riesgo cardiovascular y proteina de soya.pdf

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ORIGINAL ARTICLE

Effect of soybean protein on novel cardiovascular disease risk

factors: a randomized controlled trialCM Rebholz1, K Reynolds1,2, MR Wofford3, J Chen1,4, TN Kelly1, H Mei1, PK Whelton1 and J He1,4

BACKGROUND/OBJECTIVES: Cardiovascular disease (CVD) is the leading cause of death in the United States and the world. Clinical

trials have suggested that soybean protein lowers lipids and blood pressure. The effect of soybean protein on novel CVD risk factors

has not been well studied. The objective of this study was to examine the effect of soybean protein on biomarkers of inflammation,

endothelial dysfunction and adipocytokines.

SUBJECTS/METHODS: The effect of 8 weeks of 40 g of soybean protein supplement (89.3 mg isoflavones), 40 g of milk protein

supplement and 40 g of complex carbohydrate placebo was examined in a randomized, placebo-controlled, double-blind, three-

phase crossover trial among adults in New Orleans, Louisiana and Jackson, Mississippi. Plasma levels of inflammation biomarkers

(C-reactive protein, interleukin-6, tumor necrosis factor-a), endothelial dysfunction biomarkers (E-selectin, intercellular adhesion

molecule-1, vascular cell adhesion molecule-1, thrombomodulin) and adipocytokines (high-molecular weight adiponectin, leptin,

resistin) were measured at baseline and at the end of each intervention using immunoturbidimetric and enzyme-linked

immunosorbent assay techniques.RESULTS: Soy protein supplementation resulted in a significant mean net change (95% confidence interval) in plasma E-selectin of

3.93 ng/ml (7.05 to 0.81 ng/ml; P0.014) compared with milk protein, and in plasma leptin of 2089.8 pg/ml (3689.3 to

490.3 pg/ml; P0.011) compared with carbohydrate. There were no significant changes in any other risk factors.

CONCLUSIONS: Soy protein supplementation may reduce levels of E-selectin and leptin. Further research is warranted to

investigate the mechanisms through which protein may confer protective effects on novel CVD risk factors.

European Journal of Clinical Nutrition (2013) 67, 5863; doi:10.1038/ejcn.2012.186; published online 28 November 2012

Keywords: adipokines; carbohydrates; clinical trial; inflammation; milk proteins; soybean proteins

INTRODUCTION

Cardiovascular disease (CVD) is the primary cause of mortality inthe United States and the world.1,2 Epidemiologic studies havefound that biomarkers of systemic inflammation, endothelialdysfunction and adipocytokines are associated with an increasedrisk of CVD morbidity and mortality, independent of traditionalCVD risk factors.35 Endothelial dysfunction is the initial detectablestep in the process of developing atherosclerotic CVD, and theinflammatory system is integral to facilitating furtheratherosclerosis lesion pathogenesis leading to clinical CVDevents.6 Adipocytokines are thought to act through severalpathways to realize cardio-metabolic effects, includingmediating glucose and lipid metabolism, the inflammatoryresponse and endothelial function.7 Examining changes inbiomarkers of the inflammatory system, endothelial function andadipocytokines allows for the detection of early indications of

change in CVD risk.Soybean protein is recommended as a healthy food for

cardiovascular health based primarily on its favorable effect oncholesterol and blood pressure.810 The American HeartAssociation recommends daily consumption of 25 g or more ofsoy protein with phytoestrogens in a diet low in saturated fat andcholesterol to improve the lipid profile and reduce CVD risk.8

Some clinical studies have investigated the effect of soy protein

on inflammatory biomarkers, endothelial dysfunction biomarkers

and adipocytokines, as early markers of CVD risk.

