# Mediatory Effect of Inflammatory Markers (IL-1β and PAI-1) on Association of Dietary Total Antioxidant Capacity and Body Composition in Overweight and Obese Women: A Cross-Sectional Study

Authors: Parisa Radkhah; Atieh Mirzababaei; Farideh Shiraseb; Dorsa Hosseininasab; Cain C. T. Clark; Khadijeh Mirzaei
Journal: International Journal of Clinical Practice
 (2022)

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## Abstract

Background. Obesity is considered a state of chronic low-grade inflammation. Different components of the diet, like antioxidants, can have anti-inflammatory effects or cause chronic inflammation. This study investigated the dietary TAC and inflammatory markers and body composition in obese and overweight women. Methods. This cross-sectional study was conducted on 259 women with overweight and obesity. Dietary intake was assessed by using an FFQ with 147 items, and DTAC was used to evaluate the antioxidant capacity of the diet. The anthropometric measurements, body composition, and biochemical assessments were measured by standard protocols. Results. We observed a significant positive association between DTAC and consumption of fruits (P = 0.021), vegetable oils (P < 0.001), potassium (P = 0.006), manganese (P = 0.003), and caffeine (P < 0.001), after adjusting confounders. After adjusting for age, energy intake, and physical activity, there was a significant correlation between DTAC and fat-free mass (FFM) (P:0.054), fat-free mass index (FFMI) (P:0.012), waist circumference (WC) (P:0.002), and visceral fat level (VFL) (P:0.063). FFM, FFMI, waist circumference (WC), and visceral fat area (VFA) were mediated by IL-1β. FFM, VFL, VFA, and WC were mediated by PAI-1. Conclusion. Some anthropometric indices were associated with DTAC, mediated by augmenting serum levels of IL-1β and PAI-1. Intake of foods rich in antioxidants could represent a protective strategy against chronic diseases, such as cardiovascular disease.


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## Body

## 1. Introduction

According to the definition of the World Health Organization (WHO), overweight and obesity are defined by abnormal or excessive fat accumulation that may impair health [1]. It is the result of an individual complex interaction of factors, including genetic predisposition, diet, metabolism, and physical activity [2,3]. The rising prevalence of overweight and obesity in several countries [4] has been described as a global pandemic [5,6]; indeed, about 13% of the world’s adult population (11% of men and 15% of women) were obese in 2016 [1]. Because of several reasons, such as lifestyle, socioeconomic indicators, and physical inactivity [7,8], the prevalence of overweight and obesity is higher in females than in males [1,9].Obesity may also be characterized by a state of chronic low-grade inflammation. The excessive accumulation of fat in adipose tissue leads to the recruitment of macrophages [10] and results in increased production of many proinflammatory cytokines and chemokines that can attract inflammatory cells [11].Diet is a major cause of obesity and overweight but also represents a foundation of treatment and prevention [10,12]. Since obesity is associated with inflammation, different components of the diet can have anti-inflammatory effects or cause chronic inflammation [11,13–15]. One of the most effective is antioxidants, which are important groups of compounds found in plants with antioxidant properties. These compounds can elicit significant weight loss through their antioxidant capacity and effects on mitochondrial biogenesis, reduction of inflammation, and regulation of the sympathetic nervous system [16–21]. Previous studies have also shown an association between diet total antioxidant capacity (TAC) and serum TAC and that dietary TAC intake was significantly lower in obese people [22]. Some studies have also shown an association between dietary TAC intake and inflammatory markers [16–18,23]; however, none have investigated the relationship, direct and mediating, between dietary TAC and inflammatory markers with body composition in obese and overweight women. Therefore, the present study sought to investigate dietary TAC, inflammatory markers, and body composition in obese and overweight women.


## 2. Methods

### 2.1. Study Design and Participants

In this cross-sectional study, 259 women (≥ 18 years), who were not menopausal, with a BMI > 25 kg/m2, from different areas of Tehran, were recruited. At the beginning of the study, pregnant and lactating women, smokers, and people with diseases, such as high blood pressure, diabetes, cancer, cardiovascular disease, and renal disease, as well as people who follow certain diets, were excluded from the study. In addition, participants were excluded if their total energy intake was outside the range of 800 and 4200 kcal/day. Participants were given complete information about the study, and written consent was obtained. All participants provided written informed consent prior to study commencement. Ethical approval for this protocol was given by TUMS (Ethics Number: IR.TUMS.VCR.REC. 51715-212-3-99).


### 2.2. Body Composition

Body composition, such as body fat mass (BFM), fat-free mass (FFM), body fat percentage (BFP), and visceral fat area (VFA), was evaluated using tetrapolar bioimpedance analysis (BIA). To prevent a possible discrepancy in measured values, before assessing body composition, participants were asked not to exercise vigorously, not to carry out any electric device, and not to consume excessive fluid or food; measurements were performed in the morning, with participants in a fasted condition and being asked to urinate just before body composition analysis [19].


### 2.3. Biochemical Assessment

Blood samples were drawn after 10–12 h of overnight fasting. Serum insulin concentrations were analyzed through the enzyme-linked immunosorbent assay (ELISA) method (Human insulin ELISA kit, DRG Pharmaceuticals, GmbH, Germany), and inflammatory markers (IL-1β and PAI-1) were measured by an immunoturbidimetric assay (Randox laboratories kit, Hitachi LTD, Tokyo, Japan). Fasting plasma glucose (FPG) was measured by the glucose oxidase phenol 4-aminoantipyrine peroxidase (GOD/PAP) method. Serum triglyceride (TG) concentrations were assayed with triacylglycerol kits (Pars Azmoon Inc, Tehran, Iran) by using the glycerol-3-phosphate oxidase phenol 4-aminoantipyrine peroxidase (GPOPAP) method. Total cholesterol levels were measured by the Enzymatic Endpoint method and direct high- and low-density lipoprotein was measured by enzymatic clearance assay.


### 2.4. Anthropometric Assessment

Anthropometric measurements were performed by trained staff following standard procedures. Bodyweight was measured, to the nearest 0.1 kg, using a calibrated electronic scale with subjects wearing minimal clothing. Body height was measured, to the nearest 0.5 cm, by using a tape measure while the subject was in a standing position and unshod, and the shoulders in a relaxed position. Body mass index (BMI) was calculated as weight (kg) divided by height (m2). Waist circumference (WC) was measured at the middle point of the iliac crest and rib cage, and hip circumference (HC) was measured at the largest circumference around the buttocks.


### 2.5. Dietary Assessment and Dietary Total Antioxidant Capacity (DTAC) Calculation

Dietary information was assessed by a validated 147-items semiquantitative food frequency questionnaire (FFQ) [20]. All dietary intakes were calculated using N4 software (version 7.0; N-squared Computing).Total antioxidant capacity (TAC), which demonstrates plasma antioxidant status, was measured using three indices, including TEAC (Trolox equivalent antioxidant capacity), TRAP (total radical-trapping antioxidant parameter), and FRAP (ferric ion reducing antioxidant power), and is based on the ability of the sample to transfer hydrogen to stabilize a free radical and reduce ferric iron to ferrous iron, respectively [19].


### 2.6. Other Variables

Items such as educational status and job status were examined by using a demographic questionnaire. Physical Activity was assessed by the validated International Physical Activity Questionnaire (IPAQ) [21].


## 2.1. Study Design and Participants

In this cross-sectional study, 259 women (≥ 18 years), who were not menopausal, with a BMI > 25 kg/m2, from different areas of Tehran, were recruited. At the beginning of the study, pregnant and lactating women, smokers, and people with diseases, such as high blood pressure, diabetes, cancer, cardiovascular disease, and renal disease, as well as people who follow certain diets, were excluded from the study. In addition, participants were excluded if their total energy intake was outside the range of 800 and 4200 kcal/day. Participants were given complete information about the study, and written consent was obtained. All participants provided written informed consent prior to study commencement. Ethical approval for this protocol was given by TUMS (Ethics Number: IR.TUMS.VCR.REC. 51715-212-3-99).


## 2.2. Body Composition

Body composition, such as body fat mass (BFM), fat-free mass (FFM), body fat percentage (BFP), and visceral fat area (VFA), was evaluated using tetrapolar bioimpedance analysis (BIA). To prevent a possible discrepancy in measured values, before assessing body composition, participants were asked not to exercise vigorously, not to carry out any electric device, and not to consume excessive fluid or food; measurements were performed in the morning, with participants in a fasted condition and being asked to urinate just before body composition analysis [19].


