Association of Dietary Inflammatory Potential (DIP) and Endothelial Function Biomarkers among Female Nurses of Isfahan Hospitals

Association of Dietary Inflammatory Potential (DIP) and Endothelial Function Biomarkers among Female Nurses of Isfahan Hospitals | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article Association of Dietary Inflammatory Potential (DIP) and Endothelial Function Biomarkers among Female Nurses of Isfahan Hospitals Mohammad Gholizadeh, Ebrahim Falahi, Ammar Hassanzadeh Keshteli, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-555465/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Dietary inflammatory index (DIP) is a new dietary index designed to evaluate individuals’ diets. In addition, adhesion molecules are important biomarkers for assessing endothelium inflammation that they related to atherosclerosis and cardiovascular disease. Also, there is no study for assessing the association between adhesion molecules and DIP until now as well as other studies that assessed the relationship between dietary inflammatory index or DIP have controversy. The purpose of this cross-sectional study was to determine the correlation between DII and endothelial markers such as E-selectin, intercellular adhesion molecule-1 (sICAM-1) and vascular cell adhesion molecule-1 (sVCAM-1) among female nurses from Isfahan. In this study, dietary inflammatory potential (DIP) was used instead of DII. Methods: This study was performed on 420 healthy nurses. The nurses were selected by random cluster sampling method from private and public Isfahan hospitals. A validated food frequency questionnaire (FFQ) was applied to assess the dietary inflammatory potential. A fasting blood sample was collected for measuring the plasma levels of the endothelial markers and other variables. Results: After adjusting different potential confounders, no statistical association was found between DIP and sICAM-1, E–selectin and sVCAM-1 in model I (P=0.57, 0.98 and 0.45), model II (P=0.57, 0.98 and 0.45) and model III (P=0.67, 0.92 and 0.50) in comparison to the crude group (P=0.35, 0.83 and 0.49, respectively). Conclusions: The results revealed that the plasma levels of endothelial markers including E-selectin, sICAM-1, and sVCAM-1 were not significantly associated with DIP in female nurses. Nutrition & Dietetics Population Biology Dietary inflammatory potential sICAM-1 endothelial markers sVCAM-1 E –selectin Figures Figure 1 Figure 2 Figure 3 Introduction Atherosclerosis is a continuing inflammatory state of the vessels [ 1 ]. The progression of atherosclerosis leads to myocardial infarction and sudden death [ 2 ]. It is believed that atherosclerosis is an inflammatory condition that is largely responsible for cardiovascular disease (CVD) mortality [ 3 , 4 ]. Endothelial dysfunction contributes to the pathogenesis of vascular disease and plays an important role in CVD as well [ 5 , 6 ]. Endothelial dysfunction is characterized by impaired activity of endothelial derived relaxant factors and increased activity of vasoconstrictor factors. However, cell adhesion molecules (CAM) including E-selectin, intercellular adhesion molecule-1 (sICAM-1) and vascular cell adhesion molecule-1 (sVCAM-1) accelerate atherosclerosis [ 4 , 7 – 9 ]. Adhesion molecules are normally expressed by the endothelium. They also play a role in leukocyte rolling, firm adhesion, and transmigration. Furthermore, they are associated with a variety of pathophysiological processes and inflammatory disorders. Atherosclerotic lesions and fatty streaks increase the expression of sICAM-1, sVCAM-1, and P- and E-selectin on the human endothelial cells [ 10 , 11 ]. E-selectin plays an important role in acute inflammation [ 12 – 14 ]. Moreover, sICAM-1 and sVCAM-1 are involved in chronic inflammation [ 15 , 16 ]. Leukocyte adhesion is an important component of some vascular diseases and atherogenesis. Leukocyte recruitment occurs in a multistep process and selectin, which is expressed on the activated endothelial cells, is involved in the initial rolling process of leukocytes [ 12 , 17 ]. The leukocyte surface has sites for selectin ligand [ 18 ]. β1 and β2 integrin are expressed on leukocytes and act as binding sites for sVCAM-1 or sICAM-1. Furthermore, selectin plays a role in the initial rolling process of leukocytes whereas sICAM-1 and sVCAM-1 mediate leukocyte arresting and firm adhesion [ 12 , 18 – 20 ]. Dietary inflammatory potential (DIP) is a new dietary index designed to evaluate the individual's diets. DIP is a tool to assess the potential inflammatory and anti-inflammatory properties of a diet based on food elements. In this index, values of + 1, 0, and − 1 indicate pro-inflammation, indifferent and anti-inflammation reactions, respectively [ 21 ]. Actually, DIP is a resource to assess pro-inflammatory effects of food ingredients based on anti-inflammatory functions [ 21 , 22 ]. DIP has been linked to a variety of systemic biomarkers such as interleukin 6 (IL-6), tumor necrosis factor alpha (TNF-α), C-reactive protein (CRP) and several metabolic diseases such as CVD, cancers, and diabetes. Many studies have found that DIP is associated with the risk of metabolic syndrome and cardiovascular diseases [ 23 – 28 ]. Many studies have reported a positive association between DIP and CVDs [ 29 , 30 ]. Due to the increase in the global risk of CVDs and related diseases in the world, it is important to find healthy dietary patterns with low inflammatory scores to tackle inflammation and CVDs. The purpose of this study was to determine the association between DIP and endothelial markers such as sICAM, sVCAM, and E-selectin in female nurses working in Isfahan hospitals. Materials And Methods Participants Four hundred and eighty healthy female nurses aged > 30 years participated in this cross-sectional study. The participants were selected randomly from seven public and private hospitals in Isfahan, Iran. The female nurses with a history of diabetes, malignancy, infections, and CVDs were excluded. Furthermore, the subjects who did not complete the FFQ questionnaire were also excluded from the study. Finally, 420 nurses were enrolled in the study. The participants fill in a consent form based on Tehran university of medical sciences ethics rules for participating on this study. The study protocol was approved by Tehran university of medical sciences (IR.TUMS.VCR.REC.1399.584). Blood sampling Blood samples were collected from the participants after 12 hours of fasting to measure the levels of endothelial markers, lipid profile, and fasting blood glucose. Then, the sample were centrifuged for 30–45 minute and frozen at 70°C. The levels of sVCAM- 1, sICAM-1, and E-selectin were measured using commercial ELISA kits (Biosource International and Bender MED Systems) according to the manufacturer’s instructions. ELISA kits were also used to measure low-density lipoprotein (LDL) and high-density lipoprotein (HDL). Dietary inflammatory score The method developed by Shivappa et al was applied to calculate the DIP scores of the diets. The Food Frequency Questionnaire (FFQ) was used to determine the dietary intake [ 21 ]. In the Iranian dietary pattern, 29 out of 45 items of DII are very common, including Macronutrients (energy, carbohydrates, fat, protein, fiber), Fat (cholesterol, saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA)), Water-soluble vitamins (pyridoxine, folic acid, niacin, thiamin, ascorbic acid and riboflavin). Fat-soluble vitamins (A,D and E), Minerals (iron, magnesium, zinc, and selenium), as well as caffeine, β-carotene, onion, garlic, pepper, and black tea [ 31 ]. Other DIP items that were uncommon in the Iranian dietary regimen were omitted form the list of FFQ. The intake of the above dietary items was adjusted according to the daily energy intake [ 32 ]. A z-score was generated for all of the 29 items of the FFQ list for each participant. For each subject, the "standard global mean" was subtracted from the mean