Association between ultra-processed food intakes with lipid profiles - A cross-sectional study

doi:10.21203/rs.3.rs-2451584/v1

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Association between ultra-processed food intakes with lipid profiles - A cross-sectional study

https://doi.org/10.21203/rs.3.rs-2451584/v1

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published 04 May, 2023

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The purpose of this cross-sectional study is to examine the association between UPFs intake level and lipid profile in Iranian people. This is a cross-sectional study which was performed on 236 individuals with the age range of 20 to 50 years in Shiraz, Iran. Food intakes of participants were evaluated by food frequency questionnaire (FFQ) which was previously validated in Iranian populations. For estimating the ultra-processed food, the classification of NOVA food group was used. Serum lipids including TC, TG, HDL-C and LDL-C were measured in blood samples. Logistic regression was used to evaluation the relation between UPF score and lipid profile. Higher UPFs intake was associated with increased OR of TG, LDL and HDL in both crude (OR: 2.60; 95% CI: 1.24–5.46 and OR: 2.05; 95% CI: 1.01–4.17 and OR: 4.34; 95% CI: 1.89–9.91 and adjusted model (OR: 2.76; 95% CI: 1.29–5.89 and OR: 2.08; 95% CI: 1.01–4.26 and OR: 4.91; 95% CI: 2.05–11.74). Thus, our results show significant associations between ultra-processed foods intake and dietary nutrient profiles that result in dyslipidemia as a risk factor of chronic diseases. In conclusion, UPF consumption worsen the nutritional profile of the diet and lead to negative changes in the lipid profile parameters.

Health sciences/Cardiology

Health sciences/Diseases

Health sciences/Health care

Health sciences/Medical research

Health sciences/Risk factors

ultra-processed food

UPF

lipid profile

Dyslipidemia is defined as any lipid abnormality including elevated total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG) and declined high-density lipoprotein cholesterol (HDL-C) ¹. Prevalence of hypertriglyceridemia, hypercholesterolemia, high LDL, and low HDL in Iran are 41.6%, 46%, 35.3% and 43.9% respectively ².

Abnormalities of lipid profile are associated to many clinical outcomes such as type 2 diabetes and cardiovascular diseases (CVDs). It is also the main cause of more than half of the cases of congenital heart disease and more than four million deaths annually ³. Studies propose a complex interaction between genetic factors and several environmental factors such as smoking, sedentary lifestyle and socio-economic situation involve in determining the lipid profile ⁴. Particularly taking foods rich in calorie, carbohydrates, sodium, cholesterol, trans and saturated fats is related to high concentration of TC and LDL-C. On the other hand, consumption of polyunsaturated fatty acids, vegetables, dietary fibers, milk and dairy products lowers the risk of dyslipidemia ⁵.

UPFs are intrinsically fatty, sugary or salty, high calorie and poor in protein, dietary fiber, micronutrients and other bioactive compounds, and usually contain no or small amount of whole food ^6,7. During the past decade UPFs displaced unprocessed or minimally processed meals and fresh foods ^8–10. As they are usually accessible, convenient, durable, ready to consume, low-cost, ultra-palatable and often habit-forming ¹¹.

Evidence also showed high consumption of UPFs is potentially related to the high rate of obesity and related comorbidities ¹². Assessment of UPFs effects on lipid profile and upcoming diseases because of their poor nutritional quality is an ongoing debate. As UPFs productions and consumption has increased extremely during the last decades, comprehending their potential effects on human health has become a major concern in health care. The purpose of this cross-sectional study was to examine the association between UPFs intake and lipid profile in Iranian adults.

Study population

This cross-sectional study was performed on 236 individuals with the age range of 20 to 50 years. In this study we used cluster random sampling for selecting participants who referred to health care centers of Shiraz-Iran. Prerequisites for participating in this study were no history of chronic disease and no adherence to a special diet (The detailed of this study have been previously published ^13,14). Written consent form was signed by all participants. The protocol of this study was authorized by Shiraz University of Medical Sciences (IR.SUMS.REC.1394.S146).

Dietary Assessment

Food intakes of the participants were evaluated by a 168-item food frequency questionnaire (FFQ) which was previously validated in Iranian populations ¹⁵. The FFQ was filled out based on family food intakes and data was changed to gram. For computing nutrient intake and energy, we used NUTRITIONIST IV (version 7.0; N-Squared Computing, Salem, OR, USA).

