e., serum carotenoids
and urinary and plasma S-methyl cysteine sulfoxide(SMCSO)). Percent self-reported adherence was calculated by dividing the number of soups consumed by
the number of total soups that should have been consumed (28 soups per intervention) and multiplyingConnolly et al. BMC Medicine(2024) 22:353

that number by 100. Urinary and plasma SMCSO wereused as objective markers of adherence to the active
intervention as SMCSO is found in higher concentrations in cruciferous vegetables, but not root and squash
vegetables [16]. Conversely, root and squash vegetables contain higher concentrations of carotenoids thancruciferous vegetables; therefore, serum carotenoids
were measured as an objective marker of control intervention adherence [17]. Please see “Biochemical analyses” for methodology used for objective measures ofadherence.
Baseline dietary assessment

To assess baseline habitual dietary intake, participants
completed the Dietary Questionnaire for Epidemiological Studies (DQES v3.2), a validated food frequency
questionnaire, under the supervision of a trained dietitian or nutritionist (ELC, CRH, BHP) [18]. A dietitian(ELC) looked at outliers for implausible energy intakeswith respect to factors such as BMI, sex, and age andreviewed for unrealistic energy intakes to support bodyfunction and lifestyle. All vegetables (including legumes and potatoes cooked without fat) were includedin the analysis of baseline total vegetable intake (g/day).
Intake of Asian greens (e.g., bok choy), coleslaw, Brussels sprouts, cauliflower, and broccoli (i.e., available
cruciferous vegetables in the DQES v3.2) were combined to create the cruciferous vegetable (g/day) variable for baseline analysis of cruciferous vegetable intake.
Outcome measurements
Ambulatory blood pressure and arterial stiffness

Trained study investigators (AHL, ELC) fitted participants with the Oscar 2 Ambulatory Blood Pressure
Monitor (ABPM) system (SunTech Medical Inc., Morrisville, NC, USA) to assess 24-h ambulatory bloodpressure at pre- and post-intervention visits (i.e., beginning and end of each 2-week period), as previously
described [13]. The ABPM system measured brachial
and aortic blood pressure every 20 min during daytime hours (6 am until 10 pm) and every 30 min during nighttime hours (10 pm until 6 am). Participants
used the same ABPM for all visits. Participants were
instructed to avoid vigorous activity whilst wearing
the monitor and to continue with regular daily activities. Participants were excluded if they were missing
more than 20% of measurements or if there were morethan four hours with no blood pressure measurements.
Ambulatory arterial stiffness was assessed using theaortic augmentation index (AIx, %) [19]. The ambulatory AIx data was obtained using the SphygmoCor

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component of the Oscar 2 ABPM at pre- and postintervention visits.
Anthropometry

Body composition (weight, height, body mass index(BMI), waist and hip circumference, body fat mass) was
assessed according to standard protocols at each pre- andpost-intervention visit [13].
Lifestyle

Online self-administered questionnaires were used toassess lifestyle factors known to influence blood pressure.
These factors included physical activity (assessed using
the Community Healthy Activities Model Program for
Seniors (CHAMPS) [20]) and stress (assessed using thePerceived Stress Scale (PSS) [14]). CHAMPS and PSSquestionnaires were completed pre- and post-intervention to assess any changes in physical activity and stresslevels, as these lifestyle factors have been shown to influence blood pressure [21].
Biochemical analyses

Blood and urine samples were collected at pre- and postintervention visits. Fasting blood samples were collectedby venipuncture into serum-separating tubes (SST) andethylenediaminetetraacetic acid (EDTA)-coated vacutainers. SST vacutainers were immediately covered inaluminum foil to protect the tubes from light, due to thelight sensitivity of carotenoids [17], and sat upright for
30 min prior to centrifugation. Whole blood was centrifuged at 4 °C at 3500 rpm for 10 min. All aliquots werestored at − 80 °C until analysis. Twenty-four-hour urine
samples were collected in sterilized containers the day
before pre- and post-intervention visits. After participants returned their sample to the clinic, samples were
weighed and urine aliquots were stored at − 80 °C until
analysis.
SMCSO was measured in urine and plasma samples.
Sulforaphane, the isothiocyanate of the glucosinolate,glucoraphanin, was also measured in plasma samples. For
the urinary SMCSO analysis, urine samples were thawedon ice and 50 µL of samples were diluted with liquid chromatography mass spectrometry (LC–MS) grade water(1:2 dilution). The diluted samples (50 µL) were transferred to a 1.5 mL centrifuge tube. The acetonitrile (ACN)solution (150 µL) containing 2 µg/mL internal standards(SMCSO-d3) was added, and the samples were vortexed for 2 min and centrifuged at 14,000 rpm and 4 °C
for 10 min. A 100 μL aliquot of supernatant was transferred to a 2 mL high-performance liquid chromatography (HPLC) vial for analysis using liquid chromatography
tandem mass spectrometry (LC–MS/MS). Chromatographic separation was performed on an UltiMate 3000

