Next Article in Journal
Nutritional Management of Irritable Bowel Syndrome
Previous Article in Journal
Current Concepts in Probiotic Safety and Efficacy
Previous Article in Special Issue
Potato Protein-Based Vegan Burgers: Discovering the Health-Promoting Benefits and Impact on the Intestinal Microbiome
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Simulated Gastrointestinal Digestion Modulates Anticholinesterase, Antioxidant, and Anti-Inflammatory Activities of Vegan Soups Rich in Natural Cholinesterase Inhibitors

by
Dorota Gajowniczek-Ałasa
1,*,
Roman Paduch
2,3,
Ewa Baranowska-Wójcik
1,
Oskar M. Szczepaniak
4 and
Dominik Szwajgier
1,*
1
Department of Biotechnology, Microbiology and Human Nutrition, University of Life Sciences in Lublin, Skromna 8, 20-704 Lublin, Poland
2
Department of Virology and Immunology, Institute of Biological Sciences, Maria Curie-Skłodowska University, Akademicka 19 Street, 20-033 Lublin, Poland
3
Department of General and Pediatric Ophthalmology, Medical University, Chmielna 1, 20-079 Lublin, Poland
4
Department of Biochemistry and Biotechnology, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland
*
Authors to whom correspondence should be addressed.
Nutrients 2026, 18(4), 698; https://doi.org/10.3390/nu18040698
Submission received: 19 January 2026 / Revised: 15 February 2026 / Accepted: 17 February 2026 / Published: 21 February 2026
(This article belongs to the Special Issue Plant-Based Diets Regulate Antioxidant-Inflammatory Balance)

Abstract

Background/Objectives: Dietary strategies targeting oxidative stress, neuroinflammation and cholinergic dysfunction are increasingly investigated as supportive approaches for maintaining cognitive health. Soups constitute a practical functional food matrix due to their compositional complexity and suitability for regular consumption. However, their bioactivity may be substantially altered during digestion. Methods: Previously, we created optimized mushroom, asparagus, leek, and sea buckthorn vegan lunch soups rich in cholinesterase inhibitors. This study evaluated digestion-induced changes in anticholinesterase, antioxidant, and anti-inflammatory activities using a standardized static in vitro digestion model (INFOGEST). Results: Fresh soups contained 90.43–247.36 µg GAE/cm3 of total polyphenols, which significantly decreased during oral–intestinal digestion, followed by stabilization or partial recovery during the colonic phase. Acetylcholinesterase and butyrylcholinesterase inhibitory activities showed soup-specific and digestion stage-dependent patterns, with an overall decline after bacterial incubation. Antioxidant capacity assessed by DPPH, ABTS•+, and cyclic voltammetry revealed dynamic redox shifts across digestion stages, while endogenous antioxidant enzymes (SOD, CAT, GR, GPx) and COX-2 activity were differentially modulated. Cell-based assays demonstrated low cytotoxicity and moderate, concentration-dependent cytokine modulation. Conclusions: Overall, gastrointestinal digestion and microbial activity markedly reshape the bioactivity of plant-based soups, indicating that the colonic phase is critical for realistic evaluation of functional food potential and supporting digestion-aware assessment of dietary strategies relevant to cognitive and inflammatory health.

1. Introduction

Previously, through several optimization steps, we designed, produced, and characterized mushroom, asparagus, leek, and sea buckthorn lunch soups exhibiting high anticholinesterase activity [1]. These soups were rich sources of phenolic compounds, a group of bioactive molecules extensively studied for their anticholinesterase, antioxidant, and neuroprotective properties, making them promising candidates for supporting cognitive health in the context of Alzheimer’s disease (AD) [2,3]. Assessment of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibition is particularly relevant, as AD is characterized by progressive cholinergic dysfunction resulting from decreased acetylcholine availability. Numerous studies indicate that food-derived cholinesterase inhibitors may contribute to maintaining acetylcholine levels and supporting cognitive function through non-pharmacological pathways [4,5,6,7]. In parallel, oxidative stress plays a central role in AD pathogenesis, and polyphenols are widely recognized for their ability to scavenge reactive oxygen species and protect neuronal cells from oxidative damage [2,3,8,9,10]. Except for the action of low-molecular-weight antioxidants, the modulation of endogenous antioxidant and inflammatory pathways is the strong point of functional foods. Enzymes such as superoxide dismutase (SOD), catalase (CAT), glutathione reductase (GR), and glutathione peroxidase (GPx) collectively regulate cellular redox homeostasis, while cyclooxygenase-2 (COX-2) and cytokine production are key mediators of inflammatory processes linked to neurodegeneration [11,12,13]. Monitoring this panel of targets allows for an integrated assessment of antioxidant and anti-inflammatory responses relevant to AD-associated pathophysiology rather than isolated single-endpoint effects. Importantly, the biological activity of polyphenol-rich foods should be assessed after the digestion. Gastrointestinal digestion induces profound changes in food composition through enzymatic hydrolysis, pH variations and microbial metabolism, which may reduce, enhance, or qualitatively modify the bioactivity of dietary compounds [14,15,16]. During digestion, phenolic compounds may undergo degradation, form complexes with macronutrients, or be released and transformed into secondary metabolites with altered bioavailability and biological properties [17,18,19].
Therefore, digestion-aware evaluation is essential for estimating the physiological relevance of functional foods. Based on our previous optimization of mushroom, asparagus, leek, and sea buckthorn soups for high anticholinesterase activity [1], the present study aimed to investigate how simulated gastrointestinal digestion affects their polyphenolic content and associated anticholinesterase, antioxidant, and anti-inflammatory activities. A standardized static in vitro digestion model (INFOGEST), including a colonic phase with bacterial inoculation, was applied to address the existing knowledge gap regarding digestion- and microbiota-driven modulation of bioactivity in complex plant-based food matrices.

2. Materials and Methods

2.1. Reagents

Chemicals used for simulated gastrointestinal digestion, biochemical assays, and cell culture experiments were obtained from commercial suppliers of analytical grade. Enzymes, substrates, antioxidant standards, and culture reagents were primarily purchased from Sigma-Aldrich (St. Louis, MO, USA), while selected solvents and auxiliary chemicals were obtained from Avantor (Gliwice, Poland). Cell culture media and supplements were supplied by Gibco (Paisley, UK), and analytical-grade CO2 was provided by Linde Gas (Lublin, Poland).
Detailed information on individual reagents, catalog numbers, and suppliers is provided in the Supplementary Materials.

2.2. Preparation of Soups

Four lunch soups were prepared in our laboratory following a formulation strategy described in our previous study [1]. Briefly, individual plant ingredients were first selected based on their high acetyl- and butyrylcholinesterase inhibitory potential and subsequently combined to obtain complete soup formulations. The soups were prepared in batch quantities using standard cooking and homogenization procedures to obtain ready-to-eat products (Tables S1 and S2, Supplementary Materials). After confirming the cholinesterase-inhibitory activity of the final soups and their sensory acceptability, four formulations were selected for further analyses (Figure 1). These final soup products were then used in the in vitro digestion experiments, without normalization to dry mass, in order to reflect a realistic consumption scenario.

2.3. In Vitro Digestions of Soups

In vitro digestion of the soups was performed using the apparatus and general procedure previously described by Baranowska-Wójcik et al., which follows the standardized INFOGEST framework proposed by Minekus [20] and later refined by Brodkorb [21], with minor adjustments [22]. A detailed description is provided in the Supplementary Materials.

2.4. Total Polyphenolic Content

Total Polyphenolic Content (TPC) was determined using the Folin–Ciocalteu colorimetric method, based on the reduction of the reagent by phenolic compounds, as described in our previous work [23]. Results were expressed as gallic acid equivalents (GAE).

2.5. Inhibition of AChE and BChE

AChE and BChE inhibitory activities were determined using Ellman’s colorimetric method, based on the hydrolysis of acetylthiocholine or butyrylthiocholine and reaction with DTNB, as described previously [24]. The false-positive effects were verified according to the method of Rhee et al. [25] The inhibitory activity was expressed as a percentage of enzyme inhibition under the test conditions.

2.6. Antioxidant Activity Testing

Antioxidant activity was determined with azino-bis(3-ethylbenzthiazoline-6-sulphonic acid (ABTS•+) and 2,2′-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assays, based on the reduction of stable radicals by antioxidant compounds, according to our previously described procedures [24]. DPPH radical scavenging activity was quantified using a Trolox calibration curve and expressed as Trolox equivalents. For cyclic voltammetry, samples were centrifuged at 6000× g to remove traces of undigested residues and matrix that might bias the electrochemical measurements.
Cyclic voltammetry (CV) was used in a three-electrode system, and antioxidant capacity was evaluated based on total reducing activity (Ei) and peak area at 0.7 V, according to our own method [26]. The working electrode was a carbon paste electrode prepared according to the patent [27], the AgCl electrode (Mineral, Warsaw, Poland) was the reference, and as a measuring electrode, Pt wire (Mineral) was used. The measurements were run within the potential range between −1 and +1.4 V. The three-electrode system was conditioned in a 0.01 M phosphate buffer saline (PBS) (pH = 7.0) with the addition of 3 mM KCl. Conditioning time and potential were 60 s and 1.7 V, respectively. Then, after checking the background of PBS, the electrode set was transferred to a vessel containing 800 µL of PBS and 200 µL of supernatant from the tested sample. Then the analyte was deposed to the electrode surface using applied potential of +0.5 V for a period of 120 s. Then the right CV measurement was performed. For each sample, 3 repetitions of the CV were performed. In addition, each trial was examined three times in independent replications. The study was conducted using potentiostat µStat i400s (Metrohm, Switzerland). The voltammograms recorded in CV analyses were then statistically analyzed using the Origin Pro 2024 software (Origin, Northhampton, MA, USA).