1116

However,the existing evidence is limited and inconsistent. Evaluating theimpact of dietary interventions, such as soybean proteinsupplementation, on novel CVD risk factors may provide justifi-cation for widespread implementation of such interventions forthe prevention and treatment of CVD and its related morbidityand mortality. The objective of this study was to examine theeffect of soybean protein, milk protein and complex carbohydratesupplementation on plasma levels of biomarkers of inflammation,endothelial dysfunction and adipocytokines.

SUBJECTS AND METHODS

Study design

This study was a randomized, double-blinded and placebo-controlled trial.

This study utilized a three-phase crossover study design with a 2-week run-in period and 3-week washout periods between interventions. Eligibleparticipants were allocated to receive 40 g/day of soy protein (89.3mgisoflavones), 40 g/day of milk protein and 40 g/day of complex carbohy-drate (placebo) in random order, each for 8 weeks.

Written informed consent was obtained from each participant beforethe initial screening visit and before randomization. The institutionalreview boards at the Tulane University Health Sciences Center and theUniversity of Mississippi Medical Center approved the study protocol.

1Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA; 2Department of Research and Evaluation, Kaiser

Permanente Southern California, Pasadena, CA, USA; 3Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA and 4Department of Medicine, Tulane

University School of Medicine, New Orleans, LA, USA. Correspondence: Dr. J He, Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine,

1440 Canal Street, Suite 2000, New Orleans, LA 70112, USA.

E-mail: [email protected]

Received 6 June 2012; revised 21 October 2012; accepted 22 October 2012; published online 28 November 2012

European Journal of Clinical Nutrition (2013) 67, 5863

& 2013 Macmillan Publishers Limited All rights reserved 0954-3007/13

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Study participants

Study participants were men and women aged 22 years or older who hada mean systolic blood pressure from 120 to 159mm Hg and a diastolicblood pressure from 80 to 95 mm Hg, based on the average of six readingsat two screening visits. Individuals were excluded if they were takingantihypertensive medication, had a self-reported history of clinical CVD,cancer, chronic kidney disease (or a serum creatinine X1.7 mg/dl(150.3mmol/l) for men and X1.5mg/dl (132.6mmol/l) for women),hypercholesterolemia (or serum total cholesterol X240 mg/dl (X6.2

mmol/l)), diabetes (or serum glucose X126mg/dl (X7 mmol/l)), a bodymass index X40kg/m2, were pregnant or intended to become pregnantduring the study, were consuming 414 alcoholic drinks per week, or wereconsuming X1.63g/kg per day (85th percentile of dietary protein intake inthe US general population) of dietary protein based on two 24-h dietaryrecalls.

Study participants were recruited by mass mailing and work-site andcommunity-based screenings in New Orleans, Louisiana and Jackson,Mississippi. Participant recruitment and the intervention occurred betweenOctober 2005 and April 2008.

Intervention

Study participants were randomly assigned to three sequences at a fixed1:1:1 allocation ratio. Randomization was stratified by clinic site, genderand hypertension status. We used a block size of six to ensure equaldistribution of participants among the sequences. Randomization assign-

ment was conducted centrally at the Data Coordinating Unit at TulaneUniversity, employing a computer-generated assignment list, which couldonly be accessed by the data coordinator. All other research personnel andthe study participants were unaware of treatment assignment.

The soy protein, milk protein and complex carbohydrate supplementswere provided by Solae, LLC (St Louis, MO, USA). Caloric and fat contentwas similar in all supplements. Milk protein supplements contained 10 mgof cholesterol, and soy protein supplements contained 89.3mg ofisoflavones, which was not present in the other supplements. Supplementpowders looked the same and were distributed in identical packets. Studyparticipants were instructed to take the supplements twice per day; oncein the morning and once in the evening in water or juice. Based on theparticipants two 24-h dietary recalls during screening visits, individualizedrecommendations were given in order for participants total energy intaketo remain constant over the intervention periods.