## 2.3. Biochemical Assessment

Blood samples were drawn after 10–12 h of overnight fasting. Serum insulin concentrations were analyzed through the enzyme-linked immunosorbent assay (ELISA) method (Human insulin ELISA kit, DRG Pharmaceuticals, GmbH, Germany), and inflammatory markers (IL-1β and PAI-1) were measured by an immunoturbidimetric assay (Randox laboratories kit, Hitachi LTD, Tokyo, Japan). Fasting plasma glucose (FPG) was measured by the glucose oxidase phenol 4-aminoantipyrine peroxidase (GOD/PAP) method. Serum triglyceride (TG) concentrations were assayed with triacylglycerol kits (Pars Azmoon Inc, Tehran, Iran) by using the glycerol-3-phosphate oxidase phenol 4-aminoantipyrine peroxidase (GPOPAP) method. Total cholesterol levels were measured by the Enzymatic Endpoint method and direct high- and low-density lipoprotein was measured by enzymatic clearance assay.


## 2.4. Anthropometric Assessment

Anthropometric measurements were performed by trained staff following standard procedures. Bodyweight was measured, to the nearest 0.1 kg, using a calibrated electronic scale with subjects wearing minimal clothing. Body height was measured, to the nearest 0.5 cm, by using a tape measure while the subject was in a standing position and unshod, and the shoulders in a relaxed position. Body mass index (BMI) was calculated as weight (kg) divided by height (m2). Waist circumference (WC) was measured at the middle point of the iliac crest and rib cage, and hip circumference (HC) was measured at the largest circumference around the buttocks.


## 2.5. Dietary Assessment and Dietary Total Antioxidant Capacity (DTAC) Calculation

Dietary information was assessed by a validated 147-items semiquantitative food frequency questionnaire (FFQ) [20]. All dietary intakes were calculated using N4 software (version 7.0; N-squared Computing).Total antioxidant capacity (TAC), which demonstrates plasma antioxidant status, was measured using three indices, including TEAC (Trolox equivalent antioxidant capacity), TRAP (total radical-trapping antioxidant parameter), and FRAP (ferric ion reducing antioxidant power), and is based on the ability of the sample to transfer hydrogen to stabilize a free radical and reduce ferric iron to ferrous iron, respectively [19].


## 2.6. Other Variables

Items such as educational status and job status were examined by using a demographic questionnaire. Physical Activity was assessed by the validated International Physical Activity Questionnaire (IPAQ) [21].


## 3. Results

### 3.1. Study Population Characteristics

This cross-sectional study was conducted on 259 women with overweight and obesity. The means (SD) of age, height, BMI, and weight of individuals were 36.672 (9.103) years, 161.223 (5.870) cm, 31.261 (4.298) kg/m2, and 81.294 (12.432) kg, respectively. The mean and SD of quantitative demographic variables are shown in Table 1. The majority of women were married (77.22%) and had an academic education (83.783%).Table 1
Frequency of general characteristics and body composition measurements in overweight and obese women (n = 259).
VariablesMeanSDMinimumMaximumAge (years)36.6729.10317.00064.000Weight (kg)81.29412.43259.500136.600Height (cm)161.2235.870142.000179.000BMI (kg/m2)31.2614.29824.20049.600FFM (kg)46.5285.71633.40067.700BFM (kg)34.7408.75319.40074.200SMM (kg)25.5603.44517.30037.900WHR1.1634.5470.81092.000WC (cm)99.61210.07779.6136.000VFL16.73512.1467.000208.000VFA (cm2)171.14291.11720.0001817.000TF (kg)16.8623.6719.70030.200TF (%)320.20769.614177.800536.600HC (cm)114.0259.672100.000160.000NC (cm)37.5467.35731.000134.500BMC (kg)2.6610.3541.8203.550SLM (kg)43.8745.39426.10059.400PBF (%)41.5505.24327.10054.300FFMI18.4148.21714.600147.800FMI13.0853.3306.90026.900ICW (%)21.1672.56116.00028.700ECW (%)13.1431.64110.00018.400BFM, body fat mass; BMC, bone mineral content; BMI, body mass index; BMI, body mass index; CI, confidence interval; DTAC, dietary total antioxidant capacity; ECW, extracellular water; FFM, fat-free mass; FFMI, fat-free mass index; FMI, fat mass index; HC, hip circumference; ICW, intracellular water; NC, Neck circumference; PBF, Percent body fat; SD, standard deviation; SLM, soft lean mass; SMM, skeletal muscle mass; TF, trunk fat; VFA, visceral fat area; VFL, visceral fat level; WC, waist circumference; WHR, waist-hip ratio.


### 3.2. Description of General Characteristics among Tertiles of DTAC

The baseline characteristics of study participants, categorized according to the DTAC, are presented in Table2. As shown in Table 2, in the crude model, there were no significant mean differences in general characteristics among DTAC tertiles. After adjustment with confounders, including energy intake, physical activity, and age, there were significant mean differences between FBS (P = 0.014) and HDL (P = 0.015) across DTAC tertiles. Potential confounding variables were selected by a literature review of studies according to the exposure and outcome of this study and using the stepwise method in regression liner. Following Bonferroni's post hoc analysis, for FBS, this difference was between tertiles 1 and 3, and for HDL, it was between tertiles 2 and 3.Table 2
Description of general characteristics among tertiles of DTAC in overweight and obese women (n = 259).
VariablesT11T2T3P valueP value∗General characteristicsAge (years)36.164 ± 8.352236.235 ± 8.78936.632 ± 8.6630.9290.408Weight (kg)80.244 ± 11.95180.789 ± 11.01780.335 ± 13.6140.9520.548Height (cm)161.305 ± 6.712161.029 ± 5.771161.436 ± 5.6740.9030.515Physical activity (MET-minutes/week)1127.953 ± 2383.966856.414 ± 749.6601518.205 ± 2328.9810.1220.098Blood pressureSBP (mm Hg)111.759 ± 13.018109.518 ± 13.211110.768 ± 13.5850.5530.563DBP (mm Hg)78.036 ± 8.79276.096 ± 9.14476.926 ± 9.4520.3910.306Blood parametersFBS (mg/dl)87.690 ± 9.161a87.385 ± 10.08786.773 ± 0.7800.8430.014TC (mg/dl)184.422 ± 36.502185.757 ± 36.735183.053 ± 36.3330.9060.239TG (mg/dl)0.395 ± 0.4910.337 ± 0.4750.298 ± 0.4600.4080.487HDL (mg/dl)47.352 ± 10.48246.828 ± 11.678b46.666 ± 11.8150.9310.015LDL (mg/dl)94.732 ± 25.12495.071 ± 23.93692.560 ± 22.7440.7890.208Inflammatory markersPAI-1 (mg/L)18.191 ± 33.87612.006 ± 21.51219.364 ± 35.5140.4610.283IL-1β (mg/L)2.636 ± 1.1352.551 ± 0.7242.825 ± 0.8520.6160.577Qualitative variablesMarital statusSingle15 (25.900)322 (37.900)21 (36.200)0.4170.302Married70 (35.000)64 (32.000)66 (33.00)Education levelIlliterate1 (33.300)2 (66.700)0 (0.000)0.2210.798Diploma and lower11 (28.900)9 (23.700)18 (47.400)Academic73 (33.600)75 (34.600)69 (31.800)Economic statusVery weak11 (39.300)6 (21.400)11 (39.300)0.8220.272Weak40 (34.200)41 (35.000)36 (30.800)Medium29 (31.900)31 (34.100)31 (34.100)Good2 (22.200)3 (33.300)4 (44.400)SupplementationYes42 (35.000)39 (32.500)39 (32.500)0.9420.727No31 (36.000)26 (30.200)29 (33.700)OccupationUnemployed45 (36.000)41 (32.800)39 (31.200)0.6160.090Employed21 (29.600)24 (33.800)26 (36.600)DBP, diastolic blood pressure; DTAC, dietary total antioxidant capacity; FBS, fasting blood sugar; HDL, high-density lipoprotein; IL-1β, interleukin-1β; LDL, low-density lipoprotein; PAI-1, type 1 plasminogen activator inhibitor; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride. 1, T1: <837.305(n = 86), T2: 837.305–1287.032 (n = 86), T3: <1287.032(n = 87). 2, Mean ± SD. 3, the numbers inside the pessary represent the percentage. a Significant difference between tertiles 1 and 3. b Significant difference between tertiles 2 and 3. P values result from ANOVA test, for categorical variables. P value result from Chi-Square Tests, ∗P value reported after adjusting total energy intake, physical activity, and age with ANCOVA for categorical variables. P value result from Univariate Tests. P value<0.05 is considered as a significance level. After adjustment the confounders, the significant mean difference between the variables and DTAC is shown in bold.


