Upper-Gastrointestinal Tract Metabolite Profile Regulates Glycaemic and Satiety Responses to Meals with Contrasting Structure

Upper-Gastrointestinal Tract Metabolite Profile Regulates Glycaemic and Satiety Responses to Meals with Contrasting Structure | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Upper-Gastrointestinal Tract Metabolite Profile Regulates Glycaemic and Satiety Responses to Meals with Contrasting Structure Gary Frost, Mingzhu Cai, Shilpa Tejpal, Martina Tashkova, Peter Ryden, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4502487/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Jun, 2025 Read the published version in Nature Metabolism → Version 1 posted You are reading this latest preprint version Abstract Dietary interventions to combat non-communicable diseases focus on optimising food intake but overlook the influence of food structure. Food processing often causes the loss of foodstructure, but how this influences human gastrointestinal digestion and the signals it generates, such as gut hormones that affect homeostatic mechanisms is unclear. In this randomised cross-over study, 10 healthy participantsconsumed iso-nutrient chickpea meals with contrasting cellular structures and underwent gastric, duodenal, and blood sampling. Here, we reported that the ‘Broken’ and ‘Intact’ cell structures of meals resulted in different digestive and metabolomic profiles, leading to distinct postprandial glycaemia, gut hormones, and satiety responses. ‘Broken' meal resulted in high starch digestibility and a sharp rise in gastric maltose within 30 minutes, which acutely elicited higher blood glycaemia, GIP, and GLP-1. ‘Intact’ meal produced a prolonged release of appetite-suppressing hormones GLP-1 and PYY, elevated duodenal amino acids, and undigested starch at 120 minutes. This work highlights how plant food structure alters upper gastrointestinal-nutrient-sensing hormones, providing insights into the adverse effects of modern diets on obesity and type 2 diabetes. Biological sciences/Physiology/Metabolism/Metabolomics Health sciences/Gastroenterology/Gastrointestinal system/Small intestine/Duodenum Health sciences/Medical research/Translational research Health sciences/Gastroenterology/Gastrointestinal hormones Health sciences/Health care/Nutrition Legumes cell wall glucose insulin gastrointestinal hormones gastrointestinal tract metabolite profile randomized cross-over Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Legumes are an important crop globally with a good nutritional and environmental profile 1 . The consumption of legumes is encouraged in global non-communicable disease policies. For example, legumes have been demonstrated to improve glycaemia and affect gut hormones to prolong satiety 2 , 3 . However, the intake of legumes is declining globally. While processing of seeds and grains can improve palatability, this often results in the loss of plant cellular structure. Current diet and food reformulation strategies focus on reducing carbohydrate and fat intakes, but one key aspect that has been largely overlooked so far is the role of food structure in regulating the extent to which these macronutrients are released into the intestinal lumen and absorbed by the body. As a consequence, it can have an impact on postprandial metabolism, gut hormone release and caloric value 4 – 6 . Food matrix structural changes are not captured in nutrient composition data, and there is an urgent need to improve understanding of how processing-induced changes to food structure impact on nutrient bioaccessibility and thereby postprandial metabolism. In raw edible plant tissues, nutrients are encapsulated within plant cells, where the surrounding plant cell wall (dietary fibre) acts as a ‘physical-barrier’, protecting intracellular macronutrients from intestinal digestive enzymes and thereby limiting the release (‘bioaccessibility’) of macronutrient digestion products (e.g., mainly malto-oligosaccharides, peptides, and fatty acids) into the intestinal lumen 7 . This so-called ‘barrier-mechanism’ is particularly well documented in cooked pulses (including chickpeas, peas and beans) 8 – 11 where in vitro digestibility studies show significantly higher starch digestion from broken cells compared with intact cells from the same source 12 – 15 . As the availability of glucose from starch digestion is known to be a key contributor to the postprandial blood glucose response 16 – 18 , it has been suggested that the processing-induced breakage of chickpea cells, for example during dry-milling into flour, compromises the low glycaemic benefits that are associated with whole pulse consumption 19 . However, the proposed direct relationship between plant cell intactness, intestinal contents, and the size of the glycaemic response evoked still requires confirmation 2 . What is less well understood is how food processing-induced changes in intestinal contents and consequently on signal systems that impact on metabolism impact such as satiety and gut hormone signalling. Interestingly, very recent human studies reported that subjective satiety was higher for meals containing intact chickpea cells than disrupted cells 20 , while another study showed a dose-dependent increase in anorexigenic gut hormone responses to bread incorporating intact chickpea cells 21 . Gut hormones such as Glucose-dependent Insulinotropic Peptide (GIP), Glucagon-like Peptide-1 (GLP-1) and Peptide-YY (PYY) are incretins (i.e., blood glucose lowering effects) with established satiety-promoting effects and are produced by enteroendocrine cells in the gastrointestinal tract. GIP is produced mainly by K-cells, located in the stomach and duodenum, whereas GLP-1 and PYY are mainly produced by L-cells, which are present in high density in the ileum and colon 22 , 23 . These enteroendocrine cells have lumen-facing receptors capable of detecting and responding to local changes in intestinal nutrient concentrations. Mapping of enteroendocrine cells and receptors across the intestine tract has been well-described 24 , 25 , a critical knowledge gap is that we still have a limited understanding of the metabolites throughout the human gastrointestinal tract, and how these change with time as a result of meal digestion and transit. Previous work has highlighted how nutrient-sensing in the ileum stimulates GLP-1 and PYY release and promotes satiety 26 – 28 . However, blood GLP-1 and PYY concentrations rise within 15 minutes following meals 29 – 31 , during the period when foods are likely still in the stomach and duodenum. Recent findings demonstrated that L-cells are present in roughly equal numbers to K-cells in human duodenum 32 , raising the possibility that the proximal intestine may play a larger role in gut hormone secretions than previously recognised. The satiety-stimulating potential of the upper gastrointestinal tract remains largely unexplored. Previous in vitro and in vivo investigations 19 – 21 , 33 have showed evidence that processing of whole grains and legumes into disrupted structure affects their postprandial responses. Understanding the mechanisms in humans is lacking. Addressing this requires investigations into how food structure impacts upper-gastrointestinal metabolites that regulate glycaemia, hormones and satiety. We combine enteric intubation techniques 34 , allowing time-resolved intestinal sample collection, with parallel blood sample collection to investigate the relationship between the digestive behaviour of foods with contrasting plant cell structures and the postprandial metabolic effects. This study aimed to investigate the effect of chickpea cotyledon cell intactness, as a model of legume processing, on digestion product concentrations within the gastric and duodenal lumen and its consequences for postprandial blood glucose, insulin, and gut hormone responses. This work first investigated human gastric and duodenal metabolites using 1 H NMR profiling 35 to explore the luminal content regulating appetite-regulating hormone responses. Despite their identical macronutrient and fibre content, the non-cellular chickpea meals were hypothesised to be more rapidly digested and thereby cause higher glycemic responses 19 , 36 than the cellular chickpea meals. Furthermore, the intact cellular meals were hypothesised to retain their structure within the stomach and duodenum, resulting in luminal metabolic profiles capable of prolonging gut hormone and satiety responses. Results Participants and Diets We admitted 6 male and 4 female adult participants aged (mean ± SEM) 30.8 y ± 2.4 years with BMI 24.9 ± 0.8 kg/m 2 and fasted glucose 4.7 ± 0.1 mmol/L (see Supplementary Tables 1 and 2 for eligibility criteria and participant characteristics) as inpatients to the NIHR Imperial Clinical Research Facility (CRF) at Hammersmith Hospital in London, UK, where they resided for 4-days. On day 1, they were fitted with two enteral feeding tubes to enable concomitant sampling from the participants’ stomach and upper-small intestine (duodenum). On the morning of days 2, 3, and 4, participants received one of three structurally different test meals in random order, and samples of blood, intestinal content and appetite visual analog scale (VAS) scores were collected up to 3h postprandially as shown in Fig. 1 A. A consort diagram showing flow of participants through the study is provided in Extended Data Fig. 1 . The test meals ( Fig. 1 B, C,D ) consisted of freshly cooked chickpea porridge in water, flavoured with low-sugar blackcurrant jam and raspberry flavoured jelly (shown in photographs in Fig. 1 B). All meals had the same nutrient content per serving (~ 242.7 kcal, 29.5 g starch, 2.3 g sugar, 6.6 g dietary fibre, 11.0 g protein and 3.7 g fat) but had been processed differently ( see Supplementary Information ‘Meal preparation’ ) so that these nutrients were either entrapped within clusters of cells ‘Intact-C’, isolated single cells ‘Intact-S’, or broken cells ‘Broken’, as confirmed with light microscopy (Fig. 1 C). Using an in vitro starch amylolysis assay, we showed that these differences in meal structure affected starch susceptibility to α-amylase digestion, with broken cell meal showing the greatest release of starch digestion products (Fig. 1 D). Non-cellular Structure Elevated Blood Glucose and Insulin Responses Meal cell structure significantly influenced postprandial glucose (P < 0.001) and insulin (P = 0.003) as shown in Fig. 2 A,D. The magnitude of glucose and insulin response was assessed using iPeak (incremental peak) and iAUC, revealing significant differences between broken and intact cellular meals. Broken cells led to a 190% increase in iPeak glucose compared to Intact-C (1.23 ± 0.56 mmol/L), and a 92% increase compared to Intact-S (0.90 ± 0.50 mmol/L) (Fig. 2 B). Similarly, insulin iPeak after Broken meals was 65% higher than after Intact-C (-27.76 ± 24.44 µU/mL, adj. P = 0.028) (Fig. 2 D). Glucose iAUC was 148% higher after consuming Broken compared to Intact-C (55.78 ± 36.56 mmol/L/min, adj. P = 0.005) (Fig. 2 C). Broken also resulted in a 74% higher insulin iAUC (-1538.92 ± 706.72 µU/mL, adj. P < 0.001) compared to Intact-C. Timing of glucose peaks occurred at approximately 34 min and did not differ between broken and intact meals (p = 0.173, Supplementary Table 4 ), likely suggesting their rates of gastric emptying were similar 37 . Intact-C had a shorter insulin response, with a mean duration of 76 min less to return to baseline levels compared to Broken and Intact-S ( Supplementary Table 4 , adj. P = 0.004 and 0.036). This can be attributed to the reduced demand for insulin secretion in the Intact-C group given the lower blood glucose levels. Overall, the chickpea porridge made from broken cells caused the highest glycaemic response, while cell clusters had the lowest impact on blood glucose and insulin. Chickpeas have been widely described as low glycaemic foods 38 , while our data clearly show that disruption of meal cell structures significantly increases the magnitude and duration of the postprandial blood glucose and insulin response. Intact-cellular Structure Enhanced GLP-1, PYY, and Satiety Responses Figure 3 shows how meal structure impacted postprandial blood GIP, GLP-1, and PYY responses. There was a significant time × meal effect on postprandial GIP ( P < 0.001), with very clear differences between meal types evident in the time-series data shown in Fig. 3 A. After Intact-S and Intact-C meals, GIP concentrations increased slowly to reach a maximum after ~ 120 min, but following the Broken meal, GIP increased rapidly to a higher peak after ~ 30 min then declined gradually to reach fasted levels after ~ 3h (see Fig. 3 A). GIP concentrations (iPeak values) for Intact-C (Fig. 3 B) were 61% lower than the Broken (53.47 ± 37.84 pg/mL, adj. P = 0.009) and 50% lower than Intact-S (34.77 ± 23.20 pg/mL, adj. P = 0.006); the iPeak of Intact-S and Broken was comparable (adj. P = 0.128). Similarly, the iAUC of Intact-C (Fig. 3 C) was 54% lower than Broken (3663.32 ± 3397.32 pg/mL/min, adj. P = 0.036) and 61% lower than Intact-S (4924.82 ± 2858.02 pg/mL/min, adj. P = 0.003). Intact-S and Broken showed no significant difference in GIP iAUC (adj. P = 0.559). Thus, the chickpea porridge containing broken plant cells caused a large and early spike in blood GIP, whereas the cellular meals elicited a more gradual and sustained GIP response. GLP-1 responses were also significantly altered by meal structure (time × meal interaction, P = 0.034) and followed similar time-trends to GIP: Fig. 3 D shows that GLP-1 increased gradually after ingestion of Intact-S and Intact-C, whereas the Broken meal caused GLP-1 concentration to reach a higher peak after ~ 30 min. The maximum postprandial rise was comparable in the three groups (Fig. 3 E, P = 0.14), while the Intact-S reached the peak 84 min later compared to Broken (adj. P = 0.003, Supplementary Table 4 ). iAUC was 63% higher in Intact-S compared with Broken (Fig. 3 F, 801 .55 ± 661.0 pmol/L/min, adj. P = 0.020). Thus, the broken cell meal elicited high GLP-1 concentrations in the early-postprandial period, whereas cellular meals caused GLP-1 concentrations to reach a similar level, but later in the postprandial period (90–180 min). Meal structure significantly affected PYY response (main effect, P = 0.003, Fig. 3 G). Intact-S elicited a higher PYY response compared to Broken: PYY iPeak for intact-S was 214% higher (17.53 ± 13.00 pmol/L) than Broken (adj. P = 0.012, Fig. 3 H). Intact-S prolonged PYY response, as evidenced by Intact-S approaching baselines 70 min later than Broken (adj. P = 0.021, Supplementary Table 4 . The first peak iAUC for Intact-S was 1604.63 ± 1113.88 pmol/L/min (568%) higher than Broken (adj. P = 0.008, Fig. 3 I). Overall, ingestion of chickpea porridge with intact separated cells (Intact-S) caused PYY concentrations to increase during the first hour of meal ingestion and remain elevated, whereas ingestion of broken cells resulted in a delayed and lower PYY response. PYY and GLP-1 have the potential to impact appetite, so to explore this further we captured the 10 participant’s responses (VAS scores) to fullness/appetite questions. Postprandial changes in fullness significantly differed between meal structures (Fig. 3 J, meal effect P = 0.007); with Intact-S resulting in higher ‘fullness’ scores compared to Intact-C and Broken (Fig. 3 K, adj .P = 0.031, < 0.001, respectively). No significant differences were observed for ‘hunger’ and ‘appetite for a meal’ (meal effect, P = 0.606 and 0.060, respectively), but ‘desire to eat’ showed a significant meal effect (P = 0.029), as did the overall composite appetite scores (meal effect, P < 0.001, calculated as the mean of ‘hunger’, ‘appetite for a meal’, ‘desire to eat’, and (100 minus ‘fullness’)) ( Extended Data Fig. 2 ) . These indicated that Intact-S resulted in lower overall appetite compared to other meals. Ad libitum food intake measured at postprandial 240 min did not show significant differences between meals (Fig. 3 L, meal effect, P = 0.123). Our findings are consistent with another recent study, which also found that consuming chickpea intact cells led to enhanced satiety compared to broken cells, without reducing food intake 20 . It is unclear why the participants’ higher satiety scores did not result in lower food intake at the subsequent meal. One possibility is that the relatively low energy density of the chickpea meals (for instance, ~ 500 mL of the ‘Broken’ meal is required to provide 242.7 Kcal), and short eating duration may not have induced a sufficient response to impact food intake at 4 h. Future study designs should consider exploring cell intactness within a more energy dense meal and impacts on satiety-responses and subsequent food intake. Overall, these results showed chickpea separated cells enhanced GLP-1 and PYY responses and appetite suppression. These are consistent with a very recent study on meals containing legume cells 21 ; our study uniquely controlled for micronutrient and fibre contents, demonstrating that the structure-derived effects would be sufficient to influence postprandial satiety through nutrient-mediated gut-hormone signalling. Cell Wall Intactness Controlled Stomach and Duodenal Starch Digestion Gastric and duodenal aspirates were collected from 10 participants and examined in terms of structure and composition to explore how differences in meal structure alter intestinal content, potentially generating signals which explain observed differences in blood responses. Light microscopy was performed on aspirated stomach and duodenal content to gather insight into the structural breakdown of these meals within the human gastrointestinal lumen. Light micrographs (Fig. 4 ) show that the characteristic meal structure which are non-cellular for Broken (Fig. 4 A,B,C), cellular for Intact-S (Fig. 4 D,E,F), and cellular clusters for Intact-C (Fig. 4 E,F,G), persisted within the stomach and duodenum, despite oral processing. Whole cells or cell clusters were found in the duodenum 15 min after meal ingestion and were present in every sample throughout the entire 180 min of sample collection. This was consistent for all participants. For the