Ultra-Processed Food Consumption and Incidence of Obesity and Cardiometabolic Risk Factors in Adults: A Systematic Review of Prospective Studies
Abstract
:1. Introduction
2. Materials and Methods
2.1. Search Strategy
2.2. Study Selection, Inclusion and Exclusion Criteria
2.3. Data Extraction
2.4. Quality Assessment
3. Results
3.1. Study Characteristics
3.2. Consumption of Ultra-Processed Food, Excess Body Weight, and Abdominal Obesity
3.3. Consumption of Ultra-Processed Food, Impaired Fasting Glucose, and Diabetes Mellitus
3.4. Consumption of Ultra-Processed Food and Hypertension
3.5. Consumption of Ultra-Processed Food and Lipid Profile
3.6. Consumption of Ultra-Processed Food and Metabolic Syndrome
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
- World Health Organization. World Obesity Day 2022—Accelerating Action to Stop Obesity; World Health Organization: Geneva, Switzerland, 2022; Available online: https://www.who.int/news/item/04-03-2022-world-obesity-day-2022-accelerating-action-to-stop-obesity (accessed on 25 January 2023).
- Engin, A. The Definition and Prevalence of Obesity and Metabolic Syndrome. Adv. Exp. Med. Biol. 2017, 960, 1–17. [Google Scholar] [CrossRef] [PubMed]
- Saklayen, M.G. The Global Epidemic of the Metabolic Syndrome. Curr. Hypertens Rep. 2018, 20, 12. [Google Scholar] [CrossRef]
- Estruch, R.; Ros, E. The role of the Mediterranean diet on weight loss and obesity-related diseases. Rev. Endocr. Metab. Disord. 2020, 21, 315–327. [Google Scholar] [CrossRef] [PubMed]
- Babio, N.; Toledo, E.; Estruch, R.; Ros, E.; Martínez-González, M.A.; Castañer, O.; Bulló, M.; Corella, D.; Arós, F.; Gómez-Gracia, E.; et al. Mediterranean diets and metabolic syndrome status in the PREDIMED randomized trial. CMAJ 2014, 186, E649–E657. [Google Scholar] [CrossRef]
- Leone, A.; De Amicis, R.; Battezzati, A.; Bertoli, S. Adherence to the Mediterranean Diet and Risk of Metabolically Unhealthy Obesity in Women: A Cross-Sectional Study. Front. Nutr. 2022, 9, 858206. [Google Scholar] [CrossRef] [PubMed]
- Leone, A.; Bertoli, S.; Bedogni, G.; Vignati, L.; Pellizzari, M.; Battezzati, A. Association between Mediterranean Diet and Fatty Liver in Women with Overweight and Obesity. Nutrients 2022, 14, 3771. [Google Scholar] [CrossRef]
- Medina-Remón, A.; Kirwan, R.; Lamuela-Raventós, R.M.; Estruch, R. Dietary patterns and the risk of obesity, type 2 diabetes mellitus, cardiovascular diseases, asthma, and neurodegenerative diseases. Crit. Rev. Food Sci. Nutr. 2018, 58, 262–296. [Google Scholar] [CrossRef]
- De Amicis, R.; Mambrini, S.P.; Pellizzari, M.; Foppiani, A.; Bertoli, S.; Battezzati, A.; Leone, A. Ultra-processed foods and obesity and adiposity parameters among children and adolescents: A systematic review. Eur. J. Nutr. 2022, 61, 2297–2311. [Google Scholar] [CrossRef]
- Leone, A.; Martínez-González, M.; Craig, W.; Fresán, U.; Gómez-Donoso, C.; Bes-Rastrollo, M. Pre-Gestational Consumption of Ultra-Processed Foods and Risk of Gestational Diabetes in a Mediterranean Cohort. The SUN Project. Nutrients 2021, 13, 2202. [Google Scholar] [CrossRef]
- Monteiro, C.A.; Cannon, G.; Moubarac, J.C.; Levy, R.B.; Louzada, M.L.C.; Jaime, P.C. The UN Decade of Nutrition, the NOVA food classification and the trouble with ultra-processing. Public Health Nutr. 2018, 21, 5–17. [Google Scholar] [CrossRef]
- Martínez Steele, E.; Juul, F.; Neri, D.; Rauber, F.; Monteiro, C.A. Dietary share of ultra-processed foods and metabolic syndrome in the US adult population. Prev. Med. 2019, 125, 40–48. [Google Scholar] [CrossRef]
- Moubarac, J.C.; Batal, M.; Louzada, M.L.; Martinez Steele, E.; Monteiro, C.A. Consumption of ultra-processed foods predicts diet quality in Canada. Appetite 2017, 108, 512–520. [Google Scholar] [CrossRef]
- Rauber, F.; da Costa Louzada, M.L.; Steele, E.M.; Millett, C.; Monteiro, C.A.; Levy, R.B. Ultra-Processed Food Consumption and Chronic Non-Communicable Diseases-Related Dietary Nutrient Profile in the UK (2008–2014). Nutrients 2018, 10, 587. [Google Scholar] [CrossRef]
