# Starch digestion: A comprehensive update on the underlying modulation mechanisms and its in vitro assessment methodologies

Daniela Freitas, Athina Lazaridou, Dorine Duijsens, Kali Kotsiou, Kendall R Corbin, Marilisa Alongi, Natalia Perez-Moral, Sebnem Simsek, Sedef Nehir El, Shannon Gwala, et al.

► To cite this version:

Daniela Freitas, Athina Lazaridou, Dorine Duijsens, Kali Kotsiou, Kendall R Corbin, et al. Starch digestion: A comprehensive update on the underlying modulation mechanisms and its in vitro assessment methodologies. Trends in Food Science and Technology, 2025, 159, pp.104969. 10.1016/j.tifs.2025.104969. hal-04998681

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# Starch digestion: A comprehensive update on the underlying modulation mechanisms and its *in vitro* assessment methodologies

Daniela Freitas<sup>a,∗</sup>, Athina Lazaridou<sup>b</sup>, Dorine Duijsens<sup>c</sup>, Kali Kotsiou<sup>b</sup>, Kendall R. Corbin<sup>d</sup>, Marilisa Alongi<sup>e</sup>, Natalia Perez-Moral<sup>f</sup>, Sebnem Simsek<sup>g</sup>, Sedef Nehir El<sup>g</sup>, Shannon Gwala<sup>a</sup>, Sibel Karakaya<sup>g</sup>, Steven Le Feunteun<sup>h</sup>, Tara Grauwet<sup>c</sup>, Mario M. Martinez<sup>i,j,1,∗∗</sup>, Cathrina H. Edwards<sup>f,1,∗∗</sup>

## ARTICLE INFO

**Keywords:**

α-amylase

Glycaemic response

Dietary fibre

Microbiota

Food structure

INFOGEST

## ABSTRACT

**Background:** Starch is the predominant carbohydrate in our diets and its impact on human health is intricately linked with its digestive process. However, despite major advancements in the field, important inconsistencies in how starch digestion is described and studied *in vitro* still persist.

**Scope and approach:** Our main objective was to provide up-to-date insights on starch digestion and fermentation in the human gastrointestinal tract, as well as related physiological responses and main drivers of variability (including inter-individual variations and structural and compositional differences between foods). A critical appraisal of digestion models and future work priorities is also presented. This work is the product of an international collaboration within the INFOGEST research network.

**Key findings and conclusions:** Starch digestion is accomplished by the concerted action of luminal and brush-border hydrolases. Mechanical and biochemical transformations occurring throughout oral, gastric and intestinal phases can be determinant, contributing to different extents to the digestive process depending on food properties and individual characteristics. Numerous methodologies, with varying complexity and capacity to reproduce key digestive processes (e.g., gastric emptying), are currently available. Some *in vitro* digestibility outcome measures can be closely correlated with the outcomes of *in vivo* studies, demonstrating the promising potential of these approaches. However, the physiological relevance of *in vitro* starch digestion studies is often compromised by the lack of methodological consensus (particularly for oral digestion) and omission of key enzymatic events. Therefore, the need to develop harmonized guidelines and validate *in vitro* protocols adapted to starch digestion studies stand out as priority areas for future work.

**Abbreviations:** DS, Digestible starch; GI, Glycaemic Index; PA, Pancreatic α-amylase; RDS, Rapidly digestible starch; RS, Resistant starch; SDS, Slowly digestible starch.

* Corresponding author. Teagasc Food Research Centre, Moorepark, Fermoy, Co Cork P61 C996, Ireland.

** Corresponding author. Center for Innovative Food (CiFOOD), Department of Food Science, Aarhus University, Agro Food Park 48, Aarhus N 8200, Denmark.

*** Corresponding author. Quadram Institute Bioscience, Norwich Research Park, NR4 7UQ, Norwich, United Kingdom.

**E-mail addresses:** daniela.freitas@teagasc.ie (D. Freitas), mm@food.au.dk, mmartinez@uva.es (M.M. Martinez), cathrina.edwards@quadram.ac.uk (C.H. Edwards).

¹ These authors contributed equally.

<footnote><sup>a</sup> Teagasc Food Research Centre, Moorepark, Fermoy, Co Cork, P61 C996, Ireland</footnote>

<footnote><sup>b</sup> Laboratory of Food Chemistry and Biochemistry, Department of Food Science and Technology, School of Agriculture, Aristotle University of Thessaloniki, P.O. Box 235, 54124, Thessaloniki, Greece</footnote>

<footnote><sup>c</sup> Laboratory of Food Technology, Department of Microbial and Molecular Systems (M2S), KU Leuven, Leuven, Belgium</footnote>

<footnote><sup>d</sup> Department of Horticulture, Martin-Gatton College of Agriculture, Food and Environment, University of Kentucky, Lexington, KY, USA</footnote>

<footnote><sup>e</sup> Department of Agricultural, Food, Environmental and Animal Sciences, University of Udine, Italy</footnote>

<footnote><sup>f</sup> Food, Microbiome and Health, Quadram Institute Bioscience, Norwich Research Park, Norwich, United Kingdom</footnote>

<footnote><sup>g</sup> Department of Food Engineering, Faculty of Engineering, Ege University, 35100, Izmir, Türkiye</footnote>

<footnote><sup>h</sup> INRAE, L'Institut Agro, STLO, 35042, Rennes, France</footnote>

<footnote><sup>i</sup> Center for Innovative Food (CiFOOD), Department of Food Science, Aarhus University, AgroFood Park 48, Aarhus N, 8200, Denmark</footnote>

<footnote><sup>j</sup> InnograinLab, Food Technology Division, Department of Agricultural Engineering, University of Valladolid, Spain</footnote>
# 1. Introduction

Starch is the predominant carbohydrate in human diets, supplying 20–50 % of the total energy intake in the form of glucose (Stylianopoulos, 2012). Depending on its digestibility, ingested starch can exert vastly different effects on postprandial glycaemia (Lehmann & Robin, 2007), hormone responses (Edwards, Grundy, et al., 2015), and the gut microbiome (Deehan et al., 2020), with longer-term implications for gut and metabolic health, appetite, and body weight regulation (Bojarczuk et al., 2022; Keenan et al., 2015; Kong et al., 2022). Thus, the impact of starch on human health is directly influenced by its digestive breakdown.

Starch digestion is now known to be affected by a variety of intrinsic and extrinsic factors, which can be targeted to develop healthier food. These factors include food structure and composition (Qadir & Wani, 2022), and are overlaid with physiological differences in digestive function (gastrointestinal physiology, enzyme activity and gut microbiome). While human studies enable the digestive profiles and metabolic consequences of starchy foods to be studied, this is often practically, ethically, and/or financially challenging (Li et al., 2020). *In vitro* methods are simpler, more economical, and reproducible alternatives, and are therefore well-suited to high-throughput screening of foods. Furthermore, *in vitro* studies enable experimental conditions to be tightly controlled and are therefore useful for mechanistic studies that complement *in vivo* and *in silico* approaches (Le Feunteun et al., 2021).

*Significance and Scope of the Review* - Recent reviews in the field of starch digestion have covered various aspects of this process separately. Some studies covered specific factors related to food products, particularly the composition and/or structural features at multiple scales (Chen et al., 2025; Chi et al., 2021; Chi et al., 2022; Ma et al., 2024; Yang et al., 2023). Other studies focused on the digestive process (Li, Hu, et al., 2023) or *in vitro* digestion and fermentation models (Ji et al., 2022). However there is a significant gap in the literature regarding the integrated discussion of these factors and their interconnections. Our review addresses this gap by providing a comprehensive updated account of the processes that govern starch digestion through each region of the digestive tract, including their impact on physiological responses and key sources of interindividual variability, never approached in earlier works. We integrate discussions on food structure and composition, their impact on starch digestion and for the first time, physiological responses and key sources of interindividual variability. This integrated approach is novel and sets our review apart from previous studies that have treated these aspects in isolation. This review goes beyond the current works and highlights key priorities for the future establishment and development of *in vitro* digestion protocols to measure starch digestion. Furthermore, our review includes a critical appraisal of the *in vitro* methodologies employed to study starch digestion, providing recommendations adapted to the research questions to be solved. This critical evaluation and the provision of future research directions are unique contributions that enhance the novelty and value of our review. This work is the result of an international cooperative effort of some members of the INFOGEST international research network (“Improving health properties of food by sharing our knowledge on the digestive process”).

# 2. Starch: structural architecture and occurrence in food

Starch is the major long-term energy storage in amyloplasts of seeds, storage roots and tubers as well as of some other plant tissues, such as the mesocarp of some fruits. It is not surprising, then, that dietary sources of starch are incredibly diverse and vary greatly in composition and structure. Cereal grains and pseudo-cereals (e.g., wheat, rice, corn, sorghum, millets, quinoa, amaranth, buckwheat), tubers (e.g., potato, yam), pulses (e.g., chickpeas, peas, beans and lentils), some fruits (e.g., banana, plantain, jackfruit, breadfruit and mangoes), some roots (e.g., arrowroot, cassava) and some stems (e.g., sago, arrowroot, bamboo) are dominant sources of starch worldwide (Holland et al., 2020). Starch is a

major nutritional component and building block of many different processed food products including bread, biscuits, cooked rice, canned pulses, pasta, breakfast cereals, and snacks, but may also be ingested raw (e.g., starchy fruits).

