Next Article in Journal
Differential Associations of Vitamin D Metabolites with Adiposity and Muscle-Related Phenotypes in Korean Adults: Results from KNHANES 2022–2023
Previous Article in Journal
Time-Restricted Eating Without Exercise Enhances Anaerobic Power and Reduces Body Weight: A Randomized Crossover Trial in Untrained Adults
Previous Article in Special Issue
Association Between Psychobehavioral Factors and the Increased Eating Rate of Ultra-Processed Versus Non-Ultra-Processed Meals in Individuals with Obesity: A Secondary Analysis of a Randomized Trial
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 17
Issue 18
10.3390/nu17183012
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Ultra-Processed Food Consumption and Systemic Inflammatory Biomarkers: A Scoping Review

by
Jacopo Ciaffi
1,2,*,
Luana Mancarella
1,
Claudio Ripamonti
1,
Veronica Brusi
1,
Federica Pignatti
1,
Lucia Lisi
1 and
Francesco Ursini
1,2
1
Medicine & Rheumatology Unit, IRCCS Istituto Ortopedico Rizzoli, 40136 Bologna, Italy
2
Department of Biomedical and Neuromotor Sciences (DIBINEM), Alma Mater Studiorum University of Bologna, 40123 Bologna, Italy
*
Author to whom correspondence should be addressed.
Nutrients 2025, 17(18), 3012; https://doi.org/10.3390/nu17183012
Submission received: 29 August 2025 / Revised: 16 September 2025 / Accepted: 18 September 2025 / Published: 20 September 2025
(This article belongs to the Special Issue Mechanisms of Ultra-Processed Foods and Health Outcomes)
Browse Figure
Versions Notes

Abstract

Background/Objectives: The consumption of ultra-processed foods (UPF) has increased worldwide and has been hypothesized to contribute to chronic diseases, including conditions characterized by inflammatory dysregulation. We conducted a scoping review to map the human evidence on the relationship between UPF consumption and systemic inflammatory biomarkers. Methods: We developed a search strategy combining terms for UPF with terms for circulating inflammatory biomarkers, including C-reactive protein (CRP/hs-CRP), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), interleukin-8 (IL-8), monocyte chemoattractant protein-1 (MCP-1), plasminogen activator inhibitor-1 (PAI-1), and leptin. Findings were synthesized separately for children/adolescents and adults. Results: A total of 24 studies were included. CRP/hs-CRP was assessed in 21; IL-6 in 9; TNF-α in 8; IL-1β in 5; leptin in 5; MCP-1 in 5; PAI-1 in 5; and IL-8 in 2. In children/adolescents, CRP/hs-CRP tended to be higher with greater UPF intake in large cohorts and in preterm infants, whereas smaller or clinically selected samples did not show an association. For other biomarkers, IL-6 generally did not vary with UPF, TNF-α and IL-1β showed no association across studies, and the two IL-8 analyses yielded mixed results. In adults, 11/17 analyses reported higher CRP/hs-CRP levels with greater UPF intake, 5/17 reported no association, and 1/17 reported an association limited to women. IL-6 was predominantly higher with greater UPF intake; TNF-α likewise tended to be higher with UPF across several settings; IL-1β showed no association; MCP-1 and PAI-1 provided limited, inconsistent signals; leptin results were mixed. Conclusions: Higher UPF consumption is frequently associated with elevated systemic inflammatory biomarkers—most consistently CRP/hs-CRP—across adults and selected pediatric contexts. Signals for IL-6 and TNF-α appear in specific populations, whereas IL-1β, MCP-1, PAI-1, and leptin show inconsistent patterns.

1. Introduction

According to the NOVA food classification, ultra-processed foods (UPFs) are industrially manufactured products with minimal whole foods, typically comprising five or more ingredients, including added sugars, oils, fats, salt, and various additives [1]. Typical examples include sugary soft drinks, sweetened cereals, packaged snacks, instant sauces, ready-to-eat meals, and processed meats [2]. The global popularity of UPF is driven by several factors: its shelf stability, little preparation requirements, and strong marketing, rendering it particularly attractive to consumers seeking convenience [3]. Nonetheless, nutritionally, UPFs are energy-dense products, with elevated levels of refined carbohydrates, saturated fats, sodium, and additives, while being deficient in fiber, vitamins, and other critical micronutrients for health [4]. UPF has become increasingly common in contemporary diets, prompting concerns over its influence on chronic diseases [5]. UPF, characterized by hyperpalatability, affordability, and convenience, is now acknowledged as a contributing cause to various chronic disorders [6,7]. In recent decades, the worldwide rise in non-communicable diseases has coincided with a significant increase in UPF consumption [8]. Many of these medical conditions, including metabolic syndrome, hypertension, cardiovascular diseases, type 2 diabetes, inflammatory bowel disease, and demyelinating diseases, are influenced by persistent low-grade inflammation [6,9]. Researchers have suggested that excessive consumption of UPF may induce systemic inflammation, hence hypothesizing an association with the development of non-communicable diseases [10,11,12,13,14].
The poor nutritional quality of UPF and its additive content has been significantly associated with negative health consequences in epidemiological research. Diets heavy in UPF are linked to excessive caloric consumption and high risks of obesity (39% increase), metabolic syndrome (79% increase), and type 2 diabetes (17% increase) [13,15,16]. Elevated intake of UPF has been associated with an increased prevalence of cardiovascular diseases, including coronary artery disease, stroke, and overall mortality [17,18,19]. Moreover, diets heavy in UPF have been associated with an increased prevalence of specific malignancies and neurological or immune-mediated diseases [7,20,21].
Chronic low-grade inflammation is a common factor interconnecting many of these conditions, and dietary patterns rich in UPF are increasingly investigated for their inflammatory potential [22]. Multiple pathways connect UPF to inflammation [6,22]. UPF-heavy diets consist of low nutritional quality and a high concentration of artificial additives and processing-derived substances, which might collectively disrupt gut health and immunological homeostasis [23]. Evidence suggests that both the nutritional composition of UPF and its non-nutritive constituents, along with the impact on the gut flora, contribute to its detrimental inflammatory effects [24]. Several UPFs contain preservatives, emulsifiers, colorants, and other compounds seldom utilized in domestic cooking; these substances may perturb the gut flora, enhance intestinal permeability, and stimulate pro-inflammatory immune responses [25,26]. The result is a pro-inflammatory interior environment (“metaflammation”) that may expedite the onset of chronic diseases [27].
Another potential mechanism is inflammation induced by the nutrients. Excessive intake of saturated fat can modify the gut microbiota towards a more inflammatory state and directly increase circulating pro-inflammatory cytokines and endotoxin concentrations [28]. The insufficient amount of dietary fiber in ultra-processed meals leads to reduced production of anti-inflammatory short-chain fatty acids by gut microbiota, hence compromising intestinal immunological equilibrium [29].
Current evidence suggests a plausible link between UPF, chronic inflammation, and diseases, but additional clarification is necessary [6,22]. To our knowledge, no prior review has systematically mapped the full spectrum of inflammatory biomarkers in relation to UPF consumption in humans. Considering the rapid rise in the consumption of UPF and its effects on metabolic and inflammatory pathways, we decided to conduct a comprehensive evaluation of the impact of UPF on inflammation. This scoping review aims to synthesize available evidence on the association between UPF consumption and inflammatory markers, using findings from studies on the general population and research involving patients with inflammation-related diseases. By analyzing epidemiological data and biological mechanisms, we seek to clarify the extent to which UPFs may affect inflammatory processes and to identify areas for future research.

2. Materials and Methods

This scoping review was conducted in accordance with the methodological guidance of the Joanna Briggs Institute [30]. Reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) checklist (Supplementary File) [31]. The protocol was registered in the Open Science Framework (OSF) Registries (https://doi.org/10.17605/OSF.IO/W8K2G) on 13 August 2025. Consistent with the process of scoping reviews, no critical appraisal or assessment of bias risk for the included studies was conducted [30].

2.1. Eligibility Criteria and Study Selection

The Population, Concept, and Context (PCC) framework was applied to define the research question and eligibility criteria:
  • Population: we included studies involving humans of any age. Results were considered separately for adults and for children/adolescents.
  • Concept: eligible studies were those that directly evaluated the association between UPF consumption and systemic inflammatory markers. We included observational or epidemiological studies (e.g., cross-sectional, case–control, cohort) as well as mechanistic human studies (e.g., short-term dietary interventions) only if they reported results on inflammatory markers across different levels of UPF intake (e.g., categories or quantiles of UPF consumption, or continuous associations). To be eligible, studies had to provide a quantitative measure of the relationship (e.g., p-value, β coefficient, odds ratio, or mean differences) for at least one systemic inflammatory marker.
The following inflammatory markers were evaluated: C-reactive protein (CRP), high-sensitivity CRP (hs-CRP), erythrocyte sedimentation rate (ESR), fibrinogen, interleukin-6 (IL-6), interleukin-1β (IL-1β), tumor necrosis factor-alpha (TNF-α), interleukin-8 (IL-8), chemokine CCL2/monocyte chemoattractant protein-1 (MCP-1), plasminogen activator inhibitor-1 (PAI-1) and leptin. Studies that reported solely surrogate indices of dietary inflammatory potential, such as the dietary inflammatory index without direct biomarker assessment, were excluded.
  • Context: studies were eligible regardless of setting, geographical region, or healthcare system. Both general population samples and patient cohorts were included.
We considered randomized controlled trials (RCTs), quasi-experimental studies, prospective and retrospective cohort studies, case–control studies, cross-sectional studies, and qualitative reports. Conference abstracts and proceedings were also eligible.

