**Association between Late-Eating Pattern and Higher Consumption of Ultra-Processed Food among Italian Adults: Findings from the INHES Study**

**Marialaura Bonaccio 1,\*, Emilia Ruggiero 1, Augusto Di Castelnuovo 2, Claudia Francisca Martínez 1, Simona Esposito 1, Simona Costanzo 1, Chiara Cerletti 1, Maria Benedetta Donati 1, Giovanni de Gaetano <sup>1</sup> and Licia Iacoviello 1,3,† on behalf of the INHES Study Investigators**


**Abstract:** Late eating is reportedly associated with adverse metabolic health, possibly through poor diet quality. We tested the hypothesis that meal timing could also be linked to food processing, an independent predictor of health outcomes. We analysed data on 8688 Italians (aged > 19years) from the Italian Nutrition & HEalth Survey (INHES) established in 2010–2013 throughout Italy. Dietary data were collected through a single 24 h dietary recall, and the NOVA classification was used to categorize foods according to increasing levels of processing: (1) minimally processed foods (e.g., fruits); (2) culinary ingredients (e.g., butter); (3) processed foods (e.g., canned fish); (4) ultra-processed foods (UPFs; e.g., carbonated drinks, processed meat). We then calculated the proportion (%) of each NOVA group on the total weight of food eaten (g/d) by creating a weight ratio. Subjects were classified as early or late eaters based on the population's median timing for breakfast, lunch and dinner. In multivariable-adjusted regression models, late eaters reported a lower intake of minimally processed food (β = −1.23; 95% CI −1.75 to −0.71), a higher intake of UPF (β = 0.93; 0.60 to 1.25) and reduced adherence to a Mediterranean Diet (β = −0.07; −0.12 to −0.03) as compared to early eaters. Future studies are warranted to examine whether increased UPF consumption may underpin the associations of late eating with adverse metabolic health reported in prior cohorts.

**Keywords:** meal timing; late eating; food processing; ultra-processed food; NOVA classification

#### **1. Introduction**

Obesity and associated cardiometabolic diseases continue to rise worldwide despite extensive public health efforts to reverse this trend [1]. Unhealthy diets, i.e., diets not meeting nutritional requirements, are major risk factors for obesity and associated diseases [2,3], and therefore, common strategies to tackle obesity and diet-related diseases have been almost exclusively focused on food composition, leading to recommendations to reduce sugar, salt and fat while emphasizing high intakes of foods that are natural sources of fibre, vitamins and minerals [4].

Among the factors that possibly contribute to the rise in obesity and cardiometabolic diseases, growing attention has been paid to the timing of food intake (i.e., the time when meals are usually consumed), which has been associated with various indicators of adiposity, possibly, but not entirely, through higher energy intake [5–12].

Population studies suggest that late eating, which refers to a delay in the timing of meals (usually the main meal of the day or the last meal, i.e., dinner) [12] may be a factor implicated in obesity and other non-communicable diseases related to nutrition [13–15].

**Citation:** Bonaccio, M.; Ruggiero, E.; Di Castelnuovo, A.; Martínez, C.F.; Esposito, S.; Costanzo, S.; Cerletti, C.; Donati, M.B.; de Gaetano, G.; Iacoviello, L. Association between Late-Eating Pattern and Higher Consumption of Ultra-Processed Food among Italian Adults: Findings from the INHES Study. *Nutrients* **2023**, *15*, 1497. https://doi.org/ 10.3390/nu15061497

Academic Editor: Megan A. McCrory

Received: 2 February 2023 Revised: 1 March 2023 Accepted: 16 March 2023 Published: 20 March 2023

**Copyright:** © 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/).

Potential mechanistic links through which meal timing may promote obesity and associated diseases include, among others, the lower diet quality and higher calorie intake observed in late eaters [16–18]. However, no prior studies to date have evaluated the possible association of meal timing with the intake of foods with different degrees of processing. Actually, it has been suggested that obesity prevalence continues to increase concomitantly with the increased consumption of ultra-processed foods (UPFs) [19]. According to the NOVA classification, UPFs are industrial formulations of ingredients, containing little or no whole food and typically including flavouring and colouring agents, emulsifiers and other cosmetic additives [20]. Consistently, population-based cohorts support a direct association of a large dietary share of UPFs with obesity [21,22] and cardiometabolic diseases [23], as well as with the incidence of major chronic diseases, regardless of the overall diet quality [24].

To fill this knowledge gap, we tested the hypothesis that the meal timing pattern is differentially associated with the intake of foods that have different food processing levels according to the NOVA classification. This study was conducted using a large dataset of adults recruited throughout Italy in 2010–2013.

#### **2. Materials and Methods**

#### *2.1. Study Population*

The data are from the Italian Nutrition & HEalth Survey (INHES), which was a 3-year telephone-based survey on nutrition and health designed to collect information on dietary habits (i.e., quality, quantity, food and meal patterns), food choice determinants, and food health awareness of the Italian population according to geographical distribution, age, gender and socioeconomic status. A total of 9422 men and women aged ≥4 years throughout Italy were enrolled between November 2010 and November 2013. Details about this cohort have been previously described [25].

To capture an adequate proportion of weekdays and weekends, a survey calendar was organized at a group level accordingly in order to distribute the sample subjects across four seasons (excluding Christmas, Easter and mid-August periods).

During the recruitment phase, the computer-assisted telephone interview method was used to collect dietary data (dietary habits and behaviour), the health status of the subjects, risk factors, anthropometric measurements (for example, height and weight) and health perception. Given the study objective, participants were excluded for the following reasons: subjects below 20 years of age (n = 571), missing data on diet (n = 2), extreme energy intakes reported (<800 kcal/d in men and <500 kcal/d in women or >4000 kcal/d in men and >3500 kcal/d in women; n = 159) and missing data on meal timing (n = 2). Therefore, a total of 8688 subjects were analysed.

#### *2.2. Assessment of Dietary Data*

A self-recorded diary, using computer-based 1-day 24-h dietary recall interview (24-HDR) software, and an Italian version of the European Food Propensity Questionnaire were used to record dietary data [26,27].

Subjects were instructed to recall and record the following data for each meal consumed: (a) time and place of food intake; (b) detailed description of foods (or beverages) and (c) the quantity of intake and the food brand chosen (for manufactured foods). Further, a picture booklet was used as a reference by the subjects to report portion sizes. Lastly, participants answered whether they were currently on any diet and whether their consumption differed from their habitual diet.

Individual food items and recipes reported by the participants were later matched with those available in the food list of the data management system INRAN-DIARIO 3.1 [26,28] by a nutritionist during the interviews.

Finally, a total of 2000 single food items extracted from the final output database were included in the software food list.