consumed food and divided by “global standard deviation”. The global means and standard deviations were obtained by the method developed by Shivappa et al [ 21 ]. To decrease the skewness of the variables, the variables were converted to a centered percentile score. This score was then extended by the impact for every item [ 21 ]. The DIP scores of all foods were summed to calculate the overall score. More positive values indicated a higher inflammatory dietary potential. Assessment of other variables A computerized scale was used for weight measurement (to the nearest 0.1 kg). The subjects were asked to wear light clothing with no shoes. The height was also measured on the same visit day. Finally, weight (kg) and height (m) were used to calculate the body mass index (BMI) according to the following formula: weight (kg)/ height (m) 2 . The International Physical Questionnaire was used to evaluate daily physical activity [ 33 , 34 ] as MET-hour per week. The factors such as education level, family size, and economic status were inquired from all the participants to determine their socioeconomic status. Moreover, covariate data including age, marital status, menopause situation, past medical history, smoking or medication/supplementation history were selfreported by all the participants. Statistical analysis The final analysis was performed on 420 individuals. Energy adjustment of the variables was carried out using the residual method. After completing the FFQ, the data were entered into an Excel datasheet and daily dietary intakes were compared using the IBM SPSS version 26 (IBM SPSS Statistics for Win, Armonk, NY) and Nutritionist IV (N4) software. Since there were three DIP groups (tertiles), one-way ANOVA was used for continuous variables including age, body mass index (BMI), weight, waist circumference, physical activity, and systolic and diastolic blood pressure, and Pearson’s chi-square test was applied to categorical variables such as oral contraceptive (OCP) use, current corticosteroid use, menopause, marital status, overweight/obesity and socioeconomic status. Similarities between the energy intakes of the participants were adjusted by linear regression. Finally, the associations between DIP and E-selectin, ICAM, and VCAM in three tertiles was analyzed using ANCOVA. Results The mean ± SD age of the participants was 34.44 ± 7.27, 34.59 ± 6.8 and 36.47 ± 7.4 years in the 1st, 2nd, and 3rd tertile, respectively. The demographic characteristics of the subjects are presented in Table 1 . Table 1 characteristics of participants by tertiles of dietary inflammatory index intake (means ± SD) Variables Tertiles of energy by DIP P-value a T1 = 133 T2 = 134 T3 = 129 Age(years) 36.47 ± 7.4 34.59 ± 6.8 34.44 ± 7.27 0.04 Weight(kg) 63.0 ± 8.7 69.2 ± 82.7 63.2 ± 10.32 0.49 BMI(kg/m 2 ) b 24.2 ± 3.37 24.0 ± 3.80 23.97 ± 3.65 0.85 WC(CM) 81.02 ± 9.91 80.51 ± 9.83 81.04 ± 10.82 0.89 PA(MET-h/wk) 60 ± 79 78 ± 77 95 ± 101 0.05 SBP 1 (mmHg) 108 ± 1.04 109 ± 1.20 107 ± 1.24 0.24 DBP(mmHg) 71 ± 0.88 70 ± 1.05 68 ± 0.97 0.08 Current OCP use (%) 5.9 7.4 5.9 0.84 Current corticosteroid use(%) 1.5 2.2 0.7 0.67 Menopausal (%) 8.1 4.4 4.4 0.30 Married (%) 71.3 73.5 73.9 0.87 Overweight or obese (%) 37.7 36.9 43 0.55 Socioeconomic status (%) c High Medium Low 24.4 44.4 31.1 34.7 40 25.3 22.3 48.9 28.7 0.34 BMI: body mass index, WC: waist circumference, SBP: systolic blood pressure,DBP: diastolic blood pressure, OCP: oral contraceptives a) Obtained from analysis of variance for continues variables and chi-square for categorical variables. b) High socioeconomic status was defined based on educational level, income, family size, being owner of the house or renting the house, house area, being owner of the car and number and kind of the car(s), number of bedrooms, and determination of who was in charge of the family. c) Body mass index ≥ 25 The distribution of the DIP score between tertiles is shown in Table 2 . Large differences in DIP scores were observed for fat (P-value:0.04), riboflavin (P-value < 0.001), folic acid (P-value < 0.001), cobalamin (P-value = 0.005), ascorbic acid (P-value < 0.001), vitamin A (P-value < 0.001), beta carotene (P-value < 0.001), zinc (P-value < 0.001), tea (P-value = 0.002), magnesium (P-value < 0.001), onion (P-value < 0.001), fiber (P-value < 0.001), caffeine (P-value = 0.03), SAFA (P-value = 0.02), and cholesterol (P-value < 0.01) between the tertiles. Table 2 dietary inflammatory index intake of participants after adjusted energy (mean ± SD) Tertile of energy –energy adjusted DIP Nutrients T1(n = 136) T2(n = 137) T3(n = 136) P-value Protein 124 ± 98 135 ± 139 102 ± 99 0.06 Fat 103 ± 17 117 ± 88 102 ± 21 0.04 Carbohydrate 338 ± 59 321 ± 61 335 ± 71 0.06 Thiamin 3 ± 2 5 ± 25 2 ± 2 0.06 Riboflavin 1.8 ± 0.51 1.6 ± 0.39 1.4 ± 0.47 < 0.001 Niacin 18.9 ± 3 18.8 ± 3 18.4 ± 5 0.6 Pyridoxine 2.2 ± 0.6 2.6 ± 8 1.6 ± 0.6 0.21 Folic acid 410 ± 121 304 ± 41 226 ± 54 < 0.001 Cobalamin 4.7 ± 2 5 ± 2 4 ± 2 0.005 Ascorbic acid 281 ± 111 181 ± 50 126 ± 40 < 0.001 Vitamin A 1928 ± 939 1276 ± 302 887 ± 335 < 0.001 Vitamin E 64 ± 16 69 ± 19 65 ± 21 0.06 Beta carotene 1527 ± 897 904 ± 291 582 ± 311 < 0.001 Vitamin D 1.4 ± 1.5 2.2 ± 11 0.9 ± 1.3 0.21 Selenium 0.04 ± 0.16 0.48 ± 5 -0.01 ± 0.17 0.31 Zinc 10 ± 4 10 ± 6 8 ± 4 < 0.001 Iron 22 ± 6 25 ± 49 20 ± 8 0.39 Tea 356 ± 263 324 ± 316 242 ± 220 0.002 Magnesium 326 ± 59 267 ± 35 216 ± 46 < 0.001 Onion 54 ± 34 40 ± 27 29 ± 19 < 0.001 Garlic 1.9 ± 0.17 1.9 ± 0.25 2 ± 0.002 0.13 Fiber 10 ± 3 7 ± 2 5 ± 1 < 0.001 Caffeine 82 ± 57 83 ± 127 59 ± 50 0.03 Pepper 10 ± 7.3 10 ± 8.7 8 ± 8.5 0.26 SAFA 25 ± 8 27 ± 14 23 ± 9 0.02 PUFA 40 ± 9 46 ± 44 39 ± 11 0.06 MUFA 32 ± 8 47 ± 146 30 ± 9 0.21 CHOL 234 ± 85 263 ± 93 232 ± 99 0.01 The mean ± SD plasma levels of endothelial markers in different tertiles are shown in Table 3 . There was no significant association between DIP and E-selectin in the crude model (P-value = 0.35) compared to model I (P-value = 0.57), model II (P-value = 0.57) and model III (P-value = 0.67) after adjusting for potential confounders (Fig. 1 ). Table 3 Index of endothelial functions across tertile categories of dietary inflammatory potential. Tertile of energy- adjusted DIP T1(n = 133) T2(n = 136) T3(n = 135) P for trend e E-selectin (ng/L) Crude 81.6 ± 4.5 85.5 ± 4.5 93.7 ± 4.5 0.35 Model I b 80 ± 5.6 88 ± 5.3 80 ± 6.0 0.57 Model II c 79 ± 5.6 88 ± 5.3 81 ± 6.1 0.57 Model III d 79 ± 5.7 88 ± 5.8 82 ± 6.7 0.67 SICAM-1 (mg/L) Crude 221 ± 6.68 211 ± 6.63 213 ± 6.65 0.83 Model I 215 ± 10.9 225 ± 10.2 215 ± 11.7 0.98 Model II 215 ± 10.9 225 ± 10.4 215 ± 10.8 0.98 Model III 214 ± 9.5 213 ± 9.6 212 ± 11.1 0.92 SVCAM-1 (mg/L) Crude 503 ± 11.76 482 ± 11.63 509 ± 11.67 0.49 Model I 479 ± 23.2 496 ± 21.8 515 ± 25.3 0.45 Model II 478 ± 23.1 490 ± 22.0 516 ± 25.2 0.45 Model III 477 ± 24.3 492 ± 24.8 502 ± 28.9 0.50 a) Values are mean ± SE in the tables and were compute by the use of ANCOVA. b) Model I: adjusted for age, energy intake, physical activity (MET-h/wk), current corticoid steroids use (yes or no), current OCP use (yes or no), marital status (categorical), menopausal status (yes or no), systolic blood pressure, diastolic blood pressure, and socioeconomic status (categorical). c) Model II: Further adjusted for BMI. d) Model III: Further adjusted for blood lipids and glucose. e) p -Value was calculated from linear regression of adhesion molecules on a categorical variable of dietary in index intake. The results showed no significant association between DIP and the plasma level of sICAM-1 in the crude model (P-value: 0.83) compared to model I (P-value: 0.98), model II (P-value: 0.98) and model III (P-value: 0.92) after adjusting for potential confounders (Fig. 2 ). In addition, no significant association was found between DIP and the plasma level of sVCAM-1 in the crude model compared to model I (P-value: 0.49), model II (P-value: 0.45) and model III (P-value: 0.50) after adjusting for potential confounders (Fig. 3 ). Discussion No