Connolly et al. BMC Medicine(2024) 22:353

Liquid Chromatograph (Thermo Scientific, CA, USA)
coupled to a Thermo Scientific TSQ Quantiva Triple
Quadrupole Mass Spectrometer equipped with an electrospray ionization (ESI) source. The optimal separation
was achieved on ACQUITY UPLC BEH Amide column(100 mm × 2.1 mm ID; Waters) with 1.7 μm particles anda mobile phase of water and ACN containing 10 mM
ammonium formate and 50 nM formic acid at pH 3. The
flow rate was 0.5 mL/min and the column temperature
was maintained at 35 °C with an injection volume of 4 µL.
The detection was performed in positive mode (3500 V)
and the spectra were acquired in multiple reaction monitoring (MRM) mode. Argon gas was selected as the collision gas and nitrogen as the nebulizer and heater gas.
For the plasma SMCSO and sulforaphane analysis,
plasma samples were thawed on ice and 50 µL of samples were transferred to a 1.5 mL centrifuge tube. The
methanol solution (150 μL) containing 0.75 µg/mL internal standards (SMCSO-d3 and SFN-d8) was added, andthe samples were vortexed for 2 min and centrifuged at4000 rpm and 4 °C for 10 min. An 80 μL aliquot of supernatant was transferred to a 2 mL HPLC vial for analysis using the LC–MS/MS. The optimal separation wasachieved on XBridge C18 column (100 × 3.0 mm packed
with 3.5µm particles; Waters) and using a mobile phaseof water and ACN both containing 0.1% formic acid. The
flow rate was 0.4 mL/min and the column temperature
was maintained at 35 °C with an injection volume of 6 µL.
As with the urine analysis, the detection was performedin positive mode (3500 V) and the spectra were acquiredin MRM mode. Argon gas was selected as the collision
gas and nitrogen as the nebulizer and heater gas.
Serum carotenoids were measured using HPLC, as previously described [22]. Briefly, ethanol, ethyl acetate, andhexane were used to extract carotenoids, with canthaxanthin as an internal standard. Dichloromethane:methanol(1:2 vol) was used to reconstitute the dried extract after
evaporation of the solvents. Using a Hypersil ODS column (100 mm × 2.1 mm × 5 μm) (Thermo Scientific, CA,USA), chromatography was performed on an Agilent
1200 HPLC system (Agilent Technologies, CA, USA).
A mobile phase of ACN:dichloromethane:methanol
0.05% ammonium acetate (85:10:5 vol:vol) at a flow rateof 0.3 mL/min with the use of a diode array detector(450 nm) was used to analyze carotenoids [22].
Plasma ­F2-isoprostanes, a biomarker of oxidative lipid
damage, were measured using electron-capture negative-ion gas chromatography–mass spectrometry as
total (free plus esterified) ­F2-isoprostanes, as previously
described [23]. Serum high sensitivity interleukin-6(hsIL-6), a marker of inflammation, was analyzed using
a commercially available enzyme-linked immunosorbent
assay (ELISA) kit (R&D Systems, Inc., Minneapolis, MN,

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USA). Urinary concentrations of creatinine, sodium, andpotassium (markers of sodium and potassium intakes)
and serum concentrations of triglycerides, total cholesterol, high-density lipoprotein (HDL) cholesterol, calculated low-density lipoprotein (LDL) cholesterol, glucose,creatinine, high sensitivity C-reactive protein (hsCRP),
sodium, and potassium were analyzed by PathWest Laboratories (Fiona Stanley Hospital, Perth, Australia).
Statistical analysis
Sample size

Based on 24-h ambulatory SBP as the primary outcome,a desired sample size of 25 participants was calculated.
This sample size was calculated to provide > 90% power todetect a 2.5 mmHg difference in mean 24-h ambulatory
SBP, assuming a standard deviation (SD) of 14 mmHg
for SBP, a within-period correlation between SBP measurements of 0.6, a between period correlation of mean
SBP of 0.6, and a minimum of 40 blood pressure measurements over each 24-h period [24], as described in
the published protocol [13]. The estimated change of
2.5 mmHg was based on plausible values for changes in
SBP following a nutritional intervention, such as thosedescribed previously from the ingestion of black tea [24],as well as also constituting a clinically meaningful change.
Statistical methods

All data were analyzed using IBM SPSS Statistics for
Windows, version 29.0 (IBM Corp., Armonk, NY, USA)and STATA, version 15.1 (Statacorp, College Station,
TX, USA). Statistical significance was set at a two-sided
type 1 error rate of P < 0.05. The Shapiro–Wilk normality test was used to assess the normality of distributionsof continuous variables. Descriptive statistics of normally
distributed variables are expressed as mean ± SD, nonnormally distributed continuous variables as medianand interquartile range (IQR), and categorical variables
as number and proportion (%). Paired t tests and Wilcoxon signed rank tests were used to compare pre- andpost-intervention measurements within the intervention
groups for normally and non-normally distributed variables, respectively.
The primary analyses were conducted according to amodified intention-to-treat protocol, including all participants for which pre-intervention visit data for bothinterventions were obtained. Secondary per-protocol
analyses were conducted on participants who completed the study and had complete data. Intention-totreat and per-protocol analyses were the same for all
outcomes except for hsCRP (extreme outlier removed)
and ambulatory aortic blood pressure and arterial
stiffness (> 10% missing data). Differences between
treatments for ambulatory brachial and aortic blood

Connolly et al. BMC Medicine(2024) 22:353

pressure and arterial stiffness were tested using linear
mixed effects regression with fixed effects for treatment, pre vs. post treatment, hour, intervention order,
and a treatment X pre-post interaction. In this analysis,“hour” referred to the blood pressure readings aggregated for each hour over the 24-h period. Participant
ID was included as a random intercept with a random
slope for treatment and pre- vs. post treatment. Akaike
information criterion (AIC) and Bayesian information criterion (BIC) were used to determine the bestmodel fit. For all other outcomes, differences between
treatments were tested using the same model without a time variable (hour). Due to ABPM error, there
was > 10% missing data for aortic blood pressure (11%
missing data) and arterial stiffness (13% missing data),
and therefore, multiple imputation was utilized as per
the published protocol [13]. Ten imputations were generated using a chained regression equation including
the following variables in the imputation model: visit,
hour, daytime/nighttime hours, treatment, intervention order, age, sex, weight, height, and screening bloodpressure. ELC and LCB had full access to all data in theFig. 2 CONSORT diagram

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study and took responsibility for its integrity and thedata analysis.

Results
Recruitment

A total of 76 individuals underwent physical screening
to be assessed for eligibility. Of these, 21 participants
were randomly assigned. Three participants withdrew
after randomization: two withdrew due to schedulingdifficulties before pre-intervention visit data was collected and one withdrew as they were unwilling to follow study requirements. The CONSORT flow diagramfor participant recruitment is shown in Fig. 2. Due to theglobal COVID-19 pandemic and restrictions put in placein Western Australia, recruitment was paused in March
2020 and later recommenced in October 2020 in a limited capacity.
Baseline demographic and clinical characteristics

Participants were aged 56 to 72 years and had a BMI rangeof 21.2 to 35.1 kg/m2 (Table 1). Most participants were
Caucasian (94%). At screening, participants presented

Connolly et al. BMC Medicine(2024) 22:353

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Table 1 Demographic and clinical characteristics of study
participants at screening/baseline
Demographic characteristics

All participants (n = 18)

Male/Female, n (%)

2 (11%)/16 (89%)