The NOVA classification [29] was used to categorize each food item into one of the following categories according to the extent and purpose of food processing: (1) fresh or minimally processed foods (e.g., fruit, meat, milk); (2) processed culinary ingredients (e.g., oils, butter, sugar); (3) processed food items (e.g., canned fish, unpackaged freshly made breads); or (4) UPFs containing predominantly industrial substances and little or no whole foods (e.g., carbonated drinks, processed meat, sweet or savoury packaged snacks). Consumption (in g/d) in each of the four NOVA groups and the percentage they represented with respect to the total amount of food eaten were determined in order to obtain a weight ratio. We used this approach instead of the energy ratio because total food amounts better account for non-nutritional factors related to food processing (e.g., neo-formed contaminants, additives and alterations to the structure of raw foods) [30]. The full list of individual foods and food groups categorized according to the NOVA classification is available in Table 1. For analyses on individual meal types, we calculated the consumption in each NOVA group separately for breakfast, lunch and dinner. Adherence to the Mediterranean Diet was evaluated by the Mediterranean Diet Score (MDS) as proposed by Trichopoulou et al. [31]. Briefly, we assigned 1 point to healthy foods (i.e., fruits and nuts, vegetables, legumes, fish, cereals, monounsaturated-to-saturated fat ratio) whose consumption was above the sex-specific medians of intake in the adult population of the whole INHES cohort; foods presumed to be detrimental (i.e., meat and dairy products) were given a positive score if their consumption was below the median. All other intakes received 0 points. For alcohol intake (ethanol), participants who consumed alcohol (men: 10–50 g/d; women: 5–25 g/d) scored 1 point; otherwise, the score was 0. The Mediterranean Diet Score potentially ranges from 0 to 9 (the latter reflecting maximum adherence).


**Table 1.** Classification of individual food items and food groups by degree of food processing according to NOVA in the INHES study, Italy, 2010–2013.

To evaluate overall diet quality, we also calculated the Food Standards Agency Nutrient Profiling System (FSAm-NPS) dietary index, which is used to compute the Nutri-Score front-of-pack labelling system that ranks food items according to their nutritional value [32].

The FSAm-NPS score was calculated as previously implemented in other population cohorts [24,33] as follows: for all foods and beverages consumed, based on composition for each 100 g of content, 0 to 40 points were allocated for nutrients that should be consumed in limited amounts (A points), i.e., total sugars (g), saturated fats (g), sodium (mg) and energy (kJ), and 0 to 15 points were given for nutrients or components that should be promoted, i.e., dietary fibre (g) and protein (g), and for fruit, vegetables, legumes and nuts (%) (C points). The total score of the product was calculated by subtracting the sum of C points from the sum of A points. Thus, the final FSAm-NPS score for each food/beverage was based on a scale that could theoretically range from −15 (healthiest food) to +40 (least healthy food). Based on this overall FSAm-NPS score, the Nutri-Score labelling system categorizes food products into five colours, associated with letters A (dark green) to E (dark orange), reflecting their nutritional quality [32]. The FSAm-NPS dietary index (DI) was computed at the individual level as an energy-weighted mean of the FSAm-NPS scores of all foods and beverages consumed by each participant using the following equation:

$$\text{FSA} - \text{NPS DI} = \frac{\sum\_{i=1}^{n} \text{FS}\_{i}\text{E}\_{i}}{\sum\_{i=1}^{n} \text{E}\_{i}}$$

FSi represents the score of food/beverage 'i', Ei is the energy intake from food/beverage 'i' specific to each participant, and 'n' is the total number of foods/beverages consumed. An increase in the FSAm-NPS dietary index values therefore reflects a decrease in the overall diet quality value.

#### *2.3. Assessment of Meal Timing*

The timing of main meals (i.e., breakfast, lunch and dinner) was obtained by using information provided by participants during the 24 h dietary recall, where they were asked to indicate the time of each eating occasion. For each main meal, we calculated the study population sample's median time and assigned 1 point to those participants reporting having (a) breakfast after 7 am (study sample median time); (b) lunch after 1 p.m. (study sample median time); and (c) dinner after 8 p.m. (study sample median time). Individuals consuming meals before the median time were given 0 points. Participants scoring ≥2 points were considered to have a late meal timing pattern; otherwise, people were classified as having an early meal timing pattern. For simplification, we called them late eaters and early eaters, respectively.

#### *2.4. Ascertainment of Covariates*

Education was based on the highest qualification attained and was categorized as up to elementary school (corresponding to ≤5 years of study), lower secondary (>5–≤8 years), upper secondary (>8–≤13 years) and postsecondary (>13 years). Present occupations were categorized into six groups: manual, non-manual, housewife, retired, student and unemployed. Marital status was defined as married/living in a couple, single, separated/divorced and widowed. The definition of urban or rural environments was based on the urbanization level described by the European Institute of Statistics (EUROSTAT definition)—obtained by the tool 'Atlante Statistico dei Comuni' provided by the Italian National Institute of Statistics [34]. Subjects were classified as never (one who has never smoked, or who has smoked less than 100 cigarettes in the lifetime), current (smoking one or more cigarettes per day at the time of the interview), former (one who had quit smoking at the time of interview) or occasional smokers (smoking less than 1 cigarette per day at the time of interview). History of cardiovascular disease and cancer and a previous diagnosis of diabetes, hyperlipidaemia or hypertension were self-reported and categorized as yes/no. Body mass index (BMI) was calculated by using self-reported measurements of height and weight, calculated as kg/m<sup>2</sup> and grouped into three categories: normal (≤25 kg/m2),

overweight (>25–<30 kg/m2) or obese (≥30 kg/m2). Self-reported sport activity was used as a categorical variable (yes/no).

#### *2.5. Statistical Analysis*

The general characteristics of the analytic sample according to early and late-eating patterns are presented as numbers and percentages for categorical variables and means with standard deviations (SDs) for continuous traits. Differences in the distribution of baseline covariates were calculated using generalized linear models adjusted for age, sex and energy intake (GENMOD procedure for categorical variables and GLM procedure for continuous variables in SAS software).

Beta coefficients with 95% confidence intervals (95% CI) from multivariable-adjusted linear regression analyses were used to evaluate the association between the meal timing pattern (independent variable) and each category of NOVA (continuous dependent variable) or dietary index (i.e., the Mediterranean Diet Score and the FSAm-NPS dietary index; continuous dependent variables). Each dietary variable was standardized to one standard deviation to allow comparison. An *a priori* approach was used to select potential covariates instead of statistical criteria [35]. Two models were ultimately fitted: model 1 was adjusted for age, sex and energy intake, and multivariable model 2 was model 1 but further adjusted for education, geographical area, place of residence, sport activity, occupation, marital status, smoking, BMI, cardiovascular disease, cancer, hypertension, diabetes and hyperlipidaemia. To maximize data availability, missing data on covariates were handled using multiple imputation (SAS PROC MI, followed by PROC MIANALYZE; n = 10 imputed datasets).

