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. 2022 Aug 9;9:945591. doi: 10.3389/fnut.2022.945591

The relationship between ultra-processed food intake and cardiometabolic risk factors in overweight and obese women: A cross-sectional study

Dorsa Hosseininasab 1, Farideh Shiraseb 2, Sahar Noori 1, Shahin Jamili 3, Fatemeh Mazaheri-Eftekhar 1, Mahshid Dehghan 4, Alessandra da Silva 5, Josefina Bressan 5, Khadijeh Mirzaei 2,*
PMCID: PMC9396040  PMID: 36017229

Abstract

Background

Cardiovascular diseases (CVDs) are the leading cause of death globally. Based on recent studies, one of the factors that can have detrimental effects on CVD is the consumption of ultra-processed foods (UPFs). The current study investigated the relationship between UPF intake and cardiometabolic risk factors among Iranian women.

Methods

The current cross-sectional study was conducted on 391 women aged 18–65 years with a body mass index (BMI) ≥ 25 kg/m2. Dietary intake was assessed using a 147-item food frequency questionnaire (FFQ). Anthropometric and biochemistry parameters were also collected. UPFs were identified using the NOVA classification.

Results

In the present study, women had a mean (standard deviation) age of 36.67 (9.10) years and the mean BMI of 31.26 (4.29) kg/m2. According to our findings, there was a significant association between UPF consumption and transforming growth factor (TGF) (β: 0.101, 95% CI: 0.023, 0.180, p = 0.012), atherogenic coefficient (AC) (β: 0.011, 95% CI: 0.001, 0.032, p = 0.034), visceral fat level (VFL) (β: 0.006, 95% CI: −0.017, 0.029, p = 0.076), and the quantitative insulin sensitivity check index (QUICKI) (β: −3.775, 95%CI: 0.001, 0.001, p = 0.042).

Conclusion

In conclusion, an increase in consumption of one gram of UPFs is associated with an increase in TGF, AC, and VFL but with a decrease in QUICKI. Despite this, further experimental studies are necessary to draw a more definite conclusion and disentangle the mechanisms by which UPFs may affect health.

Keywords: ultra-processed food, cardiovascular diseases, obesity, overweight, cardiometabolic risk

Introduction

Cardiovascular diseases (CVDs) are the leading cause of death globally; an estimated 17.9 million people died from CVDs in 2019, representing 32% of all global deaths (1). About 85% of the deaths were due to heart attack and stroke (1). According to previous studies conducted in 2016 and 2017, CVD has been the major cause of mortality in Iran, accounting for 46% of all deaths and 20–23% of the disease burden (2, 3).

The global consumption of ultra-processed foods (UPF) has risen exponentially. UPFs account for between 25 and 60% of total daily energy consumption, according to the Nationwide Food Surveys (4–14). According to the NOVA classification system, UPFs are defined as foods made up entirely or predominantly from unhealthy components containing higher levels of total fat, saturated fat, added sugar, energy density, and salt, and lower quantities of fiber and vitamin density (15). UPF packaging contains materials that come into contact with food, such as Bisphenol A, which, according to a meta-analysis of observational studies, may increase the risk of cardiometabolic disorders, even though prospective cohort studies are still limited (16, 17). Some studies reported that consumption of UPF is associated with adverse health outcomes, including CVDs, and obesity (18–20). Srour et al. reported a higher risk of CVD associated with the consumption of ultra-processed foods (21).

Given the high prevalence of CVDs in Iran, it is necessary to find dietary factors that may associate with the disease (22). The main objective of this study was to investigate the relationship between UPF intake and cardiometabolic risk factors among Iranian women, and the secondary objectives were to exhibit the association between UPF consumption, food groups, and demographic variables.

Methods

Study population

The research was conducted in Tehran, Iran, using a multi-stage cluster random sampling procedure on 391 overweight and obese women with a body mass index (BMI) ranging from 25 to 40 kg/m2 and aged 18–48 years, recruited from the community health center of the Tehran University of Medical Sciences (TUMS) in 2018. We used the sample size formula N =([(Z 1−α+Z1−β)×1-r2]/r)2+2), β = 95%, and α = 0.05, r = 0.25. Participants were excluded from the study if they reported a total daily energy intake outside of 800–4,200 kcal (17,556–3,344 kJ) (23) or if they reported a history of diseases such as CVD, diabetes, cancer, kidney disease, thyroid disease, menopause, pregnancy, and breastfeeding. In addition, individuals on lipid-lowering agents, individuals on blood glucose-lowering agents, and those who consumed alcohol or smoked were excluded from the study. Furthermore, the food frequency questionnaire (FFQ) did not include individuals who did not respond to more than 70 questions and had significant fluctuations in their weight over the past year. After learning about the study's objectives, all the participants signed an informed consent form. The Human Ethics Committee of Tehran University of Medical Sciences approved the study protocol (Ethics number: IR.TUMS.VCR.REC.1398.142, Date of reference number: 5 April 2019).

Dietary assessment and NOVA calculation

To evaluate the food consumption of participants during the previous year, we used a validated semi-quantitative FFQ, whose validity and reliability have already been authorized (24, 25). Trained dietitians were responsible for applying the FFQ. In total, one hundred forty-seven food items were included in this FFQ with a standard serving size, and participants assessed their consumption frequency according to four categories: daily, weekly, monthly, and infrequent. Using home measures, the portion sizes of the consumed foods were converted to grams (23). Nutrient and energy intakes were calculated using NUTRITIONIST IV software (version 7.0; N-Squared Computing, Salem, OR). The following food and beverage items are classified as UPFs in the NOVA food group classification, which is the subject of this research, and are grouped into the FFQ into seven food groups (daily intake was calculated as grams): (1) Non-dairy beverages (coffee, cola, nectar, and industrial sweet drink), (2) dairy beverages (ice cream, pasteurized and non-pasteurized, chocolate milk, and cocoa milk), (3) cakes and cookies (cookies, biscuits, pastries (creamy and non-creamy), cake, pancake, industrial bread, toasted bread, noodles, and pasta), (4) fast food and processed meat (burger, sausage, pizza, and bologna), (5) salty snacks (chips, crisps, crackers, and cheese puff), (6) oil and sauce (mayonnaise, margarine, and ketchup), (7) sweets (Gaz, Sohan, Noghl, sesame halva, chocolate, candies, rock candies, jam, and sweets) (26). All the NOVA components were adjusted for energy intake.

Anthropometry and body composition

Participants were advised to fast for 12 h the night before the assessment and avoid unusual physical activity for 72 h before the anthropometrics and body composition assessments. A digital stadiometer (Seca) was used to measure height (m) with a precision of 0.5 cm. The waist circumference (WC) (cm) and hip circumference (HC) (cm) with an accuracy of 0.5 cm were measured within the largest and the littlest circumference separately. The waist-to-hip ratio (WHR) was computed as WC (cm)/HC (cm).