association was observed between dietary inflammatory potential (DIP) and endothelial biomarkers including E-selectin, sVCAM-1 and sICAM-1 in the participants. This dissociation remained significant after adjusting for possible confounders. This is the first study of the association between adhesion molecules and DIP. Other studies assessed the correlation between DIP and cardiovascular disease. Adhesion of circulating molecules, including E-selectin, sICAM-1 and sVCAM-1, plays an essential role in endothelial dysfunction and atherosclerosis [ 14 , 35 – 39 ]. Furthermore, reactive oxygen species (ROS) activate endothelial markers by inducing E-selectin, sICAM-1 and sVCAM-1. It has been reported that sICAM-1 plays an important role as a predictor of CVD [ 40 ]. Moreover, the sVCAM-1 expression represents the inflammatory conditions of the vascular walls and predicts fatal coronary artery disease in the future [ 39 , 41 ]. Plasma levels of endothelial markers such as sE-selectin and sICAM-1 correlate with prognosis [ 11 ]. Many studies have assessed the correlation of DIP with CVD. The results of the present study are consistent with a study by Imran khan et al who carried out a cohort study on 1111 subjects to evaluate the relationship between DIP and cardiovascular disease (CVD). The results showed no a significant correlation between DIP and CVD in females while a significant relationship was found in male subjects [ 42 ]. Similarly, Gabriela Pocovi-Gerardino et al conducted a cross-sectional study on 105 women with a mean age of 45.4 years old and found no significant correlation between the DIP score and CVD markers [ 43 ]. A study of 585 women aged 50–55 years old by Linda E. T. Vissers et al failed to show any correlation between DIP and CVD, ischemic heart disease, and myocardial infarction (MI) [ 44 ]. Furthermore, a prospective case-control study of 100000 participants showed no significant relationship between DII and MI [ 45 ]. By contrast, Bondonno et al reported that a high DIP score was associated with atherosclerotic vascular disease in women aged over 70 although they did not find any association between DIP and carotid plaque severity [ 46 ]. Moreover, Stefanos Tyrovolas et al carried out a dose-dependent study to assess the correlation between DIP and CVD risk factors. They found a significant correlation between DIP and CVD risk factors such as diabetes mellitus, obesity, hypertension, and hypercholesterolemia. In addition, the participants with a high DIP score in the 3rd and 4th quartile had at least one CVD risk factor in comparison to the participants in the 1st quartile [ 47 ]. It was difficult to sort out consistent results with our findings because many studies were carried out on subjects with unhealthy conditions. Moreover, there were differences between the studies in terms of the sample size. The geographic dietary pattern may also affect the results. Furthermore, many studies did not measure the plasma levels of sICAM-1, sVCAM-1 and E-selectin directly. Therefore, more studies are required to assess the correlation between endothelial markers and the DIP score. This study had some limitations. For example, it had a cross-sectional design and therefore no conclusions can be made regarding causality. Moreover, there were some unknown confounders including shift time, bias in reporting food items, and difference in the dietary pattern between nurses in private and public hospitals, which could affect the results. Studies with larger sample sizes are required to obtain concrete results. Conclusion In summary, the findings suggest that the plasma levels of endothelial markers including E-selectin, ICAM-1 and sVCAM-1 have no significant correlation with dietary inflammatory potential in females. Abbreviations sICAM-1 Soluble intercellular adhesion molecule-1; DIP:Dietary inflammatory potential; FFQ:Food frequency questionnaire; CVD:Cardiovascular disease; CAM:Cell adhesion molecules; IL-6:Interleukin 6; TNF-α:Tumor necrosis factor alpha; CRP:C-reactive protein; LDL:Low-density lipoprotein; HDL:High-density lipoprotein; SFA:Saturated fatty acids; MUFA:Monounsaturated fatty acids; PUFA:Polyunsaturated fatty acids; BMI:Body mass index; OCP:Oral contraceptive Declarations Acknowledgments: The authors thank the Board of Directors of Isfahan Nursing Organization for this study. We are also thankful to the staff of Isfahan hospitals who contribute at this study. Also, we acknowledge the helps from Farzaneh Barak to collect data. Authors’ contributions: The conception and design of the study performed by MG, ES, AS. Analysis and interpretation of data carried out by MG, AE and confirmed by AS. The collecting samples used to by MG, AH, AY, EF and PS. Manuscript wrote by MG, AE and revised by AS. Funding: This research supported by Isfahan University Medical Sciences, Lorestan University of Medical Sciences and Tehran University of Medical Sciences. Availability of data and materials: The data collected and analyzed during in current study. All data are available from professor Ahmad Esmaeilzadeh, Ahmad Saedisomeolia on reasonable request. Declarations: Ethics approval and consent to participate The study protocol was approved by Tehran university of medical sciences (IR.TUMS.VCR.REC.1399.584). Consent for publication The participants fill in a consent form based on Tehran university of medical sciences ethics rules for participating on this study. Competing interests: The authors declare that they have no competing interests References Hansson GK, Robertson A-KL, Söderberg-Nauclér C. Inflammation and atherosclerosis. Annu Rev Pathol Mech Dis. 2006;1:297–329. Hansson GK. Inflammation, atherosclerosis, and coronary artery disease. 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Soluble adhesion molecules and prediction of coronary heart disease: a prospective study and meta-analysis. The Lancet. 2001;358(9286):971–5. Luc G, Arveiler D, Evans A, Amouyel P, Ferrieres J, Bard J-M, Elkhalil L, Fruchart J-C, Ducimetiere P, Group PS. Circulating soluble adhesion molecules ICAM-1 and VCAM-1 and incident coronary heart disease: the PRIME Study. Atherosclerosis. 2003;170(1):169–76. Blankenberg S, Rupprecht HJ, Bickel C, Peetz D, Hafner G, Tiret L, Meyer Jr, Investigators A. Circulating cell adhesion molecules and death in patients with coronary artery disease. Circulation. 2001;104(12):1336–42. Khan I, Kwon M, Shivappa N, Hébert JR, Kim MK. Positive Association of Dietary Inflammatory Index with Incidence of Cardiovascular Disease: Findings from a Korean Population-Based Prospective Study. Nutrients. 2020;12(2):588. Pocovi-Gerardino G, Correa-Rodríguez M, Callejas-Rubio J-L, Ríos-Fernández R, Martín-Amada M, Cruz-Caparros M-G, Rueda-Medina B, Ortego-Centeno N. Dietary Inflammatory Index Score and Cardiovascular Disease Risk Markers in Women with Systemic Lupus Erythematosus. Journal of the Academy of Nutrition Dietetics. 2020;120(2):280–7. Vissers LE, Waller MA, van der Schouw YT, Hebert JR, Shivappa N, Schoenaker DA, Mishra GD. The relationship between the dietary inflammatory index and risk of total cardiovascular disease, ischemic heart disease and cerebrovascular disease: Findings from an Australian population-based prospective cohort study of women. Atherosclerosis. 2016;253:164–70. Bodén S, Wennberg M, Van Guelpen B, Johansson I, Lindahl B, Andersson J, Shivappa N, Hebert JR, Nilsson LM. Dietary inflammatory index and risk of first myocardial infarction; a prospective population-based study. Nutrition journal. 2017;16(1):21. Bondonno NP, Lewis JR, Blekkenhorst LC, Shivappa N, Woodman RJ, Bondonno CP, Ward NC, Hébert JR, Thompson PL, Prince RL. Dietary inflammatory index in relation to sub-clinical atherosclerosis and atherosclerotic vascular disease mortality in older women. Br J Nutr. 2017;117(11):1577–86. Tyrovolas S, Koyanagi A, Kotsakis GA, Panagiotakos D, Shivappa N, Wirth MD, Hebert JR, Haro JM. Dietary inflammatory potential is linked to cardiovascular disease risk burden in the US adult population. Int J Cardiol. 