To estimating the ultra-processed foods, classification of NOVA food group was used ^16,17. Accordingly, total daily consumption of 27 items of foods and beverages in FFQ were recognized as ultra-processed food. To define the contribution of every food group to overall consumption of ultra-processed foods, the mean daily intake of every 9 subgroups of ultra-processed foods (dairy products, non-dairy beverages, margarine and sauces, cakes and cookies, chips and snacks, breads, fast foods and meat, sweets and others) was divided by daily consumption of ultra-processed foods and then multiplied by 100.

Biochemical Assessments

Serum lipids including TC, TG, HDL-C and LDL-C were measured in blood samples (5 cc) taken from the participants. Serum lipids were determined by commercially accessible enzyme kits (Pars Azmoon, Tehran, Iran).

Socio-demographic And Anthropometric Assessments

Sex, age, smoking habit and alcohol intake were evaluated by using a demographic questionnaire. Also we used International Physical Activity Questionnaire (IPAQ) ¹⁸ to assess physical activity level of the participants. Anthropometric indices (weight (kg), waist circumference (cm) and height (cm)) were measured. Then, according to weight and height, BMI was calculated.

Statistical analysis

Normal distribution of the variables was checked by Kolmogorov-Smirnov test. We analyzed data by SPSS for windows (version 20.0, SPSS Inc., Chicago IL, USA). The level of significance was P-value < 0.05. The relationship between quantitative variables was evaluated by analysis of variance test and the relationship between qualitative variables was evaluated by Chi-square test. Crude and adjusted model of logistic regression was used to evaluate the relation between UPF score with anthropometric index and lipid profiles. In adjusted model, the effects of age, energy, physical activity, BMI, sex and smoking history were controlled.

According to Table 1 there was no significant difference between age, weight, height, BMI, waist, WHR, TC, LDL, HDL, Non-HDH, LDL to HDL ratio, physical activity, sex, smoking and alcohol history and education of the participants in the first and last tertile of UBFs, but the difference for mean of serum TG was significant (P = 0.01).

Table 1

Baseline characteristics according to tertile of UPFs.
Variables	UPFs
	T₁	T₂	T₃	P
Age (year)	46.20 ± 11.06	47.56 ± 12.96	44.20 ± 11.12	0.20
Weight (kg)	74.05 ± 14.73	74.75 ± 14.93	75.45 ± 13.32	0.82
Height (cm)	162.18 ± 10.68	161.65 ± 9.09	163.51 ± 9.05	0.47
BMI (kg/m²)	28.07 ± 4.63	28.56 ± 5.01	28.23 ± 4.47	0.80
Waist (cm)	93.98 ± 11.04	94.29 ± 12.05	94.37 ± 11.11	0.97
WHR	0.90 ± 0.07	0.89 ± 0.07	0.90 ± 0.08	0.47
TG (mg/dL)	109.92 ± 52.55	120.39 ± 66.75	140.27 ± 74.00	0.01
TC (mg/dL)	179.12 ± 47.21	178.60 ± 40.90	182.45 ± 39.65	0.85
LDL-C (mg/dL)	111.42 ± 36.74	106.55 ± 32.82	109.69 ± 30.19	0.63
HDL-C (mg/dL)	38.06 ± 11.59	39.24 ± 11.11	36.37 ± 10.18	0.31
Non-HDL	141.06 ± 44.42	139.36 ± 38.13	146.08 ± 37.43	0.60
LDL-C to HDL-C ratio	3.23 ± 1.66	2.92 ± 1.32	3.30 ± 1.59	0.27
Physical Activity (Met.h/day)	21.56 ± 4.75	23.60 ± 4.20	17.23 ± 4.46	0.64
Sex (%) Male Female	35.1 35.3	29.9 33.1	35.1 31.7	0.82
Smoking history (%) Yes No	32.1 35.6	32.1 31.7	35.7 32.7	0.92
Alcohol history (%) Yes No	25.0 35.3	25.0 31.9	50.0 32.8	0.76
Education Less than high school (%) High school and higher (%)	39.4 31.6	34.3 30.1	26.3 38.3	0.14
UPF; ultra-processed Food, BMI; body mass index, WHR; waist to hip ratio, TG, triglyceride, TC; total cholesterol, LDL; low density lipoprotein, HDL; low density lipoprotein.
Values are mean ± (SD) for continuous and percentage for categorical variables.
Using one-way ANOVA for continuous and Chi-square test for categorical variables.

