Ultra-processed foods: Implications for gastrointestinal health

Abstract

Ultra-processed foods (UPFs) are believed to contribute to the development of multiple chronic inflammatory diseases, including inflammatory bowel diseases and metabolic syndrome, based on epidemiological studies and emerging preclinical and clinical research. Several aspects of food processing and formulation in the development of chronic inflammatory diseases are currently being studied. Ongoing research emphasizes epidemiological evidence and mechanistic insights regarding UPFs and their interaction with the intestinal microbiota. In this review, we explore UPFs, their interaction with the intestinal microbiota, and the implications for gastrointestinal health.

Key Words: Ultra-processed foods; Gastrointestinal health; Intestinal microbiota; Inflammatory bowel disease; Cancer; Metabolic dysfunction associated steatotic liver disease

Core Tip: Ultra-processed foods (UPFs) are believed to play a role in various chronic inflammatory diseases, such as inflammatory bowel diseases and metabolic syndrome, based on epidemiological studies and emerging preclinical and clinical research. The recent development of a new classification for UPFs has improved our understanding of the connection between gastrointestinal (GI) disorders and UPFs. The introduction of this new classification system has expanded our knowledge about the role of ultra-processed foods in various GI disorders and their associations, although this research remains preliminary. Further research is needed to define specific thresholds of UPFs intake that could be considered harmful within daily diets. These thresholds should also account for differences across geographical locations and populations.

INTRODUCTION

Ultra-processed foods (UPFs) have become increasingly prevalent in global diets, comprising over 50% of caloric intake in many high-income countries[1]. Defined by their industrial formulations containing ingredients not commonly used in home cooking, such as emulsifiers, colorants, flavor enhancers, and preservatives, UPFs offer convenience and extended shelf life at the expense of nutritional quality[2]. Growing evidence links high UPFs consumption to a range of adverse health outcomes, including obesity, metabolic syndrome, cardiovascular disease, gastrointestinal (GI) disorders, and cancers[2,3]. The rising burden of non-communicable diseases globally shows the importance of understanding the role of UPFs on GI health. In this review, we aim to discuss the impact of UPFs on various GI diseases

UPFS: DEFINITION AND CLASSIFICATIONS

UPFs are a distinct category of food products that have undergone extensive industrial formulation, characterized by the addition of cosmetic additives, industrial ingredients, and little to no intact whole food content. These products are typically designed to be hyper-palatable, convenient, and shelf-stable, often replacing minimally processed or home-prepared meals in modern diets. The concept has gained prominence through the NOVA food classification system, which categorizes foods based on the extent and purpose of processing, rather than nutrient content alone[1] (Table 1). The NOVA classification system classifies foods into four groups: Unprocessed or minimally processed foods, processed culinary ingredients, processed foods, and UPFs. Unlike traditional nutrient-based models, NOVA emphasizes food processing as a key determinant of diet quality and health outcomes. Its growing use in nutritional epidemiology reflects its ability to better capture real-world dietary patterns and its strong association with the rising consumption of UPFs and the global burden of chronic disease.

Table 1 NOVA classification of foods[1].

Category	Description	Examples
Unprocessed or minimally processed foods	Natural foods altered by removal of inedible parts, drying, freezing, or pasteurization without adding substances	Fresh fruits, vegetables, grains, eggs, milk, and meat
Processed culinary ingredients	Substances extracted from group 1 foods or nature, used in home cooking to prepare and season dishes	Oils, butter, sugar, salt, starch
Processed foods	Products are made by adding salt, sugar, oil, or other group 2 ingredients to group 1 foods, typically to increase shelf life or palatability	Canned vegetables, cheese, salted nuts, bread
UPFs	Industrial formulations with little or no whole foods, containing additives for flavor, texture, and shelf life. Designed for convenience and hyper-palatability	Soft drinks, packaged snacks, instant noodles, and ice cream

UPFs: Ultra-processed foods.

The classification of processed foods, however, remains complex and somewhat contested as multiple classification systems are present (Table 2)[4-7]. Sadler et al[8] highlighted the conceptual challenges in categorizing foods purely by processing level, noting the blurred lines between different processing techniques and the context-dependent nature of food processing. Nevertheless, the NOVA system has proven useful in epidemiological studies linking high UPFs intake with adverse health outcomes.

Table 2 Alternative classification systems for ultra-processed foods[1,4-7].

Classification system	Developed by/source	Basis of classification	Key features
NOVA	Monteiro et al[1], Brazil (FAO, 2018)	Extent and purpose of food processing	Categorizes foods into 4 groups; emphasizes the health risks of UPFs
UNC system	University of North Carolina	Processing categories based on ingredient lists and barcodes	Categorizes > 12000 United States foods from NHANES; focuses on industrial processing
IFPRI classification	International Food Policy Research Institute	Technological processes and ingredient function	Emphasizes processing techniques like extrusion, hydrolysis
EU FP7 (Food4Me Project)	European Commission	Combining processing level with nutritional and matrix properties	Assess the impact of the food matrix and nutrient profile
EPIC-Soft/GloboDiet	European Prospective Investigation into Cancer	Harmonized food description for dietary recall	Focuses on preparation and preservation methods

NHANES: National Health and Nutrition Examination Survey; UPFs: Ultra-processed food; FAO: Food and Agriculture Organization of the United Nations.

UPFs AND GI DISEASE: EPIDEMIOLOGICAL VIEWPOINT

Multiple cohort studies have demonstrated that higher consumption of UPFs is associated with an increased risk of all-cause mortality and several morbidities, including coronary artery disease[9], cardiovascular disease[10], type 2 diabetes mellitus[11], and various forms of cancer[12]. In recent times, there has been a lot of emerging data suggesting a role of UPFs and various GI disorders, including inflammatory bowel disease (IBD), Functional GI disorders, and various GI malignancies. Though the exact mechanism of this association between UPFs and GI disorders remains unclear.

Among the GI disorders, the association between UPFs and IBD is demonstrated by multiple studies. Large cohort studies, including PURE, Nurses’ Health Studies, NutriNet-Santé, Expanded Prostate Cancer Index Composite, and the United Kingdom Biobank, consistently report that individuals with higher UPFs consumption have an increased hazard ratio for Crohn’s disease (CD), even after adjusting for confounders such as age, sex, body mass index, smoking, physical activity, and overall diet quality. However, the association between UPFs and ulcerative colitis (UC) was generally weaker and not statistically significant in most studies[13-17]. Notably, while some studies showed strong associations, others, like NutriNet-Santé, had limited power due to short follow-up times and low case numbers. The findings of the available studies suggest a differential effect of diet-related exposures on IBD subtypes and highlight the need for more precise dietary assessment and long-term prospective studies to strengthen causal inferences. The studies on the association between UPFs and other GI disorders are limited and mainly restricted to functional GI disorders and cancers, i.e., colorectal and pancreatic cancer.