2017;240:409–13. Supplementary Files Graphicalabstract.tif Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-555465","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":31537027,"identity":"8df32488-5717-4a17-aa1a-912371d9ac7b","order_by":0,"name":"Mohammad Gholizadeh","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Gholizadeh","suffix":""},{"id":31537028,"identity":"bd891ea9-355d-4c11-9b0c-a54e7f302d52","order_by":1,"name":"Ebrahim Falahi","email":"","orcid":"","institution":"Lorestan University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ebrahim","middleName":"","lastName":"Falahi","suffix":""},{"id":31537029,"identity":"aa5e6ab3-80ea-4aed-adb1-6d883c1b80c3","order_by":2,"name":"Ammar Hassanzadeh Keshteli","email":"","orcid":"","institution":"Isfahan University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ammar","middleName":"Hassanzadeh","lastName":"Keshteli","suffix":""},{"id":31537030,"identity":"041d2a30-aafa-4861-88b2-19ec8e73d4bf","order_by":3,"name":"Ahmadreza Yazdan Nik","email":"","orcid":"","institution":"Isfahan University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ahmadreza","middleName":"Yazdan","lastName":"Nik","suffix":""},{"id":31537031,"identity":"f5a9ff2b-0f75-48fd-b995-0856fd8091e6","order_by":4,"name":"Parvaneh Saneei","email":"","orcid":"","institution":"Isfahan University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Parvaneh","middleName":"","lastName":"Saneei","suffix":""},{"id":31537032,"identity":"8d496763-d965-4e33-9300-72716cf7b6ae","order_by":5,"name":"Ahmad Esmaeilzadeh","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"","lastName":"Esmaeilzadeh","suffix":""},{"id":31537033,"identity":"b156dcb2-b858-4000-8384-756dba89d87c","order_by":6,"name":"Ahmad Saedisomeolia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYNACNgk5BJtYLcY8pGphSOwh2kn8DewPP3wos0jfL5Gd/IGhxo6BT/oAfi0SB3iMJWeck8jtkcjdJsFwLJmBjS+BgDUHeBikedsgWoAuPMDAxkNAh/wB9se//7ZJpPNI5G7+wPCPCC0GBxjMpBnbJBKAWjZIMLYRocXwAI+ZZc85CcOeM2+3SST2JfMQ1CIHdNiNH2V18uztQId9+GYnJ08wuOUfIHESGBgI2TEKRsEoGAWjgBgAAIl5NmGrQ9hkAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Cellular and Molecular Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"","lastName":"Saedisomeolia","suffix":""}],"badges":[],"createdAt":"2021-05-23 20:28:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-555465/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-555465/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":10294815,"identity":"5c359bdd-b4a2-41bc-b359-089d702caffd","added_by":"auto","created_at":"2021-06-12 16:20:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":25407,"visible":true,"origin":"","legend":"The E-selectin plasma concentration in tertiles (mean±SEM)","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-555465/v1/bbaa645955067b54b305752d.jpg"},{"id":10294816,"identity":"0a28e881-2455-4f80-bc24-e0c3a73ed65f","added_by":"auto","created_at":"2021-06-12 16:20:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23999,"visible":true,"origin":"","legend":"The ICAM-1 plasma concentration in tertiles (mean±SEM)","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-555465/v1/eb7e7b2b7ee630aaef73569a.jpg"},{"id":10295205,"identity":"3eaa8172-ddca-4310-9e8f-99463aaea7a5","added_by":"auto","created_at":"2021-06-12 16:23:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26456,"visible":true,"origin":"","legend":"The sVCAM-1 plasma concentration in tertiles (mean±SEM)","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-555465/v1/732b5f545192a4563d3846c8.jpg"},{"id":13698318,"identity":"253ae070-d1a7-4e1c-8d4b-f8aa1831f54a","added_by":"auto","created_at":"2021-09-17 13:13:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":432781,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-555465/v1/c24b2031-c1c9-47a9-9805-198c8663b707.pdf"},{"id":10295206,"identity":"ad326db7-af74-4918-b853-47116c2f32ba","added_by":"auto","created_at":"2021-06-12 16:23:23","extension":"tif","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":169834,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.tif","url":"https://assets-eu.researchsquare.com/files/rs-555465/v1/dc1bcf00dbff3d1aa1e6198a.tif"}],"financialInterests":"","formattedTitle":"\u003cp\u003eAssociation of Dietary Inflammatory Potential (DIP) and Endothelial Function Biomarkers among Female Nurses of Isfahan Hospitals\u003c/p\u003e","fulltext":[{"header":"Introduction","content":" \u003cp\u003eAtherosclerosis is a continuing inflammatory state of the vessels [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The progression of atherosclerosis leads to myocardial infarction and sudden death [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is believed that atherosclerosis is an inflammatory condition that is largely responsible for cardiovascular disease (CVD) mortality [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Endothelial dysfunction contributes to the pathogenesis of vascular disease and plays an important role in CVD as well [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Endothelial dysfunction is characterized by impaired activity of endothelial derived relaxant factors and increased activity of vasoconstrictor factors. However, cell adhesion molecules (CAM) including E-selectin, intercellular adhesion molecule-1 (sICAM-1) and vascular cell adhesion molecule-1 (sVCAM-1) accelerate atherosclerosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdhesion molecules are normally expressed by the endothelium. They also play a role in leukocyte rolling, firm adhesion, and transmigration. Furthermore, they are associated with a variety of pathophysiological processes and inflammatory disorders. Atherosclerotic lesions and fatty streaks increase the expression of sICAM-1, sVCAM-1, and P- and E-selectin on the human endothelial cells [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. E-selectin plays an important role in acute inflammation [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moreover, sICAM-1 and sVCAM-1 are involved in chronic inflammation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Leukocyte adhesion is an important component of some vascular diseases and atherogenesis. Leukocyte recruitment occurs in a multistep process and selectin, which is expressed on the activated endothelial cells, is involved in the initial rolling process of leukocytes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The leukocyte surface has sites for selectin ligand [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. β1 and β2 integrin are expressed on leukocytes and act as binding sites for sVCAM-1 or sICAM-1. Furthermore, selectin plays a role in the initial rolling process of leukocytes whereas sICAM-1 and sVCAM-1 mediate leukocyte arresting and firm adhesion [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDietary inflammatory potential (DIP) is a new dietary index designed to evaluate the individual's diets. DIP is a tool to assess the potential inflammatory and anti-inflammatory properties of a diet based on food elements. In this index, values of +\u0026thinsp;1, 0, and \u0026minus;\u0026thinsp;1 indicate pro-inflammation, indifferent and anti-inflammation reactions, respectively [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Actually, DIP is a resource to assess pro-inflammatory effects of food ingredients based on anti-inflammatory functions [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. DIP has been linked to a variety of systemic biomarkers such as interleukin 6 (IL-6), tumor necrosis factor alpha (TNF-α), C-reactive protein (CRP) and several metabolic diseases such as CVD, cancers, and diabetes. Many studies have found that DIP is associated with the risk of metabolic syndrome and cardiovascular diseases [\u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMany studies have reported a positive association between DIP and CVDs [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Due to the increase in the global risk of CVDs and related diseases in the world, it is important to find healthy dietary patterns with low inflammatory scores to tackle inflammation and CVDs. The purpose of this study was to determine the association between DIP and endothelial markers such as sICAM, sVCAM, and E-selectin in female nurses working in Isfahan hospitals.