MeasurementsStudy participants were instructed to fast for 10 h before their clinic visitsfor blood sample collection. Blood samples were promptly centrifuged at3000r.p.m. for 10 min at 4 1C. Serum and plasma were separated andaliquoted for different analyses at the clinical laboratory. Specimens werestored at 85 1C until analysis. Specimens from 102 study participantswere available for this study. Blind duplicates of samples were collected,and biomarker levels were similar in the blind duplicates compared withthe original samples.

Plasma levels of C-reactive protein (CRP) were measured using a latexparticle-enhanced, high-sensitivity immunoturbidimetric assay on theOlympus AU400e Analyzer (Beckman Coulter, Brea, CA, USA) with Kamiyareagents (Kamiya Biomedical Company, Seattle, WA, USA).17 Ten percent of

the samples were measured in duplicate. Plasma levels of all otherbiomarkers were measured using commercially available, sandwichenzyme-linked immunosorbent assay methods, with high-sensitivityassays for interleukin-6 (IL-6) and tumor necrosis factor-a (TNF-a) only(Quantikine human immunoassays, R&D Systems, Inc., Minneapolis, MN,USA). Optical density was measured using a microplate reader (ASYSExpert Plus microplate reader, Biochrom Ltd., Cambridge, UK). All samplesthat were measured with enzyme-linked immunosorbent assay methodswere run in duplicate.

A baseline questionnaire was administered to record demographic andlifestyle characteristics and medical history. Blood pressure, body weight,height and waist circumference were also measured at baseline. Computersoftware was used to calculate nutrient intakes from 24-h dietary recalls atbaseline and at the end of each phase (Minnesota Nutrition Data Systemfor Research, University of Minnesota, 2002). An overnight timed urinesample was collected at the baseline and termination visits to measureexcretion of micronutrients. Side effects and compliance were assessedusing a symptom questionnaire, counts of returned unconsumed packetsand self-reported supplement calendar report.

Statistical analysis

The primary outcome of interest was the net change in biomarkers, whichwas calculated as the difference in biomarker levels at the end of eachintervention/control phase. When duplicate measurements of sampleswere taken, the means of the two measurements were used for analysis.PROC MIXED of SAS version 9.2 (SAS Institute Inc., Cary, NC, USA) was usedto obtain point estimates and standard errors of the treatment andsequence effects and to test for differences between treatments. Anautoregressive correlation structure was used to account for within-subjectcorrelation due to repeated measurements in the crossover study design.Mean levels of biomarkers according to intervention phase and net changein biomarkers according to all three comparisons between phases alongwith corresponding 95% confidence intervals (CIs) were estimated with theLSMEANS statement. The carryover effect was assessed by testing theinteraction between phase and treatment and the interaction was notstatistically significant. The intention-to-treat principle was used for allprimary analyses. Sensitivity analyses were performed by (1) using log-transformed values, (2) restricting to those participants that completed allphases of the study and (3) those that consumed at least 85% ofsupplements based on returned packet counts. All tests are two-tailed andstatistical significance was assessed at an a level of 0.0167 (0.05/3 for theBonferroni correction of multiple comparisons).

For the power calculation, the standard deviations corresponding to netchanges in biomarkers between phases were abstracted from previouslypublished trials with a similar follow-up period as the present study.1820

With a pre-determined sample size and a Bonferroni-adjusted significancelevel, the power for determining the difference in mean biomarkers in acrossover design study for two-tailed t tests was calculated. We estimatedthat it should be possible to recognize relatively small but important netchanges in plasma levels of novel biomarkers with 80% statistical power.

RESULTS

The flow diagram summarizes study assignment according torandomization group and period (Figure 1). Conditional follow-up

Randomized (n=102)

Completed soy protein (n=29) Completed milk protein (n=34) Completed carbohydrate (n=33)

Allocated to sequence A (n=32) Allocated to sequence B (n=35) Allocated to sequence C (n=35)

Completed milk protein (n=23) Completed carbohydrate (n=28) Completed soy protein (n=27)

Completed carbohydrate (n=21) Completed soy protein (n=27) Completed milk protein (n=24)

Figure 1. Flow diagram of study participant assignment and participation.