### 3.3. Description of Dietary Intake across Tertiles of DTAC

Dietary intakes of participants across tertiles of DTAC are presented in Table2 and Figure 1, for macronutrients, including protein, carbohydrates, and total fat; before the adjustment in the crude model, there was a significant mean difference among DTAC tertiles (P < 0.05). After controlling for confounders, including energy intake, significance was lost (P > 0.05).Figure 1
Description of general characteristics among tertiles of DTAC in overweight and obese women (n = 259). DBP, diastolic blood pressure; DTAC, dietary total antioxidant capacity; FBS, fasting blood sugar; HDL, high-density lipoprotein; IL-1β, interleukin-1β; LDL, low-density lipoprotein; PAI-1, type 1 plasminogen activator inhibitor; SBP, systolic blood pressure; TG, triglyceride.
(a)(b)(c)In food groups, in the crude model, the relationship between fruits (P < 0.001), tea and coffee (P < 0.001), dairy (P = 0.005), meat (P = 0.067), and DTAC was significant. After adjustment for confounders, the significance between tea and coffee (P = 0.368), dairy (P = 0.527), meat (P = 0.613), and DTAC was lost, but the significance for fruits remained (P = 0.021), which indicated that the group with the highest DTAC score had a significantly higher intake of fruits (Table 3).Table 3
Description of dietary intake within tertiles of DTAC in overweight and obese women (n = 259).
VariablesT11T2T3P valueP value∗Mean ± SDEnergy and macronutrientsEnergy intake (kcal/d)2272.564 ± 568.2982642.461 ± 796.4312837.647 ± 748.079<0.001−Protein (g/d)78.271 ± 24.31990.189 ± 31.12995.314 ± 28.793<0.0010.890Carbohydrates (g/d)321.941 ± 93.655370.609 ± 127.948407.543 ± 121.537<0.0010.503Total fat (g/d)81.869 ± 26.50897.024 ± 36.690101.689 ± 31.704<0.0010.710Food groupsWhole grains (g/d)8.108 ± 9.9226.837 ± 10.1106.555 ± 8.9520.5330.181Fruits (g/d)411.467 ± 265.815480.806 ± 272.618624.280 ± 344.464<0.0010.021Vegetables (g/d)379.385 ± 253.359452.118 ± 259.251445.944 ± 236.9850.1090.594Nuts (g/d)11.778 ± 10.89813.792 ± 17.26315.370 ± 17.2240.3100.985Legumes (g/d)53.670 ± 44.31849.521 ± 37.68655.714 ± 42.2470.6100.391Tea and coffee (g/d)304.095 ± 207.827580.499 ± 221.6381103.696 ± 443.628<0.0010.368Sweetened beverages (g/d)20.581 ± 53.25931.221 ± 68.53422.561 ± 71.2910.5230.477Refined grains (g/d)401.556 ± 166.684417.490 ± 207.439461.121 ± 232.8800.1410.728Dairy (g/d)328.685 ± 198.891387.820 ± 224.714445.443 ± 267.8750.0050.527Eggs (g/d)20.490 ± 12.67522.804 ± 16.56221.503 ± 12.6020.5590.863Fish and seafood (g/d)10.572 ± 11.50612.336 ± 14.71911.460 ± 10.8940.6530.354Meat (g/d)53.808 ± 38.60366.148 ± 56.23670.869 ± 52.0510.0670.613Fast food2 (g/d)19.490 ± 17.86826.638 ± 27.19924.026 ± 28.0590.1630.979VitaminsVitamin A (IU/d)657.329 ± 306.822794.190 ± 450.351829.259 ± 412.3590.0110.767β-carotene (µg/d)4324.444 ± 2745.2675550.012 ± 4249.2635557.640 ± 3088.8470.0260.438Vitamin C (mg/d)158.571 ± 103.664183.619 ± 96.291225.301 ± 122.146<0.0010.133Vitamin D (µg/d)1.812 ± 1.3752.073 ± 1.5622.165 ± 1.9310.3420.891Vitamin E (IU/d)16.966 ± 9.44217.046 ± 9.63417.293 ± 8.3870.9700.220Thiamin (mg/d)1.894 ± 0.5652.091 ± 0.7272.200 ± 0.6620.0080.073Riboflavin (mg/d)1.905 ± 0.6342.263 ± 0.8202.351 ± 0.841<0.0010.648Niacin (mg/d)22.470 ± 7.32326.052 ± 11.69826.637 ± 8.5470.0070.236Vitamin B6 (µg/d)1.907 ± 0.5872.175 ± 0.7912.338 ± 0.710<0.0010.947Folate (mg/d)543.213 ± 153.162595.764 ± 191.544650.256 ± 168.709<0.0010.378Vitamin B12 (µg/d)3.797 ± 2.234.247 ± 1.9534.838 ± 2.9090.0180.699Biotin (µg/d)33.418 ± 13.62540.261 ± 21.13540.922 ± 14.7710.0060.598Vitamin B5 (µg/d)5.727 ± 1.6696.534 ± 3.0407.018 ± 2.2020.0020.964Vitamin k (µg/d)194.883 ± 119.059241.906 ± 324.028210.072 ± 126.5520.3340.358MineralsSodium (mg/d)3858.392 ± 1330.3664301.205 ± 1576.4724446.184 ± 1381.8300.0210.510Potassium (mg/d)3558.357 ± 1311.7794291.144 ± 1601.7394944.556 ± 1548.121<0.0010.006Calcium (mg/d)1016.170 ± 345.1651188.614 ± 457.5751278.289 ± 468.104<0.0010.851Iron (mg/d)16.649 ± 4.91319.856 ± 14.39919.861 ± 5.7320.0360.213Magnesium (mg/d)392.680 ± 124.269457.339 ± 155.461506.831 ± 142.959<0.0010.185Zinc (g/d)11.249 ± 3.38513.035 ± 4.29313.940 ± 4.310<0.0010.984Copper (mg/d)1.797 ± 0.5712.000 ± 0.8952.098 ± 0.6570.0210.095Manganese (mg/d)5.811 ± 2.0976.872 ± 2.9758.089 ± 2.385<0.0010.003Selenium (µg/d)109.380 ± 37.942122.295 ± 47.856126.290 ± 43.2750.0280.194Chromium (mg/d)0.100 ± 0.0820.112 ± 0.0930.120 ± 0.0850.3070.907Other componentsVegetable oils (g/d)22.649 ± 20.37523.899 ± 25.44924.284 ± 20.5510.880<0.001SFA (g/d)23.759 ± 8.39528.643 ± 10.69330.800 ± 12.029<0.0010.592MUFA (g/d)28.042 ± 10.25732.205 ± 13.05833.025 ± 11.6630.0120.638PUFA (g/d)18.735 ± 8.49920.349 ± 10.67420.670 ± 8.3500.3380.393Linoleic (g/d)16.296 ± 8.19217.554 ± 10.11617.785 ± 7.7830.4860.419Linolenic (g/d)1.136 ± 0.6361.247 ± 0.7261.307 ± 0.7020.2590.711EPA (g/d)0.029 ± 0.0350.035 ± 0.0410.032 ± 0.0350.5600.763DHA (g/d)0.096 ± 0.1080.115 ± 0.1310.108 ± 0.1080.5440.755Trans fatty acid (g/d)0.000 ± 0.0020.000 ± 0.0030.001 ± 0.0020.5920.690Total fiber (g/d)40.793 ± 17.53746.660 ± 21.89847.036 ± 17.8000.0580.126Caffeine (mg/d)64.554 ± 43.754121.258 ± 46.183225.582 ± 89.754<0.001<0.001DHA, docosahexaenoic acid; DTAC, dietary total antioxidant capacity; EPA, eicosapentaenoic acid; MUFA, monosaturated fat; PUFA, polyunsaturated fatty acid; SFA, saturated fat. 1, T1: <837.305(n = 86), T2: 837.305–1287.032(n = 86), T3: <1287.032(n = 87). 2. Fast food consists of pizza, hamburger, sausage, and mortadella. P values result from the ANOVA test. ∗P value reported after adjusting energy intake with ANCOVA. P value <0.05 is considered a significant level. In the crude model and after adjustment for confounders, significant relationships between food groups and DTAC are shown in bold.For micronutrients, after adjustment for confounders, the relationship between vegetable oils (P < 0.001), potassium (P = 0.006), manganese (P = 0.003), caffeine (P < 0.001), and DTAC remained significant, indicating a significantly higher intakes of these substances in the highest tertile of DTAC (Table 2).