cellular meals, there was no evidence of progressive structural breakdown of plant cells with gastrointestinal residence time or region (stomach vs duodenum). When cells were ingested as clusters, clusters were clearly visible to the naked eye within the digesta, these were successfully captured using light microscopy, although some images give the illusion of cell separation which results from pressing on the coverslip. Thus, the clusters can break down into separated cells, although this process could not be captured quantitatively. No intact chickpea cells were detected after ingestion of the broken cell meals. After ingestion of broken cell chickpea porridge, partially digested starch granules were evident already within the first collection point from the stomach. In summary, the microscopic analyses showed that the cellular meals largely remained intact in the gastric and duodenal aspirates during the entire postprandial sampling period, thus providing effective barriers as starch remained encapsulated within those plant cells. Effects of meal structure on starch and starch-digestion products within the aspirated fluid are shown in Fig. 5 . Starch digestion products (maltose > maltotriose > glucose) were present in postprandial (0–3 h) gastric and duodenal aspirate supernatants, and gastric concentrations were higher than duodenal. Maltose (Fig. 5 A,B ) , being the main product of starch amylolysis, was present at ~ 50% higher concentrations than maltotriose ( Supplementary Table 5 ) and glucose (Fig. 5 C,D ) . Gastric maltose concentrations (Fig. 5 A) differed between meal types ( P = 0.006, One-way RM ANOVA). Pairwise comparison (Tukey’s) showed that the Broken meal elicited a 2.13 ± 1.85 mg/mL (233%) and 2.39 ± 1.74 mg/mL (370%) higher gastric maltose response than both cellular meals Intact-S ( adj. P = 0.027) and Intact-C ( adj. P = 0.011), while there was no difference between Intact-S and Intact-C. Sucrose concentrations (Supplementary Table 5) , included as a meal marker, were not significantly different in gastric aspirates between meal types ( P = 0.055 for both gastric samples), and the time-series data indicated a gradual drop in gastric sucrose concentrations (presumably reflecting liquid-phase gastric emptying, suggesting most of the digesta had been emptied from the stomach from ~ 60 min). Duodenal maltose concentrations (Fig. 5 B) were also significantly different between meal types ( P = 0.020 ) , with pairwise comparisons showing that the Broken meal elicited 0.689 ± 0.686 mg/mL (140%) higher duodenal maltose concentrations than Intact-C ( adj. P = 0.049), while differences between the other meals were not significant. Meal type had similar effects on the duodenal maltotriose concentrations (main effect P = 0.018, Broken vs Intact-C, mean difference 0.504 ± 0.473 mg/mL, 410% higher, adj. P = 0.038) and duodenal glucose concentrations (main effect P = 0.003, Broken vs Intact-C, mean difference 0.253 ± 0.173 mg/mL, 132% higher, adj. P = 0.007) as shown in Fig. 5 C,D and Extended Data Fig. 3 . Sucrose (liquid phase meal marker) was present at ~ 1.4 x higher concentrations in the gastric fluid compared to the duodenal fluid, and duodenal sucrose concentrations were significantly different between meal types (P = 0.041), with Broken having a 0.080 ± 0.074 (90%) higher sucrose concentration than Intact-C ( adj. P = 0.035) – this could suggest a greater dilution of the intact-C meals. No significant differences in duodenal sucrose concentrations were seen between Intact-S and Broken. Thus, the differences in maltose concentrations observed within these gastric or duodenal fluids after Intact-S and Broken can be taken to mainly reflect differences in meal starch digestibility, while Intact-C results may also be influenced by gastric emptying or dilution effects. Pellet masses contained the inaccessible and undigested meal components from the aspirated gastric and duodenal fluids. Typically, one mL of digesta aspirated from the stomach contained between 7 and 48 mg of pellet dry mass, and duodenal aspirates contained between 3 and 18 mg of pellet dry mass (see Supplementary Table 5 for further details). Regardless of meal type, the duodenal pellets were comprised of ~ 43–54% ‘meal-derived’ carbohydrate, of which starch was the main component (Fig. 5 E). There was a highly significant main meal effect ( P < 0.001) on duodenal pellet carbohydrate composition, with duodenal pellets from Broken comprising a 30.54 ± 6.47 mg/100 mg (46%) lower proportion of starch than Intact-S ( adj. P < 0.001) and 33.01 ± 9.00 mg/100 mg (43%) lower than Intact C ( adj. P < 0.001). Taking into account the typical pellet masses for each meal, it is possible to estimate that duodenal fluid after Intact-S, Intact-C and Broken (0–3 h) contained on average 6.353 ± 2.678, 3.263 ± 1.667, and 1.306 ± 0.456 mg non-bioaccessible starch per mL aspirated duodenal fluid (Fig. 5 F). Based on the microstructure of the digesta (Fig. 4 ), it is likely that the higher levels of non-bioaccessible starch in digesta from the cellular meals are due to its encapsulation within the cell wall (type 1 resistant starch). Overall, the cellular meals showed similar digestion properties, whereas the non-cellular Broken meal resulted in less undigested starch in the pellet, while having higher local maltose and maltotriose concentrations. Our in vivo observations support the cell wall barrier mechanism proposed in in vitro studies 39 – 42 that the intact cell wall is the key to controlling starch digestion. The oral processing of digestion deserves more attention. The role of salivary amylase is often overlooked due to the short duration of the oral phase. Our study suggested that disrupting the intact cell wall (‘Broken’ meal) increased starch digestibility in the mouth, which caused local concentrations of starch digestion products (maltose > maltotriose > glucose) to increase rapidly within the stomach and duodenum within the early postprandial period after Broken meal consumption. Additionally, we noted that the gastric pH remained at around 4 for the first 15–30 min after meals (see Supplementary Table 6 ), likely allowing salivary amylase to retain 50% of its activity 43 . Our observations thus imply that salivary amylase digestion of accessible starch may continue in the stomach in humans and that the extent to which this contributes to overall glucose release from starch depends on meal cellular structure. We recommend future in vitro simulated digestion models incorporate the oral digestion phase and account for dynamic changes in gastric pH. Gastric Maltose Correlated with Early Blood Glucose and Incretin Responses Early peaks in blood responses (0–30 min) were tightly correlated with initial rates of starch digestion (Fig. 5 G). Correlations analyses were performed between the 0–30 min delta changes in blood responses with increments of starch digestion products: gastric glucose (GasGlu), gastric maltose (GasMal), duodenal glucose (DuoGlu), and duodenal maltose (DuoMal) across all meal types. GasMal emerged as the most robust correlator, positively correlating with elevations in glucose, GIP, and GLP-1 levels (Fig. 5 G, Spearman rho = 0.64, 0.47, 0.42 with P < 0.01, P = 0.014, P = 0.034 respectively). GasGlu played similar but less-pronounced correlations ( Extended Data Fig. 4 , rho = 0.32, P = 0.082 for blood glucose, rho = 0.44, p = 0.017 for GIP). No significant correlation was found between DuoGlu or DuoMal with blood responses (all P > 0.05, Extended Data Fig. 4 ), possibly due to dilution effects of gastric fluid entering the duodenum and/or limited duodenal samples (see Supplementary Table 3 f or overview of sample collection and analyses). No correlation was found between PYY and luminal starch digestion products ( Extended Data Fig. 4 , all P > 0.05). Our data demonstrated a direct relationship between cell intactness, intestinal carbohydrates, and the magnitude of the glycaemic response. The observation that spikes in blood glucose and GIP within 0–30 min were associated with increments of gastric carbohydrates is an interesting finding. This can be supported by previous studies that identified SGLT-1 and GLUT-1, the main glucose transporters 44 , 45 as well as the GIP-positive K-cells 46 in the human and mouse stomach. Mechanisms of carbohydrate absorption and GIP-sensing are well described in the small intestine 47 – 49 , whilst our study may be the first to suggest that the early stage of digestion i.e., the oral and gastric phase, can play a more important role in postprandial glucose and GIP than previously thought. Our results also showed that the initial starch digestion rates correlated with GLP-1 but not PYY, which aligned with previous observations. The upper segment of the mice intestine could secrete GLP-1 but not PYY 50 . Intragastric or intraduodenal glucose infusion in humans resulted in a remarkable increase in GLP-1 but had a lesser impact on PYY secretions 51 , 52 . Our findings supported the role of gastric and duodenal carbohydrates in the early GLP-1 response with minor effects on PYY. Duodenal ‘Undigested Starch’ Associated with PYY and Later-Phase GLP-1 Responses We then explored whether the PYY response was a result of carbohydrates arriving at the lower gut (e.g., the jejunum and ileum) where a higher density of L-cells is present. The undigested starch contents in duodenal aspirates, indicating the availability of carbohydrates in the lower gut, positively correlated with PYY iAUC (0-180min) (rho = 0.39, P = 0.048, Fig. 5 H). Non-digestible carbohydrates have been reported to arrive in the ileum as early as the first 30 minutes after meals in healthy subjects 53 ; ileal infusion of glucose increased PYY, whereas no such effects were observed with duodenal infusion 28 . These findings supported our analyses indicating that the undigested starch escaping to the lower intestinal segment promoted PYY secretion. The undigested starch showed no correlation with the iAUC of GLP-1 or GIP within 0-180 minutes (data not shown). However, it exhibited significant correlations with their iAUC from 30 to 180 minutes (rho = 0.62 and 0.62, both P < 0.001, as shown in Fig. 5 H). Thus, carbohydrate arrival in the lower intestinal segment promoted PYY and later-phase GLP-1 responses. Food Structure Impacted Gastric and Duodenal Metabolite Profiles Enteroendocrine cells (EECs) secrete gut hormones in response to metabolites in the gut lumen 22 , 54 . EECs and receptors have been well-mapped throughout the gut 55 , yet little is known about how meal ingestion and transit influence the luminal metabolites. This study used 1 H NMR metabolomic profiling to measure the metabolomics in gastric and duodenal aspirates collected pre- and post-prandially at 30, 60, 120 and 180 min. An overview of profiling and peak assignments is presented in Fig. 6 A and Supplementary Table 7. Repeated measures, Monte-Carlo Cross-validation, Partial Least Squares Discriminant Analysis (RM-MCCV-PLS-DA) identified the metabolites associated with food structure interventions. No baseline differences between meals were detected ( Extended Data Fig. 5 ). Robust separations between the ‘Intact’ meal and ‘Broken’ meal were observed between postprandial 30–120 min (see 30 min and 120 min in Fig. 6 B-E and others in Extended Data Fig. 5 ). At postprandial 30 min, when GIP and GLP-1 levels peaked for Broken and were lowest for Intact-C, robust differences in their luminal metabolites were observed (gastric model: R 2 Y 0.97, Q 2 Y 0.89; duodenal model: R 2 Y 0.99, Q 2 Y 0.57). Broken exhibited increased levels of starch digestion products, including maltose, glucose, and oligosaccharides in gastric and duodenal aspirates. Broken showed decreased levels of tyrosine, phenylalanine, and tryptophan in gastric aspirates, and decreased conjugated bile acids in duodenal aspirates compared to Intact-C (Fig. 6 F, P values reported in Supplementary Table 8 ). At postprandial 120 min, GLP-1 levels in Intact-S significantly surpassed those in Broken, with distinct differences observed in their gastric and duodenal metabolomes (gastric model: R 2 Y 0.99, Q 2 Y 0.89; duodenal model: R 2 Y 0.89, Q 2 Y 0.71). Intact-S exhibited elevated levels of amino acids and bile acids in duodenal aspirates, including valine, leucine/isoleucine, alanine, tyrosine, and taurine-/glycine- conjugated bile acids. Although starch digestion products in gastric aspirates remained lower in Intact-S compared to Broken, these carbohydrates were comparable in their duodenal aspirates (Fig. 6 F, P values reported in Supplementary Table 8 ). Dynamics of Intestinal Metabolites and Postprandial Responses Figure 7 A overviews how luminal metabolites change over time and correspond with blood responses. Key metabolites were quantified from their 1 H NMR profiling and presented in a heatmap. Consistent with the quantitative analyses, this figure also shows that cell breakage increased glucose and maltose concentrations within the stomach and the duodenum, which coincided with higher blood glucose, insulin, GLP-1 and GIP concentrations. Time-series comparison of meal-derived components such as trigonelline ( a marker of legume intake 56 ), stachyose/raffinose (a source from legume meal), fumarate/fumaric acid (a food additive from the background diet jelly) with starch-digestion products (maltose, glucose) confirmed that the elevated levels of starch digestion products coincided with meal transit. Amino acids like alanine, tyrosine, and glutamine were more concentrated in the fasted state and decreased postprandially, aligning with a dilution effect from postprandial intestinal secretions and meal transit. The duodenal amino acid concentrations rose in the later postprandial period (i.e., 60 and 120 min), with the increase being more pronounced for the Intact-cellular meals and concurring with their later GIP /GLP-1 response. Partial Least Squares Regression (PLSR) explored the relationships between luminal metabolites (X matrix) and blood responses (Y matrix). PLSR models for early and later postprandial periods revealed a shift of luminal metabolites and enteroendocrine signalling. At postprandial 30 min, Fig. 7 B showed a distinct clustering of blood glucose (BldGlu), insulin, GIP and GLP-1 alongside gastric maltose. The network correlation analysis (Fig. 7 C ) highlighted the significant correlations (with False Discovery Rate -adjusted p values < 0.05), involving maltose, glucose, fumarate and trigonelline in the stomach. In the later postprandial stage, BldGlu, Insulin, and GIP were most closely associated with duodenal maltose, trigonelline, and fumarate (Fig. 7 D). GLP-1 showed the same vector direction with a series of duodenal amino acids (Fig. 7 D). Among these were valine, alanine, glutamine, tyrosine, and histamine, highlighted as significant correlators (Fig. 7 E). Food structure has been hypothesised to impact gut hormone secretion through modulating the amount of nutrients arriving in the distal intestine 5 . Less attention has been paid to the proximal intestine. This study provides insight into the upper gastrointestinal mechanisms by which broken and intact food structures induce different gut hormone responses. Gastric and duodenal metabolite profiles significantly differed between food structure interventions, with the differences driven by bile acids, amino acids, and starch digestion products. Furthermore, the PLSR models demonstrated the dynamic associations between luminal metabolites and postprandial blood responses: Initial glycaemia and GIP responses were predominantly influenced by a surge in maltose, particularly in the gastric region. In the later postprandial phase, the metabolic drivers shift towards a range of duodenal amino acids that have implications for GLP-1 levels. Specifically, we showed that elevated valine, alanine, and tyrosine (Fig. 6 E,F) may be linked to heightened GLP-1 response in Intact-S at postprandial 120 min. Emerging evidence suggests individual amino acids can stimulate GLP-1 with different potencies and through different mechanisms 57 . Valine has been suggested as a potent stimulator of GLP-1 secretion when infused in the perfused rat small intestine 58 . In human subjects, intraduodenal infusion of L-tryptophan 59 or L-glutamine 60 induced GLP-1 response; other amino acids have yet to be studied. Our data indicates that the relationship between the duodenal amino acid profile and GLP-1 response warrants further investigation. Further research is also needed to uncover the impact of food structure on luminal amino acid releases to complete the understanding of how chickpea meals containing intact cell walls promote GLP-1 secretion. Strengths and Limitations A strength of this study was the use of precisely controlled meal structures in combination with the parallel sampling of blood and digesta from healthy humans within a controlled clinical environment. The meals were matched by amount and type of carbohydrate, while clear differences in the cellular structure were achieved. This allowed meal structure-dependent differences to be elucidated. Insight into the gastrointestinal mechanisms that underpinned the different postprandial blood responses to meals with contrasting structures were possible through the use of enteric intubation technique which enabled postprandial sampling of digesta in parallel to blood collections. This technique helps to overcome limitations of in vitro digestion models, which do not, for instance, replicate oral phase processing and enteroendocrine feedback responses. A learning from this enteric intubation (pilot) study relates to the sampling frequency of postprandial duodenal digesta, as the low volume of digesta prevented some sample collections, resulting in insufficient sample numbers/volumes for reliable insight into digesta time trends. Future studies should consider less frequent postprandial sampling of stomach and duodenal content to improve sample collection. Sampling should expand to the distal gut area to develop a complete picture of the interactions between gut metabolites and hormones. Discussion Previous studies 19 – 21 , 33 have shown that food structure