Soy protein and novel cardiovascular risk factors

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rates for the first, second and third phases were 94.1, 81.3 and92.3%, respectively. Overall follow-up during the entire studyperiod was similar for the three interventions: 81.4% for soyprotein, 79.4% for milk protein and 80.4% for carbohydrate.

Supplement consumption was similar for all three interventionsand all three periods. According to supplement calendar reportsfor all interventions combined, study participants consumed anaverage of 92.1% of supplements provided for each phase and84.5% of participants consumed at least 85% of their supplements.Based on overall counts by study staff of returned andunconsumed supplement packets, study participants consumedan average of 90.5% of their supplements and 76.0% ofparticipants consumed at least 85% of their supplements. Theaverage level of overnight urinary excretion of urea nitrogen wassignificantly increased during the soy protein (467.4 mg/l) andmilk protein (484.0 mg/l) supplementation interventions com-pared with the carbohydrate supplementation intervention(376.9mg/l; P0.01). The urea:creatinine ratio was also increasedduring protein interventions (soy: 8.7, milk: 8.7, carbohydrate: 6.8;Po0.0001).

Baseline characteristics of the 102 study participants are shownin Table 1 according to randomization group. For the total studypopulation, mean age was 46 years, 67% were males, and 29%were African-American. At baseline, mean body mass index was29.9kg/m2, mean fasting glucose level was 96.1 mg/dl (5.3mmol/l),mean systolic/diastolic blood pressure was 127.5/82.3 mm Hgand mean total cholesterol was 196.5 mg/dl (5.1 mmol/l).

There were no statistically significant differences in baseline

characteristics by randomization sequence assignment. Thereappeared to be variation in age and college education byrandomization group, but it was not statistically significant.

The daily dietary nutrient intake based on 24-h dietary recall ispresented according to intervention phase in Table 2. Total dietaryprotein intake was significantly higher by an average of 30.2 g/daydue to the higher intake of vegetable protein (29.9 g/day) duringthe soy protein intervention compared with the carbohydrateintervention. During the milk protein intervention, total dietaryprotein was significantly higher by 33.2 g/day due to the higherintake of animal protein (33.8 g/day) compared with thecarbohydrate intervention. Dietary carbohydrate intake was lowerby 36.7 g/day during the soy protein intervention and by 41.3 g/day during the milk protein intervention compared with thecarbohydrate intervention. Daily intake of other dietary nutrientswas not significantly different across intervention phases.

Mean plasma levels of biomarkers at baseline and at the end ofeach intervention with corresponding CIs are presented in Table 3.During the study, the lowest level of several biomarkers, includingCRP, IL-6, TNF-a, E-selectin, intercellular adhesion molecule-1(ICAM-1) and leptin, were observed at the end of the soysupplementation intervention. The lowest levels of vascular celladhesion molecule-1 (VCAM-1) and resistin were observed at theend of milk protein supplementation intervention. The meanplasma levels of E-selectin and resistin were statistically signifi-cantly different by phase, after accounting for multiple compar-isons. Mean plasma levels of all other biomarkers were notstatistically significantly different by phase.

Table 1. Baseline characteristics* of 102 trial participants

Randomization groups

A(n35)

B(n32)

C(n35)

P-value

Age, years 48.2 (11.7) 42.7 (11.7) 47.6 (9.2) 0.08Male, n (%) 25 (71.4%) 20 (62.5%) 23 (65.7%) 0.73