2.7. Effect on Catalase Activity

CAT activity was evaluated based on the decomposition of hydrogen peroxide. The assay was performed according to the method of Watanabe et al. [28] with minor modifications. Briefly, changes in hydrogen peroxide concentration were monitored spectrophotometrically at 240 nm. The results are presented as the residual H2O2 concentration measured after incubation with the tested samples, reflecting CAT-mediated H2O2 decomposition. The reaction system consisted of EDTA solution (56.5 mM; 0.02 mL), tested sample (0.01 mL), 3% H2O2 (0.02 mL), and catalase solution diluted 4000-fold in TRIS buffer (pH 7.0, 1 M). All reagents, except the analyzed sample, were prepared in the same buffer, and the final reaction volume was adjusted to 0.31 mL. In the control system, distilled de-ionized water was used instead of the sample, whereas sample background was determined in buffer-completed mixtures lacking enzymatic reagents. Absorbance was monitored at 240 nm immediately after mixing and after 5 min of incubation at room temperature. Catalase-related activity was evaluated by comparing the decrease in H2O2 absorbance between tested and control systems. Quantification was based on a calibration curve prepared from eleven H2O2 standards (0.754–6.032 mmol/L). Results were expressed both as percentage inhibition and as the rate of H2O2 depletion (mmol L−1 min−1).

2.8. Effect on GPx, GR, SOD and COX-2 Activity

The activities of GPx, GR, SOD, and COX-2 were determined using commercially available assay kits, strictly following the procedures described by Studzińska-Sroka et al. [29]. Enzyme activities were expressed as percentage modulation relative to the control conditions, whereas COX-2 activity was additionally presented as acetylsalicylic acid equivalents (mg/cm3), according to the assay specification.

2.9. Cultivation of Cell Lines and Cytokine Concentrations

Tests using cell cultures were essentially performed as described in our previous work [30]. HT-29 cells (ATCC No. HTB-38), human colorectal adenocarcinoma (grade I), were cultured in RPMI 1640 medium supplemented with 10% fetal calf serum (FCS) (Gibco, Paisley, UK) and antibiotics (100 U/mL penicillin, 100 µg/mL streptomycin, and 0.25 µg/mL amphotericin B) at 37 °C in a humidified atmosphere with 5% CO2. CCD 841 CoTr cells (ATCC No. CRL-1807) and human normal colon epithelial cells (SV40-transformed) were maintained in RPMI 1640/DMEM (1:1) medium (Sigma-Aldrich Co. LLC, St. Louis, MO, USA) supplemented with 10% FCS and antibiotics at 34 °C in a humidified atmosphere with 5% CO2. For cytokine analysis, cells were seeded at densities appropriate for each assay to ensure optimal attachment and exponential growth and allowed to adhere for 24 h prior to treatment. Digested soup samples were then applied for 24 h at concentrations selected based on preliminary cytotoxicity screening to maintain cell viability above 80% and to reflect biologically relevant exposure conditions. Following treatment, culture supernatants were collected and centrifuged (3000 rpm) prior to analysis. The concentrations of IL-1β, IL-6, and IL-10 were determined using commercially available ELISA kits (E0143Hu, E0090Hu, and E0102Hu, respectively; Bioassay Technology Laboratory, Shanghai, China) according to the manufacturers’ instructions. Briefly, 100 µL of each supernatant was added to the appropriate wells and incubated for 2 h. After washing, enzyme-conjugated secondary antibodies (100 µL) were added and incubated for 1 h, followed by substrate addition. The colorimetric reaction was stopped after 30 min with 2 M H2SO4, and absorbance was measured at 450 nm using a microplate reader (Molecular Devices Corp., Emax, San Jose, CA, USA). Cytokine concentrations were calculated from standard curves. The detection limits were 10.07 pg/mL (IL-1β), 1.03 ng/mL (IL-6), and 2.59 pg/mL (IL-10).

2.10. Statistical Analysis

Statistical analysis for non-cell-based measurements included calculation of mean values and standard deviations. Differences among groups were evaluated using one-way analysis of variance (ANOVA) followed by Tukey’s HSD post hoc test, with statistical significance accepted at p < 0.05. Relationships between analyzed parameters were assessed using Pearson’s correlation coefficient (r). All calculations were performed with Statistica software (version 13.1, StatSoft, Cracow, Poland). Results obtained from cell culture experiments are expressed as mean ± SD from three independent experiments (n = 3). These data were analyzed by one-way ANOVA followed by Dunnett’s multiple comparison test, applying a significance threshold of p < 0.05. In the case of cyclic voltammetry measurements, variability was evaluated using two-way ANOVA, and group differences were subsequently determined with Tukey’s post hoc test at α = 0.05.

3. Results

3.1. Total Polyphenol Content

As repeatedly pointed out in the past, polyphenols are one of the main groups of bioactive compounds in raw materials used in presented study for the preparation of soups [31]. Fresh soups were characterized by high polyphenol levels (90.43–247.36 μg GAE/cm3; Figure 2). A statistically significant (p < 0.05) reduction in TPC occurred throughout oral, gastric, and small-intestinal digestion, preceding the addition of the microbial inoculum. Some differences between soups appeared. The TPC of asparagus soup further insignificantly decreased until the end of digestion, whereas in the case of other soups, significant (p < 0.05, sea buckthorn soup) or insignificant (p > 0.05, mushroom and leek soups) increases were observed (Figure 2). Generally, a common pattern of TPC changes can be observed: the loss of TPC during three initial stages of digestion followed by significant increase (sea buckthorn soup) or stabilization (other soups) until the end of the process.

3.2. Anticholinesterase Activities

Anti-AChE and anti-BChE activities are crucial in the light of the prophylaxis of AD. In the case of undigested soups, the highest anti-AChE (Figure 3) and anti-BChE activity (Figure 4) activities were observed for mushroom and leek soups, reaching approximately 40–70% inhibition, followed by the remaining freshly prepared soups with lower inhibitory potential. Two distinct patterns of anti-AChE evolution during digestion were identified: a gradual, consistent decrease in activity for mushroom and leek soups, and a transient increase until bacterial inoculum addition for asparagus and sea buckthorn soups. Nevertheless, in all cases, anti-AChE activity was significantly reduced at the final stage of digestion in the large intestine (p < 0.05), falling to approximately 10–30% inhibition. A continuous and statistically significant decline in anti-BChE activity throughout digestion was observed for mushroom, asparagus, and leek soups, whereas sea buckthorn soup showed an initial increase followed by a marked decrease at the final digestion stage (Figure 4). Notably, although undigested mushroom soup exhibited the highest inhibitory activity toward both cholinesterases, this activity decreased during digestion to levels comparable with the other soups at the small-intestinal stage and declined further after bacterial incubation in the large intestine (p < 0.05).
Overall, these quantitative and statistically supported changes demonstrate that gastrointestinal digestion and microbial activity markedly reduce cholinesterase inhibitory potential, indicating that in vitro digestion provides a more realistic estimate of physiological bioactivity than analysis of undigested products alone.

3.3. Antioxidant Activity Measured Using DPPH and ABTS•+

Antioxidant capacity assessed using the DPPH assay (Figure 5) was initially high for all undigested soups but declined significantly during the early stages of digestion (oral, gastric, or small-intestinal phase, depending on the soup), with reductions reaching approximately 20–50% of initial values (p < 0.05). After microbial inoculation, divergent patterns emerged: activity increased significantly in sea buckthorn soup and showed a non-significant upward trend in asparagus soup, remained relatively stable in mushroom soup, and slightly decreased in leek soup. In contrast, ABTS•+-based antioxidant activity (Figure 6) showed a continuous and significant reduction in sea buckthorn soup throughout digestion. The remaining soups also exhibited significant losses relative to their undigested forms; however, in each case a marked increase in activity was observed at the end of the small-intestinal phase. Notably, although undigested mushroom soup displayed high initial DPPH scavenging capacity compared with the other soups, this activity decreased substantially during digestion, reaching levels comparable to digested asparagus soup and significantly lower than those measured for sea buckthorn soup at the final stage (p < 0.05). An opposite trend was observed for ABTS•+ analysis, where the initially high antioxidant activity of sea buckthorn soup decreased to values comparable with mushroom and asparagus soups. Overall, the statistically significant and method-dependent quantitative shifts observed across digestion stages (p < 0.05) demonstrate that gastrointestinal and microbial processes critically determine the final antioxidant capacity of the soups, reflecting differential stability and transformation of redox-active compounds within complex plant-based matrices.

3.4. Antioxidant Activity Measured Using Cyclic Voltammetry

In the case of the CV analyses, for the majority of samples, signals in potential range 0.0–0.2 V and 0.6–0.85 V were noted (Figure 7). The first signal is characteristic for vitamin C present in the analyte [32], while the other may be related to the presence of aromatic and heterocyclic compounds, e.g., phenolics, including anthocyanins [33]. To assess the effect of digestion on the bioavailability of antioxidants from the soup samples, two parameters were studied independently. First, “total reducing activity” was assessed by measuring the Ei parameter for each digestion stage. The other was registering only peak area at E = 0.7 V to illustrate the bioavailability of plant secondary metabolites in the samples.
Cyclic voltammetry (CV) analysis revealed significant differences in electrochemical behavior among the soups across the simulated digestion stages (Figure 7). In CV, the recorded current response reflects the overall reducing power of electroactive compounds, such as ascorbic acid, phenolic acids, and flavonoids, which can undergo reversible oxidation–reduction reactions at the electrode surface. Sea buckthorn soup exhibited the highest anodic current intensity during the oral digestion phase (Stage M), significantly exceeding the values recorded for the remaining soups (p < 0.05), indicating a strong presence of readily oxidizable low-molecular-weight antioxidants. In contrast, its signal markedly decreased during the gastric and duodenal stages (Stages S i-m and S i-e), consistent with digestion-related degradation or complex formation of these compounds. Conversely, mushroom, leek, and asparagus soups showed the strongest electrochemical responses at the intestinal stages (L i 2h and L i-e), suggesting digestion-dependent release or transformation of phenolic constituents. The calculated total reducing activity (Ei) quantitatively confirmed these trends, with the highest value observed for the oral phase of sea buckthorn soup. A distinct oxidation peak at approximately 0.7 V, associated with phenylpropanoid-derived phenolics, was most pronounced in the final digestion phase of sea buckthorn soup (Figure 8). Although a smaller peak near 0.7 V was also detected in the negative control, the peak area did not differ significantly among most samples, except for sea buckthorn soup, which maintained a significantly stronger response (p < 0.05).
Cyclic voltammetry profiles revealed a distinct oxidation peak at approximately 0.7 V in all soup samples. The magnitude of this electrochemical signal varied in a digestion-stage- and soup-dependent manner, with statistically significant differences observed between selected stages (p < 0.05), indicating quantitative modulation of the pool of electroactive compounds contributing to the overall redox response. These findings confirm that gastrointestinal digestion dynamically reshapes the electrochemical antioxidant profile of complex plant-based matrices.