Age, years

68 (66–70)

Marital status, n (%) -Married

10 (55.6%) -Single

4 (22.2%) -Divorced

3 (16.7%) -De facto

1 (5.6%)Ethnic background, n (%)

vegetables was 0 g/day (0–0 g/day) during the control
intervention; four participants reported consuming cruciferous vegetables ranging from 0.3–12.3 g/day during
their control intervention. During the active intervention,
median (IQR) intake of cruciferous vegetables was 300 g/
day (293–300 g/day); three participants reported consuming cruciferous vegetables outside of the intervention
soups ranging from 2.1 to 10 g/day during their active
intervention.
Ambulatory blood pressure -Caucasian

17 (94%) -Asian

1 (6%)

BMI, kg/m2

28.1 ± 3.9

Ex-smoker, n (%)

7 (39%)

Medications
Blood pressure medication -No

16 (88.9%) -Yes

2 (11.1%)

Clinic blood pressure   
Systolic blood pressure, mmHg

135.9 ± 10.0

Diastolic blood pressure, mmHg

76.4 ± 7.9Heart rate, beats/min

69.1 ± 8.8

Biochemistry, mmol/L
Total cholesterol

5.5 ± 0.9

LDL cholesterol

3.2 ± 0.5

HDL cholesterol

1.6 (1.3–1.7)

Triglycerides

1.3 ± 0.5

Glucose

5.5 ± 0.5

Values are mean ± standard deviation, median (IQR), or number (%) as indicatedAbbreviations: HDL high-density lipoprotein, IQR interquartile range, LDL lowdensity lipoproteinwith mean ± SD brachial SBP of 135.9 ± 10.0 mmHg anda DBP of 76.4 ± 7.9 mmHg. Two participants used bloodpressure-lowering medication, which remained unalteredthroughout the trial. Median (IQR) baseline habitual
daily intake of cruciferous vegetable was 26.0 g (18.5–
52.9 g) and most people consumed at least 3.5 servings ofvegetables per day (Additional File 1: Table S2).
Dietary intervention

For both soups, 72% of participants had 100% soupadherence (median (IQR) adherence: 100% (96.4–100%)for both interventions). No adverse events were reported.
Energy, macronutrient, and food group consumption
during both intervention periods is presented in Additional File 1: Table S3. Only protein was significantly different between interventions (P = 0.001). Median (IQR)
intake of total vegetables per day was 481 g (458–526 g)
and 493 g (458–503 g) for the control and active interventions, respectively. Median (IQR) intake of cruciferous

Twenty-four-hour brachial SBP was significantly reducedin the active intervention compared to the control intervention (mean difference active vs. control: − 2.5 mmHg,
95% CI − 4.1, − 0.9, P = 0.002) (Table 2). This result was
driven by the daytime period (mean difference active
vs. control: − 3.6 mmHg, 95% CI − 5.4, − 1.7, P < 0.001).
No significant difference was seen for nighttime SBP
nor 24-h, daytime, and nighttime brachial DBP between
interventions (Table 2). Figure 3 shows 24-h brachial SBP
at the pre- and post-intervention visit for both interventions. No carryover effects were seen for 24-h brachial
SBP (P = 0.877) or DBP (P = 0.556).
Between interventions, 24-h and daytime aortic SBP
were significantly reduced in the active intervention compared to the control intervention (mean difference active
vs. control: − 2.1 mmHg, 95% CI − 3.7, -0.5, P = 0.010
and − 3.2 mmHg, 95% CI − 5.0, − 1.4, P = 0.001, respectively) (Table 3). Nighttime aortic DBP was significantly
increased in the active compared to the control intervention between interventions (mean difference active vs.
control: 2.9 mmHg, 95% CI 0.6, 5.2, P = 0.014). Ambulatory aortic blood pressure prior to multiple imputations
is shown in Additional File 1: Table S4.
Relative to control, there was a significant increase in
24-h and nighttime heart rate for the active intervention (mean difference active vs. control: 2.1 beats/min,
95% CI 1.1, 3.2, P < 0.001 and 2.0 beats/min, 95% CI 0.6,
3.3, P = 0.004, respectively; Table 2). However, this difference was driven by significant reductions in the control intervention for 24-h (P = 0.048) and nighttime heart
rate (P = 0.004) from pre- to post-intervention visits,
which appeared to cause the significant between-groupdifferences.
Arterial stiffness

The mean difference in AIx was not significantly different
between interventions for 24-h, daytime, or nighttime
measurements (Table 3). AIx prior to multiple imputations is shown in Supplementary Table 4. No carryover
effects were noted (P = 0.645).

Connolly et al. BMC Medicine(2024) 22:353

Table 2 Ambulatory brachial blood pressure by intervention
and differences between interventions*

Intervention (n = ­17 )Control

ActiveMean difference (mm Hg)
active vs. control (95% CI)

Overall 24-h SBP, mmHg
Pre125.5 ± 12.1

126.8 ± 12.6

Post

124.8 ± 13.1

124.4 ± 11.8

− 2.5 (− 4.1, − 0.9)
P = 0.002

Overall 24-h DBP
Pre68.8 ± 9.4

69.1 ± 10.8

Post

68.4 ± 11.2

68.9 ± 9.9− 0.5 (− 1.7, 0.8)
P = 0.466

Overall HR, beats/min
Pre67.7 ± 10.0

67.5 ± 9.3

Post64.4 ± 11.3†

66.3 ± 11.4

133.1 ± 8.0

135.0 ± 8.6

Post133.0 ± 8.7

131.4 ± 6.6

Pre74.3 ± 6.8

75.4 ± 8.0

74.0 ± 9.0

74.3 ± 7.4− 3.6 (− 5.4, − 1.7)
P < 0.001− 1.0 (− 2.4, 0.4)
P = 0.149

Daytime HR, beats/min
Pre72.1 ± 8.5

72.2 ± 8.0

Post

69.9 ± 9.972.2 ± 9.3

2.2 (0.9, 3.5)
P = 0.001

Nighttime SBP, mmHg
Pre117.9 ± 10.8

118.6 ± 10.6

Post

116.6 ± 11.6

117.5 ± 11.8

There was no overall difference in the mean
­F2-isprostanes between interventions (Table 4). Plasma­F2-isoprostanes were significantly lower at the postintervention visit compared with the pre-intervention
visit within the active intervention (P = 0.013). However, a similar non-significant drop in ­F2-isprostanes
was seen in the control intervention. There was no significant difference in hsCRP and hsIL-6 between interventions (Table 4). One participant was excluded fromthe per-protocol analysis of hsCRP due to an extreme
outlier (hsCRP value: 89.6 mg/L; mean difference active
vs. control 0.2 mg/L, 95% CI − 0.9, 1.4, P = 0.708). No
carryover effects were seen for either ­F2-isprostanes(P = 0.901) or hsCRP (P = 0.553).
Markers of adherence and metabolism
Urinary/plasma SMCSO and plasma sulforaphane