African-American, n (%) 8 (22.9%) 9 (28.1%) 13 (37.1%) 0.42Some college education, n (%) 33 (94.3%) 27 (84.4%) 26 (74.3%) 0.07Current smoking, n (%) 1 (2.9%) 4 (12.5%) 4 (11.4%) 0.30Alcohol drinking, n (%) 15 (42.9%) 12 (37.5%) 14 (40.0%) 0.90Physical activity X3 times/week, n (%) 18 (51.4%) 23 (74.2%) 20 (58.8%) 0.16Body-mass index, kg/m2 30.4 (4.8) 29.5 (3.8) 29.7 (4.9) 0.71Systolic blood pressure, mm Hg 127.0 (8.3) 126.9 (8.5) 128.4 (11.3) 0.79Diastolic blood pressure, mm Hg 81.6 (4.9) 82.7 (4.8) 82.7 (7.0) 0.67

Total cholesterol, mmol/l 4.9 (0.7) 5.3 (0.6) 5.1 (0.7) 0.13Glucose, mmol/l 5.3 (0.5) 5.4 (0.5) 5.3 (0.5) 0.85Creatinine, mmol/l 88.4 (26.5) 97.2 (17.7) 97.2 (17.7) 0.87

*Mean (s.d.) or frequency (percentage).

Table 2. Mean (s.d.) daily dietary nutrient intake according to intervention phase

Nutrient Baseline(n102)

Soy protein(n83)

Milk protein(n81)

Carbohydrate(n82)

P-value

Energy, kcal 2021.8 (566.9) 2102.7 (705.7) 2099.8 (596.5) 2113.2 (643.1) 0.99Protein, g 87.3 (29.5) 114.3 (30.9) 117.3 (32.2) 84.1 (30.4) o0.0001Animal protein, g 61.4 (26.4) 59.7 (25.7) 93.2 (29.9) 59.4 (26.0) o0.0001Vegetable protein, g 25.7 (12.3) 54.4 (16.2) 24.0 (10.7) 24.5 (10.9) o0.0001Carbohydrate, g 234.2 (78.6) 231.4 (91.0) 226.8 (80.4) 268.1 (89.7) 0.005Fat, g 79.4 (27.8) 77.0 (34.6) 78.0 (28.7) 77.8 (32.5) 0.98Saturated fat, g 25.0 (10.8) 24.9 (13.4) 25.2 (10.5) 25.5 (12.6) 0.95Polyunsaturated fat, g 16.5 (7.4) 14.3 (6.9) 15.8 (6.8) 15.7 (7.3) 0.29Monounsaturated fat, g 31.0 (11.0) 30.2 (14.0) 30.0 (12.3) 29.8 (13.5) 0.98Cholesterol, mg 320.8 (185.9) 317.5 (168.4) 341.9 (193.8) 300.2 (184.9) 0.35Daidzein, mg 1.0 (2.7) 0.9 (3.2) 0.9 (1.8) 1.1 (4.4) 0.92Genistein, mg 1.1 (3.3) 1.1 (4.1) 0.9 (2.0) 1.2 (4.5) 0.92


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Net change in plasma levels of inflammatory biomarkers alongwith CIs and P-values are provided for the three comparisons ofinterest in Table 4. Compared with milk protein, soy proteinsupplementation resulted in a significant mean net change (95%CI) in plasma E-selectin of 3.93 ng/ml (7.05 to 0.81 ng/ml;P0.014), even after correction for multiple comparisons.Compared with carbohydrate, soy protein supplementationresulted in a significant mean net change (95% CI) in plasmaleptin of 2089.8 pg/ml (3689.3 to 490.3pg/ml; P0.011),which attained statistical significance even after adjusting for

multiple comparisons. There were no statistically significant netchanges in plasma levels of CRP, IL-6, TNF-a, VCAM-1, ICAM-1,thrombomodulin, high-molecular weight (HMW) adiponectin,leptin or resistin.

The magnitude and significance of the mean levels and netchange estimates were not different in the pre-specified sensitivityanalyses which included using log-transformed values, when theanalysis was restricted to participants who completed all phasesand those who consumed at least 85% of their supplementpackets.