\u003c/p\u003e "},{"header":"Materials And Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eFour hundred and eighty healthy female nurses aged\u0026thinsp;\u0026gt;\u0026thinsp;30 years participated in this cross-sectional study. The participants were selected randomly from seven public and private hospitals in Isfahan, Iran. The female nurses with a history of diabetes, malignancy, infections, and CVDs were excluded. Furthermore, the subjects who did not complete the FFQ questionnaire were also excluded from the study. Finally, 420 nurses were enrolled in the study. The participants fill in a consent form based on Tehran university of medical sciences ethics rules for participating on this study.\u003c/p\u003e \u003cp\u003e The study protocol was approved by Tehran university of medical sciences (IR.TUMS.VCR.REC.1399.584).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBlood sampling\u003c/h2\u003e \u003cp\u003eBlood samples were collected from the participants after 12 hours of fasting to measure the levels of endothelial markers, lipid profile, and fasting blood glucose. Then, the sample were centrifuged for 30\u0026ndash;45 minute and frozen at 70\u0026deg;C. The levels of sVCAM- 1, sICAM-1, and E-selectin were measured using commercial ELISA kits (Biosource International and Bender MED Systems) according to the manufacturer\u0026rsquo;s instructions. ELISA kits were also used to measure low-density lipoprotein (LDL) and high-density lipoprotein (HDL).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDietary inflammatory score\u003c/h2\u003e \u003cp\u003eThe method developed by Shivappa \u003cem\u003eet al\u003c/em\u003e was applied to calculate the DIP scores of the diets. The Food Frequency Questionnaire (FFQ) was used to determine the dietary intake [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In the Iranian dietary pattern, 29 out of 45 items of DII are very common, including Macronutrients (energy, carbohydrates, fat, protein, fiber), Fat (cholesterol, saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA)), Water-soluble vitamins (pyridoxine, folic acid, niacin, thiamin, ascorbic acid and riboflavin). Fat-soluble vitamins (A,D and E), Minerals (iron, magnesium, zinc, and selenium), as well as caffeine, β-carotene, onion, garlic, pepper, and black tea [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Other DIP items that were uncommon in the Iranian dietary regimen were omitted form the list of FFQ.\u003c/p\u003e \u003cp\u003eThe intake of the above dietary items was adjusted according to the daily energy intake [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A z-score was generated for all of the 29 items of the FFQ list for each participant. For each subject, the \"standard global mean\" was subtracted from the mean consumed food and divided by \u0026ldquo;global standard deviation\u0026rdquo;. The global means and standard deviations were obtained by the method developed by Shivappa \u003cem\u003eet al\u003c/em\u003e [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. To decrease the skewness of the variables, the variables were converted to a centered percentile score. This score was then extended by the impact for every item [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The DIP scores of all foods were summed to calculate the overall score. More positive values indicated a higher inflammatory dietary potential.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of other variables\u003c/h2\u003e \u003cp\u003eA computerized scale was used for weight measurement (to the nearest 0.1 kg). The subjects were asked to wear light clothing with no shoes. The height was also measured on the same visit day. Finally, weight (kg) and height (m) were used to calculate the body mass index (BMI) according to the following formula: weight (kg)/ height (m) \u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe International Physical Questionnaire was used to evaluate daily physical activity [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] as MET-hour per week. The factors such as education level, family size, and economic status were inquired from all the participants to determine their socioeconomic status. Moreover, covariate data including age, marital status, menopause situation, past medical history, smoking or medication/supplementation history were selfreported by all the participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe final analysis was performed on 420 individuals. Energy adjustment of the variables was carried out using the residual method. After completing the FFQ, the data were entered into an Excel datasheet and daily dietary intakes were compared using the IBM SPSS version 26 (IBM SPSS Statistics for Win, Armonk, NY) and Nutritionist IV (N4) software. Since there were three DIP groups (tertiles), one-way ANOVA was used for continuous variables including age, body mass index (BMI), weight, waist circumference, physical activity, and systolic and diastolic blood pressure, and Pearson\u0026rsquo;s chi-square test was applied to categorical variables such as oral contraceptive (OCP) use, current corticosteroid use, menopause, marital status, overweight/obesity and socioeconomic status. Similarities between the energy intakes of the participants were adjusted by linear regression. Finally, the associations between DIP and E-selectin, ICAM, and VCAM in three tertiles was analyzed using ANCOVA.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cp\u003eThe mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD age of the participants was 34.44\u0026thinsp;\u0026plusmn;\u0026thinsp;7.27, 34.59\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8 and 36.47\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4 years in the 1st, 2nd, and 3rd tertile, respectively. The demographic characteristics of the subjects are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003echaracteristics of participants by tertiles of dietary inflammatory index intake (means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eTertiles of energy by DIP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1\u0026thinsp;=\u0026thinsp;133\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2\u0026thinsp;=\u0026thinsp;134\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT3\u0026thinsp;=\u0026thinsp;129\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.47\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.59\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.44\u0026thinsp;\u0026plusmn;\u0026thinsp;7.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.2\u0026thinsp;\u0026plusmn;\u0026thinsp;82.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.97\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC(CM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.02\u0026thinsp;\u0026plusmn;\u0026thinsp;9.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.51\u0026thinsp;\u0026plusmn;\u0026thinsp;9.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.04\u0026thinsp;\u0026plusmn;\u0026thinsp;10.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA(MET-h/wk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u0026thinsp;\u0026plusmn;\u0026thinsp;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78\u0026thinsp;\u0026plusmn;\u0026thinsp;77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95\u0026thinsp;\u0026plusmn;\u0026thinsp;101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003csup\u003e1\u003c/sup\u003e (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent OCP use (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent corticosteroid use(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenopausal (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight or obese (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocioeconomic status (%)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003eMedium\u003c/p\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.4\u003c/p\u003e \u003cp\u003e44.4\u003c/p\u003e \u003cp\u003e31.