Compared to the first tertile, individuals in the last tertile of UPFs had higher consumption of energy (P = 0.01), carbohydrate (P = 0.02), protein (P = 0.04), fat (P = 0.01), cholesterol (P = 0.03), SFA (P = 0.004), MUFA (P = 0.001), PUFA (P = 0.005), vitamin B12 (P = 0.01) and E (P = 0.01). But, we observed no significant association between intake of vitamin B6, B9, C, calcium, magnesium, zinc and selenium with UPFs intake (Table 2).

Table 2

The study participants’ nutrients intakes among the tertiles of UPFs.
Variable	UPFs
Variable	T1	T2	T3	P-value
Energy (kcal/d)	2819.19 ± 993.36	2493.850.92	2992.42 ± 1231.91	0.01
Carbohydrate (g/d)	440.40 ± 162.78	391.54 ± 148.39	462.05 ± 185.38	0.02
Protein (g/d)	91.93 ± 34.50	81.42 ± 27.64	96.20 ± 45.71	0.04
Fat (g/d)	83.09 ± 37.58	72.77 ± 28.17	91.84 ± 46.73	0.01
Cholesterol (mg/d)	225.45 ± 121.26	193.20 ± 76.95	244.82 ± 157.76	0.03
SFA (mg/d)	23.28 ± 11.86	19.87 ± 7.28	26.10 ± 13.72	0.004
MUFA (mg/d)	25.76 ± 12.32	23.50 ± 9.57	31.54 ± 17.23	0.001
PUFA (mg/d)	18.63 ± 9.28	17.63 ± 8.20	22.98 ± 13.02	0.005
Vitamin B₆ (mg/d)	2.24 ± 0.86	2.00 ± 0.75	2.23 ± 0.93	0.15
Vitamin B₉ (µg/d)	732.49 ± 251.79	678.80 ± 239.41	767.21 ± 275.83	0.10
Vitamin B₁₂ (µg/d)	3.53 ± 1.84	3.34 ± 1.59	4.35 ± 2.93	0.01
Vitamin C (mg/d)	201.22 ± 109.24	186.30 ± 167.73	214.19 ± 111.32	0.42
Vitamin E (mg/d)	9.26 ± 4.69	8.49 ± 2.53	10.43 ± 4.39	0.01
Calcium (mg/d)	1210.06 ± 468.89	1048.58 ± 416.29	1158.68 ± 434.80	0.07
Magnesium (mg/d)	488.48 ± 236.25	430.46 ± 188.83	492.35 ± 224.05	0.14
Zinc (mg/d)	13.29 ± 5.83	12.52 ± 5.64	14.13 ± 7.18	0.30
Selenium (mg/d)	130.70 ± 74.55	119.30 ± 61.64	133.08 ± 71.68	0.43
UPFs; ultra-processed Foods.
Values are mean ± (SD).
Using one-way ANOVA.

As presented in Table 3, participants in the last tertile of UPFs had significantly higher intakes of processed and red meat (P = 0.007) and breads (P = 0.02), also lower intake of margarine and sauces (P < 0.001). There were no differences in non-dairy beverages, cookies and cakes, dairy products, potato chips and salty snacks, sweets and other food items intakes among the UPFs tertiles.

Table 3

Daily intakes of UPFs subgroups across the tertiles of UPFs.
Variables	UPFs
	T₁	T₂	T₃	P
Non-dairy Beverage (%)	11.94 ± 1.69	9.75 ± 1.58	11.61 ± 1.53	0.59
Cookies and cakes (%)	19.91 ± 2.16	19.84 ± 2.29	23.48 ± 2.21	0.45
Dairy products (%)	11.34 ± 1.66	10.27 ± 1.18	8.68 ± 1.09	0.37
Potato chips and salty snacks (%)	7.52 ± 1.06	5.42 ± 1.27	6.14 ± 1.17	0.43
Processed meat and fast food (%)	7.01 ± 1.06	10.40 ± 1.49	13.42 ± 1.77	0.007
Margarine and sauces (%)	16.55 ± 1.51	15.70 ± 1.37	7.99 ± 0.70	˂0.001
Sweets (%)	5.42 ± 0.82	5.72 ± 1.31	3.10 ± 0.46	0.09
Bread (%)	5.99 ± 0.96	5.17 ± 0.86	10.57 ± 2.20	0.02
Others (%)	14.33 ± 1.93	17.29 ± 2.12	15.01 ± 1.97	0.55
UPFs; ultra-processed Food.
Values are mean ± (SD).
Using one-way ANOVA.