Daytime DBP, mmHg
Post

Biomarkers of oxidative stress and inflammation

2.1 (1.1, 3.2)
P < 0.001

Daytime SBP, mmHg
Pre

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0.7 (− 2.2, 3.5)
P = 0.653

The mean differences in urinary and plasma SMCSO(mean difference active vs. control: 22.93 mg/mL,
95% CI 15.62, 30.23, P < 0.0001 and 5.46 mg/mL, 95%
CI 4.40, 6.51, P < 0.0001, respectively) and plasma sulforaphane concentrations were significantly higher
following the active intervention compared to the control intervention (mean difference active vs. control:
0.15 ng/mL, 95% CI 0.06, 0.23, P < 0.001) (Table 5).
Urinary and plasma SMCSO concentrations, as well as
plasma sulforaphane, were significantly higher at the
post-intervention visit compared with the pre-intervention visit in the active group (P < 0.05 for all) (Table 5).

Nighttime DBP, mmHg
Pre63.3 ± 8.6

62.7 ± 9.5

Post62.8 ± 10.6

63.6 ± 9.3

Serum carotenoids
1.6 (− 0.5, 3.6)
P = 0.144

Nighttime HR, mmHg
Pre
Post

63.3 ± 9.6
58.9 ± 10.0

62.9 ± 8.2‡

60.3 ± 10.4

2.0 (0.6, 3.3)
P = 0.004

Values are mean ± standard deviation, unless otherwise indicatedP-values for pre- vs. post-intervention comparison within the intervention
group were obtained using the paired t test for normally distributed data.
The difference between interventions was tested using linear mixed effects
regression with fixed effects for treatment, pre- vs. post treatment, hour,intervention order, and a treatment X pre-post interaction. Participant ID was
included as a random intercept with a random slope for treatment and pre vs.
post treatment
CI confidence interval, DBP diastolic blood pressure, HR heart rate, Pre preintervention, Post post-intervention, SBP systolic blood pressure*

One participant was excluded from ambulatory blood pressure analysis due todeclining to wear monitor†

P < 0.05 for pre- vs. post-intervention comparison within the intervention
group‡
P < 0.01 for pre- vs. post-intervention comparison within the intervention
group

Mean differences in total carotenoids, lutein, lycopene, a-carotene, and b-carotene were significantly
higher following the control intervention compared tothe active intervention (P < 0.05 for all) (Table 6). Total
and individual carotenoids, excluding b-cryptoxanthin,
were significantly higher at the post-intervention visit
compared with the pre-intervention visit in the control
intervention (P < 0.05 for all). In the active intervention,
only a-carotene was significantly different at the postintervention visit compared with the pre-intervention
visit.
Serum and urinary sodium, potassium, and creatinine

Between interventions, there were no significant meandifferences for serum sodium, potassium, or creatine.
Urinary sodium, potassium, and creatinine were also notsignificantly different between interventions (Table 7).

Connolly et al. BMC Medicine(2024) 22:353

Page 9 of 15

Fig. 3 24-h ambulatory systolic blood pressure (SBP) aggregated hourly for the control (A) and active (B) interventions
at the pre- and post-intervention visits

Serum lipids and glucose

Serum triglycerides were significantly lower in the active
intervention compared to the control intervention(mean difference active vs. control: − 0.2 mmol/L, 95%
CI − 0.4, − 0.0, P = 0.047) (Table 7). No carryover effects
were noted (P = 0.7897). There were no significant meandifferences between interventions for serum total cholesterol, LDL cholesterol, HDL cholesterol, or serum glucose. However, serum total, LDL, and HDL cholesteroland serum glucose were significantly decreased at the
post-intervention visit compared with the pre-intervention visit for both interventions (P < 0.05 for all).
Anthropometry, energy expenditure from physical activity,and stress

There were no significant mean differences between
interventions for any anthropometric measures, energy
expenditure from physical activity, or perceived stress(P > 0.05 for all) (Additional File 1: Table S5). However, weight, BMI, and body fat mass were significantly
reduced at the post-intervention visit compared with thepre-intervention visit for both interventions.

Discussion
In this randomized, controlled, crossover trial, we foundthat consumption of four serves per day of cruciferous
vegetables (active intervention) resulted in a statisticallysignificant reduction in SBP compared with four serves
per day of root and squash vegetables (control intervention), supporting our hypothesis. This reduction in SBP isclinically relevant; in a meta-analysis of randomized controlled trials involving pharmacological interventions, a
reduction in SBP of 5 mmHg was found to reduce the riskof major cardiovascular events by ~ 10% [25]. Therefore,
the 2.5 mmHg reduction in SBP resulting from increasing

cruciferous vegetable intake could translate to a 5% lower
risk of major cardiovascular events.
Weight reduction is an important component of nonpharmacological management of hypertension [26],with a loss of 1 kg weight associated with approximately
1 mmHg reduction in SBP [27]. Both interventions
resulted in a statistically significant reduction in weight
post-intervention (control: 1.9 kg; active: 1.3 kg). However, there was no significant difference seen between
interventions. Therefore, the improvements in SBP seen
with the cruciferous vegetable intervention are likely
independent of weight reduction. In addition, there wasno significant difference in urinary sodium or potassium excretion between interventions, indicating that the
reduction in SBP was independent of dietary sodium andpotassium intake.
This blood pressure result is in alignment with otherresearch investigating the breakdown products of cruciferous vegetables, which include glucosinolates and isothiocyanates [28]. Research into the cardio-protective
properties of these compounds has largely focused on
glucoraphanin, a major glucosinolate found in broccoli,
and its isothiocyanate, sulforaphane [5]. Previous studies
have investigated the effects of glucosinolates and isothiocyanates on blood pressure in animal models (i.e., rats),
with results demonstrating blood pressure-lowering
effects [29–32]. However, studies involving humans havebeen inconsistent. In a study including 40 participants
with hypertension (baseline mean blood pressure: control group = 158.6/98; active group = 158.5/96 mmHg),
daily ingestion of 10 g dried broccoli sprouts for 4 weeks
did not improve blood pressure or endothelial function[33]. Conversely, a 4-week study including participants
with type two diabetes and positive for H. Pylori (baseline mean blood pressure: standard triple therapy group(n = 33) 130/80.4; broccoli sprouts powder group (n = 28)