### 3.4. Association between DTAC Score and IL-1β and PAI-1

The association between DTAC and inflammatory markers (IL-1β and PAI-1) is shown in Table 4.Table 4
Association between DTAC and IL-1β and PAI-1 in overweight and obese women (n = 259).
Inflammatory markersModelsβ ± SE95% CIIL-1β (mg/L)Crude2.453 ± 0.1952.064, 2.842Adjusted2.837 ± 1.1600.508, 5.167PAI-1 (mg/L)Crude18.232 ± 4.7418.864, 27.599Adjusted−26.468 ± 28.377−82.626, 29.689CI, confidence Interval; DTAC, dietary total antioxidant capacity; IL-1β, interleukin-1β; PAI-1, type 1 plasminogen activator inhibitor; SE, standard error. Adjusted model: controlled with total energy intake, physical activity, and age as covariates resulting from linear regression.Regarding IL-1β, in the crude model (β: 2.453, 95% CI: 2.064, 2.842) and in the adjusted model (with controlling for total energy intake, physical activity, and age) (β: 2.837, 95% CI: 0.508, 5.167), there was no significant association.The association between PAI-1 and DTAC score in the crude model was not significant (β: 18.232, 95% CI: 8.864, 27.599), while after adjustment for potential confounders, there was a significant negative association (β: −26.468, 95% CI: −82.626, 29.689).


### 3.5. Association between DTAC on Anthropometric Indices

In Table5 and Figure 2, the association between DTAC and anthropometric indices is shown. In the crude model, a marginally significant inverse association was shown between DTAC and WC (β: −0.003, 95% CI: −0.006, 0.000, P: 0.065), and after adjustment for confounders (age, energy intake, physical activity), the association remained significant (β: −0.006, 95% CI: −0.010, 0.002, P: 0.002). In the crude model, no significant relationship was seen between DTAC and FFM, VFL, VFA, and FFMI (P > 0.05), but after adjustment for confounders, FFM (β: 0.001, 95% CI: −0.001, 0.000, P: 0.054) and VFL (β: −0.002, 95% CI: −0.001,0.000, P: 0.063) were marginally significant, and VFA (β: −0.012, 95% CI: −0.034,0.000, P: 0.029) and FFMI (β: 0.001, 95% CI: 0.000, 0.004, P: 0.012) were significant.Table 5
Association between DTAC on anthropometric indices in overweight and obese women (n = 259).
VariablesCrudeModel 1βSE95% CIP valueβSE95% CIP valueWC (cm)−0.0030.002−0.006, <0.0010.065−0.0060.002−0.010, 0.0020.002TF (kg)<0.001<0.001−0.001,0.0010.941<0.001<0.001−0.001, <0.0010.570TF (%)0.0020.006−0.009,0.0130.663−0.0010.006−0.013, 0.0110.854BFM (kg)<0.0010.001-0.001,0.0020.788<0.0010.001−0.002, 0.0010.661FFM (kg)<0.001<0.001−0.001,0.0010.6630.0010.001−0.001, <0.0010.054SMM (kg)<0.001<0.001−0.001, <0.0010.553<0.001<0.001−0.001, <0.0010.426BMI<0.001<0.001−0.001,0.0010.652<0.001<0.001−0.001,0.0010.690WHR<0.001<0.001−0.001,0.0010.905<0.001<0.001<0.001, <0.0010.077VFA (cm)−0.0090.009−0.027,0.0080.296−0.0120.011−0.034, <0.0010.029VFL0.0010.001−0.001,0.0040.282−0.0020.002−0.001, <0.0010.063HC )cm)0.0010.001−0.001,0.0020.547−0.0010.001−0.002,0.0010.481NC (cm)<0.0010.001−0.001,0.0020.609<0.001<0.001−0.001, <0.0010.352BMC (kg)<0.001<0.001<0.001, <0.0010.636<0.001<0.001<0.001, <0.0010.804SLM (kg)<0.001<0.001−0.001, <0.0010.373−0.001<0.001−0.002, <0.0010.235PBF (%)<0.001<0.001−0.001,0.0010.816<0.001<0.001−0.001,0.0010.821FFMI (kg/m2)0.0010.001<0.001,0.0030.0740.0010.001<0.001,0.0040.012FMI (kg/m2)<0.001<0.001<0.001,0.0010.190<0.001<0.001<0.001,0.0010.472ICW (%)<0.001<0.001−0.001, <0.0010.594<0.001<0.001−0.001, <0.0010.455ECW (%)<0.001<0.001<0.001, <0.0010.830<0.001<0.001<0.001, <0.0010.624BFM, body fat mass; BMC, bone mineral content; BMI, body mass index; CI, confidence interval; DTAC, dietary total antioxidant capacity; ECW, extracellular water; FFM, fat-free mass; FFMI, fat-free mass index; FMI, fat mass index; HC, hip circumference; ICW, intracellular water; NC, neck circumference; PBF, percent body fat; SE, standard error; SLM, soft lean mass; SMM, skeletal muscle mass; TF, trunk fat; VFA, visceral fat area; VFL, visceral fat level; WC, waist circumference; WHR, waist hip ratio. Model 1: adjusted by age, energy intake, and physical activity.Figure 2
Association between DTAC on anthropometric indices in overweight and obese women (n = 259). DTAC, dietary total antioxidant capacity; FFM, fat-free mass; FFMI, fat-free mass index; VFA, visceral fat area; WC, waist circumference.