At a molecular level, starch is exclusively composed of glucose residues, assembled into two structurally different polymers, amylose and amylopectin. In amylose, most glucose residues are linked through α-(1,4) glycosidic bonds, forming an essentially linear structure. Amylopectin is a much larger polymer whose glucose residues are linked through α-(1,4) and α-(1,6) glycosidic bonds, which respectively create linear segments and branch points in the macromolecule. The chain length distribution, degree of branching and blockiness (i.e., branching pattern) and molecular weight/size, of both amylose and amylopectin vary greatly between and within species (Martinez, Li, et al., 2018; Roman et al., 2020). Amylose and amylopectin are synthesised as tightly packed, semicrystalline and water-insoluble aggregates termed starch granules. These granules vary in size, shape, crystallinity, surface porosity, amylose to amylopectin ratio, and presence of trace amounts of other components such as proteins and lipids, depending on their botanical origin (Buleon et al., 1998; Wang et al., 1998).

Within a plant, most dietary starch granules are formed in amyloplasts and hence are inherently confined within the native barriers of the plant cell, such as the plant cell wall, a highly hydrated composite consisting of non-oriented cellulose microfibrils interlocked by hemi-celluloses or pectin (depending on the species). Plant cell walls are typically classified as Type I, ubiquitous in vegetables and fruits and consisting of cellulose, pectin and xyloglucan, or Type II, predominantly exist in cereals and consisting of cellulose, arabinoxylan, and mixed linkage (1,3)(1,4)-β-glucan (Johnson et al., 2018). Although some starch is ingested raw, starch is usually consumed after processing, which alters the original semi-crystalline structure and natural physical barriers. Furthermore, in a food product, starch and/or starch-rich ingredients (e. g., flour, whole grains) are often combined with other food ingredients and processed to form a more complex food matrix, where the starch is interspersed with other food components. Importantly, the structural features and composition of the starch and the surrounding food matrix can have a major impact on its digestibility (this is discussed further in Section 5). This represents an interlocked conundrum affecting the digestive breakdown of starch α-glucan chains and is therefore an important consideration in such investigation.

# 3. Starch digestion and fermentation through the human gastrointestinal tract

Starch digestion is a dynamic and complex process involving the concerted action of different enzymes throughout the gastrointestinal tract. An overview of the main digestive steps and enzymes involved is presented in Fig. 1. A detailed explanation of each stage of the digestion process and a brief summary of key aspects of the fermentation of undigested starch in the large intestine is presented below. Various methods have been used to determine amylase activity, making comparisons between different studies a difficult task. For the sake of consistency, and to enable comparisons between oral and intestinal amylase concentrations, one unit is defined as the amount of enzyme that releases 1 mg of maltose monohydrate from starch over 3 min as determined at 25 °C, using the protocol described by Dahlqvist (1962).

## 3.1. Mouth

In the mouth, the food is physically disrupted by mastication ('chewing'), with larger particles reduced into smaller particles that mix with saliva to form a swallowable bolus. Saliva is a complex mixture consisting of approximately 99 % water that contains several electrolytes (including bicarbonate), proteins (including enzymes, immunoglobulins, glycoproteins and albumin), as well as mucins, glucose, urea and ammonia (de Almeida Pdel, Gregio, Machado, de Lima, & Azevedo,
2008) and plays a role in lubrication and biochemical digestion of starch. It is secreted at a rate of 1–3 mL/min under stimulated conditions (de Almeida Pdel et al., 2008). Salivary α-amylase (α-(1,4) D-glucan- glucanohydrolase, EC 3.2.1.1) is the main enzyme present in saliva. In adults average activities range between 150 and 200 units per mL of saliva (Rossiter et al., 1974). It specifically cleaves α-(1,4) glycosidic bonds in the linear segments of starch molecules to produce mainly maltose, maltotriose and α-limit dextrins. The enzymatic hydrolysis of starch is thus initiated from the very beginning of digestion, during mastication ('chewing'). Among the aspects that are critical to this enzymatic digestion process, the chewing times, levels of salivary impregnation and changes to size and food structure can be highlighted. All of these are greatly influenced by food properties.

The duration of mastication varies between food types, with the time needed to form a bite of food into a bolus reported in the literature varying between 7 s for instant mashed potatoes and 59 s for boiled potatoes. Other starch-rich foods such as bread, breakfast cereal, toast, pasta and rice have been reported to fall within this range (Aguayo-Mendoza et al., 2019; Forde et al., 2013; Gaviao et al., 2004; Hoebler et al., 2000; Loret et al., 2011; Moongngarm et al., 2012). The levels of salivary impregnation for these foods can differ by more than 10-fold. Pasta is often in the lower end of the spectrum (between 0.03 and 0.07 grams of saliva per gram of food in the final bolus), whereas lower moisture foods such as wheat bread and toast are usually at the higher end (with around 0.2 and 1.07 grams of saliva per gram of food in the final bolus, respectively) (Gaviao et al., 2004; Hoebler et al., 2000). Despite the fact that food exposure to oral processing conditions is brief and generally lasts less than 1 min, the extent of starch hydrolysis during the oral stage can be substantial for some foods. For bread and pasta, for example, starch hydrolysis during oral digestion can represent up to 15 % of the total starch (Freitas et al., 2018; Woolnough et al., 2010). In contrast, for foods where starch is encapsulated by cell walls, like whole cooked chickpeas or lentils, the contribution of salivary α-amylase to starch degradation during the oral phase was minimal (Duijsens, Ver- kempinck, et al., 2024; Edwards et al., 2021).

## 3.2. Stomach

Each food bolus formed during mastication is passed down the oesophagus into the stomach. In the fasted state, gastric pH varies between 1 and 2. However, as the food arrives in the stomach, the pH of gastric contents increases to levels close to the pH of the food(s) consumed, typically between 4.5 and 6.7 (Dressman et al., 1990; Gardner et al., 2002; Malagelada et al., 1979). As digestion progresses, gastric juice (containing hydrochloric acid, lipase, pepsinogen and salts) is secreted, inducing a gradual acidification of the gastric content. Although no additional amylase is secreted in the stomach, salivary α-amylase associated with the bolus can retain some activity depending on the pH: the amylolytic activity of saliva is optimal in the range pH 6–7, decreases slowly with decreasing pH, and is irreversibly lost at around pH 3 (Bernfeld et al., 1948; Freitas et al., 2018). Considering that an average gastric pH of 3 is typically reached 1 h after meal intake (Dressman et al., 1990; Malagelada et al., 1979), salivary α-amylase continues starch hydrolysis within the stomach long after food intake. Additionally, it is important to note that gastric contents are heterogeneous and there can be areas where local conditions (e.g., pH, bolus integrity) are conducive to preserving amylase activity for longer. Different levels of intra-gastric pH variations, particularly between the proximal and distal regions, have been observed for meals with different macronutrient profiles. For a high fat meal (fat supplying approximately 50 % of the total calories), for example, areas with pH close to 4 persisted for up to 150 min after ingestion (Simonian et al., 2005). The level of de-structuration of food boluses in the stomach is another factor to consider. Bolus formation is aided by the increased viscosity generated from the starchy food mixing with saliva, so degradation in the stomach varies depending on the structure formed in the mouth. Some starch-rich foods such as bread can form boluses that retain their shape for up to 2.5 h, thereby possibly conferring longer protection to internal amylase from acid inactivation (Freitas, Boue, et al., 2022). In contrast, a meal of chickpea cells does not form a viscous bolus, and showed limited digestion in the oral and gastric phase, presumably due to the

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Overview of the main stages of starch digestion and fermentation throughout the human gastrointestinal tract.</figcaption></figure>
inaccessibility and resistance of encapsulated starch to salivary α-amylase (Edwards et al., 2021).

Although several *in vivo* studies from the early 20th century found that a considerable proportion of starch can be hydrolysed before reaching the small intestine (Davenport, 1992), the exact contribution of the gastric phase to starch digestion has remained largely unexplored up to recently. This idea was brought back into light by semi-dynamic digestion studies *in vitro* reporting that salivary α-amylase can hydrolyse up to 70–80 % of bread starch and 13–30 % of pasta starch at the gastric stage (Freitas et al., 2018; Freitas & Le Feunteun, 2019b). Since then, a number of *in vitro* and *in vivo* (animal) studies have confirmed the significant contribution of enzymatic starch hydrolysis during the gastric phase for a variety of starchy foods (Duijsens, Verkempinck, et al., 2024; Freitas et al., 2023; Freitas, Souchon, & Le Feunteun, 2022; Martens et al., 2020; Nadia, Bronlund, et al., 2022; Nadia et al., 2022). If the predominant factor explaining these results is the delay before the gastric pH becomes low enough to inactivate salivary α-amylase, then starch digestion within the stomach can also be influenced by mechanical and chemical actions. The mixing of the gastric content performed by the movements of the stomach walls can impact enzyme accessibility to the substrate, for example. It has even been suggested that, in the absence of micro- and macro-structural barriers in the food matrix, acid hydrolysis of starch could take place and enhance the starch hydrolysis initiated by salivary α-amylase (Nadia, Bronlund, et al., 2022).

Gastric mixing is a slow process dependent on physiological factors (such as gastric secretions, motility and emptying) as well as meal properties (e.g., buffering capacity, physical properties and rate of breakdown), which is achieved by different types of contractions in the proximal and distal regions of the stomach as reviewed elsewhere (Bornhorst, 2017). These contractions act to physically breakdown food by generating forces of up to 0.65 N (Marciani et al., 2001). The effects of mixing on the local biochemical environments of food in the stomach are determinant for acid hydrolysis and enzyme reactions as well as pH-mediated enzyme activity (Bornhorst, 2017) which is particularly relevant for starch digestion.