Connolly et al. BMC Medicine(2024) 22:353

Page 10 of 15

Table 3 Ambulatory aortic blood pressure and arterial stiffness
by intervention and between intervention differences

Table 4 Biomarkers of oxidative stress and inflammation byintervention and between intervention differences

Intervention (n = ­17*)
ControlIntervention (n = 18)

ActiveMean difference
active vs. control(95% CI)Pre

37.7 ± 0.5

36.9 ± 0.5

Post37.1 ± 0.5

36.8 ± 0.5

0.6 (− 1.2, 2.3)
P = 0.516

Overall 24-h aortic SBP, mmHg
Post

Mean difference
active vs. control (95% CI)Pre537.66 ± 177.06

518.06 ± 123.83

Post

475.00 ± 102.40

469.07 ± 88.65*

13.67 (− 54.74, 82.09)
P = 0.695

Serum hsCRP, mg/L

119.9 ± 0.5

120.6 ± 0.5

120.5 ± 0.5†

119.1 ± 0.5− 2.1 (− 3.7, − 0.5)
P = 0.010

Pre

2.2 (1.2–3.2)

2.7 (1.6–3.2)

Post

2.1 (1.2–2.9)

2.0 (1.6–3.0)

5.0 (− 2.7, 12.7)
P = 0.206

Serum hsIL-6, pg/mL

Overall 24-h aortic DBP, mmHg
Pre73.0 ± 0.4

73.7 ± 0.5

Post72.8 ± 0.4

73.6 ± 0.4

0.1 (− 1.2, 1.4)
P = 0.879

Daytime AIx (%)

Pre

2.9 (2.1–4.4)

2.9 (2.1–3.7)

Post

3.7 (2.8–4.2)

3.0 (2.7–4.1)− 0.4 (− 1.3, 0.5)
P = 0.358

Values are mean ± standard deviation or median (interquartile range)

Pre35.4 ± 0.6

34.2 ± 0.6

Post35.1 ± 0.6

35.0 ± 0.6

1.2 (− 0.9, 3.4)
P = 0.271

Daytime aortic SBP, mmHg
Pre

ActivePlasma ­F2-isoprostanes, pmol/L

Overall 24-h AIx (%)

PreControl

122.6 ± 0.5

123.5 ± 0.6

123.8 ± 0.5

121.3 ± 0.5‡

CI confidence interval, hsCRP high sensitivity C-reactive protein, hsIL-6 highsensitivity interleukin 6, Pre pre-intervention, Post post-intervention− 3.2 (− 5.0, − 1.4)
P = 0.001

Daytime aortic DBP, mmHg
Pre75.8 ± 0.4

76.9 ± 0.5

Post75.9 ± 0.5

76.1 ± 0.5− 0.7 (− 2.2, 0.7)
P = 0.325

Nighttime AIx (%)
Pre

44.5 ± 0.7

44.9 ± 0.8

Post43.0 ± 0.8

42.5 ± 0.8†

− 1.5 (− 4.5, 1.5)
P = 0.319

Nighttime aortic SBP, mmHg
Pre111.6 ± 1.0

112.1 ± 1.0

Post110.2 ± 1.0

112.1 ± 1.0

1.4 (− 1.6, 4.5)
P = 0.351

Nighttime aortic DBP, mmHg
Pre64.8 ± 0.8

64.7 ± 0.9

Post63.5 ± 0.8†

65.7 ± 0.8

Differences between treatments were tested using linear mixed effects
regression with fixed effects for treatment, pre vs. post treatment, intervention
order, and a treatment X pre-post interaction. Participant ID was included as arandom intercept with a random slope for treatment and pre vs. post treatment

2.9 (0.6, 5.2)
P = 0.014

Values are mean ± standard error
Within intervention, differences were measured using a mixed effects regression
following multiple imputation. The difference between interventions was tested
using linear mixed effects regression with fixed effects for treatment, pre vs.
post treatment, hour, intervention order, and a treatment X pre-post interaction.
Participant ID was included as a random intercept with a random slope fortreatment and pre vs. post treatment
AIx augmentation index, CI confidence interval, DBP diastolic blood pressure, Pre
pre-intervention, Post post-intervention, SBP systolic blood pressure*

One participant was excluded from ambulatory blood pressure analysis due todeclining to wear the monitor†
P < 0.05 for pre- vs. post-intervention comparison within the intervention
group‡
P < 0.01 for pre- vs. post-intervention comparison within the intervention
group

*

P < 0.05 for pre- vs. post-intervention comparison within the intervention group125/80.4; combination group (n = 25) 136/89.8 mmHg)
found that there was a significant reduction in SBP and
DBP in participants who received 6 g/day broccoli sprout
powder in combination with standard triple therapy for
H. Pylori (14 mmHg and 9.4 mmHg reduction in SBP and
DBP, respectively) [34]. In a dose escalation study including 12 pregnant women with preeclampsia, activatedbroccoli extract equivalent to 32 mg or 64 mg of sulforaphane resulted in a trend towards an approximately
10% decrease in DBP (P = 0.05) but not SBP [35].
Plasma sulforaphane was significantly higher after theactive intervention compared with the control, indicating that glucosinolates (i.e., glucoraphanin) were present
in the soup and may explain the beneficial effect seen on
blood pressure. These compounds have been proposed tohave anti-inflammatory and antioxidant properties due
to involvement in increasing Nrf2 activity and inhibition
of NF-ĸB [36]. Whilst our findings show that cruciferous vegetable intake did not have a significant effect on
our marker of oxidative lipid damage, relative to root andsquash vegetables, plasma ­F2-isoprostanes were significantly lower after the active intervention. This highlights
the potential efficacy for the antioxidant capabilities of
cruciferous vegetables [37], although our study does notprovide evidence that this explains the observed difference in SBP. Evidence for cruciferous vegetables alteringoxidative stress and inflammation has been inconsistentConnolly et al. BMC Medicine(2024) 22:353