has a profound effect on blood glycaemia and gut hormone responses. Our study supports these findings as we demonstrate chickpea meals with the same nutrient contents and contrasting cell intactness were shown to elicit significantly different postprandial blood glucose, insulin, GIP, GLP-1, PYY and satiety responses. However, the current study goes beyond these observations by exploring the relationship between the structure of the food matrix, luminal metabolites of the small intestine and the generation of signals which affect physiology. We showed that disruption of cellular structure (‘Broken’ meal) significantly increased starch bioaccessibility in the upper gastrointestinal tract, leading to a 2 to 4-fold increase in the magnitude of the (peak) glucose response evoked. ‘Broken’ meal also increased insulin and incretin release to combat the rapid rise in plasma glucose. Furthermore, ‘Broken’ meal shortened the duration of the GIP, GLP-1 and PYY responses, and lowered subjective satiety compared to a nutrient-matched meal in which the cellular structure remained intact (Intact-S). It is important to realise the physiological benefits for glycaemic control and weight loss, as reported in earlier epidemiological and interventional studies where whole cooked pulses, with their cellular structure presumed intact, were usually the main structural form consumed 61 , 62 . However, this study shows that these benefits can be compromised when plant cell intactness is disrupted. Food cellular structure and processing level should be considered when making dietary recommendations or developing health-promoting ingredients for fibre-rich products. The nutrient-sensing system on EECs related to the release of gut hormones is a topic of growing interest. Unlike the ileum and colon, lesser attention has been paid to the proximal intestine about the secretions of anorexigenic gut hormones such as GLP-1. Our study emphasises the role of the postprandial small intestinal metabolites, demonstrating how digestion of ‘Broken’ and ‘Intact-cellular’ meals shapes different upper gastrointestinal metabolite profiles, resulting in distinct endocrine and metabolic consequences. Significant alterations in metabolite profiles were observed, particularly in starch digestion products, amino acids, and conjugated bile acids. These findings offer new insights to explore the interaction between food and gut signalling. We showed the ‘Broken’ meal elicited a higher acute GIP and GLP-1 response as a consequence of a rapid rise of gastric maltose, whereas the ‘Intact-cellular’ meal elevated tyrosine, valine, and alanine which were associated with prolonged GLP-1 levels. Future research should focus on the mechanisms underlying these findings and validate their roles in gut hormone secretion. Evidence from well-controlled human gut infusion studies is warranted. Phamaceutical adaptions of gut hormones are creating a new generation of therapies for diabetes and obesity. Our study indicates that food structure can be a promising tool to target gut hormones by controlling the release and delivery of nutrients in the gut which could offer a public health strategy to prevent non-communicable disease. Processing which disrupts the cellular structure and increases intestinal luminal maltose and glucose could have positive effects on K-cell release of GIP whereas intact structures and the change in metabolite profiles related to these structures lead to increased GLP-1 and PYY from L-cells. Simple changes in food structure could therefore offer an effective strategy for optimising gut peptides and postprandial metabolism. Methods Human intubation study design This trial was approved by the Health Research Authority and London-Camden and King’s Cross Research Ethics Committee (REC 19/LO/0962) before the commencement of any study procedures. The study was prospectively registered at ISRCTN (ISRCTN18097249) before the enrollment of the first participant. All the participants received a participants information sheet and signed informed consent before they started the clinical trial. A CONSORT diagram and key study dates are shown in Extended Data Fig. 1 . A human study was conducted with 10 healthy participants aged 18–65 y old with a Body Mass Index (BMI) ranging from 18.5 to 30 kg/m 2 . All participants were recruited from the healthy volunteer database of NIHR Imperial Clinical Research Facility. Participants who expressed an interest in this study were asked to fill in a pre-screening form and attend a screening visit. Their eligibility was checked and confirmed by a medical doctor based on their health history, anthropometric measurements, ECGs, and blood test results. Participants with an abnormal ECG, screening blood values outside the clinical reference range, a history of cancer, diabetes, gastrointestinal disease and/or requiring medication likely to interfere with metabolic and hormone responses were excluded. Inclusion and exclusion criteria were as listed in Supplementary Table 1. This study followed a double-blinded, randomized crossover design. Randomization was performed using a sealed envelope system (Sealed Envelope Ltd. 2022). Participants were assigned into three intervention groups in a randomized order using a balanced allocation ratio of 1:1:1. The randomiszation was performed by an independent researcher who was not involved in this trial. The allocation of treatment sequence was blinded to the investigators, the technicians performing analysis of blood samples and participants. Investigators and participants remained blinded until the completion of the study and data analysis. Study procedure Each participant attended one 4-day inpatient study visit at NIHR Imperial Clinical Research Facility (CRF) at Hammersmith Hospital. The day before the study visit, participants were asked to refrain from caffeine, alcohol, and strenuous exercise. Participants were also requested to fast overnight. On day 1, an enteral feeding tube was placed in the participants’ small intestine (duodenum), following procedures previously reported 33 . On days 2, 3, and 4 participants received three chickpea test meals (see ‘ Dietary intervention’ ) in a randomized order. Intestinal samples were taken before meals (T = -10 and 0 min) and subsequently at 15-minute intervals for 180 min for microscopic and carbohydrate analyses. Blood samples were taken before and after the test meals for 180 min (T = -10, 0, 15, 30, 45, 60, 90, 120, 150, and 180 min). Blood samples were collected through a cannula placed in the antecubital fossa used for measuring blood glycaemia and hormonal markers. Visual Analogues Scale (VAS) questionnaire was collected for the same period and at the same frequent intervals to measure the subjective appetite levels of participants. A lunchtime meal (at 4 h) was provided to measure their ad libitum food intake. On day 4, the enteral tube was removed, and participants were discharged. Dietary interventions Participants received different test meals on day 2, 3, 4 corresponding to the 3 different chickpea structures: broken cells (‘Broken’), intact single cells (‘Intact S’), and cell clusters (‘Intact-C’). All test meals were prepared from the same batch of whole chickpeas, Cicer arietinum L. , Kabuli type (Argentine variety, supplied by AGT Poortman Ltd.), which were abrasively dehulled, dry-milled (if applicable) and sieved, then weighed into test-meal specific portions, labelled, and sealed by a researcher independent from this study. Cooked test meals were prepared fresh for each study visit, using the concealed ingredient portions with the corresponding standardised cooking programme and a Vorwerk Thermomix Version 5 (see Supplementary Information ‘Meal preparation’ for further details) which was developed to enable reproducible production of meals with contrasting structures. The portion size was controlled based on the moisture content of the chickpea portion to ensure delivery of 30 g total starch per serving for all meal types. Typically, the freshly cooked chickpea serving (containing 60 g chickpea dry solids) weighed 490, 280 and 230 g for meals Broken, Intact-S, and Intact-C, respectively, reflecting their different water content, and were served with 270, 480, and 530 g water to achieve a consistent total portion size of 760 g, including test meal, water and flavouring (15 g ‘no sugar added blackcurrant jam’, Stute Foods Ltd., Bristol, UK, and 115 g ‘Hartley’s no added-sugar raspberry flavoured jelly’, Histon Sweet Spreads Ltd., Leeds, UK). Based on proximate analysis of the chickpea component (performed by accredited food testing provider ALS Laboratories Ltd., Chatteris, UK) and nutrition labels on food packaging (jelly and jam), each test meal serving provided (mean of triplicate with SD, 41.16 ± 0.4 g available carbohydrate of which 29.5 ± 0.04 g total starch and 2.29 ± 0.00 g sugars, 6.58 ± 0.43 g dietary fiber, 11.02 ± 0,03 g protein, 3.74 ± 0.06g fat for a total 242.7 ± 0.76 kcal. Thus, all meals contained the same ingredients and macronutrient composition per serving but were designed to differ in microstructure, i.e., consisting of either mainly broken cells (Broken), individual cells (Intact-S) or cell clusters (Intact-C). Sample analysis The primary outcome was the blood gut hormone response. Co-secondary outcomes included intestinal content analysis, blood glucose and insulin response, subjective appetite changes and ab libitum energy intake. Blood sample analysis Blood samples were collected into tubes (BD Vacutainer® tubes: fluoride/oxalate tubes for glucose analysis; SST™ serum tubes for insulin analysis), and into lithium heparin tubes with DPP-IV (10 µL/mL blood, Merck Millipore), aprotinin (10,000 KIU/mL blood, Nordic Pharma) and AEBSF (pefabloc, 1 mg/mL blood) for GIP, GLP-1 and PYY analysis. Plasma glucose was measured using GLUC-PAP kits (Randox Laboratories Ltd., UK). Serum insulin concentrations were determined by a Human Insulin Specific RIA kit (HI-14K, Merck, UK). Plasma GIP concentrations were measured by Human GIP ELISA kits from (EZHGIP-54K, Merck, UK). All these assays were performed as per the manufacturers’ instructions. GLP-1 and PYY concentrations were measured by the in-house RIA method 63 . Gastric and duodenal starch digestion analysis Defrosted gastric and duodenal samples were centrifuged for 15 min at 10,000 g to separate the undigested solids (pellet) from the intestinal fluid. The supernatants were decanted into 15 mL centrifuge tubes. Absolute ethanol was added to the supernatant (3 mL) and pellet (1 mL) to kill bacteria before drying the sample fractions at 51°C for 3 h in a centrifugal evaporator (EZ-2 Elite, Genevac). Supernatants were then analysed by LCMS to determine concentrations of starch digestion products (maltose, maltotriose) within the aspirate fluid. The dried supernatants were resuspended in 0.5 mL water (aided by vortex mixing and sonication), and then centrifuged at 13,000 g for 5 min. The supernatants were then diluted in (milliQ) water and 45 µL of each sample was transferred to an HPLC vial and 5 µL D-glucose- 13 C 6 -Glc 99% atom C (0.1 mg/mL) was added as an internal standard, before analysis by LC-MS (Agilent 6490 mass spectroscopy) on a reverse phase UPLC column (Thermo Hypercarb 100 x 2.1 mm 3 µm column) using 0.1% formic acid in water and 0.1% formic acid in acetonitrile as the mobile phase. Maltose, sucrose, and maltotriose were included as standards. Sugars were quantified by selected ion monitoring. Gastric and duodenal glucose was measured by GLUC-PAP kit (Randox Laboratories Ltd., UK) based on the instructions, except for an additional step at the beginning. Defrosted samples were first centrifuged at room temperature for 10 minutes at 13,000 rpm. A volume of 20 µL from the supernatant was used to perform the remaining steps according to the GLUC-PAP kit instructions. Pellets were analysed for estimation of their carbohydrate composition: samples of the dry pellet powders < 3 mg were weighed out to an accuracy of 0.1 mg into glass culture tubes, then treated with 100 µL 72% w/w H 2 SO 4 for 3 h at room temperature, then diluted with water to 4% acid, and heated at 121°C for 1 h in a Techne Dri-block heater, and finally cooled on ice for 10 min 64 Monosaccharide analyses of the resulting acid hydrolyzates were then performed based on the method 65 .A mixed standard solution containing 200 µg/mL of 9 monosaccharides (arabinose, fucose, galactose, glucose, galacturonic acid, glucuronic acid, mannose, rhamnose and xylose) was prepared and diluted to concentrations of 160, 120, 80, and 40 µg/mL. Next, 100–300 µL of 1 mg/mL Talose internal standard) was added to each hydrolysate and standards mixture. Sample hydrolyzates were then pH neutralised with 2 M CaCO 3 , and centrifuged (2500 rpm, 10 min) to remove the precipitate. The supernatants were filtered through 0.45 µm syringe filters. Finally, samples plus 5 µL D -glucose- 13 C 6 , 99% Atom C (0.1 mg/mL), were derivatized with 3-methyl-1-phenyl-2-pyrazoline-5-one (PMP) and the monosaccharides quantified by UPLC analyses (Agilent 6490 Mass Spectrometer) using the method of Xu et al. (2018) 65 , which was chosen for its high sensitivity with multiple reaction monitoring mode detection. Sample dry matter (g/mL) was calculated from pellet mass loss upon drying. The total monosaccharide content of the acid hydrolysates (derived from cell walls and starch) was calculated as the sum of anhydro masses of monosaccharide constituents and expressed per mg pellet dry mass. An indication of the amount of starch in the pellet samples was obtained as per Eq. 1 . The glucose measured derives from starch, cellulose and xyloglucan. By an in-house acid hydrolysis of the chickpea cell wall purified of intracellular contents, the ratio of arabinose to glucose was found to be 1: 0.469. Assuming no solubilisation or fermentation of pectic arabinan in the upper gut, the arabinose value may be used to estimate the non-starch glucan content. This was subtracted from the total glucose to give an indication of the starch content of the pellets. \(Starch \%=Glu\%-\left(0.469 x Ara\%\right)\) x Total sugars (µg/mg dry matter) (Eq. 1 ) Equation 1 Proportion of pellet total sugars that is derived from starch (Starch %) is estimated from the proportion of Glucose (Glu) and Arabinose (Ara) measured in the acid hydrolysate, which are expressed as a percentage of the total sugar content (sum of anhydro masses of monosaccharide constituents) within the acid hydrolysate. Appetite and food intake analysis VAS consisted of a set of questions to measure hunger (“how hungry do you feel right now?”), fullness (“How full do you feel right now?”), desire to eat (“How strong is your desire to eat”), and prospective food intake (“How strong is your appetite for a meal?”). Participants were asked to answer these questions by drawing a vertical line across a 100mm scale ranging from “not at all” (right extreme) and “extremely” (left extreme). A composite appetite score (CAS) was calculated by combining the four measurements: [hunger+(100-fullness) + desire to eat + appetite for a meal]/4. An excessive, homogenous pasta meal was served at 240 min to measure ad libitum food intake. The meal consisted of 3kg boiled white pasta, mixed thoroughly with 2 pots of tomato sauce (Hearty Food Co. Tomato Herb Pasta Sauce 440 g) and 50 g of vegetable oil (Flora sunflower oil) to provide approximately 2500 kcal. Food intake was measured by weighing the grams of food before and after consumption and calculating the difference. Sample Size and Statistical Analysis This was a pilot study. No similar study had been conducted pior to this study so the target of 15 participants was estimated. During the study, Petropoulou, K. et al. used similar methodology to investigate the effects of resistant starch from peas on human gastric and duodenal digestion with 10 participants and reported significant differences on outcome measures such as blood glycaemia, gut hormone and intestinal starch digestion 33 . Thus, 10 participants were recruited for this study. Blood biochemical data were checked for normality by the Shapiro-Wilk test. For data that did not pass the test (P < 0.05), a log transformation was applied before conducting parametric tests. Two measurements of baselines before breakfast were combined as the baseline value. For time-course data, two-way repeated ANOVA was performed to measure the effect of time, meal (treatment) and their interaction. The Greenhouse-Geisser correlation was applied to correct for violations in sphericity, and the assumption of normality of residuals was assessed using QQ plots. Post hoc Tukey’s Tests were performed for pairwise comparisons when significant treatment × time effects were detected, and multiplicity adjusted p -values reported (‘adj.’ P '), and mean differences (MD) with 95% CI reported in the text (expressed as MD ± 95% margin of error(MoE)). Incremental peak (iPeak) was calculated as the maximum rise from fasted concentrations for each individual. Incremental Area under curve (iAUC) refers to the ‘first peak ‘area and was calculated using the trapezoidal rule, ignoring the area under the baseline. The ‘Peak X’ and ‘Last X’ refer to the time to peak and return to baseline levels. Summary variables (iPeak, iAUC, Peak X, and Last X) were analyzed by one-way repeated measure ANOVA, followed by post hoc Tukey’s Test. For VAS time-series data, changes from baselines were used for two-way repeated ANOVA followed by pairwise comparisons. Data were analyzed using GraphPad Prism 9.0 (Graphpad Software USA, Biomatters, Ltd.). Results were considered statistically significant at P < 0.05. For aspirate analyses data, two-way mixed effects ANOVA was planned for the time-series, however the large number of missing values (due to potentially non-random variable availability of intestinal content) meant that this statistical test could not reliably be performed on the intestinal samples. Time series data is presented as mean data, and Supplementary Table 3 includes a table showing number of samples used to compute the means for each time point. Mean analyte concentrations in samples collected from each participant over the 3 h period were analysed by One-Way repeated measures ANOVA, to test for main meal effects. Tukey’s test was performed post hoc when significant main effects were observed. Mean difference with 95% CI of difference, along with multiplicity adjusted P-values are reported for post hoc pairwise comparisons. Partial Least Squares Regression (PLSR) was performed with luminal metabolites as predictors and blood glucose, insulin, GIP, GLP-1, and PYY as outcomes. PLSR was conducted using the ‘plsr’ package in R 68 . Data was scaled enabling each variable to contribute uniformly to the model. The number of principal components used was determined through the Root mean square error of prediction (RMSEP) to achieve the highest prediction accuracy. The model was validated by the ‘leave one out’ method. Declarations Acknowledgements The authors would like to thank the study participants for their time and efforts in participating in this trial. We thank Oyinkansola Olotu, Echo Junceng Dong, Claire Ho, Dr Hannah Stephen, and Jennifer Pugh for assistance with the trial and sample collection. Author Contribution Conceptualization: M.C., G.F., C.E.; Methodology: M.C., M.T., P.R., G.F., C.E.; Investigation: M.C., S.T., M.T., P.R., N.P., I.G.P., J.I.C., J.W., E.H., A.B., B.D., G.B., Formal analysis: M.C., S.T., P.R., G.B., I.G.P., J.W., C.E.; Data Curation: M.C., P.R., N.P., S.S., I.G.P., J.I.C.; J.W.; Visualization: M.C., C.E., Supervision: G.F., C.E.; Project administration: G.F., C.E.; Funding acquisition: G.F., C.E.; Writing- Original Draft: M.C., C.E.; Writing- Review & Editing: M.C., C.E., G.F.; All authors [M.C., S.T., M.T., P.R., N.P., S.S., I.G.P., J.I.C., J.W., E.H., A.B., B.D., G.B., G.F., C.E.] have read and approved the manuscript. Funding Disclosure This research was funded by the Biotechnology and Biological Sciences Research Council, UK (BBSRC) Institute Strategic Programme grant BB/R012512/1 and its constituent projects BBS/E/F/000PR10343 and BBS/E/F/00044427. MC is funded by the China Scholarship Council (CSC) from the Ministry of Education of P.R. China for her PhD. Declaration of Interest All authors have no conflicts of interest to declare. Additional Information The clinical trial has been registered with the ISRCTN ( https://www.isrctn.com/ISRCTN18097249). Data Availability The data reported in this study are available from Mendeley Data Database at https://data.mendeley.com/preview/4vn35twm9v?a=2e3eda4b-23d2-4a9a-ab80-263b57e58caa and will also be shared by the corresponding authors upon request. Code Availability This study does not report original code. References Foyer, C. H. et al. Neglecting legumes has compromised human health and sustainable food production. 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Rapid Determination of Cellulose, Neutral Sugars, and Uronic Acids from Plant Cell Walls by One-step Two-step Hydrolysis and HPAEC-PAD. Bio-protocol 6, e1978 (2016). https://doi.org/10.21769/BioProtoc.1978 Xu, G., Amicucci, M. J., Cheng, Z., Galermo, A. G. & Lebrilla, C. B. Revisiting monosaccharide analysis – quantitation of a comprehensive set of monosaccharides using dynamic multiple reaction monitoring. Analyst 143, 200–207 (2018). https://doi.org/10.1039/C7AN01530E Dona, A. C. et al. Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping. Anal Chem 86, 9887–9894 (2014). https://doi.org/10.1021/ac5025039 Gu, Z. Complex heatmap visualization. iMeta 1, e43 (2022). https://doi.org/https://doi.org/10.1002/imt2.43 Mevik, B.-H. & Wehrens, R. Introduction to the pls Package. Help section of the “Pls” package of R studio software, 1–23 (2015). Additional Declarations There is NO Competing Interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4502487","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":311038687,"identity":"e7b98444-a318-4377-a9e9-3f5fdb775145","order_by":0,"name":"Gary 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London","correspondingAuthor":false,"prefix":"","firstName":"Georgia","middleName":"","lastName":"Becker","suffix":""},{"id":311038701,"identity":"ce1d6a5f-1a4e-45bb-ac92-ea6a0c0ca8a8","order_by":14,"name":"Cathrina Edwards","email":"","orcid":"https://orcid.org/0000-0003-4952-0229","institution":"Quadram Institute","correspondingAuthor":false,"prefix":"","firstName":"Cathrina","middleName":"","lastName":"Edwards","suffix":""}],"badges":[],"createdAt":"2024-05-30 10:56:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4502487/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4502487/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s42255-025-01309-7","type":"published","date":"2025-06-20T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59463983,"identity":"a5b1e652-7995-4118-9f0b-b55df0aae721","added_by":"auto","created_at":"2024-07-02 06:03:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":352847,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of study design and test meals.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Overview of study interventions. Study interventions include serial blood and intestinal sampling, visual analog scale assessments, ad libitum food intake assessment. Abbreviation: glucose-dependent insulinotropic peptide (GIP), Glucagon-like Peptide-1 (GLP-1) and peptide-YY (PYY). (B) Photographs of nutrient-matched test meals with contrasting structures. Flavoured cooked chickpea porridge Intact-C, Intact-S, and Broken, served with water. All meal servings have the same mass and nutrient content – the different volume in the bowls reflect differences in water holding capacity. NB: Meals consumed by human participants were freshly prepared, but these photos are of frozen and thawed meals to enable side-by-side comparison. Annotations: Dashed line shows water level. Scalebar bottom right is 20 mm. (C) Structural microscopy of cooked chickpea porridge. Light micrographs of cooked porridge showing clusters of intact cells (‘Intact-C’), separate intact cells (Intact-S) and debris from broken cells ‘Broken’. Scalebar 200 µm. (D) Starch amylolysis curves show starch digestion progress during in vitro digestion with pancreatic α-amylase.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4502487/v1/a975fe38cd530a47e5950786.png"},{"id":59464419,"identity":"aebbfea5-9799-4d4e-9b7e-5397feed2b92","added_by":"auto","created_at":"2024-07-02 06:11:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":499277,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBlood glucose and insulin responses to chickpea meals with contrasting structures. \u003c/strong\u003ePostprandial (3h) blood glucose (ABC) and insulin (DEF) responses in healthy participants (n=10) to macronutrient-matched chickpea meals (30 g starch/serving) in which cotyledon cells are present as cell clusters ‘Intact-C’, separated cells ‘Intact-S’, or no longer cellular ‘Broken’. Time-series (AD) show mean with SEM (n=10). Two-way repeated measures ANOVA showed significant meal × time effects for glucose (p\u0026lt;0.001) and insulin (p= 0.003), and time-point annotations show significant differences (adj. P\u0026lt;0.05, Tukey’s) based on post-hoc pairwise comparisons: a Intact-S vs Intact-C; b Intact-S vs Broken; and c Intact-C vs Broken. Bar charts (BCEF) show (BE) maximum rise from fasted concentrations (‘iPeak’) and (CF) incremental AUC for glucose (BC) and insulin (EF), where bars show the mean responses to each meal and connected data points are from the the same individual. One way repeated measures ANOVA showed significant main meal effects on iPeak (P \u0026lt;0.001 and P=0.015 for glucose and insulin) and iAUC (P= 0.008 and P\u0026lt;0.001 for glucose and insulin) and annotations are the result of post hoc pairwise comparison ; Tukey’s multiplicity adj. P: *P\u0026lt;0.05, **P\u0026lt;0.01, *** P\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4502487/v1/7aac939db591118584a205e8.png"},{"id":59463592,"identity":"8dff7ab7-2a49-48f3-ac05-f9de027201da","added_by":"auto","created_at":"2024-07-02 05:55:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":425997,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGut hormone and appetite responses to chickpea meals with contrasting structures\u003c/strong\u003e.\u003cstrong\u003e \u003c/strong\u003ePostprandial (3h) blood GIP (ABC), GLP-1 (DEF), PYY (GHI) and subjective appetite (JK) responses and ad libitum food intake (L) in healthy participants (n=10) to macronutrient matched chickpea meals (30 g starch/serving) in which cotyledon cells are present as cell clusters ‘Intact-C’, separated cells ‘Intact-S’, or no longer cellular ‘Broken’. Time-series (ADGJ) show mean with SEM (n=10). Two-way repeated measures ANOVA showed significant meal × time effects for GIP (p\u0026lt;0.001) and GLP-1 (p= 0.034) and main effects for PYY (p= 0.003) and time-point annotations show significant differences (adj. P\u0026lt;0.05, Tukey’s) based on post-hoc pairwise comparisons: a Intact-S vs Intact-C; b Intact-S vs Broken; and c Intact-C vs Broken. Bar charts show (BCEFHI) maximum rise from fasted concentrations (iPeak) and (CFI) incremental AUC for GIP (BC) , GLP-1(EF), and PYY (HI), where bars show mean responses to each meal and data points connected by a line are from the same individual. One-way repeated measures ANOVA showed significant main meal effects on iPeak (P = 0.002 and P=0.144, P=0.012 for GIP, GLP-1 and PYY) and iAUC (P= 0.002, P= 0.070 and P=0.008 for GIP, GLP-1, and PYY) and annotations are the result of post hoc pairwise comparisons; Tukey’s multiplicity adj. P: *p\u0026lt;0.05, **p\u0026lt;0.01, *** p\u0026lt;0.001. Bar chart (KL) shows incremental area of fullness and ad libitum food intake (4h) where bars show mean responses to each meal and data points connected by a line are from the same individual. One-way repeated measures ANOVA showed significant main effect on fullness score (p=0.007) but not on food intake (P=0.176).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4502487/v1/61e075b6850313c2f69b0a76.png"},{"id":59463599,"identity":"c31b7c97-6eeb-4725-b714-02a7a3c81ff9","added_by":"auto","created_at":"2024-07-02 05:55:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":808011,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrographs of chickpea cell structures during gastric and duodenal digestion. \u003c/strong\u003eLight micrographs showing characteristics structures from Broken (ABC), Intact-S (DEF) and Intact-C (GHI) meals (ADG) and digesta aspirated from the stomach (BEH) and duodenum (CFI). Images are taken from the digesta of the same individual.\u003cstrong\u003e \u003c/strong\u003eScalebar is 100 µm.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4502487/v1/0bfb508441eb9836713c6cc3.png"},{"id":59463982,"identity":"38445e68-e87f-4e36-9db6-a70137abe04d","added_by":"auto","created_at":"2024-07-02 06:03:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":918948,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntestinal concentrations of starch digestion products, estimated undigested starch contents and their relationship with blood responses.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Time-series and average concentrations for gastric maltose. Connected data points in bar charts are from the same individual. n=9. (B)Time-series and average concentrations for duodenal maltose. Connected data points are from the same individual. n=9. (C)Time-series and average concentrations for gastric glucose. Connected data points are from the same individual. n=9. (D) Time-series and average concentrations for duodenal glucose. Connected data points are from the same individual. n=9. (E) Time-series and average concentrations for duodenal undigested starch expressed as a proportion of ‘total pellet sugars’. Connected data points are from the same individual. n=9. (F) Time-series and average concentrations for duodenal undigested starch calculated from the aspirated solid content and its pellet composition. n=9. (G) Spearman correlations of 0-30min delta changes in gastric maltose (mg/mL) \u0026nbsp;with blood glucose (mmol/L) , GIP (pg/ml) and GLP-1 (pmol/L). Scatterplots displayed individual points with a regression line and 95% CI,n=9. BldGlu. Blood glucose. (H) Spearman correlations of undigested starch in duodenal aspirates with PYY iAUC pmol/L*(0-180min), GLP-1 iAUC pmol/L*(30-180min) and GIP iAUC pg/mL*(30-180min). Scatterplots displayed individual points with a regression line and 95% CI, n=9.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4502487/v1/7661c5cb2111159beddc1190.png"},{"id":59463595,"identity":"39259685-80da-4c92-954a-a0c90fc44642","added_by":"auto","created_at":"2024-07-02 05:55:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":337436,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFood structure impacted gastric and duodenal metabolomics.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Overviewing mean \u003csup\u003e1\u003c/sup\u003eH nuclear magnetic resonance (NMR) spectrum of duodenal aspirates with annotations for peak assignments. Chemical shifts for NMR peak assignments are reported in \u003cstrong\u003eSupplementary Table 7\u003c/strong\u003e. (B-E) Repeated Measures, Monte-Carlo Cross-Validation, Partial Least Squares Discriminant Analysis (RM-MCCV-PLSDA) models on \u003csup\u003e1\u003c/sup\u003eH-NMR spectra derived from gastric and duodenal aspirates comparing participants receiving Broken and Intact-S / Intact-C meal at postprandial 30 and 120min. Dots represent individual participant metabolic profiles. Abbreviations:\u0026nbsp;Q\u003csup\u003e2\u003c/sup\u003eY, capability of prediction;\u0026nbsp;R\u003csup\u003e2\u003c/sup\u003eY, explained variance. Model scores: (B) Gastric postprandial T=30 (n = 9, R\u003csup\u003e2\u003c/sup\u003eY 0.97, Q\u003csup\u003e2\u003c/sup\u003eY 0.89). (C) Gastric postprandial T=120 (n = 9, R\u003csup\u003e2\u003c/sup\u003eY 0.99, Q\u003csup\u003e2\u003c/sup\u003eY 0.89). (D) Duodenal postprandial T=30 (n = 5, R\u003csup\u003e2\u003c/sup\u003eY 0.99, Q\u003csup\u003e2\u003c/sup\u003eY 0.57). (E) Duodenal postprandial T=120 (n = 8, R\u003csup\u003e2\u003c/sup\u003eY 0.89, Q\u003csup\u003e2\u003c/sup\u003eY 0.71). (F) List of luminal metabolites that were significantly associated with food structure interventions at postprandial 30 and 120 min (adjusted p values\u0026lt;0.05). P-values were adjusted for multiple testing using the Benjamini-Hochberg False Discovery Rate (FDR) and reported in \u003cstrong\u003eSupplementary Table 8. \u003c/strong\u003eAbbreviations:\u0026nbsp; G30, Gastric T=30min; G120, Gastric T=120min; D30, Duodenal T=30min; D120, Duodenal T=120min. BAs, bile acids; TCBAs, taurine-conjugated bile acids; GCBAs, glycine-conjugated bile acids.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4502487/v1/67ff581dee1869904496f281.png"},{"id":59463600,"identity":"46acce14-09ee-4420-b907-940bf5798b35","added_by":"auto","created_at":"2024-07-02 05:55:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":347435,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInterplay between luminal metabolites and blood metabolic responses.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Dynamics of luminal metabolites and blood responses. Each block represented the mean concentrations of participants (n=10), Each variable was normalized using Z-score transformation. Abbreviations: BldGlu, Blood Glucose; SR, Stachyose/Raffinose. (B, D) Partial Least Squares Regression (PLSR) Model Correlation Loadings for metabolites and blood responses at postprandial 30 and 120 min. Metabolites and associated blood responses were analysed via PLSR models (X [metabolites] / Y [blood responses], n=10). Positively correlated variables are proximal within the correlation circle (angle \u0026lt;0), while negative correlations appear diametrically opposed (angle \u0026lt;180). Variables lacking correlation are situated at angles \u0026lt;90 relative to each other. The X- and Y-axes denote the explained variance percentages by the PLSR factor. Abbreviations: BldGlu, Blood Glucose; PLS, Partial Least Squares. Annotations: gastric and duodenal metabolites are indicated with a suffix '.g' or '.d', respectively. Standard three-letter abbreviations are used for amino acids. (C, E) Correlation networks showing significant correlations between X [metabolites] and Y [blood responses] at postprandial 30 and 120 min (n=10), using a false discovery rate-adjusted p-value threshold of \u0026lt;0.05 using the Benjamini–Hochberg method. Annotations: gastric and duodenal metabolites are indicated with a suffix '.g' or '.d'. Standard three-letter abbreviations are used for amino acids.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4502487/v1/01c92b1ac1231a2a3dc29f39.png"},{"id":85101764,"identity":"f57bde69-cc92-4a9d-b913-e98398ccd8e6","added_by":"auto","created_at":"2025-06-21 07:08:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5277921,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4502487/v1/ea118161-427e-464f-87f3-7e8826d6a76f.pdf"},{"id":59463597,"identity":"e46fecf8-7f2f-43b4-949f-c4d1bcdebfdb","added_by":"auto","created_at":"2024-07-02 05:55:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":246420,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4502487/v1/537f94512e520abae6d2a2a4.docx"},{"id":59463984,"identity":"819c021e-b3a2-49f1-a4cc-2a66e30c3d2c","added_by":"auto","created_at":"2024-07-02 06:03:57","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1049976,"visible":true,"origin":"","legend":"","description":"","filename":"ExtendedDataFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-4502487/v1/d02929abc253ede07d7a0898.