Gastric emptying is a continuous process that can start during ingestion. The first emptying episode can occur as early as around 7 min after a meal is started (Minderhoud et al., 2004), with liquids emptying earlier than solids. The extent of starch digestion and of disruption of the food matrix as the chyme enters the duodenum may affect subsequent digestive processes as well. It is well established that gastric emptying rates of carbohydrates and sugars are important determinants of postprandial glycaemia. Fast emptying of available carbohydrates, in particular, may cause postprandial hyperglycaemia and is recognized as a major factor in the pathogenesis and management of diabetes mellitus (Goyal et al., 2019). Gastric emptying is regulated by the physical and chemical nature of food, through complex neuro-hormonal control mechanisms. The process is highly dependent on the physical properties of the food (e.g., particle size), meal volume, osmolality, chemical composition and caloric density. It has been estimated that the stomach empties, on average, 1–4 kcal/min and gastric emptying usually lasts between 2 and 3 h (Goyal et al., 2019). However, this is likely to vary considerably depending on meal size and types as well as individual factors.

### 3.3. Small intestine (lumen and brush border membrane)

The small intestine is the main site of nutrient absorption. It has been the focus of most starch digestion studies due to its longer retention time compared to the mouth and the stomach (Mikolajczyk et al., 2015; C. H. M. Versantvoort & Rompelberg, 2004) and the direct contribution of luminal and brush-border enzymes to the breakdown of starch to the monomeric unit, glucose (Martinez, 2021). As opposed to what occurs in the mouth and the stomach, the contribution of small intestinal motility to the physical breakdown of solid food particles is limited (Nadia et al.,

2021). Starch digestion within the duodenum is thus regulated mostly by factors affecting the activity and substrate access/binding of amylolytic enzymes.

In the fasted state, the pH in the duodenum ranges from 5.8 to 6.5 (Dressman et al., 1990). As gastric contents are emptied into the duodenum, they are mixed with bile and pancreatic juice. Bile is an aqueous solution composed predominantly of bile salts (in addition to various other components such as phospholipids, cholesterol and conjugated bilirubin electrolytes). Bile salts are also the most significant constituent of bile in the context of digestion, particularly lipid digestion, with a basal output ranging between 10 and 20 µmol/min increasing three to six fold with meal ingestion, as reviewed by Keller & Layer (Keller & Layer, 2005). Pancreatic juice contains enzymes, including pancreatic α-amylase (α-(1,4) D-glucan-glucanohydrolase, EC 3.2.1.1), and bicarbonate. In the postprandial state, the duodenal pH fluctuates due to the periodic emptying of (acidic) chyme from the stomach followed by neutralization with pancreatic bicarbonate (Dressman et al., 1990). Overall, after a meal, duodenal pH has been found to vary between 4.2 and 6.7 (Dedlovskaya, 1968; Dressman et al., 1990) creating a favourable environment for enzymatic amyolysis. In the fed state, pancreatic enzymes (including α-amylase) are continuously secreted into the duodenum surging quickly after eating and peaking within the initial hour or even just 20–30 min after meal ingestion. After reaching this peak, the secretion of pancreatic α-amylase gradually decreases to a more consistent rate, albeit at lower levels. This stable secretion continues for around 3–4 h after eating, but the degree and duration can vary based on the meal's size and composition (Keller & Layer, 2005). Average activity levels of pancreatic α-amylase in duodenal contents are around 150 U/mL, however significant interindividual variations have been reported (from 40 to >500 U/mL, with the same meal) (Dahlqvist, 1962; Worning & Mullertz, 1966). In addition to pancreatic α-amylase, there is some evidence that salivary α-amylase can also be present in the small intestine in its active form, and hence contribute to the intestinal amylolysis process. According to the literature, about 14 % of the total amylolytic activity measured in the small intestine over a 3 h postprandial period could, indeed, be attributed to salivary α-amylase (Fried et al., 1987). Both amylases hydrolyse the α-(1,4) linked straight-chain regions of starch to liberate oligomers with 2–5 glucose residues (mainly maltose and maltotriose), as well as larger and highly branched molecules, usually termed α-limit dextrins (Quezada-Calvillo et al., 2007). Linear and branched glucose oligomers from amylolysis must be further hydrolysed by four α-(1,4) exoglucosidases comprising the sucrase-isomaltase (SI; EC 3.2.1.48 and EC 3.2.1.10) and maltase-glucoamylase (MGAM; EC 3.2.1.20 and EC 3.2.1.3) complexes present in the brush border membrane. All four enzymes have α-(1,4) glucosidic activity and one enzyme, isomaltase, has substantial α-(1,6) glucosidic activity that cleaves the linkages present in the branch points of α-limit dextrins (Gray, 1992; Jones et al., 1983). The resulting glucose monomer units are then absorbed into the portal vein through the protein-membrane transporters sodium-dependent glucose transporter (SGLT1) and the facilitated-transporter glucose transporter (GLUT2) from the polarized epithelial cells (enterocytes). An example of the importance of this enzyme complementarity can be seen in patients with congenital SI deficiency, where full starch digestion will be missing in addition to the well-known sucrose malabsorption (Karnsakul et al., 2002).

Starch digestion continues as it passes from the duodenum through the jejunal and ileal regions of the small intestine. Transit time through the upper-gastrointestinal tract is variable depending on the meal, context and individual factors, but a recent study observed that the first part of the meal reaches the ileum within 1 h of ingestion, with most of the meal reaching the ileum within 4–6 h of ingestion. Compared to other digestive regions, the ileum is relatively poorly understood due to the difficulty in accessing this region in humans. Recently, naso-enteric intubation and novel sampling techniques have enabled new insight into this region of the gut. Remarkably, the ileum has been shown to be
colonised by a complex community of microorganisms (estimated up to $10^8$ bacteria/g) presenting enzymatic machineries required to metabolize starch (and therefore able to hydrolase it) (Shalon et al., 2023). The microbial density in the small intestine increases from around $10^3$ to around $10^8$ bacteria/g, with microbial density increasing from the duodenum to the terminal ileum (Ruigrok et al., 2023). A review previously highlighted that the reduced motility and pooling in the ileum during an overnight fast allows time for some microbial degradation of the food residue to occur (Maurer, 2015). However, more recently, a new study has demonstrated that the ileal environment changes dramatically from the fasted to fed state, with the arrival of digesta causing a rapid decrease in microbial abundance and fermentation products present (Dagbasi et al., 2024). Thus, the ileum is a highly dynamic region of the gastrointestinal tract which is dominated by non-microbial products of digestion in the postprandial stage, but may also contain microbial metabolites in the fasted stage including overnight. These recent insights contribute to an improved understanding of the potential for nutrients or metabolites within this region to stimulate gut peptides responses involved in regulation of digestion, metabolism and appetite.

### 3.4. Large intestine

The mammalian lower gastrointestinal tract contains a variety of distinct microbial habitats along the small intestine, caecum and large intestine (colon). The microbial diversity and load in the small intestine are rather low compared to the colon, with up to $10^{12}$ bacteria/g of luminal content (Ohland & Jobin, 2015). Physiological variations along the lengths of the small intestine and colon include chemical and nutrient gradients, as well as compartmentalized host immune activity, all of which are known to influence bacterial community composition (Donaldson et al., 2016). Many gut bacteria are known to degrade starch (El Kaoutari et al., 2013), suggesting that the manipulation of the colonic microbiota could be achieved by introducing and/or modifying the type of starch in the diet (as occurs through other non-starch dietary fibres). Indeed, a recent study on the effect of resistant starch (RS) supplementation in overweight or obese participants over eight weeks (40 g/d, RS2), confirmed a change in gut microbiome composition which played a pivotal role in the weight loss facilitated by the intervention (mean body weight decrease of 2.8 kg) (Li et al., 2024). Previous attempts to estimate the proportion of starch that reaches the colon, based on analyses of human ileal content of participants with ileostomies, provided values ranging from less than 1 % in white rice to 6 % in baked beans, suggesting that the majority of starch in processed foods is digested by amylase in the upper-gastrointestinal tract (Muir & O'Dea, 1993). Higher proportions of resistant starch have been observed with raw food sources, for example an ileostomy study of green banana flours recovered approximately 84 % of α-glucans (most of which from starch) reaching the ileum (Faisant et al., 1995). It is important to note that the levels of starch digestion/absorption can be influenced by other factors. Investigations of the effect of transit time and carbohydrate load indicate a direct relationship between starch output and the quantity ingested as well as small intestinal transit times (Chapman et al., 1985). However, a limitation of ileostomy studies is that the gut microbiome and the physiology are not fully representative of the intact human gastrointestinal tract in healthy humans. Naso-enteric intubation studies in healthy human participants can provide further insight, but their invasive and specialised nature means that very few foods have been studied in this way. Currently, it remains challenging to accurately determine the amount of resistant starch that is truly available to the lower gut microbiome.

Although microbial fermentation of starch begins in the ileum, the main site of fermentation is thought to be the ascending colon, where the arrival of starch triggers a cascading web of metabolic interactions (Dobranowski & Stintzi, 2021). The ability of the gut microbiota to ferment starch in the colon depends on several factors, including the

amount and type of starch consumed, the composition of the gut microbiota, and the transit time of the digesta in the colon (Li, Hu, et al., 2023). Colonic bacteria initiate their metabolic process by adhering to the starch substrate, subsequently hydrolysing it into oligomeric compounds. These oligomers traverse into the periplasmic space of the bacterial cell, undergoing enzymatic degradation into glucose, which is then assimilated into the cell for metabolic utilization. The ability of colonic bacteria to bind to starch, and access their distinct e.g., crystalline structures, represent necessary specialised adaptations needed to competitively access and utilize the molecular structures of starch (Deehan et al., 2020). This broad and differentiating mechanism for starch utilization has been for long reported to be present in Gram (-) bacteria, particularly in the Bacteroidetes phylum (Martens et al., 2009). However, the development of next-generation sequencing techniques, especially 16S rRNA-based approaches, have pointed out that changes in microbiota composition are not limited to increases in probiotics such as *Bifidobacterium* and *Lactobacillus* species. Anaerobic Clostridium, including *Ruminococcus*, *Eubacterium*, *Roseburia*, *Faecalibacterium* and *Coprococcus*, can also ferment starch (Wang, Wichienchot, et al., 2019), with marked differences in their ability to bind and access it. For example, *Eubacterium rectale* is unable to bind crosslinked tapioca starch, while *Eubacterium rectale* and *Ruminococcus bromii* have limited ability to grow on the substrate. Other bacteria, however, have shown good adherence and utilization of crosslinked tapioca starch, for example *Bifidobacterium adolescentis* and *Parabacteroides distasonis* (Deehan et al., 2020).