The frequency of side effects was similar for the soy protein,milk protein and carbohydrate supplementation interventions.

The most commonly self-reported changes in symptoms were achange in appetite (29.6%), increased energy level (16.7%) andconstipation (14.2%). Participants reported more belching during

the milk protein supplementation (18.0%) compared with soyprotein (6.2%) and carbohydrate supplementation (6.2%; P0.02).

There was a slightly larger percentage of participants reportingimproved mood during the soy protein supplementation (18.5%)compared with milk protein (7.7%) and carbohydrate supplemen-tation (8.6%), although it was not statistically significant (P0.06).

DISCUSSION

This randomized controlled trial suggests that soy protein

supplementation reduces plasma levels of E-selectin comparedwith milk protein and reduces plasma levels of leptin comparedwith carbohydrate. However, soy protein supplementation maynot have a meaningful effect on plasma levels of CRP, IL-6, TNF-a,VCAM-1, ICAM-1, thrombomodulin, HMW adiponectin or resistin.

These study findings add to our understanding of the effects ofsoy protein, especially considering the paucity of rigorouslydesigned and conducted soy protein clinical trials on novel CVDrisk factors.

The beneficial effect of soy protein on traditional CVD riskfactors, primarily lipids and blood pressure, has been demon-strated previously.9,10 The evidence for an association of dietaryprotein intake on novel CVD risk factors is less conclusive. Ourfindings appear to be consistent with the majority of the fewpublished soy protein clinical trials, demonstrating no statistically

Table 3. Mean (95% confidence interval) plasma levels of biomarkers according to comparison phase

Biomarker Baseline Soy protein Milk protein Carbohydrate P-value

C-reactive protein, mg/l 2.44 (1.81, 3.06) 2.33 (1.71, 2.95) 2.65 (2.03, 3.26) 2.54 (1.92, 3.15) 0.74Interleukin-6, pg/ml 2.29 (1.91, 2.66) 1.57 (1.22, 1.93) 1.69 (1.33, 2.04) 1.76 (1.41, 2.12) 0.05

Tumor necrosis factor-a,pg/ml

1.45 (1.28, 1.62) 1.20 (1.03, 1.37) 1.22 (1.05, 1.39) 1.22 (1.05, 1.38) 0.04

E-selectin, ng/ml 49.91 (45.34, 54.48) 42.43 (37.75, 47.10) 46.36 (41.70, 51.01) 43.17 (38.53, 47.81) 0.002Vascular cell adhesionmolecule-1, ng/ml

674.5 (612.3, 736.8) 746.0 (685.7, 806.3) 737.2 (677.4, 797.0) 760.4 (701.2, 819.8) 0.23

Intercellular adhesionmolecule-1, ng/ml

254.4 (232.3, 276.5) 262.6 (240.2, 285.1) 268.2 (245.8, 290.6) 266.1 (243.9, 288.4) 0.69

Thrombomodulin, pg/ml 4457.4 (4204.5, 4710.6) 4514.2 (4259.1, 4769.4) 4589.0 (4335.5, 4842.5) 4455.9 (4203.7, 4708.1) 0.64High-molecular weightadiponectin, ng/ml

2830.3 (2126.6, 3533.9) 3332.6 (2618.6, 4046.6) 3202.0 (2489.1, 3914.9) 3439.0 (2726.7, 4151.4) 0.018

Leptin, pg/ml 17 6 68 (14 0 92, 21 2 45) 17 5 38 (13 9 00, 21 1 76) 18 1 95 (14 5 64, 21 8 25) 19 6 28 (16 0 01, 23 2 55) 0.06Resistin, ng/ml 10.92 (9.57, 12.28) 8.28 (7.08, 9.48) 7.57 (6.38, 8.76) 8.30 (7.11, 9.48) 0.009

To convert C-reactive protein to nmol/l, multiply by 9.524. To convert interleukin-6 to IU/ml, multiply by 0.124. To convert tumor necrosis factor-a to IU/ml,

multiply by 0.086. To our knowledge, there is no conversion to International Units currently available for the remaining novel biomarkers.