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Upper-Gastrointestinal Tract Metabolite Profile Regulates Glycaemic and Satiety Responses to Meals with Contrasting Structure","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLegumes are an important crop globally with a good nutritional and environmental profile \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The consumption of legumes is encouraged in global non-communicable disease policies. For example, legumes have been demonstrated to improve glycaemia and affect gut hormones to prolong satiety \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. However, the intake of legumes is declining globally. While processing of seeds and grains can improve palatability, this often results in the loss of plant cellular structure. Current diet and food reformulation strategies focus on reducing carbohydrate and fat intakes, but one key aspect that has been largely overlooked so far is the role of food structure in regulating the extent to which these macronutrients are released into the intestinal lumen and absorbed by the body. As a consequence, it can have an impact on postprandial metabolism, gut hormone release and caloric value \u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Food matrix structural changes are not captured in nutrient composition data, and there is an urgent need to improve understanding of how processing-induced changes to food structure impact on nutrient bioaccessibility and thereby postprandial metabolism.\u003c/p\u003e \u003cp\u003eIn raw edible plant tissues, nutrients are encapsulated within plant cells, where the surrounding plant cell wall (dietary fibre) acts as a \u0026lsquo;physical-barrier\u0026rsquo;, protecting intracellular macronutrients from intestinal digestive enzymes and thereby limiting the release (\u0026lsquo;bioaccessibility\u0026rsquo;) of macronutrient digestion products (e.g., mainly malto-oligosaccharides, peptides, and fatty acids) into the intestinal lumen \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. This so-called \u0026lsquo;barrier-mechanism\u0026rsquo; is particularly well documented in cooked pulses (including chickpeas, peas and beans) \u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e where \u003cem\u003ein vitro\u003c/em\u003e digestibility studies show significantly higher starch digestion from broken cells compared with intact cells from the same source \u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. As the availability of glucose from starch digestion is known to be a key contributor to the postprandial blood glucose response \u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, it has been suggested that the processing-induced breakage of chickpea cells, for example during dry-milling into flour, compromises the low glycaemic benefits that are associated with whole pulse consumption \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. However, the proposed direct relationship between plant cell intactness, intestinal contents, and the size of the glycaemic response evoked still requires confirmation \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhat is less well understood is how food processing-induced changes in intestinal contents and consequently on signal systems that impact on metabolism impact such as satiety and gut hormone signalling. Interestingly, very recent human studies reported that subjective satiety was higher for meals containing intact chickpea cells than disrupted cells \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, while another study showed a dose-dependent increase in anorexigenic gut hormone responses to bread incorporating intact chickpea cells \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGut hormones such as Glucose-dependent Insulinotropic Peptide (GIP), Glucagon-like Peptide-1 (GLP-1) and Peptide-YY (PYY) are incretins (i.e., blood glucose lowering effects) with established satiety-promoting effects and are produced by enteroendocrine cells in the gastrointestinal tract. GIP is produced mainly by K-cells, located in the stomach and duodenum, whereas GLP-1 and PYY are mainly produced by L-cells, which are present in high density in the ileum and colon \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. These enteroendocrine cells have lumen-facing receptors capable of detecting and responding to local changes in intestinal nutrient concentrations. Mapping of enteroendocrine cells and receptors across the intestine tract has been well-described \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, a critical knowledge gap is that we still have a limited understanding of the metabolites throughout the human gastrointestinal tract, and how these change with time as a result of meal digestion and transit. Previous work has highlighted how nutrient-sensing in the ileum stimulates GLP-1 and PYY release and promotes satiety \u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. However, blood GLP-1 and PYY concentrations rise within 15 minutes following meals \u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, during the period when foods are likely still in the stomach and duodenum. Recent findings demonstrated that L-cells are present in roughly equal numbers to K-cells in human duodenum \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, raising the possibility that the proximal intestine may play a larger role in gut hormone secretions than previously recognised. The satiety-stimulating potential of the upper gastrointestinal tract remains largely unexplored.\u003c/p\u003e \u003cp\u003ePrevious in vitro and in vivo investigations \u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e have showed evidence that processing of whole grains and legumes into disrupted structure affects their postprandial responses. Understanding the mechanisms in humans is lacking. Addressing this requires investigations into how food structure impacts upper-gastrointestinal metabolites that regulate glycaemia, hormones and satiety.\u003c/p\u003e \u003cp\u003eWe combine enteric intubation techniques \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, allowing time-resolved intestinal sample collection, with parallel blood sample collection to investigate the relationship between the digestive behaviour of foods with contrasting plant cell structures and the postprandial metabolic effects. This study aimed to investigate the effect of chickpea cotyledon cell intactness, as a model of legume processing, on digestion product concentrations within the gastric and duodenal lumen and its consequences for postprandial blood glucose, insulin, and gut hormone responses. This work first investigated human gastric and duodenal metabolites using \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH NMR profiling \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e to explore the luminal content regulating appetite-regulating hormone responses. Despite their identical macronutrient and fibre content, the non-cellular chickpea meals were hypothesised to be more rapidly digested and thereby cause higher glycemic responses \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e than the cellular chickpea meals. Furthermore, the intact cellular meals were hypothesised to retain their structure within the stomach and duodenum, resulting in luminal metabolic profiles capable of prolonging gut hormone and satiety responses.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and Diets\u003c/h2\u003e \u003cp\u003eWe admitted 6 male and 4 female adult participants aged (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM) 30.8 y\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4 years with BMI 24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 kg/m\u003csup\u003e2\u003c/sup\u003e and fasted glucose 4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 mmol/L (see \u003cb\u003eSupplementary Tables\u0026nbsp;1 and 2\u003c/b\u003e for eligibility criteria and participant characteristics) as inpatients to the NIHR Imperial Clinical Research Facility (CRF) at Hammersmith Hospital in London, UK, where they resided for 4-days. On day 1, they were fitted with two enteral feeding tubes to enable concomitant sampling from the participants\u0026rsquo; stomach and upper-small intestine (duodenum). On the morning of days 2, 3, and 4, participants received one of three structurally different test meals in random order, and samples of blood, intestinal content and appetite visual analog scale (VAS) scores were collected up to 3h postprandially as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. A consort diagram showing flow of participants through the study is provided in \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe test meals \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, C,D\u003cb\u003e)\u003c/b\u003e consisted of freshly cooked chickpea porridge in water, flavoured with low-sugar blackcurrant jam and raspberry flavoured jelly (shown in photographs in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). All meals had the same nutrient content per serving (~\u0026thinsp;242.7 kcal, 29.5 g starch, 2.3 g sugar, 6.6 g dietary fibre, 11.0 g protein and 3.7 g fat) but had been processed differently \u003cb\u003e(\u003c/b\u003esee \u003cb\u003eSupplementary Information \u0026lsquo;Meal preparation\u0026rsquo;\u003c/b\u003e) so that these nutrients were either entrapped within clusters of cells \u0026lsquo;Intact-C\u0026rsquo;, isolated single cells \u0026lsquo;Intact-S\u0026rsquo;, or broken cells \u0026lsquo;Broken\u0026rsquo;, as confirmed with light microscopy (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Using an \u003cem\u003ein vitro\u003c/em\u003e starch amylolysis assay, we showed that these differences in meal structure affected starch susceptibility to α-amylase digestion, with broken cell meal showing the greatest release of starch digestion products (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eNon-cellular Structure Elevated Blood Glucose and Insulin Responses\u003c/h2\u003e \u003cp\u003eMeal cell structure significantly influenced postprandial glucose (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and insulin (P\u0026thinsp;=\u0026thinsp;0.003) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA,D. The magnitude of glucose and insulin response was assessed using iPeak (incremental peak) and iAUC, revealing significant differences between broken and intact cellular meals. Broken cells led to a 190% increase in iPeak glucose compared to Intact-C (1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56 mmol/L), and a 92% increase compared to Intact-S (0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50 mmol/L) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Similarly, insulin iPeak after Broken meals was 65% higher than after Intact-C (-27.76\u0026thinsp;\u0026plusmn;\u0026thinsp;24.44 \u0026micro;U/mL, adj. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Glucose iAUC was 148% higher after consuming Broken compared to Intact-C (55.78\u0026thinsp;\u0026plusmn;\u0026thinsp;36.56 mmol/L/min, adj. P\u0026thinsp;=\u0026thinsp;0.005) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Broken also resulted in a 74% higher insulin iAUC (-1538.92\u0026thinsp;\u0026plusmn;\u0026thinsp;706.72 \u0026micro;U/mL, adj.\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to Intact-C.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTiming of glucose peaks occurred at approximately 34 min and did not differ between broken and intact meals (p\u0026thinsp;=\u0026thinsp;0.173, \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e), likely suggesting their rates of gastric emptying were similar \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Intact-C had a shorter insulin response, with a mean duration of 76 min less to return to baseline levels compared to Broken and Intact-S (\u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e, adj.\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004 and 0.036). This can be attributed to the reduced demand for insulin secretion in the Intact-C group given the lower blood glucose levels.\u003c/p\u003e \u003cp\u003eOverall, the chickpea porridge made from broken cells caused the highest glycaemic response, while cell clusters had the lowest impact on blood glucose and insulin. Chickpeas have been widely described as low glycaemic foods \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, while our data clearly show that disruption of meal cell structures significantly increases the magnitude and duration of the postprandial blood glucose and insulin response.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIntact-cellular Structure Enhanced GLP-1, PYY, and Satiety Responses\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows how meal structure impacted postprandial blood GIP, GLP-1, and PYY responses. There was a significant time \u0026times; meal effect on postprandial GIP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with very clear differences between meal types evident in the time-series data shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. After Intact-S and Intact-C meals, GIP concentrations increased slowly to reach a maximum after ~\u0026thinsp;120 min, but following the Broken meal, GIP increased rapidly to a higher peak after ~\u0026thinsp;30 min then declined gradually to reach fasted levels after ~\u0026thinsp;3h (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). GIP concentrations (iPeak values) for Intact-C (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) were 61% lower than the Broken (53.47\u0026thinsp;\u0026plusmn;\u0026thinsp;37.84 pg/mL, adj.\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) and 50% lower than Intact-S (34.77\u0026thinsp;\u0026plusmn;\u0026thinsp;23.20 pg/mL, adj. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006); the iPeak of Intact-S and Broken was comparable (adj. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.128). Similarly, the iAUC of Intact-C (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) was 54% lower than Broken (3663.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3397.32 pg/mL/min, adj.\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036) and 61% lower than Intact-S (4924.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2858.02 pg/mL/min, adj.\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). Intact-S and Broken showed no significant difference in GIP iAUC (adj. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.559). Thus, the chickpea porridge containing broken plant cells caused a large and early spike in blood GIP, whereas the cellular meals elicited a more gradual and sustained GIP response.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGLP-1 responses were also significantly altered by meal structure (time \u0026times; meal interaction, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034) and followed similar time-trends to GIP: Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD shows that GLP-1 increased gradually after ingestion of Intact-S and Intact-C, whereas the Broken meal caused GLP-1 concentration to reach a higher peak after ~\u0026thinsp;30 min. The maximum postprandial rise was comparable in the three groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, P\u0026thinsp;=\u0026thinsp;0.14), while the Intact-S reached the peak 84 min later compared to Broken (adj.\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e). iAUC was 63% higher in Intact-S compared with Broken (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF, \u003cb\u003e801\u003c/b\u003e.55\u0026thinsp;\u0026plusmn;\u0026thinsp;661.0 pmol/L/min, adj. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020). Thus, the broken cell meal elicited high GLP-1 concentrations in the early-postprandial period, whereas cellular meals caused GLP-1 concentrations to reach a similar level, but later in the postprandial period (90\u0026ndash;180 min).\u003c/p\u003e \u003cp\u003eMeal structure significantly affected PYY response (main effect, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Intact-S elicited a higher PYY response compared to Broken: PYY iPeak for intact-S was 214% higher (17.53\u0026thinsp;\u0026plusmn;\u0026thinsp;13.00 pmol/L) than Broken (adj.\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). Intact-S prolonged PYY response, as evidenced by Intact-S approaching baselines 70 min later than Broken (adj.\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021, \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e. The first peak iAUC for Intact-S was 1604.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1113.88 pmol/L/min (568%) higher than Broken (adj.\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI). Overall, ingestion of chickpea porridge with intact separated cells (Intact-S) caused PYY concentrations to increase during the first hour of meal ingestion and remain elevated, whereas ingestion of broken cells resulted in a delayed and lower PYY response.\u003c/p\u003e \u003cp\u003e PYY and GLP-1 have the potential to impact appetite, so to explore this further we captured the 10 participant\u0026rsquo;s responses (VAS scores) to fullness/appetite questions. Postprandial changes in fullness significantly differed between meal structures (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ, meal effect P\u0026thinsp;=\u0026thinsp;0.007); with Intact-S resulting in higher \u0026lsquo;fullness\u0026rsquo; scores compared to Intact-C and Broken (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eK, \u003cem\u003eadj\u003c/em\u003e.P\u0026thinsp;=\u0026thinsp;0.031, \u0026lt;\u0026thinsp;0.001, respectively). No significant differences were observed for \u0026lsquo;hunger\u0026rsquo; and \u0026lsquo;appetite for a meal\u0026rsquo; (meal effect, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.606 and 0.060, respectively), but \u0026lsquo;desire to eat\u0026rsquo; showed a significant meal effect (P\u0026thinsp;=\u0026thinsp;0.029), as did the overall composite appetite scores (meal effect, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, calculated as the mean of \u0026lsquo;hunger\u0026rsquo;, \u0026lsquo;appetite for a meal\u0026rsquo;, \u0026lsquo;desire to eat\u0026rsquo;, and (100 minus \u0026lsquo;fullness\u0026rsquo;)) (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. These indicated that Intact-S resulted in lower overall appetite compared to other meals. \u003cem\u003eAd libitum\u003c/em\u003e food intake measured at postprandial 240 min did not show significant differences between meals (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eL, meal effect, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.123).