We found that, higher UPFs intake was associated with increased OR of serum TG, LDL and HDL in both crude (OR: 2.60; 95% CI: 1.24–5.46; P-trend = 0.01 and OR: 2.05; 95% CI: 1.01–4.17; P-trend = 0.04 and OR: 4.34; 95% CI: 1.89–9.91; P-trend = 0.001) and adjusted model (OR: 2.76; 95% CI: 1.29–5.89; P-trend = 0.008 and OR: 2.08; 95% CI: 1.01–4.26; P-trend = 0.04 and OR: 4.91; 95% CI: 2.05–11.74; P-trend < 0.001) (Table 4).

Table 4

Crude and multivariable-adjusted odds ratios and 95% CIs for lipid profile across UPFs tertile.
Variables	UPFs
	T₁	T₂	P	T₃	P	P_trend
TG (mg/day)
Crude Model	Ref.	1.79 (0.83, 3.86)	0.13	2.60 (1.24, 5.46)	0.01	0.01
Adjusted Model	Ref.	1.76 (0.80, 3.88)	0.15	2.76 (1.29, 5.89)	0.008	0.008
TC (mg/day)
Crude Model	Ref.	0.72 (0.33, 1.55)	0.40	1.48 (0.74, 2.96)	0.26	0.25
Adjusted Model	Ref.	0.82 (0.37, 1.80)	0.62	1.55 (0.76, 3.15)	0.22	0.22
LDL (mg/day)
Crude Model	Ref.	1.13 (0.53, 2.43)	0.73	2.05 (1.01, 4.17)	0.04	0.04
Adjusted Model	Ref.	1.18 (0.54, 2.55)	0.67	2.08 (1.01, 4.26)	0.04	0.04
HDL (mg/day)
Crude Model	Ref.	1.36 (0.69, 2.66)	0.36	4.34 (1.89, 9.91)	˂0.001	0.001
Adjusted Model	Ref.	1.11 (0.55, 2.23)	0.76	4.91 (2.05, 11.74)	˂0.001	˂0.001
Non-HDL
Crude Model	Ref.	0.97 (0.53, 1.77)	0.92	1.10 (0.57, 2.12)	0.77	0.79
Adjusted Model	Ref.	0.94 (0.50, 1.75)	0.85	1.07 (0.54, 2.11)	0.83	0.86
LDL to HDL ratio
Crude Model	Ref.	0.99 (0.53, 1.84)	0.99	1.28 (0.65, 2.51)	0.47	0.50
Adjusted Model	Ref.	0.92 (0.59, 1.72)	0.79	1.19 (0.59, 2.39)	0.60	0.66
WC (cm)
Crude Model	Ref.	0.77 (0.42, 1.41)	0.40	0.86 (0.45, 1.65)	0.66	0.60
Adjusted Model	Ref.	0.48 (0.19, 1.18)	0.11	1.29 (0.51, 3.30)	0.58	0.78
WHR
Crude Model	Ref.	0.89 (0.45, 1.75)	0.74	0.90 (0.44, 1.83)	0.77	0.75
Adjusted Model	Ref.	0.75 (0.35, 1.60)	0.46	0.91 (0.42, 1.99)	0.82	0.76
UPFs; ultra-processed Foods, TG, triglyceride; TC, total cholesterol; LDL, low density lipoprotein; HDL, low density lipoprotein; WC, waist circumference; WHR, waist to hip ratio.
Adjusted for age, energy intake, BMI, smoking and sex.
These values are odds ratio (95% CIs).
Obtained using logistic regression.

We found that higher intake of UPFs was associated with elevated lipid profile markers including TG, LDL and HDL. In terms of other blood lipids such as TC, non-HDL and LDL to HDL ratio, while they were not significantly associate with UPFs intake, all were higher in last tertile of UPFs intake.

In agreement with our results, a cross-sectional on Ecuadorian adolescents showed that dietary patterns consist of processed food was associated with increased blood level of LDL and cholesterol ¹⁹. Also, a longitudinal study in Brazil on preschool children found that intake of UPFs was a predictor of a higher total cholesterol and LDL cholesterol but not HDL and TG ²⁰. Moreover, Lima et al reported that higher UPFs consumption was associated with dyslipidemia ²¹. Furthermore, a large cohort of older adults in Spain showed that consumption of UPFs was associated with dyslipidemia and higher CVD risk ²². However, in contrast to our findings, a systematic review and meta-analysis resulted in negative association between UPFs consumption and HDL-cholesterol levels ²³.