Page 11 of 15

Table 5 Urinary and plasma SMCSO and plasma sulforaphane
concentration by intervention and between-interventiondifferences

Table 6 Serum carotenoid concentration by intervention andbetween-intervention differences
Intervention (n = 18)

Intervention (n = 18)
Control

ActiveControl
Mean difference
active vs. control(95% CI)2.61 (1.93–4.81)

2.58 (1.53–5.04)

Pre

1.619 (1.159–1.873)

1.588 (1.307–2.493)

Post

2.433 (2.249–3.207)*

1.885 (1.545–2.693)

Post 1.83 (1.37–2.89)* 22.15 (14.54–25.74)† 22.93 (15.62, 30.23)
P < 0.0001
0.82 (0.40–1.41)

0.52 (0.42, 1.47)

Post 0.51 (0.36–0.59)* 5.30 (4.14–7.64)†

5.46 (4.40, 6.51)
P < 0.0001

Plasma sulforaphane, ng/mL
Pre

1.05 (1.01–1.15)

1.04 (1.02–1.08)

Post 1.06 (1.02–1.11)

1.22 (1.12–1.27)*

Pre

0.314 (0.244–0.448)

0.308 (0.234–0.567)

Post

0.446 (0.342–0.576)*

0.365 (0.299–0.513)− 0.090(− 0.162, − 0.019)
P = 0.013

B-Cryptoxanthin, mg/mL
0.15 (0.06, 0.23)
P = 0.001

Pre

0.178 (0.139–0.277)

0.183 (0.130–0.280)

Post

0.198 (0.133–0.318)

0.209 (0.122–0.253)

Values are median (interquartile range)
P-values for pre- vs. post-intervention comparison within the intervention groupwere obtained using Wilcoxon signed ranks test for non-normally distributed
data. The difference between interventions was tested using linear mixed effects
regression with fixed effects for treatment, pre- vs post treatment, intervention
order, and a treatment X pre-post interaction. Participant ID was included as arandom intercept with a random slope for treatment and pre- vs. post treatment

Lycopene, mg/mL

CI confidence interval, Pre pre-intervention, Post post-intervention, SMCSO
S-methyl cysteine sulfoxide

α-Carotene, mg/mL*− 0.974(− 1.525, − 0.423)
P = 0.001

Lutein, mg/mL

Plasma SMCSO, mg/mL
Pre

Mean difference
active vs. control(95% CI)

Total carotenoids, mg/mL

Urinary SMCSO, mg/mL
Pre

ActiveP < 0.05 for pre- vs. post-intervention comparison within the intervention group†

P < 0.001 for pre- vs. post-intervention comparison within the intervention
group

Pre

0.653 (0.333–1.020)

0.642 (0.532–0.945)

Post

1.295 (0.965–1.558)*

1.026 (0.668–1.638)†

Pre0.023 (0.017–0.032)

0.032 (0.023–0.041)

Post

0.048 (0.036–0.061)*

0.021 (0.015–0.039)†− 0.028 (− 0.075,
0.019)
P = 0.237− 0.366(− 0.725, − 0.008)
P = 0.045− 0.037(− 0.049, − 0.024)
P < 0.001

b-carotene, mg/mL[38–40] and further studies are needed to investigate
the anti-inflammatory and antioxidant capacity of cruciferous vegetables when consumed as part of the diet.
Although there were no significant differences in oxidative stress and inflammatory biomarkers between interventions in our study, this could be due to similar benefitsof other vegetables in the control treatment. The control
soup contained carotenoid-rich vegetables, which also
have both antioxidant and anti-inflammatory properties[41]. Furthermore, these biomarkers were within normal
expected ranges [42–44], which may also explain thisresult.
Whilst we report SMCSO as a biomarker of adherenceto cruciferous vegetable intake in this study, SMCSO has
also been recently identified as a key metabolite associated with the antihypertensive benefits of the Dietary
Approaches to Stop Hypertension (DASH) diet [45].
SMCSO contributes a greater proportion of sulfur incruciferous vegetables than glucosinolates, yet whether
SMCSO mediates some of the therapeutic benefits ofthese vegetables in humans remains largely unexplored[46]. Sulfur-rich vegetables (i.e., cruciferous) are a goodsource contributing to hydrogen sulfide (­H2S): the third

Pre

0.212 (0.159–0.352)

0.363 (0.232–0.476)

Post

0.611 (0.503–0.738)*

0.330 (0.217–0.466)− 0.452(− 0.658, − 0.247)
P < 0.001

Values are mean ± standard deviation or median (interquartile range) as
indicated
P-values for pre- vs. post-intervention comparison within the intervention groupwere obtained using Wilcoxon signed ranks test for non-normally distributed
data and paired t test for normally distributed data. The difference between
interventions was tested using linear mixed effects regression with fixed effects
for treatment, pre vs. post treatment, intervention order, and a treatment X
pre-post interaction. Participant ID was included as a random intercept with arandom slope for treatment and pre vs. post treatment
CI confidence interval, Pre pre-intervention, Post post-intervention*

P < 0.001 for pre- vs. post-intervention comparison within the intervention
group

†

P < 0.05 for pre- vs. post-intervention comparison within the intervention
group

most important gaseous signaling molecule. As such, thismay be a potential mechanism through which these vegetables modulate endothelial function [47]. The contribution of ­H2S to the anti-hypertensive benefits of sulfur-richallium vegetables (e.g., onion, garlic) has been recently
explored [48]; however, more research is required. Both

Connolly et al. BMC Medicine(2024) 22:353

Page 12 of 15

Table 7 Biochemical analyses by intervention and betweenintervention differences
Intervention (n = 18)
Control

ActiveMean difference active
vs. control (95% CI)