Table 4.Net change (95% confidence interval) and P-value for plasma levels of biomarkers according to comparison phases

Biomarker Soy protein vs carbohydrate Milk protein vs carbohydrate Soy protein vs milk protein

C-reactive protein, mg/l 0.21 (0.78, 0.36) 0.47 0.11 (0.47, 0.68) 0.71 0.32 (0.90, 0.26) 0.28Interleukin-6, pg/ml 0.19 (0.55, 0.18) 0.31 0.08 (0.44, 0.29) 0.69 0.11 (0.48, 0.26) 0.55

Tumor necrosis factor-a, pg/ml 0.01 (0.14, 0.11) 0.84 0.01 (0.12, 0.13) 0.93 0.02 (0.15, 0.11) 0.78E-selectin, ng/ml 0.74 (3.79, 2.30) 0.63 3.19 (0.10, 6.27) 0.04 3.93 (7.05, 0.81) 0.014Vascular cell adhesion molecule-1,ng/ml

14.44 (74.78, 45.89) 0.64 23.25 (84.29, 37.79) 0.45 8.81 (52.66, 70.27) 0.78

Intercellular adhesion molecule-1,ng/ml

3.47 (20.92, 13.97) 0.70 2.09 (15.57, 19.75) 0.82 5.56 (23.41, 12.29) 0.54

Thrombomodulin, pg/ml 58.4 (155.7, 272.4) 0.59 113.2 (83.5, 349.8) 0.23 74.8 (293.6, 144.0) 0.50High-molecular weight adiponectin,ng/ml

106.4 (385.4, 172.5) 0.45 237.1 (519.9, 45.8) 0.10 130.6 (155.8, 417.0) 0.37

Leptin, pg/ml 2089.8 (3689.3, 490.3) 0.011 1433.3 (3055.1, 188.4) 0.08 656.5 (2298.2, 985.3) 0.43Resistin, ng/ml 0.02 (1.46, 1.42) 0.98 0.72 (2.17, 0.72) 0.33 0.71 (0.74, 2.15) 0.34

To convert C-reactive protein to nmol/l, multiply by 9.524. To convert interleukin-6 to IU/ml, multiply by 0.124. To convert tumor necrosis factor-a to IU/ml,

multiply by 0.086. To our knowledge, there is no conversion to International Units currently available for the remaining novel biomarkers.


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significant change in the inflammatory biomarkers, endothelialdysfunction biomarkers and adipocytokines.1116 The observedreduction in plasma levels of E-selectin after soy proteinsupplementation compared with milk protein supplementationin the present study was consistent with one other study.16 In acrossover study of 42 postmenopausal women with metabolicsyndrome, an 8-week intervention on the soy nut diet (30 g/daysoy protein and 102mg/day isoflavones) resulted in a 11.4%decrease (Po0.01) in E-selectin compared with 8 weeks on thecontrol diet.16 In contrast, there was no effect of soy protein onE-selectin in three other studies.1315 The observed reduction inplasma leptin was documented in one other study of 90overweight and obese men and women in which a 6-weekintervention of two daily servings of a soy drink resulted in18.2 ng/ml leptin reduction compared with 6.97ng/ml in thecontrol group (Po0.01).21 In contrast, six other soy protein clinicaltrials reported no significant change in leptin levels.12,2226 To thebest of our knowledge, the present study is one of the largestrandomized trials to investigate the effect of proteinsupplementation on multiple biomarkers of inflammation,endothelial dysfunction and adipocytokines, and is the secondto report on thrombomodulin and resistin. Whereas the majorityof previous studies have limited their participants to Caucasianfemales, our study population is more generalizable due to itrepresenting men, women, Caucasians and African-Americans.