We conducted subgroup analyses to test the robustness of the findings by analysing the potential effect modification of the association of the meal timing pattern with each dietary score by various risk factors, such as age (19–50 years; 51–65 years and 66–97 years) and sex. We used SAS/STAT software, version 9.4 (SAS Institute Inc., Cary, NC, USA), for the analysis.

#### **3. Results**

The analytic sample consists of 4053 men (46.7%) and 4635 women (53.3%) with a mean age of 56.9 years (±14.6). The average (SD) weight contributions of unprocessed/minimally processed foods, culinary ingredients, processed foods and UPFs to the diet were 73.7% (±12.0), 2.6% (±1.2), 15.9% (±10.7) and 7.8% (±7.0), respectively. More than half (58.1%) of the total calories came from unprocessed/minimally processed foods and culinary ingredients, while 24.6% came from processed food, and 17.3% were from UPFs.

The characteristics of the study participants according to the meal timing pattern are presented in Table 2. As compared to early eaters, late eaters were younger, were more likely to live in Southern Italy and urban environments, had a higher educational level and were prevalently non-manual workers. Additionally, late eaters were less likely to report chronic diseases (e.g., CVD) or other health conditions (e.g., hypertension and hyperlipidaemia). No relevant differences in BMI, diabetes or history of cancer were found. Differences in dietary factors were also observed between meal timing patterns. Specifically, late eaters tended to consume less energy from carbohydrates while reporting higher energy from fats (Table 3).

In multivariable-adjusted regression analyses, we found that late eaters were less likely to consume unprocessed/minimally processed foods as compared to early eaters (β = −0.10; 95% CI −0.14 to −0.06) while reporting the increased consumption of UPFs (β = 0.13; 95% CI 0.09 to 0.18) and processed culinary ingredients (β = 0.05; 95% CI 0.01 to 0.10); eating late was also found to be inversely associated with adherence to the Mediterranean Diet (β = −0.07; 95% CI −0.12 to −0.03) and directly associated with the FSAm-NPS dietary index (β = 0.10; 95% CI 0.05 to 0.14) (Table 4; Model 2). The direction and strengths of these associations were substantially confirmed in all age groups and in men and women, especially for UPF consumption and diet quality indices (Supplementary Tables S1 and S2); however, the relationships of late eating with unprocessed/minimally processed food or processed food intake were stronger in the young group than in the elderly (Supplementary Table S1). Additionally, an effect modification by sex was observed in relation to the consumption of unprocessed/minimally processed foods and culinary ingredients (Supplementary Table S2).


**Table 2.** Characteristics of 8688 participants (20–97 years) in the INHES study, Italy, 2010–2013.


**Table 2.** *Cont.*

Values are reported as numbers and percentages unless otherwise stated. Means were adjusted for age, sex and energy intake. *p*-values were obtained using generalized linear models for both continuous and categorical dependent variables adjusted for age, sex and energy intake.

**Table 3.** Dietary factors associated with meal timing pattern in 8688 participants (20–97 years) from the INHES study, Italy, 2010–2013.


TEI = total energy intake. MUFA = monounsaturated fats. PUFA = polyunsaturated fats. Means and *p*-values obtained from general linear regression models adjusted for sex, age and energy intake.

Analyses separated by meal type showed that late breakfast eating was associated with the reduced consumption of unprocessed/minimally processed foods and processed foods and a higher intake of UPFs at breakfast, as well as with lower adherence to the Mediterranean Diet and a higher FSAm-NPS dietary index. Similarly, participants who had delayed dinners were more likely to eat processed foods or UPFs and tended to reduce the intake of unprocessed/minimally processed foods, and also reported less adherence to a Mediterranean Diet and a larger dietary share of foods with poor nutritional quality. Finally, late lunch eaters reported a higher intake of processed culinary ingredients (Figure 1).


**Table 4.** Association of food processing according to NOVA classification with meal timing pattern in 8688 participants (20–97 years) from the INHES study, Italy 2010–2013.

Model 1: Multivariable-adjusted linear regression including age, sex and energy intake. Model 2: Multivariableadjusted linear regression including age, sex, energy intake, place of residence, educational level, occupation, marital status, smoking status, sport activity, body mass index, history of cardiovascular disease, history of cancer, diabetes, hyperlipidaemia and hypertension. FSAm-NPS = Food Standards Agency Nutrient Profiling System. Each dietary variable was standardized to allow comparison.


**Figure 1.** Timing of food intake for individual meals (late vs. early eaters) associated with food processing according to NOVA classification, adherence to the Mediterranean Diet and the Food Standards Agency Nutrient Profiling System (FSAm-NPS) dietary index in 8688 participants (20–97 years) from the INHES study, Italy, 2010–2013. Regression coefficients β with 95% CIs from a multivariableadjusted linear regression including age, sex, energy intake, place of residence, educational level, occupation, marital status, smoking status, sport activity, body mass index, history of cardiovascular disease, history of cancer, diabetes, hyperlipidaemia and hypertension. Each dietary variable was standardized to allow comparison.

#### **4. Discussion**

In this large cohort of 8688 adults from the general Italian population, a late-eating pattern was associated with both a higher consumption of UPFs and a lower intake of unprocessed/minimally processed foods, as well as with poorer diet quality. Evidence from population studies has consistently suggested that the timing of meal intake is a reliable predictor of cardiometabolic health outcomes, with late eating being reportedly associated with obesity and glucose intolerance in observational studies [10,36]. The key role of timed meals has been also supported by animal [37] and intervention studies in humans showing that late eating may adversely impact the success of weight-loss therapy [38].

Mechanistic hypotheses to support the association of late eating with adverse cardiometabolic health are likely multifactorial and include the fact that late eating may contribute to circadian misalignment, i.e., a lack of synchrony of light/dark cycles and behavioural rhythms with the endogenous circadian system [38–40], which was found to adversely impact both energy balance and glycaemic control [41] and changes in the diversity of the microbiota [42].

A number of studies indicate that late eaters tend to have a lower overall diet quality and higher energy intake [16,17,43,44], which may in part explain the adverse cardiometabolic health associated with delaying meals to later in the day; this was also confirmed by our analyses showing that late eating was associated with reduced adherence to a traditional Mediterranean Diet and higher values of the FSAm-NPS dietary index, which is used to compute the Nutri-Score front-of-pack labels and reflects the consumption of less-nutrient-dense foods. However, others reported that energy intake and overall diet quality were not found to vary significantly across eating times [39].

As all prior studies were focused on the nutritional composition of diets, regardless of food processing levels, we used a complementary approach by examining whether meal timing is differentially associated with the food intakes with different levels of processing according to the NOVA classification.