### 4. Physiological responses to starch: relevance to nutrition and health

Depending on how starch is digested in the gastrointestinal tract, it can evoke contrasting effects on postprandial metabolism and the gut microbiome. Notably, the amount and rate of release and absorption of glucose from starch during digestion impacts directly on blood glucose concentrations (Priebe et al., 2018), which are implicated in various cardiometabolic diseases. Comparisons between postprandial glycaemic response studies in humans and *in vitro* digestion studies have clearly shown that the same amount of starch within different food matrices can elicit vastly different glycaemic responses, depending on its estimated digestibility in the upper-gastrointestinal tract (Edwards et al., 2019; Freitas, Souchon, & Le Feunteun, 2022; Goñi et al., 1997; Jenkins, Ghafari, et al., 1982; Jenkins et al., 1982; Monro & Mishra, 2022). When undigested starch is instead fermented by the lower gut microbiome, short chain fatty acids (SCFAs) such as acetate, propionate and butyrate, are the main metabolites generated, which are thought to have several functions in gut health, including activation of specific immune and adipose receptors, provision of energy for colonocytes, and lowering the colonic pH (Bojarczuk et al., 2022; DeMartino & Cockburn, 2020; Ribeiro et al., 2022). As the fermentation of starch into short chain fatty acids yields less energy (2 kcal/g) than its digestion into glucose (~4 kcal/g), the region and mechanism by which starch is digested has major implications for its caloric value (Liversey, 1994), which has in turn prompted further discussion about the classification of resistant starch as dietary fibre for food labelling, as reviewed by Nugent (2005).

More broadly, glucose and/or SCFAs products of starch digestion may also act as signalling molecules by binding to receptors of enteroendocrine cells. A number of candidate glucose-sensing and lipid-sending mechanisms have been described, including metabolism-dependent closure of ATP-sensitive potassium (KATP) channels, sweet taste receptor activation, transport through sodium-coupled glucose transporters (SGLTs), and sensing of G-protein-coupled receptors (GPCRs) (Gribble & Reimann, 2016). Therefore, starch digestion and fermentation products have the potential to trigger the release of gut hormones such as glucose-dependent insulinotropic peptide (GIP), peptide YY (PYY) and glucagon-like peptide-1 (GLP-1). These hormones are involved in the regulation of digestion (through effects on motility
and enzyme secretions, including the ‘ileal break’), appetite, and
metabolism (Baggio & Drucker, 2007; Maljaars et al., 2008; Schwartz
et al., 2000). The positive repercussions of influencing the secretion of
these hormones, particularly in terms of the activation of the ileal break
and the modulation of postprandial glucose responses to subsequent
meals has been reviewed elsewhere (Kong et al., 2022).

Differences in starch digestibility and physiological responses are highly relevant when considering the health effects of starch-rich foods. Diets with a higher overall glycaemic index (e.g., mediated by highly digestible starch-rich foods) appear to be associated with a higher risk of stroke and type-2-diabetes incidence (Bhupathiraju et al., 2014; Livesey et al., 2019), coronary heart disease in women (Dong et al., 2012; Mirrahimi et al., 2012), breast cancer (Mullie et al., 2016) and obesity-related complications (Schwingshackl & Hoffmann, 2013). Low glycaemic sources of starch, on the other hand, are encountered in healthier diets. This is often reflected in dietary guidance, where complex/slowly digested sources of carbohydrate are favoured. Furthermore, there is an approved EFSA health claim on the beneficial effect of consuming cereal products high in slowly digestible starch compared to cereal products low in this type of starch (EFSA, 2011). It should also be noted that even when slowly digestible starch sources do not necessarily produce a lower glycaemic response, if they successfully reduce the rate of glucose uptake, they could still have beneficial, potentially long-term, benefits. In a study by Eelderink et al. (2012), it was observed that a slower intestinal uptake of glucose from a starchy food can result in lower postprandial insulin and glucose-dependent insulinotropic polypeptide (GIP) concentrations, but not necessarily in lower glycaemic responses because of a slower glucose clearance rate.

The term “resistant starch” (RS), first introduced by Englyst et al. in the early 1980s, describes a fraction of starch that resists breakdown by α-amylase both *in vitro* and in the human small intestine (H. Englyst et al., 1982; Englyst & Cummings, 1985). The introduction of an enzyme assay for measuring nutritionally relevant starch fractions subsequently enabled important distinctions to be made between the physiological and health effects of starch-sources with contrasting digestibility. Since then, many researchers have made connections between RS intakes and outcomes such as gut health, weight loss, increased satiety, improved lipid profiles and management of diabetes with lowered glycaemic response and/or improved insulin sensitivity (Birt et al., 2013; Bojarczuk et al., 2022; DeMartino & Cockburn, 2020; Fuentes-Zaragoza et al., 2010; Guo et al., 2022; Keenan et al., 2015; Ribeiro et al., 2022). However, study findings are inconsistent. For instance, a review of 22 different multiple randomized crossover and intervention studies found no conclusive evidence that RS has statistically significant effects on appetite, hunger, food intake, and satiety biomarkers (Guo et al., 2023). Studies investigating the effects of RS on glucose metabolism also show mixed results (Pugh et al., 2023). Recent reviews suggest that the sub-type of RS may be important. Resistant starch has been classified into five different categories based on its structural characteristics; physically entrapped starch (RS₁), non-gelatinized native starch granules (RS₂), retrograded starch (RS₃), chemically modified starch (RS₄), and amylose-lipid complex (RS₅). For instance, there is now some indication that starch that is resistant due to its encapsulation with a food matrix (RS₁) is more effective at lowering glucose, whereas raw starch (RS₂) may be more effective at altering insulin responses (Pugh et al., 2023). Assigning a numerical classification to RS types can present challenges. For example, most amorphous extruded high-amylose starches exhibit enzyme-resistant properties comparable to those observed in native high-amylose starch granules (Htoon et al., 2009; Lopez-Rubio et al., 2007). The resistance in extruded high amylose starch, however, does not arise from the retrogradation of amylose or amylopectin chains (RS₃), the formation of amylose-lipid complexes (RS₅), or the preservation of an intact granular structure (RS₂). Instead, this resistance likely results from the dense packing of amorphous chains, creating a substrate with a polymer density similar to that of granular starch. In light of this, the current RS classification system (RS₁

to RS5) may benefit from revision. A more comprehensive framework
could consider the kinetic aspects of resistance, focusing ideally on the
primary factors driving resistance, specifically the rate-limiting steps of
enzyme access and binding to the substrate and the subsequent con-
version of substrate to product once bound, as previously suggested
(Dhital et al., 2017). In any case, current meta-analyses are based on a
limited number of small studies and further investigation is required.
Clearly, an improved understanding of the mechanisms by which
different starch sources are digested and metabolised is needed to clarify
whether the observed physiological effects and health outcomes are due
to the presence of resistant starch or the absence of digestible starch.

4.1. *Interindividual differences in digestive function and metabolism*

The interplay between all the intrinsic elements that make up each
individual's unique physiology determines the nature of its interactions
with extrinsic factors, with possible repercussions on digestive perfor-
mance and metabolic responses. Postprandial studies in relatively ho-
mogeneous populations where healthy individuals consume starch-rich
test meals, report wide variations between- and within-individuals
(coefficients of variability up to 25 % and 43 %, respectively)
(Matthan et al., 2016; Vega-Lopez et al., 2007). Further interactions of
other differentiating factors will then contribute additional layers of
complexity and variability. The relationships between factors such as
age and sex on salivary α-amylase levels (U/mL) has been investigated in
various studies. One of the earliest studies on the effect of age indicated
that despite a wide scatter of values, concentrations increased from very
low levels at birth to reach adult levels by the age of 6 months (Rossiter
et al., 1974). A constant amylase concentration from childhood into
adulthood has been confirmed in another study comparing samples of
individuals between 5 and 42 years old (Gillard et al., 1978) study.
However, conflicting evidence can be found in other studies showing
either a decrease (Strahler et al., 2010) or an increase(Kalipatnapu et al.,
1983) in amylase concentrations between childhood and adulthood.
Investigations of different age groups throughout adulthood seem to
converge in showing similar amyloolytic activity levels in younger and
older adults (Kalipatnapu et al., 1983; Nassar et al., 2014; Strahler et al.,
2010). Interestingly however, as well as finding no differences between
different age groups (U/mL of saliva), some of these studies have found a
decline in specific activity with advancing age (U/mg of protein)
(Gillard et al., 1978; Kalipatnapu et al., 1983). The effect of sex on
amylase activity levels is not clear either with some research indicating
higher concentrations in men (van Stegeren et al., 2008) while others do
not observe any significant difference (Gillard et al., 1978; Prodan et al.,
2015). It is important to note that the differences across these studies
may be due to variations in study design, age groupings, saliva collection
protocols and methodological approaches. The impact of age on
pancreatic α-amylase concentrations has also been investigated with
different studies finding evidence of reduced amylase activity or output
in older adults compared to younger adults (Dreiling et al., 1985; Firky,
1968; Ishibashi et al., 1991; Vellas et al., 1988). While a thorough re-
view of all the aspects that contribute to this variability is outside the
scope of this article, it is noteworthy that variables such as chewing,
genetic polymorphisms, and gut microbiota are associated with differ-
ences in oral processing, digestion, and intestinal absorption of carbo-
hydrates (Walther et al., 2019).