### 3.6. Assessment of the Mediating Role of Inflammatory Markers

The association between DTAC and anthropometric indices in the relationship with inflammatory markers, including IL-1β and PAI-1 as mediatory markers, is shown in Table 6.Table 6
Association of the mediating effect of inflammatory factors (IL-1β and PAI-1) for the association between DTAC on anthropometric indices in overweight and obese women (n = 259).
VariablesInflammatory markersβSE95% CIP valueWC (cm)IL-1β−0.0120.005−0.022, −0.0020.018PAI-1−0.0040.002−0.008, 0.0010.103TF (kg)IL-1β<0.0010.001−0.001, 0.0010.923PAI-1<0.001<0.001−0.001, 0.0010.888TF (%)IL-1β−0.0020.011−0.025, 0.0200.849PAI-10.0020.008−0.013, 0.0170.763BFM (kg)IL-1β<0.0010.001−0.002, 0.0030.773PAI-1<0.0010.001−0.002, 0.0010.884FFM (kg)IL-1β0.0010.001−0.001, 0.0030.315PAI-1<0.0010.001−0.002, 0.0010.714SMM (kg)IL-1β0.0010.001−0.001, 0.0020.330PAI-1<0.001<0.001−0.001, 0.0010.643BMIIL-1β<0.0010.001−0.001, 0.0010.986PAI-1<0.001<0.001−0.001, 0.0010.890WHRIL-1β<0.001<0.001<0.001, <0.0010.883PAI-1<0.001<0.001<0.001, <0.0010.678VFA (cm)IL-1β−0.0060.007−0.019, 0.0080.381PAI-1−0.0040.004−0.013, 0.0050.372VFLIL-1β0.0050.005−0.004, 0.0140.273PAI-10.0040.002−0.001, 0.0080.086HC )cm)IL-1β<0.0010.002−0.004, 0.0030.928PAI-1−0.0010.001−0.003, 0.0010.279NC (cm)IL-1β<0.0010.001−0.001, 0.0010.829PAI-1<0.001<0.001−0.001, 0.0010.514BMC (kg)IL-1β<0.001<0.001<0.001, <0.0010.277PAI-1<0.001<0.001<0.001, <0.0010.714SLM (kg)IL-1β0.0010.001−0.001, 0.0030.315PAI-1−0.0010.001−0.002, 0.0010.303PBF (%)IL-1β−0.0010.001−0.002, 0.0010.531PAI-1<0.0010.001−0.001, 0.0010.969FFMIIL-1β<0.001<0.001<0.001, 0.0010.803PAI-10.0030.001<0.001, 0.0060.047FMIIL-1β0.0010.001<0.001, 0.0020.177PAI-1<0.001<0.001−0.001, 0.0010.829ICW (%)IL-1β<0.001<0.001<0.001, 0.0010.287PAI-1<0.001<0.001−0.001, <0.0010.658ECW (%)IL-1β<0.001<0.001<0.001, 0.0010.287PAI-1<0.001<0.001<0.001, <0.0010.816BFM, body fat mass; BMC, bone mineral content; BMI, body mass index; CI, confidence interval; DTAC, dietary total antioxidant capacity; ECW, extracellular water; FFM, fat-free mass; FFMI, fat-free mass index; FMI, fat mass index; HC, hip circumference; ICW, intracellular water; NC, neck circumference; PBF, percent body fat; SE, standard error; SLM, soft lean mass; SMM, skeletal muscle mass; TF, trunk fat; VFA, visceral fat area; VFL, visceral fat level; WC, waist circumference; WHR, waist hip ratio. All variables were adjusted for age, energy intake, and physical activity, and linear regression was used. Significant associations are shown in bold.By including IL-1β as a confounding variable, we observed that in FFM (β: 0.001, 95% CI: −0.001, 0.003, P: 0.315), VFA (β: −0.006, 95% CI: −0.019, 0.008, P: 0.381), VFL (β: 0.005, 95% CI: −0.004, 0.014, P: 0.273), and FFMI (β: −0.000, 95% CI: 0.000, 0.001, P: 0.803), the significance was eliminated, and in WC (β: −0.012, 95% CI: −0.022, 0.002, P: 0.018), the P value increased, suggesting that IL-1β may represent a mediatory marker.For TF, BFM, BMI, WHR, HC, NC, and SLM, there was no significant association; however, by including IL-1β as a confounding variable, the P value increased, highlighting that IL-1β probably could be considered as a mediatory marker.For SMM, BMC, PBF, FMI, ICW, and ECW, theP value decreased such that IL-1β likely has no mediating effect.By including PAI-1 as a confounder, the significance was eliminated in WC (β: −0.004, 95% CI: −0.008, 0.001, P: 0.103), FFM (β: 0.000, 95% CI: −0.002, 0.001, P: 0.884), VFA (β: −0.004, 95% CI: −0.013, 0.005, P: 0.372), and VFL (β: 0.004, 95% CI: −0.001, 0.008, P: 0.086), demonstrating that they were mediated by PAI-1; however, the association in FFMI (β: 0.003, 95% CI: 0.000, 0.006, P: 0.047) remained significant.For variables including SMM, TF, BFM, BMI, WHR, PBF, FMI, ICW, ECW, NC, and SLM, there was no significant association, and by including PAI-1 as a confounding variable, theP value increased, meaning that PAI-1 could be considered as a mediatory marker, but for BMC and HC, the P value decreased indicating that PAI-1 probably does not have a mediating effect on these two variables.


## 3.1. Study Population Characteristics

This cross-sectional study was conducted on 259 women with overweight and obesity. The means (SD) of age, height, BMI, and weight of individuals were 36.672 (9.103) years, 161.223 (5.870) cm, 31.261 (4.298) kg/m2, and 81.294 (12.432) kg, respectively. The mean and SD of quantitative demographic variables are shown in Table 1. The majority of women were married (77.22%) and had an academic education (83.783%).Table 1
Frequency of general characteristics and body composition measurements in overweight and obese women (n = 259).
VariablesMeanSDMinimumMaximumAge (years)36.6729.10317.00064.000Weight (kg)81.29412.43259.500136.600Height (cm)161.2235.870142.000179.000BMI (kg/m2)31.2614.29824.20049.600FFM (kg)46.5285.71633.40067.700BFM (kg)34.7408.75319.40074.200SMM (kg)25.5603.44517.30037.900WHR1.1634.5470.81092.000WC (cm)99.61210.07779.6136.000VFL16.73512.1467.000208.000VFA (cm2)171.14291.11720.0001817.000TF (kg)16.8623.6719.70030.200TF (%)320.20769.614177.800536.600HC (cm)114.0259.672100.000160.000NC (cm)37.5467.35731.000134.500BMC (kg)2.6610.3541.8203.550SLM (kg)43.8745.39426.10059.400PBF (%)41.5505.24327.10054.300FFMI18.4148.21714.600147.800FMI13.0853.3306.90026.900ICW (%)21.1672.56116.00028.700ECW (%)13.1431.64110.00018.400BFM, body fat mass; BMC, bone mineral content; BMI, body mass index; BMI, body mass index; CI, confidence interval; DTAC, dietary total antioxidant capacity; ECW, extracellular water; FFM, fat-free mass; FFMI, fat-free mass index; FMI, fat mass index; HC, hip circumference; ICW, intracellular water; NC, Neck circumference; PBF, Percent body fat; SD, standard deviation; SLM, soft lean mass; SMM, skeletal muscle mass; TF, trunk fat; VFA, visceral fat area; VFL, visceral fat level; WC, waist circumference; WHR, waist-hip ratio.


## 3.2. Description of General Characteristics among Tertiles of DTAC

The baseline characteristics of study participants, categorized according to the DTAC, are presented in Table2. As shown in Table 2, in the crude model, there were no significant mean differences in general characteristics among DTAC tertiles. After adjustment with confounders, including energy intake, physical activity, and age, there were significant mean differences between FBS (P = 0.014) and HDL (P = 0.015) across DTAC tertiles. Potential confounding variables were selected by a literature review of studies according to the exposure and outcome of this study and using the stepwise method in regression liner. Following Bonferroni's post hoc analysis, for FBS, this difference was between tertiles 1 and 3, and for HDL, it was between tertiles 2 and 3.Table 2
Description of general characteristics among tertiles of DTAC in overweight and obese women (n = 259).
VariablesT11T2T3P valueP value∗General characteristicsAge (years)36.164 ± 8.352236.235 ± 8.78936.632 ± 8.6630.9290.408Weight (kg)80.244 ± 11.95180.789 ± 11.01780.335 ± 13.6140.9520.548Height (cm)161.305 ± 6.712161.029 ± 5.771161.436 ± 5.6740.9030.515Physical activity (MET-minutes/week)1127.953 ± 2383.966856.414 ± 749.6601518.205 ± 2328.9810.1220.098Blood pressureSBP (mm Hg)111.759 ± 13.018109.518 ± 13.211110.768 ± 13.5850.5530.563DBP (mm Hg)78.036 ± 8.79276.096 ± 9.14476.926 ± 9.4520.3910.306Blood parametersFBS (mg/dl)87.690 ± 9.161a87.385 ± 10.08786.773 ± 0.7800.8430.014TC (mg/dl)184.422 ± 36.502185.757 ± 36.735183.053 ± 36.3330.9060.239TG (mg/dl)0.395 ± 0.4910.337 ± 0.4750.298 ± 0.4600.4080.487HDL (mg/dl)47.352 ± 10.48246.828 ± 11.678b46.666 ± 11.8150.9310.015LDL (mg/dl)94.732 ± 25.12495.071 ± 23.93692.560 ± 22.7440.7890.208Inflammatory markersPAI-1 (mg/L)18.191 ± 33.87612.006 ± 21.51219.364 ± 35.5140.4610.283IL-1β (mg/L)2.636 ± 1.1352.551 ± 0.7242.825 ± 0.8520.6160.577Qualitative variablesMarital statusSingle15 (25.900)322 (37.900)21 (36.200)0.4170.302Married70 (35.000)64 (32.000)66 (33.00)Education levelIlliterate1 (33.300)2 (66.700)0 (0.000)0.2210.798Diploma and lower11 (28.900)9 (23.700)18 (47.400)Academic73 (33.600)75 (34.600)69 (31.800)Economic statusVery weak11 (39.300)6 (21.400)11 (39.300)0.8220.272Weak40 (34.200)41 (35.000)36 (30.800)Medium29 (31.900)31 (34.100)31 (34.100)Good2 (22.200)3 (33.300)4 (44.400)SupplementationYes42 (35.000)39 (32.500)39 (32.500)0.9420.727No31 (36.000)26 (30.200)29 (33.700)OccupationUnemployed45 (36.000)41 (32.800)39 (31.200)0.6160.090Employed21 (29.600)24 (33.800)26 (36.600)DBP, diastolic blood pressure; DTAC, dietary total antioxidant capacity; FBS, fasting blood sugar; HDL, high-density lipoprotein; IL-1β, interleukin-1β; LDL, low-density lipoprotein; PAI-1, type 1 plasminogen activator inhibitor; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride. 1, T1: <837.305(n = 86), T2: 837.305–1287.032 (n = 86), T3: <1287.032(n = 87). 2, Mean ± SD. 3, the numbers inside the pessary represent the percentage. a Significant difference between tertiles 1 and 3. b Significant difference between tertiles 2 and 3. P values result from ANOVA test, for categorical variables. P value result from Chi-Square Tests, ∗P value reported after adjusting total energy intake, physical activity, and age with ANCOVA for categorical variables. P value result from Univariate Tests. P value<0.05 is considered as a significance level. After adjustment the confounders, the significant mean difference between the variables and DTAC is shown in bold.


