Edited by: Mainul Haque, National Defence University of Malaysia, Malaysia
Reviewed by: Iffat Jahan, Eastern Medical College and Hospital, Bangladesh; Susmita Sinha, Khulna City Medical College and Hospital, Bangladesh
This article was submitted to Nutritional Epidemiology, a section of the journal Frontiers in Nutrition
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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.
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.
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,
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.
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 (
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 (
Given the high prevalence of CVDs in Iran, it is necessary to find dietary factors that may associate with the disease (
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
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 (
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.
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 (
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). (
Blood pressure was measured using an automated sphygmomanometer according to standard procedures (OMRON, Germany).
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 (
The Kolmogorov–Smirnov test was used to check the quantitative variable's normality (
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
General characteristics among tertiles of NOVA score in obese and overweight women (
36.480 ± 9.138 | 38.759 ± 8.77 | 34.860 ± 9.352 | |||
1,465.171 ± 231.881 | 834.995 ± 235.775 | 1,353.665 ± 254.709 | 0.098 | 0.154 | |
81.958 ± 12.382 | 79.884 ± 10.975 | 81.669 ± 13.320 | 0.337 | 0.365 | |
161.574 ± 5.888 | 160.115 ± 5.881 | 161.763 ± 5.796 | 0.869 | ||
31.141 ± 0.440 | 30.847 ± 0.449 | 30.459 ± 0.483 | 0.946 | 0.576 | |
113.163 ± 8.516 | 113.638 ± 7.477 | 116.295 ± 13.637 | 0.247 | 0.592 | |
2.676 ± 0.376 | 2.622 ± 0.342 | 2.661 ± 0.330 | 0.445 | 0.643 | |
25.954 ± 3.281 | 25.347 ± 3.300 | 25.333 ± 3.4205 | 0.247 | 0.311 | |
44.083 ± 5.759 | 43.585 ± 5.126 | 43.587 ± 5.317 | 0.693 | 0.454 | |
0.311 | |||||
Yes % | 58 (36.7) | 47 (29.7) | 53 (33.5) | ||
No % | 51 (29.0) | 61 (34.7) | 64 (36.4) | ||
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) | ||
0.275 | 0.880 | ||||
Single | 35 (32.1) | 31 (28.4) | 43 (39.4) | ||
Married | 92 (33.6) | 96 (35) | 86 (31.4) | ||
0.137 | |||||
Unemployed | 2 (100) | 0 (0) | 0 (0) | ||
Employed | 128 (33.2) | 129 (33.5) | 128 (33.2) | ||
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) |
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.
significant difference was seen between T3 and T2.
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 of all the participants among tertiles of UPF consumption are presented in
Dietary intakes among tertiles of the NOVA score in obese and overweight women (
Nondairy beverages (g/d) | 177.351 ± 93.223 | 124.069 ± 25.540 | 157.152 ± 27.648 | 251.242 ± 126.711 | ||
Cookies-cakes (g/d) | 98.913 ± 44.205 | 75.570 ± 25.626 | 97.288 ± 28.007 | 124.061 ± 57.167 | ||
Dairy beverages (g/d) | 47.833 ± 27.952 | 37.472 ± 18.4894 | 46.629 ± 22.117 | 59.479 ± 35.795 | ||
Potato chips- salty | 22.106 ± 13.893 | 17.354 ± 9.094 | 22.652 ± 10.166 | 26.348 ± 18.853 | ||
Processed meat- fast food (g/d) | 41.138 ± 25.424 | 28.402 ± 12.600 | 40.230 ± 14.202 | 54.881 ± 35.167 | ||
Oil_ Sause (g/d) | 18.269 ± 8.727 | 16.764 ± 8.5494 | 17.861 ± 7.4184 | 20.194 ± 9.766 | ||
Sweet (g/d) | 36.861 ± 24.0635 | 30.679 ± 15.1176 | 36.916 ± 17.1858 | 43.037 ± 33.8778 | ||
Refined grains (g/d) | 432.348 ± 220.133 | 474.142 ± 191.103 | 380.801 ± 207.529 | 444.129 ± 253.5120 | 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 | 0.340 | |
Vegetables (g/d) | 433.577 ± 263.259 | 526.618 ± 264.203 | 382.927 ± 226.814 | 385.498 ± 275.073 | ||
Nuts (g/d) | 14.370 ± 16.1868 | 17.821 ± 17.786 | 11.449 ± 14.354 | 13.795 ± 15.697 | 0.518 | |
Legumes (g/d) | 52.691 ± 41.2788 | 63.432 ± 49.5718 | 45.834 ± 35.7690 | 48.313 ± 34.0807 | ||