UPFs AND GI DISEASES: MECHANISTIC INSIGHTS

The adverse impact of UPFs on GI health occurs through multiple direct and indirect mechanisms. The following outlines how UPFs contribute to GI dysfunction and disease (Figure 1).

Figure 1  Ultra-processed foods and gastrointestinal dysfunction.

Impact on the nutritional quality of food

Excessive consumption of UPFs, as classified by the NOVA system, carries important implications for human health. In developed countries, UPFs contribute up to 80% of total caloric intake, with confectionery and sugar-sweetened beverages representing the most frequently consumed categories[18]. Higher intake of UPFs is associated with greater dietary energy density and increased total caloric consumption, primarily due to elevated intake of free sugars. However, compared to fresh and minimally processed foods, UPFs exhibit lower nutritional quality, characterized by high levels of added sugars, unhealthy fats, and sodium, and lower concentrations of fiber, vitamins, and minerals.

Patterns of UPFs consumption are closely linked to dietary habits, with higher intakes observed among individuals adhering to vegan and vegetarian diets[19] and lower intakes among those following national dietary guidelines or the Mediterranean dietary pattern[20]. In addition to their suboptimal nutrient profile, UPFs typically undergo several intense industrial processes, such as molding and high-temperature extrusion, and frequently contain cosmetic additives and industrial ingredients aimed at enhancing flavor, appearance, and palatability. These features may contribute to potential long-term adverse health outcomes[21].

Role of the food matrix

The concept of the "food matrix” refers to the physical and chemical structure of a food item, including how its nutrients and non-nutrients are organized and interact within the whole food. It is increasingly recognized as more important than consideration of nutrient composition alone. Ultra-processing techniques extensively disrupt the natural food matrix[22].

UPFs are characterized by extreme matrix degradation through the addition of cosmetic additives (such as colors, flavors, and texturizers), the extraction and purification of isolated compounds (e.g., refined sugars, protein isolates, hydrogenated oils), and the application of drastic processing techniques (e.g., extrusion, puffing, molding). This matrix breakdown by UPFs has multilevel impacts like speeding up digestion (leading to blood sugar spikes), reducing satiety (leading to overeating), and decreasing nutrient synergy and bioavailability. It also negatively affects gut health and microbiome diversity and promotes food addiction through hyperpalatable artificial textures and tastes[22].

It is important to recognize that individuals consume whole food matrices, not isolated nutrients. For example, a sugar molecule embedded within the natural matrix of an apple has markedly different metabolic effects compared to sugar dissolved in a soft drink. Ignoring the food matrix in favor of a purely nutrient-centric perspective risks oversimplification and may mislead dietary guidance. A paradigm shift toward matrix-focused assessment of foods is warranted to more accurately evaluate health impacts.

Impact of food additives on gut microbiota

The United States Food and Drug Administration defines a food additive as “any substance the intended use of which results, or may reasonably be expected to result-directly or indirectly, in it becoming a component or otherwise affecting the characteristics of any food” [Federal Food, Drug, and Cosmetic Act 201(s)]. Acceptable daily intake levels for food additives are established based on lifetime exposure risk assessments conducted by expert committees such as the Joint Food and Agriculture Organization of the United Nations/World Health Organization Expert Committee on Food Additives (JECFA) and the European Food Safety Authority (EFSA). Among the most commonly employed food additives are emulsifiers, low-calorie sweeteners, inorganic nanoparticles, and preservatives.

Emulsifiers: Emulsifiers are compounds that facilitate the stabilization of emulsions and improve the appearance, texture, and mouthfeel of foods, particularly low-fat products. Broadly, the term “emulsifier” encompasses substances that also function as thickeners, including proteins, phospholipids, and carbohydrates, used individually or in combination. While emulsifiers are prevalent in the food supply, they are not ubiquitous as sometimes suggested[23]. More than 100 emulsifiers are approved for use in food. Common examples include: Carboxymethylcellulose (CMC), Polysorbate 80 (P80), Lecithins (E322), Propylene glycol alginate (E405), and various gums such as acacia, Arabic, xanthan, and guar gum, etc.[24].

Emulsifiers can adversely affect gut health by decreasing bacterial diversity, promoting the expansion of pro-inflammatory microbial populations, altering microbial gene expression, reducing mucus layer thickness, and impairing gut barrier integrity via disruption of tight junction proteins. These alterations collectively activate inflammatory pathways and contribute to the development of colitis

Non-nutritive sweeteners: Non-nutritive sweeteners are low- to no-calorie substances used in food products. Preclinical studies indicate that artificial sweeteners can alter gut microbiota, impair gut barrier function, and trigger inflammation, although their effects vary by type of sweetener. Acesulfame K consistently reduces microbial diversity and promotes inflammation[25], while sucralose has mixed effects; sometimes worsening gut permeability and at other times showing little impact[26-28]. Aspartame shifts microbiota composition toward a potentially harmful profile[29], whereas stevia generally increases beneficial bacteria and short-chain fatty acids[30].

Non-nutritive sweeteners have been demonstrated to reduce microbial diversity by impairing the production of beneficial short-chain fatty acids such as butyrate, and increase intestinal permeability, thereby activating inflammatory cascades, including the colitis-associated nuclear factor kappa-B pathway and promoting the secretion of tumor necrosis factor and MAdCAM1. Furthermore, the metabolism of food colorants by intestinal microbiota generates pro-inflammatory metabolites, such as 1-amino-2-naphthol-6-sulfonic acid sodium salt, which can drive IL-23R-dependent inflammation.

Nanoparticles in food: Dietary microparticles are defined as inorganic, bacterial-sized particles (0.1-1 μm) often used as food additives to influence color, consistency, or appearance[31]. The application of inorganic nanoparticles in the food industry is expanding, encompassing food processing, packaging, and nutritional enhancement. Titanium dioxide (TiO₂) and iron (III) oxide (Fe₂O₃) are among the most prevalent nanomaterials utilized as food colorants and functional ingredients in dairy products, beverages, seeds, and processed foods[32]. Nanoparticles commonly incorporated into UPFs have been shown to reduce the abundance of beneficial species such as Faecalibacterium prausnitzii and activate the NLRP3 inflammasome, resulting in the release of pro-inflammatory cytokines[33].

Titanium dioxide (TiO₂) is the most studied nanoparticle with its effect on GI health. In animal studies, TiO₂ was found to build up in Peyer’s patches and worsen colitis by activating the inflammasome. Long-term exposure to TiO₂ also increased oxidative stress, changed gene activity, and promoted the development of dysplasia and colorectal cancer in rodents[34,35]. Similarly, the studies have pointed out the effect of TiO₂ in the pathogenesis of IBD[36,37]. In healthy individuals, TiO₂ is trapped within the lumen by the intestinal mucus layer[38]. In IBD patients, these particles have been detected within phagocytes in intestinal lymphoid aggregates. As compared to healthy controls, patients with acute severe UC have higher serum Titanium levels. Lomer et al[36] also showed a significant reduction in ileal disease activity in CD with a low microparticle diet. Most evidence regarding the effects of nanoparticles on gut health and the microbiome originates from in vitro studies and animal models. Consequently, the applicability of these findings to humans remains uncertain and requires further investigation in well-designed human studies.

CLINICAL STUDIES EXAMINING UPFs AND THE GUT MICROBIOME

Gut microbiota is important in maintaining human health. It plays a role in the metabolism of macro- and micronutrients, gut immunity, and disease susceptibility, risk, progression, and severity of disease[39-41]. Dietary modification is the simplest and an efficient way of modifying gut microbiota. Thus, recent studies have started focusing on the food variations and gut microbiome. With the availability of the NOVA classification system, more and more studies are examining the effect of UPFs on the gut microbiota[42]. Recent studies have identified UPFs based on the NOVA classification using food frequency questionnaire or 24-hour dietary recall methods[43-46].

Four observational studies have examined the association between UPFs and the human gut microbiome[43-46]. Table 3 summarizes these studies on UPFs and the gut microbiome. Cuevas-Sierra et al[43] identified foods as UPFs and compared "high” UPFs consumption vs “low” UPFs consumption, while Atzeni et al[44] analyzed the tertiles of proportions (percentage of total daily calorie intake) of UPFs consumption (low = first tertile, medium = second tertile, and high = third tertile). García-Vega et al[46] simply classified all food items as either ultra-processed or not ultra-processed[46]. Despite the heterogeneity in food assessment, all studies analyzed the fecal microbiome using a single fecal sample and the 16S rRNA sequencing of various hypervariable regions.

Table 3 Summary of available studies on the effect of ultra-processed food on the gut microbiome[43-46].

Ref.	Alpha diversity	Beta diversity	Bacterial composition changes about UPFs		Composition changes related to specific UPFs	Clinical outcome
			Increases	Decreases
Atzeni et al[44], 2022	No significant difference	No significant difference	Positive association between Alloprevotella spp. (P = 0.041) and Sutterella spp. (P = 0.116) vs tertile 2; Positive association between Alloprevotella spp. (P = 0.065), Negativibacillus spp. (P = 0.096), and Prevotella spp. (P = 0.116) vs tertile 3		No significant differences found between bacterial taxa and UPFs categories	UPFs consumption is positively associated with higher total energy intake; No significant differences in cholesterol, TG, or HbA1c between the tertiles. No association between bacterial taxa and cardiovascular risk factors
Cuevas-Sierra et al[43], 2021	Men consuming > 5 servings/day of UPFs showed lower richness compared to men consuming < 3 servings/day (observed P = 0.03, Shannon P = 0.01, Chao1 P = 0.04). No differences in women or the whole population	No significant difference	Gemmiger spp. (P < 0.001); Granulicatella spp. (P < 0.001); Parabacteroides spp. (P < 0.001); Shigella spp. (P < 0.001); Bifidobacterium spp. (P < 0.001); Anaerofilum spp. (P = 0.001); Cc_115 spp. (P = 0.007); Oxalobacter spp. (P = 0.008); Collinsella spp. (P = 0.008)	Lachnospira spp. (P = 0.003); Roseburia spp. (P = 0.003)	Women: Dairy and pizza positively correlated with Actinobacteria (P < 0.05), and pizza positively correlated with Bifidobacterium spp. (P < 0.05)	Consumption of UPFs is associated with ↑ serum triglycerides (P = 0.004) and ↓ HDL-c levels (P = 0.04). UPFs consumption is associated with ↑ depression, anxiety, and weight in women, while ↑ weight and BMI in men
Fernandes et al[45], 2023	No associations between food processing level and alpha diversity	NA	Clostridium butyricum; Odoribacter splanchnicus, Barnesiella intestinihominis, Alistipes onderdonkii, Alistipes indistinctus	Ruminococcus sp., (Ruminococcus) gnavus Bacteroides vulgatus Bacteroides plebeius	NA	Consumption of UPFs is associated with leptin resistance
García-Vega et al[46], 2020	Higher in females than males (Shannon, P = 0.046), higher in middle-aged than younger individuals (Shannon, P = 0.012). No significant association between diet quality (including UPFs intake) and alpha diversity	Differences according to participants’ city of origin (P = 0.001), sex (P = 0.001), socioeconomic level (P = 0.024) and BMI (P = 0.002). No significant association between diet quality (including UPFs intake) and beta diversity	Bifidobacterium adolescentis, Prevotella melaninogenica, Subdoligranulum variabile, Veillonella dispar, Ruminococcus spp., Bilophila spp., Oscillospira spp.	Prevotella copri Clostridium hathewayi, Ruminococcaceae unclassified spp. Gemella spp. Lachnospira spp. Oscillospira spp.	OTUs from Oscillospira spp., Unclassified Ruminococcaceae, Ruminococcus spp., Lachnospira spp. Positively associated with the intake of plant-derived food groups, rich in dietary fiber; Bifidobacterium adolescentis is associated with plant-derived food groups; Bile-tolerant Bilophila sp., Prevotella copri, and the opportunistic pathogen Prevotella melaninogenica were associated with increased intake of animal-derived foods	Diets enriched with plant-derived foods have more diverse gut microbiota and ↑ levels of SCFA-producing bacteria

HDL-c: High-density lipoprotein cholesterol; OTU: Operational taxonomic unit; SCFA: Short-chain fatty acid; TG: Triglyceride; UPFs: Ultra-processed food; BMI: Body mass index; HbA1c: Glycated hemoglobin.

Alpha and beta diversity are key ecological metrics used to study biodiversity. Alpha diversity refers to the variety of species within a particular area or ecosystem, typically expressed through species richness or indices like Shannon or Simpson. it reflects local diversity and how evenly species are distributed. Beta diversity, on the other hand, measures the difference in species composition between two or more ecosystems or habitats. It indicates the species turnover across environments and assesses the ecological variation. Alpha diversity was assessed in all studies, while beta diversity was investigated in 3 studies. Only one of the four UPFs studies showed a difference in alpha diversity, i.e., lower richness in men consuming a higher portion of UPFs compared to those consuming a lesser portion of UPFs[39]. None of the studies observed significant differences in beta diversity based on the UPFs consumption. Studies identified the change in gut microbiome with the consumption of UPFs. Two studies noted the increase in Prevotella spp.[44,46] while two studies reported a decrease in Lachnospira spp.[43,46] and Ruminococcus spp.[45,46] with increased UPFs consumption. The outcome of these observational studies is not limited to the gut microbiome assessment, and they simultaneously assessed the metabolic and clinical outcomes.

Overall, these studies point towards the negative health outcomes associated with the consumption of UPFs. Though a reasonable clinical outcome cannot be drawn. Further studies with adequate food processing measurement, gut microbiota assessment, and relevant clinical endpoints are required before any definitive conclusion of the UPFs on the gut microbiome and health can be drawn.

UPFs and impact on IBD

IBD is a condition characterized by chronic inflammation of the gut mucosa. An alteration in the gut microbiota of IBD patients has been demonstrated to contribute to disease progression. Multiple mechanisms have been proposed, including decreased mucosal function, impaired intestinal barrier, and abnormal immune system activation[47]. Studies have reported decreased gut microbial diversity in both UC and CD[48]. Furthermore, an increase in pro-inflammatory bacterial species, i.e., Escherichia coli, Ruminococcus gnavus, or Fusbobacterium spp., has been noted[49]. These changes in gut microbiota are associated with disease exacerbations and active inflammation, while the gut microbiota of patients with quiescent IBD closely resembles that of healthy individuals[50]. However, it remains unclear if the changes are the cause or consequence of the disease.

Evidence that the non-nutritional components of UPFs can negatively affect the intestinal barrier is growing. Both prospective and retrospective studies have investigated this aspect with variable outcomes (Table 4)[13-17,51-69]. Among the nine prospective cohort studies[13-16,57,59,62,65,66], five have reported a positive association between UPFs and increased risk of IBD (UC and CD). In comparison, four studies have not shown an increased risk of IBD. Similarly, retrospective studies have reported variable outcomes with few associated with increased risk of IBD, while others have shown no association between consumption of UPFs and risk of IBD[14,15,51,58,60,61,63-65]. A meta-analysis of both retrospective and prospective studies identified that UPFs intake was associated with an increased risk of IBD [relative risk (RR): 1.13, 95%CI: 1.06-1.21][70]. The risk was increased for CD (RR: 1.19, 95%CI: 1.00-1.41) but not for UC (RR: 1.11, 95%CI: 0.99-1.26). The study identified that UPFs intake as per the NOVA food classification was significantly associated with an increased risk of IBD (RR: 1.27, 95%CI: 1.10-1.46) but not as per the Western-type diet pattern and fast-food consumption. This could explain the variation in the outcome of the available studies. A recent meta-analysis included studies with UPFs defined as per the NOVA classification and identified that UPFs are associated with increased risk of CD pooled hazard ratio (HR): 1.71, 95%CI: 1.37-2.14) but not with UC (pooled HR: 1.17, 95%CI: 0.86-1.61)[71].

Table 4 Summary of available studies on the use of ultra-processed food and risk of inflammatory bowel disease[13-17,51-69].

Ref.	Study design	Population (n)	Assessment method	Diagnostic methods	Main findings
Studies on UPFs and the risk of UC
Persson et al[51], 1992	Case-control	n = 755; Cases: 445, Controls: 310	FFQ	Medical records	Higher fast-food intake: ↑ risk of UC
[52], 1994	Case-control	n = 245; Cases: 146, Controls: 109	Dietary history questionnaire	Self-reported	Western diet: ↑ risk of UC
Rashvand et al[60], 2018	Case-control	n = 186; Cases: 81, Control: 105	FFQ	Medical report	Processed meat: ↑ risk of UC
Akbari et al[67], 2022	Case-control	n = 244; Cases: 85, Control: 158	FFQ	Diagnosis by gastroenterologists	Western dietary pattern: No increased risk of UC
Studies on UPFs and the risk of CD
Cohen et al[57], 2013	Cohort study	n = 6768	FFQ	Self-reported	Higher intake of sweetened beverages: ↑ risk of CD
Peters et al[65], 2022	Cohort study	n = 125445	FFQ	Self-reported	Western diet pattern: ↑ risk of CD
Narula et al[13], 2021	Cohort study	n = 116037	FFQ/NOVA classification	Self-reported	UPFs consumption: ↑ risk of CD
Studies on UPFs and risk of IBD (both UC and CD
Klein et al[53], 1998	Case-control	n = 87; Cases: 60, Control: 27	Dietary history questionnaire	Diagnosis by gastroenterologists	Higher total sugar intake: ↑ risk of both UC & CD
Russel et al[54], 1998	Case-control	n = 1304; Cases: 668, Control: 636	Dietary history questionnaire	Medical records	Chocolate intake: No increased risk of IBD
Sakamoto et al[55], 2005	Case-control	n = 319; Cases: 156, Control: 163	FFQ	Self-administered questionnaire	Higher sweets intake was associated with ↑ the risk of IBD; Risk for CD > UC
Maconi et al[56], 2010	Case-control	n = 243; Cases: 146, Control: 97	FFQ	Medical records	Processed meat and refined sugar intake: ↑ risk of IBD
Ng et al[58], 2015	Case-control	n = 1382; Cases: 775, Control: 607	Food habits questionnaire	Diagnosis by gastroenterologists	Western dietary pattern: No increased risk of IBD
Ananthakrishnan et al[59], 2015	Cohort study	n = 84803	FFQ	Self-reported and medical reports	Western dietary pattern: No increased risk of IBD
Racine et al[61], 2016	Case-control	n = 366351	FFQ	Hospital-based registries, pathology records	Sugar and soft drinks: No increased risk of IBD
Khalili et al[62], 2019	Cohort study	n = 83042	FFQ	Medical records	Sweetened beverage intake: ↑ risk of IBD
Preda et al[63], 2020	Case-control	n = 185	Dietary history questionnaire	Diagnosis by gastroenterologists	Intake of sweets/sweetened drinks, processed meat, fried foods, ice-cream, and mayonnaise: ↑ risk of IBD
Han et al[64], 2020	Case-control	n = 103789; Cases: 46456, Control: 57333	Dietary history questionnaire	Self-reported	Fast food, ice cream, processed meat, cookies, candy, sugar-sweetened beverages: ↑ risk of IBD
Vasseur et al[14], 2021	Cohort study	n = 105832	24-hour dietary/NOVA classification	Self-reported	UPFs consumption: No increased risk of IBD
Meyer et al[15], 2023	Cohort study	n = 413590	FFQ/NOVA classification	Self-reported	UPFs consumption: No increased risk of IBD
Dong et al[66], 2022	Cohort study	n = 413590	FFQ	Self-reported	Processed meat intake: No increased risk of IBD
Lo et al[16], 2022	Cohort study	n = 245112	FFQ/NOVA classification	Self-reported and medical report	UPFs consumption: ↑ risk of IBD

CD: Crohn’s disease; FFQ: Food frequency questionnaire; IBD: Inflammatory bowel disease; UC: Ulcerative colitis; UPFs: Ultra-processed foods.

UPFs and metabolic dysfunction-associated steatotic liver disease

Non-alcoholic fatty liver disease, now renamed as metabolic dysfunction-associated steatotic liver disease (MASLD)[72], is the most common chronic liver disease in the world, affecting approximately one-third of the world population[73]. Its spectrum includes hepatic fat accumulation, inflammation, fibrosis, and ultimately cirrhosis. The global rise in MASLD parallels the increased consumption of UPFs. UPFs, characterized by high energy density, added sugars, unhealthy fats, and various additives, have been implicated in metabolic disturbances. A diet high in UPFs contributes highly to this spectrum of MASLD, as shown by the United Kingdom National Diet and Nutrition Survey, where 53% of daily calorie intake was from UPFs and had a direct association with the prevalence of obesity[74]. Henney et al[75], in their comprehensive analysis of nine studies involving 60000 participants, found that high UPFs intake significantly increased the risk of MASLD (pooled RR: 1.42; 95%CI: 1.16-1.75). Definite correlation was found by Grinshpan et al[76] in their extensive review of twenty-seven studies between MASLD markers and high UPFs diet. Zhang et al[77] in their UK-Biobank study cohort of 143073 individuals found a 26% increased risk of severe MASLD with higher consumption of UPFs-rich diet, defined by hospitalization or death. Four other cross-sectional studies from the United States, Spain, Brazil, and the United Kingdom showed similar evidence of higher odds of the development of hepatic steatosis with high UPD diet consumption. Konieczna et al[78,79] in their prospective cohort studies (PREDIMED-Pls trial) on patients with metabolic syndrome, showed a strong correlation between visceral fat deposition and high UPFs intake, ultimately leading to hepatic steatosis. All available studies have been formulated in Table 5[77,79,80-102]. These studies underscore a consistent association between UPFs intake and MASLD risk across diverse populations. Available data on negative associations are concluded in Table 6[74,103-106].

Table 5 Summary of available studies on positive association of ultra-processed foods with non-alcoholic fatty liver disease/obesity/metabolic syndrome[77,79,80-102].

Ref.	Study design & population (n)	UPFs assessment method	Diagnosis of MASLD	Main findings
Zhang et al[77], 2024	Prospective cohort (United Kingdom Biobank, n = 143073)	24-hour diet recall, NOVA classification	Hospitalizations, mortality records (ICD codes)	26% ↑ risk of severe MASLD (HR: 1.26; 95%CI: 1.15-1.38)
Zhang et al[80], 2022, China	Prospective, n = 16168	NOVA (g/1000 kcal/day)	AUS	The highest UPFs quartile had 18% higher MASLD risk (HR 1.18); dose-response noted
Liu et al[81], 2023	Cross-sectional (NHANES 2011-2018, n = 5499)	24-hour dietary recall, NOVA classification	US-FLI > 30	83% ↑ odds of MASLD (OR: 1.83; 95%CI: 1.42-2.37)
Konieczna et al[79], 2022	Prospective cohort (PREDIMED-Plus subset), n = 5867	FFQ, NOVA classification	FLI	Greater UPFs consumption: Associated with ↑ ALT, AST, and hepatic fat accumulation, especially in high-risk metabolic groups
Rauber et al[82], 2018	Cross-sectional (United Kingdom, 2008-2014)	National Diet and Nutrition Survey data + NOVA classification	Nutrient profile analysis; indirect MASLD risk via dietary patterns	Diets high in UPFs had ↓ fiber, ↑ sugars, and ↑ fats-suggestive of ↑ MASLD risk
Hall et al[83], 2020, United States	RCT, n = 20	NOVA (controlled feeding trial)	MRS	No significant change in liver fat after 2-week UPFs or unprocessed diet
Fridén et al[84], 2022, Sweden	Cross-sectional, n = 286	NOVA (% of kcal)	MRI	Positive crude association with liver fat; not significant after adjustment
Ivancovsky-Wajcman et al[85], 2021, Israel	Cross-sectional, n = 789	NOVA (% of kcal)	AUS + FibroMax panel	No direct UPFs-MASLD link; higher UPFs linked to ↑ NASH and fibrosis in smokers and MASLD patients
Canhada et al[86], 2023, Brazil	Prospective, n = 8065	Semi-quantitative 114-item FFQ NOVA classification	-	Higher UPFs consumption was associated with a 19% ↑ risk of incident MetS. 150 g increase in UPFs consumption/day: Associated with a 4% ↑ risk of incident MetS
Pan et al[87], 2023, China	Prospective, n = 5147	24-hour dietary recall, Cumulative mean UPF intake, NOVA classification (g/day)	-	Higher UPFs consumption: Associated with 17% ↑ risk for MetS (HR 1.17, 95%CI: 1.01-1.35)
Martínez Steele et al[88], 2019, United States	Cross-sectional (NHANES), n = 6385	24-hour dietary recall NOVA classification	-	A 10% ↑ increase in UPFs consumption was associated with a 4% higher prevalence of MetS (PR 1.04, 95%CI: 1.02-1.07). Higher UPFs consumption: Associated with a ↑ prevalence of MetS (PR 1.28, 95%CI: 1.09-1.50)
Lavigne-Robichaud et al[89], 2018, Canada	Cross-sectional, n = 811	24-hour dietary recall NOVA classification	-	Higher UPFs consumption: Associated with ↑ prevalence of MetS (OR 1.90, 95%CI: 1.14-3.17; P for trend = 0.04)
Li et al[90], 2021, China	Prospective, n = 12451	24-hour dietary recall of 3 consecutive days at each survey, Cumulative mean UPF intake NOVA classification (g/day)	-	Higher UPFs consumption: Associated with ↑ risk of overweight/obesity and central obesity
Cordova et al[91], 2021, 9 European countries	EPIC study, prospective, n = 348748	Quantitative dietary questionnaires or semi-quantitative FFQ, or a combination of semi-quantitative FFQ and 7- and 14-day records, NOVA classification	-	Higher consumption of UPFs (per 1 SD increment) was positively associated with weight gain (0.12 kg/5 years, 95%CI: 0.09-0.15)
Rauber et al[92], 2021, United Kingdom	Prospective, n = 18218	24-hour dietary recall, NOVA classification	-	Higher UPFs consumption: Associated with ↑ risk for obesity (HR = 1.79, 95%CI: 106-3.03), and abdominal obesity (HR = 1.30, 95%CI: 113-1.48)
Sandoval-Insausti H et al[93], 2020, Spain	Prospective, n = 652	Face-to-face dietary history, recording all food consumed in a typical week in the preceding year, NOVA classification	-	Participants with a higher UPFs consumption were more likely to develop abdominal obesity (OR = 1.62, 95%CI: 104-2.54; P for linear trend = 0.037)
Beslay et al[94], 2020, France	NutriNet-Sante cohort, n = 110260	24-hour dietary recall, NOVA classification	-	Risk of overweight (HR for an absolute ↑of 10% of UPFs = 1.11, 95%CI: 1.08-1.14, P < 0.001), and for obesity (HR for an absolute increment of 10% of UPF = 1.09, 95%CI: 1.05-1.13, P < 0.001)
Canhada et al[95], 2020, Brazil	Prospective, n = 11827	Semi-quantitative 114-items FFQ NOVA classification	-	UPFs consumption: Associated with a ↑ risk of weight gain and waist gain, overweight/obesity incidence (RR = 1.20, 95%CI: 1.03-1.40), and obesity incidence (RR = 1.02, 95%CI: 0.85-1.21)
Mendonça et al[96], 2016, Spain	SUN project, prospective, n = 8451	Self-administered semi-quantitative 136-item FFQ, NOVA classification	-	↑ incidence of overweight and obesity with ↑ baseline quartiles of UPFs
Silva Meneguelli et al[97], 2022, Brazil	Cross-sectional, n = 325	24-hour dietary recall NOVA classification	-	Positive associations between UPF consumption and excessive body weight (PR = 1.004, 95%CI: 1.00-1.01), and abdominal obesity (PR = 1.004, 95%CI: 1.00-1.01)
Martinez-Perez et al[98], 2021, Spain	Cross-sectional, PREDIMED-Plus trial, n = 5636	Semi-quantitative 143-items FFQ NOVA, IARC, IFIC, and UNC classification	-	5% ↑ in UPFs consumption: Associated with 0.11 higher BMI (95%CI: 0.05-0.18)
Machado et al[99], 2020, Australia	Cross-sectional, NNPAS, n = 7411	24-hour dietary recall NOVA classification	-	UPFs consumption: Associated with higher BMI and WC and ↑ prevalence of obesity and abdominal obesity (P < 0.001 for all outcomes)
Nardocci et al[100], 2021, Canada	Cross-sectional, CCHS, n = 13608	24-hour dietary recall NOVA classification	-	10% ↑ in UPFs consumption: Associated with 6% ↑ odds of obesity (OR 1.06, 95%CI: 1.02-1.11)
Juul et al[101], 2018, United States	Cross-sectional, NHANES, n = 15977	24-hour dietary recall NOVA classification	-	Higher UPFs consumption is associated with a 161-unit increase in BMI (95%CI: 1.11-2.10), a 407 cm increase in WC (95%CI: 2.94-5.19), and greater odds of being overweight (OR 1.48, 95%CI: 1.25-1.76), obese (OR 1.53, 95%CI: 1.29-1.81), and having abdominal obesity (OR 1.62, 95%CI: 1.39-1.89)
Silva et al[102], 2018, Brazil	Brazilian Longitudinal Study of Adult Health (ELSA-Brazil), cross-sectional, n = 8977	Semi-quantitative FFQ, NOVA classification	-	Higher UPFs consumption: Associated with a higher BMI (b = 0.80, 95%CI: 0.53-1.07 kg/m2), WC (b = 1.71, 95%CI: 1.02-2.40 cm) and higher odds for being overweight (OR 1.31, 95%CI: 1.13-1.51), obese (OR 1.41, 95%CI: 1.18-1.69) and increased WC (OR 1.41, 95%CI: 1.20-1.66)

AUS: Abdominal ultrasound; FLI: Fatty Liver Index; US: Ultrasound; MRS: Magnetic resonance spectroscopy; MRI: Magnetic resonance imaging; NNPAS: Cross-sectional, National Nutrition and Physical Activity Survey; CCHS: Canadian Community Health Survey; AST: Aspartate aminotransferase; ALT: Alanine aminotransferase; BMI: Body Mass index; CI: Confidence interval; FFQ: Food frequency questionnaire; HOMA-IR: Homeostatic model assessment for insulin resistance; HR: Hazard ratio; HSI: Hepatic steatosis index; IARC: International Agency for Research on Cancer; IFIC: International food information council; IR: Insulin resistance; Mets: Metabolic syndrome; MASLD: Non-alcoholic fatty liver disease; NHANES: National Health and Nutrition Examination Survey; OR: Odds ratio; QUICKI: Quantitative insulin-sensitivity check index; RCT: Randomized controlled trial; RR: Relative risk; UNC: University of North Carolina; UPF: Ultra-processed food; WC: Waist circumference.

Table 6 Studies with a negative association between ultra-processed foods and non-alcoholic fatty liver disease/obesity/metabolic syndrome.

Ref.	Design & population (n)	UPFs assessment method	Outcomes
Magalhães et al[103], 2022, Brazil	Prospective, n = 896	Semi-quantitative 83-item FFQ, NOVA classification	UPFs consumption: No association with MetS
Barbosa et al[104], 2023, Brazil	Cross-sectional, n = 895	24-hour dietary recall, NOVA classification, NOVA score	Higher UPFs consumption: Not associated with a ↑ prevalence of MetS
Nasreddine et al[105], 2018, Lebanon	Community-based survey, n = 302	Semi-quantitative 80-item FFQ NOVA classification	Ultra-processed dietary pattern: No association with MetS (OR 1.11, 95%CI: 0.26-4.65)
Asma et al[106], 2019, Malaysia	Cross-sectional, n = 200	24-hour dietary recall NOVA classification	UPFs consumption: Not associated with BMI, WC, and% body fat
Adams and White[74], 2015, United Kingdom	Cross-sectional, n = 2174	Food diary and UPF	UPFs consumption: No association with markers of body weight

BMI: Body mass index; CI: Confidence interval; FFQ: Food frequency questionnaire; Mets: Metabolic syndrome; OR: Odds ratio; UPF: Ultra-processed food; WC: Waist circumference.

UPFs and risk of cancer

The association between the type of food and the risk of cancer has been hypothesized for a long time. The World Cancer Research Fund’s continuous update project for the first time reviewed the association between the type of processed food consumption and risk of cancer development[107]. A study suggested a strong association between the consumption of processed meat and the risk of colon cancer. Others also reported a strong association between alcoholic drinks and risk for laryngopharyngeal cancer, GI cancers, breast cancer, and kidney cancer. With the introduction of the NOVA food processing framework, evidence for the association between UPFs intake and cancer risk has started emerging. UPFs hypothetically increases the cancer risk through its obesogenic properties and the effect of food additives and preservatives[95,108,109]. Although these additives and preservatives are used within the safety limits, the impact of their cumulative effect on humans is largely unknown, while animal studies point towards carcinogenic potential[110,111].

Five large prospective cohort studies have investigated the relationship between consumption of UPFs and cancers[109,112-115]. Fiolet et al[109] in a large French cohort of 104980 participants evaluated the association between UPFs and risk of overall cancer, breast cancer, colorectal cancer, and prostate cancer. They found that a 10% increment in the proportion of UPFs in the diet was associated with a 13% increased risk of overall cancer (HR 1.13; 95%CI: 1.07-1.18) and an 11% increased risk of breast cancer (HR 1.11, 95%CI: 1.02-1.22). Though no significant risk of colorectal cancer (CRC) and Prostate cancer was noted. Wang et al[112] in a cohort of 206248 participants evaluated the risk of CRC and noted a 29% increased risk of CRC (HR 1.29, 95%CI: 1.08-1.53) in males with the highest quintile of UPFs compared to males with the lowest quintile of UPFs. The risk of CRC was not noted among the females (HR 1.04, 95%CI: 0.90-1.20). Zhong et al[113] evaluated and found the increased risk of pancreatic cancers (HR 1.49, 95%CI: 1.07-2.07) with the consumption of UPFs. Recent European and United Kingdom cohorts have identified an increased risk of overall cancer and site-specific cancers with > 10% increased consumption of UPFs[114,115]. Several retrospective studies have also noted an association with consumption of UPFs and increased risk of overall cancer, colorectal cancer, breast cancer, chronic lymphocytic leukemia, and central nervous system tumors[116-123]. Table 7 summarizes all the available studies on UPFs and its association with malignancy.

Table 7 Summary of available studies on the use of ultra-processed food and risk of cancers.

Ref.	Study design	Population characteristics, n	Outcome parameters	Main findings
Fiolet et al[109], 2018, France	Prospective cohort (NutriNet-Sante)	Adults > 18 years, n = 104980	Overall cancer, breast cancer, CRC, prostate cancer	A 10% ↑ increase in the proportion of UPFs in the diet is associated with; 13% ↑ risk of overall cancer, an 11% ↑ risk of breast cancer. No significant association between UPFs consumption and risk of CRC or prostate cancer
Wang et al[112], 2022, United States	Prospective cohort	Adults: 25-75 years, 206248	CRC	Males in the highest quintile of UPFs consumption had a 29% ↑ risk of CRC compared to males in the lowest quintile. No association was found between UPFs consumption and CRC risk in females
Zhong et al[113], 2023, United States	Prospective cohort	Adults: 55-74 years, n = 98265	Pancreatic cancer	Adults in the highest quartile of UPFs consumption had a 49% ↑ risk of pancreatic cancer compared to individuals in the lowest quartile
[114], 2023	Multicentric prospective cohort	n = 450111	Head and neck cancers, esophageal cancer, gastric cancer, colon cancer, rectal cancer, hepatocellular carcinoma, gallbladder cancer, pancreatic cancer, lung cancer, renal cell carcinoma, bladder cancer, glioma, thyroid cancer, multiple myeloma, non-Hodgkin lymphoma, leukaemia, melanoma, breast cancer (premenopausal and postmenopausal), cervical cancer, endometrial cancer, ovarian cancer, and prostate cancer	A substitution of 10% of processed foods with an equal amount of minimally processed foods was associated with ↓ a risk of: Overall cancer (HR 0.96, 95%CI: 0.95-0.97), head and neck cancers (HR 0.80, 95%CI: 0.75-0.85); Esophageal squamous cell carcinoma (HR 0.57, 95%CI: 0.51-0.64); Colon cancer (HR 0.88, 95%CI: 0.85-0.92); Rectal cancer (HR 0.90, 95%CI: 0.85-0.94); Hepatocellular carcinoma (HR 0.77, 95%CI: 0.68-0.87), and Postmenopausal breast cancer (HR 0.93, 95%CI: 0.90-0.97); Replacement of processed and ultra-processed foods and drinks with an equal amount of minimally processed foods might ↓ the risk of various cancer types
Chang et al[115], 2023, United Kingdom	Prospective cohort	Adults: 40-69 years	Overall, cancer and 34-site site-specific cancers	Every 10% point ↑ in UPF consumption was associated with: ↑ overall cancer (HR: 1.02, 95%CI: 1.01-1.04); ↑ ovarian cancer (HR: 1.19, 95%CI: 1.08-1.30)
El Kinany et al[118], 2022, Morocco	Case-control	Adults > 18 years; Cases (n = 1453); Controls (n = 1453)	CRC	Individuals in the highest tertile of UPFs consumption compared to the lowest tertile had: 40% ↑ OR of having overall CRC; 36% ↑ OR of having colon cancer; 44% ↑ OR of having rectal cancer
Jafari et al[119], 2023, Iran	Case-control	Adults: 40-75 years; Cases (n = 71); Controls (n = 142)	CRC	Individuals in the highest tertile of UPFs consumption had a 332% ↑ OR of CRC compared to individuals in the lowest tertile
Romaguera et al[117], 2021, Spain	Case-control	Adults: 20-85 years; CRC; Cases (n = 1852); Controls (n = 3447); Breast cancer; Cases (n = 1486); Controls (n = 1652); Prostate cancer; Cases (n = 953); Controls (n = 1283)	CRC; breast cancer; prostate cancer	Individuals in the highest tertile of UPFs consumption had a 30% ↑ OR of CRC compared to the lowest tertile; No significant association between UPFs consumption and prostate cancer or overall breast cancer
Romieu et al[121], 2022, Chile, Colombia, Costa Rica, Mexico	Case-control	Adults 20-45 years; Cases (n = 525); Controls (n = 525)	Premenopausal breast cancer	Participants in the highest tertile of UPFs consumption had a 93% ↑ OR of having overall premenopausal breast cancer compared to the lowest tertile
Solans et al[116], 2021, Spain	Case-control	Adults: 20-85 years; Cases (n = 230); Controls (n = 1634)	CLL	In incident cases only, a 10% ↑ in UPFs in the diet was associated with a 22% ↑ OR of being diagnosed with CLL
Trudeau et al[122], 2020, Canada	Case-control (PROtEuS)	Adults: 39-75 years; Cases (n = 1919); Controls (n = 1991)	Prostate cancer	No association was found between UPFs consumption and prostate cancer when comparing quartiles of UPF consumption
Esposito et al[123], 2023, Italy	Case-control	Adults, > 18 years; Cases (n = 44); Controls (n = 88)	CNS tumours	1% ↑ in UPFs in diet was associated with: 6% ↑ OR of overall CNS tumors; 9% ↑ OR of malignant CNS tumors
Jacobs et al[120], 2022, South-Africa	Case-control	Adults > 18 years; Cases (n = 396); Controls (n = 396)	Breast cancer	No statistically significant association between UPFs consumption and breast cancer when comparing tertiles of UPFs consumption

CLL: Chronic lymphocytic leukemia; CNS: Central nervous system; CRC: Colorectal cancer; HR: Hazard ratio; OR: Odds ratio; UPF: Ultra-processed foods; CI: Confidence interval.

Despite the limited literature, the studies show evidence of a positive association between consumption of UPFs and cancer risk. The risk appears to be increased for overall cancers, CRC, breast cancer, and pancreatic cancer.

Human trials of dietary emulsifier restriction

The harmful effects of food emulsifiers are long established in animal studies. Among the common emulsifiers, carrageenan is most commonly studied and has properties to induce IBD in murine models. Though the human studies are still in the infancy stage, with limited available studies. Chassaing et al[124] for the first time showed that emulsifiers can have a direct impact on the human gut microbiota by changing the relative abundance of bacteria and gene expression and driving the intestinal inflammation. Table 8 summarizes the interventional studies on the role of emulsifiers on gut health.

Table 8 Available randomised studies on the dietary emulsifier restriction and their effect.

Ref.	Type of study	Population characteristics, n	Outcome parameters	Key findings
Bhattacharyya et al[125], 2017	RCT	Cases (n = 5): UC in remission on 100mg carrageenan-containing capsule; Controls (n = 7): UC in remission on placebo capsules	Relapse in two groups at different time points (3, 6, 9, and 12 months); Relapse is defined as an increase of two (or more) points on the SCCAI	Relapses were higher with the carrageenan diet (P = 0.046); Increase in interleukin-6 (P = 0.02) and fecal calprotectin (P = 0.06) in carrageenan diet group
Chassaing et al[126], 2022	RCT	Cases (n = 7): Diet enriched with 15 g/day of CMC; Controls (n = 9): Emulsifier-free diets	Metabolic impact in healthy volunteers: Effect on human gut microbiota composition and gene expression	CMC reduced microbiota richness with a decrease in evenness and Shannon indices; CMC consumption affected the fecal metabolome with depletion of short-chain fatty acids and free amino acids
Fitzpatrick et al[127], 2025	RCT	HED (n = 12) vs LED (n = 12) was provided for 4 weeks in patients with CD		The emulsifier content did not influence disease activity in CD
Bancil et al[128], 2025	Multicentric RCT	LED (n = 75) vs LED plus emulsifier re-supplementation (Controls, n = 79) was provided for 8 weeks in active CD	Primary endpoint: Proportion of patients achieving CDAI response (≥ 70 reduction) at 8 weeks; Secondary endpoint: CDAI remission and fecal calprotectin	CDAI response was achieved in 39 (49.4%) on LED vs 23 (30.7%) in the control group (P = 0.019); Patients on LED are more than twice as likely to experience CDAI remission (adjusted RR: 2.1, 95%CI: 1.0-4.42) and > 50% reduction in FCP (adjusted RR: 2.9, 95%CI: 1.1-8.0)

CD: Crohn’s disease; CDAI: Crohn’s disease activity index; FCP: Fecal calprotectin; RCT: Randomized controlled trial; UC: Ulcerative colitis; SCCAI: Simple Clinical Colitis Activity Index; CMC: Carboxymethylcellulose; HED: High emulsifier diet; LED: Low emulsifier diet; RR: Relative risk.

Four randomized clinical trials (RCTs) have evaluated the effect of emulsifier on the gut microbiome and IBD. Bhattacharyya et al[125], for the first time, showed the detrimental effect of carrageenan in the diet with increased relapse rates of UC. Though subsequent studies showed variable results with Chassaing et al[126] showed the altered gut microbiome with CMC consumption, while Fitzpatrick et al[127] showed no effect of emulsifier content on the disease activity of CD. However, a limitation of these studies was the limited sample size. A recent multicentric RCT randomised 154 patients with active CD and identified that consumption of low low-emulsifier diet is associated with improved clinical and laboratory response[128].

FUTURE RESEARCH

The availability of a newer classification system has expanded our knowledge of the role of UPFs in various GI disorders and their associations. However, our understanding remains limited, with much still to be discovered. Future research should determine the cut-off ranges of UPFs in daily food intake and categorize them as harmful accordingly. These cut-offs should also be identified for different geographic locations and populations. More importantly, observational associations should be confirmed through randomized studies, with a concurrent focus on experimental research to understand the exact mechanisms of UPFs and GI disorders. Strategies involving dietary guidelines and recommendations for reducing UPFs consumption worldwide should be promoted.

CONCLUSION

GI diseases include a wide range of disorders with an incomplete understanding of etiopathogenesis. Often, these disorders are associated with a change in gut microbiota with an incompletely understood mechanism. With the recent availability of novel classification of UPFs, there is a better understanding of the link between GI disorders and UPFs. The available studies point towards higher consumption of UPFSs and increased risk of overweight and obesity, abdominal obesity, metabolic syndrome, functional GI disorders, CD, colorectal cancer, breast cancer, pancreatic cancer, and overall cancers. However, evidence supporting protective dietary interventions remains limited, based on small-scale studies with inconsistent findings. Secondly, the majority of the evidence is derived from Western countries, particularly Europe and North America, largely due to the availability of large, well-established cohorts in these regions. However, this geographic concentration raises concerns regarding the generalizability of the findings to populations in underrepresented regions.