A multi-frequency bioelectric impedance analyzer (BIA) (Inbody Co., Seoul, Korea) scanner evaluated body composition. This electrical impedance analyzer measures the resistance of body tissues to the passage of an electrical signal given through the feet and hands. The body composition analyzer was used to assess the individuals' weight, BMI, fat mass (FM), fat-free mass (FFM), body fat percentage (%), and the others, according to a predetermined methodology. The participants were instructed to urinate before measuring their body composition according to the fabricant recommendations.

Biochemical assessment

The blood samples were obtained between 8:00 and 10:00 a.m. at the Nutrition and Biochemistry lab of the School of Nutritional Sciences and Dietetics, TUMS, after an overnight fast and deposited in tubes containing 0.1 percent ethylenediaminetetraacetic acid (EDTA). The serum was centrifuged, aliquoted, and stored at −70°C. The glucose oxidase phenol 4-aminoantipyrine peroxidase (GOD/PAP) technique determined fasting blood glucose levels (FBG). To evaluate blood triglyceride (TG) levels, enzyme colorimetric assays with GPO–PAP were utilized. Total cholesterol was assessed using phenol 4-aminoantipyrine peroxidase (CHOD–PAP), low-density lipoprotein (LDL), and high-density lipoprotein (HDL) were measured using the direct approach and immunoinhibition. The serum high-sensitivity C-reactive protein (hs-CRP) was measured using an immunoturbidimetric method. The Enzyme-Linked Immunosorbent Assays (ELISA) technique was used to evaluate the levels of IL-1β and PA-I (Human PAI-1*96 T ELIZA kit Crystal Company). The serum insulin concentrations were determined using the enzyme-linked immunosorbent assay (ELISA kit). The ELISA kit was also used to quantify serum MCP-1 levels (Zell Bio GmbH, Germany, assay range:5 ng/L−1,500 ng/L, sensitivity:2.4 ng/L, CV10 percent inter-assay variability). All of the kits were given by Pars Azmoon (Pars Azmoon Inc. Tehran, Iran). Insulin resistance was assessed using a homeostasis model (HOMA–IR). The index was computed using the algorithm (plasma glucose mmol/ l / × fasting plasma insulin mIU/ l)/22.5 (27). The quantitative insulin sensitivity check index (QUICKI) was also used to evaluate insulin resistance through the formula 1/[log (fasting insulin) + log (fasting glucose)] (27). From biochemical parameters, FBG, TG, HDL, LDL, hs-CRP, and IL_1β variables are considered as CVD risk factors in this study.

The atherogenic index of plasma (AIP) was calculated using the logarithmic of (TG/HDL-C). TC/HDL, LDL/HDL, and (TC-HDL) /LDL were used to determine castelli's risk index 1 (CRI- I), castelli's risk index 2 (CRI- II), and atherogenic coefficient (AC), respectively. The following formula was used to compute CHOLIndex: CHOLIndex = LDL-C – HDL-C (TG <400) = LDL-C – HDL-C + 1/5 TG (TG >400). (28). Ln (FBG (mg/dl) * TG (mg/dl)/2) was used to determine triglyceride–glucose index (TyG index) (29). The terms triglyceride glucose-waist circumference (TyG–WC) and triglyceride glucose–body mass index (TyG–BMI) were obtained through the formulas: [Ln (FBG (mg/dl) * TG (mg/dl)/2)] * WC and [Ln (FBG (mg/dl) * TG (mg/dl)/2)] * BMI), respectively (30).

Blood pressure assessment

Blood pressure was measured using an automated sphygmomanometer according to standard procedures (OMRON, Germany).

Other collected data

General information about the participants, such as their age, job status (employed, unemployed), education level (illiterate, under diploma, diploma, and bachelor and higher) (what are the categories? Detail the methodology here, as well as the other variables), marital status (single and married), economic status (low, middle, and high class), standard questionnaires, were collected. The physical activity status was obtained using the validated International Physical Activity Questionnaire (IPAQ). Afterward, metabolic equation hours per day (MET-min/week) were calculated for each subject. After that, each subject's metabolic equation hours per day score (MET-min/week) was calculated. Trained professionals were responsible for applying the questionnaires (31, 32).

Statistical analyses

The Kolmogorov–Smirnov test was used to check the quantitative variable's normality (P > 0.05). Categorical data were reported as absolute and relative frequencies, and quantitative data were reported as means and standard deviation (SD). According to the NOVA score, the participants were categorized into tertiles of UPF consumption in grams. To compare the mean difference of quantitative and frequency of categorical variables across UPF tertiles, a one-way analysis of variance (ANOVA) and Pearson chi-square (χ2) tests were performed, respectively. Analysis of covariance (ANCOVA) adjusted for potential confounders (age, BMI, energy intake, and physical activity) and considering BMI as a collinear variable for anthropometrics and body composition variables were performed. The Bonferroni post-hoc test was used to detect the statistically significant difference among UPF tertiles. Linear regression was performed to evaluate the association of the UPF consumption (independent variable) with cardiometabolic risk factors (dependent variable). Model 1 was adjusted for age, BMI, physical activity, total energy intake, supplements intake, and job status. Model 2 was further adjusted for legumes and vegetables. This analysis was presented as the β-value and a confidence interval of 95% (CI). SPSS v.26 software (SPSS Inc., IL, USA) was used for statistical analysis. The significance level was set at p < 0.05.

Results

A total of 391 participants were included in the present study. Women had a mean (SD) age of 36.67 (9.10) years and a mean BMI of 31.26 (4.29) kg/m2. The majority of women were employed (97%), 47% were highly educated (bachelor's degree and higher), and 45.5% had a middle income. The mean of UPF intake in our sample was 442.47 (127.91) g or 96.8 %.

The general characteristics of participants among UPF tertiles are presented in Table 1. The average UPF consumption in tertile 1 was <383,681 g, in tertile 2 was from 383,681 g to 467,713 g, and in tertile 3 was >467,713 g. The mean of age (P = 0.003) was statistically different between UPF tertiles in the crude model and after controlling for confounding variables. The mean height (P = 0.047) was statistically different between UPF tertiles in the crude model. According to the Bonferroni's post-hoc test, the significant mean difference in age was between T2 and T3, and the mean difference was higher in T2 than in T3. In the categorical variables, the supplementation intake (P = 0.057) and job status (P = 0.073) were marginally significant between UPF tertiles after controlling for cofounders. There was no significant difference for other variables (Table 1).

Table 1.

General characteristics among tertiles of NOVA score in obese and overweight women (n = 391).