UPF intake is on arise worldwide and constitutes more than half of the total calories eaten in the US, UK and Canada [45–47] while being less consumed in Mediterranean countries, such as Italy [48] and Spain [49]. An increasing number of large-scale population studies indicate that elevated intakes of UPFs can be a major threat to human health, being directly associated with an increased risk of cardiovascular disease, cancer and diabetes, as well as reduced survival [23,24]. A systematic review summarizing the evidence for the association between food processing and cardiometabolic factors in adults found that a large dietary share of UPFs is positively associated with worse cardiometabolic health, as reflected by increased levels of overweight and obesity, metabolic syndrome and high blood pressure [50]. Additionally, a high proportion of UPFs in the diet was linked to altered levels of inflammation [51], which was found to be increased in association with mistimed meals in both animals [52] and humans [53].

Both the direct association of the meal timing pattern with UPFs and its inverse relationship with unprocessed/minimally processed foods observed in our study suggest that the degree of food processing could be among the potential mechanisms/factors that link mistimed meals to impaired cardiometabolic outcomes. Besides being nutrient-poor (e.g., rich in fat, sodium and salt, and low in fibre and nutrients), UPFs are a major dietary source of chemicals (e.g., endocrine-disrupting chemicals such as bisphenol and phthalates commonly used in food packaging) and neo-formed compounds (e.g., acrylamide), which may have severe implications for health, as suggested by robust research, ranging from laboratory-based to prospective epidemiological studies [54].

Most importantly, food processing impacts both the nutritional composition (e.g., decreased antioxidant potential of some foods resulting from removing germ and bran) and food matrix (i.e., the 'architecture' of the food, which derives from nutrient interactions), which is crucial to the food's overall health potential, specifically in satiety and glycaemic responses, as well as in determining nutrient bioavailability [55].

While complex, natural, minimally or unprocessed foods have more or less intact structures, and their nutritional properties are substantially unaltered [55], highly processed foods are typically unstructured, fractionated and usually heavily supplemented with free glucose and sucrose, which renders glucose more available for absorption, thereby increasing blood glycaemic response [56]. Diets with a large share of foods with a high glycaemic index are well-established risk factors for cardiometabolic diseases and mortality [57].

Interestingly, in our study late eating was associated with an approximately absolute 1% higher proportion of UPF intake relative to the total food eaten; prior cohort studies showed that even such a small increment possibly leads to a higher risk of mortality both in general populations [24] and among people with pre-existing cardiovascular disease [58]. Despite consuming more UPFs, late eaters also tended to report lower diet quality overall, and in this regard, it is worth noting that most highly processed foods are typically less nutrient-dense [59]. In addition, diets high in UPFs were found to have a higher impact on mortality than the overall diet quality [24].

Lastly, a late meal pattern in our study was associated with younger age, a higher educational level and being single; all these characteristics were reportedly associated with a higher consumption of UPFs in previous cohort studies [48,60], while unmarried individuals were also found to have lower diet quality overall [61,62]. However, our estimates were from multivariable-adjusted models that also account for these socioeconomic and demographic factors, and other drivers for UPF consumption need consideration (e.g., heavy marketing, availability, low cost, attractiveness, high palatability and domination of food supply chains) [20].

#### *Strengths and Limitations*

To the best of our knowledge, this is the first study that analysed meal timing in association with food processing and also with the dietary index underpinning the Nutri-Score front-of-pack label. The major strengths of this study include a large sample size representative of the Italian population, with a complete assessment of diet, lifestyle and other covariates used to minimize, at least in part, confounding. Moreover, the use of 24 h recall is more advantageous than other tools (e.g., food frequency questionnaires) to assess participants' diets and to classify foods based on the extent of processing according to NOVA [63]. Despite its strengths, among its limitations, we acknowledge the observational nature of our study and the cross-sectional design of the analyses, which limits causal inference. Further, errors in the visual display of foods and potential bias could have been introduced by the interviewer in the telephone-based survey. Additionally, the decline in the use of landline phones may have resulted in an under-representation of respondents. Another weakness is that the study relied on self-reported dietary data, which are susceptible to bias and error, including social desirability and recall bias, imprecision in assessing portion sizes and inadequacies in food composition tables; however, data were collected by trained interviewers, and each participant received by mail, beforehand, a short photograph atlas and guidance notes to estimate food portion sizes. It was not possible to include some unmeasured factors as confounders due to their unavailability; however, it is a weakness in any observational study. Limitations also include that we dichotomized our population into early and late eaters using the population median timing, as a consensus on the most suitable approach to quantifying food timing is still lacking [39]. We also acknowledge that the NOVA classification remains controversial, mainly due to its equivocal definition of ultra-processed food and multiple revisions and refinements over time [64]; however, its utility value in nutrition epidemiology research has been widely acknowledged allowing comparison with previous studies. Finally, the generalizability of our findings might be limited to the Italian population.

#### **5. Conclusions**

As well as reporting poor diet quality overall, late eaters are prone to consume more UPFs and fewer minimally processed food than early eaters. These findings contribute to increased knowledge on the mechanisms underpinning the association between late eating and adverse cardiometabolic health previously reported in several experimental and observational studies [12,13,39]. Anticipating the timing of meals may provide a complementary strategy for reducing UPF consumption and increasing unprocessed or minimally processed food intakes, which typically require more time and effort than ready-to-eat/heat meals. Undeniably, mistimed meals are strongly influenced by several factors, especially socioeconomic conditions that are difficult to tackle. Further research is warranted to test whether the consumption of UPFs could be a mediator of the association between mistimed meals and adverse cardiometabolic health.

**Supplementary Materials:** The following supporting information can be downloaded at https:// www.mdpi.com/article/10.3390/nu15061497/s1. Table S1: Association of food processing according to NOVA classification with meal timing pattern across age groups from the INHES Study, Italy 2010–2013; Table S2: Association of food processing according to NOVA classification with meal timing pattern in men and women from the INHES Study, Italy 2010–2013.

**Author Contributions:** M.B., E.R. and A.D.C. conceived the present study, contributed to its design and to the interpretation of data; C.F.M. contributed to data interpretation and critically revised the manuscript; S.C. and A.D.C. managed data collection; E.R. and S.E. analysed the data; M.B. and C.F.M. wrote the manuscript; M.B.D., C.C., G.d.G. and L.I. originally inspired the INHES study and critically reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** The enrolment phase of the INHES study was supported by a research grant from Barilla SpA. The present analyses were partially supported by the Italian Ministry of Health—Ricerca Corrente 2022–2024. The present analyses were funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3—Call for tender No. 341 of 15/03/2022 of the Italian Ministry of University and Research funded by the European Union—NextGenerationEU. Project code PE0000003, Concession Decree No. 1550 of 11/10/2022 adopted by the Italian Ministry of University and Research, CUP D93C22000890001, Project title "Research and innovation network on food and nutrition Sustainability, Safety and Security–Working ON Foods" (ONFoods). The funders had no role in the study design or the collection, analysis or interpretation of data; in the writing of the report; or in the decision to submit this article for publication.

**Institutional Review Board Statement:** This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human subjects were approved by the Ethical Committee of the Catholic University of Rome.