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.7\u003c/p\u003e \u003cp\u003e40\u003c/p\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.3\u003c/p\u003e \u003cp\u003e48.9\u003c/p\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eBMI: body mass index, WC: waist circumference, SBP: systolic blood pressure,DBP: diastolic blood pressure, OCP: oral contraceptives\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ea) Obtained from analysis of variance for continues variables and chi-square for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eb) High socioeconomic status was defined based on educational level, income, family size, being owner of the house or renting the house, house area, being owner of the car and number and kind of the car(s), number of bedrooms, and determination of who was in charge of the family.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ec) Body mass index\u0026thinsp;\u0026ge;\u0026thinsp;25\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe distribution of the DIP score between tertiles is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Large differences in DIP scores were observed for fat (P-value:0.04), riboflavin (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), folic acid (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), cobalamin (P-value\u0026thinsp;=\u0026thinsp;0.005), ascorbic acid (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), vitamin A (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), beta carotene (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), zinc (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), tea (P-value\u0026thinsp;=\u0026thinsp;0.002), magnesium (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), onion (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), fiber (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), caffeine (P-value\u0026thinsp;=\u0026thinsp;0.03), SAFA (P-value\u0026thinsp;=\u0026thinsp;0.02), and cholesterol (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) between the tertiles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003edietary inflammatory index intake of participants after adjusted energy (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eTertile of energy \u0026ndash;energy adjusted DIP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eT1(n\u0026thinsp;=\u0026thinsp;136)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eT2(n\u0026thinsp;=\u0026thinsp;137)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eT3(n\u0026thinsp;=\u0026thinsp;136)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124\u0026thinsp;\u0026plusmn;\u0026thinsp;98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135\u0026thinsp;\u0026plusmn;\u0026thinsp;139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e102\u0026thinsp;\u0026plusmn;\u0026thinsp;99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117\u0026thinsp;\u0026plusmn;\u0026thinsp;88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e102\u0026thinsp;\u0026plusmn;\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbohydrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e338\u0026thinsp;\u0026plusmn;\u0026thinsp;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e321\u0026thinsp;\u0026plusmn;\u0026thinsp;61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e335\u0026thinsp;\u0026plusmn;\u0026thinsp;71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThiamin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026thinsp;\u0026plusmn;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRiboflavin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNiacin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e18.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePyridoxine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFolic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e410\u0026thinsp;\u0026plusmn;\u0026thinsp;121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e304\u0026thinsp;\u0026plusmn;\u0026thinsp;41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e226\u0026thinsp;\u0026plusmn;\u0026thinsp;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCobalamin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscorbic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e281\u0026thinsp;\u0026plusmn;\u0026thinsp;111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181\u0026thinsp;\u0026plusmn;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e126\u0026thinsp;\u0026plusmn;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1928\u0026thinsp;\u0026plusmn;\u0026thinsp;939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1276\u0026thinsp;\u0026plusmn;\u0026thinsp;302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e887\u0026thinsp;\u0026plusmn;\u0026thinsp;335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69\u0026thinsp;\u0026plusmn;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e65\u0026thinsp;\u0026plusmn;\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta carotene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1527\u0026thinsp;\u0026plusmn;\u0026thinsp;897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e904\u0026thinsp;\u0026plusmn;\u0026thinsp;291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e582\u0026thinsp;\u0026plusmn;\u0026thinsp;311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelenium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e8\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIron\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u0026thinsp;\u0026plusmn;\u0026thinsp;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e356\u0026thinsp;\u0026plusmn;\u0026thinsp;263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e324\u0026thinsp;\u0026plusmn;\u0026thinsp;316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e242\u0026thinsp;\u0026plusmn;\u0026thinsp;220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e326\u0026thinsp;\u0026plusmn;\u0026thinsp;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e267\u0026thinsp;\u0026plusmn;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e216\u0026thinsp;\u0026plusmn;\u0026thinsp;46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u0026thinsp;\u0026plusmn;\u0026thinsp;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u0026thinsp;\u0026plusmn;\u0026thinsp;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e29\u0026thinsp;\u0026plusmn;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGarlic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e5\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaffeine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82\u0026thinsp;\u0026plusmn;\u0026thinsp;57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83\u0026thinsp;\u0026plusmn;\u0026thinsp;127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e59\u0026thinsp;\u0026plusmn;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePepper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e23\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePUFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u0026thinsp;\u0026plusmn;\u0026thinsp;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e39\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMUFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u0026thinsp;\u0026plusmn;\u0026thinsp;146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e30\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e234\u0026thinsp;\u0026plusmn;\u0026thinsp;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e263\u0026thinsp;\u0026plusmn;\u0026thinsp;93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e232\u0026thinsp;\u0026plusmn;\u0026thinsp;99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD plasma levels of endothelial markers in different tertiles are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. There was no significant association between DIP and E-selectin in the crude model (P-value\u0026thinsp;=\u0026thinsp;0.35) compared to model I (P-value\u0026thinsp;=\u0026thinsp;0.57), model II (P-value\u0026thinsp;=\u0026thinsp;0.57) and model III (P-value\u0026thinsp;=\u0026thinsp;0.67) after adjusting for potential confounders (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndex of endothelial functions across tertile categories of dietary inflammatory potential.