Quantitative variables NOVA tertiles P-value P-value*
T1 T2 T3
<383.681 383.681–467.713 >467.713
N = 131 N = 130 N = 130
Mean ±SD
Age (year) a 36.480 ± 9.138 38.759 ± 8.77 34.860 ± 9.352 0.003 0.004
PA (MET-min -week) 1,465.171 ± 231.881 834.995 ± 235.775 1,353.665 ± 254.709 0.098 0.154
Weight (kg) 81.958 ± 12.382 79.884 ± 10.975 81.669 ± 13.320 0.337 0.365
Height (cm) 161.574 ± 5.888 160.115 ± 5.881 161.763 ± 5.796 0.047 0.869
BMI (kg/m 2 ) 31.141 ± 0.440 30.847 ± 0.449 30.459 ± 0.483 0.946 0.576
WC (cm) 113.163 ± 8.516 113.638 ± 7.477 116.295 ± 13.637 0.247 0.592
BMC (Kg) 2.676 ± 0.376 2.622 ± 0.342 2.661 ± 0.330 0.445 0.643
SMM (Kg) 25.954 ± 3.281 25.347 ± 3.300 25.333 ± 3.4205 0.247 0.311
SLM (Kg) 44.083 ± 5.759 43.585 ± 5.126 43.587 ± 5.317 0.693 0.454
Categorical variables
Supplementation intake n (%)b 0.311 0.057
Yes % 58 (36.7) 47 (29.7) 53 (33.5)
No % 51 (29.0) 61 (34.7) 64 (36.4)
Income status n (%) 0.582 0.185
Low class 33 (37.5) 31 (35.2) 24 (27.3)
Middle class 60 (33.0) 61 (33.5) 61 (33.5)
High class 35 (32.7) 31 (29.0) 41 (38.3)
Marital status n (%) 0.275 0.880
Single 35 (32.1) 31 (28.4) 43 (39.4)
Married 92 (33.6) 96 (35) 86 (31.4)
Job status n (%) 0.137 0.073
Unemployed 2 (100) 0 (0) 0 (0)
Employed 128 (33.2) 129 (33.5) 128 (33.2)
Educational status n (%) 0.753 0.744
Illiterate 1 (25) 1 (25) 2 (50)
Under diploma 12 (26.1) 17 (37) 17 (37)
Diploma 46 (30.9) 54 (36.2) 49 (32.9)
Bachelor and higher 68 (37) 55 (29.9) 61 (33.2)
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PA, physical activity; BMI, body mass index; WC, waist circumference; BMC, bone mineral content; SMM, skeletal muscle mass; SLM, soft lean mass.

Values are represented as means and SD and number (%) for categorical variables.

ANCOVA (P-value*) was performed to adjust potential confounding factors; age, energy intake, PA, BMI. BMI consider as the collinear variable for body composition, and anthropometric measurements.

p <0.05 were considered as significant.

a

significant difference was seen between T3 and T2.

b

significant difference was seen between T2 and T3.

A p < 0.05 were considered as significant and p-values of 0.05, 0.06, and 0.07 were considered as marginally significant.

Dietary intakes among the UPF tertiles

Dietary intakes of all the participants among tertiles of UPF consumption are presented in Table 2. The mean of non-dairy beverages (P = 0.001), dairy beverages (P = 0.001), cookies (cakes) (P = 0.001), potato chips (salty) (P = 0.001), processed meat (fast food) (P = 0.001), oil (sauce group) (P = 0.005), sweet (P = 0.001) were statistically different among UPF tertiles, with it being higher in the third tertile. With increasing UPF consumption, non-dairy beverages, cookies (cakes), dairy beverages, potato chips (salty), processed meat (fast food), oil, sauce, and sweet have increased in the crude and adjusted model.

Table 2.

Dietary intakes among tertiles of the NOVA score in obese and overweight women (n = 391).

Total UPF consumption tertiles P-value P-value*
T1 (n = 131)
<383.681 		T2 (n = 130) 383.681-467.713 T3 (n = 130)
>467.713
NOVA score components
Nondairy beverages (g/d) 177.351 ± 93.223 124.069 ± 25.540 157.152 ± 27.648 251.242 ± 126.711 0.001 0.001
Cookies-cakes (g/d) 98.913 ± 44.205 75.570 ± 25.626 97.288 ± 28.007 124.061 ± 57.167 0.001 0.001
Dairy beverages (g/d) 47.833 ± 27.952 37.472 ± 18.4894 46.629 ± 22.117 59.479 ± 35.795 0.001 0.001
Potato chips- salty 22.106 ± 13.893 17.354 ± 9.094 22.652 ± 10.166 26.348 ± 18.853 0.001 0.001
Processed meat- fast food (g/d) 41.138 ± 25.424 28.402 ± 12.600 40.230 ± 14.202 54.881 ± 35.167 0.001 0.001
Oil_ Sause (g/d) 18.269 ± 8.727 16.764 ± 8.5494 17.861 ± 7.4184 20.194 ± 9.766 0.005 0.005
Sweet (g/d) 36.861 ± 24.0635 30.679 ± 15.1176 36.916 ± 17.1858 43.037 ± 33.8778 0.001 0.001
Food groups
Refined grains (g/d) 432.348 ± 220.133 474.142 ± 191.103 380.801 ± 207.529 444.129 ± 253.5120 0.008 0.969
Whole grains (g/d) 7.586 ± 10.410 9.144 ± 11.2396 6.769 ± 9.0196 6.746 ± 10.831 0.177 0.361
Fruits (g/d) 528.904 ± 338.1681 605.778 ± 317.153 466.287 ± 317.377 513.252 ± 370.044 0.011 0.340
Vegetables (g/d) 433.577 ± 263.259 526.618 ± 264.203 382.927 ± 226.814 385.498 ± 275.073 0.001 0.003
Nuts (g/d) 14.370 ± 16.1868 17.821 ± 17.786 11.449 ± 14.354 13.795 ± 15.697 0.018 0.518
Legumes (g/d) 52.691 ± 41.2788 63.432 ± 49.5718 45.834 ± 35.7690 48.313 ± 34.0807 0.005 0.045
Dairy (g/d) 387.451 ± 246.357 438.192 ± 267.952 330.196 ± 224.147 394.927 ± 233.413 0.007 0.769
Eggs (g/d) 21.687 ± 14.174 22.105 ± 12.3656 21.235 ± 12.394 21.732 ± 17.7520 0.909 0.569
Fish and seafood (g/d) 11.408 ± 12.1569 12.086 ± 11.932 10.743 ± 11.2257 11.399 ± 13.4774 0.735 0.990
Meats (g/d) 64.571 ± 50.1758 67.371 ± 40.9762 54.081 ± 41.6793 73.518 ± 65.0100 0.022 0.250
Red meat (g/d) 21.479 ± 18.5197 24.003 ± 20.368 17.760 ± 15.8117 22.894 ± 18.722 0.038 0.947
Macronutrients and energy
Energy intake (kcal/d) 2633.280 ± 809.432 2916.675 ± 654.474 2267.608 ± 712.433 2713.37 ± 904.867 0.001 -
Micronutrients
SFA (mg/d) 28.409 ± 11.545 30.861 ± 11.417 24.761 ± 10.291 29.587 ± 12.033 0.001 0.628
MUFA (mg/d) 32.008 ± 12.917 35.155 ± 13.593 27.591 ± 10.563 33.253 ± 13.241 0.001 0.817
PUFA (mg/d) 20.082 ± 9.568 22.589 ± 10.515 17.403 ± 8.316 20.235 ± 9.087 0.001 0.717
Trans fat (g/d) 0.0007 ± 0.002 0.001 ± 0.003 0.0006 ± 0.001 0.0005 ± 0.001 0.097 0.120
Total fiber (g/d) 47.344 ± 21.360 57.263 ± 21.377 40.359 ± 19.203 44.333 ± 19.795 0.078 0.001
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pro, protein; Cho, carbohydrate; SAFA, saturated fatty acid; MUFA, monounsaturated fatty acid; PUFA, polyunsaturated fatty acid.