It is well established that diet plays an important role inmodifying CVD risk. Soy food products have several bioactivecomponents and properties that could be responsible forimproving cardiovascular health, including fiber, polyunsaturatedfatty acids and arginine.2729 Replacement of carbohydrate withdietary protein intake is thought to offer cardiovascular benefitdue to the lower glycemic index of protein and associatedreduction of inflammatory activity.30 A leading hypothesis for thepostulated mechanism for an effect of soy protein on CVD riskfactors is through the estrogen-like structure and biologicalactivity of soy-derived isoflavones.31,32 Isoflavones, such asgenistein, have been shown to improve endothelial function byincreasing nitric oxide, causing smooth muscle relaxation, andsubsequently resulting in vasodilation.3337 Genistein has alsobeen shown to inhibit differentiation of fat cells, which secreteadipocytokines, through several pathways including expression ofendothelial nitric oxide synthase, inhibition of p38 mitogen-activated kinase phosphorylation and inhibition of fatty acidsynthase.3840

This randomized, double-blind, placebo-controlled trial had ahigh level of compliance, high rate of study completion, andlimited variability in diet and lifestyle behaviors. The prolonged3-week washout period limited the carryover effect, and there wasno statistical evidence of carryover between periods. Blindduplicates of samples were measured for quality assurance, andthe same laboratory technician and equipment was usedthroughout the study to minimize systematic error. High-sensitivity assay techniques were employed such that low levels

of inflammatory biomarkers could be detected. Random variabilitywas decreased by using average values of duplicate measures inthe analysis. The estimation of mean levels of biomarkers andmean net change in biomarkers was consistent in sensitivityanalyses.

Although our study has a relatively large sample size comparedwith previously published studies, this study still has insufficientstatistical power to detect small changes in biomarkers due to thesupplement interventions. However, the clinical significance ofsmall changes in these novel CVD risk factors is unknown. Thecontribution of participants was maximized with the crossoverstudy design and mixed effects regression model. Anotherlimitation is that the potential day-to-day variation in biomarkerlevels could have increased random error and variance estimatesand therefore reduced statistical power. In our study, the 95% CI

estimates of net changes in biomarkers were large, even for thosethat reached statistical significance. Future studies should collectmultiple blood samples on different days in order to assess andaccount for this potential source of variability. An additionallimitation is the relatively short intervention duration of 2 months.However, the majority of soy protein clinical trials on novel CVDrisk factors similarly have an intervention duration of 6 or 8weeks.11,1316

In conclusion, this study suggests that soy protein may improveendothelial function by decreasing plasma levels of E-selectincompared with milk protein and may improve metabolic functionby decreasing plasma levels of leptin compared with carbohy-drate. This study suggests that soy protein may not significantlyimpact plasma levels of CRP, IL-6, TNF-a, ICAM-1, VCAM-1,thrombomodulin, HMW adiponectin or resistin. A randomizedcontrolled trial with a larger sample size, longer interventionduration and multiple measurements of biomarkers may be bettersuited to assess the impact of soy protein on novel CVD riskfactors. A meta-analysis could be conducted to pool results fromthe few available clinical trials with small sample sizes in order toestimate the effect of soy protein on novel CVD risk factors withgreater statistical power. This study provides moderate support forthe role of soy protein as a healthy food for cardiovascular healththrough E-selectin and leptin reduction, which adds to the existingevidence for the cholesterol and blood pressure reducing effectsof soy protein.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

ACKNOWLEDGEMENTS

This work was supported by a research grant (R01 HL68057) from the National Heart,

Lung, and Blood Institute of National Institutes of Health, Bethesda, MD, USA, and the

American Medical Association Foundation 2011 Seed Grant Research Program. The

study supplements were provided by Solae, LLC. National Heart, Lung, and Blood

Institute, American Medical Association Foundation, and Solae, LLC had no role in the

design, data collection and analysis, decision to publish or preparation of themanuscript.

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