2.2. Search Strategy

We carried out a comprehensive literature search in Medline (via PubMed), Web of Science (WOS), and Embase, on 17 August 2025.
The search strategy combined terms related to dietary exposures (e.g., “ultra-processed food,” “ultra processed food,” “ultraprocessed food,” “ultra-processed foods,” “ultra processed foods,” “ultraprocessed foods”) with those referring to systemic inflammatory markers. Specifically, we included CRP, hs-CRP, ESR, fibrinogen, IL-6, IL-1β, TNF-α, IL-8, CCL2/MCP-1, PAI-1 and leptin.
To enhance comprehensiveness, we conducted manual searches employing pertinent keywords and examined the reference lists of included research to identify additional eligible papers. The development and execution of the search strategy were conducted independently by two investigators (J.C. and L.M.) under the guidance of a senior reviewer (F.U.). No time limits were imposed on the search.
The complete search strings for each database are available in the Supplementary Materials to guarantee transparency and reproducibility.

2.3. Study Selection and Data Charting

Following the removal of duplicate records, two reviewers (J.C. and L.M.) independently evaluated all titles and abstracts of the obtained publications. A comprehensive evaluation was subsequently conducted for all studies considered potentially eligible. Disagreements throughout the selection process were addressed by discussion, with a senior investigator (F.U.) consulted where consensus was unattainable.
For each study included, the following data were extracted using a standardized collection form: first author, year of publication, country, study design, sample size, study population, method of exposure assessment for UPF consumption, inflammatory markers evaluated, and main findings concerning the relationship between UPF intake and systemic inflammation.

2.4. Synthesis of Results

We conducted a narrative synthesis, organizing findings by population and, within each population, by biomarker. For each biomarker, we prioritize adjusted estimates, report the direction and statistical significance of associations, and note sex-stratified or sensitivity analyses when available. Where a study includes both adolescents and adults, subgroup findings are summarized in both population sections. CRP and hs-CRP are considered jointly a priori.
  • UPF consumption and systemic inflammatory biomarkers in children and adolescents
  • This section summarizes studies assessing the association between UPF consumption and systemic inflammatory biomarkers in pediatric and adolescent populations, presented per biomarker.
  • UPF consumption and systemic inflammatory biomarkers in adults
  • This section collects evidence from research assessing the influence of UPF consumption on systemic inflammatory markers in adult populations, with results presented per biomarker.
  • Summary tables
  • Descriptive tables report study characteristics (author, year, country, design, population, sample size), UPF exposure assessment, biomarkers assessed, and key findings to support the narrative synthesis, and summary tables visually highlight the presence of association between UPF intake and each inflammatory biomarker in the included studies.

3. Results

The database search yielded 204 records (PubMed: 51; Web of Science: 81; Embase: 72). An additional 18 studies were identified through reference screening. After removal of duplicates, 140 unique records remained. Following title/abstract screening and full-text assessment against eligibility criteria, 24 studies met inclusion and were retained for qualitative synthesis, comprising 23 full papers and 1 conference abstract. A PRISMA flow diagram of the selection process is provided in Figure 1. The included studies were published between 2019 and 2025 and originated from Brazil (6), United States (U.S.) (5), Spain (3), Iran (3), Canada (2), and one each from Norway, Republic of Korea, France, Australia, and Ireland. By population, 5 studies enrolled children or adolescents, 17 enrolled adults, and 2 included both adolescents and adults. Nineteen studies sampled the general population, while 5 focused on specific clinical groups (metabolic syndrome, survivors of childhood acute lymphoblastic leukemia, celiac disease in children, preterm infants, and adults with colon cancer). By design, 19 were cross-sectional, 3 prospective, 1 retrospective, and 1 RCT. Overall, CRP/hs-CRP was assessed in 21/24 studies; IL-6 in 9/24; TNF-α in 8/24; IL-1β in 5/24; leptin in 5/24; MCP-1 in 5/24; PAI-1 in 5/24; and IL-8 in 2/24. ESR and fibrinogen were not assessed in any study; IL-8 was not assessed in adults, and PAI-1 was not assessed in pediatric populations. All study-level details are reported in Table 1, while Table 2 and Table 3 present visual summaries of UPF–biomarker associations for children/adolescents and adults, respectively, with checks (✓) for associations and crosses (X) for no association.

3.1. UPF Consumption and Systemic Inflammatory Biomarkers in Children and Adolescents

3.1.1. UPF and CRP/hs-CRP

Six studies investigated the relationship between UPF intake and CRP or hs-CRP concentrations in pediatric populations (Table 2) [35,38,46,51,52,56].
In the large nationwide ERICA cohort of Brazilian adolescents (n = 6316), high UPF consumption was modestly associated with a greater prevalence of elevated CRP levels, suggesting low-grade systemic inflammation [38]. Similarly, in a cross-sectional study of 391 adolescents in Northeastern Brazil, higher UPF intake was associated with increased hs-CRP concentrations [46]. Among younger children, a Brazilian study including preterm and term infants (n = 90) found that higher UPF intake was independently associated with higher CRP levels, with a significant interaction between prematurity and UPF exposure [52].
By contrast, in a cohort of 151 Brazilian schoolchildren, UPF consumption was linked to adverse lipid and liver profiles, but no consistent associations were observed with hs-CRP [51]. Similarly, cross-sectional analysis including 806 American adolescents reported no association between UPF intake and hs-CRP [56]. Finally, in Canadian survivors of childhood acute lymphoblastic leukemia (n = 241; including 85 under 18 years), diet quality indices and UPF consumption were associated with dyslipidemia and insulin resistance, but no significant associations emerged with CRP [35].
Overall, across the six pediatric studies, three reported higher CRP/hs-CRP with greater UPF intake, whereas three found no association, indicating signals concentrated in large adolescent cohorts and preterm infants while smaller or clinically selected samples more often showed no association.

3.1.2. UPF and IL-6

Four pediatric studies evaluated the association between UPF consumption and circulating IL-6 (Table 2) [35,46,48,51]. In Canadian survivors of childhood acute lymphoblastic leukemia (n = 241; including 85 under 18 years), stratified models by obesity status showed higher IL-6 at greater UPF intake in both obese and non-obese participants [35].
By contrast, in a population-based study of Brazilian adolescents (n = 391), higher UPF consumption was not associated with elevated IL-6 levels [46]. Consistent findings emerged in Spanish children with celiac disease (n = 53) compared to healthy controls (n = 32), where greater UPF intake was not linked to increased IL-6 concentrations [48]. Finally, in Brazilian schoolchildren (n = 151), higher UPF consumption was also not associated with higher IL-6 values in multivariable analyses [51].
Overall, pediatric evidence does not show a consistent association between UPF and IL-6; the only positive signal appears among childhood cancer survivors but requires confirmation.

3.1.3. UPF and TNF-α

Four studies investigated the relationship between UPF intake and TNF-α levels in pediatric populations, and all had negative results (Table 2) [35,46,48,51]. In Canadian survivors of childhood acute lymphoblastic leukemia (n = 241; including 85 under 18 years), no significant associations were observed between UPF consumption and TNF-α concentrations [35]. In a population-based sample of Brazilian adolescents (n = 391), higher UPF was related to higher hs-CRP and leptin, but TNF-α was not associated [46]. Similarly, among Spanish children with celiac disease (53 cases) and healthy controls (n = 32), TNF-α concentrations did not differ across UPF intake categories or versus controls [48]. Finally, in Brazilian schoolchildren (n = 151), UPF intake related to lipid and liver enzymes but not to TNF-α [51].
Overall, the pediatric evidence indicates no association between UPF consumption and TNF-α.

3.1.4. UPF and IL-1β

Only one pediatric study examined IL-1β in relation to UPF consumption (Table 2). In Spanish children with celiac disease (n = 53) and healthy controls (n = 32), higher UPF intake was not associated with increased IL-1β concentrations [48].
The evidence in pediatric populations is very limited and does not support an association between UPF intake and higher IL-1β; confirmation in larger and more diverse cohorts is needed.

3.1.5. UPF and IL-8

Evidence for IL-8 derives from two pediatric studies (Table 2) [46,48]. In a population-based sample of Brazilian adolescents (n = 391), those in the highest UPF tertile showed significantly higher IL-8 concentrations than the lowest tertile in adjusted models [46]. In contrast, among Spanish children with celiac disease (n = 53) and healthy controls (n = 32), IL-8 concentrations did not differ across UPF intake categories after adjustment [48].
Overall, pediatric evidence on IL-8 is limited and mixed, suggesting a positive association in one adolescent cohort but no differences in a disease-specific sample, warranting confirmation in additional populations.

3.1.6. UPF and MCP-1

Among the pediatric studies, MCP-1 was evaluated only in Spanish children with celiac disease (n = 53) and healthy controls (n = 32) by comparing celiac participants with <50% vs. ≥50% of energy from UPF (plus controls) (Table 2) [48]. MCP-1 concentrations did not differ significantly across UPF intake categories, indicating no clear association between UPF consumption and MCP-1 in this sample.
Overall, pediatric evidence on MCP-1 is very limited and, in the only available study, does not support a relationship between UPF intake and MCP-1.