During oral processing, food can be exposed to a wide range of
salivary α-amylase activity levels. There is a more than 10-fold differ-
ence between activity levels of individuals in the lower and upper ends
of the ranges reported in the literature (Butterworth et al., 2011; Ros-
siter et al., 1974), with the variation explained by both sampling
methodology and inter- and intra-individual variations. An aspect of
particular interest is that individual salivary α-amylase activity levels
increase significantly upon transition from fasted to fed states, and even
in the fed state they can be further impacted by satiety levels
(Harthoorn, 2008). In addition to salivary α-amylase activity, variations
in other elements of the bolus formation process, such as increases in the number of chews (Zhu et al., 2014) and degree of particle size breakdown (Ranawana et al., 2010) have both been linked with higher postprandial blood glucose concentrations. On a mechanistic level, increased particle size reduction in the oral phase would increase the release of starch digestion products by salivary α-amylase. The maltose-oligosaccharides are then carried rapidly through the stomach and emptied with the liquid phase into the duodenum. This early arrival of starch-digestion products into the duodenum drives the early postprandial rise in blood glucose.

Another relevant aspect is the copy number of the salivary α-amylase gene (AMY1) which encodes salivary α-amylase and has been shown to vary from 2 to 15 in a study of 50 individuals, with a proportion of individuals following a high-starch diet with at least six AMY1 copies reported to be nearly two times greater than that for low-starch populations (Perry et al., 2007). The AMY1 copy number has been significantly associated with the production and activity of salivary α-amylase (Carpenter et al., 2017; Mandel et al., 2010; Perry et al., 2007). It has been found in an observational study that high AMY1 copy number can significantly increase postprandial glucose concentrations depending on the starch dose (Farrell et al., 2021). In that study, median fasting salivary α-amylase activity was more than four times higher in those with high (≥10 copies) vs. those with low (≤4 copies) AMY1 copy number. Interestingly, it should be noted that the underlying mechanisms may extend beyond salivary α-amylase levels as it has been reported that while the production of this enzyme is correlated with AMY1 copy number, the majority of interindividual variations comes from other sources (Carpenter et al., 2017) such as circadian rhythms (Nater et al., 2007) and stress (Granger et al., 2007). Copy number variations in AMY1 have been shown to be correlated to the pancreatic α-amylase gene (AMY2A), although the former varied to a greater extent than the latter (Carpenter et al., 2015). Another possible source of interindividual variability pertains to the absorption of glucose in the small intestine. By analysing data from over 15,000 individuals, Seidelmann et al. (2018) concluded that specific genetic variants of the sodium glucose cotransporter protein SGLT1 were linked to a lower glycaemic response during a 2-h glucose tolerance test and had lower odds of impaired glucose tolerance and could thereby buffer against diet-induced hyperglycaemia.

Other elements which should be considered include gut microbiome activity and anthropometric factors, both known to influence individual glycaemic responses (Tily et al., 2022). Several large studies support the “proof of concept” that combining genotypic, phenotypic (clinical measures), microbiome, and lifestyle (diet and exercise) factors can be used to generate predictive algorithms for glycaemic response. Zeevi et al. (2015) concluded that personal and microbiome features enable accurate glucose response prediction that is superior to common practice, although others argued that dietary recommendations to improve glycaemic response are just as good (Wolever, 2016). A large-scale high-resolution study on 1002 twins and unrelated healthy adults showed large interindividual variability (as measured by the population coefficient of variation (s.d./mean, %) in postprandial responses of blood glucose (68 %) and insulin (59 %) following identical meals (Berry et al., 2020) Nevertheless, person-specific factors, such as gut microbiome, had lower influence than did meal macronutrients (6 % and 15 %, respectively), with genetic variants having a modest impact on predictions (10 %). Although these individualized algorithms support better predictions of glycaemic response, the question whether they can be scalable and affordable remains, since they rely on large amount of information, such as food diaries, continuous glucose monitoring, microbiome composition and metabolome. In this regard, the design of precise food structures to address interindividual variability could be more realistic, emphasizing the importance of food chemists/engineers to precisely target physiological, metabolic, or immune response through the design and/or understanding of food matrices.

# 5. Factors influencing starch digestibility in food products

Starch digestion is influenced by a range of food structural and compositional factors. Developing an understanding of how these factors modulate starch digestibility, bioaccessibility (release) and bioavailability (absorption) is relevant for the design and interpretation of *in vitro* digestion experiments. Food structure and composition can also be targeted during food product design to achieve desirable starch digestibility profiles for improved glycaemic control, energy release and/or prebiotic effects.

For foods with the same composition, differences in their structural properties alone can have a significant impact on how starch is digested. Structural features at all length-scales, from the molecular organization of starch to the food macrostructure can influence the digestive breakdown of and enzyme accessibility to the starch substrate with potential repercussions for postprandial glycaemia.

## 5.1. Native supramolecular and granular architecture

In native (raw) starch, enzymatic adsorption and digestion rates will depend on crystalline polymorphism, granule size, boundary surface area, and presence of non-starchy substances (Dhital et al., 2017). Most starch is not consumed in the native state, and as the starch structure changes during processing, these properties become less relevant when it comes to modulating digestibility of processed foods.

A common way of processing starch-based food includes heating in the presence of water where starch undergoes gelatinization, swelling and even granular disruption if shear stress is present. Gelatinization consists of an order-to-disorder transition with the irreversible loss of molecular organization which in most cases increases enzyme susceptibility considerably compared to the native state. Gelatinization of starch is therefore associated with vast increases in digestibility. Starch gelatinization occurs in hydrated products such as bread (Martinez, Roman, & Gomez, 2018) and cakes (Martinez, Li, et al., 2018), but not in low-moisture products like cookies (Varriano-Marston et al., 1980). Therefore, the moisture-temperature history plays a crucial role in determining the digestion rate and sets the foundation for strategies aimed at slowing starch digestion. It should be noted that the gelatinization of normal starch - without modified biosynthesis or chemical crosslinking - cannot be entirely prevented in some food products. Notably, high-amylose starch demonstrates greater thermal stability than conventional starches. This enhanced stability allows high-amylose starches to better maintain their granular and semicrystalline structures under typical cooking conditions, thereby preserving their resistance to digestion in final food products. Cooling of the gelatinized starch will result in retrogradation, or a reorganization of amylose and amylopectin chains into a more ordered structure (Miles et al., 1985; Wang et al., 2015), which can potentially lead to the recovery of the slow/resistant digestion property of native starch. Starch retrogradation is known to somewhat reduce the digestion rate and extent of gelatinized starch. Structure-digestion relationships have positioned some starches as potential candidates to promote slowly digestible or resistant supramolecular structures, which offers the potential to reverse the hyperglycaemic metabolic outcomes of starch in processed foods (Mario M. Martinez, 2021). On the one hand, gelatinized and retrograded starch structures resulting in slow digestion property are particularly formed from amylopectin molecules possessing: i) high proportions of long chains, that is, higher molar ratio of long to short chains (Martinez, Li, et al., 2018); ii) chains with longer average length (M. M. Martinez, Li, et al., 2018; Roman et al., 2019), and/or; iii) induced lower molecular sizes through processes such as acid-conversion or high shear extrusion (Martinez, Li, et al., 2018; Roman et al., 2019; Roman & Martinez, 2019). A recent study has also suggested that it is the organization of the chains and the subsequent double helical packing, and not simply the unit chain size-distribution and double helix formation, that dictates the slow digestion property of amylopectin (Roman et al., 2020). On the
other hand, amylose self-interactions during retrogradation (RS<sub>3</sub>), re-
sults in α-amylase resistance in which digestion rate and extension
depend on the amylose content (L. Li et al., 2008) and amylose
chain-lengths (Gidley & Bulpin, 1989; Yee et al., 2021). However, it
appears that amylose content alone is insufficient to explain slow
digestible properties of high amylose starches. High amylose starches
have lower proportions of amylose short chains and amylopectin with
significantly longer chains compared to regular starches (Li, Zhang,
et al., 2023; Lin et al., 2016; Martinez, Li, et al., 2018). For instance,
sago, potato, banana and pea starches possess longer amylopectin chains
that confer their relatively lower digestion (Martinez, Li, et al., 2018;
Roman et al., 2020).

These findings highlight the opportunities for breeding and precision
processing strategies targeting starch molecular structures with slow/
low digestibility. For instance, a starch branching enzyme II (SBEII)
wheat mutant with increased amylose content and larger proportions of
long chains resulted in breads with lower susceptibility to in vitro
amylolysis (Corrado et al., 2023). Such effects have also been observed
in vivo, where attenuated blood glucose levels were present when
healthy adults consumed high amylose bread (Belobrajdic et al., 2019)
or noodles (Ang et al., 2020). Besides in planta modifications, starch
digestibility can also be modulated by using exogenous enzymes. For
example, by increasing the branch density of starch using a combination
of β-amylase and transglucosidase (Ao et al., 2007), or by elongating
amylopectin chains using amylosucrase (Kim et al., 2013). In addition to
retrogradation-driven RS, amylose can impart enzyme resistance
through the higher thermal stability of its native semicrystalline struc-
ture (Hoebler et al., 1999; Roman & Martinez, 2019), also known as
'Granular Resistant Starch' (RS<sub>2</sub>). Amylose can also form
enzyme-resistant inclusion complexes with lipids (RS<sub>5</sub>), which can
naturally exist in high amylose starches or be formed upon cooking and
baking (Roman & Martinez, 2019).