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eTertile of energy- adjusted DIP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1(n\u0026thinsp;=\u0026thinsp;133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2(n\u0026thinsp;=\u0026thinsp;136)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT3(n\u0026thinsp;=\u0026thinsp;135)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP for trend\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eE-selectin (ng/L)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel I\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel II\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel III\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eSICAM-1 (mg/L)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221\u0026thinsp;\u0026plusmn;\u0026thinsp;6.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e211\u0026thinsp;\u0026plusmn;\u0026thinsp;6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213\u0026thinsp;\u0026plusmn;\u0026thinsp;6.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e215\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e215\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e213\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e212\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eSVCAM-1 (mg/L)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e503\u0026thinsp;\u0026plusmn;\u0026thinsp;11.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e482\u0026thinsp;\u0026plusmn;\u0026thinsp;11.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e509\u0026thinsp;\u0026plusmn;\u0026thinsp;11.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e479\u0026thinsp;\u0026plusmn;\u0026thinsp;23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e496\u0026thinsp;\u0026plusmn;\u0026thinsp;21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e515\u0026thinsp;\u0026plusmn;\u0026thinsp;25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e478\u0026thinsp;\u0026plusmn;\u0026thinsp;23.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e490\u0026thinsp;\u0026plusmn;\u0026thinsp;22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e516\u0026thinsp;\u0026plusmn;\u0026thinsp;25.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e477\u0026thinsp;\u0026plusmn;\u0026thinsp;24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e492\u0026thinsp;\u0026plusmn;\u0026thinsp;24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e502\u0026thinsp;\u0026plusmn;\u0026thinsp;28.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ea) Values are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE in the tables and were compute by the use of ANCOVA.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eb) Model I: adjusted for age, energy intake, physical activity (MET-h/wk), current corticoid steroids use (yes or no), current OCP use (yes or no), marital status (categorical), menopausal status (yes or no), systolic blood pressure, diastolic blood pressure, and socioeconomic status (categorical).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ec) Model II: Further adjusted for BMI.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ed) Model III: Further adjusted for blood lipids and glucose.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ee) \u003cem\u003ep\u003c/em\u003e-Value was calculated from linear regression of adhesion molecules on a categorical variable of dietary in index intake.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results showed no significant association between DIP and the plasma level of sICAM-1 in the crude model (P-value: 0.83) compared to model I (P-value: 0.98), model II (P-value: 0.98) and model III (P-value: 0.92) after adjusting for potential confounders (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, no significant association was found between DIP and the plasma level of sVCAM-1 in the crude model compared to model I (P-value: 0.49), model II (P-value: 0.45) and model III (P-value: 0.50) after adjusting for potential confounders (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e "},{"header":"Discussion","content":" \u003cp\u003eNo association was observed between dietary inflammatory potential (DIP) and endothelial biomarkers including E-selectin, sVCAM-1 and sICAM-1 in the participants. This dissociation remained significant after adjusting for possible confounders. This is the first study of the association between adhesion molecules and DIP. Other studies assessed the correlation between DIP and cardiovascular disease.\u003c/p\u003e \u003cp\u003eAdhesion of circulating molecules, including E-selectin, sICAM-1 and sVCAM-1, plays an essential role in endothelial dysfunction and atherosclerosis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36 CR37 CR38\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Furthermore, reactive oxygen species (ROS) activate endothelial markers by inducing E-selectin, sICAM-1 and sVCAM-1. It has been reported that sICAM-1 plays an important role as a predictor of CVD [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Moreover, the sVCAM-1 expression represents the inflammatory conditions of the vascular walls and predicts fatal coronary artery disease in the future [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Plasma levels of endothelial markers such as sE-selectin and sICAM-1 correlate with prognosis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Many studies have assessed the correlation of DIP with CVD.\u003c/p\u003e \u003cp\u003eThe results of the present study are consistent with a study by Imran khan \u003cem\u003eet al\u003c/em\u003e who carried out a cohort study on 1111 subjects to evaluate the relationship between DIP and cardiovascular disease (CVD). The results showed no a significant correlation between DIP and CVD in females while a significant relationship was found in male subjects [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Similarly, Gabriela Pocovi-Gerardino \u003cem\u003eet al\u003c/em\u003e conducted a cross-sectional study on 105 women with a mean age of 45.4 years old and found no significant correlation between the DIP score and CVD markers [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. A study of 585 women aged 50\u0026ndash;55 years old by Linda E. T. Vissers \u003cem\u003eet al\u003c/em\u003e failed to show any correlation between DIP and CVD, ischemic heart disease, and myocardial infarction (MI) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Furthermore, a prospective case-control study of 100000 participants showed no significant relationship between DII and MI [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBy contrast, Bondonno \u003cem\u003eet al\u003c/em\u003e reported that a high DIP score was associated with atherosclerotic vascular disease in women aged over 70 although they did not find any association between DIP and carotid plaque severity [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Moreover, Stefanos Tyrovolas \u003cem\u003eet al\u003c/em\u003e carried out a dose-dependent study to assess the correlation between DIP and CVD risk factors. They found a significant correlation between DIP and CVD risk factors such as diabetes mellitus, obesity, hypertension, and hypercholesterolemia. In addition, the participants with a high DIP score in the 3rd and 4th quartile had at least one CVD risk factor in comparison to the participants in the 1st quartile [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt was difficult to sort out consistent results with our findings because many studies were carried out on subjects with unhealthy conditions. Moreover, there were differences between the studies in terms of the sample size. The geographic dietary pattern may also affect the results. Furthermore, many studies did not measure the plasma levels of sICAM-1, sVCAM-1 and E-selectin directly. Therefore, more studies are required to assess the correlation between endothelial markers and the DIP score.