3.1.7. UPF and Leptin

Two studies investigated the association between UPF intake and leptin levels in pediatric populations (Table 2) [35,46]. In Canadian survivors of childhood acute lymphoblastic leukemia (n = 241; including 85 under 18 years), diet quality indices and UPF consumption were associated with several cardiometabolic outcomes, but no significant associations were found with leptin concentrations [35]. Conversely, in a population-based study of Brazilian adolescents (n = 391), those in the highest UPF consumption tertile showed significantly higher leptin levels compared to peers with lower intake [46].
Overall, current pediatric evidence suggests inconsistent findings, with a positive association between UPF consumption and leptin reported in a general adolescent cohort, but not among childhood cancer survivors.

3.2. UPF Consumption and Systemic Inflammatory Biomarkers in Adults

3.2.1. UPF and CRP/hs-CRP

Seventeen studies examined UPF in relation to CRP/hs-CRP in adults (Table 3) [33,34,35,36,37,39,40,41,42,43,44,45,47,53,54,55,56]. Of these, 11/17 reported a positive association, 5/17 described no association, and 1/17 showed a sex-specific pattern (positive in women, absent in men). Large general-population studies, such as those by Baric et al. (Canada; n = 6517), Kityo et al. (U.K. Biobank; n = 72,817), Lane et al. (Australia; n = 2018), Millar et al. (Ireland; n = 1986), and Zhao et al. (U.K. Biobank; n = 173,889 and NHANES cohort; n = 2734), reported correlation between greater UPF intake and higher CRP/hs-CRP [34,43,44,47,55,56].
Positive associations also emerged in specific contexts: pregnancy cohorts in the U.S. (n = 350) and Norway (n = 2984), colon cancer patients in the U.S. (n = 796), and regional samples of obese and overweight women in Iran (n = 391 and n = 285) [33,37,41,42,54].
In contrast, absence of association between UPF consumption and CRP/hs-CRP was reported in a Canadian sample of leukemia survivors (n = 241; including 156 above 18 years), a Spanish outpatient sample (n = 152), another Iranian cohort (n = 221), a short randomized crossover feeding trial from the U.S. (n = 20), and a French cohort (n = 1594) [35,36,39,40,53].
A sex-specific pattern was observed by Lopes et al. in a Brazilian study on civil servants from public universities/research institutions (n = 8468), where higher UPF consumption was related to higher CRP in women but not in men after sociodemographic adjustment.
Overall, adult studies generally show an association between greater UPF intake and higher CRP/hs-CRP across large population cohorts and several clinical settings, alongside variability across study designs, including absence of association in short feeding trials, and a single cohort reporting a female-specific association.

3.2.2. UPF and IL-6

Six studies evaluated UPF in relation to circulating IL-6 in adults (Table 3) [35,37,47,49,50,54]. Of these, 5/6 reported a positive association and 1/6 found no association.
Higher IL-6 with greater UPF intake was observed in Canadian long-term leukemia survivors (n = 241; including 156 above 18 years), Spanish older adults with metabolic syndrome (n = 92), young-adult cohorts from Portugal and Brazil (n = 524 and n = 2888, with a sex-specific pattern: the association is present in women in Portugal and in men in Brazil), an Irish primary-care sample (n = 1986), and colon cancer patients from the U.S. (n = 796) [35,47,49,50,54]. In contrast, no association was detected in an American pregnancy cohort (n = 350) [37].
Overall, adult IL-6 evidence points to a prevailing positive association between higher UPF intake and higher IL-6 across diverse settings, with sex-specific patterns in young adults and a single negative finding in pregnancy.

3.2.3. UPF and TNF-α

Five studies assessed UPF in relation to circulating TNF-α in adults; three reported a positive association and two reported no association (Table 3) [35,37,47,49,54].
Higher TNF-α with greater UPF intake was observed in Spanish older adults with metabolic syndrome (n = 92), U.S. colon cancer patients (n = 796), and an Irish primary-care sample (n = 1986) [47,49,54].
In contrast, no association was detected in a U.S. pregnancy cohort (n = 350) or Canadian long-term leukemia survivors (n = 241; including 156 above 18 years) [35,37].
Overall, adult TNF-α evidence points toward a prevailing positive association between higher UPF intake and higher TNF-α, including in primary-care and disease-specific settings, while in pregnancy and long-term cancer survivor cohorts no association was found.

3.2.4. UPF and IL-1β

Four studies assessed UPF in relation to circulating IL-1β in adults, and all four reported no association (Table 3) [33,39,41,49]. This included three cohorts of overweight/obese adult women in Iran (n = 285, n = 221, n = 391) and a cohort of older adults with metabolic syndrome in Spain (n = 92) [33,39,41,49].
Overall, available adult evidence does not support an association between UPF consumption and IL-1β levels.

3.2.5. UPF and MCP-1

Four studies assessed UPF in relation to circulating MCP-1 in adults; three reported no association and one reported a borderline positive association (Table 3) [33,39,41,49].
No association was observed in two samples of overweight/obese women in Iran (n = 285 and n = 391) and in older adults with metabolic syndrome in Spain (n = 92), whereas a third Iranian cohort of overweight/obese women (n = 221) reported a borderline increase in MCP-1 with higher UPF intake [33,39,41,49].
Overall, adult evidence for MCP-1 is limited and does not consistently support an association with UPF consumption.

3.2.6. UPF and PAI-1

Five studies assessed UPF in relation to circulating PAI-1 in adults; one reported a positive association and four reported no association (Table 3) [33,39,40,41,47]. A positive relationship was observed in overweight/obese women in Iran (n = 221) [39].
No association was found in two additional cohorts of overweight/obese women in Iran (n = 285 and n = 391), in a short, randomized crossover feeding trial conducted in a metabolic ward in the U.S. (n = 20), or in a primary-care adult sample in Ireland (n = 1986) [33,40,41,47].
Overall, adult evidence for PAI-1 is limited, with a single positive report and four analyses finding no association.

3.2.7. UPF and Leptin

Four studies assessed UPF in relation to circulating leptin in adults; two reported a positive association and two reported no association (Table 3) [35,40,47,49].
Higher leptin with greater UPF intake was observed in older adults with metabolic syndrome in Spain (n = 92) and in a primary-care adult sample in Ireland (n = 1986) [47,49].
In contrast, no association was detected in Canadian long-term leukemia survivors (n = 241; including 156 above 18 years) or in a short, randomized crossover feeding trial conducted in a metabolic ward from the U.S. (n = 20) [35,40].
Overall, adult evidence for leptin is mixed, with signals in metabolic-risk and primary-care cohorts, while cancer-survivor and short feeding-trial settings report no association.