**Informed Consent Statement:** Verbal informed consent was obtained from all subjects. Verbal consent was witnessed and formally recorded.

**Data Availability Statement:** The data underlying this article will be shared on reasonable request to the corresponding author. The data are stored in an institutional repository (https://repository. neuromed.it) and access is restricted by ethical approval and the legislation of the European Union.

**Acknowledgments:** We thank the Research Group of the Cardiovascular Epidemiology Observatory/Health Examination Survey 2008 for making available the list of subjects recruited in their survey. ER was supported by Fondazione Umberto Veronesi, who is gratefully acknowledged. CFM was financially supported by the Joint Platform Laboratory of Umberto Veronesi Foundation—Department of Epidemiology and Prevention at IRCCS Neuromed in Pozzilli, Italy.

**Conflicts of Interest:** All authors of the present manuscript declare that they have no conflicts of interest to disclose.

#### **Appendix A**

**INHES Study Investigators** Principal Investigator: Licia Iacoviello Study coordinator: Americo Bonanni

**Scientific Committee:** Marialaura Bonaccio, Francesca Bracone, Chiara Cerletti, Simona Costanzo, Augusto Di Castelnuovo, Mariarosaria Persichillo, Maria Benedetta Donati, Giovanni de Gaetano.

**Dietary questionnaire validation**: Mariarosaria Persichillo and Francesco Zito. **Questionnaire administration:** Lucia Aurisano, Paola Barisciano, Valentina Bonaccio, Francesca De Lucia, Giovanna Galuppo, Filippo Petrucci, Anna Sciarretta, Angelita Verna. **Data management:** Simona Costanzo, Augusto Di Castelnuovo, Marco Olivieri.

#### **References**


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### *Review* **Low-Grade Inflammation and Ultra-Processed Foods Consumption: A Review**

**Marta Tristan Asensi 1, Antonia Napoletano 1, Francesco Sofi 1,2 and Monica Dinu 1,\***

<sup>1</sup> Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy

<sup>2</sup> Unit of Clinical Nutrition, Careggi University Hospital, 50134 Florence, Italy

**\*** Correspondence: monica.dinu@unifi.it; Tel.: +39-3791439612

**Abstract:** Low-grade inflammation alters the homeostasis of the organism and favors the onset of many chronic diseases. The global growth in the prevalence of noncommunicable diseases in recent years has been accompanied by an increase in the consumption of ultra-processed foods (UPF). Known to be hyperpalatable, economic and ready-to-eat, increased consumption of UPF has already been recognized as a risk factor for several chronic diseases. Different research groups have tried to investigate whether UPF consumption could promote low-grade inflammation and thus favor the development of noncommunicable diseases. Current evidence highlights the adverse health effects of UPF characteristics, not only due to the nutrients provided by a diet rich in UPF, but also due to the non-nutritive components present in UPF and the effect they may have on gut health. This review aims to summarize the available evidence on the possible relationship between excessive UPF consumption and modulation of low-grade inflammation, as potential promoters of chronic disease.

**Keywords:** ultra-processed foods; NOVA classification; low-grade inflammation; chronic diseases

#### **1. Introduction**

Inflammation is an immunosurveillance response essential for host defense, which serves to repair damaged tissues and eliminate toxic agents [1]. However, when this response becomes chronic, it results in the presence of immune system cells for an increasing period of time. This state of low-grade inflammation can lead to dysmetabolic conditions that disrupt homeostasis, favoring the development of a wide range of noncommunicable diseases such as cancer, diabetes and cardiovascular diseases [2].

Current evidence highlights diet among the modifiable behavioral risk factors for the development of noncommunicable diseases [3]. In recent years, particular attention has been paid to the increased consumption of ultra-processed foods (UPF) worldwide [4]. Characterized by being hyperpalatable, affordable and ready-to-eat, UPF have led to a worsening of the diet quality due to their nutritional composition [5] and have already been recognized as a risk factor for diet-related diseases [6].

Recent scientific research has sought to investigate whether UPF consumption could promote low-grade inflammation and thus favor the development of noncommunicable diseases. Emerging evidence attributes the negative effects of UPF consumption not only to the nutrients provided by a diet rich in UPF, but also to the non-nutritive components and the effect they may have on the gut microbiota. This review aims to summarize the available evidence on the possible relationship between excessive UPF consumption and modulation of low-grade inflammation as potential promoters of chronic diseases.

#### **2. Low-Grade Inflammation**

The inflammatory response is a defense mechanism of the innate immune system [7] that protects the host from harmful stimuli such as viruses, bacteria, toxins and infections by eliminating pathogens and promoting the repair of damaged tissues [1]. At the onset

**Citation:** Tristan Asensi, M.; Napoletano, A.; Sofi, F.; Dinu, M. Low-Grade Inflammation and Ultra-Processed Foods Consumption: A Review. *Nutrients* **2023**, *15*, 1546. https://doi.org/10.3390/ nu15061546

Academic Editor: Samuel Fernández-Tomé

Received: 28 February 2023 Revised: 14 March 2023 Accepted: 21 March 2023 Published: 22 March 2023

**Copyright:** © 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/).

of inflammation, the innate immune cells perceive pathogen invasion or cell damage and initiate the inflammatory cascade by actively releasing soluble proinflammatory mediators. These signals also activate leukocytes and microvascular changes, such as increased vasodilation and vascular permeability, allowing leukocytes to reach the affected tissues from the blood [8]. Such inflammatory activity should resolve once the threat is overcome, becoming temporarily restricted and self-limiting to maintain homeostasis [9,10]. However, failure of immune resolution or continued exposure to environmental and biological factors that promote the activation of the inflammatory response can lead to a chronic inflammatory process. This results in the presence of immune cells such as lymphocytes, macrophages and plasma cells in the tissue for long periods of time, as well as of proinflammatory cytokines, chemokines and other proinflammatory molecules [11,12]. Although this condition recognized as low-grade inflammation has minimal or no clinical manifestations, the prolonged inflammatory response can cause consequences for tissue health, which can develop into tissue fibrosis and possible loss of function [13].

The presence of low-grade inflammation disrupts the homeostatic balance, altering the crosstalk between immune and metabolic responses and promoting chronic metabolic inflammation. This so-called "metainflammation" is primarily caused by metabolic and nutrient excess and triggers immune cell infiltration and the secretion of inflammatory cytokines into the tissue environment, which may inhibit glucose uptake or alter lipid metabolism [2,14]. As a result, chronic metabolic inflammation is particularly associated with an increased risk of noncommunicable diseases, such as cancer, diabetes and cardiovascular disease. An example is insulin resistance caused by chronic exposure to inflammatory biomarkers, which often lead to diabetes [15]. Low-grade inflammation plays an important role also in the development of cardiovascular diseases, due to its involvement in atheroprogression [16], and may favor the progression of different types of cancer by promoting cell proliferation, decreasing apoptosis and increasing angiogenesis and metastasis [17]. At present, it is not well-established which biomarkers can best represent low-grade inflammation, although among the most widely used in scientific studies are soluble mediators (chemokines and cytokines), acute-phase proteins (fibrinogen and C-Reactive Protein (CRP)) or blood cellular markers (granulocytes and total white blood cells) [18].