\u003c/p\u003e \u003cp\u003eThis study had some limitations. For example, it had a cross-sectional design and therefore no conclusions can be made regarding causality. Moreover, there were some unknown confounders including shift time, bias in reporting food items, and difference in the dietary pattern between nurses in private and public hospitals, which could affect the results. Studies with larger sample sizes are required to obtain concrete results.\u003c/p\u003e "},{"header":"Conclusion","content":" \u003cp\u003eIn summary, the findings suggest that the plasma levels of endothelial markers including E-selectin, ICAM-1 and sVCAM-1 have no significant correlation with dietary inflammatory potential in females.\u003c/p\u003e "},{"header":"Abbreviations","content":" \u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003esICAM-1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSoluble intercellular adhesion molecule-1; DIP:Dietary inflammatory potential; FFQ:Food frequency questionnaire; CVD:Cardiovascular disease; CAM:Cell adhesion molecules; IL-6:Interleukin 6; TNF-α:Tumor necrosis factor alpha; CRP:C-reactive protein; LDL:Low-density lipoprotein; HDL:High-density lipoprotein; SFA:Saturated fatty acids; MUFA:Monounsaturated fatty acids; PUFA:Polyunsaturated fatty acids; BMI:Body mass index; OCP:Oral contraceptive\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Board of Directors of Isfahan Nursing Organization for this study. We are also thankful to the staff of Isfahan hospitals who contribute at this study. Also, we acknowledge the helps from Farzaneh Barak to collect data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe conception and design of the study performed by MG, ES, AS. Analysis and interpretation of data carried out by MG, AE and confirmed by AS. The collecting samples used to by MG, AH, AY, EF and PS. Manuscript wrote by MG, AE and revised by AS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research supported by Isfahan University Medical Sciences, Lorestan University of Medical Sciences and Tehran University of Medical Sciences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data collected and analyzed during in current study. All data are available from professor Ahmad Esmaeilzadeh, Ahmad Saedisomeolia on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by Tehran university of medical sciences (IR.TUMS.VCR.REC.1399.584).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe participants fill in a consent form based on Tehran university of medical sciences ethics rules for participating on this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors declare that they have no competing interests\u0026nbsp;\u003c/p\u003e "},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHansson GK, Robertson A-KL, S\u0026ouml;derberg-Naucl\u0026eacute;r C. 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The relationship between the dietary inflammatory index and risk of total cardiovascular disease, ischemic heart disease and cerebrovascular disease: Findings from an Australian population-based prospective cohort study of women. Atherosclerosis. 2016;253:164\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBod\u0026eacute;n S, Wennberg M, Van Guelpen B, Johansson I, Lindahl B, Andersson J, Shivappa N, Hebert JR, Nilsson LM. Dietary inflammatory index and risk of first myocardial infarction; a prospective population-based study. Nutrition journal. 2017;16(1):21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBondonno NP, Lewis JR, Blekkenhorst LC, Shivappa N, Woodman RJ, Bondonno CP, Ward NC, H\u0026eacute;bert JR, Thompson PL, Prince RL. Dietary inflammatory index in relation to sub-clinical atherosclerosis and atherosclerotic vascular disease mortality in older women. Br J Nutr. 2017;117(11):1577\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTyrovolas S, Koyanagi A, Kotsakis GA, Panagiotakos D, Shivappa N, Wirth MD, Hebert JR, Haro JM. Dietary inflammatory potential is linked to cardiovascular disease risk burden in the US adult population. Int J Cardiol. 2017;240:409\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dietary inflammatory potential, sICAM-1, endothelial markers, sVCAM-1, E –selectin","lastPublishedDoi":"10.21203/rs.3.rs-555465/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-555465/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBackground\u003c/em\u003e\u003c/strong\u003e: Dietary inflammatory index (DIP) is a new dietary index designed to evaluate individuals’ diets.\u0026nbsp;In addition, adhesion molecules are important biomarkers for assessing endothelium inflammation that they related to atherosclerosis and cardiovascular disease. Also, there is no study for assessing the association between adhesion molecules and DIP until now as well as other studies that assessed the relationship between dietary inflammatory index or DIP have controversy. The purpose of this cross-sectional study was to determine the correlation between DII and endothelial markers such as E-selectin, intercellular adhesion molecule-1 (sICAM-1) and vascular cell adhesion molecule-1 (sVCAM-1) among female nurses from Isfahan. In this study, dietary inflammatory potential (DIP) was used instead of DII.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMethods:\u003c/em\u003e\u003c/strong\u003e This study was performed on 420 healthy nurses. The nurses were selected by random cluster sampling method from private and public Isfahan hospitals. A validated food frequency questionnaire (FFQ) was applied to assess the dietary inflammatory potential. A fasting blood sample was collected for measuring the plasma levels of the endothelial markers and other variables. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cem\u003eResults: \u003c/em\u003e\u003c/strong\u003eAfter adjusting different potential confounders, no statistical association was found between DIP and sICAM-1, E–selectin and sVCAM-1 in model I (P=0.57, 0.98 and 0.45), model II (P=0.57, 0.98 and 0.45) and model III (P=0.67, 0.92 and 0.50) in comparison to the crude group (P=0.35, 0.83 and 0.49, respectively). \u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConclusions:\u003c/em\u003e\u003c/strong\u003e The results revealed that the plasma levels of endothelial markers including E-selectin, sICAM-1, and sVCAM-1 were not significantly associated with DIP in female nurses.\u0026nbsp;\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Association of Dietary Inflammatory Potential (DIP) and Endothelial Function Biomarkers among Female Nurses of Isfahan Hospitals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-06-12 16:20:21","doi":"10.21203/rs.3.rs-555465/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1440ebfb-6e71-49cc-aa43-25dbe8d9546c","owner":[],"postedDate":"June 12th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":4970056,"name":"Nutrition \u0026 Dietetics"},{"id":4970057,"name":"Population Biology"}],"tags":[],"updatedAt":"2021-07-11T18:24:02+00:00","versionOfRecord":[],"versionCreatedAt":"2021-06-12 16:20:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-555465","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-555465","identity":"rs-555465","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}