Dairy (g/d) | 387.451 ± 246.357 | 438.192 ± 267.952 | 330.196 ± 224.147 | 394.927 ± 233.413 | 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 | 0.250 | |
Red meat (g/d) | 21.479 ± 18.5197 | 24.003 ± 20.368 | 17.760 ± 15.8117 | 22.894 ± 18.722 | 0.947 | |
Energy intake (kcal/d) | 2633.280 ± 809.432 | 2916.675 ± 654.474 | 2267.608 ± 712.433 | 2713.37 ± 904.867 | - | |
SFA (mg/d) | 28.409 ± 11.545 | 30.861 ± 11.417 | 24.761 ± 10.291 | 29.587 ± 12.033 | 0.628 | |
MUFA (mg/d) | 32.008 ± 12.917 | 35.155 ± 13.593 | 27.591 ± 10.563 | 33.253 ± 13.241 | 0.817 | |
PUFA (mg/d) | 20.082 ± 9.568 | 22.589 ± 10.515 | 17.403 ± 8.316 | 20.235 ± 9.087 | 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 |
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.
The association of CVD risk factors among UPF consumption tertiles is shown in
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 (
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 | |
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 |
Crude |
38.338 ± 12.042 | 36.958 ± 2.702 | 37.430 ± 3.942 | 0.537 |
Model 1 |
36.130 ± 0.421 | 37.791 ± 0.445 | 36.420 ± 0.571 | ||
Model 2 |
36.233 ± 0.433 | 37.723 ± 0.452 | 36.354 ± 0.575 | ||
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 | |
Model 1 | 2.941 ± 0.190 | 3.770 ± 0.189 | 3.143 ± 0.204 | 0.011 | |
Model 2 |
3.031 ± 0.196 | 3.738 ± 0.189 | 3.078 ± 0.207 | ||
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 2 |
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 | ||
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 | ||
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 1 |
718.567 ± 26.978 | 838.143 ± 28.287 | 754.874 ± 32.243 | ||
Model 2 |
741.185 ± 27.740 | 842.278 ± 28.573 | 760.139 ± 32.637 | ||
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 2 |
49.350 ± 15.871 | 78.440 ± 16.862 | 88.086 ± 11.597 | ||
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 1 |
3.905 ± 0.727 | 1.385 ± 0.871 | 6.109 ± 0.702 | ||
Model 2 |
3.972 ± 1.006 | 0.566 ± 1.069 | 6.390 ± 0.735 |
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.
Significant difference was seen between T1 and T2.
Significant difference was seen between T2 and T3.
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 in crude and adjusted models present with β-value and a 95% CI is shown in
Association between NOVA score and CVD risk factors, anthropometric measurements, body composition, biochemical variables, and inflammatory factors in obese and overweight women (
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 | - | ||
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 | - | ||
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 | - | ||
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 | - | ||
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 | - | ||
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 |
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 |
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 | |||
Model 2 | 0.101 (0.040) | 0.023, 0.180 | - | ||
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 | - | ||
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 | - | ||
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 | - | ||
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 |
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.
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 (
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 (
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 (
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.
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.
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,
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.
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.
This study is funded by grant from the Tehran University of Medical Sciences (TUMS) (Grant ID: 97-03-161-41017).
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.
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.
We are grateful to all the participants for their contribution to this research.