\u003c/p\u003e \u003cp\u003eOur findings are consistent with another recent study, which also found that consuming chickpea intact cells led to enhanced satiety compared to broken cells, without reducing food intake \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. It is unclear why the participants\u0026rsquo; higher satiety scores did not result in lower food intake at the subsequent meal. One possibility is that the relatively low energy density of the chickpea meals (for instance, ~\u0026thinsp;500 mL of the \u0026lsquo;Broken\u0026rsquo; meal is required to provide 242.7 Kcal), and short eating duration may not have induced a sufficient response to impact food intake at 4 h. Future study designs should consider exploring cell intactness within a more energy dense meal and impacts on satiety-responses and subsequent food intake.\u003c/p\u003e \u003cp\u003eOverall, these results showed chickpea separated cells enhanced GLP-1 and PYY responses and appetite suppression. These are consistent with a very recent study on meals containing legume cells \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e; our study uniquely controlled for micronutrient and fibre contents, demonstrating that the structure-derived effects would be sufficient to influence postprandial satiety through nutrient-mediated gut-hormone signalling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCell Wall Intactness Controlled Stomach and Duodenal Starch Digestion\u003c/h2\u003e \u003cp\u003e Gastric and duodenal aspirates were collected from 10 participants and examined in terms of structure and composition to explore how differences in meal structure alter intestinal content, potentially generating signals which explain observed differences in blood responses.\u003c/p\u003e \u003cp\u003eLight microscopy was performed on aspirated stomach and duodenal content to gather insight into the structural breakdown of these meals within the human gastrointestinal lumen. Light micrographs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) show that the characteristic meal structure which are non-cellular for Broken (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA,B,C), cellular for Intact-S (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD,E,F), and cellular clusters for Intact-C (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE,F,G), persisted within the stomach and duodenum, despite oral processing. Whole cells or cell clusters were found in the duodenum 15 min after meal ingestion and were present in every sample throughout the entire 180 min of sample collection. This was consistent for all participants. For the cellular meals, there was no evidence of progressive structural breakdown of plant cells with gastrointestinal residence time or region (stomach vs duodenum). When cells were ingested as clusters, clusters were clearly visible to the naked eye within the digesta, these were successfully captured using light microscopy, although some images give the illusion of cell separation which results from pressing on the coverslip. Thus, the clusters can break down into separated cells, although this process could not be captured quantitatively. No intact chickpea cells were detected after ingestion of the broken cell meals. After ingestion of broken cell chickpea porridge, partially digested starch granules were evident already within the first collection point from the stomach.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn summary, the microscopic analyses showed that the cellular meals largely remained intact in the gastric and duodenal aspirates during the entire postprandial sampling period, thus providing effective barriers as starch remained encapsulated within those plant cells.\u003c/p\u003e \u003cp\u003eEffects of meal structure on starch and starch-digestion products within the aspirated fluid are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Starch digestion products (maltose\u0026thinsp;\u0026gt;\u0026thinsp;maltotriose\u0026thinsp;\u0026gt;\u0026thinsp;glucose) were present in postprandial (0\u0026ndash;3 h) gastric and duodenal aspirate supernatants, and gastric concentrations were higher than duodenal. Maltose (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA,B\u003cb\u003e)\u003c/b\u003e, being the main product of starch amylolysis, was present at ~\u0026thinsp;50% higher concentrations than maltotriose (\u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e) and glucose (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC,D\u003cb\u003e)\u003c/b\u003e. Gastric maltose concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) differed between meal types (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006, One-way RM ANOVA). Pairwise comparison (Tukey\u0026rsquo;s) showed that the Broken meal elicited a 2.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85 mg/mL (233%) and 2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74 mg/mL (370%) higher gastric maltose response than both cellular meals Intact-S (\u003cem\u003eadj. P\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) and Intact-C (\u003cem\u003eadj. P\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), while there was no difference between Intact-S and Intact-C. Sucrose concentrations \u003cb\u003e(Supplementary Table\u0026nbsp;5)\u003c/b\u003e, included as a meal marker, were not significantly different in gastric aspirates between meal types (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.055 for both gastric samples), and the time-series data indicated a gradual drop in gastric sucrose concentrations (presumably reflecting liquid-phase gastric emptying, suggesting most of the digesta had been emptied from the stomach from ~\u0026thinsp;60 min).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuodenal maltose concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) were also significantly different between meal types (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.020\u003cem\u003e)\u003c/em\u003e, with pairwise comparisons showing that the Broken meal elicited 0.689\u0026thinsp;\u0026plusmn;\u0026thinsp;0.686 mg/mL (140%) higher duodenal maltose concentrations than Intact-C (\u003cem\u003eadj. P\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049), while differences between the other meals were not significant. Meal type had similar effects on the duodenal maltotriose concentrations (main effect \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018, Broken vs Intact-C, mean difference 0.504\u0026thinsp;\u0026plusmn;\u0026thinsp;0.473 mg/mL, 410% higher, \u003cem\u003eadj. P\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038) and duodenal glucose concentrations (main effect \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.003, Broken vs Intact-C, mean difference 0.253\u0026thinsp;\u0026plusmn;\u0026thinsp;0.173 mg/mL, 132% higher, \u003cem\u003eadj. P\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC,D and \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Sucrose (liquid phase meal marker) was present at ~\u0026thinsp;1.4 x higher concentrations in the gastric fluid compared to the duodenal fluid, and duodenal sucrose concentrations were significantly different between meal types (P\u0026thinsp;=\u0026thinsp;0.041), with Broken having a 0.080\u0026thinsp;\u0026plusmn;\u0026thinsp;0.074 (90%) higher sucrose concentration than Intact-C (\u003cem\u003eadj. P\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035) \u0026ndash; this could suggest a greater dilution of the intact-C meals. No significant differences in duodenal sucrose concentrations were seen between Intact-S and Broken. Thus, the differences in maltose concentrations observed within these gastric or duodenal fluids after Intact-S and Broken can be taken to mainly reflect differences in meal starch digestibility, while Intact-C results may also be influenced by gastric emptying or dilution effects.\u003c/p\u003e \u003cp\u003ePellet masses contained the inaccessible and undigested meal components from the aspirated gastric and duodenal fluids. Typically, one mL of digesta aspirated from the stomach contained between 7 and 48 mg of pellet dry mass, and duodenal aspirates contained between 3 and 18 mg of pellet dry mass (see \u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e for further details).\u003c/p\u003e \u003cp\u003eRegardless of meal type, the duodenal pellets were comprised of ~\u0026thinsp;43\u0026ndash;54% \u0026lsquo;meal-derived\u0026rsquo; carbohydrate, of which starch was the main component (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). There was a highly significant main meal effect (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on duodenal pellet carbohydrate composition, with duodenal pellets from Broken comprising a 30.54\u0026thinsp;\u0026plusmn;\u0026thinsp;6.47 mg/100 mg (46%) lower proportion of starch than Intact-S (\u003cem\u003eadj. P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 33.01\u0026thinsp;\u0026plusmn;\u0026thinsp;9.00 mg/100 mg (43%) lower than Intact C (\u003cem\u003eadj. P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Taking into account the typical pellet masses for each meal, it is possible to estimate that duodenal fluid after Intact-S, Intact-C and Broken (0\u0026ndash;3 h) contained on average 6.353\u0026thinsp;\u0026plusmn;\u0026thinsp;2.678, 3.263\u0026thinsp;\u0026plusmn;\u0026thinsp;1.667, and 1.306\u0026thinsp;\u0026plusmn;\u0026thinsp;0.456 mg non-bioaccessible starch per mL aspirated duodenal fluid (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Based on the microstructure of the digesta (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), it is likely that the higher levels of non-bioaccessible starch in digesta from the cellular meals are due to its encapsulation within the cell wall (type 1 resistant starch).\u003c/p\u003e \u003cp\u003e Overall, the cellular meals showed similar digestion properties, whereas the non-cellular Broken meal resulted in less undigested starch in the pellet, while having higher local maltose and maltotriose concentrations. Our \u003cem\u003ein vivo\u003c/em\u003e observations support the cell wall barrier mechanism proposed in \u003cem\u003ein vitro\u003c/em\u003e studies \u003csup\u003e\u003cspan additionalcitationids=\"CR40 CR41\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e that the intact cell wall is the key to controlling starch digestion.\u003c/p\u003e \u003cp\u003e The oral processing of digestion deserves more attention. The role of salivary amylase is often overlooked due to the short duration of the oral phase. Our study suggested that disrupting the intact cell wall (\u0026lsquo;Broken\u0026rsquo; meal) increased starch digestibility in the mouth, which caused local concentrations of starch digestion products (maltose\u0026thinsp;\u0026gt;\u0026thinsp;maltotriose\u0026thinsp;\u0026gt;\u0026thinsp;glucose) to increase rapidly within the stomach and duodenum within the early postprandial period after Broken meal consumption. Additionally, we noted that the gastric pH remained at around 4 for the first 15\u0026ndash;30 min after meals (see \u003cb\u003eSupplementary Table\u0026nbsp;6\u003c/b\u003e), likely allowing salivary amylase to retain 50% of its activity \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Our observations thus imply that salivary amylase digestion of accessible starch may continue in the stomach in humans and that the extent to which this contributes to overall glucose release from starch depends on meal cellular structure. We recommend future \u003cem\u003ein vitro\u003c/em\u003e simulated digestion models incorporate the oral digestion phase and account for dynamic changes in gastric pH.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGastric Maltose Correlated with Early Blood Glucose and Incretin Responses\u003c/h3\u003e\n\u003cp\u003eEarly peaks in blood responses (0\u0026ndash;30 min) were tightly correlated with initial rates of starch digestion (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). Correlations analyses were performed between the 0\u0026ndash;30 min delta changes in blood responses with increments of starch digestion products: gastric glucose (GasGlu), gastric maltose (GasMal), duodenal glucose (DuoGlu), and duodenal maltose (DuoMal) across all meal types. GasMal emerged as the most robust correlator, positively correlating with elevations in glucose, GIP, and GLP-1 levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG, Spearman rho\u0026thinsp;=\u0026thinsp;0.64, 0.47, 0.42 with P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, P\u0026thinsp;=\u0026thinsp;0.014, P\u0026thinsp;=\u0026thinsp;0.034 respectively). GasGlu played similar but less-pronounced correlations (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, rho\u0026thinsp;=\u0026thinsp;0.32, P\u0026thinsp;=\u0026thinsp;0.082 for blood glucose, rho\u0026thinsp;=\u0026thinsp;0.44, p\u0026thinsp;=\u0026thinsp;0.017 for GIP). No significant correlation was found between DuoGlu or DuoMal with blood responses (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), possibly due to dilution effects of gastric fluid entering the duodenum and/or limited duodenal samples (see \u003cb\u003eSupplementary Table\u0026nbsp;3 f\u003c/b\u003eor overview of sample collection and analyses). No correlation was found between PYY and luminal starch digestion products (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eOur data demonstrated a direct relationship between cell intactness, intestinal carbohydrates, and the magnitude of the glycaemic response. The observation that spikes in blood glucose and GIP within 0\u0026ndash;30 min were associated with increments of gastric carbohydrates is an interesting finding. This can be supported by previous studies that identified SGLT-1 and GLUT-1, the main glucose transporters \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e as well as the GIP-positive K-cells \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e in the human and mouse stomach. Mechanisms of carbohydrate absorption and GIP-sensing are well described in the small intestine \u003csup\u003e\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, whilst our study may be the first to suggest that the early stage of digestion i.e., the oral and gastric phase, can play a more important role in postprandial glucose and GIP than previously thought.\u003c/p\u003e \u003cp\u003eOur results also showed that the initial starch digestion rates correlated with GLP-1 but not PYY, which aligned with previous observations. The upper segment of the mice intestine could secrete GLP-1 but not PYY \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Intragastric or intraduodenal glucose infusion in humans resulted in a remarkable increase in GLP-1 but had a lesser impact on PYY secretions \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Our findings supported the role of gastric and duodenal carbohydrates in the early GLP-1 response with minor effects on PYY.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDuodenal \u0026lsquo;Undigested Starch\u0026rsquo; Associated with PYY and Later-Phase GLP-1 Responses\u003c/h2\u003e \u003cp\u003eWe then explored whether the PYY response was a result of carbohydrates arriving at the lower gut (e.g., the jejunum and ileum) where a higher density of L-cells is present. The undigested starch contents in duodenal aspirates, indicating the availability of carbohydrates in the lower gut, positively correlated with PYY iAUC (0-180min) (rho\u0026thinsp;=\u0026thinsp;0.39, P\u0026thinsp;=\u0026thinsp;0.048, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). Non-digestible carbohydrates have been reported to arrive in the ileum as early as the first 30 minutes after meals in healthy subjects \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e; ileal infusion of glucose increased PYY, whereas no such effects were observed with duodenal infusion \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. These findings supported our analyses indicating that the undigested starch escaping to the lower intestinal segment promoted PYY secretion.\u003c/p\u003e \u003cp\u003eThe undigested starch showed no correlation with the iAUC of GLP-1 or GIP within 0-180 minutes (data not shown). However, it exhibited significant correlations with their iAUC from 30 to 180 minutes (rho\u0026thinsp;=\u0026thinsp;0.62 and 0.62, both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). Thus, carbohydrate arrival in the lower intestinal segment promoted PYY and later-phase GLP-1 responses.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eFood Structure Impacted Gastric and Duodenal Metabolite Profiles\u003c/h2\u003e \u003cp\u003eEnteroendocrine cells (EECs) secrete gut hormones in response to metabolites in the gut lumen \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. EECs and receptors have been well-mapped throughout the gut \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, yet little is known about how meal ingestion and transit influence the luminal metabolites. This study used \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH NMR metabolomic profiling to measure the metabolomics in gastric and duodenal aspirates collected pre- and post-prandially at 30, 60, 120 and 180 min. An overview of profiling and peak assignments is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cb\u003eSupplementary Table\u0026nbsp;7.