Serum triglycerides, mmol/L
Pre

1.4 ± 0.6

1.5 ± 0.6Post

1.4 ± 0.4

1.3 ± 0.4

− 0.2 (− 0.4, − 0.0)
P = 0.047

Serum total cholesterol, mmol/L
Pre

6.0 ± 1.2

6.0 ± 1.0

Post

5.6 ± 1.0*

5.4 ± 0.8†

− 0.2 (− 0.5, 0.1)
P = 0.264

Serum LDL, mmol/L
Pre

3.7 ± 0.9

3.6 ± 0.8

Post

3.4 ± 0.9*

3.3 ± 0.7*

− 0.1 (− 0.4, 0.1)
P = 0.369

Serum HDL cholesterol, mmol/LPre

1.5 (1.2–1.8)

1.5 (1.3–1.8)

Post

1.3 (1.1–1.6)†

1.3 (1.2–1.6)†

0.0 (− 0.1, 0.1)
P = 0.600

Serum glucose, mmol/L
Pre

5.4 ± 0.5

5.4 ± 0.5Post

5.1 ± 0.4†

5.2 ± 0.6*

0.1 (− 0.1, 0.3)
P = 0.357

Serum creatinine, mmol/L
Pre

67.6 ± 10.7

67.3 ± 11.7

Post

68.8 ± 11.9

67.8 ± 11.3

− 0.7 (− 2.8, 1.4)
P = 0.509

Serum sodium, mmol/L
Pre

138.7 ± 2.0

138.5 ± 1.8

Post138.7 ± 1.9

138.4 ± 2.1− 0.1 (− 0.7, 0.5)
P = 0.768

Serum potassium, mmol/L
Pre

4.6 ± 0.3

4.5 ± 0.3

Post

4.4 ± 0.3

4.4 ± 0.2

0.1 (− 0.1, 0.2)
P = 0.382

Urinary creatinine, mmol/day
Pre

9.2 ± 1.7

8.8 ± 1.9

0.3 (− 0.6, 1.2)

Post

8.5 ± 1.6

8.4 ± 1.8

P = 0.526

Urinary sodium, mmol/day
Pre116.7 ± 39.7

113.2 ± 43.2

− 1.9 (− 26.4, 22.8)

Post

105.7 ± 39.8

100.3 ± 41.1

P = 0.883

Urinary potassium, mmol/day
Pre

73.1 ± 13.2

63.4 ± 21.0

− 5.1 (− 18.3, 8.0)

Post

75.8 ± 16.1

60.9 ± 17.7

P = 0.446

Values are mean ± standard deviation or median (interquartile range) as
indicated
P-values for pre- vs. post-intervention comparison within the intervention groupwere obtained using Wilcoxon signed ranks test for non-normally distributed
data and paired t test for normally distributed data. The difference between
interventions was tested using linear mixed effects regression with fixed effects
for treatment, pre vs. post treatment, intervention order, and a treatment X
pre-post interaction. Participant ID was included as a random intercept with arandom slope for treatment and pre vs. post treatment
CI confidence interval, HDL high-density lipoprotein, LDL low-density
lipoprotein, Pre pre-intervention, Post post-intervention*†

P < 0.05 for pre- vs. post-intervention comparison within the intervention group

P < 0.001 for pre- vs. post-intervention comparison within the intervention
group

SMCSO and glucosinolates may act as ­H2S donors [45,47], and the subsequent vasodilation may be partly
responsible for the reduction in blood pressure observedin the active intervention.
We also found that serum triglycerides were significantly lower after the active intervention compared withthe control intervention. Although pre-clinical evidence
suggests that cruciferous vegetables and their glucosinolates may play a role in the reduction of blood lipids[49–52], there is limited evidence in humans. Humantrials have shown that broccoli sprouts and glucoraphanin-rich broccoli can improve HDL cholesterol [53] andreduce LDL cholesterol [54], respectively. There were no
significant differences in biomarkers of total cholesterol,
LDL, HDL, and glucose between interventions in ourstudy. However, this could be due to similar benefits ofother vegetables in the control treatment, as there weresignificant within-group changes in these biomarkers following both interventions.
This study had multiple strengths. First, to our knowledge, our study is the only intervention study in humans
to show improvements in blood pressure in middle-aged
and older adults with mildly elevated blood pressure after
increased short-term consumption of cruciferous vegetables compared to commonly consumed root and squash
vegetables. Furthermore, this study is also novel in thatthe study design included a dietary intervention utilizing a combination of cruciferous vegetables as wholefoods (not extracts of cruciferous vegetables or theirbioactives). Second, the study had a crossover design
which allowed all participants to act as their own control,
mitigating potential differences between participants.
A 2-week washout period was selected between interventions to avoid potential carryover effects, as this wasconsidered enough time for objective markers of intake
to be adequately excreted (i.e., urinary SMCSO in theactive intervention and serum carotenoids in the control
intervention). This washout period has been used in priorstudies, demonstrating that these biomarkers return tonormal levels within 2 weeks [55, 56]. Third, in addition
to self-reported food diaries, objective markers of intake
were measured to corroborate adherence to both dietaryinterventions. Additionally, we carefully controlled thebackground diet of participants. The provision of lunch
and dinner meals throughout the study likely substantially reduced the background variation in vegetableintake and other foods.
This study also has limitations. Although a significant reduction in our primary outcome of 24-h SBP was
observed, we did not reach our desired sample size. The
reduced sample size resulting from COVID-19-related
issues may be relevant for secondary outcomes in thisstudy. As we were unable to recruit the desired sample