4. Discussion

UPF accounts for an increasing proportion of dietary intake worldwide and has been associated with several chronic diseases characterized by low-grade systemic inflammation [6]. In this context, we conducted a scoping review to summarize human evidence regarding the association between UPF consumption and circulating inflammatory biomarkers across various age groups, examining patterns by biomarker and population while identifying methodological sources of heterogeneity that may explain inconsistent findings.
We included 24 studies; all were published between 2019 and 2025. CRP/hs-CRP emerged as the most consistently associated biomarker, particularly in adults. For cytokines, IL-6 and TNF-α generally showed positive associations with UPF intake, whereas IL-1β, MCP-1, PAI-1, and leptin yielded less consistent evidence. Notably, ESR and fibrinogen were not evaluated, and IL-8 was examined only in pediatric populations. In children and adolescents, larger cohorts of teenagers and preterm-infant samples suggested higher CRP/hs-CRP with greater UPF intake, while smaller or clinically selected samples more often did not show an association. For pediatric markers beyond CRP/hs-CRP, evidence was limited: IL-6 generally did not vary with UPF, TNF-α showed no association across studies, IL-1β showed no association in the single study available, and IL-8 evidence was limited and mixed across two studies.
Overall, the adult evidence aligns higher UPF intake with higher CRP/hs-CRP, with more variability for cytokines and other markers. This pattern is biologically plausible. Diets high in UPF are typically energy-dense, abundant in added sugars and saturated fats, and deficient in fiber and micronutrients—characteristics consistently associated with elevated inflammatory biomarkers in children, adolescents, and adults [57].
In a nationally representative investigation from the U.S., UPF contributed approximately 58% of total energy and almost 90% of added sugars; each 5% increase in energy from UPF was associated with a 1% increase in energy from added sugars, exhibiting a nearly linear gradient across UPF quintiles [58]. Added sugars, refined starches, and high-glycaemic loads can exacerbate postprandial glycaemia and lipaemia, oxidative stress, and subsequent cytokine responses, potentially increasing CRP/hs-CRP and IL-6 levels [58,59].
Saturated fats may further contribute: a systematic review revealed a positive correlation between saturated fatty acids (SFA) and IL-6 as well as endothelial activation markers such as soluble intercellular adhesion molecule-1 (sICAM-1), whereas data regarding hs-CRP were inconsistent and there was little support for the relationship between SFA and adipokines [60].
Additionally, dietary-pattern research suggests that the diet–inflammation relationship is partially mediated by adiposity: higher-quality dietary patterns correlate with reduced CRP and leptin levels, while “Western” dietary patterns are associated with higher levels, but this correlation attenuates after adjusting for BMI [61,62]. Adiposity, particularly central or visceral fat, is strongly related to hs-CRP through inflammation in adipose tissue and hepatic acute-phase responses [63]. The evidence substantiates a mediation pathway (UPF → adiposity → inflammation) that aligns with the attenuation observed in several studies included in our review after comprehensive covariate adjustment.
Importantly, biomarkers such as CRP, IL-6, TNF-α, and leptin are frequently elevated in individuals with overweight or obesity independent of dietary exposures [64,65,66]. Accordingly, the associations observed in included studies should not be interpreted as solely attributable to UPF intake, but rather as the result of interactions between adiposity, metabolic status, and dietary patterns.
However, nutrient content and adiposity are unlikely to be the whole story. A large cross-sectional study from the Moli-sani cohort linked increased consumption of UPF with an elevated composite inflammatory score (hs-CRP, white blood cells, platelets, granulocyte–lymphocyte ratio) [67]. When the energy-adjusted Dietary Inflammatory Index (E-DII) into the models, the relationship with processed foods diminished significantly (~88% attenuation), but the association with UPF exhibited only a ~33% attenuation, indicating a substantial non-nutritional component unique to ultra-processing [67].
Proposed mechanisms encompass additives (e.g., emulsifiers, non-nutritive sweeteners), compounds derived from processing (e.g., acrylamide, advanced glycation end-products), and contaminants from packaging (e.g., bisphenols), which exert effects through microbiota disturbance, barrier impairment, endotoxemia, and activation of the innate immune system [67,68].
Prospective data further reinforce both the epidemiologic signal and mechanistic plausibility. In the Malmö Diet and Cancer Study, increased consumption of UPF was associated with elevated risks of cardiovascular disease outcomes [69]. Substitution analyses indicated that replacing UPF with unprocessed or minimally processed foods could reduce these risks [69]. Proteomic analysis in a subcohort revealed that higher levels of inflammatory and endothelial-related proteins (e.g., IL-18, TNF-R1/2, CSF-1, thrombomodulin, resistin) correlated with higher UPF intake, many of which were prospectively linked to cardiovascular disease [69].
In individuals with type 2 diabetes, increased consumption of UPF predicted cardiovascular outcomes, with a detectable mediation by CRP and BMI, among other variables [70]. A similar analysis described higher risks of microvascular complications, particularly diabetic kidney disease, again mediated by BMI and CRP [71]. Beyond cardiometabolic disease, increased intake of UPF was associated with to the onset of rheumatoid arthritis in the U.K. Biobank, with partial mediation through CRP, high-density lipoprotein cholesterol, white blood cell count, and composite inflammatory indices [72]. These studies collectively suggest that systemic inflammation is integral to the UPF–disease relationship and that multiple, partially overlapping pathways—nutrient-driven, adiposity-related, and processing-specific—likely operate synergistically.
Two additional considerations are relevant when comparing studies. First, classification choices for UPF can materially affect results. In NHANES 2015–2018, the CRP association was evident in adjusted models only when UPF definitions excluded products with high whole-grain content; the standard definition diluted the signal [73]. This highlights intra-category variability and the necessity of describing UPF subsets (e.g., beverages, sweets, savory snacks) individually when feasible. Second, design and measurement matter: most studies were cross-sectional with single-time-point biomarkers; the only short metabolic-ward trial did not detect hs-CRP differences over two weeks [40]. Small quasi-experimental studies that reduce UPF by enhancing plant-food variety over a period of weeks similarly demonstrate minimal changes in biomarkers, perhaps due to the brief duration, small sample sizes, and inadequate assays [74]. These patterns argue for longer interventions with repeated measures and panels beyond CRP alone.
The mediation results align with clinical observations from nutritional interventions [67,70,72]. In very-low-calorie ketogenic diet (VLCKD) trials, significant clinical improvement was observed in knee osteoarthritis associated with weight reduction, consistent with adiposity-mediated advantages, while in fibromyalgia, improvements were not statistically attributable to changes in BMI, indicating partially weight-independent mechanisms [75,76]. Mechanistically, ketosis and β-hydroxybutyrate inhibit NLRP3 inflammasome signaling and modulate cytokines, illustrating that dietary exposures can influence inflammation both through and beyond adiposity [77]. While these interventions are not specifically aimed at reducing UPF, they support a nuanced mediation paradigm where adiposity, nutrient quality (e.g., sugars/saturated fatty acids), and non-nutritional characteristics of ultra-processing collectively influence systemic inflammation.
The strengths of our scoping review encompass the extensive coverage of inflammatory biomarkers, parallel synthesis in pediatric and adult cohorts, clear presentation of counts by biomarker, and the utilization of summary matrices to enable quick comparisons.
Limitations reflect those of the underlying literature: predominance of cross-sectional designs; variability in UPF exposure metrics and in the measurement of CRP versus hs-CRP and cytokines; limited amount of prospective and experimental evidence; and potential misclassification of processing categories. IL-8 was evaluated in pediatric population only, while PAI-1 was analyzed solely in adults; ESR and fibrinogen were not assessed in the studies included, hence limiting the possibility of inference. The absence of consistent association between UPF consumption and biomarkers such as IL-1β, MCP-1, PAI-1, and leptin, may reflect methodological limitations or population differences but could also indicate a genuine lack of correlation. Further research is needed to determine whether these negative or inconsistent findings represent true absence of effects or context-dependent variations. Finally, residual confounding may persist despite multivariable adjustment in several studies, and RCTs with standardized dietary interventions in healthy individuals would be needed to establish whether UPF directly alters systemic inflammatory biomarkers.
Considering these limitations, it is also important to consider future directions. Our view is that reducing the consumption of UPF should be regarded as a public health priority. Convenience, affordability, and aggressive marketing largely drive the widespread use of UPF, but regulatory and policy measures are needed to counteract these factors and promote healthier alternatives. Solid evidence already demonstrates that high UPF intake increases the risk of metabolic syndrome, cardiovascular disease, cancer, and musculoskeletal conditions [7,20,78]. By showing consistent associations with systemic inflammation, our review reinforces the notion that UPF is detrimental to health across multiple domains and should be limited as much as possible.

5. Conclusions

Human evidence—most consistently for CRP/hs-CRP in adults—supports an association between greater UPF consumption and higher levels of systemic inflammatory biomarkers, with additional signals for IL-6 and TNF-α in selected contexts and mixed findings for other markers. Differences in UPF classification, exposure metrics, populations, and biomarker panels likely explain part of the heterogeneity. Future work should prioritize standardized UPF metrics, repeated biomarker measures, explicit mediation analyses (adiposity, diet quality, and processing-specific factors), and adequately powered prospective studies and longer interventions. Such designs will clarify causality, identify modifiers, and better define the clinical significance of UPF-related inflammatory changes.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu17183012/s1.