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRepeated measures, Monte-Carlo Cross-validation, Partial Least Squares Discriminant Analysis (RM-MCCV-PLS-DA) identified the metabolites associated with food structure interventions. No baseline differences between meals were detected (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Robust separations between the \u0026lsquo;Intact\u0026rsquo; meal and \u0026lsquo;Broken\u0026rsquo; meal were observed between postprandial 30\u0026ndash;120 min (see 30 min and 120 min in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-E and others in \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt postprandial 30 min, when GIP and GLP-1 levels peaked for Broken and were lowest for Intact-C, robust differences in their luminal metabolites were observed (gastric model: R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eY 0.97, Q\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eY 0.89; duodenal model: R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eY 0.99, Q\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eY 0.57). Broken exhibited increased levels of starch digestion products, including maltose, glucose, and oligosaccharides in gastric and duodenal aspirates. Broken showed decreased levels of tyrosine, phenylalanine, and tryptophan in gastric aspirates, and decreased conjugated bile acids in duodenal aspirates compared to Intact-C (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF, P values reported in \u003cb\u003eSupplementary Table\u0026nbsp;8\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eAt postprandial 120 min, GLP-1 levels in Intact-S significantly surpassed those in Broken, with distinct differences observed in their gastric and duodenal metabolomes (gastric model: R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eY 0.99, Q\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eY 0.89; duodenal model: R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eY 0.89, Q\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eY 0.71). Intact-S exhibited elevated levels of amino acids and bile acids in duodenal aspirates, including valine, leucine/isoleucine, alanine, tyrosine, and taurine-/glycine- conjugated bile acids. Although starch digestion products in gastric aspirates remained lower in Intact-S compared to Broken, these carbohydrates were comparable in their duodenal aspirates (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF, P values reported in \u003cb\u003eSupplementary Table\u0026nbsp;8\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDynamics of Intestinal Metabolites and Postprandial Responses\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA overviews how luminal metabolites change over time and correspond with blood responses. Key metabolites were quantified from their \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH NMR profiling and presented in a heatmap. Consistent with the quantitative analyses, this figure also shows that cell breakage increased glucose and maltose concentrations within the stomach and the duodenum, which coincided with higher blood glucose, insulin, GLP-1 and GIP concentrations. Time-series comparison of meal-derived components such as trigonelline ( a marker of legume intake \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e), stachyose/raffinose (a source from legume meal), fumarate/fumaric acid (a food additive from the background diet jelly) with starch-digestion products (maltose, glucose) confirmed that the elevated levels of starch digestion products coincided with meal transit.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmino acids like alanine, tyrosine, and glutamine were more concentrated in the fasted state and decreased postprandially, aligning with a dilution effect from postprandial intestinal secretions and meal transit. The duodenal amino acid concentrations rose in the later postprandial period (i.e., 60 and 120 min), with the increase being more pronounced for the Intact-cellular meals and concurring with their later GIP /GLP-1 response.\u003c/p\u003e \u003cp\u003ePartial Least Squares Regression (PLSR) explored the relationships between luminal metabolites (X matrix) and blood responses (Y matrix). PLSR models for early and later postprandial periods revealed a shift of luminal metabolites and enteroendocrine signalling. At postprandial 30 min, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB showed a distinct clustering of blood glucose (BldGlu), insulin, GIP and GLP-1 alongside gastric maltose. The network correlation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e highlighted the significant correlations (with False Discovery Rate -adjusted p values\u0026thinsp;\u0026lt;\u0026thinsp;0.05), involving maltose, glucose, fumarate and trigonelline in the stomach.\u003c/p\u003e \u003cp\u003eIn the later postprandial stage, BldGlu, Insulin, and GIP were most closely associated with duodenal maltose, trigonelline, and fumarate (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). GLP-1 showed the same vector direction with a series of duodenal amino acids (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Among these were valine, alanine, glutamine, tyrosine, and histamine, highlighted as significant correlators (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eFood structure has been hypothesised to impact gut hormone secretion through modulating the amount of nutrients arriving in the distal intestine \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Less attention has been paid to the proximal intestine. This study provides insight into the upper gastrointestinal mechanisms by which broken and intact food structures induce different gut hormone responses. Gastric and duodenal metabolite profiles significantly differed between food structure interventions, with the differences driven by bile acids, amino acids, and starch digestion products. Furthermore, the PLSR models demonstrated the dynamic associations between luminal metabolites and postprandial blood responses: Initial glycaemia and GIP responses were predominantly influenced by a surge in maltose, particularly in the gastric region. In the later postprandial phase, the metabolic drivers shift towards a range of duodenal amino acids that have implications for GLP-1 levels. Specifically, we showed that elevated valine, alanine, and tyrosine (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE,F) may be linked to heightened GLP-1 response in Intact-S at postprandial 120 min. Emerging evidence suggests individual amino acids can stimulate GLP-1 with different potencies and through different mechanisms \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Valine has been suggested as a potent stimulator of GLP-1 secretion when infused in the perfused rat small intestine \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. In human subjects, intraduodenal infusion of L-tryptophan \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e or L-glutamine \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e induced GLP-1 response; other amino acids have yet to be studied. Our data indicates that the relationship between the duodenal amino acid profile and GLP-1 response warrants further investigation. Further research is also needed to uncover the impact of food structure on luminal amino acid releases to complete the understanding of how chickpea meals containing intact cell walls promote GLP-1 secretion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eA strength of this study was the use of precisely controlled meal structures in combination with the parallel sampling of blood and digesta from healthy humans within a controlled clinical environment. The meals were matched by amount and type of carbohydrate, while clear differences in the cellular structure were achieved. This allowed meal structure-dependent differences to be elucidated. Insight into the gastrointestinal mechanisms that underpinned the different postprandial blood responses to meals with contrasting structures were possible through the use of enteric intubation technique which enabled postprandial sampling of digesta in parallel to blood collections. This technique helps to overcome limitations of in vitro digestion models, which do not, for instance, replicate oral phase processing and enteroendocrine feedback responses. A learning from this enteric intubation (pilot) study relates to the sampling frequency of postprandial duodenal digesta, as the low volume of digesta prevented some sample collections, resulting in insufficient sample numbers/volumes for reliable insight into digesta time trends. Future studies should consider less frequent postprandial sampling of stomach and duodenal content to improve sample collection. Sampling should expand to the distal gut area to develop a complete picture of the interactions between gut metabolites and hormones.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrevious studies \u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e have shown that food structure has a profound effect on blood glycaemia and gut hormone responses. Our study supports these findings as we demonstrate chickpea meals with the same nutrient contents and contrasting cell intactness were shown to elicit significantly different postprandial blood glucose, insulin, GIP, GLP-1, PYY and satiety responses. However, the current study goes beyond these observations by exploring the relationship between the structure of the food matrix, luminal metabolites of the small intestine and the generation of signals which affect physiology.\u003c/p\u003e \u003cp\u003eWe showed that disruption of cellular structure (\u0026lsquo;Broken\u0026rsquo; meal) significantly increased starch bioaccessibility in the upper gastrointestinal tract, leading to a 2 to 4-fold increase in the magnitude of the (peak) glucose response evoked. \u0026lsquo;Broken\u0026rsquo; meal also increased insulin and incretin release to combat the rapid rise in plasma glucose. Furthermore, \u0026lsquo;Broken\u0026rsquo; meal shortened the duration of the GIP, GLP-1 and PYY responses, and lowered subjective satiety compared to a nutrient-matched meal in which the cellular structure remained intact (Intact-S). It is important to realise the physiological benefits for glycaemic control and weight loss, as reported in earlier epidemiological and interventional studies where whole cooked pulses, with their cellular structure presumed intact, were usually the main structural form consumed \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. However, this study shows that these benefits can be compromised when plant cell intactness is disrupted. Food cellular structure and processing level should be considered when making dietary recommendations or developing health-promoting ingredients for fibre-rich products.\u003c/p\u003e \u003cp\u003eThe nutrient-sensing system on EECs related to the release of gut hormones is a topic of growing interest. Unlike the ileum and colon, lesser attention has been paid to the proximal intestine about the secretions of anorexigenic gut hormones such as GLP-1. Our study emphasises the role of the postprandial small intestinal metabolites, demonstrating how digestion of \u0026lsquo;Broken\u0026rsquo; and \u0026lsquo;Intact-cellular\u0026rsquo; meals shapes different upper gastrointestinal metabolite profiles, resulting in distinct endocrine and metabolic consequences. Significant alterations in metabolite profiles were observed, particularly in starch digestion products, amino acids, and conjugated bile acids. These findings offer new insights to explore the interaction between food and gut signalling. We showed the \u0026lsquo;Broken\u0026rsquo; meal elicited a higher acute GIP and GLP-1 response as a consequence of a rapid rise of gastric maltose, whereas the \u0026lsquo;Intact-cellular\u0026rsquo; meal elevated tyrosine, valine, and alanine which were associated with prolonged GLP-1 levels. Future research should focus on the mechanisms underlying these findings and validate their roles in gut hormone secretion. Evidence from well-controlled human gut infusion studies is warranted.\u003c/p\u003e \u003cp\u003ePhamaceutical adaptions of gut hormones are creating a new generation of therapies for diabetes and obesity. Our study indicates that food structure can be a promising tool to target gut hormones by controlling the release and delivery of nutrients in the gut which could offer a public health strategy to prevent non-communicable disease. Processing which disrupts the cellular structure and increases intestinal luminal maltose and glucose could have positive effects on K-cell release of GIP whereas intact structures and the change in metabolite profiles related to these structures lead to increased GLP-1 and PYY from L-cells. Simple changes in food structure could therefore offer an effective strategy for optimising gut peptides and postprandial metabolism.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cstrong\u003eHuman intubation study design\u003c/strong\u003e \u003cp\u003eThis trial was approved by the Health Research Authority and London-Camden and King\u0026rsquo;s Cross Research Ethics Committee (REC 19/LO/0962) before the commencement of any study procedures. The study was prospectively registered at ISRCTN (ISRCTN18097249) before the enrollment of the first participant. All the participants received a participants information sheet and signed informed consent before they started the clinical trial. A CONSORT diagram and key study dates are shown in \u003cb\u003eExtended Data Fig.\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eA human study was conducted with 10 healthy participants aged 18\u0026ndash;65 y old with a Body Mass Index (BMI) ranging from 18.5 to 30 kg/m\u003csup\u003e2\u003c/sup\u003e. All participants were recruited from the healthy volunteer database of NIHR Imperial Clinical Research Facility. Participants who expressed an interest in this study were asked to fill in a pre-screening form and attend a screening visit. Their eligibility was checked and confirmed by a medical doctor based on their health history, anthropometric measurements, ECGs, and blood test results. Participants with an abnormal ECG, screening blood values outside the clinical reference range, a history of cancer, diabetes, gastrointestinal disease and/or requiring medication likely to interfere with metabolic and hormone responses were excluded. Inclusion and exclusion criteria were as listed in \u003cb\u003eSupplementary Table\u0026nbsp;1.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study followed a double-blinded, randomized crossover design. Randomization was performed using a sealed envelope system (Sealed Envelope Ltd. 2022). Participants were assigned into three intervention groups in a randomized order using a balanced allocation ratio of 1:1:1. The randomiszation was performed by an independent researcher who was not involved in this trial. The allocation of treatment sequence was blinded to the investigators, the technicians performing analysis of blood samples and participants. Investigators and participants remained blinded until the completion of the study and data analysis.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStudy procedure\u003c/strong\u003e \u003cp\u003eEach participant attended one 4-day inpatient study visit at NIHR Imperial Clinical Research Facility (CRF) at Hammersmith Hospital. The day before the study visit, participants were asked to refrain from caffeine, alcohol, and strenuous exercise. Participants were also requested to fast overnight. On day 1, an enteral feeding tube was placed in the participants\u0026rsquo; small intestine (duodenum), following procedures previously reported \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. On days 2, 3, and 4 participants received three chickpea test meals (see \u0026lsquo;\u003cem\u003eDietary intervention\u0026rsquo;\u003c/em\u003e) in a randomized order. Intestinal samples were taken before meals (T = -10 and 0 min) and subsequently at 15-minute intervals for 180 min for microscopic and carbohydrate analyses. Blood samples were taken before and after the test meals for 180 min (T = -10, 0, 15, 30, 45, 60, 90, 120, 150, and 180 min). Blood samples were collected through a cannula placed in the antecubital fossa used for measuring blood glycaemia and hormonal markers. Visual Analogues Scale (VAS) questionnaire was collected for the same period and at the same frequent intervals to measure the subjective appetite levels of participants. A lunchtime meal (at 4 h) was provided to measure their \u003cem\u003ead libitum\u003c/em\u003e food intake. On day 4, the enteral tube was removed, and participants were discharged.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDietary interventions\u003c/strong\u003e \u003cp\u003eParticipants received different test meals on day 2, 3, 4 corresponding to the 3 different chickpea structures: broken cells (\u0026lsquo;Broken\u0026rsquo;), intact single cells (\u0026lsquo;Intact S\u0026rsquo;), and cell clusters (\u0026lsquo;Intact-C\u0026rsquo;). All test meals were prepared from the same batch of whole chickpeas, \u003cem\u003eCicer arietinum L.\u003c/em\u003e, Kabuli type (Argentine variety, supplied by AGT Poortman Ltd.), which were abrasively dehulled, dry-milled (if applicable) and sieved, then weighed into test-meal specific portions, labelled, and sealed by a researcher independent from this study. Cooked test meals were prepared fresh for each study visit, using the concealed ingredient portions with the corresponding standardised cooking programme and a Vorwerk Thermomix Version 5 (see \u003cb\u003eSupplementary Information \u0026lsquo;Meal preparation\u0026rsquo;\u003c/b\u003e for further details) which was developed to enable reproducible production of meals with contrasting structures. The portion size was controlled based on the moisture content of the chickpea portion to ensure delivery of 30 g total starch per serving for all meal types. Typically, the freshly cooked chickpea serving (containing 60 g chickpea dry solids) weighed 490, 280 and 230 g for meals Broken, Intact-S, and Intact-C, respectively, reflecting their different water content, and were served with 270, 480, and 530 g water to achieve a consistent total portion size of 760 g, including test meal, water and flavouring (15 g \u0026lsquo;no sugar added blackcurrant jam\u0026rsquo;, Stute Foods Ltd., Bristol, UK, and 115 g \u0026lsquo;Hartley\u0026rsquo;s no added-sugar raspberry flavoured jelly\u0026rsquo;, Histon Sweet Spreads Ltd., Leeds, UK). Based on proximate analysis of the chickpea component (performed by accredited food testing provider ALS Laboratories Ltd., Chatteris, UK) and nutrition labels on food packaging (jelly and jam), each test meal serving provided (mean of triplicate with SD, 41.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 g available carbohydrate of which 29.