## 3.3. Description of Dietary Intake across Tertiles of DTAC

Dietary intakes of participants across tertiles of DTAC are presented in Table2 and Figure 1, for macronutrients, including protein, carbohydrates, and total fat; before the adjustment in the crude model, there was a significant mean difference among DTAC tertiles (P < 0.05). After controlling for confounders, including energy intake, significance was lost (P > 0.05).Figure 1
Description of general characteristics among tertiles of DTAC in overweight and obese women (n = 259). DBP, diastolic blood pressure; DTAC, dietary total antioxidant capacity; FBS, fasting blood sugar; HDL, high-density lipoprotein; IL-1β, interleukin-1β; LDL, low-density lipoprotein; PAI-1, type 1 plasminogen activator inhibitor; SBP, systolic blood pressure; TG, triglyceride.
(a)(b)(c)In food groups, in the crude model, the relationship between fruits (P < 0.001), tea and coffee (P < 0.001), dairy (P = 0.005), meat (P = 0.067), and DTAC was significant. After adjustment for confounders, the significance between tea and coffee (P = 0.368), dairy (P = 0.527), meat (P = 0.613), and DTAC was lost, but the significance for fruits remained (P = 0.021), which indicated that the group with the highest DTAC score had a significantly higher intake of fruits (Table 3).Table 3
Description of dietary intake within tertiles of DTAC in overweight and obese women (n = 259).
VariablesT11T2T3P valueP value∗Mean ± SDEnergy and macronutrientsEnergy intake (kcal/d)2272.564 ± 568.2982642.461 ± 796.4312837.647 ± 748.079<0.001−Protein (g/d)78.271 ± 24.31990.189 ± 31.12995.314 ± 28.793<0.0010.890Carbohydrates (g/d)321.941 ± 93.655370.609 ± 127.948407.543 ± 121.537<0.0010.503Total fat (g/d)81.869 ± 26.50897.024 ± 36.690101.689 ± 31.704<0.0010.710Food groupsWhole grains (g/d)8.108 ± 9.9226.837 ± 10.1106.555 ± 8.9520.5330.181Fruits (g/d)411.467 ± 265.815480.806 ± 272.618624.280 ± 344.464<0.0010.021Vegetables (g/d)379.385 ± 253.359452.118 ± 259.251445.944 ± 236.9850.1090.594Nuts (g/d)11.778 ± 10.89813.792 ± 17.26315.370 ± 17.2240.3100.985Legumes (g/d)53.670 ± 44.31849.521 ± 37.68655.714 ± 42.2470.6100.391Tea and coffee (g/d)304.095 ± 207.827580.499 ± 221.6381103.696 ± 443.628<0.0010.368Sweetened beverages (g/d)20.581 ± 53.25931.221 ± 68.53422.561 ± 71.2910.5230.477Refined grains (g/d)401.556 ± 166.684417.490 ± 207.439461.121 ± 232.8800.1410.728Dairy (g/d)328.685 ± 198.891387.820 ± 224.714445.443 ± 267.8750.0050.527Eggs (g/d)20.490 ± 12.67522.804 ± 16.56221.503 ± 12.6020.5590.863Fish and seafood (g/d)10.572 ± 11.50612.336 ± 14.71911.460 ± 10.8940.6530.354Meat (g/d)53.808 ± 38.60366.148 ± 56.23670.869 ± 52.0510.0670.613Fast food2 (g/d)19.490 ± 17.86826.638 ± 27.19924.026 ± 28.0590.1630.979VitaminsVitamin A (IU/d)657.329 ± 306.822794.190 ± 450.351829.259 ± 412.3590.0110.767β-carotene (µg/d)4324.444 ± 2745.2675550.012 ± 4249.2635557.640 ± 3088.8470.0260.438Vitamin C (mg/d)158.571 ± 103.664183.619 ± 96.291225.301 ± 122.146<0.0010.133Vitamin D (µg/d)1.812 ± 1.3752.073 ± 1.5622.165 ± 1.9310.3420.891Vitamin E (IU/d)16.966 ± 9.44217.046 ± 9.63417.293 ± 8.3870.9700.220Thiamin (mg/d)1.894 ± 0.5652.091 ± 0.7272.200 ± 0.6620.0080.073Riboflavin (mg/d)1.905 ± 0.6342.263 ± 0.8202.351 ± 0.841<0.0010.648Niacin (mg/d)22.470 ± 7.32326.052 ± 11.69826.637 ± 8.5470.0070.236Vitamin B6 (µg/d)1.907 ± 0.5872.175 ± 0.7912.338 ± 0.710<0.0010.947Folate (mg/d)543.213 ± 153.162595.764 ± 191.544650.256 ± 168.709<0.0010.378Vitamin B12 (µg/d)3.797 ± 2.234.247 ± 1.9534.838 ± 2.9090.0180.699Biotin (µg/d)33.418 ± 13.62540.261 ± 21.13540.922 ± 14.7710.0060.598Vitamin B5 (µg/d)5.727 ± 1.6696.534 ± 3.0407.018 ± 2.2020.0020.964Vitamin k (µg/d)194.883 ± 119.059241.906 ± 324.028210.072 ± 126.5520.3340.358MineralsSodium (mg/d)3858.392 ± 1330.3664301.205 ± 1576.4724446.184 ± 1381.8300.0210.510Potassium (mg/d)3558.357 ± 1311.7794291.144 ± 1601.7394944.556 ± 1548.121<0.0010.006Calcium (mg/d)1016.170 ± 345.1651188.614 ± 457.5751278.289 ± 468.104<0.0010.851Iron (mg/d)16.649 ± 4.91319.856 ± 14.39919.861 ± 5.7320.0360.213Magnesium (mg/d)392.680 ± 124.269457.339 ± 155.461506.831 ± 142.959<0.0010.185Zinc (g/d)11.249 ± 3.38513.035 ± 4.29313.940 ± 4.310<0.0010.984Copper (mg/d)1.797 ± 0.5712.000 ± 0.8952.098 ± 0.6570.0210.095Manganese (mg/d)5.811 ± 2.0976.872 ± 2.9758.089 ± 2.385<0.0010.003Selenium (µg/d)109.380 ± 37.942122.295 ± 47.856126.290 ± 43.2750.0280.194Chromium (mg/d)0.100 ± 0.0820.112 ± 0.0930.120 ± 0.0850.3070.907Other componentsVegetable oils (g/d)22.649 ± 20.37523.899 ± 25.44924.284 ± 20.5510.880<0.001SFA (g/d)23.759 ± 8.39528.643 ± 10.69330.800 ± 12.029<0.0010.592MUFA (g/d)28.042 ± 10.25732.205 ± 13.05833.025 ± 11.6630.0120.638PUFA (g/d)18.735 ± 8.49920.349 ± 10.67420.670 ± 8.3500.3380.393Linoleic (g/d)16.296 ± 8.19217.554 ± 10.11617.785 ± 7.7830.4860.419Linolenic (g/d)1.136 ± 0.6361.247 ± 0.7261.307 ± 0.7020.2590.711EPA (g/d)0.029 ± 0.0350.035 ± 0.0410.032 ± 0.0350.5600.763DHA (g/d)0.096 ± 0.1080.115 ± 0.1310.108 ± 0.1080.5440.755Trans fatty acid (g/d)0.000 ± 0.0020.000 ± 0.0030.001 ± 0.0020.5920.690Total fiber (g/d)40.793 ± 17.53746.660 ± 21.89847.036 ± 17.8000.0580.126Caffeine (mg/d)64.554 ± 43.754121.258 ± 46.183225.582 ± 89.754<0.001<0.001DHA, docosahexaenoic acid; DTAC, dietary total antioxidant capacity; EPA, eicosapentaenoic acid; MUFA, monosaturated fat; PUFA, polyunsaturated fatty acid; SFA, saturated fat. 1, T1: <837.305(n = 86), T2: 837.305–1287.032(n = 86), T3: <1287.032(n = 87). 2. Fast food consists of pizza, hamburger, sausage, and mortadella. P values result from the ANOVA test. ∗P value reported after adjusting energy intake with ANCOVA. P value <0.05 is considered a significant level. In the crude model and after adjustment for confounders, significant relationships between food groups and DTAC are shown in bold.For micronutrients, after adjustment for confounders, the relationship between vegetable oils (P < 0.001), potassium (P = 0.006), manganese (P = 0.003), caffeine (P < 0.001), and DTAC remained significant, indicating a significantly higher intakes of these substances in the highest tertile of DTAC (Table 2).