#### *Diet as a Risk Factor for Low-Grade Inflammation*

Among the environmental and lifestyle factors that can promote or intensify inflammation, increasing scientific evidence supports the role of diet. Potential nutritional compounds influencing inflammation processes include macro- and micronutrients, bioactive molecules such as polyphenols and specific food components [19]. Overall, plant-based dietary patterns with a high consumption of vegetables, fruits and whole grains, a moderate consumption of legumes and fish and a low consumption of red meat have been associated with a greater anti-inflammatory potential (Figure 1). These include several traditional healthy diets, such as the Mediterranean or the Nordic diet, which are usually based on minimally processed or unprocessed foods [20,21]. A meta-analysis that evaluated a total of 2300 subjects from 17 clinical trials showed that greater adherence to the Mediterranean diet was associated with lower levels of inflammatory biomarkers, particularly CRP and interleukin-6 (IL-6) [22]. These findings were confirmed in a recent meta-analysis assessing the effect of multiple dietary patterns on inflammatory biomarkers [23]. The authors concluded that the Mediterranean diet appeared as the dietary pattern with the most significant reductions in inflammatory biomarkers, including IL-6 and CRP [23]. Similar results were observed for the Nordic diet, with a review of intervention and observational studies revealing its beneficial influence on low-grade inflammation amelioration [24].

**Figure 1.** Dietary patterns and inflammation.

A growing number of studies show that the protective effects of these dietary patterns against inflammation are related to the dietary pattern as a whole, not just to its individual components [19]. All these dietary models share the presence of whole grains, fiber, vegetables, fruits, fish, polyunsaturated fatty acids (PUFAs), particularly marine *n*-3 PUFAs, vitamin C, vitamin E and carotenoids. In contrast, dietary factors that promote inflammation are oxidized lipids, saturated fatty acids (SFAs) and trans fatty acids, which are present at high levels in Western dietary patterns. Unfortunately, in recent years, the increased availability and variety of foods has led to a change in traditional dietary patterns, favoring a nutritional transition and a globalization of the diet towards a Western dietary pattern [25]. This dietary pattern, characterized by a high caloric intake and a high consumption of sweets, refined cereals, red and processed meats, snacks and sugary drinks, has been associated with an increased pro-inflammatory potential and higher levels of CRP and IL-6 [26].

To further investigate the role of diet in modulating inflammation, several literaturebased indices have been developed. The energy-adjusted dietary inflammatory index (E-DII) analyzes the potential effect of 45 dietary elements on 6 inflammatory markers, both pro-inflammatory (IL-1b, IL-6, tumor necrosis factor (TNF)-α and CRP) and antiinflammatory (IL-4, IL-10). The Empirical Diet Inflammatory Pattern (EDIP) is based on food group consumption and divides the dietary intake into nine inflammatory and nine anti-inflammatory food groups according to their impact on the CRP, IL-6 and TNF-αR2 biomarkers of inflammation [27]. Using these indices, many studies have assessed the potential inflammatory effect of diet on the health status. Recently, an umbrella review was conducted on DII and human health [28]. Umbrella reviews are overviews of systematic reviews and meta-analyses that provide a comprehensive and systematic evaluation of the scientific literature available for a specific research topic and offer the possibility to understand the strength of the evidence and the extent of potential biases [29]. In their umbrella review [28], authors found strong evidence supporting the relationship between a high dietary inflammatory index and an increased risk of myocardial infarction. They also found highly suggestive evidence for increased risk of cancer, in particular oral, respiratory, pancreatic and colorectal cancer, and all-cause mortality [28]. As for EDIP, several observational studies have associated a higher score with increased fasting blood sugar and decreased high-density lipoprotein (HDL) cholesterol levels, as well as with an increased risk of weight gain, metabolic syndrome, nonalcoholic fatty liver disease, heart failure and depression [30–36].

#### **3. Ultra-Processed Foods (UPF)**

One of the cornerstones of the Western diet are UPF, widely available and increasingly consumed in the contemporary society [4,37]. The possible role of UPF in the nutrition– health relationship was first highlighted by Monteiro et al. in 2009, with the introduction of the NOVA classification [38]. NOVA is a system that groups foods according to the nature, extent and purpose of the industrial processes they undergo, rather than in terms of the nutrients they contain [38]. In this classification, foods are assigned to one of four groups: Group 1 contains unprocessed or minimally processed foods, i.e., the edible parts of plants or animals taken directly from nature or minimally modified/preserved; Group 2 contains processed culinary ingredients, such as salt, sugar, oil or starch, produced from Group 1 foods; Group 3 contains processed foods such as canned vegetables or freshly baked bread, produced by combining Group 1 and Group 2 foods; Group 4 contains UPFs, defined as "formulations of ingredients, mostly of exclusive industrial use, that have little or none of the food intact and are typically created by a range of industrial techniques and processes" [38]. UPFs are identified by a long list of ingredients, are ready-to-eat, highly palatable, and usually inexpensive. The most commonly consumed UPFs include soft and sweetened beverages, processed bread, refined breakfast cereals, confectionery products, pre-packaged sauces, ready-to-heat meals and processed meats products [39]. Possible mechanisms behind their link with the health status may involve both their nutritional composition and "processing". Indeed, in terms of nutritional composition, UPF are typically nutritionally unbalanced due to their ingredients [40]. Most UPF are energy-dense products high in added sugars, saturated and trans fatty acids and sodium and low in protein, fiber and certain micronutrients including potassium, magnesium, vitamin C, vitamin D, zinc, phosphorus, vitamin B12 and niacin [40].

UPF are also characterized by the presence of non-nutritive components, such as additives and chemicals. Additives are frequently added to make the final product more palatable, with better sensory qualities and longer shelf life. Commonly used additives in the manufacture of UPF include flavorings, emulsifiers and sweeteners such as aspartame, cyclamate or stevia-derived compounds [41]. As to the supposed presence of harmful chemicals in UPF, it has been suggested that they may derive from the processing or packaging of these products [42]. Processing could also alter the physical properties of food products, leading to a higher glycemic load and a reduced gut–brain satiety signaling, both responsible for overconsumption [43].

According to previous studies, all these aspects could explain the reason why the incidence of several chronic noncommunicable diseases is increasing along with UPF consumption [41]. Among adults, multiple meta-analyses found that a higher UPF consumption is significantly associated with an increased risk of overweight and obesity, metabolic syndrome, hypertension, diabetes and cardiovascular disease [6,44–47]. A higher UPF consumption has also been associated with a higher risk of cancer, particularly breast cancer [6,48], anxiety and depression [49] and all-cause mortality [50,51]. In children and adolescents, significant relationships were found with overweight and obesity [25,52].