We found a strong association between the dietary contribution of UPFs and dietary content of energy, carbohydrates, proteins, fat, cholesterol and SFAs. Moreover, MUFAs and PUFAs increased significantly as UPFs consumption increased probably because of high fat intake, which could be the main reason for higher HDL level in our study. In addition, higher HDL level in last tertile of UPFs intake is an explanation why LDL to HDL ratio was not associated with UPFs consumption.

With regard to anthropometric indices, although WC and WHR were not significantly associated with UPFs intake, participants in the last tertile of UPFs had higher average of WC and WHR. Cohort studies, on children from childhood to early adulthood ^24,25, and on adults ^26,27 showed that higher UPFs intake was associated with greater adiposity accumulation, higher BMI, weight gaining and incidence of obesity ²⁸. Besides, another cohort study on overweight and obese participants aged 55–75 years reported that higher UPFs intake was associated with higher age-related increase in visceral and overall adiposity ²⁹. Another similar study also showed positive association between UPFs intake and incidence of abdominal obesity in adults ³⁰. A systematic review and met-analysis and a multi-national cohort study also reported positive association between higher consumption of UPFs and overweight, obesity and high WC ^31,32. Basal characteristics of our participant’s shows that the average weight, WHR and WC of them were already high, it is probably why we did not found association between them and UPFs intake.

UPFs are often calorie-dense, contain large amounts of fats, saturated fats, trans fats, sodium and simple sugars with high glycemic index and contain no or small amount of fibers, vitamins, minerals or other bioactive compounds that naturally exist in fresh foods, so they are nutritionally unbalanced ³³.

Several mechanisms have been suggested to explain adverse effects of UFPs on lipid profile and health. First of all, UPFs consumption may result in physiological disruption of hunger and satiety patterns because of over consumption due to their intrinsic palatability, consequently leads to more and more intake of them ^34–37. Besides, energy-dense foods usually contain high amount of free sugar and trans fats which leads to decreased oxidation and higher fatty acid aggregation in tissues and blood ³⁸, higher lipogenesis ³⁷, so production and release of very low-density lipoproteins in to the blood ³⁸. Also, partially hydrogenated vegetable oils in UPFs contains trans fatty acids which have adverse impact on lipid profile. In addition, there is scientific evidence which support the hypothesis that the interacting effects of ingredients and substances produced through high-heat processing of oils, determines the health effects of them ³⁹.

On the other hand, the impact of carbohydrates on lipid profile also depends on their sources, processing methods and its level. While consumption of free sugar elevates serum TG, whole grains intake decreases total and LDL cholesterol and TG ⁴⁰. Consumption of minimally-processed whole grains such as oatmeal, instead of highly processed refined grains may improve lipid profile ⁴¹.

There are some limitations of this study that we want to discuss about. Firstly, we could not assay causal correlations between UPFs intake and lipid profile, because of the nature of cross-sectional study. In the second place, the study was done in Shiraz city, so we should consider caution to extend our results to all Iranian adults. Ultimately, although we have adjusted some confounders, there may be other ones that have not been recognized in this study.

In conclusion, our results showed significant associations between ultra-processed foods intake and dietary nutrient profiles that result in dyslipidemia as a risk factor of chronic diseases.

Acknowledgements: no.

Authors’ Contributions: M.N, S.E, M.R, N.O and M.N; Contributed to data collection and writing the first draft. M.N; Contributed to all data and statistical analysis, and interpretation of data. S.F.; Contributed to the research concept, supervised the work and revised the manuscript. All authors read and approved the final manuscript.

Ethical Approval: The present study was approved by The Research Ethics Committee of Shiraz University of Medical Sciences, Shiraz, Iran (IR.SUMS.REC.1394.S146)

Disclosure statement: All authors declare that they have no conflict of interest.

Availability of data and materials: Data available on request from the corresponding author.

Use of human participants: all experiments were performed in accordance with relevant guidelines and regulations

Informed Consent: all participants singed informed consent form.

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No competing interests reported.

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published 04 May, 2023

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Association between ultra-processed food intakes with lipid profiles - A cross-sectional study

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Socio-demographic And Anthropometric Assessments

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