## 3.4. Association between DTAC Score and IL-1β and PAI-1

The association between DTAC and inflammatory markers (IL-1β and PAI-1) is shown in Table 4.Table 4
Association between DTAC and IL-1β and PAI-1 in overweight and obese women (n = 259).
Inflammatory markersModelsβ ± SE95% CIIL-1β (mg/L)Crude2.453 ± 0.1952.064, 2.842Adjusted2.837 ± 1.1600.508, 5.167PAI-1 (mg/L)Crude18.232 ± 4.7418.864, 27.599Adjusted−26.468 ± 28.377−82.626, 29.689CI, confidence Interval; DTAC, dietary total antioxidant capacity; IL-1β, interleukin-1β; PAI-1, type 1 plasminogen activator inhibitor; SE, standard error. Adjusted model: controlled with total energy intake, physical activity, and age as covariates resulting from linear regression.Regarding IL-1β, in the crude model (β: 2.453, 95% CI: 2.064, 2.842) and in the adjusted model (with controlling for total energy intake, physical activity, and age) (β: 2.837, 95% CI: 0.508, 5.167), there was no significant association.The association between PAI-1 and DTAC score in the crude model was not significant (β: 18.232, 95% CI: 8.864, 27.599), while after adjustment for potential confounders, there was a significant negative association (β: −26.468, 95% CI: −82.626, 29.689).


## 3.5. Association between DTAC on Anthropometric Indices

In Table5 and Figure 2, the association between DTAC and anthropometric indices is shown. In the crude model, a marginally significant inverse association was shown between DTAC and WC (β: −0.003, 95% CI: −0.006, 0.000, P: 0.065), and after adjustment for confounders (age, energy intake, physical activity), the association remained significant (β: −0.006, 95% CI: −0.010, 0.002, P: 0.002). In the crude model, no significant relationship was seen between DTAC and FFM, VFL, VFA, and FFMI (P > 0.05), but after adjustment for confounders, FFM (β: 0.001, 95% CI: −0.001, 0.000, P: 0.054) and VFL (β: −0.002, 95% CI: −0.001,0.000, P: 0.063) were marginally significant, and VFA (β: −0.012, 95% CI: −0.034,0.000, P: 0.029) and FFMI (β: 0.001, 95% CI: 0.000, 0.004, P: 0.012) were significant.Table 5
Association between DTAC on anthropometric indices in overweight and obese women (n = 259).
VariablesCrudeModel 1βSE95% CIP valueβSE95% CIP valueWC (cm)−0.0030.002−0.006, <0.0010.065−0.0060.002−0.010, 0.0020.002TF (kg)<0.001<0.001−0.001,0.0010.941<0.001<0.001−0.001, <0.0010.570TF (%)0.0020.006−0.009,0.0130.663−0.0010.006−0.013, 0.0110.854BFM (kg)<0.0010.001-0.001,0.0020.788<0.0010.001−0.002, 0.0010.661FFM (kg)<0.001<0.001−0.001,0.0010.6630.0010.001−0.001, <0.0010.054SMM (kg)<0.001<0.001−0.001, <0.0010.553<0.001<0.001−0.001, <0.0010.426BMI<0.001<0.001−0.001,0.0010.652<0.001<0.001−0.001,0.0010.690WHR<0.001<0.001−0.001,0.0010.905<0.001<0.001<0.001, <0.0010.077VFA (cm)−0.0090.009−0.027,0.0080.296−0.0120.011−0.034, <0.0010.029VFL0.0010.001−0.001,0.0040.282−0.0020.002−0.001, <0.0010.063HC )cm)0.0010.001−0.001,0.0020.547−0.0010.001−0.002,0.0010.481NC (cm)<0.0010.001−0.001,0.0020.609<0.001<0.001−0.001, <0.0010.352BMC (kg)<0.001<0.001<0.001, <0.0010.636<0.001<0.001<0.001, <0.0010.804SLM (kg)<0.001<0.001−0.001, <0.0010.373−0.001<0.001−0.002, <0.0010.235PBF (%)<0.001<0.001−0.001,0.0010.816<0.001<0.001−0.001,0.0010.821FFMI (kg/m2)0.0010.001<0.001,0.0030.0740.0010.001<0.001,0.0040.012FMI (kg/m2)<0.001<0.001<0.001,0.0010.190<0.001<0.001<0.001,0.0010.472ICW (%)<0.001<0.001−0.001, <0.0010.594<0.001<0.001−0.001, <0.0010.455ECW (%)<0.001<0.001<0.001, <0.0010.830<0.001<0.001<0.001, <0.0010.624BFM, body fat mass; BMC, bone mineral content; BMI, body mass index; CI, confidence interval; DTAC, dietary total antioxidant capacity; ECW, extracellular water; FFM, fat-free mass; FFMI, fat-free mass index; FMI, fat mass index; HC, hip circumference; ICW, intracellular water; NC, neck circumference; PBF, percent body fat; SE, standard error; SLM, soft lean mass; SMM, skeletal muscle mass; TF, trunk fat; VFA, visceral fat area; VFL, visceral fat level; WC, waist circumference; WHR, waist hip ratio. Model 1: adjusted by age, energy intake, and physical activity.Figure 2
Association between DTAC on anthropometric indices in overweight and obese women (n = 259). DTAC, dietary total antioxidant capacity; FFM, fat-free mass; FFMI, fat-free mass index; VFA, visceral fat area; WC, waist circumference.