#### **4. UPF and Low-Grade Inflammation**

The number of human studies investigating whether the consumption of UPF could promote low-grade inflammation, so favoring the development of noncommunicable diseases, is still limited. The available studies have focused mainly on two aspects: how excessive UPF consumption may affect the presence of biomarkers of inflammation, and how the nutritional composition or non-nutritional components of UPF may influence the development of chronic inflammation and gut dysbiosis, previously correlated with a pro-inflammatory state (Figure 2).

**Figure 2.** Possible mechanisms explaining the relationship between UPF and low-grade inflammation. ↑ increased; ↓ reduced.

The vast majority of studies that have examined the relationship between UPF consumption and inflammation are observational, either cross-sectional or cohort studies (Table 1), with only one clinical trial currently available [53].

**Table 1.** Observational studies assessing the relationship between UPF consumption and inflammatory biomarkers.


UPF: ultra-processed foods; CRP: C-reactive protein; BMI: body mass index; hs-CRP: high-sensitivity C-reactive protein; IL: interleukin; TNF: tumor necrosis factor; INFLA: low-grade inflammation; E-DII: energy-adjusted dietary inflammatory index.

CRP is the most investigated inflammatory biomarker to date in relation to UPF consumption. In the only available clinical trial, subjects assigned to a diet based on unprocessed foods showed a significant reduction in hs-CRP levels, while subjects on a diet rich in UPF did not report significant changes [53]. The authors suggested that these results might indicate that the subjects were already regularly consuming a large amount of UPF, as already observed in the US population [53]. As for data from observational studies, they are not consistent and suggest that the relationship may depend on gender and body mass index (BMI). For example, in the ELSA-Brasil study, a significant association between high UPF consumption and higher CRP levels was found in women, but the association lost its significance when adjusting for BMI [54]. Similarly, in the Melbourne Collaborative Cohort Study, the association between high UPF consumption and CRP levels remained significant only in men, after adjustment for BMI [55]. In adolescents, Martins et al. found that subjects consuming more UPF in their diet had higher CRP and IL-8 values, but the association was significant only for IL-8 [56]. Other biomarkers studied to a lesser extent are some proinflammatory cytokines such as IL-6. Dos Santos et al. investigated the possible relationship between UPF consumption and IL-6 concentrations in two cohorts, showing an association only in women in the Portuguese cohort and only in men in the Brazilian cohort [57]. The conclusion was that the UPF intake could be associated with higher IL-6 levels, although the relation was not explained by adiposity [57].

As to the E-DII score, a cross-sectional study in Brazil found a direct relationship between a higher dietary energy intake from UPF and a higher rate of dietary inflammation in pregnant women [58]. Similar findings were obtained in the Italian cohort Moli-Sani, where a higher consumption of UPF was related to a higher pro-inflammatory potential of the adults' diet [59]. In this cohort, further analyses were performed using the lowgrade inflammation (INFLA)-Score, which allows the assessment of the possible intensity of low-grade inflammation through the effects of biomarkers of inflammation (platelets, white blood cell (WBC), CRP and granulocyte-to-lymphocyte ratio), obtaining the same association [59].

#### **5. Possible Mechanisms Explaining the Relationship between UPF and Low-Grade Inflammation**

#### *5.1. Nutritional Aspects*

UPF consumption could contribute to an inflammatory state through several mechanisms. First, it could be the high intake of sugars, salt, saturated fats and trans fatty acids typical of a UPF-rich diet that directly promotes the development of chronic inflammation [61]. When high intakes of these nutrients and their possible relationship to the modulation of inflammation are considered individually, the results to date are mixed. UPF are usually high in simple sugars, in the form of either sucrose or a high-fructose syrup, so they tend to be foods that raise the blood glucose markedly and rapidly, i.e., with a high glycemic index/glycemic load [62]. This postprandial increase in the glucose levels in turn causes an increase in insulin levels, which promotes a proinflammatory state [63]. Although these mechanisms appear to play an important role in diet and the promotion of low-grade inflammation, intervention studies are not very clear in this regard. In the TOSCA.IT study, an association was found between the intake of added sugars ≥10% of the daily energy intake and increased CRP levels in adults with diabetes [64]. Other observational studies associated a higher consumption of sugar-sweetened beverages with increased levels of CRP and IL-6 in adults and children [65–67]. Regarding the glycemic response, although an intervention study found a positive association between glycemic load and plasma hs-CRP in healthy middle-aged women [68], a recent meta-analysis including 28 randomized controlled trials found no association between the glycemic index and different markers of inflammation in adults [69].

UPF also have a high salt content, contributing to a high sodium intake. Several cross-sectional studies associated a higher salt intake with higher CRP levels in adults and elderly people [70,71], although this association was not found in adolescents [72]. A recent meta-analysis also found no associations between dietary sodium level and markers of inflammation, although it should be noted that the researchers pointed out that their findings were likely due to methodological errors [73].

As for the fat content of UPF, their inflammatory potential derives not only from a higher consumed quantity with respect to other foods, but also from a poorer quality. In fact, trans fatty acids resulting from the industrial process are associated with a higher presence of low-grade inflammation. Specifically, they have been related to higher levels of hs-CRP, IL-6 and TNF-α [74–76]. Diets with a high processed-food content have also been associated with a higher intake of omega-6 fatty acids, resulting in a higher omega-6/omega-3 ratio and the potential promotion of low-grade inflammation [77].

Finally, consuming large amounts of UPF sometimes results in the replacement of foods that are the basis of a healthy and balanced diet. Examples are fruits and vegetables, which are correlated with an anti-inflammatory effect thanks to the presence of numerous phytocompounds [78,79]. Recent studies clearly show how people consuming more UPF have a lower intake of fruit and vegetables [80] and consequently ingest less substances with an anti-inflammatory effect. A low fruit and vegetable consumption also results in a low dietary fiber intake. In the E-DIITM, fiber is considered one of the factors that reduce diet-related inflammation. In previous studies, an adequate fiber intake was shown to be important in maintaining low CRP levels and in maintaining homeostasis of the gut microbiota [81]. A high UPF consumption can also lead to deficiencies of micronutrients considered to be anti-inflammatory factors in the diet, such as magnesium, vitamin C, vitamin D, zinc and niacin [82].