Overall, starch molecular and granular structures govern the avail-
ability of α-glucan chains to amylase hydrolysis and can be targeted
through processing and/or genetic approaches to modulate its digestion.

5.2. *Natural barriers in the plant tissue matrix*

Natural barriers in the plant tissue matrix can limit the bio-
accessibility of starch during digestive transit. When starch is consumed
in its naturally occurring form as part of an edible plant tissue, the starch
is encapsulated by primary plant cell walls of the starch-storage tissue.
Plant cell walls are complex, supramolecular assemblies consisting
mainly of polysaccharides such as cellulose, hemicelluloses and/or
pectins which are not digested by mammalian enzymes in the human
upper-gastrointestinal tract (Jarvis, 2011). As a result, they play a key
role in regulating amylase access to intracellular starch during digestion
of food containing whole cells or plant tissues.

Plant cell wall properties of the starch-storage tissues vary between plant species and can change during plant maturation/development, post-harvest storage and with food processing conditions (Khorasaniha et al., 2023), so the likelihood of ingesting starch encapsulated within intact plant cells depends on the botanical source and processing treatment applied.

Numerous studies have shown that primary plant cell walls of cooked
legumes can act as physical barriers that slow the ingress of digestive
enzymes into the plant cells and thereby delay and/or limit the release of
maltooligosaccharides from starch into the intestinal lumen (Xiong
et al., 2022). Starch encapsulated within intact cells (an example of Type
1 resistant starch) is therefore considerably less digestible than isolated
legume starch or non-cellular legume flours (Xiong et al., 2022). Legume
cotyledons also tend to cell-separate after hydrothermal processing, so
ingestion of intact plant cells with encapsulated starch is likely when
pulse seeds are cooked whole. Human studies have found that a high
proportion of legume cells appears to remain intact after mastication
(Pallares Pallares et al., 2019), and during upper-gastrointestinal transit

(Noah et al., 1998). The encapsulation of starch by cell walls in legumes
is a key reason for the low glycaemic response to whole cooked pulses
(Bajka et al., 2023; Petropoulou et al., 2020).

Cereals have different plant cell walls compared to legumes, and the
cells in their starch-storage tissues do not readily cell separate (Jarvis
et al., 2003). However, cell-encapsulated starch from cereals can be
ingested if particles are ingested as a whole, cracked or coarsely milled
grains. In particles that are large enough to contain intact plant cells, cell
wall that surrounds the starch provide a barrier to digestive enzymes.
This effect has been observed in vitro with wheat flour (Tagliasco et al.,
2022) and rye bread (Tagliasco et al., 2024), demonstrating that particle
size affects starch digestibility. However, when these flours are pro-
cessed into bread, the effect either vanishes or is significantly reduced,
making this strategy less feasible for high-moisture foods. Moreover,
depending on the cereal this barrier effect may be less effective when
compared with pulses. For instance, durum wheat endosperm walls were
found to be thinner and more permeable to digestive enzymes than
chickpea cell walls, meaning that only the rate but not the extent of
starch digestion was affected (Edwards et al., 2021). Increased
hard-to-cook levels due to postharvest aging of legume seeds is also an
important factor to consider, since the increased hardness in cooked
samples was associated with greater cotyledon cell wall rupture during
simulated (in vitro) mastication, with implications for starch digestibility
(Gwala et al., 2019). Cell encapsulation of starch during processing has
also been shown the limit starch gelatinization and swelling during
hydrothermal processing which contributes to the lower digestibility of
encapsulated starch (Edwards, Warren, et al., 2015).

Within whole grains, starch-containing plant tissues are further
protected by other plant tissues such as pericarp, testa or ‘bran layers’.
These fibre-rich tissues are highly resistant to digestion. Grain properties
also determine their milling performance, and thereby the properties of
the resulting flour. For instance, maize endosperm hardness has been
shown to affect the granular and molecular damage of the starch within
the resultant flours, meaning that raw flours from hard maize have
higher digestibility than raw flours made from the soft endosperm. Once
cooked, however, these flours had similar digestibility after cooking
(Roman et al., 2017). Thus, plant cell walls and intracellular protein
matrices present within some foods can serve as barriers that slow down
starch digestion in whole cooked legumes or coarsely milled/cracked or
whole grains. Food processing methods that disrupt these naturally
resistant native starch structures and/or barriers contribute to
increasing starch susceptibility in processed food. However, processing
can also result in the formation of new food structures which exhibit
reduced starch digestibility. For example, formation of a high density
molecular network with low permeability to digestive enzymes, i.e.,
cooked starch/protein composite matrices (e.g., pasta, rice) consisting of
retrograded amylopectin and amylose chain assemblies (compact B-type
crystallites).

5.3. Food macrostructure

The natural barriers described above are likely to be present in many
minimally processed or wholefoods. Macrostructure and texture of
complex food matrices can also prevent access/binding of enzyme to a
large fraction of the starch and hence result in foods with lower gly-
caemic responses (P. M. Burton et al., 2011; Fardet et al., 2006; Le
Feunteun et al., 2021). A good example is pasta, which exhibits low
enzymatic starch hydrolysis due to its compact and dense macrostruc-
ture. A clinical study showed that the consumption of pasta exhibited the
lowest postprandial levels of blood glucose compared to a flat bread and
a typical bread with a porous macrostructure (Eelderink et al., 2015).
Moreover, higher density breads also exhibit lower postprandial gly-
caemic responses than lower density (more porous) breads (P. Burton &
Lightowler, 2006; Eelderink et al., 2015). This inverse relationships
between density and glycaemic responses are reflected in *in vitro* di-
gestibility studies where higher bread density has been associated with
slower starch digestion (Freitas, Souchon, & Le Feunteun, 2022; Gallo et al., 2022; Gkountenoudi-Eskitzi et al., 2023; Lazaridou et al., 2014; Martinez, Roman, & Gomez, 2018). Fardet et al. (2006), who reviewed several studies on cereal products, reached the conclusion that maintenance of the integrity of food structure during digestion could be a more important factor for restricting starch digestion than the degree of starch crystallinity or the presence of dietary fibres in the composite matrix of baked products.

The textural differences, rather than variations in composition, also appear to be the determinant factor for the *in vitro* starch digestibility of extruded rye products (snack food model), since the latter was positively correlated with the crispiness index and the number of particles in the bolus of the digested food (Alam et al., 2019); i.e., the less dense, less hard and crispier (more fragile) extrudates released more starch granules and thus increased their susceptibility to amylolytic enzymes.

## 5.4. Interactions with other food components

Food macro- and micro-nutrients as well as bioactive compounds might affect the rate of starch hydrolysis, and the subsequent glycaemic response. These components can be present as a structure and constitute physical barriers to starch digestion. Their impact is modulated mainly by physicochemical interactions with either the starch molecules themselves or the key enzymes (α-amylases and α-glucosidases) involved in this process.

Starch digestibility is strongly affected by the presence of proteins in the food matrix (Bhattarai et al., 2016; Tang et al., 2019). It has been shown that endogenous proteins in complex raw materials exhibit a stronger inhibitory effect for amyolysis of the co-existing starch component compared to added proteins (Bhattarai et al., 2016; Do et al., 2023), while in some cases the type of protein was also found to differently affect starch digestibility (Zheng et al., 2020). The protein-starch interactions, which restrict starch hydration and swelling and therefore gelatinization and changes in the physical state of starch, are greatly responsible for the observed effects (López-Barón et al., 2017; Moreton et al., 2023). In fact, denatured and/or hydrolysed plant proteins have been found to be even more effective in restricting enzymatic cleavage of the α-D-glucans by promoting starch-protein interactions (López-Barón et al., 2017). Specifically, microscopic observations of starch-protein systems after cooking revealed that the proteins interact with starch by being located on the surface and encapsulating the starch granules (X. Chen et al., 2017; Yang et al., 2019) and/or being embedded, due to the cracking and swelling, within the starch matrix forming an extensive polymeric network (Yang et al., 2019), and thereby resulting in limited accessibility of starch molecules to digestive enzymes. Finally, the binding of α-amylase by proteins, acting competitively against starch, has been suggested as another potential mechanism for modulation of starch digestion rate (Bhattarai et al., 2016; Chen et al., 2017).

The presence of lipids in the food structure has also been shown to reduce the amount of glucose released during *in vitro* starch digestion (Tang et al., 2019). In this case, formation of lipid complexes between starch (mainly amylose) and lipid molecules (particularly of mono-acyl type) reduces the amyolysis rates (Vernon-Carter et al., 2020). The development of an oily physical barrier around the starch granules has also been suggested as a possible mechanism contributing to the decreased levels of starch digestibility (Vernon-Carter et al., 2020). In most cases, the type of fatty acids, in terms of saturation or chain length, plays a role in the observed effects. As an example, for long-chain fatty acids (18-carbons), linoleic acid was more effective than stearic acid in decreasing starch hydrolysis in a composite matrix, like instant rice noodles (Chen et al., 2022), whereas the digestion of starch-lipids inclusion complexes has been negatively correlated with the fatty acid chain length (Sun et al., 2021). The latter effects have been linked with the ability of fatty acids to generate more or less compact structures for the self-assembled starch-lipid V-type complexes (RS<sub>5</sub>) (Sun et al.,

2021). Finally, the synergistic effect of protein and lipids in reducing starch digestibility has also been widely reported (Tang et al., 2019), with the effect of lipids in such systems being more significant (He et al., 2022).