## 3.6. Assessment of the Mediating Role of Inflammatory Markers

The association between DTAC and anthropometric indices in the relationship with inflammatory markers, including IL-1β and PAI-1 as mediatory markers, is shown in Table 6.Table 6
Association of the mediating effect of inflammatory factors (IL-1β and PAI-1) for the association between DTAC on anthropometric indices in overweight and obese women (n = 259).
VariablesInflammatory markersβSE95% CIP valueWC (cm)IL-1β−0.0120.005−0.022, −0.0020.018PAI-1−0.0040.002−0.008, 0.0010.103TF (kg)IL-1β<0.0010.001−0.001, 0.0010.923PAI-1<0.001<0.001−0.001, 0.0010.888TF (%)IL-1β−0.0020.011−0.025, 0.0200.849PAI-10.0020.008−0.013, 0.0170.763BFM (kg)IL-1β<0.0010.001−0.002, 0.0030.773PAI-1<0.0010.001−0.002, 0.0010.884FFM (kg)IL-1β0.0010.001−0.001, 0.0030.315PAI-1<0.0010.001−0.002, 0.0010.714SMM (kg)IL-1β0.0010.001−0.001, 0.0020.330PAI-1<0.001<0.001−0.001, 0.0010.643BMIIL-1β<0.0010.001−0.001, 0.0010.986PAI-1<0.001<0.001−0.001, 0.0010.890WHRIL-1β<0.001<0.001<0.001, <0.0010.883PAI-1<0.001<0.001<0.001, <0.0010.678VFA (cm)IL-1β−0.0060.007−0.019, 0.0080.381PAI-1−0.0040.004−0.013, 0.0050.372VFLIL-1β0.0050.005−0.004, 0.0140.273PAI-10.0040.002−0.001, 0.0080.086HC )cm)IL-1β<0.0010.002−0.004, 0.0030.928PAI-1−0.0010.001−0.003, 0.0010.279NC (cm)IL-1β<0.0010.001−0.001, 0.0010.829PAI-1<0.001<0.001−0.001, 0.0010.514BMC (kg)IL-1β<0.001<0.001<0.001, <0.0010.277PAI-1<0.001<0.001<0.001, <0.0010.714SLM (kg)IL-1β0.0010.001−0.001, 0.0030.315PAI-1−0.0010.001−0.002, 0.0010.303PBF (%)IL-1β−0.0010.001−0.002, 0.0010.531PAI-1<0.0010.001−0.001, 0.0010.969FFMIIL-1β<0.001<0.001<0.001, 0.0010.803PAI-10.0030.001<0.001, 0.0060.047FMIIL-1β0.0010.001<0.001, 0.0020.177PAI-1<0.001<0.001−0.001, 0.0010.829ICW (%)IL-1β<0.001<0.001<0.001, 0.0010.287PAI-1<0.001<0.001−0.001, <0.0010.658ECW (%)IL-1β<0.001<0.001<0.001, 0.0010.287PAI-1<0.001<0.001<0.001, <0.0010.816BFM, body fat mass; BMC, bone mineral content; BMI, body mass index; CI, confidence interval; DTAC, dietary total antioxidant capacity; ECW, extracellular water; FFM, fat-free mass; FFMI, fat-free mass index; FMI, fat mass index; HC, hip circumference; ICW, intracellular water; NC, neck circumference; PBF, percent body fat; SE, standard error; SLM, soft lean mass; SMM, skeletal muscle mass; TF, trunk fat; VFA, visceral fat area; VFL, visceral fat level; WC, waist circumference; WHR, waist hip ratio. All variables were adjusted for age, energy intake, and physical activity, and linear regression was used. Significant associations are shown in bold.By including IL-1β as a confounding variable, we observed that in FFM (β: 0.001, 95% CI: −0.001, 0.003, P: 0.315), VFA (β: −0.006, 95% CI: −0.019, 0.008, P: 0.381), VFL (β: 0.005, 95% CI: −0.004, 0.014, P: 0.273), and FFMI (β: −0.000, 95% CI: 0.000, 0.001, P: 0.803), the significance was eliminated, and in WC (β: −0.012, 95% CI: −0.022, 0.002, P: 0.018), the P value increased, suggesting that IL-1β may represent a mediatory marker.For TF, BFM, BMI, WHR, HC, NC, and SLM, there was no significant association; however, by including IL-1β as a confounding variable, the P value increased, highlighting that IL-1β probably could be considered as a mediatory marker.For SMM, BMC, PBF, FMI, ICW, and ECW, theP value decreased such that IL-1β likely has no mediating effect.By including PAI-1 as a confounder, the significance was eliminated in WC (β: −0.004, 95% CI: −0.008, 0.001, P: 0.103), FFM (β: 0.000, 95% CI: −0.002, 0.001, P: 0.884), VFA (β: −0.004, 95% CI: −0.013, 0.005, P: 0.372), and VFL (β: 0.004, 95% CI: −0.001, 0.008, P: 0.086), demonstrating that they were mediated by PAI-1; however, the association in FFMI (β: 0.003, 95% CI: 0.000, 0.006, P: 0.047) remained significant.For variables including SMM, TF, BFM, BMI, WHR, PBF, FMI, ICW, ECW, NC, and SLM, there was no significant association, and by including PAI-1 as a confounding variable, theP value increased, meaning that PAI-1 could be considered as a mediatory marker, but for BMC and HC, the P value decreased indicating that PAI-1 probably does not have a mediating effect on these two variables.


## 4. Discussion

To our knowledge, this is the first study designed to assess the relationship between dietary total antioxidant capacity and body composition, in addition to the mediating role of inflammatory markers (IL-1β and PAI-1), among overweight and obese Iranian women.Our finding of lower FBS and HDL in participants with higher scores of DTAC is in contrast with several studies [24,25]. Valtueña et al. did not report any significant changes in FBS and HDL levels before and after a high total antioxidant capacity diet. Instead, they observed a mild but considerable advantageous effect of dietary antioxidants on systemic inflammation and liver dysfunction. Moreover, a reduction in ALT, alkaline phosphatase, GGT activities, and plasma concentrations of hs-CRP was shown following a diet high in antioxidants compared with a diet low in antioxidants [24]. In another study, Mohammadi et al. also failed to detect any significant changes between FBS and HDL levels and DTAC tertiles [25]. However, another study on healthy Brazilian teens displayed an inverse relationship between DTAC and lipid profile [26].In our study, participants who had higher DTAC scores had a higher intake of fruits, vegetable oils, potassium, manganese, and caffeine in their diets. The desirable effects of these antioxidant-rich foods for the improvement of lipid profiles, glucose homeostasis, insulin resistance, adiposity, and obesity have been shown in preclinical and some clinical studies. Indeed, the effects of food antioxidants, including carotenoids, polyphenols, and vitamins, might occur due to the correction of lipids and carbohydrate metabolism, incremented insulin sensitivity, and balancing of both appetite and adipocytokines [27].The findings of this study showed that high consumption of foods rich in antioxidants could decrease WC, FFM, VFL, VFA, and FFMI, and, as a result, the risk of central obesity may reduce. In line with our study, Bahadoran et al. reported a positive association between DTAC and WC [28].Results from mediation analyses for IL-1β and PAI-1 were as hypothesized. Indeed, based on our results, IL-1β and PAI-1 can likely be considered mediatory markers in the association between FFM, VFA, WC, and DTAC.Although the molecular mechanisms remain unknown, oxidative stress accompanying obesity and its complications have been shown to be decreased by weight loss, caloric restriction, or antioxidant-rich diets [29], which might modify the synthesis of inflammatory markers and total antioxidant capacity of a diet [30]. The anti-inflammatory attributes of some vitamins and other bioactive compounds with antioxidant capacity have been ascribed to their capability of modifying the NF-kB DNA-binding action. NF-kB activation is chiefly elevated by oxidative stress, It causes the cytokine-induced expression of cell adhesion molecule (CAM) in the vascular endothelium, and the TNF-a and IL-6-induced production of CRP by the liver. Because the antioxidant vitamins (a-tocopherol, b-carotene, and vitamin C) [31–33] and the effective flavonoids (quercetin and apigenin) [34,35] are all able to prevent NF-kB DNA-binding activity, in vitro, it is plausible to consider that the anti-inflammatory effects of single antioxidants would depend on their redox potential, rather than on their special molecular structure.The inverse association between DTAC and obesity-related properties in some studies supports the oxidative stress-induced obesity hypothesis (36). According to this assumption, exposure to high levels of reactive oxygen species (ROS) stimulates adipocyte differentiation, which has been shown in bothin vitro [36] and in vivo surveys [37]. As an outcome of this differentiation, major adipose tissue can produce higher ROS [38], and this damaged cycle is a pathogenic mechanism that can exacerbate obesity-related features [38]. Hence, dietary antioxidants can also impact other facets of obesity-related metabolic pathways, including prevention of intestinal fat absorption, improvement of catabolism in adipose tissue, prevention of proliferation, differentiation, angiogenesis in preadipocytes, and induction of apoptosis in mature adipocytes [39,40]. Some other dietary antioxidants might inhibit adiposity by the regulation of brown adipose tissue metabolism and augmentation of thermogenesis, reducing adiponectin and leptin gene expression in adipocytes [41,42].The obvious limitations of the present study include the cross-sectional nature of the study, which precludes the ability to suggest a causal relationship between dietary total antioxidant capacity and body composition. Also, there might be small errors in the dietary assessment, mostly due to (mis)remembering the data and misclassification errors by using FFQ. Moreover, because our study only included women, the results are not generalizable to men. Further studies are needed and should include people of different ages and sexes, in addition to experimental studies, to confirm the veracity of our findings.The strengths of the current study included selecting obese and overweight individuals in a large sample. Dietary intake was assessed using a locally validated questionnaire, and the FFQ was conducted via interview by an experienced dietitian to minimize measurement errors.


## 5. Conclusion

In conclusion, DTAC was positively associated with some anthropometric indices and mediated by augmenting serum levels of IL-1β and PAI-1. Intake of foods rich in antioxidants could represent a protective strategy against chronic diseases, such as cardiovascular disease.



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