#### *5.2. Non-Nutritional Aspects*

Results from an Italian cohort study suggested that only part of the proinflammatory effect of a high UPF consumption can be directly attributed to the nutritional components of the diet, while the rest could be attributed to non-nutritional factors that may promote lowgrade inflammation [59]. One of the non-nutritional factors present in UPF are additives, which are added to mimic or intensify the sensory qualities of foods [83]. Among the most studied are sweeteners, especially non-caloric ones such as acesulfame potassium, sucralose or aspartame, due to their widespread use in soft drinks to provide a sweet taste without the energy value of sugars [84]. Recently, there has also been growing interest in the harmful effect of emulsifiers used to improve the shelf life and texture of food products. Although scientific evidence to date is limited, animal and in vitro studies suggest that sweeteners and emulsifiers may contribute to the inflammatory cascade [85–87]. One of the hypothesized mechanisms is the modulation of the microbiota, but data are inconsistent, and further studies are needed to investigate these mechanisms [88,89]. It has also been hypothesized that the non-caloric sweeteners' harmful effect might be due to an acute metabolic response [90]. However, data from two recent meta-analyses do not support this hypothesis, as they found no association between the consumption of non-caloric sweetened beverages and an increased insulinemic effect or acute glycemic response [91,92].

Non-nutrient components such as bisphenol or phthalates may also be present in UPF due to the migration of chemical substances that are part of food packaging. In fact, several cross-sectional studies reported higher levels of both substances in the urine of people with a high UPF consumption [42,93–96]. Because of their structure, bisphenol and phthalates can disrupt various aspects of the hormonal action and are therefore called endocrine disruptors. They can interfere with the synthesis, secretion, transport, signaling and metabolism of hormones; therefore, they have been associated with adverse health consequences, including the development of diseases such as obesity, diabetes and cardiovascular disease [97,98].

A recent meta-analysis investigating the role of different endocrine disruptors on the inflammatory response showed that increased exposure to Bisphenol A (BPA) is significantly associated with higher levels of IL-6 and CRP, while increased exposure to phthalates is associated with higher levels of CRP, IL-6 and IL-10 [99]. Although the adverse effects

of BPA have led to various restrictions on its use, the analogs that replaced it appear to have similar effects [100]. On the other hand, UPF may contain chemicals derived from food processing, especially due to the heat treatment to which food is subjected. One example is acrylamide as a result of the Maillard reaction between amino acids and sugars, exposure to which in adults has been associated with an increased presence of biomarkers of inflammation such as CRP or Mean Platelet Volume (MPV) [101]. Another chemical instead derived from lipid oxidation is acrolein, high exposure to which has been associated with a higher concentration of Hs-CRP in adults in the United States [102] and of CRP in adults in China [103].

#### *5.3. Gut Microbiota Modulation*

The human gut microbiota is a dynamic and complex network composed of hundreds of thousands of microorganisms, including bacteria, fungi, archaea, viruses and protozoa [104]. When in its normal state of homeostasis, the gut microbiota plays a key role in host health through the immune system function and protection against pathogens. However, when the gut microbiota is altered compared to the community found in healthy individuals, gut dysbiosis occurs [84]. This dysbiosis is associated with a high degree of inflammation, caused by a lower presence of short- chain-fatty-acids-(SCFAs)-producing bacteria, and increased permeability of the gut [105]. Both diet quality and the presence of the additives previously described may influence intestinal dysbiosis, offering a possible explanation for the mechanism linking an increased consumption of UPF with the presence of low-grade inflammation.

In fact, it has been suggested that a diet rich in fiber can decrease the systemic inflammatory response by improving the intestinal barrier function and modulating the intestinal microbiota [81]. This is because dietary fiber is essential for the formation of SCFAs, which are thought to play a key role in neuroimmunoendocrine regulation [106]. In fact, SCFAs are associated with a lower concentration of CRP and plasma lipopolysaccharide, an endotoxin used as a marker to assess intestinal permeability linked to increased low-grade inflammation [107–110]. In contrast, Western diets with a high fat content have been associated with increased intestinal permeability due to a greater presence of lipopolysaccharides in humans and mice [111,112]. Similar results were observed in mice fed a diet rich in refined sugar, also associated with an atypical composition of the intestinal microbiota [113]. In a cross-sectional study conducted in the U.S.A., the increased consumption of highly processed food was associated with intestinal permeability biomarkers [114]. Also in a study conducted in Italy, intestinal permeability tended to increase in subjects with low adherence to the Mediterranean diet, who also reported a high intake of food high in fat and sugar, referred to as junk food [115]. Finally, a French study involving 862 healthy adults found that the regular consumption of foods such as soft drinks, fatty sweet products, fried foods, processed meats, ready-to-eat meals, cheese and desserts, most of them recognized as UPF, was associated with reduced bacterial diversity, indicating an altered microbiota composition [116]. In contrast, the PREDIMED-PLUS study in older adults found no such association and suggested that perhaps the contradictory results with the previous study were due to the lower UPF consumption of the studied population [117].

Several studies have also highlighted additives as possible factors affecting the microbiota. Studies in murine models suggested different mechanisms through which emulsifying additives could contribute to intestinal dysbiosis, increasing intestinal permeability and promoting a proinflammatory state [89,118]. However, these studies remain limited, and the results in humans are contrasting. For example, a double-blind controlled study comparing seven adults on an emulsifier-rich diet to nine adults on an emulsifier-free diet observed changes in the gut microbiome and metabolome that may be related to chronic inflammatory diseases [119]. In contrast, a cross-sectional study involving 588 adults found no association with biomarkers related to increased intestinal permeability, although it found an association with increased levels of systemic inflammation [114]. Similarly, studies in murine models suggested that artificial sweeteners can alter the intestinal microbiota,

favoring the enrichment of proinflammatory bacteria that promote the formation of endotoxins such as lipopolysaccharides [85,86,120]. However, the results to date are inconsistent, and further research will be needed to investigate these mechanisms.

#### **6. Conclusions and Future Perspectives**

Low-grade inflammation plays a pivotal role in the pathogenesis of noncommunicable diseases, which are becoming increasingly prevalent worldwide. In recent years, diet has been highlighted as one of the main risk factors for these diseases, together with the increased consumption of UPF, which through different mechanisms, may contribute to promote a proinflammatory state. Although the evidence on the association between UPF consumption and inflammation is still limited and, in some cases, the results are discordant, considering the potential impact of their excessive consumption on the health status, as well as their potential role in favoring the presence of chronic inflammation, public policies that limit their consumption are required. These public policies should also include the promotion of traditional diets based on unprocessed or minimally processed foods, in order to modulate low-grade inflammation and improve people's health status. Future human research evaluating clusters of inflammation markers instead of individual biomarkers may help to better understand the mechanism involved in the modulation of low-grade inflammation by a high consumption of UPF. This information could also be useful in establishing policies that promote the reformulation of UPF to minimize their adverse health effects.

**Author Contributions:** F.S., M.D. and M.T.A., conceptualization; M.T.A. and A.N., revision of the literature; M.T.A. and A.N., writing—original draft preparation; F.S. and M.D., critical revision; F.S. and M.D., writing—review and editing; F.S. and M.D., supervised the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no specific grant from any funding agency, commercial or not-forprofit sectors.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Additional data are available from the corresponding author on reasonable request.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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