Author Contributions

Conceptualization, J.C., L.M. and F.U.; methodology, J.C., L.M. and C.R.; software, V.B., F.P. and L.L.; validation, J.C. and F.U.; formal analysis, L.M., C.R. and F.P.; investigation, L.M., V.B., F.P. and L.L.; resources, C.R. and F.U.; data curation, J.C., F.P., L.L. and F.U.; writing—original draft preparation, J.C., L.M. and C.R.; writing—review and editing, J.C., L.M., C.R., V.B., F.P., L.L. and F.U.; visualization, J.C.; supervision, J.C. and F.U.; project administration, L.M., C.R. and V.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Monteiro, C.A.; Cannon, G.; Levy, R.B.; Moubarac, J.-C.; Louzada, M.L.; Rauber, F.; Khandpur, N.; Cediel, G.; Neri, D.; Martinez-Steele, E.; et al. Ultra-Processed Foods: What They Are and How to Identify Them. Public Health Nutr. 2019, 22, 936–941. [Google Scholar] [CrossRef] [PubMed]
  2. Mertens, E.; Colizzi, C.; Peñalvo, J.L. Ultra-Processed Food Consumption in Adults across Europe. Eur. J. Nutr. 2022, 61, 1521–1539. [Google Scholar] [CrossRef]
  3. 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. [Google Scholar] [CrossRef]
  4. Zhang, Y.; Giovannucci, E.L. Ultra-Processed Foods and Health: A Comprehensive Review. Crit. Rev. Food Sci. Nutr. 2023, 63, 10836–10848. [Google Scholar] [CrossRef]
  5. Baker, P.; Machado, P.; Santos, T.; Sievert, K.; Backholer, K.; Hadjikakou, M.; Russell, C.; Huse, O.; Bell, C.; Scrinis, G.; et al. Ultra-Processed Foods and the Nutrition Transition: Global, Regional and National Trends, Food Systems Transformations and Political Economy Drivers. Obes. Rev. 2020, 21, e13126. [Google Scholar] [CrossRef]
  6. Tristan Asensi, M.; Napoletano, A.; Sofi, F.; Dinu, M. Low-Grade Inflammation and Ultra-Processed Foods Consumption: A Review. Nutrients 2023, 15, 1546. [Google Scholar] [CrossRef]
  7. Ciaffi, J.; Mancarella, L.; Ripamonti, C.; D’Amuri, A.; Brusi, V.; Pignatti, F.; Lisi, L.; Ursini, F. Ultra-Processed Food and Its Impact on Bone Health and Joint Diseases: A Scoping Review. Nutrients 2025, 17, 1188. [Google Scholar] [CrossRef] [PubMed]
  8. Afshin, A.; Sur, P.J.; Fay, K.A.; Cornaby, L.; Ferrara, G.; Salama, J.S.; Mullany, E.C.; Abate, K.H.; Abbafati, C.; Abebe, Z.; et al. Health Effects of Dietary Risks in 195 Countries, 1990–2017: A Systematic Analysis for the Global Burden of Disease Study 2017. Lancet 2019, 393, 1958–1972. [Google Scholar] [CrossRef] [PubMed]
  9. Hotamisligil, G.S. Inflammation, Metaflammation and Immunometabolic Disorders. Nature 2017, 542, 177–185. [Google Scholar] [CrossRef]
  10. Juul, F.; Vaidean, G.; Parekh, N. Ultra-Processed Foods and Cardiovascular Diseases: Potential Mechanisms of Action. Adv. Nutr. 2021, 12, 1673–1680. [Google Scholar] [CrossRef]
  11. Narula, N.; Chang, N.H.; Mohammad, D.; Wong, E.C.L.; Ananthakrishnan, A.N.; Chan, S.S.M.; Carbonnel, F.; Meyer, A. Food Processing and Risk of Inflammatory Bowel Disease: A Systematic Review and Meta-Analysis. Clin. Gastroenterol. Hepatol. 2023, 21, 2483–2495.e1. [Google Scholar] [CrossRef]
  12. Mannino, A.; Daly, A.; Dunlop, E.; Probst, Y.; Ponsonby, A.-L.; van der Mei, I.A.F.; Ausimmune Investigator Group; Black, L.J. Higher Consumption of Ultra-Processed Foods and Increased Likelihood of Central Nervous System Demyelination in a Case-Control Study of Australian Adults. Eur. J. Clin. Nutr. 2023, 77, 611–614. [Google Scholar] [CrossRef]
  13. Delpino, F.M.; Figueiredo, L.M.; Bielemann, R.M.; da Silva, B.G.C.; Dos Santos, F.S.; Mintem, G.C.; Flores, T.R.; Arcêncio, R.A.; Nunes, B.P. Ultra-Processed Food and Risk of Type 2 Diabetes: A Systematic Review and Meta-Analysis of Longitudinal Studies. Int. J. Epidemiol. 2022, 51, 1120–1141. [Google Scholar] [CrossRef]
  14. Qu, Y.; Hu, W.; Huang, J.; Tan, B.; Ma, F.; Xing, C.; Yuan, L. Ultra-Processed Food Consumption and Risk of Cardiovascular Events: A Systematic Review and Dose-Response Meta-Analysis. eClinicalMedicine 2024, 69, 102484. [Google Scholar] [CrossRef]
  15. Pagliai, G.; Dinu, M.; Madarena, M.P.; Bonaccio, M.; Iacoviello, L.; Sofi, F. Consumption of Ultra-Processed Foods and Health Status: A Systematic Review and Meta-Analysis. Br. J. Nutr. 2021, 125, 308–318. [Google Scholar] [CrossRef] [PubMed]
  16. Dicken, S.J.; Dahm, C.C.; Ibsen, D.B.; Olsen, A.; Tjønneland, A.; Louati-Hajji, M.; Cadeau, C.; Marques, C.; Schulze, M.B.; Jannasch, F.; et al. Food Consumption by Degree of Food Processing and Risk of Type 2 Diabetes Mellitus: A Prospective Cohort Analysis of the European Prospective Investigation into Cancer and Nutrition (EPIC). Lancet Reg. Health Eur. 2024, 46, 101043. [Google Scholar] [CrossRef] [PubMed]
  17. Du, S.; Kim, H.; Rebholz, C.M. Higher Ultra-Processed Food Consumption Is Associated with Increased Risk of Incident Coronary Artery Disease in the Atherosclerosis Risk in Communities Study. J. Nutr. 2021, 151, 3746–3754. [Google Scholar] [CrossRef]
  18. Bhave, V.M.; Oladele, C.R.; Ament, Z.; Kijpaisalratana, N.; Jones, A.C.; Couch, C.A.; Patki, A.; Garcia Guarniz, A.-L.; Bennett, A.; Crowe, M.; et al. Associations Between Ultra-Processed Food Consumption and Adverse Brain Health Outcomes. Neurology 2024, 102, e209432. [Google Scholar] [CrossRef]
  19. Dai, S.; Wellens, J.; Yang, N.; Li, D.; Wang, J.; Wang, L.; Yuan, S.; He, Y.; Song, P.; Munger, R.; et al. Ultra-Processed Foods and Human Health: An Umbrella Review and Updated Meta-Analyses of Observational Evidence. Clin. Nutr. 2024, 43, 1386–1394. [Google Scholar] [CrossRef]
  20. Isaksen, I.M.; Dankel, S.N. Ultra-Processed Food Consumption and Cancer Risk: A Systematic Review and Meta-Analysis. Clin. Nutr. 2023, 42, 919–928. [Google Scholar] [CrossRef] [PubMed]
  21. Ejtahed, H.-S.; Mardi, P.; Hejrani, B.; Mahdavi, F.S.; Ghoreshi, B.; Gohari, K.; Heidari-Beni, M.; Qorbani, M. Association between Junk Food Consumption and Mental Health Problems in Adults: A Systematic Review and Meta-Analysis. BMC Psychiatry 2024, 24, 438. [Google Scholar] [CrossRef]
  22. Maki, K.A.; Sack, M.N.; Hall, K.D. Ultra-Processed Foods: Increasing the Risk of Inflammation and Immune Dysregulation? Nat. Rev. Immunol. 2024, 24, 453–454. [Google Scholar] [CrossRef]
  23. Rondinella, D.; Raoul, P.C.; Valeriani, E.; Venturini, I.; Cintoni, M.; Severino, A.; Galli, F.S.; Mora, V.; Mele, M.C.; Cammarota, G.; et al. The Detrimental Impact of Ultra-Processed Foods on the Human Gut Microbiome and Gut Barrier. Nutrients 2025, 17, 859. [Google Scholar] [CrossRef]
  24. Whelan, K.; Bancil, A.S.; Lindsay, J.O.; Chassaing, B. Ultra-Processed Foods and Food Additives in Gut Health and Disease. Nat. Rev. Gastroenterol. Hepatol. 2024, 21, 406–427. [Google Scholar] [CrossRef]
  25. Song, Z.; Song, R.; Liu, Y.; Wu, Z.; Zhang, X. Effects of Ultra-Processed Foods on the Microbiota-Gut-Brain Axis: The Bread-and-Butter Issue. Food Res. Int. 2023, 167, 112730. [Google Scholar] [CrossRef]
  26. Bevilacqua, A.; Speranza, B.; Racioppo, A.; Santillo, A.; Albenzio, M.; Derossi, A.; Caporizzi, R.; Francavilla, M.; Racca, D.; Flagella, Z.; et al. Ultra-Processed Food and Gut Microbiota: Do Additives Affect Eubiosis? A Narrative Review. Nutrients 2024, 17, 2. [Google Scholar] [CrossRef]
  27. Charles-Messance, H.; Mitchelson, K.A.J.; De Marco Castro, E.; Sheedy, F.J.; Roche, H.M. Regulating Metabolic Inflammation by Nutritional Modulation. J. Allergy Clin. Immunol. 2020, 146, 706–720. [Google Scholar] [CrossRef]
  28. Geladari, E.V.; Kounatidis, D.; Christodoulatos, G.S.; Psallida, S.; Pavlou, A.; Geladari, C.V.; Sevastianos, V.; Dalamaga, M.; Vallianou, N.G. Ultra-Processed Foods and Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD): What Is the Evidence So Far? Nutrients 2025, 17, 2098. [Google Scholar] [CrossRef] [PubMed]
  29. Spiller, A.L.; Costa, B.G.D.; Yoshihara, R.N.Y.; Nogueira, E.J.Z.; Castelhano, N.S.; Santos, A.; Brusco De Freitas, M.; Magro, D.O.; Yukie Sassaki, L. Ultra-Processed Foods, Gut Microbiota, and Inflammatory Bowel Disease: A Critical Review of Emerging Evidence. Nutrients 2025, 17, 2677. [Google Scholar] [CrossRef] [PubMed]
  30. Peters, M.D.J.; Marnie, C.; Tricco, A.C.; Pollock, D.; Munn, Z.; Alexander, L.; McInerney, P.; Godfrey, C.M.; Khalil, H. Updated Methodological Guidance for the Conduct of Scoping Reviews. JBI Evid. Implement. 2021, 19, 3–10. [Google Scholar] [CrossRef] [PubMed]
  31. Tricco, A.C.; Lillie, E.; Zarin, W.; O’Brien, K.K.; Colquhoun, H.; Levac, D.; Moher, D.; Peters, M.D.J.; Horsley, T.; Weeks, L.; et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann. Intern. Med. 2018, 169, 467–473. [Google Scholar] [CrossRef]
  32. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef]
  33. Bahrampour, N.; Shiraseb, F.; Noori, S.; Clark, C.C.T.; Mirzaei, K. Is There Any Putative Mediatory Role of Inflammatory Markers on the Association between Ultra-Processed Foods and Resting Metabolic Rate? Front. Nutr. 2022, 9, 932225. [Google Scholar] [CrossRef]
  34. Baric, A.; Malik, V.S.; Christoforou, A. Ultra-Processed Food Consumption and Cardiometabolic Risk in Canada: A Cross-Sectional Analysis of the Canadian Health Measures Survey. Nutr. Metab. 2025, 22, 37. [Google Scholar] [CrossRef]
  35. Bérard, S.; Morel, S.; Teasdale, E.; Shivappa, N.; Hebert, J.R.; Laverdière, C.; Sinnett, D.; Levy, E.; Marcil, V. Diet Quality Is Associated with Cardiometabolic Outcomes in Survivors of Childhood Leukemia. Nutrients 2020, 12, 2137. [Google Scholar] [CrossRef]
  36. Contreras-Rodriguez, O.; Reales-Moreno, M.; Fernández-Barrès, S.; Cimpean, A.; Arnoriaga-Rodríguez, M.; Puig, J.; Biarnés, C.; Motger-Albertí, A.; Cano, M.; Fernández-Real, J.M. Consumption of Ultra-Processed Foods Is Associated with Depression, Mesocorticolimbic Volume, and Inflammation. J. Affect. Disord. 2023, 335, 340–348. [Google Scholar] [CrossRef] [PubMed]
  37. Cummings, J.R.; Lipsky, L.M.; Schwedhelm, C.; Liu, A.; Nansel, T.R. Associations of Ultra-Processed Food Intake with Maternal Weight Change and Cardiometabolic Health and Infant Growth. Int. J. Behav. Nutr. Phys. Act. 2022, 19, 61. [Google Scholar] [CrossRef] [PubMed]
  38. Dos Santos, G.R.; Rosa, P.B.Z.; Martins, N.N.F.; Mendes, L.L.; Do Carmo, A.S.; Schaan, B.D.; Cureau, F.V. Association between Consumption of Ultra-Processed Foods and C-Reactive Protein: Findings from Study of Cardiovascular Risks in Adolescents (ERICA). Br. J. Nutr. 2025, 133, 1395–1403. [Google Scholar] [CrossRef]