Connolly et al. BMC Medicine(2024) 22:353

size of 25 participants (28 to account for participant
drop-out) due to the COVID-19 pandemic, this study
may be considered a pilot RCT rather than a phase 2
trial. However, despite this, we were still able to demonstrate a statistically significant result for our primary outcome, brachial 24-h ambulatory SBP, thereby eliminating
the possibility of making a type-2 error for the primary
outcome. We were aiming to have roughly an equal distribution of males and females; however, this was notpossible due to limitations resulting from the COVID-19
pandemic (e.g., logistical and financial) and led to mostly
female participants. Most participants were also Caucasian. Therefore, these results may not be as generalizable to males and other ethnicities. Second, participants
included in this study had a baseline reported intake ofvegetables higher than that of the general population(327 g vs.195 g per day) [57], which may reduce the generalizability of these results. However, this may also beseen as a strength by reinforcing that the type of vegetable consumed matters. Nonetheless, investigation of
cruciferous vegetable intake in individuals with lower
baseline intake of vegetables is warranted to determine
if there is a more profound effect with a greater increase
from baseline habitual intake. Third, given the nature of
the dietary interventions, participants were unable to becompletely blinded, as the different soups had clear differences in color and taste. However, participants were
not informed which soup was the active or control orwhat vegetables were included in each soup. Lastly,
self-reported protein intake was significantly different
between interventions (7 g higher in the active groupin comparison to the control, P = 0.001), and increasing protein intake has been shown to influence bloodpressure. However, this difference is unlikely to have aneffect, based on results of a meta-analysis that indicate
amounts of approximately 40 g per day are needed tohave a similar effect on blood pressure [58]. It is important to note that cruciferous vegetables do not contain
only glucosinolates and SMCSO at higher concentrations than root and squash vegetables. Cruciferous vegetables also contain higher nitrate and vitamin K levels.
In addition, they also provide smaller but higher levelsof other nutrients and phytochemicals such as magnesium, flavonoids, vitamin C, and folate, all of which havepotential to contribute to benefits on blood pressure [11,12]. As such, we are not able to fully elucidate which
specific components are responsible for the beneficial
effects that we observed.

Conclusions
Daily consumption of four serves of cruciferous vegetables over a 2-week period resulted in reduced SBP
in middle-aged and older adults with mildly elevatedPage 13 of 15

blood pressure compared with root and squash vegetables. Increased intake of a variety of different
vegetables has many health benefits due to the presence of vitamins, minerals, and many other bioactive compounds. Future research is needed to informwhether targeted recommendations to increase cruciferous vegetable intake within a healthy diet that
includes a variety of vegetables can reduce the publichealth burden of CVD. Furthermore, the study could
be implemented in other regions worldwide to obtain
multi-ethnic data.

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03577-8.
Additional file 1. Table S1 Detailed inclusion and exclusion criteria. Table S2
Dietary intakes of study participants at baseline obtained using a foodfrequency questionnaire. Table S3 Comparison of energy, macronutrients,and food groups consumed during both interventions for all participants
who completed the study (n = 18). Table S4 Ambulatory aortic bloodpressure and arterial stiffness by intervention and between interventiondifferences. Table S5 Anthropometric measurements, energy expenditurefrom physical activity, and perceived stress by intervention.
Acknowledgements
We would like to thank Kim Luu, a lab technician for Nutrition & Dietetics, forthe preparation of the intervention soups.
Authors’ contributions
LCB, RM, and JMH designed the research. ELC conducted the study withassistance from AHL, LM, SRB, AD, MS, CRH, BHP, SKG, CJS, CPB, JRL, JMH, and
LCB. AS, MCB, KDC, LGW, LM, and HK conducted the laboratory analysis. ELC
performed statistical analysis in consultation with NPB, RJW, JMH, and LCB.
ELC wrote the original draft manuscript in consultation and with editing from
LCB. ELC and LCB had primary responsibility for the final content. All authors
critically revised the manuscript, provided intellectual contribution to theinterpretation of results, and read and approved the final manuscript.
Authors’ Twitter handles@LCBlekkenhorst (Lauren C. Blekkenhorst).
Funding
This research was funded by Edith Cowan University Early Career Researcher
Grant 2019 (grant number G1004405) and Department of Health Western Aus‑
tralia Near Miss Merit Awards 2019 (grant number G1004519). Funding sources
had no involvement in the study design; collection, analysis, and interpreta‑tion of the data; or writing of the manuscript, and there are no restrictions
regarding publication. EC is supported by an Australian Government ResearchTraining Program Scholarship at Edith Cowan University. MS is supported by a
Royal Perth Hospital Research Foundation Fellowship (ID: CAF 130/2020) andan Emerging Leader Fellowship from the Western Australian Future HealthResearch and Innovation Fund. CPB is supported by a Royal Perth Hospital
Research Foundation ‘Lawrie Beilin’ Career Advancement Fellowship (ID: CAF
127/2020) and the Western Australian Future Health Research and Innova‑
tion Fund (ID: IG2021/5). LCB is supported by a National Health and Medical
Research Council of Australia Emerging Leadership Investigator Grant (ID:1172987) and a National Heart Foundation of Australia Post-Doctoral Research
Fellowship (ID: 102498). JRL is supported by a National Heart Foundation
Future Leader Fellowship (ID: 102817).
Availability of data and materials
The datasets used and analyzed during the current study are available fromthe corresponding author on reasonable request.

Connolly et al. BMC Medicine(2024) 22:353

Declarations
Ethics approval and consent to participate
Ethics approval was granted by the Edith Cowan University Human Research
Ethics Committee (2019–00356-BLEKKENHORST). All participants provided
written informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Nutrition and Health Innovation Research Institute, School of Medi‑cal and Health Sciences, Royal Perth Hospital Research Foundation, Edith
Cowan University, Perth, WA, Australia. 2 Centre for Integrative Metabolomics
and Computational Biology, School of Science, Edith Cowan University, Joon‑
dalup, WA, Australia. 3 School of Science, Edith Cowan University, Joondalup,
WA, Australia. 4 School of Biomedical Science and Pharmacy, New Lambton
Heights, University of Newcastle, NSW, Australia. 5 School of Biomedical Sci‑
ences, Royal Perth Hospital Unit, University of Western Australia, Perth, WA,
Australia. 6 Medical School, University of Western Australia, Perth, WA, Australia.
7
The Danish Cancer Institute, Copenhagen, Denmark. 8 Liggins Institute, Uni‑
versity of Auckland, Auckland, New Zealand. 9 Department of Endocrinology
and Diabetes, Royal Perth Hospital, Perth, WA, Australia. 10 Department of Car‑diology, Royal Perth Hospital, Perth, WA, Australia. 11 Flinders Health and Medi‑
cal Research Institute, Flinders University, Adelaide, SA, Australia. 12 Centre
for Kidney Research, Children’s Hospital at Westmead, School of Public Health,Sydney Medical School, The University of Sydney, Sydney, NSW, Australia.
Received: 30 November 2023 Accepted: 22 August 2024

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