Regarding dietary fibre, both soluble and insoluble fibres, have been reported to be effective in restricting *in vitro* starch digestibility (Brennan et al., 1996; Brennan et al., 2004; Liu et al., 2021; Tang et al., 2019; Zheng et al., 2021). Beyond its role as a natural cell wall barrier described in Section 5a, dietary fibre has also been suggested to bind or absorb α-amylase, thereby restricting its activity (Wang et al., 2021). The effect of viscous soluble dietary fibres has also been associated to its interaction with starch chains (Brennan et al., 1996). It has been suggested that non-covalent interactions, such as hydrogen bonds and van der Waals forces, drive a rearrangement of starch chains into more-ordered and compact aggregates, which are more resistant to amylase action (Zheng et al., 2021). However, the ability of soluble dietary fibres to build a three-dimensional hydrated polymeric network is also considered an important contributing factor affecting the rate of enzymatic digestibility along with a reduction of glucose diffusion-absorption rates in the intestinal epithelium (Brennan et al., 2004; Zheng et al., 2021). An important source of insoluble dietary fibre is cellulose, which has long chains that are tightly arranged in a dense crystal structure. In natural cellulose, besides the crystalline structure, there is an amorphous region with loosely arranged chains. Zhu et al. (2022) reported that the crystalline structure of cellulose can reduce starch hydrolysis by inhibiting the activity of the digestive enzymes and therefore influence postprandial hyperglycaemia.

Among the bioactive compounds present in food matrices, phenolic compounds have been known for their ability to significantly reduce the extent of starch digestion by inhibiting key enzymes involved in starch digestion and the subsequent intestinal absorption (Martinez, 2021). For example, flavan-3-ols from fruits, tea and cocoa, and especially those with higher degree of polymerization, and flavonol aglycones (i.e. quercetin, myricetin, and fisetin) have been reported to be strong inhibitors of pancreatic α-amylase (Bordenave et al., 2014; Lo Piparo et al., 2008). Inhibitions of mammalian brush border α-glucosidases by polyphenols have also been reported (Simsek et al., 2015). Polyphenols were reported to inhibit the trans-epithelial transport of glucose-derived starch, as shown for example in oat-based snacks (M. Li et al., 2017) and banana cakes (Pico et al., 2019). Moreover, they may also exhibit the ability to form inclusion and non-inclusion complexes with starch (non-covalent interactions) that are highly resistant to enzymatic digestion (Giuberti et al., 2020; Xiao et al., 2013). The inhibitory effect of phenolic compounds on starch digestion is found to be dependent on their physicochemical characteristics; for instance, phenolic compounds with more than one hydroxyl group exhibited a stronger inhibitory action against α-amylase, whereas glycosylation of the hydroxyl group weakened the inhibition (Bordenave et al., 2014; Xiao et al., 2013). Other than phenolic compounds, food micro components such as vitamins might also regulate starch digestibility. For example, the ability of specific water-soluble vitamins to inhibit α-amylase activity has been recently explored, and the kinetic responses revealed allosteric and competitive mechanisms of α-amylase inhibition for ascorbic acid and folic acid, respectively (Borah et al., 2019).

Overall, there are a broad range of structural and compositional factors that are known to influence starch digestion progress from starch-rich foods. It is therefore important that these key factors are adequately represented and/or controlled within *in vitro* digestion models used to study and predict physiological responses to starch digestion.

## 6. Critical appraisal of *in vitro* methods for starch digestion studies

The interest in developing *in vitro* digestion models to simulate the digestion of starch from foods has led to many different approaches and
protocols being suggested, with a handful of them becoming widely used, sometimes standardized, and ready to execute using conventional laboratory equipment (Fig. 2). In some cases, carbohydrates are just one of several nutrients assessed in a general digestion model, while other models focus specifically on the digestion of starch. These models differ in important factors such as whether they are static or dynamic, omitting the oral and/or gastric phase, pH, type and concentration of enzymes, duration, inclusion of electrolytes and bile salts (Fig. 2). As our

knowledge of digestion of starch-rich food has progressed, digestion models have become more advanced. At one end of the spectrum, the simpler enzyme-kinetic approaches provide well-controlled conditions and enable clear conclusions to be drawn about mechanisms, but risk not being physiologically relevant. Insights into time-dependent starch hydrolysis (i.e., amylolysis rates) during *in vitro* digestion, can only be established when following a kinetic experimental approach (Duijsens et al., 2022). While this can be achieved with static models, their

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Portfolio of relevant *in vitro* starch digestion protocols available to perform using simple instrumentation that can be used in a wide range of laboratories. This overview is not intended to be exhaustive of all protocols ever applied. Superscript numbers reference specific literature examples employing these digestion models. Boxes with green outline were correlated with glycaemic response *in vivo*. 1. Englyst et al. (1992). 2. Goñi et al. (2017). 3. Edwards et al. (2019). 4. Butterworth et al. (2022). 5. Brodkorb et al. (2019). 6. Gallego-Lobillo et al. (2021). 7. Mulet-Cabero et al. (2020). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>
physiological relevance can be limited. For more in-depth studies, that mimic human digestion more closely, semi-dynamic or, ultimately, fully dynamic approaches, that account for time-dependent changes in the digestive environment, are required. However, these models also tend to be more resource-demanding, and their complexity can present challenges and uncertainty for result interpretation. Ultimately, the choice of which model to follow should depend on the application and the use that will be given to the information obtained from the *in vitro* digestion (Duijsens et al., 2022).

## 6.1. Overview of *in vitro* methods to simulate digestion of starch in the upper GI tract

The first methods to simulate *in vitro* the digestion of starch were based on analytical techniques used to measure starch and fibre in foods, as reviewed by Woolnough et al. (2008). The first methods for measuring available (Southgate, 1969a) and unavailable (Southgate, 1969b) carbohydrates in foods were proposed in 1969. Later on, to include the starch not accounted for when applying these protocols, Englyst proposed a method (Englyst et al., 1982) where the portion of starch resistant to α-amylolysis by amylase and pullulanase was exposed to a more intense hydrolysis with amyloglucosidase, which was later on modified to more closely mimic physiological conditions (Berry, 1986). The protocols subsequently developed focused either on the classification of starch based on digestion rates or in correlations with the GI. Englyst et al. (1992) developed a classification scheme for digestible starch according to the rates of hydrolysis under controlled conditions. The protocol, which was widely adopted, introduced a classification scheme based on a combination of rapidly digestible starch (RDS) and slowly digestible starch (SDS). RDS and SDS were defined as the fraction of total starch hydrolysed after incubation with an excess of pancreatic α-amylase and amyloglucosidase at 37°C for 20 min and a further 120 min, respectively. The non-hydrolysed residue remaining after 120 min of incubation was regarded as RS. It is important to stress that RDS and SDS are not independent starch entities - these classifications were intended to reflect the rate at which glucose from starch becomes available for absorption in the human small intestine, based on *in vivo* data available at the time (Englyst et al., 1999). However, there are fundamental problems with the Englyst Classification methodology, including that it fails to control the enzyme-substrate ratio, which is a major determinant of starch amylolysis rate, and also the reliance on few time points which may not be representative of the starch digestion rate. Enzymologists therefore advocate moving away from the 'Englyst methodology' used for RSD/SDS classification and instead using more robust amylolysis assays in which kinetic parameters are derived from analyses of the full starch digestibility profile under a controlled enzyme-substrate ratio (Butterworth et al., 2022; Dhital et al., 2017). The study of starch hydrolysis in a simplified (*in vitro*) system has provided mechanistic insight into hydrolysis patterns. In this straightforward manner, substrate-enzyme interactions can be studied in a stable, simple, and static system. Moreover, the rate of amylolysis, and release of metabolites into the GI tract where they could potentially be absorbed, has been accepted to be highly relevant in the context of physiological responses such as glycaemic index (Edwards et al., 2019; Mackie et al., 2020). Indeed, well-executed amylolysis assays have been successfully used by many authors, and were found to positively correlate with the GI. For instance, Jenkins et al. observed early correlations between postprandial blood glucose response and *in vitro* starch digestion rates over 2 h using pooled human saliva and post-prandial jejunal juice on 14 foods (Jenkins, Ghafari, et al., 1982). In 1997, Goñi et al. studied the patterns of the kinetics of starch digestion using pepsin and α-amylase to hydrolyse digestible starch and a treatment with 2M KOH to solubilise resistant starch that was incubated with amyloglucosidase to produce glucose. They proposed a single exponential decay equation $C = C_{\infty}(1 - e^{-kt})$ to describe the starch hydrolysis at different times, the

total concentration digested at the equilibrium, and the kinetic constant in different foods (Goñi et al., 1997). Mathematical modelling of the first order kinetics of starch amylolysis was further used to characterize and quantify starch digestion patterns. For example, the Logarithm of Slope (LOS) method was used to identify and quantify nutritionally important starch fractions by analysing whether amylolysis was a single-phase or two-phase pseudo-first order process with two digestion rates (Edwards et al., 2014). However, it is important to note that, in some cases, starch digestion kinetics deviates from the classical first-order kinetic behaviour and alternative models are better suited (Pallares Pallares et al., 2019). Building on these enzyme-kinetic principles, simple and high-throughput amylolysis assays have since been shown to be useful both for mechanistic understanding and in the estimation and comparison of glycaemic effects of different food products (Duijsens et al., 2022; Edwards et al., 2019; Le Feunteun et al., 2021; Nguyen & Sopade, 2018). Thus, despite its simplicity, a digestion of starch-rich food material with α-amylase seems to provide a good indication of the glycaemic response. These single-enzyme amylolysis assays appear to adequately capture differences in early degradation of starch-rich foods by amylase, as occurs in the mouth or duodenum, but are limited in that they do not factor in the potential influence of other digestive processes which determine the extent of starch digestion in the upper-gastrointestinal tract.