  39. Hajmir, M.M.; Shiraseb, F.; Hosseininasab, D.; Aali, Y.; Hosseini, S.; Mirzaei, K. The Mediatory Role of Inflammatory Markers on the Relationship between the NOVA Classification System and Obesity Phenotypes among Obese and Overweight Adult Women: A Cross-Sectional Study. Front. Nutr. 2023, 10, 1226162. [Google Scholar] [CrossRef] [PubMed]
  40. Hall, K.D.; Ayuketah, A.; Brychta, R.; Cai, H.; Cassimatis, T.; Chen, K.Y.; Chung, S.T.; Costa, E.; Courville, A.; Darcey, V.; et al. Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake. Cell Metab. 2019, 30, 67–77.e3. [Google Scholar] [CrossRef]
  41. Hosseininasab, D.; Shiraseb, F.; Noori, S.; Jamili, S.; Mazaheri-Eftekhar, F.; Dehghan, M.; Da Silva, A.; Bressan, J.; Mirzaei, K. The Relationship between Ultra-Processed Food Intake and Cardiometabolic Risk Factors in Overweight and Obese Women: A Cross-Sectional Study. Front. Nutr. 2022, 9, 945591. [Google Scholar] [CrossRef] [PubMed]
  42. Kelsey, P.T.; Papadopoulou, E.; Borge, T.C.; Dahl, C.; Brantsæter, A.L.; Erlund, I.; Meltzer, H.M.; Haug, L.S.; Caspersen, I.H. Ultra-Processed Food Consumption and Associations with Biomarkers of Nutrition and Inflammation in Pregnancy: The Norwegian Environmental Biobank. Front. Nutr. 2022, 9, 1052001. [Google Scholar] [CrossRef]
  43. Kityo, A.; Choi, B.; Lee, J.-E.; Kim, C.; Lee, S.-A. Association of Ultra-Processed Food-Related Metabolites with Selected Biochemical Markers in the UK Biobank. Nutr. J. 2025, 24, 21. [Google Scholar] [CrossRef]
  44. Lane, M.M.; Lotfaliany, M.; Forbes, M.; Loughman, A.; Rocks, T.; O’Neil, A.; Machado, P.; Jacka, F.N.; Hodge, A.; Marx, W. Higher Ultra-Processed Food Consumption Is Associated with Greater High-Sensitivity C-Reactive Protein Concentration in Adults: Cross-Sectional Results from the Melbourne Collaborative Cohort Study. Nutrients 2022, 14, 3309. [Google Scholar] [CrossRef]
  45. Lopes, A.E.D.S.C.; Araújo, L.F.; Levy, R.B.; Barreto, S.M.; Giatti, L. Association between Consumption of Ultra-Processed Foods and Serum C-Reactive Protein Levels: Cross-Sectional Results from the ELSA-Brasil Study. Sao Paulo Med. J. 2019, 137, 169–176. [Google Scholar] [CrossRef]
  46. Martins, G.M.D.S.; França, A.K.T.D.C.; Viola, P.C.D.A.F.; Carvalho, C.A.D.; Marques, K.D.S.; Santos, A.M.D.; Batalha, M.A.; Alves, J.D.D.A.; Ribeiro, C.C.C. Intake of Ultra-Processed Foods Is Associated with Inflammatory Markers in Brazilian Adolescents. Public Health Nutr. 2022, 25, 591–599. [Google Scholar] [CrossRef]
  47. Millar, S.R.; Harrington, J.M.; Perry, I.J.; Phillips, C.M. Associations between Ultra-Processed Food and Drink Consumption and Biomarkers of Chronic Low-Grade Inflammation: Exploring the Mediating Role of Adiposity. Eur. J. Nutr. 2025, 64, 150. [Google Scholar] [CrossRef]
  48. Nestares, T.; Martín-Masot, R.; Flor-Alemany, M.; Bonavita, A.; Maldonado, J.; Aparicio, V.A. Influence of Ultra-Processed Foods Consumption on Redox Status and Inflammatory Signaling in Young Celiac Patients. Nutrients 2021, 13, 156. [Google Scholar] [CrossRef]
  49. Quetglas-Llabrés, M.M.; Monserrat-Mesquida, M.; Bouzas, C.; Mateos, D.; Ugarriza, L.; Gómez, C.; Tur, J.A.; Sureda, A. Oxidative Stress and Inflammatory Biomarkers Are Related to High Intake of Ultra-Processed Food in Old Adults with Metabolic Syndrome. Antioxidants 2023, 12, 1532. [Google Scholar] [CrossRef] [PubMed]
  50. Silva Dos Santos, F.; Costa Mintem, G.; Oliveira De Oliveira, I.; Lessa Horta, B.; Ramos, E.; Lopes, C.; Petrucci Gigante, D. Consumption of Ultra-Processed Foods and IL-6 in Two Cohorts from High- and Middle-Income Countries. Br. J. Nutr. 2023, 129, 1552–1562. [Google Scholar] [CrossRef] [PubMed]
  51. Silva-Luis, C.C.; Lopes, M.S.; Gomes, S.M.; Cantalice Matias, P.K.; Brandini, F.P.; Costa, P.C.T.; De Moraes, R.C.S.; Baccin Martins, V.J.; De Brito Alves, J.L. Ultra-Processed Food Consumption and Cardiometabolic Risk Factors in Children Living in Northeastern Brazil. Nutrients 2024, 16, 3944. [Google Scholar] [CrossRef]
  52. Vivi, A.C.P.; Azevedo-Silva, T.R.; Neri, D.; Strufaldi, M.W.L.; Lebrão, C.W.; Fonseca, F.L.A.; Sarni, R.O.S.; Suano-Souza, F.I. Association between Ultraprocessed Food Intake and C-Reactive Protein Levels in Preterm and Term Infants. Nutrition 2022, 99–100, 111649. [Google Scholar] [CrossRef] [PubMed]
  53. Xia, L.L.C.H.; Girerd, N.; Lamiral, Z.; Duarte, K.; Merckle, L.; Leroy, C.; Nazare, J.-A.; Van Den Berghe, L.; Seconda, L.; Hoge, A.; et al. Association between Ultra-Processed Food Consumption and Inflammation: Insights from the STANISLAS Cohort. Eur. J. Nutr. 2025, 64, 94. [Google Scholar] [CrossRef]
  54. Zatsepina, A.; Sayeed, S.; Mohsin, F.; Al Shihabi, A.; Roland, B.; Salem, A.E. The Impact of Dietary Patterns on Inflammation and Colon Cancer Risk: A Retrospective Study of 796 Patients. J. Clin. Oncol. 2025, 43, 3618. [Google Scholar] [CrossRef]
  55. Zhao, L.; Clay-Gilmour, A.; Zhang, J.; Zhang, X.; Steck, S.E. Higher Ultra-Processed Food Intake Is Associated with Adverse Liver Outcomes: A Prospective Cohort Study of UK Biobank Participants. Am. J. Clin. Nutr. 2024, 119, 49–57. [Google Scholar] [CrossRef]
  56. Zhao, L.; Zhang, X.; Martinez Steele, E.; Lo, C.-H.; Zhang, F.F.; Zhang, X. Higher Ultra-Processed Food Intake Was Positively Associated with Odds of NAFLD in Both US Adolescents and Adults: A National Survey. Hepatol. Commun. 2023, 7, e0240. [Google Scholar] [CrossRef]
  57. Bujtor, M.; Turner, A.; Torres, S.; Esteban-Gonzalo, L.; Pariante, C.; Borsini, A. Associations of Dietary Intake on Biological Markers of Inflammation in Children and Adolescents: A Systematic Review. Nutrients 2021, 13, 356. [Google Scholar] [CrossRef]
  58. Martínez Steele, E.; Baraldi, L.G.; Louzada, M.L.D.C.; Moubarac, J.-C.; Mozaffarian, D.; Monteiro, C.A. Ultra-Processed Foods and Added Sugars in the US Diet: Evidence from a Nationally Representative Cross-Sectional Study. BMJ Open 2016, 6, e009892. [Google Scholar] [CrossRef]
  59. Teeman, C.S.; Kurti, S.P.; Cull, B.J.; Emerson, S.R.; Haub, M.D.; Rosenkranz, S.K. Postprandial Lipemic and Inflammatory Responses to High-Fat Meals: A Review of the Roles of Acute and Chronic Exercise. Nutr. Metab. 2016, 13, 80. [Google Scholar] [CrossRef]
  60. Santos, S.; Oliveira, A.; Lopes, C. Systematic Review of Saturated Fatty Acids on Inflammation and Circulating Levels of Adipokines. Nutr. Res. 2013, 33, 687–695. [Google Scholar] [CrossRef]
  61. Ko, B.-J.; Park, K.H.; Shin, S.; Zaichenko, L.; Davis, C.R.; Crowell, J.A.; Joung, H.; Mantzoros, C.S. Diet Quality and Diet Patterns in Relation to Circulating Cardiometabolic Biomarkers. Clin. Nutr. 2016, 35, 484–490. [Google Scholar] [CrossRef]
  62. Esmaillzadeh, A.; Kimiagar, M.; Mehrabi, Y.; Azadbakht, L.; Hu, F.B.; Willett, W.C. Dietary Patterns and Markers of Systemic Inflammation among Iranian Women. J. Nutr. 2007, 137, 992–998. [Google Scholar] [CrossRef]
  63. Brooks, G.C.; Blaha, M.J.; Blumenthal, R.S. Relation of C-Reactive Protein to Abdominal Adiposity. Am. J. Cardiol. 2010, 106, 56–61. [Google Scholar] [CrossRef]
  64. Obradovic, M.; Sudar-Milovanovic, E.; Soskic, S.; Essack, M.; Arya, S.; Stewart, A.J.; Gojobori, T.; Isenovic, E.R. Leptin and Obesity: Role and Clinical Implication. Front. Endocrinol. 2021, 12, 585887. [Google Scholar] [CrossRef]
  65. Yu, J.-Y.; Choi, W.-J.; Lee, H.-S.; Lee, J.-W. Relationship between Inflammatory Markers and Visceral Obesity in Obese and Overweight Korean Adults: An Observational Study. Medicine 2019, 98, e14740. [Google Scholar] [CrossRef]
  66. Khanna, D.; Khanna, S.; Khanna, P.; Kahar, P.; Patel, B.M. Obesity: A Chronic Low-Grade Inflammation and Its Markers. Cureus 2022, 14, e22711. [Google Scholar] [CrossRef]
  67. Mignogna, C.; Costanzo, S.; Di Castelnuovo, A.; Ruggiero, E.; Shivappa, N.; Hebert, J.R.; Esposito, S.; De Curtis, A.; Persichillo, M.; Cerletti, C.; et al. The Inflammatory Potential of the Diet as a Link between Food Processing and Low-Grade Inflammation: An Analysis on 21,315 Participants to the Moli-Sani Study. Clin. Nutr. 2022, 41, 2226–2234. [Google Scholar] [CrossRef]
  68. Mariath, A.B.; Machado, A.D.; Ferreira, L.D.N.M.; Ribeiro, S.M.L. The Possible Role of Increased Consumption of Ultra-Processed Food Products in the Development of Frailty: A Threat for Healthy Ageing? Br. J. Nutr. 2022, 128, 461–466. [Google Scholar] [CrossRef]
  69. Li, H.; Wang, Y.; Sonestedt, E.; Borné, Y. Associations of Ultra-Processed Food Consumption, Circulating Protein Biomarkers, and Risk of Cardiovascular Disease. BMC Med. 2023, 21, 415. [Google Scholar] [CrossRef]
  70. Li, Y.; Lai, Y.; Geng, T.; Xia, P.; Chen, J.; Tu, Z.; Yang, K.; Liao, Y.; Liu, G.; Pan, A. Association of Ultraprocessed Food Consumption with Risk of Cardiovascular Disease Among Individuals with Type 2 Diabetes: Findings from the UK Biobank. Mol. Nutr. Food Res. 2024, 68, 2300314. [Google Scholar] [CrossRef]
  71. Li, Y.; Lai, Y.; Geng, T.; Zhang, Y.-B.; Xia, P.-F.; Chen, J.-X.; Yang, K.; Zhou, X.-T.; Liao, Y.-F.; Franco, O.H.; et al. Association of Ultraprocessed Food Consumption with Risk of Microvascular Complications among Individuals with Type 2 Diabetes in the UK Biobank: A Prospective Cohort Study. Am. J. Clin. Nutr. 2024, 120, 674–684. [Google Scholar] [CrossRef]
  72. Zhao, H.; Bai, Y.; Liu, Y.; Xing, Y.; Yan, Y.; Chen, G.; Chen, J.; Wang, X.; Chen, C.; Zhang, Z. Association of Ultraprocessed Food Consumption with Risk of Rheumatoid Arthritis: A Retrospective Cohort Study in the UK Biobank. Am. J. Clin. Nutr. 2024, 120, 927–935. [Google Scholar] [CrossRef]
  73. Price, E.J.; Du, M.; McKeown, N.M.; Batterham, M.J.; Beck, E.J. Excluding Whole Grain-Containing Foods from the Nova Ultraprocessed Food Category: A Cross-Sectional Analysis of the Impact on Associations with Cardiometabolic Risk Measures. Am. J. Clin. Nutr. 2024, 119, 1133–1142. [Google Scholar] [CrossRef]
  74. Mitra, A.; Thames, K.; Brown, A.; Shuster, I.; Rosenfield, M.; Baumler, M.D. Consumption of a Variety of Plant Foods, Ultra-Processed Foods, and Risk for Chronic Disease: A Dietary Intervention. Hum. Nutr. Metab. 2024, 36, 200258. [Google Scholar] [CrossRef]
  75. Ciaffi, J.; Lisi, L.; Mari, A.; Mancarella, L.; Brusi, V.; Pignatti, F.; Ricci, S.; Vitali, G.; Stefanelli, N.; Assirelli, E.; et al. Efficacy, Safety and Tolerability of Very Low-Calorie Ketogenic Diet in Obese Women with Fibromyalgia: A Pilot Interventional Study. Front. Nutr. 2023, 10, 1219321. [Google Scholar] [CrossRef]
  76. Ciaffi, J.; Mancarella, L.; Pederzani, G.; Lisi, L.; Brusi, V.; Pignatti, F.; Ricci, S.; Vitali, G.; Faldini, C.; Ursini, F. Efficacy, Safety, and Tolerability of a Very Low-Calorie Ketogenic Diet in Women with Obesity and Symptomatic Knee Osteoarthritis: A Pilot Interventional Study. Nutrients 2024, 16, 3236. [Google Scholar] [CrossRef]