Values are represented as means (SD).

ANCOVA (P-value*) was performed to adjust potential confounding factors (energy intake).

A P-value under 0.05 is considered significant.

CVD risk factors among the UPF tertiles

The association of CVD risk factors among UPF consumption tertiles is shown in Table 3. UPF consumption was associated with the HOMA–IR index (P = 0.024), hs-CRP (P = 0.001), and TYG–WC (P = 0.026). On the contrary, it was marginally associated with the markers TGF (P = 0.077), AC (P = 0.072), CRI-1 (P = 0.062), and NC (P = 0.068).

Table 3.

CVD risk factors consist of anthropometric measurements and body composition, biochemical variables, and inflammatory factors among tertiles of NOVA score in obese and overweight women (n = 391).

Variables UPF consumption tertiles P-value
T1 <383.681 T2 383.681– 467.713 T3 >467.713
Body Composition
FFM (Kg) Crude 47.019 ± 5.938 46.217 ± 5.444 46.263 ± 5.616 0.440
Model 1 46.402 ± 0.982 47.858 ± 1.037 46.017 ± 1.332 0.513
Model 2 46.286 ± 1.014 47.917 ± 1.058 46.121 ± 1.347 0.499
FFMI Crude 18.977 ± 1.618 17.977 ± 1.443 17.672 ± 11.450 0.266
Model 1 17.801 ± 0.246 18.153 ± 0.260 17.838 ± 0.334 0.625
Model 2 17.729 ± 0.252 18.202 ± 0.263 17.882 ± 0.33 0.479
FMI Crude 13.422 ± 3.163 13.318 ± 3.235 13.610 ± 3.799 0.784
Model 1 12.214 ± 0.590 12.903 ± 0.623 12.217 ± 0.800 0.716
Model 2 12.168 ± 0.612 12.929 ± 0.638 12.255 ± 0.813 0.700
BF (%) Crude 42.238 ± 5.016 41.890 ± 5.255 42.550 ± 6.196 0.629
Model 1 40.208 ± 1.026 41.033 ± 1.084 39.571 ± 1.392 0.725
Model 2 40.174 ± 1.066 41.058 ± 1.112 39.588 ± 1.416 0.726
BFM (Kg) Crude 34.936 ± 8.395 33.830 ± 7.801 35.421 ± 9.887 0.325
Model 1 31.494 ± 1.421 33.926 ± 1.501 31.150 ± 1.927 0.450
Model 2 31.387 ± 1.471 33.978 ± 1.535 31.251 ± 1.955 0.450
TF (kg) Crude 16.965 ± 3.489 16.5070 ± 3.411 17.103 ± 4.086 0.393
Model 1 15.609 ± 0.624 16.608 ± 0.660 15.450 ± 0.847 0.493
Model 2 15.571 ± 0.648 16.630 ± 0.676 15.479 ± 0.861 0.492
TF (%) Crude 320.916 ± 65.872 317.959 ± 68.968 322.384 ± 75.379 0.875
Model 1 298.070 ± 12.616 312.018 ± 13.326 297.691 ± 17.107 0.736
Model 2 297.023 ± 13.092 312.746 ± 13.660 298.300 ± 17.394 0.712
Anthropometric measurements
WC (cm) Crude 97.281 ± 16.058 97.138 ± 12.693 97.951 ± 17.058 0.933
Model 1 92.021 ± 3.239 98.703 ± 3.421 89.857 ± 4.391 0.259
Model 2 91.161 ± 3.329 99.287 ± 3.473 90.382 ± 4.423 0.207
WHR Crude 0.939 ± 0.054 1.637 ± 8.018 0.936 ± 0.051 0.372
Model 1 0.931 ± 0.009 0.940 ± 0.010 0.911 ± 0.013 0.236
Model 2 0.931 ± 0.010 0.939 ± 0.010 0.911 ± 0.013 0.233
VFA (CM2) Crude 168.858 ± 36.720 176.087 ± 150.293 168.733 ± 42.799 0.764
Model 1 154.422 ± 6.889 161.912 ± 7.276 147.917 ± 9.341 0.526
Model 2 154.298 ± 7.155 161.986 ± 7.465 148.012 ± 9.506 0.536
VFL Crude 17.122 ± 12.037 15.612 ± 3.307 17.514 ± 17.260 0.423
Model 1 14.815 ± 0.605 15.404 ± 0.639 14.262 ± 0.820 0.576
Model 2 14.826 ± 0.628 15.397 ± 0.656 14.252 ± 0.835 0.589
NC a (cm) Crudea 38.338 ± 12.042 36.958 ± 2.702 37.430 ± 3.942 0.537
Model 1b 36.130 ± 0.421 37.791 ± 0.445 36.420 ± 0.571 0.036
Model 2a 36.233 ± 0.433 37.723 ± 0.452 36.354 ± 0.575 0.068
Biochemical variables
SBP (mmHg) Crude 113.000 ± 15.0006 112.227 ± 12.386 108.333 ± 17.190 0.083
Model 1 113.074 ± 1.661 111.479 ± 1.691 108.975 ± 1.734 0.231
Model 2 113.374 ± 1.4497 110.824 ± 1.504 119.552 ± 1.164 0.222
DBP (mmHg) Crude 77.969 ± 9.586 77.930 ± 9.454 76.547 ± 12.418 0.590
Model 1 77.745 ± 1.194 77.736 ± 1.216 77.931 ± 1.247 0.992
Model 2 78.237 ± 1.064 76.992 ± 1.069 78.117 ± 1.172 0.677
HOMA-IR Crude 3.240 ± 1.346 3.585 ± 1.388 3.142 ± 1.007 0.073
Model 1 2.941 ± 0.190 3.770 ± 0.189 3.143 ± 0.204 0.011
Model 2a 3.031 ± 0.196 3.738 ± 0.189 3.078 ± 0.207 0.024
Insulin (mIU/ ml) Crude 1.205 ± 0.245 1.240 ± 0.234 1.194 ± 0.197 0.415
Model 1 1.229 ± 0.035 1.235 ± 0.034 1.190 ± 0.037 0.636
Model 2a 1.227 ± 0.036 1.237 ± 0.035 1.189 ± 0.038 0.629
FBG (mg/dL) Crude 87.569 ± 9.927 88.912 ± 10.343 85.536 ± 7.919 0.089
Model 1 84.834 ± 1.382 88.262 ± 1.372 84.393 ± 1.482 0.126
Model 2 85.057 ± 1.439 88.199 ± 1.388 84.209 ± 1.516 0.136
TC (mg/dL) Crude 182.383 ± 37.483 189.395 ± 33.851 183.000 ± 37.733 0.371
Model 1 177.717 ± 4.406 179.295 ± 4.417 184.293 ± 466 0.569
Model 2 177.026 ± 4.583 179.669 ± 4.472 184.648 ± 4.741 0.514