- Lane, M.M.; Davis, J.A.; Beattie, S.; Gómez-Donoso, C.; Loughman, A.; O’Neil, A.; Jacka, F.; Berk, M.; Page, R.; Marx, W.; et al. Ultraprocessed food and chronic noncommunicable diseases: A systematic review and meta-analysis of 43 observational studies. Obes Rev. 2021, 22, e13146. [Google Scholar] [CrossRef]
- Moola, S.; Munn, Z.; Tufanaru, C.; Aromataris, E.; Sears, K.; Sfetcu, R.; Currie, M.; Qureshi, R.; Mattis, P.; Lisy, K.; et al. Chapter 7: Systematic Reviews of Etiology and Risk; Aromataris, E., Munn, Z., Eds.; JBI Manual for Evidence Synthesis: Adelaide, Australia, 2020. [Google Scholar]
- Canhada, S.L.; Luft, V.C.; Giatti, L.; Duncan, B.B.; Chor, D.; Fonseca, M.; Matos, S.M.A.; Molina, M.; Barreto, S.M.; Levy, R.B.; et al. Ultra-processed foods, incident overweight and obesity, and longitudinal changes in weight and waist circumference: The Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Public Health Nutr. 2020, 23, 1076–1086. [Google Scholar] [CrossRef]
- Scaranni, P.; Cardoso, L.O.; Chor, D.; Melo, E.C.P.; Matos, S.M.A.; Giatti, L.; Barreto, S.M.; da Fonseca, M.J.M. Ultra-processed foods, changes in blood pressure and incidence of hy.ypertension: The Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Public Health Nutr. 2021, 24, 3352–3360. [Google Scholar] [CrossRef]
- Scaranni, P.; de Oliveira Cardoso, L.; Griep, R.H.; Lotufo, P.A.; Barreto, S.M.; da Fonseca, M.J.M. Consumption of ultra-processed foods and incidence of dyslipidemias: The Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Br. J. Nutr. 2022, 129, 336–344. [Google Scholar] [CrossRef]
- Magalhães, E.; de Oliveira, B.R.; Rudakoff, L.C.S.; de Carvalho, V.A.; Viola, P.; Arruda, S.P.M.; de Carvalho, C.A.; Coelho, C.; Bragança, M.; Bettiol, H.; et al. Sex-Dependent Effects of the Intake of NOVA Classified Ultra-Processed Foods on Syndrome Metabolic Components in Brazilian Adults. Nutrients 2022, 14, 3126. [Google Scholar] [CrossRef]
- Beslay, M.; Srour, B.; Méjean, C.; Allès, B.; Fiolet, T.; Debras, C.; Chazelas, E.; Deschasaux, M.; Wendeu-Foyet, M.G.; Hercberg, S.; et al. Ultra-processed food intake in association with BMI change and risk of overweight and obesity: A prospective analysis of the French NutriNet-Santé cohort. PLoS Med. 2020, 17, e1003256. [Google Scholar] [CrossRef]
- Srour, B.; Fezeu, L.K.; Kesse-Guyot, E.; Alles, B.; Debras, C.; Druesne-Pecollo, N.; Chazelas, E.; Deschasaux, M.; Hercberg, S.; Galan, P.; et al. Ultraprocessed Food Consumption and Risk of Type 2 Diabetes Among Participants of the NutriNet-Sante Prospective Cohort. JAMA Intern. Med. 2019, 180, 283–291. [Google Scholar] [CrossRef]
- Monge, A.; Silva Canella, D.; López-Olmedo, N.; Lajous, M.; Cortés-Valencia, A.; Stern, D. Ultraprocessed beverages and processed meats increase the incidence of hypertension in Mexican women. Br. J. Nutr. 2021, 126, 600–611. [Google Scholar] [CrossRef] [PubMed]
- Duan, M.J.; Vinke, P.C.; Navis, G.; Corpeleijn, E.; Dekker, L.H. Ultra-processed food and incident type 2 diabetes: Studying the underlying consumption patterns to unravel the health effects of this heterogeneous food category in the prospective Lifelines cohort. BMC Med. 2022, 20, 7. [Google Scholar] [CrossRef]
- Sandoval-Insausti, H.; Jiménez-Onsurbe, M.; Donat-Vargas, C.; Rey-García, J.; Banegas, J.R.; Rodríguez-Artalejo, F.; Guallar-Castillón, P. Ultra-Processed Food Consumption Is Associated with Abdominal Obesity: A Prospective Cohort Study in Older Adults. Nutrients 2020, 12, 2368. [Google Scholar] [CrossRef] [PubMed]
- Mendonca, R.D.; Pimenta, A.M.; Gea, A.; de la Fuente-Arrillaga, C.; Martinez-Gonzalez, M.A.; Lopes, A.C.; Bes-Rastrollo, M. Ultraprocessed food consumption and risk of overweight and obesity: The University of Navarra Follow-Up (SUN) cohort study. Am. J. Clin. Nutr. 2016, 104, 1433–1440. [Google Scholar] [CrossRef] [PubMed]
- Mendonca, R.D.; Lopes, A.C.; Pimenta, A.M.; Gea, A.; Martinez-Gonzalez, M.A.; Bes-Rastrollo, M. Ultra-Processed Food Consumption and the Incidence of Hypertension in a Mediterranean Cohort: The Seguimiento Universidad de Navarra Project. Am. J. Hypertens 2017, 30, 358–366. [Google Scholar] [CrossRef] [PubMed]
- Llavero-Valero, M.; Escalada-San Martín, J.; Martínez-González, M.A.; Basterra-Gortari, F.J.; de la Fuente-Arrillaga, C.; Bes-Rastrollo, M. Ultra-processed foods and type-2 diabetes risk in the SUN project: A prospective cohort study. Clin. Nutr. 2021, 40, 2817–2824. [Google Scholar] [CrossRef]
- Donat-Vargas, C.; Sandoval-Insausti, H.; Rey-García, J.; Moreno-Franco, B.; Åkesson, A.; Banegas, J.R.; Rodríguez-Artalejo, F.; Guallar-Castillón, P. High Consumption of Ultra-Processed Food is Associated with Incident Dyslipidemia: A Prospective Study of Older Adults. J. Nutr. 2021, 151, 2390–2398. [Google Scholar] [CrossRef]
- Levy, R.B.; Rauber, F.; Chang, K.; Louzada, M.L.D.C.; Monteiro, C.A.; Millett, C.; Vamos, E.P. Ultra-processed food consumption and type 2 diabetes incidence: A prospective cohort study. Clin. Nutr. 2021, 40, 3608–3614. [Google Scholar] [CrossRef]
- Rauber, F.; Chang, K.; Vamos, E.P.; da Costa Louzada, M.L.; Monteiro, C.A.; Millett, C.; Levy, R.B. Â Ultra-processed food consumption and risk of obesity: A prospective cohort study of UK Biobank. Eur. J. Nutr. 2021, 60, 2169–2180. [Google Scholar] [CrossRef]
- Li, M.; Shi, Z. Ultra-Processed Food Consumption Associated with Overweight/Obesity among Chinese Adults-Results from China Health and Nutrition Survey 1997-2011. Nutrients 2021, 13, 2796. [Google Scholar] [CrossRef]
- Cordova, R.; Kliemann, N.; Huybrechts, I.; Rauber, F.; Vamos, E.P.; Levy, R.B.; Wagner, K.H.; Viallon, V.; Casagrande, C.; Nicolas, G.; et al. Consumption of ultra-processed foods associated with weight gain and obesity in adults: A multi-national cohort study. Clin. Nutr. 2021, 40, 5079–5088. [Google Scholar] [CrossRef]
- Rauber, F.; Steele, E.M.; Louzada, M.L.d.C.; Millett, C.; Monteiro, C.A.; Levy, R.B. Ultra-processed food consumption and indicators of obesity in the United Kingdom population (2008–2016). PLoS ONE 2020, 15, e0232676. [Google Scholar] [CrossRef]
- Srour, B.; Fezeu, L.K.; Kesse-Guyot, E.; Alles, B.; Mejean, C.; Andrianasolo, R.M.; Chazelas, E.; Deschasaux, M.; Hercberg, S.; Galan, P.; et al. Ultra-processed food intake and risk of cardiovascular disease: Prospective cohort study (NutriNet-Sante). BMJ 2019, 365, l1451. [Google Scholar] [CrossRef]
- Fardet, A. Minimally processed foods are more satiating and less hyperglycemic than ultra-processed foods: A preliminary study with 98 ready-to-eat foods. Food Funct. 2016, 7, 2338–2346. [Google Scholar] [CrossRef]
- Sadeghirad, B.; Duhaney, T.; Motaghipisheh, S.; Campbell, N.R.; Johnston, B.C. Influence of unhealthy food and beverage marketing on children’s dietary intake and preference: A systematic review and meta-analysis of randomized trials. Obes. Rev. 2016, 17, 945–959. [Google Scholar] [CrossRef]
- Pérez-Escamilla, R.; Obbagy, J.E.; Altman, J.M.; Essery, E.V.; McGrane, M.M.; Wong, Y.P.; Spahn, J.M.; Williams, C.L. Dietary energy density and body weight in adults and children: A systematic review. J. Acad. Nutr. Diet. 2012, 112, 671–684. [Google Scholar] [CrossRef]
- Canella, D.S.; Levy, R.B.; Martins, A.P.; Claro, R.M.; Moubarac, J.C.; Baraldi, L.G.; Cannon, G.; Monteiro, C.A. Ultra-processed food products and obesity in Brazilian households (2008–2009). PLoS ONE 2014, 9, e92752. [Google Scholar] [CrossRef]
- Lam, M.C.L.; Adams, J. Association between home food preparation skills and behaviour, and consumption of ultra-processed foods: Cross-sectional analysis of the UK National Diet and nutrition survey (2008–2009). Int. J. Behav. Nutr. Phys. Act. 2017, 14, 68. [Google Scholar] [CrossRef]
- Robinson, E.; Aveyard, P.; Daley, A.; Jolly, K.; Lewis, A.; Lycett, D.; Higgs, S. Eating attentively: A systematic review and meta-analysis of the effect of food intake memory and awareness on eating. Am. J. Clin. Nutr. 2013, 97, 728–742. [Google Scholar] [CrossRef]
- Robinson, E.; Almiron-Roig, E.; Rutters, F.; de Graaf, C.; Forde, C.G.; Tudur Smith, C.; Nolan, S.J.; Jebb, S.A. A systematic review and meta-analysis examining the effect of eating rate on energy intake and hunger. Am. J. Clin. Nutr. 2014, 100, 123–151. [Google Scholar] [CrossRef]
- Schulte, E.M.; Avena, N.M.; Gearhardt, A.N. Which foods may be addictive? The roles of processing, fat content, and glycemic load. PLoS ONE 2015, 10, e0117959. [Google Scholar] [CrossRef]
- Carter, A.; Hendrikse, J.; Lee, N.; Yücel, M.; Verdejo-Garcia, A.; Andrews, Z.B.; Hall, W. The Neurobiology of “Food Addiction” and Its Implications for Obesity Treatment and Policy. Annu. Rev. Nutr. 2016, 36, 105–128. [Google Scholar] [CrossRef] [PubMed]
- Hall, K.D. A review of the carbohydrate-insulin model of obesity. Eur. J. Clin. Nutr. 2017, 71, 323–326. [Google Scholar] [CrossRef] [PubMed]
- Filippini, T.; Malavolti, M.; Whelton, P.K.; Vinceti, M. Sodium Intake and Risk of Hypertension: A Systematic Review and Dose-Response Meta-analysis of Observational Cohort Studies. Curr. Hypertens Rep. 2022, 24, 133–144. [Google Scholar] [CrossRef]
- Lustig, R.H. Fructose: Metabolic, hedonic, and societal parallels with ethanol. J. Am. Diet. Assoc. 2010, 110, 1307–1321. [Google Scholar] [CrossRef]
- Zakim, D. The effect of fructose on hepatic synthesis of fatty acids. Acta Med. Scand. Suppl. 1972, 542, 205–214. [Google Scholar] [CrossRef]
- Mensink, R.P.; Zock, P.L.; Kester, A.D.; Katan, M.B. Effects of dietary fatty acids and carbohydrates on the ratio of serum total to HDL cholesterol and on serum lipids and apolipoproteins: A meta-analysis of 60 controlled trials. Am. J. Clin. Nutr. 2003, 77, 1146–1155. [Google Scholar] [CrossRef]
- Matthan, N.R.; Welty, F.K.; Barrett, P.H.; Harausz, C.; Dolnikowski, G.G.; Parks, J.S.; Eckel, R.H.; Schaefer, E.J.; Lichtenstein, A.H. Dietary hydrogenated fat increases high-density lipoprotein apoA-I catabolism and decreases low-density lipoprotein apoB-100 catabolism in hypercholesterolemic women. Arterioscler. Thromb. Vasc. Biol. 2004, 24, 1092–1097. [Google Scholar] [CrossRef]
- Wijendran, V.; Hayes, K.C. Dietary n-6 and n-3 fatty acid balance and cardiovascular health. Annu. Rev. Nutr. 2004, 24, 597–615. [Google Scholar]
- El-Ezaby, M.M.; Abd-El Hamide, N.-A.H.; El-Maksoud, M.A.E.; Shaheen, E.M.; Embashi, M.M.R. Effect of some food additives on lipid profile, kidney function and liver function of adult male albino rats. J. Bas Environ. Sci. 2018, 5, 52–59. [Google Scholar]
- Bhattacharyya, S.; Feferman, L.; Tobacman, J.K. Carrageenan Inhibits Insulin Signaling through GRB10-mediated Decrease in Tyr(P)-IRS1 and through Inflammation-induced Increase in Ser(P)307-IRS1. J. Biol. Chem. 2015, 290, 10764–10774. [Google Scholar] [CrossRef]
- Chassaing, B.; Koren, O.; Goodrich, J.K.; Poole, A.C.; Srinivasan, S.; Ley, R.E.; Gewirtz, A.T. Dietary emulsifiers impact the mouse gut microbiota promoting colitis and metabolic syndrome. Nature 2015, 519, 92–96. [Google Scholar] [CrossRef]
- Bertoli, S.; Leone, A.; Battezzati, A. Human Bisphenol A Exposure and the “Diabesity Phenotype”. Dose Response 2015, 13, 1559325815599173. [Google Scholar] [CrossRef]
- Rolfo, A.; Nuzzo, A.M.; De Amicis, R.; Moretti, L.; Bertoli, S.; Leone, A. Fetal-Maternal Exposure to Endocrine Disruptors: Correlation with Diet Intake and Pregnancy Outcomes. Nutrients 2020, 12, 1744. [Google Scholar] [CrossRef]
- Tonini, C.; Segatto, M.; Bertoli, S.; Leone, A.; Mazzoli, A.; Cigliano, L.; Barberio, L.; Mandalà, M.; Pallottini, V. Prenatal Exposure to BPA: The Effects on Hepatic Lipid Metabolism in Male and Female Rat Fetuses. Nutrients 2021, 13, 1970. [Google Scholar] [CrossRef]
- Bird, S.R.; Hawley, J.A. Update on the effects of physical activity on insulin sensitivity in humans. BMJ Open Sport Exerc. Med. 2017, 2, e000143. [Google Scholar] [CrossRef]
- Schulze, M.B.; Martínez-González, M.A.; Fung, T.T.; Lichtenstein, A.H.; Forouhi, N.G. Food based dietary patterns and chronic disease prevention. BMJ 2018, 361, k2396. [Google Scholar] [CrossRef]
- Fardet, A.; Rock, E. Toward a new philosophy of preventive nutrition: From a reductionist to a holistic paradigm to improve nutritional recommendations. Adv. Nutr. 2014, 5, 430–446. [Google Scholar] [CrossRef]
- Thompson, F.E.; Subar, A.F. Chapter 1 Dietary Assessment Methodology. In Nutrition in the Prevention and Treatment of Disease; Academic Press: Cambridge, MA, USA, 2017; pp. 5–48. [Google Scholar]
- Willett, W.C.; Howe, G.R.; Kushi, L.H. Adjustment for total energy intake in epidemiologic studies. Am. J. Clin. Nutr. 1997, 65, 1220S–1228S. [Google Scholar] [CrossRef]
- Lorenzoni, G.; Di Benedetto, R.; Silano, M.; Gregori, D. What Is the Nutritional Composition of Ultra-Processed Food Marketed in Italy? Nutrients 2021, 13, 2364. [Google Scholar] [CrossRef]
- Mann, C.J. Observational research methods. Research design II: Cohort, cross sectional, and case-control studies. Emerg. Med. J. 2003, 20, 54–60. [Google Scholar] [CrossRef] [PubMed]
Author (Year) | Country (Cohort) | Subjects (n) and Baseline Characteristics | Outcome | Follow-Up Time | Dietary Assessment | Covariates Included in the Fully Adjusted Model | Type of Exposure | Results |
---|---|---|---|---|---|---|---|---|
Mendonça et al. (2016) [26] | Spain (SUN cohort) | 8451 participants 35.1% men 64.9% women Age: 37.6 ± 11.0 years | Overweight/obesity | Median follow-up: 8.9 years | Semi-quantitative FFQ (136 items) | Sex, age, baseline BMI, educational status, marital status, physical activity, smoking status, siesta sleep, television watching, following a special diet at baseline, snacking between meals, and consumption of fruit and vegetables. | servings/d | Participants in the fourth quartile of UPF consumption had a higher risk of developing overweight or obesity (HR = 1.26, 95% CI: 1.10, 1.45, Ptrend = 0.001) than participants in the first quartile. |
Canhada et al. (2019) [17] | Brazil (ELSA cohort) | 11,827 participants 45% men 55% women Age: 51.3 ± 8.7 years | Overweight/obesity | Mean follow-up: 3.8 years | FFQ (114 items) | Age, sex, school achievement, center, and color/race, as well as smoking and physical activity, waist/weight gain, incidence of overweight/obesity, baseline BMI, and baseline waist circumference. | %UPFenergy | Participants in the fourth quartile of UPF consumption (>30.8 %) presented 20% greater risk (RR:1.20; 95% CI: 1.03, 1–40) of incident overweight and obesity than participants in the first quartile (<17.8%). No association between UPF quartiles and risk of incident obesity among overweight participants was observed (RR:1.02; 95% CI: 0.85, 1.21). |
Beslay et al. (2020) [21] | France (French NutriNet-Santè cohort) | 110,260 participants 22.8% men 78.2% women Age: 43.1 ± 14.6 years | Overweight/obesity | Median follow-up: 4.1 years | 24 h dietary record | Age, sex, marital status, BMI, educational level, physical activity, smoking status, alcohol intake, number of 24 h dietary records, energy intake, health, and Western dietary pattern. | %UPFintake | Normal-weight participants with low UPF consumption had a lower risk of developing overweight or obesity during follow-up (HRQ4 vs.Q1 = 1.22, 95% CI: 1.14, 1.31, Ptrend < 0.001) than those with a higher intake. Moreover, a 10% increment of UPF intake was associated with a higher risk of developing overweight or obesity (HR = 1.10, 95% CI: 1.07, 1.13; P < 0.001). Non-obese subjects with low UPF consumption had a lower risk of developing obesity during follow-up (HRQ4 vs.Q1 = 1.20, 95% CI: 1.08, 1.33, Ptrend < 0.001) than those with a higher intake. Moreover, a 10% increment of UPFs intake was associated with a higher risk of developing obesity (HR = 1.11, 95% CI: 1.07, 1.15; P < 0.001). |
Sandoval-Insausti et al. (2020) [25] | Spain (Seniors-ENRICA-1) | 652 participants 55.7% men 44.3% women Age: 67.08 ± 5.8 years | Abdominal obesity | Median follow-up: 6 years | Dietary history (DH-ENRICA) record | Age, sex, educational level, marital status, ex-drinker status, smoking, physical activity in the household, physical activity during leisure time, prevalence of chronic disease, number of medications consumed daily, and adherence to Mediterranean diet. | %UPFenergy | Participants in the first tertiles of UPF consumption had a higher risk of developing abdominal obesity (RR: 1.61; 95% CI: 1.01, 2.56, Ptrend=0.048) than participants in the first tertile. |
Cordova et al. (2021) [33] | Denmark, Germany, Italy, France, Greece, the Netherlands, Spain, Norway, Sweden and the UK (EPIC cohort) | 348,748 participants 26.6% men 73.4% women Age: 51.7 ± 9.0 years | Overweight/obesity | Median follow-up: 5 years | (a) Quantitative FFQ (Italy, Spain, the Netherlands, Germany, and France) (b) Semi-quantitative FFQ (Denmark, Naples (Italy), Norway, and Umeå (Sweden), (c) A combination of semi-quantitative FFQ and 7- and 14-day records in the UK and Malmo (Sweden). | Age, sex, BMI baseline, education level, smoking history, physical activity, alcohol intake, Mediterranean diet score, and plausibility of dietary energy reporting. | g/day | Normal-weight participants in the fifth quintile of UPF consumption had a 15% higher risk (RR = 1.15, 95% CI: 1.11, 1.19, Ptrend <0.001) of becoming overweight or obese during follow-up than participants in the first quintile. Similarly, participants with overweight in the highest quintile of UPF consumption had a 16% higher risk (RR = 1.16; 95% CI: 1.09, 1.23, Ptrend <0.001) of becoming obese during follow-up than participants in the lowest quintile. |
Li et al. (2021) [32] | China (CNHS cohort) | 12,451 participants 48.7% men 51.3% women Age: 43.7 ± 14.7 years | Overweight/obesity and abdominal obesity | 10 years | 3-day 24 h dietary recall | Age, sex, income, urbanization, education, smoking, alcohol drinking, and physical activity, energy intake, fat intake, and dietary patterns. | g/day | Participants consuming 1–19 g/day, 20–49 g/day, or ≥ 50 g/day of UPF were at a higher risk of developing overweight and obesity and abdominal obesity than non-consumers. Adjusted ORs for overweight and obesity were 1.45 (95% CI: 1.26, 1.65), 1.34 (95% CI: 1.15–1.57), and 1.45 (95% CI: 1.21–1.74), respectively. Adjusted ORs for abdominal obesity were 1.54 (95% CI: 1.38, 1.72), 1.35 (95% CI: 1.19, 1.54), and 1.50 (95% CI: 1.29, 1.74), respectively. |
Rauber et al. (2021) [31] | England, Scotland and Wales (UK Biobank) | 22,659 participants 47.9% men 52.1% women Age: 55.9 ± 7.4 years | General and abdominal obesity | Median follow-up: 5 years | 24 h dietary recall | Sex, BMI, waist circumference or body fat at baseline, smoking status, level of physical activity, sleep duration, Index of Multiple Deprivation (IMD). | %UPFenergy | Non-obese participants in the uppermost quartile of UPF consumption were at a higher risk of developing obesity (HR = 1.79, 95% CI: 1.06, 3.03) than participants in the lowest quartile. Similarly, participants with normal waist circumference at baseline but in the first quartile of UPF consumption were at a higher risk of developing abdominal obesity (HR = 1.30, 95% CI: 1.14, 1.48) than participants in the lowest quartile. |
DaSilva Magalhães et al. (2022) [20] | Brazil (Ribeirão Preto cohort) | 896 particpants 44.3% men 55.7% women Age: 23–25 years | MetS and its components | 14–16 years | Semi-quantitative FFQ (83 items) | Sex, age, education, marital status, skin color, family income, smoking, level of physical activity, and alcohol consumption. In the analyses with the consumption of UPF in %g, total energy intake was additionally included. | %UPFenergy and %UPFintake | UPF consumption was not associated with the risk of metabolic syndrome (%kcal PR: 1.00; 95% CI: 0.99–1.01; %g PR: 1.00; 95% CI: 0.99–1.01). However, women with higher UPF consumption were at a higher risk of developing abdominal obesity (%kcal: RR = 1.01, 95% CI: 1.00, 1.02, p = 0.030; %g: RR = 1.01, 95% CI: 1.00, 1.02, p = 0.003) and low HDL-cholesterol (%kcal: RR = 1.02, 95% CI: 1.01, 1.04, p = 0.041). No significant associations between UPF consumption and other metabolic syndrome components were observed. |
Mendonca et al. (2017) [27] | Spain (SUN cohort) | 14,790 36.3% men 63.7% women Age: 36.3 ± 10.3 years | Hypertension | Mean follow-up: 9.1 years | Semi-quantitative FFQ (136 items) | Sex, age, baseline BMI, physical activity, hours of television watching, smoking status, following a special diet at baseline, use of analgesics, alcohol consumption, family history of hypertension, hypercholesterolemia, total energy intake, fruit and vegetable consumption, and olive oil intake. | servings/d | Participants in the third tertile of UPF consumption were at a higher risk of developing hypertension (HR = 1.21, 95% CI: 1.06, 1.37, Ptrend = 0.004) than participants in the first tertile. |
Monge et al. (2021) [23] | Mexico (Mexican Teachers’ Cohort) | 64934 participants (only women) Age: 41.7 ± 7.2 years | Hypertension | Median follow-up: 2.2 years | Semi-quantitative FFQ (140 items) | Age, smoking status, physical activity, menopausal status, ethnicity, internet access and insurance for serious conditions, family history of hypertension, total energy intake, and multivitamin supplementation. | %UPFenergy | No association between categories of %UPFenergy (≤20%, 21–25%, 26–35%, 36–45% >45% energy/d) and incident hypertension was found. Compared with the first category, IRRs were 0.96 (95% CI: 0.86, 1.07), 0.92 (95% CI: 0.84, 1.02), 0.95 (95% CI: 0.85, 1.06), and 0.98 (95% CI: 0.84, 1.14). |
Scaranni et al. (2021) [18] | Brazil (ELSA cohort) | 8754 participants 42% men 58% women Median age: 49.0 years | Hypertension | Mean follow-up: 3.9 years | 114-item FFQ | Sex, age, self-declared color/ race, education, smoking, alcohol consumption, antihypertensive drug use, Na consumption, physical activity, total daily energy intake, and BMI. | %UPFenergy | Participants with higher UPF consumption had a marginally significant greater risk of developing hypertension (OR = 1.17; 95% CI: 1.00, 1.37) than participants with lower UPF consumption. |
Srour et al. (2019) [22] | France (French NutriNet-Santè cohort) | 1047,07 participants 20.8% men 79.2% women Age: 42.7 ± 14.5 years | Type 2 Diabetes | Median follow-up: 6 years | 24 h dietary record | Sex, age, BMI, weight change during follow-up, educational level, smoking status, physical activity level, number of 24 h dietary records, alcohol intake, energy intake without alcohol, overall diet quality, family history of diabetes, baseline dyslipidemia and hypertension, and treatments for these conditions. | g/day | An increment of 10% of UPFs in diet was associated with an increased risk of T2D (HR = 1.13, 95% CI: 1.03, 1.23, p = 0.04). Similarly, a 100g/day increment in UPF consumption was associated with the risk of T2D (HR = 1.05; 95% CI: 1.02, 1.08, p = 0.003). |
Duan et al. (2022) [24] | Netherlands (Lifelines cohort) | 70,421 participants 41.4% men 58.6% women Age 49.1 ± 8.8 years | Type 2 Diabetes | Median follow-up: 3.4 years | Semi-quantitative FFQ (110 items) | Sex, age, BMI, educational level, energy intake, alcohol intake, Life diet score, smoking status, physical activity, and TV-watching time. | %UPFintake | An increment of 10% in UPF consumption was associated with a 25% higher risk of developing T2D (OR = 1.25; 95% CI: 1.16, 1.34). |
Levy et al. (2021) [30] | England, Scotland and Wales (UK Biobank) | 21,730 participants 47.1% men 52.9% women Age: 55.8 ± 7.4 years | Type 2 Diabetes | Mean follow-up: 5.4 years | 24 h dietary recall | Sex, age, BMI, smoking, physical activity level, ethnicity, family history of T2D, Index of Multiple Deprivation (IMD), and total energy intake. | %UPFintake | Participants in the highest quartile of UPF consumption were at a higher risk for T2D (HR = 1.44; 95% CI: 1.04, 2.02, Ptrend < 0.028) than participants in the lowest quartile. Moreover, a 10%-point increment in UPF consumption was associated with a 12% increased risk of T2D (HR = 1.12, 95% CI: 1.04, 1.20). |
Llavero-Valero et al. (2021) [28] | Spain (SUN cohort) | 20,060 participants 38.5% men 61.5% women Age: 37.4 ± 12.2 years | Type 2 Diabetes | Median follow-up: 12 years | Semi-quantitative FFQ (136 items) | Age, sex, BMI, educational level, smoking status, 8-item active + sedentary lifestyle score, following a special diet at baseline, snacking, and family history of diabetes. | g/day | Participants in the highest tertile of UPF consumption were at a higher risk of T2D than participants in the lowest tertile (HR = 1.53, 95% CI: 1.06, 2.22, Ptrend = 0.024). After using repeated measurements of UPF consumption, the association remained significant (HR = 1.65, 95% CI: 1.14, 2.38). |
Donat-Vargas et al. (2021) [29] | Spain (ENRICA cohort) | 1082 participants 48% men 52% women Age: 68 ± 6 years | Dyslipidemia | 5–7 years | Dietary history (DH-ENRICA) record | Sex, age, BMI, smoking status, physical activity, educational level, marital status, total energy intake, alcohol consumption, fiber intake, consumption of unprocessed or minimal processed foods, number of medications, and number of chronic diseases. | %UPFenergy | Participants in the uppermost tertile of UPF consumption were at a higher risk for incident low HDL cholesterol (OR = 2.23; 95% CI: 1.22, 4.05; Ptrend = 0.012) and hypertriglyceridemia (OR = 2.66, 95% CI: 1.20, 5.90; Ptrend = 0.011) than participants in the lowest tertile. However, the consumption of UPF was not associated with the incident risk of high LDL cholesterol. |
Scaranni et al. (2022) [19] | Brazil (ELSA cohort) | 5275 participants 42.2% men 57.8% women Age: 50.6 ± 8.8 years | Dyslipidemia | 4 years | Semi-quantitative FFQ (114 items) | Sex, age, BMI, schooling, smoking, physical activity, alcohol consumption, total energy intake, diabetes and time since baseline, and Brazilian Healthy Eating Index—Revised (BHEI-R). | g/day | Individuals with medium and high consumption of UPF had higher risks of developing isolated hypertriacylglycerolemia (OR = 1.14, 95% CI: 1.03, 1.26 and OR = 1.30, 95% CI: 1.17, 1.45), isolated hypercholesterolemia (OR = 1.12, 95% CI: 1.00, 1.27 and OR = 1.28, 95% CI: 1.12, 1.47), mixed hyperlipidemia (OR = 1.21, 95% CI: 1.05, 1.39 and OR = 1.38, 95% CI: 1.18, 1.62), and low HDL (OR = 1.12, 95% CI: 1.00, 1.24 and OR = 1.18, 95% CI: 1.05, 1.32), respectively, than participants who consumed less UPF. |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Mambrini, S.P.; Menichetti, F.; Ravella, S.; Pellizzari, M.; De Amicis, R.; Foppiani, A.; Battezzati, A.; Bertoli, S.; Leone, A. Ultra-Processed Food Consumption and Incidence of Obesity and Cardiometabolic Risk Factors in Adults: A Systematic Review of Prospective Studies. Nutrients 2023, 15, 2583. https://doi.org/10.3390/nu15112583
Mambrini SP, Menichetti F, Ravella S, Pellizzari M, De Amicis R, Foppiani A, Battezzati A, Bertoli S, Leone A. Ultra-Processed Food Consumption and Incidence of Obesity and Cardiometabolic Risk Factors in Adults: A Systematic Review of Prospective Studies. Nutrients. 2023; 15(11):2583. https://doi.org/10.3390/nu15112583
Chicago/Turabian StyleMambrini, Sara Paola, Francesca Menichetti, Simone Ravella, Marta Pellizzari, Ramona De Amicis, Andrea Foppiani, Alberto Battezzati, Simona Bertoli, and Alessandro Leone. 2023. "Ultra-Processed Food Consumption and Incidence of Obesity and Cardiometabolic Risk Factors in Adults: A Systematic Review of Prospective Studies" Nutrients 15, no. 11: 2583. https://doi.org/10.3390/nu15112583