  77. Ciaffi, J.; Mitselman, D.; Mancarella, L.; Brusi, V.; Lisi, L.; Ruscitti, P.; Cipriani, P.; Meliconi, R.; Giacomelli, R.; Borghi, C.; et al. The Effect of Ketogenic Diet on Inflammatory Arthritis and Cardiovascular Health in Rheumatic Conditions: A Mini Review. Front. Med. 2021, 8, 792846. [Google Scholar] [CrossRef]
  78. Mendoza, K.; Smith-Warner, S.A.; Rossato, S.L.; Khandpur, N.; Manson, J.E.; Qi, L.; Rimm, E.B.; Mukamal, K.J.; Willett, W.C.; Wang, M.; et al. Ultra-Processed Foods and Cardiovascular Disease: Analysis of Three Large US Prospective Cohorts and a Systematic Review and Meta-Analysis of Prospective Cohort Studies. Lancet Reg. Health Am. 2024, 37, 100859. [Google Scholar] [CrossRef]
Nutrients 17 03012 g001
Figure 1. PRISMA 2020 flow diagram. Adapted From: Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews [32].
Figure 1. PRISMA 2020 flow diagram. Adapted From: Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews [32].
Nutrients 17 03012 g001
Table 1. Characteristics of the Included Studies.
Table 1. Characteristics of the Included Studies.
First AuthorYear of PublicationCountry of Affiliation of First AuthorPublication TypeStudy DesignSample SizeStudy PopulationExposure AssessmentInflammatory Markers AssessedKey Findings
Bahrampour N. [33]2022IranFull-text articleCross-sectional285Overweight and obese adult womenUPF % of total dietary intakehs-CRP, IL-1β, MCP-1, PAI-1Higher UPF consumption was associated with increased hs-CRP levels, while no significant associations were observed for MCP-1, PAI-1, or IL-1β.
Baric L. [34]2025CanadaFull-text articleCross-sectional6517Adults from the Canadian Health Measures SurveyUPF % of total dietary intakeCRPHigher UPF consumption was associated with increased CRP levels.
Bérard A. [35]2020CanadaFull-text articleCross-sectional241 (156 adults; 85 <18 years)Long-term survivors of childhood acute lymphoblastic leukemiaUPF % of total dietary intakeCRP, IL-6, TNF-α, leptinHigher UPF consumption was associated with increased IL-6 levels in both obese and non-obese participants. A non-significant trend was observed for higher TNF-α. No associations were found for CRP or leptin.
Contreras-Rodriguez O. [36]2023SpainFull-text articleCross-sectional152Adults consulting for weight lossUPF % of total dietary intakehs-CRPUPF consumption was not associated with hs-CRP.
Cummings J. [37]2022U.S.Full-text articleProspective cohort458 enrolled; biomarker subsample n = 350Pregnant women from a metropolitan area in North CarolinaUPF % of total energy intakeCRP, IL-6, TNF-αHigher UPF consumption during pregnancy was associated with increased CRP, while associations with IL-6 and TNF-α were not significant.
Dos Santos G.R. [38]2025BrazilFull-text articleCross-sectional6316AdolescentsUPF % of total energy intakeCRPHigher total UPF consumption was modestly associated with a higher prevalence of elevated CRP.
Hajmir M. [39]2023IranFull-text articleCross-sectional221Overweight and obese adult womenUPF intake categorized by quartileshs-CRP, IL-1β, MCP-1, PAI-1UPF quartiles were significantly associated with PAI-1, marginally associated with MCP-1, and not associated with hs-CRP or IL-1β after adjustment.
Hall K.D. [40]2019U.S.Full-text articleRandomized controlled trial20Adults hospitalized in metabolic ward, undergoing two 2-week periods: UPF diet vs. unprocessed diet, ad libitum intake.UPF-based diet vs. unprocessed diet, in crossover designhs-CRP, PAI-1, leptinNo statistically significant differences were observed between the ultra-processed and unprocessed diets in hs-CRP, leptin or PAI-1 levels, although hs-CRP tended to be lower after the unprocessed diet compared with baseline.
Hosseininasab D. [41]2022IranFull-text articleCross-sectional391Overweight and obese adult womenUPF intake categorized by quartileshs-CRP, IL-1β, MCP-1, PAI-1Higher UPF consumption was associated with increased hs-CRP levels in tertile-based analyses, but no association was observed for IL-1β, MCP-1 and PAI-1.
Kelsey P.T. [42]2022NorwayFull-text articleCross-sectional2984Nationally recruited pregnant womenUPF % of total energy intakeCRPHigher UPF intake was weakly associated with higher CRP.
Kityo A. [43]2025Republic of KoreaFull-text articleCross-sectional72,817U.K. Biobank adultsUPF % of total dietary intakeCRPHigher UPF intake was associated with higher CRP after multivariable adjustment.
Lane M. [44]2022AustraliaFull-text articleCross-sectional2018Adults from the Melbourne Collaborative Cohort StudySelf-reported UPF intakehs-CRPHigher UPF intake was associated with higher hs-CRP.
Lopes A.E.S.C. [45]2019BrazilFull-text articleCross-sectional8468Civil servants from public universities/research institutionsUPF % of total energy intakehs-CRPAmong women, the highest vs. lowest UPF tertile was associated with 14% higher mean CRP after adjustment for sociodemographics, smoking, and physical activity; among men, no association remained after sociodemographic adjustment.
Martins G.M.S. [46]2022BrazilFull-text articleCross-sectional391AdolescentsUPF % of total energy intakehs-CRP, IL-6, TNF-α, IL-8, leptinHigher UPF consumption was associated with increased hs-CRP, leptin, and IL-8 levels, while no significant associations were found for TNF-α or IL-6.
Millar S.R. [47]2025IrelandFull-text articleCross-sectional1986Primary-care adult patientsUPF % of total dietary intakeCRP, IL-6, TNF-α, PAI-1, leptinHigher UPF intake was associated with higher CRP, IL-6, TNF-α and leptin, while PAI-1 was not significant.
Nestares T. [48]2021SpainFull-text articleCross-sectional85 children (53 with celiac disease, 32 healthy controls)Children with celiac disease on a gluten-free diet and healthy controlsUPF % of total dietary intakeIL-6, IL-1β, TNF-α, IL-8, MCP-1No significant differences were found in TNF-α, IL-1β, IL-6, IL-8 or MCP-1 between celiac disease children consuming >50% of energy from UPF compared to those consuming <50% and to healthy controls.
Quetglas-Llabrés M.M. [49]2023SpainFull-text articleCross-sectional92Older adults with metabolic syndromeUPF % of total dietary intakeIL-6, IL-1β, TNF-α, MCP-1, leptinHigher UPF intake was associated with higher IL-6, TNF-α and leptin, while IL-1β and MCP-1 did not differ between UPF groups.
Silva Dos Santos F. [50]2023BrazilFull-text articleProspective cohort524 (EPITeen, Portugal) + 2888 (Pelotas, Brazil)Young adults from Porto (Portugal) and from Pelotas (Brazil)UPF % of total energy intakeIL-6Higher UPF consumption was associated with increased IL-6 concentrations among females in the EPITeen cohort and males in the Pelotas cohort.
Silva-Luis C.C. [51]2024BrazilFull-text articleCross-sectional151Children enrolled in public schoolsUPF % of total energy intakehs-CRP, IL-6, TNF-αUPF consumption was not associated with hs-CRP, IL-6, or TNF-α levels among children.
Vivi A.C.P. [52]2022BrazilFull-text articleCross-sectional90Preterm infants from a hospital follow-up clinic and healthy term infants from primary careUPF % of total energy intake (all types of milk excluded from the energy calculation)CRPHigher UPF consumption was associated with higher CRP, with a stronger association in preterm infants.
Xia L. [53]2025FranceFull-text articleCross-sectional1594Community adultsSelf-reported UPF consumptionCRPTotal UPF consumption was not associated with CRP in fully adjusted models.
Zatsepina A. [54]2025U.S.Conference proceedingRetrospective cohort796Adults with histologically confirmed colon cancerSelf-reported dietary patternCRP, IL-6, TNF-αPatients consuming high-UPF diets had elevated CRP, IL-6, and TNF-α compared to those on anti-inflammatory diets.
Zhao L. [55]2024U.S.Full-text articleProspective cohort173,889U.K. Biobank adultsUPF % of total dietary intakeCRPHigher UPF intake was associated with increased odds of elevated CRP.
Zhao L. [56]2023U.S.Full-text articleCross-sectional806 adolescents and 2734 adultsCommunity-dwelling participants from NHANES cohortSelf-reported UPF consumptionhs-CRPIn adults, higher UPF intake was associated with higher hs-CRP, whereas no association with hs-CRP was observed in adolescents.
Abbreviations: UPF, ultra-processed foods; CRP, C-reactive protein; hs-CRP, high-sensitivity C-reactive protein; IL, interleukin; TNF-α, tumor necrosis factor-α; MCP-1, monocyte chemoattractant protein-1; PAI-1, plasminogen activator inhibitor-1; U.S., United States; U.K., United Kingdom; NHANES, National Health and Nutrition Examination Survey.
Table 2. Biomarker-by-study summary of associations between ultra-processed food consumption and systemic inflammatory biomarkers in children/adolescents. A check mark (✓) denotes an association, whereas a cross (X) indicates no association.
Table 2. Biomarker-by-study summary of associations between ultra-processed food consumption and systemic inflammatory biomarkers in children/adolescents. A check mark (✓) denotes an association, whereas a cross (X) indicates no association.
First Author, YearCRPhs-CRPIL-6IL-1βTNF-αIL-8MCP-1PAI-1Leptin
Bérard A., 2020 [35]X ✓ X X
Dos Santos G.R., 2025 [38]✓
Martins G.M.S., 2022 [46] ✓X X✓ ✓
Nestares T., 2021 [48] XXXXX
Silva-Luis C.C., 2024 [51] XX X
Vivi A.C.P., 2022 [52]✓
Zhao L., 2023 [56] X
Table 3. Biomarker-by-study summary of associations between ultra-processed food consumption and systemic inflammatory biomarkers in adults. A check mark (✓) denotes an association, whereas a cross (X) indicates no association.
Table 3. Biomarker-by-study summary of associations between ultra-processed food consumption and systemic inflammatory biomarkers in adults. A check mark (✓) denotes an association, whereas a cross (X) indicates no association.
First Author, YearCRPhs-CRPIL-6IL-1βTNF-αIL-8MCP-1PAI-1Leptin
Bahrampour N., 2022 [33] ✓ X XX
Baric L., 2025 [34]✓
Bérard A., 2020 [35]X ✓ X X
Contreras-Rodriguez O., 2023 [36] X
Cummings J., 2022 [37]✓ X X
Hajmir M., 2023 [39] X X ✓✓
Hall K.D., 2019 [40] X XX
Hosseininasab D., 2022 [41] ✓ X XX
Kelsey P.T., 2022 [42]✓
Kityo A., 2025 [43]✓
Lane M., 2022 [44] ✓
Lopes A., 2019 [45]X ✓  *
Millar S.R., 2025 [47]✓ ✓ ✓ X✓
Quetglas-Llabrés M.M., 2023 [49] ✓X✓ X ✓
Silva dos Santos F., 2023 [50] ✓
Xia L., 2025 [53]X
Zatsepina A., 2025 [54]✓ ✓ ✓
Zhao L., 2024 [55]✓
Zhao L., 2023 [56] ✓
* Positive association between UPF consumption and CRP levels was found in women, but not in men.
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.