TG (mg/dL) Crude 118.267 ± 55.944 120.022 ± 59.970 116.144 ± 64.776 0.922
Model 1 115.533 ± 8.472 126.502 ± 8.494 118.257 ± 8.966 0.666
Model 2 114.907 ± 4.583 179.669 ± 8.609 118.752 ± 9.126 0.653
HDL (mg/dL) Crude 47.267 ± 10.965 47.340 ± 11.662 45.536 ± 9.567 0.518
Model 1 48.039 ± 1.328 45.996 ± 1.331 47.130 ± 1.405 0.584
Model 2 48.149 ± 1.383 45.941 ± 1.350 47.068 ± 1.431 0.561
LDL (mg/dL) Crude 95.244 ± 23.856 97.109 ± 24.795 92.029 ± 23.850 0.420
Model 1 98.986 ± 3.071 98.070 ± 3.078 99.803 ± 3.249 0.931
Model 2 98.176 ± 3.186 98.486 ± 3.109 100.248 ± 3.296 0.890
GOT (mg/dL) Crude 17.720 ± 7.441 18.604 ± 8.179 16.927 ± 5.976 0.358
Model 1 18.502 ± 1.037 18.579 ± 1.021 16.154 ± 1.104 0.202
Model 2 18.365 ± 1.064 18.675 ± 1.022 16.194 ± 1.112 0.221
GPT (mg/dL) Crude 19.209 ± 14.249 20.373 ± 13.793 17.478 ± 9.842 0.378
Model 1 21.013 ± 1.886 21.034 ± 1.857 16.240 ± 2.008 0.144
Model 2 21.125 ± 1.945 21.047 ± 1.868 16.093 ± 2.033 0.131
AIP Crude 0.366 ± 0.236 0.362 ± 0.240 0.361 ± 0.272 0.990
Model 1 0.343 ± 0.034 0.403 ± 0.034 0.353 ± 0.036 0.449
Model 2 0.339 ± 0.035 0.404 ± 0.034 0.356 ± 0.036 0.434
CRI-I Crude 4.029 ± 1.206 4.194 ± 1.209 4.294 ± 2.100 0.542
Model 1 3.778 ± 0.118 3.998 ± 0.118 4.025 ± 0.125 0.292
Model 2 3.755 ± 0.122 4.010 ± 0.119 4.038 ± 0.127 0.062
CRI-II Crude 2.075 ± 0.531 2.132 ± 0.642 2.075 ± 0.580 0.765
Model 1 2.114 ± 0.080 2.191 ± 0.080 2.187 ± 0.085 0.760
Model 2 2.091 ± 0.083 2.202 ± 0.081 2.200 ± 0.086 0.593
AC Crude 3.029 ± 1.206 3.194 ± 1.209 3.294 ± 2.100 0.542
Model 1 2.778 ± 0.118 2.998 ± 0.118 3.025 ± 0.125 0.292
Model 2 2.755 ± 0.122 3.010 ± 0.119 3.038 ± 0.127 0.072
CHOLIndex Crude 47.976 ± 21.460 49.769 ± 23.040 46.492 ± 23.306 0.657
Model 1 50.947 ± 3.040 52.074 ± 3.048 52.674 ± 3.218 0.925
Model 2 50.027 ± 3.151 52.545 ± 3.075 53.180 ± 3.260 0.775
TyG index Crude 8.446 ± 0.478 8.466 ± 0.486 8.377 ± 0.494 0.516
Model 1 8.404 ± 0.068 8.517 ± 0.068 8.402 ± 0.072 0.434
Model 2 8403 ± 0.070 8.517 ± 0.069 8.404 ± 0.73 0.449
TyG-BMI Crude 261.876 ± 40.206 261.827 ± 41.224 255.425 ± 46.928 0.571
Model 1 253.760 ± 7.272 246.303 ± 7.625 254 ± 337 ± 8.691 0.586
Model 2 252.295 ± 7.491 265.151 ± 7.716 255.342 ± 8.813 0.510
TyG-WC Crude 810.989 ± 177.111 812.087 ± 135.178 791.246 ± 132.359 0.76
Model 1b 718.567 ± 26.978 838.143 ± 28.287 754.874 ± 32.243 0.057
Model 2b 741.185 ± 27.740 842.278 ± 28.573 760.139 ± 32.637 0.026
Inflammatory biomarkers
PAL-1 (mg/dl) Crude 20.265 ± 39.585 14.200 ± 24.731 13.319 ± 20.377 0.405
Model 1 31.411 ± 12.591 23.991 ± 15.100 5.875 ± 12.168 0.429
Model 2 47.091 ± 17.465 20.579 ± 18.555 9.139 ± 12.762 0.255
MCP1 (mg/dl) Crude 57.514 ± 94.785 54.332 ± 109.983 36.967 ± 54.657 0.389
Model 1 83.110 ± 28.588 52.148 ± 34.285 25.798 ± 27.629 0.301
Model 2 117.021 ± 40.136 57.515 ± 42.640 25.996 ± 29.328 0.224
TGF (ng/ml) Crude 74.436 ± 39.046 80.671 ± 61.1695 80.775 ± 41.250 0.733
Model 1 55.998 ± 10.530 78.067 ± 12.628 88.626 ± 10.177 0.295
Model 2c 49.350 ± 15.871 78.440 ± 16.862 88.086 ± 11.597 0.077
IL_1β (ng/ml) Crude 2.585 ± 0.895 2.745 ± 1.022 2.843 ± 0.927 0.647
Model 1 2.625 ± 0.274 3.010 ± 0.328 2.715 ± 0.264 0.538
Model 2 2.307 ± 0.418 3.052 ± 0.445 2.708 ± 0.306 0.580
hs_CRP (mg/l) Crude 4.300 ± 4.624 4.219 ± 4.641 4.480 ± 4.773 0.942
Model 1b 3.905 ± 0.727 1.385 ± 0.871 6.109 ± 0.702 0.001
Model 2 b 3.972 ± 1.006 0.566 ± 1.069 6.390 ± 0.735 0.001
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AC, atherogenic coefficient; BFM, body fat mass; BF, body fat; FFM, fat-free mass; FMI, fat mass index; FFMI, fat-free mass index; WC, waist circumference; WHR, Waist-to-Hip Ratio; NC, Neck circumference; IL-1β, interleukin-1 beta; MCP-1, monocyte chemoattractant protein-1; CRI, Cardiac risk index; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; FBS, Fasting Blood Sugar; TG, Triglyceride; HDL, High-density lipoprotein; LDL, Low-density lipoprotein; GPT, Glutamic-pyruvic transaminase; GOT, Glutamic-oxaloacetic transaminase; PAI-1, plasminogen activator inhibitor- 1, TF, Trunk Fat, VFA, Visceral fat area, VFL, Visceral fat level, SD Standard deviation; hs-CRP, high sensitive- C reactive protein; TC, Total cholesterol; AIP, Atherogenic index of plasma; TyG, Triglyceride-glucose; TGF, Transforming growth factor.

Quantitative variables were shown by means ± SD and categorical variables were shown by number (%).

P-values resulted from one-way ANOVA analysis and thechi-squared test. A p-value < 0.05 was considered significant and p-values equal to 0.05, 0.06, and 0.07 were considered marginally significant.

*P-values resulted from ANCOVA analysis and were adjusted.

Model 1: Adjusted for age, BMI, physical activity, total energy intake, supplements intake, and job status (BMI consider as a collinear variable).

Model 2: Additionally controlled for the effect of vegetables and legumes.

The Bonferroni post-hoc test was used to investigate differences between tertiles, significant difference between two means with p < 0.05. 0.05, and 0.06 considered as marginally significant.

a

Significant difference was seen between T1 and T2.

b

Significant difference was seen between T2 and T3.

c

Significant difference was seen between T1 and T3.

Association between UPF consumption and CVD risk factors, anthropometric measurements, body composition, biochemical variables, and inflammatory factors

Association between UPF consumption and CVD risk factors, anthropometric measurements, body composition, biochemical variables, and inflammatory factors in crude and adjusted models present with β-value and a 95% CI is shown in Table 4. In the model 1, there was a significant association between UPF consumption and TGF (β: 0.101, 95% CI: 0.023, 0.180, p = 0.012). Also, there was a significant association between UPF consumption and AC (β: 0.011, 95%CI: 0.001, 0.032, p = 0.034), VLF (β: 0.006, 95% CI: −0.017, 0.029, p = 0.076), and ISQIUKI (β: −3.775, 95% CI: 0.001, 0.001, P = 0.042). With increasing one gram of UPF intake, AC increases to 0.011, VFL increases by 0.006, and QUICKI is significantly reduced by −3.775 mg/lit. The other variables in Table 3 had no significant association.

Table 4.

Association between NOVA score and CVD risk factors, anthropometric measurements, body composition, biochemical variables, and inflammatory factors in obese and overweight women (n = 391).

Variables NOVA score P-value P-value*
β (SE) CI (95%)
Body composition
FFM (Kg) Crude −0.001 (0.002) −0.005, 0.003 0.682 -
Model 1 −0.004 (0.004) −0.011, 0.003 - 0.278
Model 2 −0.004 (0.004) 0.001, 0.001 - 0.056
FFMI Crude −0.004 (0.003) −0.009, 0.001 0.115 -
Model 1 −0.001 (0.001) −0.003, 0.001 - 0.201
Model 2 −0.001 (0.001) −0.003, 0.001 - 0.376
FMI Crude −0.004 (0.001) −0.014, 0.006 0.427 -
Model 1 0.001 (0.002) −0.005, 0.004 - 0.878
Model 2 −5.286 (0.002) −0.004, 0.004 - 0.981
BF (%) Crude 0.003 (0.002) −0.001, 0.007 0.148 -
Model 1 0.001 (0.004) −0.006, 0.008 - 0.768
Model 2 0.001 (0.004) −0.006, 0.008 - 0.768
BFM (Kg) Crude 0.006 (0.003) −0.001, 0.013 0.084 -
Model 1 −0.002 (0.006) −0.013, 0.009 - 0.719
Model 2 −0.002 (0.006) −0.014, 0.009 0.658
TF (kg) Crude 0.002 (0.001) 0.001, 0.005 0.097 -
Model 1 −0.001 (0.002) −0.005, 0.004 - 0.812
Model 2 −0.001 (0.002) −0.006, 0.004 - 0.709
TF (%) Crude 0.035 (0.028) −0.020, 0.089 0.213 -
Model 1 0.001 (0.044) −0.086, 0.088 - 0.988
Model 2 −0.005 (0.045) −0.094, 0.083 - 0.904
VFA (CM2) Crude 0.028 (0.037) −0.044, 0.100 0.442 -
Model 1 −0.005 (0.082) −0.168, 0.157 - 0.951
Model 2 −0.007 (0.085) −0.174, 0.159 - 0.930
VFL Crude 0.002 (0.005) −0.007, 0.012 0.647 -
Model 1 0.005 (0.011) −0.017, 0.028 - 0.651
Model 2 0.006 (0.012) −0.017, 0.029 - 0.076
Biochemical variables
Insulin (mIU/ml) Crude −6.160 (0.001) 0.001, 0.001 0.617 -
Model 1 0.001 (0.001) −0.001, 0.000 - 0.230
Model 2 0.001 (0.001) 0.001, 0.001 - 0.273
HOMA_IR Crude 0.001 (0.001) −0.0002, 0.001 0.671 -
Model 1 −2.096 (0.001) −0.002, 0.002 - 0.981
Model 2 0.001 (0.001) 0.001, 0.033 - 0.055
QUICKI (mg/lit) Crude −1.731 (0.001) 0.001, 0.001 0.205 -
Model 1 −4.306 (0.001) −0.001, 0.001 - 0.720
Model 2 −3.775 (0.001) 0.001, 0.001 - 0.042
hs-CRP (mg/l) Crude 0.001 (0.003) −0.005, 0.005 0.962 -
Model 1 0.001 (0.003) −0.006, 0.007 - 0.875
Model 2 0.001 (0.003) −0.006, 0.007 - 0.916
FBG (mg/dL) Crude −0.004 (0.005) −0.014, 0.006 0.427 -
Model 1 −0.004 (0.006) −0.017, 0.009 - 0.506
Model 2 −0.006 (0.007) −0.020, 0.007 - 0.335
SBP (mmHg) Crude −0.012 (0.007) −0.027, 0.002 0.102 -
Model 1 −0.015 (0.008) −0.032, 0.002 - 0.032
Model 2 0.017 (0.008) −0.001, 0.020 - 0.148
DBP (mmHg) Crude −0.008 (0.005) −0.018, 0.002 0.134 -
Model 1 −0.004 (0.086) −0.016, 0.008 - 0.503
Model 2 −0.007 (0.006) −0.019, 0.005 - 0.236
TC (mg/dL) Crude 0.003 (0.019) −0.036, 0.041 0.893 -
Model 1 0.012 (0.022) −0.032, 0.055 - 0.598
Model 2 0.020 (0.022) −0.024, 0.064 - 0.073
TG (mg/dL) Crude 0.003 (0.032) −0.060, 0.067 0.916 -
Model 1 0.031 (0.042) −0.052, 0.115 - 0.456
Model 2 0.041 (0.043) −0.044, 0.126 - 0.344
HDL
(mg/dL)		 Crude −0.005 (0.006) −0.016, 0.006 0.391 -
Model 1 −0.004 (0.007) −0.017, 0.010 - 0.588
Model 2 −0.002 (0.007) −0.016, 0.011 - 0.720
LDL
(mg/dL)		 Crude −0.010 (0.013) −0.036, 0.015 0.433 -
Model 1 −6.043 (0.015) −0.030, 0.030 - 0.997
Model 2 0.007 (0.016) −0.024, 0.038 - 0.662
GOT (mg/dL) Crude −0.004 (0.004) −0.012, 0.0003 0.274 -
Model 1 −0.007 (0.005) −0.017, 0.003 - 0.167
Model 2 −0.006 (0.005) −0.017, 0.004 - 0.245
GPT (mg/dL) Crude −0.006 (0.007) −0.020, 0.008 0.391 -
Model 1 −0.016 (0.009) −0.034, 0.003 - 0.097
Model 2 −0.015 (0.010) −0.034, 0.004 - 0.117
PAI-1 (mg/dL) Crude −0.019 (0.022) −0.063, 0.025 0.401 -
Model 1 −0.012 (0.033) −0.077, 0.053 - 0.705
Model 2 −0.005 (0.033) −0.071, 0.061 - 0.883
MCP1 (mg/dL) Crude −0.047 (0.052) −0. 15, 0.055 0.363 -
Model 1 −0.041 (0.072) −0.184, 0.102 - 0.570
Model 2 −0.039 (0.073) −0.184, 0.107 -
TGF (mg/dL) Crude 0.034 (0.036) −0.038, 0.106 0.106
Model 1 0.092 (0.038) 0.016, 0.167 - 0.018
Model 2 0.101 (0.040) 0.023, 0.180 - 0.012
IL-1 β (mg/dL) Crude 0.001 (0.001) −0.001, 0.003 0.250 -
Model 1 0.001 (0.001) −0.002, 0.003 - 0.596
Model2 0.001 (0.001) −0.001, 0.003 - 0.060
AIP (mg/dL) Crude 2.472 (0.001) 0.001, 0.01 0.853 -
Model 1 0.001 (0.001) 0.001, 0.001 - 0.482
Model2 0.001 (0.001) −0.001, 0.011 - 0.072
CRI-I Crude 0.001 (0.001) −0.001, 0.002 0.476 -
Model 1 0.001 (0.001) −0.001, 0.002 - 0.277
Model2 0.001 (0.001) −0.001, 0.002 - 0.064
CRI-II Crude −3.679 (0.001) −0.001, 0.001 0.907 -
Model 1 0.001 (0.001) −0.001, 0.001 - 0.574
Model2 0.001 (0.001) 0.001, 0.001 - 0.431
AC Crude 0.001 (0.001) −0.001, 0.002 0.476 -
Model 1 0.001 (0.001) −0.001, 0.002 - 0.277
Model2 0.011 (0.001) 0.001, 0.032 - 0.034
CHOlIndex Crude −0.005 (0.012) −0.029, 0.019 0.688 -
Model 1 0.004 (0.015) −0.026, 0.033 - 0.811
Model2 0.009 (0.015) −0.021, 0.040 - 0.547
TyG Crude −6.963 (0.001) −0.001, 0.001 0.794 -
Model 1 0.001 (0.001) 0.001, 0.001 - 0.610
Model2 0.001 (0.001) 0.001, 0.001 - 0.501
TyG-BMI Crude 0.010 (0.023) −0.035, 0.055 0.648 -
Model 1 −0.012 (0.028) −0.67, 0.044 - 0.685
Model 2 −0.003 (0.029) −0.060, 0.054 - 0.917
TyG-WC Crude 0.061 (0.105) −0.146, 0.267 0.563 -
Model 1 −0.056 (0.177) −0.408, 0.296 - 0.752
Model 2 0.048 (0.188) −0.324, 0.421 - 0.797
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AC, atherogenic coefficient; BFM, body fat mass; BF, body fat; FFM, fat-free mass; FMI, fat mass index; FFMI, fat-free mass index; IL-1β, interleukin-1 beta; MCP-1, monocyte chemoattractant protein-1; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBS, fasting blood sugar; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; GPT, glutamic-pyruvic transaminase; GOT, glutamic-oxaloacetic transaminase; PAI-1, plasminogen activator inhibitor- 1, SD standard deviation; hs-CRP, high sensitive- C reactive protein; TF, trunk Fat; VFA, visceral fat area; VFL, visceral fat level; TC, total cholesterol; AIP, Atherogenic index of plasma; TyG, Triglyceride-glucose; TGF, Transforming growth factor; CRI: Cardiac risk index.

Model 1: Adjusted for age, BMI, physical activity, total energy intake, supplements intake, and job status (BMI considered as a collinear variable).

Model 2: In addition controlled for the role of vegetables and legumes.

*A P-value obtained from adjustment. All of the p-values obtained from the analysis of the linear regression.

A P < 0.05 was considered significant and p-values equal to 0.05, 0.06, and 0.07 were considered marginally significant.

Discussion

To the best of our knowledge, this is the first study investigating the relationship between UPF intake and cardiometabolic risk in overweight and obese Iranian women.