3.5. Effect on SOD and CAT Activities

SOD converts the superoxide anion (O2) into molecular oxygen and hydrogen peroxide, which is subsequently removed by CAT, both enzymes being evaluated in the present study. The preservation of both enzymes is essential for the efficient removal of reactive oxygen species from cells. As shown in Figure 9, SOD activity increased significantly (p < 0.05) in all studied soups already at the mouth stage compared to the corresponding undigested samples, rising from approximately 5–25% to about 20–60%, depending on soup type. This elevated SOD activity was maintained until the end of the small-intestinal phase, with no further statistically significant changes observed during later digestion stages. In contrast, CAT activity was assessed indirectly based on residual hydrogen peroxide concentration (Figure 10), where increased H2O2 levels indicate reduced enzymatic activity. Quantitatively, CAT activity decreased significantly at the mouth stage for all soups (p < 0.05), corresponding to increased residual H2O2 levels and indicating reduced hydrogen peroxide-degrading capacity. At the final stage of digestion, CAT activity showed a significant change only in mushroom soup, while no statistically significant differences were observed for the remaining soups. Overall, these quantitative and statistically supported findings demonstrate digestion-dependent modulation of endogenous antioxidant defense enzymes, highlighting the dynamic balance between superoxide dismutation and hydrogen peroxide removal during gastrointestinal processing.
In contrast to the other enzymes, catalase activity was evaluated indirectly through residual hydrogen peroxide concentration (Figure 10). Accordingly, increased H2O2 levels correspond to reduced CAT activity, whereas lower H2O2 concentrations indicate higher enzymatic efficiency. Quantitative analysis revealed that CAT activity decreased significantly (p < 0.05) already at the oral stage of digestion, demonstrating an early digestion-dependent reduction in hydrogen peroxide-degrading capacity.

3.6. Effect on GR and GPx Activities

GR and GPx are another two so-called “antioxidant” enzymes. As shown in Figure 11, similar changes in GR activity increased significantly during digestion (p < 0.05), rising from low levels in undigested soups (approximately 30–80% inhibition, depending on soup type) to peak values at intermediate small-intestinal stages (approximately 45–90%). After bacterial inoculation in the large-intestinal phase, GR activity decreased (to roughly 20–50%), followed by a partial recovery toward the end of digestion. All undigested soups exhibited the lowest GPx activity (approximately 0–35%; Figure 12). A statistically significant increase in GPx activity (p < 0.05) was observed from the oral phase onward, reaching about 5–20% for mushroom and leek soups and up to ~25–30% for sea buckthorn soup during digestion. The exception was sea buckthorn soup at the final large-intestinal stage, where GPx activity decreased significantly. Notably, although undigested asparagus soup showed significantly lower GPx activity than the other soups, digestion led to values comparable to mushroom and leek soups and significantly higher than those of sea buckthorn soup (p < 0.05). Overall, these quantitative and statistically supported trends demonstrate digestion-dependent modulation of endogenous antioxidant enzyme activity, indicating that in vitro digestion provides a more physiologically relevant assessment of soup bioactivity than analysis of undigested products alone.

3.7. Effect on COX-2 Activity

COX-2 activity should be monitored due to its pro-inflammatory properties in the tissues. It can be seen (Figure 13) that early digestion stages (undigested, oral, and gastric phases) were associated with relatively low COX-2 inhibitory activity in mushroom, asparagus, and sea buckthorn soups (approximately 4–7 mg acetylsalicylic acid equivalents/cm3), with statistically significant reductions observed compared to later stages (p < 0.05), while leek soup showed no significant early-stage change. During subsequent digestion, COX-2 inhibitory activity increased, reaching approximately 10–13 mg/cm3 at the final stages in mushroom, asparagus, and sea buckthorn soups (p < 0.05). In leek soup, inhibition rose from about 4 mg/cm3 in early digestion to approximately 9 mg/cm3 at intermediate intestinal stages, followed by a significant decline at the end of digestion (p < 0.05). Overall, these quantitative and statistically supported changes demonstrate digestion-dependent modulation of COX-2 inhibition, reflecting dynamic variation in the anti-inflammatory potential of the soups during gastrointestinal processing.

3.8. Correlation Between TPC and Selected Biochemical Markers

Table 1 presents correlation coefficients (r) between TPC and selected biochemical markers monitored during in vitro digestions. Positive, medium correlations were observed for TPC and CAT in the case of mushroom and asparagus soups (r = 0.52 and r = 0.47, respectively). Medium negative correlations were calculated in the case of TPC and BChE (sea buckthorn soup, r = −0.56), TPC and ABTS•+ (mushroom soup, r = −0.41; leek soup, r = −0.49), TPC and GPx (leek soup, r = −0.56, sea buckthorn soup, r = −0.42), and TPC and COX-2 (asparagus soup, r = −0.56). Strong and very strong negative correlations were observed in the case of asparagus soup (r = −0.83) and leek soup (r = −0.72) for TPC and DPPH.

3.9. Cytokine Levels in Cell Cultures

Incubation for 24 h of human normal colon epithelial cells 841 CoTr, (Table 2) with samples of soups at the conc. of 0.1%, or with digestive fluid (DF) applied singly, revealed no significant changes in the pro-inflammatory IL-1β and IL-6 and anti-inflammatory IL-10 levels in studied samples, as compared to corresponding controls. The highest increase in IL-1β as compared to untreated control was found for leek soup. The highest decrease in IL-6 levels compared to the untreated control was observed when cells were incubated with digested asparagus soup. The highest decrease in IL-10 was observed for mushroom soup.
When the samples were incubated for 24 h with the addition of 1% of soups or DF applied singly, no significant changes in IL-1β levels were observed. However, IL-6 levels were increased in the cultivation media containing non-digested asparagus and sea buckthorn soups. Also, application of 1% of non-digested mushroom and leek soups caused the increase in IL-10 concentrations. However, the same result concerning IL-10 was obtained in the case of the samples containing DF singly, so these observations should be taken with reserve as DF was present in all samples of digested soups taken at the end of the in vitro digestion at the “small intestine” phase.
Incubation of human colon adenocarcinoma cells (HT-29, Table 3) with samples of soups at the conc. of 0.1% or 1%, or DF (applied singly), revealed that the higher concentration of tested soup (1%) led to the more pronounced effect on the production of cytokines. Incubation of cells with samples applied at 0.1% for 24 h revealed no significant changes in the pro-inflammatory IL-1β levels. IL-6 levels were increased after the incubation of cells with non-digested sea buckthorn and leek soups as well as digested leek soup. However, the levels of IL-6 were also raised after the incubation of DF applied singly, so the results concerning digested leek soup should be taken with caution as DF was present in all samples of digested soups at the “small intestine” phase. IL-10 levels were decreased after the incubation of cells with digested mushroom, leek and sea buckthorn soups. Also, the levels of this cytokine were also raised after the incubation of DF applied singly to the cultivation medium, so the results concerning IL-10 levels should also be taken with caution.
Incubation of cells with samples applied at 1% for 24 h resulted in statistically significant modulation of cytokine levels (p < 0.05). Specifically, IL-1β concentrations decreased in samples containing both non-digested and digested mushroom soups, as well as DF applied alone. A reduction in IL-6 was observed in the presence of digested mushroom and leek soups, whereas increased IL-6 levels occurred in samples containing asparagus soup and DF alone. In addition, IL-10 levels decreased in samples treated with undigested asparagus and sea buckthorn soups, as well as digested leek and sea buckthorn soups. Overall, these quantitative and statistically significant changes indicate a moderate, concentration-dependent immunomodulatory response rather than a strong pro- or anti-inflammatory effect.

4. Discussion

The application of acetyl- and butyrylcholinesterase inhibitors remains the only universally accepted pharmacological approach for the symptomatic treatment of AD. In contrast, the present study does not address therapeutic intervention, but rather explores the potential of food-derived cholinesterase-inhibiting compounds as part of a broader nutritional context. Our results highlight the anticholinesterase properties of four functional soups previously designed by our group, with mushroom and leek soups showing the highest inhibitory effects on both AChE and BChE in their undigested states. These findings should not be interpreted as evidence for disease prevention, but instead suggest that such products may contribute to dietary patterns supporting cholinergic function as one of many factors relevant to cognitive health [34]. In the past, several authors pointed out that soups can be a rich source of acetylcholinesterase inhibitors. For example, Li [35] isolated acetylcholinesterase inhibitors from the dried root of Astragalus membranaceus (Fish.) Bge (radix astragali; family Fabaceae). This plant is a traditional health food supplement in Asian populations, and it is used for the production of ethnic tonifying soups due to the proven immunostimulant, diuretic, antidiabetic and analgesic activity. In the present work, two distinct patterns of AChE inhibition were observed during simulated digestion: a consistent decrease in activity for mushroom and leek soups, and an initial increase followed by a decline for asparagus and sea buckthorn soups. The addition of bacterial inoculum negatively affected both AChE and BChE activities across all soups, emphasizing the role of gut microbiota in modulating the bioactivity of cholinesterase-inhibiting compounds. This effect may be attributed to microbial metabolism leading to the formation of small phenolic acids, short-chain fatty acids, and other redox-active metabolites capable of altering enzyme conformation, local pH, or redox conditions, thereby reducing enzymatic inhibition [36,37]. However, microbial metabolism should not be interpreted solely as a loss of bioactivity. Previous studies, including our own work using the same in vitro digestion model, demonstrated that these soups may also support bacterial viability and promote the formation of microbial metabolites with potential health relevance, including anti-inflammatory and cytoprotective effects mediated through mechanisms independent of cholinesterase inhibition [30]. Additional mechanisms may involve oxidative or enzymatic degradation of phenolic conjugates within the gut microenvironment [38,39]. Notably, specific microbial metabolites such as 3,4-dihydroxyphenylacetic acid, a breakdown product of quercetin and other flavonoids, have been shown to interact directly with the catalytic triad (Ser-His-Glu) of AChE, altering its conformation and decreasing catalytic efficiency [40]. Also, competition for resources can take place as the presence of bacteria may also lead to competition for substrates necessary for maintaining the activity of cholinesterase inhibitors. As bacteria metabolize these substrates, there may be a reduction in available compounds that would otherwise inhibit AChE and BChE effectively [41]. Despite the high initial anticholinesterase activity observed in undigested mushroom soup, this activity was substantially reduced during digestion, particularly following bacterial incubation, indicating that digestive processes can markedly attenuate the bioactivity of such compounds.
As mentioned in the Introduction, it is well known that food polyphenols are excellent cholinesterase inhibitors [7,42]. The analysis of TPC in various soups before and after in vitro digestion revealed significant insights into the behavior of these compounds during this process. Initially, all soups exhibited high TPC, indicating that the raw ingredients used are rich sources of these bioactive compounds. However, a marked decrease in TPC was observed across all soups when subjected to simulated digestive conditions, particularly up to the bacterial inoculum stage. This reduction underscores the vulnerability of polyphenols to digestive mechanisms, aligning with findings from other studies that report similar decreases in TPC due to enzymatic activity and pH changes during digestion [43,44,45]. Interestingly, the evolution of TPC changes varied among the soups. Polyphenol levels in asparagus soup maintained relatively stable, suggesting that its specific polyphenolic profile may confer a degree of resistance to degradation during digestion. This stability is noteworthy, as it contrasts with sea buckthorn soup, which showed a significant increase in TPC after the bacterial addition. This increase may indicate microbial transformations that enhance polyphenol release from food matrix, highlighting the complex interactions between gut microbiota and dietary polyphenols [46,47,48]. In comparison, mushroom and leek soups experienced minor increases in TPC, which may reflect mild transformations that preserve or slightly elevate their polyphenol content. These observations point to the intricate dynamics of polyphenol bioavailability during digestion, where the composition of each soup plays a critical role in determining how well polyphenols are retained or transformed. The findings suggest that while initial digestion generally reduces TPC across all soups, subsequent retention and transformation are highly dependent on the unique characteristics of the ingredients used [43,44,46]. Mechanistically, this pattern likely reflects the combined effects of upper-gastrointestinal polyphenol instability and subsequent microbiota-driven hydrolysis of matrix-bound phenolics in the colonic phase, which together determine the apparent TPC measured after digestion.
Antioxidant activity is considered relevant in the context of neurodegeneration prevention. All undigested soups initially exhibited high radical-scavenging capacity, which decreased significantly during the oral, gastric, and small-intestinal stages, indicating susceptibility of antioxidant compounds to enzymatic degradation, pH-dependent instability, and matrix interactions occurring during digestion [43,44,49,50,51]. In contrast to the DPPH results, ABTS•+ analysis revealed divergent patterns, including a continuous decrease in sea buckthorn soup and a late-stage increase in the remaining soups. This discrepancy reflects fundamental differences in reaction mechanisms between the assays, as ABTS•+ can detect a broader spectrum of redox-active compounds through combined single-electron and hydrogen-atom transfer pathways, whereas DPPH is more limited and sensitive to environmental conditions [52,53,54,55]. Collectively, these findings indicate that digestion not only reduces antioxidant activity but also reshapes the measurable redox profile through transformation of phenolic structures and altered assay reactivity, highlighting the importance of digestion-stage-dependent interpretation of antioxidant capacity in complex plant matrices.
CV analysis across digestion stages (Figure 7) reflects digestion-dependent changes in the pool of electroactive antioxidants. The high reducing activity of sea buckthorn soup during the oral phase is consistent with the rapid release of water-soluble antioxidants such as ascorbic acid, which is readily liberated but unstable under subsequent gastric conditions [49,56]. In contrast, the stronger electrochemical responses observed for the remaining soups during intestinal and colonic stages indicate delayed liberation of matrix-bound phenolics, including phenylpropanoid-derived compounds requiring enzymatic or microbial hydrolysis for release [56]. Collectively, these findings demonstrate that the redox profile of complex plant matrices is governed by the balance between early degradation of labile antioxidants and later microbiota-driven transformation of bound phenolic constituents. This mechanistic shift explains the emergence of late-stage electrochemical signals, including the ~0.7 V oxidation peak, as a marker of digestion-generated phenolic metabolites rather than native compounds.
The analysis of SOD and CAT activities provides insight into the antioxidant-related effects of the studied soups during digestion. The significant increase in SOD activity observed already at the oral stage (p < 0.05) may reflect rapid interactions between released low-molecular antioxidants and the assay system, suggesting an early shift in redox balance [57]. In contrast, the gradual decrease in GPx inhibitory activity during digestion, with the exception of sea buckthorn soup showing a late-stage increase, is consistent with digestion-dependent transformation of redox-active compounds, as reported previously [58]. Taken together, the coordinated changes in SOD, GPx, and GR activities indicate that gastrointestinal processing modifies the antioxidant response rather than simply reducing it, pointing to a dynamic reorganization of redox-active metabolites during digestion that may also be relevant under physiological conditions.
The digestion-dependent modulation of COX-2 activity suggests that microbial metabolism of soup-derived compounds may reshape the local redox and inflammatory balance. Late-stage increases in COX-2-related responses could reflect the formation of bioactive fermentation products, including oxidized phenolic derivatives or small lipophilic metabolites capable of interacting with NF-κB- or MAPK-associated signaling pathways [59]. Rather than indicating overt pro-inflammatory effects, such changes may correspond to mild physiological immune modulation occurring in the intestinal environment. These observations highlight the importance of microbiota-driven biotransformation in determining the final inflammatory profile of complex plant-based foods and their potential relevance for neuroinflammatory processes.
The cytokine responses observed in 841 CoTr and HT-29 cells indicate that the immunomodulatory potential of the soups is strongly concentration-dependent and influenced by the digestive matrix. The absence of significant effects at 0.1% suggests that low exposure levels are insufficient to trigger measurable inflammatory signaling, whereas the moderate cytokine modulation detected at 1% likely reflects interactions between released bioactive compounds and cellular redox- or inflammation-related pathways [60]. Importantly, the presence of digestive fluid complicates direct attribution of these effects to the soups alone, as DF itself may exert intrinsic immunomodulatory activity. Therefore, the overall pattern is more consistent with mild, locally regulated immune modulation rather than a strong pro- or anti-inflammatory response. This supports the view that digestion-derived metabolites of complex plant foods primarily influence intestinal immune homeostasis instead of inducing systemic inflammatory effects.
The cytokine profile observed in HT-29 cells confirms that the immunological impact of the soups is strongly concentration-dependent and shaped by digestion-derived metabolites. More pronounced modulation at 1% likely reflects increased cellular exposure to redox-active and signaling-relevant compounds capable of influencing pro- and anti-inflammatory pathways [61]. At the same time, the divergent direction of cytokine changes indicates context-dependent regulation rather than a uniform inflammatory effect. Collectively, these findings support the interpretation that digested plant-based food matrices primarily induce subtle, locally regulated immune responses in intestinal cells, highlighting the complexity of food-derived immunomodulation instead of suggesting clear pro- or anti-inflammatory activity.
From a mechanistic perspective, the partial recovery or increase in selected antioxidant and anticholinesterase activities in the colonic phase results from microbiota-driven degradation of complex polyphenols and the concurrent formation of low-molecular-weight phenolic metabolites. Small-intestinal digestion reduces the concentration of native phenolic compounds, whereas colonic fermentation promotes hydrolysis of ester- and glycoside-bound forms and releases simple phenolic acids such as gallic and caffeic acids together with flavonol aglycones [62,63]. Microbial catabolism additionally generates metabolites including protocatechuic acid, which exhibit strong redox activity and measurable cholinesterase inhibition [64]. These transformations increase compound solubility and accessibility within the fermented matrix, thereby modifying the measurable antioxidant and enzyme-modulating activity after bacterial incubation [65]. Consequently, digestion-dependent bioactivity reflects microbiota-mediated chemical conversion rather than the native phenolic profile of the undigested product.
In summary, the results of this study demonstrate that changes in the content of polyphenolic compounds and associated biological activities during in vitro digestion of functional soups are multidirectional and difficult to predict. This complexity reflects the dynamic interplay between food matrix composition, digestive processes, and microbial transformations, emphasizing the need to evaluate bioactivity beyond the undigested state. A major strength of the present work lies in the comprehensive assessment of complex food matrices using a standardized in vitro digestion model encompassing oral, gastric, intestinal, and colonic stages, allowing a more physiologically relevant interpretation of digestion-dependent bioactivity. Moreover, the integration of enzymatic and microbiota-related effects provides insight into mechanisms that cannot be captured by analyses of isolated compounds alone. At the same time, several limitations should be acknowledged. The applied in vitro model cannot fully reproduce in vivo gastrointestinal conditions or interindividual variability in gut microbiota composition, and the observed effects should not be interpreted as therapeutic outcomes.
Nevertheless, the moderate modulation of cytokine production combined with retained COX-2 inhibition observed in this study suggests a potential for gut-mediated immunomodulatory effects rather than a strong systemic anti-inflammatory response. Such effects are consistent with the concept of functional foods acting through long-term dietary exposure and local intestinal mechanisms rather than as acute therapeutic agents. As noted in the Introduction, both prevention of Alzheimer’s disease and intervention in its symptomatic stages require modulation of multiple, interacting pathways involved in disease pathogenesis. Therefore, the findings of this study should be viewed as indicative of potential, digestion-dependent bioactivity within a broader nutritional and mechanistic context rather than as evidence of clinical efficacy.

5. Conclusions

This study demonstrates that functional soups rich in food-derived cholinesterase inhibitors undergo dynamic changes in bioactivity during simulated gastrointestinal digestion, with significant modulation by intestinal microbiota.
Although not all measured parameters were modified during the colonic phase, a great number of the parameters (anti-AChE, anti-BChE and antioxidant activities, effect on CAT, GR, GPx and COX-2 activities) were modulated by inoculum introduced during the colonic phase, pointing out the important role of this stage in the formation of the above-mentioned activities.
Despite the fact that digestion reduces anticholinesterase activity, the results highlight the importance of food matrix and microbial interactions in shaping the biological potential of functional foods. Overall, the findings support the relevance of soups as complex dietary systems for studying digestion-dependent bioactivity rather than static sources of bioactive compounds.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/nu18040698/s1, Table S1. Plant materials used for preparation of functional soup components. Table S2. Composition and preparation procedure of the investigated soups.

Author Contributions

D.G.-A.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources Writing—original draft Writing—review and editing, Project administration. E.B.-W.: Data curation, Investigation, Methodology, Formal analysis. R.P.: Data curation, Investigation, Methodology. O.M.S.: Data curation, Investigation, Methodology. D.S.: Conceptualization, Data curation, Formal analysis, Methodology, Supervision, Writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

Financial support for this study was provided by the University of Life Sciences in Lublin (internal grant fo PhD students no. SD/84/TŻ/2023).

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Gajowniczek-Alasa, D.; Szwajgier, D.; Baranowska-Wojcik, E. Plant Soup Formulations Show Cholinesterase Inhibition Potential in the Prevention of Alzheimer’s Disease. Curr. Alzheimer Res. 2024, 21, 81–89. [Google Scholar] [CrossRef] [PubMed]
  2. Caruso, G.; Godos, J.; Privitera, A.; Lanza, G.; Castellano, S.; Chillemi, A.; Bruni, O.; Ferri, R.; Caraci, F.; Grosso, G. Phenolic Acids and Prevention of Cognitive Decline: Polyphenols with a Neuroprotective Role in Cognitive Disorders and Alzheimer’s Disease. Nutrients 2022, 14, 819. [Google Scholar] [CrossRef]
  3. Akter, R.; Chowdhury, M.A.R.; Rahman, M.H. Flavonoids and Polyphenolic Compounds as Potential Talented Agents for the Treatment of Alzheimer’s Disease and their Antioxidant Activities. Curr. Pharm. Des. 2021, 27, 345–356. [Google Scholar] [CrossRef] [PubMed]
  4. Wiegand, I.; Hilpert, K.; Hancock, R.E. Agar and broth dilution methods to determine the minimal inhibitory concentration (MIC) of antimicrobial substances. Nat. Protoc. 2008, 3, 163–175. [Google Scholar] [CrossRef] [PubMed]
  5. Gajendra, K.; Pratap, G.K.; Poornima, D.V.; Shantaram, M.; Ranjita, G. Natural acetylcholinesterase inhibitors: A multi-targeted therapeutic potential in Alzheimer’s disease. Eur. J. Med. Chem. Rep. 2024, 11, 100154. [Google Scholar] [CrossRef]
  6. Saud, A.; Krishnaraju, V.; Taha, A.; Kalpana, K.; Malarkodi, V.; Durgaramani, S.; Vinoth Prabhu, V.; Saleh, F.A.; Ezhilarasan, S. Potential acetylcholinesterase inhibitors to treat Alzheimer’s disease. Eur. Rev. Med. Pharmacol. Sci. 2024, 28, 2522–2537. [Google Scholar] [CrossRef]
  7. Aluko, R.E. Food-derived Acetylcholinesterase Inhibitors as Potential Agents against Alzheimer’s Disease. eFood 2021, 2, 49–58. [Google Scholar] [CrossRef]
  8. Mountaki, C.; Dafnis, I.; Panagopoulou, E.A.; Vasilakopoulou, P.B.; Karvelas, M.; Chiou, A.; Karathanos, V.T.; Chroni, A. Mechanistic insight into the capacity of natural polar phenolic compounds to abolish Alzheimer’s disease-associated pathogenic effects of apoE4 forms. Free Radic. Biol. Med. 2021, 171, 284–301. [Google Scholar] [CrossRef]
  9. Ekundayo, B.E.; Obafemi, T.O.; Adewale, O.B.; Obafemi, B.A.; Oyinloye, B.E.; Ekundayo, S.K. Oxidative Stress, Endoplasmic Reticulum Stress and Apoptosis in the Pathology of Alzheimer’s Disease. Cell Biochem. Biophys. 2024, 82, 457–477. [Google Scholar] [CrossRef]
  10. Hussain, F.; Tahir, A.; Jan, M.S.; Fatima, N.; Sadiq, A.; Rashid, U. Exploitation of the multitarget role of new ferulic and gallic acid derivatives in oxidative stress-related Alzheimer’s disease therapies: Design, synthesis and bioevaluation. RSC Adv. 2024, 14, 10304–10321. [Google Scholar] [CrossRef]
  11. Dembo, G.; Park, S.B.; Kharasch, E.D. Central nervous system concentrations of cyclooxygenase-2 inhibitors in humans. Anesthesiology 2005, 102, 409–415. [Google Scholar] [CrossRef]
  12. Basak, S.; Gokhale, J. Immunity boosting nutraceuticals: Current trends and challenges. J. Food Biochem. 2022, 46, e13902. [Google Scholar] [CrossRef]
  13. Hannam, J.A.; Murto, K.T.; Anderson, B.J.; Dembo, G.; Kharasch, E.D. Modeling adult COX-2 cerebrospinal fluid pharmacokinetics to inform pediatric investigation. Paediatr. Anaesth. 2023, 33, 291–302. [Google Scholar] [CrossRef]
  14. Bohn, T.; Carriere, F.; Day, L.; Deglaire, A.; Egger, L.; Freitas, D.; Golding, M.; Le Feunteun, S.; Macierzanka, A.; Menard, O.; et al. Correlation between in vitro and in vivo data on food digestion. What can we predict with static in vitro digestion models? Crit. Rev. Food Sci. Nutr. 2018, 58, 2239–2261. [Google Scholar] [CrossRef] [PubMed]
  15. Nayak, P.K.; Chandrasekar, C.M.; Sundarsingh, A.; Kesavan, R.K. Effect of in-vitro digestion on the bio active compounds and biological activities of fruit pomaces. J. Food Sci. Technol. 2020, 57, 4707–4715. [Google Scholar] [CrossRef]
  16. Ketnawa, S.; Reginio, F.C., Jr.; Thuengtung, S.; Ogawa, Y. Changes in bioactive compounds and antioxidant activity of plant-based foods by gastrointestinal digestion: A review. Crit. Rev. Food Sci. Nutr. 2022, 62, 4684–4705. [Google Scholar] [CrossRef] [PubMed]
  17. Zhang, L.; Hao, Z.; Zhao, C.; Zhang, Y.; Li, J.; Sun, B.; Tang, Y.; Yao, M. Taste compounds, affecting factors, and methods used to evaluate chicken soup: A review. Food Sci. Nutr. 2021, 9, 5833–5853. [Google Scholar] [CrossRef] [PubMed]
  18. Sandoval-Sicairos, E.S.; Milan-Noris, A.K.; Luna-Vital, D.A.; Milan-Carrillo, J.; Montoya-Rodriguez, A. Anti-inflammatory and antioxidant effects of peptides released from germinated amaranth during in vitro simulated gastrointestinal digestion. Food Chem. 2021, 343, 128394. [Google Scholar] [CrossRef] [PubMed]
  19. Fernandez-Tome, S.; Hernandez-Ledesma, B. Gastrointestinal Digestion of Food Proteins under the Effects of Released Bioactive Peptides on Digestive Health. Mol. Nutr. Food Res. 2020, 64, e2000401. [Google Scholar] [CrossRef]
  20. Minekus, M.; Alminger, M.; Alvito, P.; Ballance, S.; Bohn, T.; Bourlieu, C.; Carriere, F.; Boutrou, R.; Corredig, M.; Dupont, D.; et al. A standardised static in vitro digestion method suitable for food—An international consensus. Food Funct. 2014, 5, 1113–1124. [Google Scholar] [CrossRef]
  21. Brodkorb, A.; Egger, L.; Alminger, M.; Alvito, P.; Assuncao, R.; Ballance, S.; Bohn, T.; Bourlieu-Lacanal, C.; Boutrou, R.; Carriere, F.; et al. INFOGEST static in vitro simulation of gastrointestinal food digestion. Nat. Protoc. 2019, 14, 991–1014. [Google Scholar] [CrossRef]
  22. Baranowska-Wojcik, E.; Szwajgier, D.; Gustaw, K.; Josko, I.; Pawlikowska-Pawlega, B.; Kapral-Piotrowska, J. Reduced bioaccessibility of TiO2 (E 171) during puree soup digestion in a gastrointestinal tract simulated in vitro. Food Res. Int. 2023, 164, 112189. [Google Scholar] [CrossRef]
  23. Szwajgier, D.; Baranowska-Wojcik, E.; Winiarska-Mieczan, A.; Gajowniczek-Alasa, D. Honeys as Possible Sources of Cholinesterase Inhibitors. Nutrients 2022, 14, 2969. [Google Scholar] [CrossRef]
  24. Gajowniczek-Ałasa, D.; Baranowska-Wójcik, E.; Szwajgier, D. Changes in Anticholinesterase and Antioxidant Activties of Fruit Products during Storage. Appl. Sci. 2024, 14, 6187. [Google Scholar] [CrossRef]
  25. Rhee, I.K.; van Rijn, R.M.; Verpoorte, R. Qualitative determination of false-positive effects in the acetylcholinesterase assay using thin layer chromatography. Phytochem. Anal. 2003, 14, 127–131. [Google Scholar] [CrossRef]
  26. Szczepaniak, O.M.; Ligaj, M.; Kobus-Cisowska, J.; Maciejewska, P.; Tichoniuk, M.; Szulc, P. Application for novel electrochemical screening of antioxidant potential and phytochemicals in Cornus mas extracts. CyTA-J. Food 2019, 17, 781–789. [Google Scholar] [CrossRef]
  27. Oczkowski, T.; Filipiak, M. Starters, Electrochemical DNA Sensor and Method of Detection of Listeria Monocytogenes Microorganisms in Organic Matter, Particularly in Foodstuffs. PL Patent 200797B1, 2007. [Google Scholar]
  28. Watanabe, M.; de Moura Neiva, L.B.; da Costa Santos, C.X.; Martins Laurindo, F.R.; de Fatima Fernandes Vattimo, M. Isoflavone and the heme oxygenase system in ischemic acute kidney injury in rats. Food Chem. Toxicol. 2007, 45, 2366–2371. [Google Scholar] [CrossRef]
  29. Studzinska-Sroka, E.; Majchrzak-Celinska, A.; Zalewski, P.; Szwajgier, D.; Baranowska-Wojcik, E.; Kapron, B.; Plech, T.; Zarowski, M.; Cielecka-Piontek, J. Lichen-Derived Compounds and Extracts as Biologically Active Substances with Anticancer and Neuroprotective Properties. Pharmaceuticals 2021, 14, 1293. [Google Scholar] [CrossRef] [PubMed]
  30. Gajowniczek-Ałasa, D.; Paduch, R.; Baranowska-Wójcik, E.; Gustaw, K.; Pawlikowska-Pawlęga, B.; Grzelczyk, J.; Szwajgier, D. Impact of in vitro digestion on the cytotoxicity and microbial viability of cholinesterase-inhibitor-rich vegan soups in human intestinal cell models. Food Res. Int. 2025, 221, 117384. [Google Scholar] [CrossRef] [PubMed]
  31. Chen, L.; Pu, Y.; Xu, Y.; He, X.; Cao, J.; Ma, Y.; Jiang, W. Anti-diabetic and anti-obesity: Efficacy evaluation and exploitation of polyphenols in fruits and vegetables. Food Res. Int. 2022, 157, 111202. [Google Scholar] [CrossRef] [PubMed]
  32. Zhao, J.; Nyein, H.Y.Y.; Hou, L.; Lin, Y.; Bariya, M.; Ahn, C.H.; Ji, W.; Fan, Z.; Javey, A. A Wearable Nutrition Tracker. Adv. Mater. 2021, 33, e2006444. [Google Scholar] [CrossRef]
  33. Szczepaniak, O.; Ligaj, M.; Kobus-Cisowska, J.; Tichoniuk, M.; Dziedzinski, M.; Przeor, M.; Szulc, P. The Genoprotective Role of Naringin. Biomolecules 2020, 10, 700. [Google Scholar] [CrossRef] [PubMed]
  34. Gajowniczek-Alasa, D.; Baranowska-Wojcik, E.; Szwajgier, D. Vegan and Vegetarian Soups Are Excellent Sources of Cholinesterase Inhibitors. Nutrients 2024, 16, 2025. [Google Scholar] [CrossRef] [PubMed]
  35. Li, S.; Liu, C.; Zhang, Y.; Tsao, R. On-line coupling pressurised liquid extraction with two-dimensional counter current chromatography for isolation of natural acetylcholinesterase inhibitors from Astragalus membranaceus. Phytochem. Anal. 2021, 32, 640–653. [Google Scholar] [CrossRef] [PubMed]
  36. Suganya, K.; Koo, B.S. Gut-Brain Axis: Role of Gut Microbiota on Neurological Disorders and How Probiotics/Prebiotics Beneficially Modulate Microbial and Immune Pathways to Improve Brain Functions. Int. J. Mol. Sci. 2020, 21, 7551. [Google Scholar] [CrossRef]
  37. Banfi, D.; Moro, E.; Bosi, A.; Bistoletti, M.; Cerantola, S.; Crema, F.; Maggi, F.; Giron, M.C.; Giaroni, C.; Baj, A. Impact of Microbial Metabolites on Microbiota-Gut-Brain Axis in Inflammatory Bowel Disease. Int. J. Mol. Sci. 2021, 22, 1623. [Google Scholar] [CrossRef]
  38. Chen, Y.; Xu, J.; Chen, Y. Regulation of Neurotransmitters by the Gut Microbiota and Effects on Cognition in Neurological Disorders. Nutrients 2021, 13, 2099. [Google Scholar] [CrossRef]
  39. Bairamian, D.; Sha, S.; Rolhion, N.; Sokol, H.; Dorothee, G.; Lemere, C.A.; Krantic, S. Microbiota in neuroinflammation and synaptic dysfunction: A focus on Alzheimer’s disease. Mol. Neurodegener. 2022, 17, 19. [Google Scholar] [CrossRef]
  40. Vauzour, D.; Vafeiadou, K.; Rodriguez-Mateos, A.; Rendeiro, C.; Spencer, J.P. The neuroprotective potential of flavonoids: A multiplicity of effects. Genes Nutr. 2008, 3, 115–126. [Google Scholar] [CrossRef]
  41. Matsuzaki, R.; Gunnigle, E.; Geissen, V.; Clarke, G.; Nagpal, J.; Cryan, J.F. Pesticide exposure and the microbiota-gut-brain axis. ISME J. 2023, 17, 1153–1166. [Google Scholar] [CrossRef]
  42. Roseiro, L.B.; Rauter, A.P.; Serralheiro, M.L.M. Polyphenols as acetylcholinesterase inhibitors: Structural specificity and impact on human disease. Nutr. Aging 2012, 1, 99–111. [Google Scholar] [CrossRef]
  43. Wanyo, P.; Chamsai, T.; Toontom, N.; Nghiep, L.K.; Tudpor, K. Differential Effects of In Vitro Simulated Digestion on Antioxidant Activity and Bioaccessibility of Phenolic Compounds in Purple Rice Bran Extracts. Molecules 2024, 29, 2994. [Google Scholar] [CrossRef] [PubMed]
  44. Mercatante, D.; Ansorena, D.; Taticchi, A.; Astiasaran, I.; Servili, M.; Rodriguez-Estrada, M.T. Effects of In Vitro Digestion on the Antioxidant Activity of Three Phenolic Extracts from Olive Mill Wastewaters. Antioxidants 2022, 12, 22. [Google Scholar] [CrossRef] [PubMed]
  45. Ozkan, G.; Sakarya, F.B.; Tas, D.; Yurt, B.; Ercisli, S.; Capanoglu, E. Effect of In Vitro Digestion on the Phenolic Content of Herbs Collected from Eastern Anatolia. ACS Omega 2023, 8, 12730–12738. [Google Scholar] [CrossRef] [PubMed]
  46. Kasprzak-Drozd, K.; Moldoch, J.; Gancarz, M.; Wojtowicz, A.; Kowalska, I.; Oniszczuk, T.; Oniszczuk, A. In Vitro Digestion of Polyphenolic Compounds and the Antioxidant Activity of Acorn Flour and Pasta Enriched with Acorn Flour. Int. J. Mol. Sci. 2024, 25, 5404. [Google Scholar] [CrossRef]
  47. Ray, S.K.; Mukherjee, S. Evolving Interplay Between Dietary Polyphenols and Gut Microbiota-An Emerging Importance in Healthcare. Front. Nutr. 2021, 8, 634944. [Google Scholar] [CrossRef]
  48. Wojtunik-Kulesza, K.; Oniszczuk, A.; Oniszczuk, T.; Combrzynski, M.; Nowakowska, D.; Matwijczuk, A. Influence of In Vitro Digestion on Composition, Bioaccessibility and Antioxidant Activity of Food Polyphenols—A Non-Systematic Review. Nutrients 2020, 12, 1401. [Google Scholar] [CrossRef]
  49. Sollano-Mendieta, X.C.; Meza-Marquez, O.G.; Osorio-Revilla, G.; Tellez-Medina, D.I. Effect of In Vitro Digestion on the Antioxidant Compounds and Antioxidant Capacity of 12 Plum (Spondias purpurea L.) Ecotypes. Foods 2021, 10, 1995. [Google Scholar] [CrossRef]
  50. Luo, X.; Tian, M.; Cheng, Y.; Ji, C.; Hu, S.; Liu, H.; Lu, J.; Ren, J. Effects of simulated in vitro gastrointestinal digestion on antioxidant activities and potential bioaccessibility of phenolic compounds from K. coccinea fruits. Front. Nutr. 2022, 9, 1024651. [Google Scholar] [CrossRef]
  51. Governa, P.; Manetti, F.; Miraldi, E.; Biagi, M. Effects of in vitro simulated digestion on the antioxidant activity of different Camellia sinensis (L.) Kuntze leaves extracts. Eur. Food Res. Technol. 2022, 248, 119–128. [Google Scholar] [CrossRef]
  52. Platzer, M.; Kiese, S.; Herfellner, T.; Schweiggert-Weisz, U.; Miesbauer, O.; Eisner, P. Common Trends and Differences in Antioxidant Activity Analysis of Phenolic Substances Using Single Electron Transfer Based Assays. Molecules 2021, 26, 1244. [Google Scholar] [CrossRef]
  53. Martysiak-Zurowska, D.; Wenta, W. A comparison of ABTS and DPPH methods for assessing the total antioxidant capacity of human milk. Acta Sci. Pol. Technol. Aliment. 2012, 11, 83–89. [Google Scholar]
  54. Gaber, N.B.; El-Dahy, S.I.; Shalaby, E.A. Comparison of ABTS, DPPH, permanganate, and methylene blue assays for determining antioxidant potential of successive extracts from pomegranate and guava residues. Biomass Convers. Biorefin. 2021, 13, 4011–4020. [Google Scholar] [CrossRef]
  55. Untea, A.; Lupu, A.; Saracila, M.; Panaite, T. Comparison of ABTS, DPPH, phosphomolybdenum assays for estimating antioxidant activity and phenolic compounds in five different plant extracts. Bull. UASVM Anim. Sci. Biotechnol. 2018, 75. [Google Scholar] [CrossRef] [PubMed]
  56. Tarko, T.; Duda-Chodak, A.; Soszka, A. Changes in Phenolic Compounds and Antioxidant Activity of Fruit Musts and Fruit Wines during Simulated Digestion. Molecules 2020, 25, 5574. [Google Scholar] [CrossRef] [PubMed]
  57. Gil, D.; Rodriguez, J.; Ward, B.; Vertegel, A.; Ivanov, V.; Reukov, V. Antioxidant Activity of SOD and Catalase Conjugated with Nanocrystalline Ceria. Bioengineering 2017, 4, 18. [Google Scholar] [CrossRef]
  58. Pei, J.; Pan, X.; Wei, G.; Hua, Y. Research progress of glutathione peroxidase family (GPX) in redoxidation. Front. Pharmacol. 2023, 14, 1147414. [Google Scholar] [CrossRef]
  59. Li, X.-J.; Shan, Q.-Y.; Wu, X.; Miao, H.; Zhao, Y.-Y. Gut microbiota regulates oxidative stress and inflammation: A double-edged sword in renal fibrosis. Cell. Mol. Life Sci. 2024, 81, 480. [Google Scholar] [CrossRef]
  60. Al-Khayri, J.M.; Sahana, G.R.; Nagella, P.; Joseph, B.V.; Alessa, F.M.; Al-Mssallem, M.Q. Flavonoids as Potential Anti-Inflammatory Molecules: A Review. Molecules 2022, 27, 2901. [Google Scholar] [CrossRef]
  61. Ju, Z.; Li, M.; Xu, J.; Howell, D.C.; Li, Z.; Chen, F.E. Recent development on COX-2 inhibitors as promising anti-inflammatory agents: The past 10 years. Acta Pharm. Sin. B 2022, 12, 2790–2807. [Google Scholar] [CrossRef]
  62. Selma, M.V.; Espin, J.C.; Tomas-Barberan, F.A. Interaction between phenolics and gut microbiota: Role in human health. J. Agric. Food Chem. 2009, 57, 6485–6501. [Google Scholar] [CrossRef]
  63. Cueva, C.; Gil-Sánchez, I.; Ayuda-Durán, B.; González-Manzano, S.; González-Paramás, A.M.; Santos-Buelga, C.; Bartolomé, B.; Moreno-Arribas, M.V. An integrated view of the effects of wine polyphenols and their relevant metabolites on gut and host health. Molecules 2017, 22, 99. [Google Scholar] [CrossRef] [PubMed]
  64. Rocchetti, G.; Gregorio, R.P.; Lorenzo, J.M.; Barba, F.J.; Oliveira, P.G.; Prieto, M.A.; Simal-Gandara, J.; Mosele, J.I.; Motilva, M.J.; Tomas, M. Functional implications of bound phenolic compounds and phenolics–food interaction: A review. Compr. Rev. Food Sci. Food Saf. 2022, 21, 811–842. [Google Scholar] [CrossRef] [PubMed]
  65. Jakobek, L.; Ištuk, J.; Tomac, I.; Matić, P. β-Glucan and aronia (Aronia melanocarpa) phenolics: Interactions during in vitro simulated gastrointestinal digestion and adsorption. Pol. J. Food Nutr. Sci. 2022, 72, 371–380. [Google Scholar] [CrossRef]
Figure 1. Workflow of performed analyses.
Figure 1. Workflow of performed analyses.
Nutrients 18 00698 g001
Figure 2. Changes in TPC observed throughout simulated gastrointestinal digestion of the soups. U: Undigested; M: Mouth; S: Stomach end; S im: Small intestine-middle; S ie: Small intestineend; L i-2h: Large intestine 2 h after bacteria addition; L ie: Large intestineend. Different letters denote statistically significant differences (p ≤ 0.05) across all results in the chart.
Figure 2. Changes in TPC observed throughout simulated gastrointestinal digestion of the soups. U: Undigested; M: Mouth; S: Stomach end; S im: Small intestine-middle; S ie: Small intestineend; L i-2h: Large intestine 2 h after bacteria addition; L ie: Large intestineend. Different letters denote statistically significant differences (p ≤ 0.05) across all results in the chart.
Nutrients 18 00698 g002
Figure 3. Changes in anti-AChE activity of the soups across successive stages of simulated gastrointestinal digestion. U: Undigested; M: Mouth; S: Stomach end; S i-m: Small intestine-middle; S i-e: Small intestine-end; L i-2h: Large intestine 2 h after bacteria addition; L i-e: Large intestine-end. Different letters denote statistically significant differences (p ≤ 0.05) across all results in the chart.
Figure 3. Changes in anti-AChE activity of the soups across successive stages of simulated gastrointestinal digestion. U: Undigested; M: Mouth; S: Stomach end; S i-m: Small intestine-middle; S i-e: Small intestine-end; L i-2h: Large intestine 2 h after bacteria addition; L i-e: Large intestine-end. Different letters denote statistically significant differences (p ≤ 0.05) across all results in the chart.
Nutrients 18 00698 g003
Figure 4. Changes in anti-BChE activity of the soups across successive stages of simulated gastrointestinal digestion. U: Undigested; M: Mouth; S: Stomach end; S i-m: Small intestine-middle; S i-e: Small intestine-end; L i-2h: Large intestine 2 h after bacteria addition; L i-e: Large intestine-end. Different letters denote statistically significant differences (p ≤ 0.05) across all results in the chart.
Figure 4. Changes in anti-BChE activity of the soups across successive stages of simulated gastrointestinal digestion. U: Undigested; M: Mouth; S: Stomach end; S i-m: Small intestine-middle; S i-e: Small intestine-end; L i-2h: Large intestine 2 h after bacteria addition; L i-e: Large intestine-end. Different letters denote statistically significant differences (p ≤ 0.05) across all results in the chart.
Nutrients 18 00698 g004
Figure 5. Evolution of antioxidant activity (DPPH) of soups during in vitro digestion. U: Undigested; M: Mouth; S: Stomach end; S i-m: Small intestine-middle; S i-e: Small intestine-end; L i 2h: Large intestine 2 h after bacteria addition; L i-e: Large intestine-end. Different letters denote statistically significant differences (p ≤ 0.05) across all results in the chart.
Figure 5. Evolution of antioxidant activity (DPPH) of soups during in vitro digestion. U: Undigested; M: Mouth; S: Stomach end; S i-m: Small intestine-middle; S i-e: Small intestine-end; L i 2h: Large intestine 2 h after bacteria addition; L i-e: Large intestine-end. Different letters denote statistically significant differences (p ≤ 0.05) across all results in the chart.
Nutrients 18 00698 g005
Figure 6. Evolution of antioxidant activity (ABTS•+) of soups during in vitro digestion. U: Undigested; M: Mouth; S: Stomach end; S i-m: Small intestine-middle; S i-e: Small intestine-end; L i-2h: Large intestine 2 h after bacteria addition; L i-e: Large intestine-end. Different letters denote statistically significant differences (p ≤ 0.05) across all results in the chart.
Figure 6. Evolution of antioxidant activity (ABTS•+) of soups during in vitro digestion. U: Undigested; M: Mouth; S: Stomach end; S i-m: Small intestine-middle; S i-e: Small intestine-end; L i-2h: Large intestine 2 h after bacteria addition; L i-e: Large intestine-end. Different letters denote statistically significant differences (p ≤ 0.05) across all results in the chart.
Nutrients 18 00698 g006
Figure 7. Cyclic voltammograms of tested soups after different digestion stages.
Figure 7. Cyclic voltammograms of tested soups after different digestion stages.
Nutrients 18 00698 g007
Figure 8. Electrochemical antiradical activity expressed as total reducing activity (Ei) and area of peak registered at 0.7 V. *, #, †, †† illustrates significant differences between digestion stages (in areas within dashed and solid lines). M: Mouth; S: Stomach end; S i-m: Small intestine-middle; S i-e: Small intestine-end; L i 2h: Large intestine 2 h after bacteria addition; L i-e: Large intestine-end. Lower case letters illustrate significant differences between the samples at given digestion stage (α = 0.05).
Figure 8. Electrochemical antiradical activity expressed as total reducing activity (Ei) and area of peak registered at 0.7 V. *, #, †, †† illustrates significant differences between digestion stages (in areas within dashed and solid lines). M: Mouth; S: Stomach end; S i-m: Small intestine-middle; S i-e: Small intestine-end; L i 2h: Large intestine 2 h after bacteria addition; L i-e: Large intestine-end. Lower case letters illustrate significant differences between the samples at given digestion stage (α = 0.05).
Nutrients 18 00698 g008
Figure 9. Effect of soups on SOD activity during in vitro digestion. U: Undigested; M: Mouth; S: Stomach end; S i-m: Small intestine-middle; S i-e: Small intestine-end; L i-h: Large intestine 2 h after bacteria addition; L i-e: Large intestine-end. Different letters denote statistically significant differences (p ≤ 0.05) across all results in the chart.
Figure 9. Effect of soups on SOD activity during in vitro digestion. U: Undigested; M: Mouth; S: Stomach end; S i-m: Small intestine-middle; S i-e: Small intestine-end; L i-h: Large intestine 2 h after bacteria addition; L i-e: Large intestine-end. Different letters denote statistically significant differences (p ≤ 0.05) across all results in the chart.
Nutrients 18 00698 g009
Figure 10. Effect of soups on CAT activity during in vitro digestion. U: Undigested; M: Mouth; S: Stomach end; S i-m: Small intestine-middle; S i-e: Small intestine-end; L i-2h: Large intestine 2 h after bacteria addition; L i-e: Large intestine-end. Different letters denote statistically significant differences (p ≤ 0.05) across all results in the chart.
Figure 10. Effect of soups on CAT activity during in vitro digestion. U: Undigested; M: Mouth; S: Stomach end; S i-m: Small intestine-middle; S i-e: Small intestine-end; L i-2h: Large intestine 2 h after bacteria addition; L i-e: Large intestine-end. Different letters denote statistically significant differences (p ≤ 0.05) across all results in the chart.
Nutrients 18 00698 g010
Figure 11. Effect of soups on GR activity during in vitro digestion. U: Undigested; M: Mouth; S: Stomach end; S i-m: Small intestine-middle; S i-e: Small intestine-end; L i-2h: Large intestine 2 h after bacteria addition; L i-e: Large intestine-end. Different letters denote statistically significant differences (p ≤ 0.05) across all results in the chart.
Figure 11. Effect of soups on GR activity during in vitro digestion. U: Undigested; M: Mouth; S: Stomach end; S i-m: Small intestine-middle; S i-e: Small intestine-end; L i-2h: Large intestine 2 h after bacteria addition; L i-e: Large intestine-end. Different letters denote statistically significant differences (p ≤ 0.05) across all results in the chart.
Nutrients 18 00698 g011
Figure 12. Effect of soups on GPx activity during in vitro digestion. U: Undigested; M: Mouth; S: Stomach end; S i-m: Small intestine-middle; S i-e: Small intestine-end; L i-2h: Large intestine 2 h after bacteria addition; L i-e: Large intestine-end. Different letters denote statistically significant differences (p ≤ 0.05) across all results in the chart.
Figure 12. Effect of soups on GPx activity during in vitro digestion. U: Undigested; M: Mouth; S: Stomach end; S i-m: Small intestine-middle; S i-e: Small intestine-end; L i-2h: Large intestine 2 h after bacteria addition; L i-e: Large intestine-end. Different letters denote statistically significant differences (p ≤ 0.05) across all results in the chart.
Nutrients 18 00698 g012
Figure 13. Effect of soups on COX−2 during in vitro digestion. U: Undigested; M: Mouth; S: Stomach end; S i-m: Small intestine-middle; S i-e: Small intestine-end; L i-2h: Large intestine 2 h after bacteria addition; L i-e: Large intestine-end. Different letters denote statistically significant differences (p ≤ 0.05) across all results in the chart.
Figure 13. Effect of soups on COX−2 during in vitro digestion. U: Undigested; M: Mouth; S: Stomach end; S i-m: Small intestine-middle; S i-e: Small intestine-end; L i-2h: Large intestine 2 h after bacteria addition; L i-e: Large intestine-end. Different letters denote statistically significant differences (p ≤ 0.05) across all results in the chart.
Nutrients 18 00698 g013
Table 1. Pearson correlation coefficients (r) between TPC and over markers. * Significant coefficient (p < 0.05).
Table 1. Pearson correlation coefficients (r) between TPC and over markers. * Significant coefficient (p < 0.05).
CorrelationCorrelation Coefficient (r)
Mushroom SoupAsparagus SoupLeek SoupSea Buckthorn Soup
TPC and AChE−0.110.24−0.17−0.29
TPC and BChE−0.29−0.08−0.30−0.56 *
TPC and ABTS•+−0.41 *−0.30−0.49 *−0.27
TPC and DPPH−0.27−0.83 *−0.72 *0.06
TPC and SOD−0.28−0.34−0.29−0.32
TPC and catalase0.52*0.47 *0.200.34
TPC and GR0.040.02−0.30−0.08
TPC and GPx−0.35−0.30−0.56 *−0.42 *
TPC and COX-2−0.35−0.59 *−0.06−0.32
Table 2. Cytokine levels (pg/mL) in human normal colon epithelial cells (CCD 841 CoTr) following 24 h exposure to samples at 0.1% and 1%.
Table 2. Cytokine levels (pg/mL) in human normal colon epithelial cells (CCD 841 CoTr) following 24 h exposure to samples at 0.1% and 1%.
Cytokine841 CoTr (Normal Colon Epithelial Cells)
Cytokine Amount (pg/mL) After Cells Incubation with Samples at 0.1% Concentration
ControlSmall Intestine FluidMushroom SoupDigested Mushroom SoupAsparagus SoupDigested ASPARAGUS SoupLeek SoupDigested Leek SoupSea Buckthorn SoupDigested Sea Buckthorn
IL-1β1637.9 ± 333.61933.6 ± 272.51827.2 ± 243.91714.3 ± 56.41820.6 ± 145.71621.3 ± 47.01916.9 ± 117.41870.4 ± 183.21815.6 ± 157.41588.0 ± 14.1
IL-61418.8 ± 157.31508.8 ± 122.01483.8 ± 129.01403.8 ± 79.61511.3 ± 125.51166.3 ± 135.11633.8 ± 157.51600.0 ± 123.71560.0 ± 154.61488.8 ± 198.7
IL-101079.5 ± 157.21071.4 ± 145.9942.4 ± 90.01008.9 ± 130.61136.6 ± 2.51060.5 ± 122.81166.4 ± 169.01061.9 ± 36.51051.0 ± 121.01037.4 ± 94.1
Cytokine Amount (pg/mL) After Cells Incubation with Samples at 1% Concentration
IL-1β1637.9 ± 333.61915.3 ± 148.01656.2 ± 178.21765.8 ± 54.01921.9 ± 44.61672.8 ± 164.11622.9 ± 120.51596.4 ± 16.41823.9 ± 28.21765.8 ± 198.3
IL-61418.8 ± 157.31582.5 ± 21.21465.0 ± 123.71493.8 ± 136.11670.0 * ± 46.01470.0 ± 17.71517.5 ± 49.51513.8 ± 8.81837.5 * ± 7.11402.5 ± 116.7
IL-101079.5 ± 157.21329.4 * ± 23.01287.3 * ± 9.61151.5 ± 28.81106.7 ± 19.21142.0 ± 53.81450.2 * ± 36.51158.3 ± 84.51124.4 ± 28.81197.7 ± 109.5
* Asterisks denote significant differences relative to the control (p ≤ 0.05).
Table 3. Cytokine levels (pg/mL) in HT-29 human colon cancer cells following 24 h exposure to samples at 0.1% and 1%.
Table 3. Cytokine levels (pg/mL) in HT-29 human colon cancer cells following 24 h exposure to samples at 0.1% and 1%.
CytokineHT-29 (Colon Adenocarcinoma Cells)
Cytokine Amount (pg/mL) After Cells Incubation with Samples at 0.1% Concentration
ControlSmall Intestine FluidMushroom SoupDigested Mushroom SoupAsparagus SoupDigested Asparagus SoupLeek SoupDigested Leek SoupSea Buckthorn SoupDigested Sea Buckthorn
IL-1β1687.7 ± 169.11591.4 ± 206.81745.9 ± 105.71679.4 ± 209.11608.0 ± 17.51707.6 ± 178.51794.0 ± 122.21579.7 ± 260.81701.0 ± 108.11563.1 ± 227.9
IL-61287.5 ± 14.11507.5 * ± 46.01232.5 ± 7.11310.0 ± 106.11507.5 ± 233.31301.3 ± 129.11491.3 * ± 93.71513.8 ± 129.01547.5 * ± 21.21146.3 ± 206.8
IL-101166.4 ± 153.3884.0 * ± 23.0939.7 ± 170.9858.2 * ± 40.31108.1 ± 90.3946.5 ± 122.91022.5 ± 118.9727.8 * ± 9.61113.5 ± 36.5873.1 * ± 130.7
Cytokine Amount (pg/mL) After Cells Incubation with Samples at 1% Concentration
IL-1β1687.7 ± 169.11448.5 * ± 18.81490.0 ± 39.91461.8 * ± 37.61476.7 ± 207.71617.9 ± 263.11647.8 ± 183.21556.5 ± 209.11669.4 ± 30.51533.3 ± 129.2
IL-61287.5 ± 14.11455.0 * ± 88.41303.8 ± 202.31182.5 * ± 3.51370.0 ± 102.51513.8 * ± 167.91343.8 ± 203.31086.3 * ± 136.11352.5 ± 109.61401.3 ± 139.7
IL-101166.4 ± 153.31082.3 ± 149.81110.8 ± 186.1984.5 ± 61.5798.4 * ± 113.21199.0 ± 149.81285.9 ± 42.2742.8 * ± 46.1794.4 * ± 49.9897.6 ± 122.8
* Asterisks denote significant differences relative to the control (p ≤ 0.05).
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

Gajowniczek-Ałasa, D.; Paduch, R.; Baranowska-Wójcik, E.; Szczepaniak, O.M.; Szwajgier, D. Simulated Gastrointestinal Digestion Modulates Anticholinesterase, Antioxidant, and Anti-Inflammatory Activities of Vegan Soups Rich in Natural Cholinesterase Inhibitors. Nutrients 2026, 18, 698. https://doi.org/10.3390/nu18040698

AMA Style

Gajowniczek-Ałasa D, Paduch R, Baranowska-Wójcik E, Szczepaniak OM, Szwajgier D. Simulated Gastrointestinal Digestion Modulates Anticholinesterase, Antioxidant, and Anti-Inflammatory Activities of Vegan Soups Rich in Natural Cholinesterase Inhibitors. Nutrients. 2026; 18(4):698. https://doi.org/10.3390/nu18040698

Chicago/Turabian Style

Gajowniczek-Ałasa, Dorota, Roman Paduch, Ewa Baranowska-Wójcik, Oskar M. Szczepaniak, and Dominik Szwajgier. 2026. "Simulated Gastrointestinal Digestion Modulates Anticholinesterase, Antioxidant, and Anti-Inflammatory Activities of Vegan Soups Rich in Natural Cholinesterase Inhibitors" Nutrients 18, no. 4: 698. https://doi.org/10.3390/nu18040698

APA Style

Gajowniczek-Ałasa, D., Paduch, R., Baranowska-Wójcik, E., Szczepaniak, O. M., & Szwajgier, D. (2026). Simulated Gastrointestinal Digestion Modulates Anticholinesterase, Antioxidant, and Anti-Inflammatory Activities of Vegan Soups Rich in Natural Cholinesterase Inhibitors. Nutrients, 18(4), 698. https://doi.org/10.3390/nu18040698

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