Share and Cite

MDPI and ACS Style

Ciaffi, J.; Mancarella, L.; Ripamonti, C.; Brusi, V.; Pignatti, F.; Lisi, L.; Ursini, F. Ultra-Processed Food Consumption and Systemic Inflammatory Biomarkers: A Scoping Review. Nutrients 2025, 17, 3012. https://doi.org/10.3390/nu17183012

AMA Style

Ciaffi J, Mancarella L, Ripamonti C, Brusi V, Pignatti F, Lisi L, Ursini F. Ultra-Processed Food Consumption and Systemic Inflammatory Biomarkers: A Scoping Review. Nutrients. 2025; 17(18):3012. https://doi.org/10.3390/nu17183012

Chicago/Turabian Style

Ciaffi, Jacopo, Luana Mancarella, Claudio Ripamonti, Veronica Brusi, Federica Pignatti, Lucia Lisi, and Francesco Ursini. 2025. "Ultra-Processed Food Consumption and Systemic Inflammatory Biomarkers: A Scoping Review" Nutrients 17, no. 18: 3012. https://doi.org/10.3390/nu17183012

APA Style

Ciaffi, J., Mancarella, L., Ripamonti, C., Brusi, V., Pignatti, F., Lisi, L., & Ursini, F. (2025). Ultra-Processed Food Consumption and Systemic Inflammatory Biomarkers: A Scoping Review. Nutrients, 17(18), 3012. https://doi.org/10.3390/nu17183012

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop