Contribution of ultra-processed foods in visceral fat deposition and other adiposity indicators: Prospective analysis nested in the PREDIMED-Plus trial

Konieczna, Jadwiga; Morey, Marga; Abete, Itziar; Bes–Rastrollo, Maira; Ruiz‐Canela, Miguel; Vioque, Jesús; González‐Palacios, Sandra; Daimiel, Lidia; Fiol, Miguel; Martín, Vicente; Estruch, Ramón; Vidal, Josep; Martínez‐González, Miguel Ángel; Canudas, Sı́lvia; Jover, A.; Fernández‐Villa, Tania; Casas, Rosa; Olbeyra, Romina; Buil‐Cosiales, Pilar; Babió, Nancy; Schröder, Helmut; Martínéz, J. Alfredo; Romaguera, Dora; investigators, PREDIMED-Plus

doi:10.1016/j.clnu.2021.01.019

Cited by 52 publications

(40 citation statements)

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“…So, despite that our mean % consumption of UPF is much lower, we found a significant direct association between UPF consumption and T2D. In addition, our results of consumption in percentage of weight are in agreement with results obtained in a subsample of the PREDIMED-PLUS study, Spanish men and women aged 55e75 years with overweight/obesity and metabolic syndrome (% of UPF consumption: 8.11, SD:7.41) [22]. Differences in percentages of UPF consumption between French and Spanish populations might be explained because of different methods to gather information on UPF consumption as well as different dietary habits.…”

Section: Discussionsupporting

confidence: 90%

Ultra-processed foods and type-2 diabetes risk in the SUN project: A prospective cohort study

et al. 2021

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Background & aim: The association between ultra-processed foods (UPF) consumption and the risk of type 2 diabetes (T2D) has not been much explored. We aimed to evaluate the association between consumption of UPF and the incidence of T2D. Methods: We assessed 20,060 participants (61.5% women) from the SUN project (Seguimiento Universidad de Navarra) followed-up every two years (median follow-up 12 years). Food and drink consumption were evaluated through a validated 136-item food frequency questionnaire and grouped according to their degree of processing by the NOVA classification. Participants were categorized into tertiles of UPF consumption adjusted for total energy intake. We fitted Cox proportional hazard models with repeated dietary measurements at baseline and updating information on food consumption after 10 years of follow-up to minimise the potential effect of diet variation. Results: During 215,149 person-years of follow-up, 175 new-onset T2D cases were confirmed. Participants in the highest baseline tertile (high consumption) of UPF consumption had a higher risk of T2D as compared to those in the lowest tertile (multivariable adjusted hazard ratio [HR] 1.53, 95% confidence interval [CI]: 1.06 to 2.22) with a significant doseeresponse relationship (p for linear trend ¼ 0.024). The multivariable adjusted HR using repeated measurements of UPF intake was 1.65 (95% CI 1.14e2.38) when comparing extreme tertiles. Conclusions: In a highly-educated Mediterranean cohort with a low absolute risk, a higher intake of UPF was independently associated with a higher risk for T2D. These results provide more evidence to encourage the limitation of UPF consumption to reduce the population burden of T2D.

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Section: Discussionsupporting

confidence: 90%

Ultra-processed foods and type-2 diabetes risk in the SUN project: A prospective cohort study

et al. 2021

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“…In two models significantly associated with T2DM, 7/7 dietary adjustments did not alter the significance [ 123 , 124 ]. Across nine studies, 23/26 models demonstrated a significant association between UPF intake and adult and child anthropometrics (weight/body mass index (BMI)/fat mass index (FMI) gain, other measures of adiposity and risk of overweight/obesity) [ 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 115 ]. 40/43 dietary adjustments did not alter the significance of these associations.…”

Section: Review Of Prospective Studies Adjusting For Dietary Qualitymentioning

confidence: 99%

“…Each 10% increment in UPF intake was associated with increases in total (β: 0.09 (0.06, 0.13)) and visceral (β: 0.09 (0.05, 0.13)) fat mass z-scores. Adjusting for overall repeated measures of saturated and trans fat, sodium, glycaemic index, alcohol and fibre intake across the 12 month study did not alter the significant association between UPF intake and increases in total (β: 0.06 (0.03, 0.09) and visceral (β: 0.06 (0.01, 0.10)) fat mass z-scores per 10% increase in daily UPF intake [ 109 ]. Adjusting instead for overall repeated measures of Mediterranean diet pattern adherence across the 12 month study also did not alter the association between each 10% increment in UPF intake and increases in total (β: 0.06 (0.02, 0.09)) and visceral (β: 0.06 (0.01, 0.10)) fat mass z-scores [ 109 ].…”

Section: Review Of Prospective Studies Adjusting For Dietary Qualitymentioning

confidence: 99%

The Role of Diet Quality in Mediating the Association between Ultra-Processed Food Intake, Obesity and Health-Related Outcomes: A Review of Prospective Cohort Studies

Dicken

Batterham

2021

Nutrients

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Prospective cohort studies show that higher intakes of ultra-processed food (UPF) increase the risk of obesity and obesity-related outcomes, including cardiovascular disease, cancer and type 2 diabetes. Whether ultra-processing itself is detrimental, or whether UPFs just have a lower nutritional quality, is debated. Higher UPF intakes are inversely associated with fruit, vegetables, legumes and seafood consumption. Therefore, the association between UPFs and poor health could simply be from excess nutrient intake or from a less healthful dietary pattern. If so, adjustment for dietary quality or pattern should explain or greatly reduce the size of the significant associations between UPFs and health-related outcomes. Here, we provide an overview of the literature and by using a novel approach, review the relative impact of adjusting for diet quality/patterns on the reported associations between UPF intake and health-related outcomes in prospective cohort studies. We find that the majority of the associations between UPFs, obesity and health-related outcomes remain significant and unchanged in magnitude after adjustment for diet quality or pattern. Our findings suggest that the adverse consequences of UPFs are independent of dietary quality or pattern, questioning the utility of reformulation to mitigate against the obesity pandemic and wider negative health outcomes of UPFs.

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“…Yet, many previously unclassified foods are often inside the phase space, indicating that they lack a dominating probability, hence the assignment of a single NOVA class is somewhat arbitrary (Figure 2B). The detection of this ambiguity is a strength of FoodProX, reflecting the observation that a four-class classification does not accurately capture the nutrient variability characterized by different food processing methods and intensities [32]. For example, while the classifier confidently assigns "Raw Onion" to NOVA 1 (p 1 = 0.9651), and "Onion rings prepared from frozen" (p 4 = 0.9921) to NOVA 4, it accurately offers an intermediate confidence for "Onion, Sauteed," placing it with probability p 4 = 0.6521 in NOVA 4, and with probability p 3 = 0.2488 in NOVA 3.…”

Section: Foodprox Algorithmmentioning

confidence: 99%

Machine Learning Prediction of Food Processing

Menichetti

Ravandi

Mozaffarian

et al. 2021

Preprint

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Despite the accumulating evidence that increased consumption of ultra-processed food has adverse health implications, it remains difficult to decide what constitutes processed food. Indeed, the current processing-based classification of food has limited coverage and does not differentiate between degrees of processing, hindering consumer choices and slowing research on the health implications of processed food. Here we introduce a machine learning algorithm that accurately predicts the degree of processing for any food, indicating that over 73% of the U.S. food supply is ultra-processed. We show that the increased reliance of an individual's diet on ultra-processed food correlates with higher risk of metabolic syndrome, diabetes, angina, elevated blood pressure and biological age, and reduces the bio-availability of vitamins. Finally, we find that replacing foods with less processed alternatives can significantly reduce the health implications of ultra-processed food, suggesting that access to information on the degree of processing, currently unavailable to consumers, could improve population health.

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Contribution of ultra-processed foods in visceral fat deposition and other adiposity indicators: Prospective analysis nested in the PREDIMED-Plus trial

Cited by 52 publications

References 50 publications

Ultra-processed foods and type-2 diabetes risk in the SUN project: A prospective cohort study

Ultra-processed foods and type-2 diabetes risk in the SUN project: A prospective cohort study

The Role of Diet Quality in Mediating the Association between Ultra-Processed Food Intake, Obesity and Health-Related Outcomes: A Review of Prospective Cohort Studies

Machine Learning Prediction of Food Processing

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