In the current study, we found an inverse association between the NOVA score and FFM. In addition, we observed a positive association between the NOVA score and VFL, AC, the HOMA-IR-index, QUICKI, TC, TGF, IL-1B, and the CRI-I levels. In other words, participants who had higher NOVA scores and consumed higher amounts of UPF had higher levels of VFL, AC, the HOMA-IR-index, QUICKI, TC, TGF, IL-1B, and CRI-I. The positive association observed between UPFs and mentioned markers might be partly explained by their poorer nutritional quality compared with the NOVA scores' lower tertiles. In fact, UPFs tend to be higher in saturated fats, sugar, and energy, and poorer in dietary fiber (5, 9, 21, 33). The positive association between consumption of UPF and inflammatory markers that have been seen among women may be explained by the greater accumulation of body fat in women (34). In line with our study, in 2021, a systematic review and meta-analysis of 7 cohort studies (207,291 adults) showed a significant positive association between UPF consumption and the risk of CVDs among adults with a BMI of more than 25 kg/m2 (35). Moreover, a recent narrative review study by Matos et al. (36) concluded that the consumption of UPFs is positively associated with the prevalence of chronic complications, including obesity, hypertension, CVDs, type 2 diabetes, and consequently the risk of all-cause mortality (36). The mechanism by which UPF is associated with CVD is summarized in Figure 1.

Figure 1.

Figure 1

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UPFs have higher levels of saturated fats, sugar, salt, additives, calories, and lower nutritional quality. Consumption of UPFs is suggested to have associations with obesity. Both obesity and consumption of UPFs could stimulate the whole chronic inflammation cascade and enhance the risk of CVD and all-cause mortality.

In our study, individuals at higher tertiles of NOVA (compared to tertile 1) had higher NC, AC, TyG-WC, HOMA-IR-INDEX, CRI-I, TGF, and hs-CRP levels. Beslay et al., in a large observational prospective study of 110,260 adults, indicated that higher consumption of UPF was associated with a gain in BMI and higher risks of overweight and obesity (37). Also, a prospective cohort of healthy subjects in Italy showed that adults in the highest quartile of UPF consumption had a higher risk of CVD (38). It is well known that adipose tissue produces cytokines that induce inflammatory markers production (39). Thus, the association between the consumption of UPFs and the inflammatory response is expected to be mostly dependent on adiposity. A cross-sectional study displayed that there might be a direct association between consumption of ultra-processed foods and CRP levels, under the assumption that the high-glycemic index of these food products could stimulate the whole chronic inflammation cascade, along with an indirect association mediated by obesity. They suggest that decreased consumption of UPFs can reduce chronic low-grade inflammation, perhaps by reducing obesity (40).

In the present study, participants with higher NOVA scores had higher consumption of cakes and sweets, processed meats, and fast foods. Bonaccio et al., in 2021, indicated that a high proportion of UPF in the diet was associated with an increased risk of CVD and all-cause mortality, probably because of its high dietary content of sugar (38). Rising evidence suggests that the consumption of UPF products determined by the low-nutritional quality and high-calorie content adversely contribute to an unhealthy dietary pattern, which enhances the risk of all-cause mortality as a substantial risk factor (36). In addition, additives in such foods containing noncaloric artificial sweeteners, emulsifiers, and thickening agents cause numerous chronic disorders such as metabolic dysfunction, systemic inflammation, endothelial dysfunction, and disrupted immune response (41–43). More than that, synthetic compounds used in the packaging of UPFs, such as bisphenol A, can act as xenohormones. Particularly, bisphenol A has been indicated to impair reproductive function and augment the risk of cancer-cause mortality (44, 45). Recently, a study conducted in the US displayed that UPF consumption was related to increased exposure to phthalates (44), which has suggested associations with obesity (46). Some food additives specific to UPFs might be involved in obesity etiology. For instance, saccharin, an artificial sweetener, could potentiate glucose-stimulated insulin release from isolated pancreatic β-cells (47), leading to insulin resistance and possibly weight gain. Several emulsifiers (such as carboxymethyl cellulose and polysorbate-80) induced metabolic perturbations, alterations to the gut microbiota, and low-grade inflammation in mice (48). Carrageenan, in the top 20 used additives, might augment insulin resistance and inhibit insulin signaling in mouse liver and human HepG2 cells (49, 50), which might, in turn, induce weight gain (51). Trans fatty acids found in UPFs containing hydrogenated oils have been associated with cardiovascular disease (52) and obesity (53), apparently by altering nutrient handling in the liver, the adipose tissues, and the skeletal muscle (54). Acrylamide, a neo-formed compound created during thermal processing of food as a result of the Maillard reaction, was shown to induce adipocyte differentiation and obesity in mice (55).

The present study possesses some strengths and limitations. At first, to the best of our knowledge, this is the first study to have evaluated the association between processed food intake and CVD risk in overweight and obese Iranian women. Second, dietary intake was assessed using a validated questionnaire. Third, in the current study, we assessed several inflammatory markers, other biochemical parameters, and body composition as risk factors for CVD.

Nevertheless, despite these strengths, we must acknowledge some limitations in the present study. First, the cross-sectional nature of this study limited the ability to suggest a causal relationship between UPF intake and the risk of cardiovascular diseases. Second, some errors may be present in the dietary assessment, mostly due to recall bias and misclassification errors; to overcome such errors, we evaluated biomarkers such as vitamin C to better capture individuals' variability in intakes. Third, our result may not be generalizable to normal-weight women. At final, although we considered known potential confounders, residual confounding cannot be ruled out.

Conclusion

In conclusion, an increase of one gram of UPFs consumption is associated with an increase in TGF, AC, and VFL but with a decrease in QUICKI. Higher consumption of UPF is significantly associated with an enhanced risk of adult inflammation and cardiometabolic risk factors. Further large studies involving participants of different ages and genders are highly warranted, in addition to experimental studies, to draw a more definite conclusion and disentangle the mechanisms by which UPFs may affect health.

Data availability statement

Participants of this study disagreed on their data being shared publicly, so supporting data is not available. Further inquiries can be directed to the corresponding author KM, mirzaei_kh@tums.ac.ir; mirzaei_kh@sina.tums.ac.ir.

Ethics statement

The studies involving human participants were reviewed and approved by Tehran University of Medical Sciences, Tehran, Iran. The patients/participants provided their written informed consent to participate in this study.

Author contributions

DH and SN wrote the paper. FS and FM-E performed the statistical analyses. SJ, MD, AS, and JB critically reviewed and revised the manuscript. KM had full access to all of the data in the study and took responsibility for the integrity and accuracy of the data. All authors read and approved the final manuscript.

Funding

This study is funded by grant from the Tehran University of Medical Sciences (TUMS) (Grant ID: 97-03-161-41017).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We are grateful to all the participants for their contribution to this research.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Participants of this study disagreed on their data being shared publicly, so supporting data is not available. Further inquiries can be directed to the corresponding author KM, mirzaei_kh@tums.ac.ir; mirzaei_kh@sina.tums.ac.ir.

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