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 g total starch and 2.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 g sugars, 6.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43 g dietary fiber, 11.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0,03 g protein, 3.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06g fat for a total 242.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76 kcal. Thus, all meals contained the same ingredients and macronutrient composition per serving but were designed to differ in microstructure, i.e., consisting of either mainly broken cells (Broken), individual cells (Intact-S) or cell clusters (Intact-C).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSample analysis\u003c/strong\u003e \u003cp\u003eThe primary outcome was the blood gut hormone response. Co-secondary outcomes included intestinal content analysis, blood glucose and insulin response, subjective appetite changes and ab libitum energy intake.\u003c/p\u003e \u003cp\u003e \u003cem\u003eBlood sample analysis\u003c/em\u003e \u003c/p\u003e \u003cp\u003eBlood samples were collected into tubes (BD Vacutainer\u0026reg; tubes: fluoride/oxalate tubes for glucose analysis; SST\u0026trade; serum tubes for insulin analysis), and into lithium heparin tubes with DPP-IV (10 \u0026micro;L/mL blood, Merck Millipore), aprotinin (10,000 KIU/mL blood, Nordic Pharma) and AEBSF (pefabloc, 1 mg/mL blood) for GIP, GLP-1 and PYY analysis. Plasma glucose was measured using GLUC-PAP kits (Randox Laboratories Ltd., UK). Serum insulin concentrations were determined by a Human Insulin Specific RIA kit (HI-14K, Merck, UK). Plasma GIP concentrations were measured by Human GIP ELISA kits from (EZHGIP-54K, Merck, UK). All these assays were performed as per the manufacturers\u0026rsquo; instructions. GLP-1 and PYY concentrations were measured by the in-house RIA method \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003eGastric and duodenal starch digestion analysis\u003c/em\u003e \u003c/p\u003e \u003cp\u003eDefrosted gastric and duodenal samples were centrifuged for 15 min at 10,000 \u003cem\u003eg\u003c/em\u003e to separate the undigested solids (pellet) from the intestinal fluid. The supernatants were decanted into 15 mL centrifuge tubes. Absolute ethanol was added to the supernatant (3 mL) and pellet (1 mL) to kill bacteria before drying the sample fractions at 51\u0026deg;C for 3 h in a centrifugal evaporator (EZ-2 Elite, Genevac). Supernatants were then analysed by LCMS to determine concentrations of starch digestion products (maltose, maltotriose) within the aspirate fluid. The dried supernatants were resuspended in 0.5 mL water (aided by vortex mixing and sonication), and then centrifuged at 13,000 \u003cem\u003eg\u003c/em\u003e for 5 min. The supernatants were then diluted in (milliQ) water and 45 \u0026micro;L of each sample was transferred to an HPLC vial and 5 \u0026micro;L D-glucose-\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC\u003csub\u003e6\u003c/sub\u003e-Glc 99% atom C (0.1 mg/mL) was added as an internal standard, before analysis by LC-MS (Agilent 6490 mass spectroscopy) on a reverse phase UPLC column (Thermo Hypercarb 100 x 2.1 mm 3 \u0026micro;m column) using 0.1% formic acid in water and 0.1% formic acid in acetonitrile as the mobile phase. Maltose, sucrose, and maltotriose were included as standards. Sugars were quantified by selected ion monitoring. Gastric and duodenal glucose was measured by GLUC-PAP kit (Randox Laboratories Ltd., UK) based on the instructions, except for an additional step at the beginning. Defrosted samples were first centrifuged at room temperature for 10 minutes at 13,000 rpm. A volume of 20 \u0026micro;L from the supernatant was used to perform the remaining steps according to the GLUC-PAP kit instructions.\u003c/p\u003e \u003cp\u003ePellets were analysed for estimation of their carbohydrate composition: samples of the dry pellet powders\u0026thinsp;\u0026lt;\u0026thinsp;3 mg were weighed out to an accuracy of 0.1 mg into glass culture tubes, then treated with 100 \u0026micro;L 72% w/w H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e for 3 h at room temperature, then diluted with water to 4% acid, and heated at 121\u0026deg;C for 1 h in a Techne Dri-block heater, and finally cooled on ice for 10 min \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e Monosaccharide analyses of the resulting acid hydrolyzates were then performed based on the method \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e.A mixed standard solution containing 200 \u0026micro;g/mL of 9 monosaccharides (arabinose, fucose, galactose, glucose, galacturonic acid, glucuronic acid, mannose, rhamnose and xylose) was prepared and diluted to concentrations of 160, 120, 80, and 40 \u0026micro;g/mL. Next, 100\u0026ndash;300 \u0026micro;L of 1 mg/mL Talose internal standard) was added to each hydrolysate and standards mixture. Sample hydrolyzates were then pH neutralised with 2 M CaCO\u003csub\u003e3\u003c/sub\u003e, and centrifuged (2500 rpm, 10 min) to remove the precipitate. The supernatants were filtered through 0.45 \u0026micro;m syringe filters. Finally, samples plus 5 \u0026micro;L \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eD\u003c/span\u003e-glucose-\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC\u003csub\u003e6\u003c/sub\u003e, 99% Atom C (0.1 mg/mL), were derivatized with 3-methyl-1-phenyl-2-pyrazoline-5-one (PMP) and the monosaccharides quantified by UPLC analyses (Agilent 6490 Mass Spectrometer) using the method of Xu et al. (2018) \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e, which was chosen for its high sensitivity with multiple reaction monitoring mode detection. Sample dry matter (g/mL) was calculated from pellet mass loss upon drying. The total monosaccharide content of the acid hydrolysates (derived from cell walls and starch) was calculated as the sum of anhydro masses of monosaccharide constituents and expressed per mg pellet dry mass. An indication of the amount of starch in the pellet samples was obtained as per \u003cb\u003eEq.\u0026nbsp;1\u003c/b\u003e. The glucose measured derives from starch, cellulose and xyloglucan. By an in-house acid hydrolysis of the chickpea cell wall purified of intracellular contents, the ratio of arabinose to glucose was found to be 1: 0.469. Assuming no solubilisation or fermentation of pectic arabinan in the upper gut, the arabinose value may be used to estimate the non-starch glucan content. This was subtracted from the total glucose to give an indication of the starch content of the pellets.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(Starch \\%=Glu\\%-\\left(0.469 x Ara\\%\\right)\\)\u003c/span\u003e \u003c/span\u003e x Total sugars (\u0026micro;g/mg dry matter) (Eq.\u0026nbsp;1\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEquation 1\u003c/strong\u003e \u003cp\u003eProportion of pellet total sugars that is derived from starch (Starch %) is estimated from the proportion of Glucose (Glu) and Arabinose (Ara) measured in the acid hydrolysate, which are expressed as a percentage of the total sugar content (sum of anhydro masses of monosaccharide constituents) within the acid hydrolysate.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAppetite and food intake analysis\u003c/h2\u003e \u003cp\u003eVAS consisted of a set of questions to measure hunger (\u0026ldquo;how hungry do you feel right now?\u0026rdquo;), fullness (\u0026ldquo;How full do you feel right now?\u0026rdquo;), desire to eat (\u0026ldquo;How strong is your desire to eat\u0026rdquo;), and prospective food intake (\u0026ldquo;How strong is your appetite for a meal?\u0026rdquo;). Participants were asked to answer these questions by drawing a vertical line across a 100mm scale ranging from \u0026ldquo;not at all\u0026rdquo; (right extreme) and \u0026ldquo;extremely\u0026rdquo; (left extreme). A composite appetite score (CAS) was calculated by combining the four measurements: [hunger+(100-fullness)\u0026thinsp;+\u0026thinsp;desire to eat\u0026thinsp;+\u0026thinsp;appetite for a meal]/4. An excessive, homogenous pasta meal was served at 240 min to measure \u003cem\u003ead libitum\u003c/em\u003e food intake. The meal consisted of 3kg boiled white pasta, mixed thoroughly with 2 pots of tomato sauce (Hearty Food Co. Tomato Herb Pasta Sauce 440 g) and 50 g of vegetable oil (Flora sunflower oil) to provide approximately 2500 kcal. Food intake was measured by weighing the grams of food before and after consumption and calculating the difference.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSample Size and Statistical Analysis\u003c/strong\u003e \u003cp\u003eThis was a pilot study. No similar study had been conducted pior to this study so the target of 15 participants was estimated. During the study, Petropoulou, K. et al. used similar methodology to investigate the effects of resistant starch from peas on human gastric and duodenal digestion with 10 participants and reported significant differences on outcome measures such as blood glycaemia, gut hormone and intestinal starch digestion \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Thus, 10 participants were recruited for this study.\u003c/p\u003e \u003cp\u003eBlood biochemical data were checked for normality by the Shapiro-Wilk test. For data that did not pass the test (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), a log transformation was applied before conducting parametric tests. Two measurements of baselines before breakfast were combined as the baseline value. For time-course data, two-way repeated ANOVA was performed to measure the effect of time, meal (treatment) and their interaction. The Greenhouse-Geisser correlation was applied to correct for violations in sphericity, and the assumption of normality of residuals was assessed using QQ plots. Post \u003cem\u003ehoc\u003c/em\u003e Tukey\u0026rsquo;s Tests were performed for pairwise comparisons when significant treatment \u0026times; time effects were detected, and multiplicity adjusted \u003cem\u003ep\u003c/em\u003e-values reported (\u0026lsquo;adj.\u0026rsquo;\u003cem\u003eP\u003c/em\u003e'), and mean differences (MD) with 95% CI reported in the text (expressed as MD\u0026thinsp;\u0026plusmn;\u0026thinsp;95% margin of error(MoE)). Incremental peak (iPeak) was calculated as the maximum rise from fasted concentrations for each individual. Incremental Area under curve (iAUC) refers to the \u0026lsquo;first peak \u0026lsquo;area and was calculated using the trapezoidal rule, ignoring the area under the baseline. The \u0026lsquo;Peak X\u0026rsquo; and \u0026lsquo;Last X\u0026rsquo; refer to the time to peak and return to baseline levels. Summary variables (iPeak, iAUC, Peak X, and Last X) were analyzed by one-way repeated measure ANOVA, followed by \u003cem\u003epost hoc\u003c/em\u003e Tukey\u0026rsquo;s Test. For VAS time-series data, changes from baselines were used for two-way repeated ANOVA followed by pairwise comparisons. Data were analyzed using GraphPad Prism 9.0 (Graphpad Software USA, Biomatters, Ltd.). Results were considered statistically significant at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eFor aspirate analyses data, two-way mixed effects ANOVA was planned for the time-series, however the large number of missing values (due to potentially non-random variable availability of intestinal content) meant that this statistical test could not reliably be performed on the intestinal samples. Time series data is presented as mean data, and \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e includes a table showing number of samples used to compute the means for each time point. Mean analyte concentrations in samples collected from each participant over the 3 h period were analysed by One-Way repeated measures ANOVA, to test for main meal effects. Tukey\u0026rsquo;s test was performed \u003cem\u003epost hoc\u003c/em\u003e when significant main effects were observed. Mean difference with 95% CI of difference, along with multiplicity adjusted P-values are reported for \u003cem\u003epost hoc\u003c/em\u003e pairwise comparisons.\u003c/p\u003e \u003cp\u003ePartial Least Squares Regression (PLSR) was performed with luminal metabolites as predictors and blood glucose, insulin, GIP, GLP-1, and PYY as outcomes. PLSR was conducted using the \u0026lsquo;plsr\u0026rsquo; package in R \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Data was scaled enabling each variable to contribute uniformly to the model. The number of principal components used was determined through the Root mean square error of prediction (RMSEP) to achieve the highest prediction accuracy. The model was validated by the \u0026lsquo;leave one out\u0026rsquo; method.\u003c/p\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the study participants for their time and efforts in participating in this trial. We thank Oyinkansola Olotu, Echo Junceng Dong, Claire Ho, Dr Hannah Stephen, and Jennifer Pugh for assistance with the trial and sample collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: M.C., G.F., C.E.; Methodology: M.C., M.T., P.R., G.F., C.E.; Investigation: M.C., S.T., M.T., P.R., N.P., I.G.P., J.I.C., J.W., E.H., A.B., B.D., G.B., Formal analysis: M.C., S.T., P.R., G.B., I.G.P., J.W., C.E.; Data Curation: M.C., P.R., N.P., S.S., I.G.P., J.I.C.; J.W.; Visualization: M.C., C.E., Supervision: G.F., C.E.; Project administration: G.F., C.E.; Funding acquisition: G.F., C.E.; Writing- Original Draft: M.C., C.E.; Writing- Review \u0026amp; Editing: M.C., C.E., G.F.; All authors [M.C., S.T., M.T., P.R., N.P., S.S., I.G.P., J.I.C., J.W., E.H., A.B., B.D., G.B., G.F., C.E.] have read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Disclosure\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Biotechnology and Biological Sciences Research Council, UK (BBSRC) Institute Strategic Programme grant BB/R012512/1 and its constituent projects BBS/E/F/000PR10343 and BBS/E/F/00044427. MC is funded by the China Scholarship Council (CSC) from the Ministry of Education of P.R. China for her PhD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical trial has been registered with the ISRCTN (\u003cu\u003ehttps://www.isrctn.com/ISRCTN18097249).\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data reported in this study are available from Mendeley Data Database at https://data.mendeley.com/preview/4vn35twm9v?a=2e3eda4b-23d2-4a9a-ab80-263b57e58caa and will also be shared by the corresponding authors upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not report original code.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFoyer, C. 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Help section of the \u0026ldquo;Pls\u0026rdquo; package of R studio software, 1\u0026ndash;23 (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Legumes, cell wall, glucose, insulin, gastrointestinal hormones, gastrointestinal tract, metabolite profile, randomized cross-over","lastPublishedDoi":"10.21203/rs.3.rs-4502487/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4502487/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDietary interventions to combat non-communicable diseases focus on optimising food intake but overlook the influence of food structure. Food processing often causes the loss of foodstructure, but how this influences human gastrointestinal digestion and the signals it generates, such as gut hormones that affect homeostatic mechanisms is unclear. In this randomised cross-over study, 10 healthy participantsconsumed iso-nutrient chickpea meals with contrasting cellular structures and underwent gastric, duodenal, and blood sampling. Here, we reported that the ‘Broken’ and ‘Intact’ cell structures of meals resulted in different digestive and metabolomic profiles, leading to distinct postprandial glycaemia, gut hormones, and satiety responses. ‘Broken' meal resulted in high starch digestibility and a sharp rise in gastric maltose within 30 minutes, which acutely elicited higher blood glycaemia, GIP, and GLP-1. ‘Intact’ meal produced a prolonged release of appetite-suppressing hormones GLP-1 and PYY, elevated duodenal amino acids, and undigested starch at 120 minutes. This work highlights how plant food structure alters upper gastrointestinal-nutrient-sensing hormones, providing insights into the adverse effects of modern diets on\u003cstrong\u003e \u003c/strong\u003eobesity and type 2 diabetes.\u003c/p\u003e","manuscriptTitle":"Upper-Gastrointestinal Tract Metabolite Profile Regulates Glycaemic and Satiety Responses to Meals with Contrasting Structure","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-02 05:55:52","doi":"10.21203/rs.3.rs-4502487/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-metabolism","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natmetab","sideBox":"Learn more about [Nature Metabolism](http://www.nature.com/natmetab/)","snPcode":"","submissionUrl":"","title":"Nature Metabolism","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b2db1622-76c1-4bb0-8e99-caf9111b7c88","owner":[],"postedDate":"July 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":32873615,"name":"Biological sciences/Physiology/Metabolism/Metabolomics"},{"id":32873616,"name":"Health sciences/Gastroenterology/Gastrointestinal system/Small intestine/Duodenum"},{"id":32873617,"name":"Health sciences/Medical research/Translational research"},{"id":32873618,"name":"Health sciences/Gastroenterology/Gastrointestinal hormones"},{"id":32873619,"name":"Health sciences/Health care/Nutrition"}],"tags":[],"updatedAt":"2025-06-21T07:07:54+00:00","versionOfRecord":{"articleIdentity":"rs-4502487","link":"https://doi.org/10.1038/s42255-025-01309-7","journal":{"identity":"nature-metabolism","isVorOnly":false,"title":"Nature Metabolism"},"publishedOn":"2025-06-20 04:00:00","publishedOnDateReadable":"June 20th, 2025"},"versionCreatedAt":"2024-07-02 05:55:52","video":"","vorDoi":"10.1038/s42255-025-01309-7","vorDoiUrl":"https://doi.org/10.1038/s42255-025-01309-7","workflowStages":[]},"version":"v1","identity":"rs-4502487","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4502487","identity":"rs-4502487","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}