In the last 15 years there has been an increasing interest in developing models that simulate the oral gastro-intestinal digestion of carbohydrates in humans. Many different digestion protocols have been proposed, varying in, for example, the use of different types and concentrations of enzymes, electrolytes, pH, temperature, times, mixing conditions, particle size, etc. A significant breakthrough occurred in 2014 when an international consensus was reached by the INFOGEST research network on the first standardised static *in vitro* digestion method for food (Minekus et al., 2014) which was further revised in 2019 (Brodkorb et al., 2019). The INFOGEST static method set a frame of parameters and conditions to simulate *in vitro* the bioaccessibility of nutrients, including starch, proteins and lipids, released from the digestion of food in the mouth, stomach and small intestine. It offers a robust platform for mechanistic studies as well as ranking of samples based on their amylolysis kinetics, with the advantage of allowing comparison between research facilities.

The models described so far are static, meaning that the conditions for the *in vitro* digestion are set at the beginning and there is no transient addition or removal of fluids or products. The dynamic conditions of the human GI tract can be however better simulated using semi-dynamic and dynamic models. The use and application of the (semi-) dynamic models have been reviewed by Li et al. (Li et al., 2020) and, more recently, by Sun et al. (Sun et al., 2022). Examples of semi and dynamic methods are the Dynamic Gastric Model (DGM) that mimics the gastric digestion and gastric residence time before emptying the food into the small intestine (López-Barón et al., 2018; Thuenemann et al., 2015); The Human Gastric Simulator (HGS) (Kong & Singh, 2010); The Gastric Digestion Simulator (GDS) (Kozu et al., 2014); The SHIME® with a simplified gastric and small intestine simulation followed by a colonic simulation (Van de Wiele et al., 2015); the DIDGI system (Ménard et al., 2015, pp. 73–81); the Model of an Infant Digestive Apparatus (M.I.D.A) (Passannanti et al., 2017); the TIM model (M. Minekus et al., 2014) that combines the stomach and the three parts of the small intestine, the duodenum, jejunum, and ileum with a large intestine model; and the standardized semi-dynamic *in vitro* model based on INFOGEST's international consensus (Mulet-Cabero et al., 2020). Other recent models are *In vitro* Mechanical Gastric System (IMGS), a closed system with a similar shape and dimension as a human stomach (Barros et al., 2016), or the Dynamic *In Vitro* Human Stomach (DHS-IV or DIVHS) (Wang, Wichienchot, et al., 2019).

Examples of other innovative tools for replicating human gastrointestinal functions in *in vitro* are digestion-on-chip and gut-on-chip technologies. Digestion-on-chip systems simulate enzymatic processes of digestion, allowing the study of nutrient breakdown and bioaccessibility
<table><caption>**Table 1**
Overview of the main advantages and limitations of various *in vitro* digestion models.</caption><thead><tr><th>Models</th><th>Main Advantages</th><th>Main Limitations</th></tr></thead><tbody><tr><td><strong>Single enzyme amylolysis assays</strong></td><td></td><td></td></tr><tr><td><strong>Englyst</strong><br>(H. N. Englyst et al., 1992)</td><td>- simple and widely adopted<br>- classification of RDS and SDS used in academic and industrial settings</td><td>- does not account for multiple digestive phases and the reduced number of sampling points prevents for the study of starch hydrolysis kinetics<br>- RDS and SDS derived nomenclature may not always be appropriate</td></tr><tr><td><strong>Goñi</strong><br>(Goñi et al., 1997)</td><td>- simple and widely adopted<br>- evaluates starch hydrolysis kinetics<br>- correlates with GI<br>- suitable for enzyme-specific studies</td><td>- does not account for multiple digestive phases<br>- no oral amylase and no intestinal proteases or lipases<br>- no mucosal enzymes</td></tr><tr><td><strong>Single enzyme high throughput model</strong><br>(Edwards et al., 2019)</td><td>- simple and high throughput<br>- evaluates starch hydrolysis kinetics<br>- correlates with GI<br>- suitable for enzyme-specific studies<br>- captures differences in early degradation</td><td>- does not account for multiple digestive phases or enzyme interactions<br>- no oral α-amylase, no gastric or intestinal proteases or lipases<br>- not standardised<br>- no mucosal enzymes<br>- standardised meal residence and transit times are not necessarily physiological</td></tr><tr><td><strong>Static digestion protocols</strong></td><td></td><td></td></tr><tr><td><strong>INFOGEST 2.0</strong><br>(Brodkorb et al., 2019)</td><td>- simple, widely used, affordable and scalable<br>- standardised enzyme activities and protocol which facilitate comparisons across studies<br>- includes oral, gastric and duodenal enzymes<br>- includes bile salts<br>- well-correlated with in vivo data on protein digestion<br>- ongoing adaptation to specific population groups</td><td>- no standardised mastication defined in oral phase<br>- expensive salivary α-amylase source<br>- may not accurately mimic in vivo digestion rates and conditions:<br>- enzyme activities found at end of each phase used since start<br>- standardised gastric pH might underestimate starch digestion depending on meal buffering capacity<br>- standardised meal residence and transit times are not necessarily physiological<br>- no ileal or colonic phase<br>- no absorption<br>- more tedious and expensive than high throughput models<br>- comparative studies investigating correlations with GI or other outcomes from starch digestion studies in vivo are lacking</td></tr><tr><td><strong>INFOGEST 2.0 including brush-border enzymes</strong><br>(Di Stasio et al., 2024)</td><td>- same as INFOGEST 2.0, but includes brush border membrane hydrolases</td><td>- same as INFOGEST 2.0<br>- brush border membrane enzymes are not commercially available<br>- limited standardization and characterisation of enzyme activities<br>- no ileal or colonic phase<br>- no absorption</td></tr><tr><td><strong>Semi-dynamic digestion protocols</strong></td><td></td><td></td></tr><tr><td><strong>Semi-dynamic INFOGEST</strong><br>(Mulet-Cabero et al., 2020)</td><td>- same as INFOGEST 2.0 but with a dynamic gastric phase<br>- simulates gradual gastric pH reduction (reduces the risk of underestimating starch digestion), fluid secretion and emptying</td><td>- heterogeneity may present a challenge for interpretation of analytical results in (semi)solid of digesta<br>- lower sample throughput (compared with INFOGEST 2.0)<br>- minimum amount of sample required<br>- no ileal or colonic phase</td></tr><tr><td><strong>Dynamic digestion protocols</strong></td><td></td><td></td></tr><tr><td><strong>Gastric models</strong><br>DGM (Thuenemann et al., 2015), HGS (F. Kong & Singh, 2010), GDS (Kozu et al., 2014), IMGS (Barros et al., 2016), DIVHS (J. Wang et al., 2019)</td><td>- more realistic simulation of stomach physiology<br>- mimics peristalsis, fluid mixing, and enzyme interaction<br>- reflects gastric residence time in digestion</td><td>- only gastric (stomach) simulation<br>- limited throughput and availability<br>- high setup cost and technical complexity<br>- no ileal or colonic phase</td></tr><tr><td><strong>Integrated upper and lower gut models</strong><br>SHIME (Van de Wiele et al., 2015), DiDGI (Ménard et al., 2015), MIDA (Passannanti et al., 2017), TIM (Bellmann et al., 2018; Mans Minekus, 2015)</td><td>- includes digestion in upper and lower intestinal gut<br>- closer to human physiological conditions<br>- some have been correlated with in vivo data<br>- accounts for starch fermentation by colonic microbiota</td><td>- simulation of some phases may be over-simplified<br>- microbiota based on faecal samples or synthetic microbial communities<br>- limited to specific experimental setups<br>- complex and requires costly equipment<br>- expensive and time-consuming<br>- poor simulation of motility and biofluidics</td></tr></tbody></table>

(continued on next page)
<table><caption>Table 1 (continued)</caption><thead><tr><th rowspan="2">Models</th><th rowspan="2">Main Advantages</th><th colspan="2">Main Limitations</th></tr><tr><th>- some models do not allow passage of large food structures</th><th>- limited throughput<br/>- needs validation against in vivo data<br/>- small lumen cannot accommodate larger food structures</th></tr></thead><tbody><tr><td colspan="4"><strong>Miniaturized platforms</strong></td></tr><tr><td>Digestion-on-chip<br/>(De Haan et al., 2019)</td><td>- simulates enzymatic processes in a miniaturized system<br/>- real-time monitoring of nutrient digestion and bioaccessibility<br/>- high control over parameters (e.g., flow rates, enzyme activity)</td><td>- limited throughput<br/>- needs validation against in vivo data<br/>- small lumen cannot accommodate larger food structures</td><td></td></tr><tr><td>Gut-on-chip<br/>(Taavitsainen et al., 2024)</td><td>- mimics gut physiology and microbiota interactions<br/>- study of intestinal barrier functions<br/>- can provide 3D architecture and dynamic flow<br/>- co-culture of cells (e.g., epithelial and microbiota)<br/>- high physiological relevance<br/>- suitable for mechanistic studies</td><td>- expensive and technically demanding<br/>- limited scalability and standardization<br/>- requires advanced equipment<br/>- small lumen cannot accommodate larger food structures<br/>- potential for cellular damage due to shear stress in small channels<br/>- require further validation against in vivo data</td><td></td></tr></tbody></table>

DIVHS, Dynamic. *In Vitro* Human Stomach; DGM, Dynamic Gastric Model; GDS, Gastric Digestion Simulator; HGS: Human Gastric Simulator; IMGS, *In Vitro* Mechanical Gastric System; MIDA, Model of an Infant Digestive Apparatus; SHIME, Simulator of the Human Intestinal Microbial Ecosystem.

(De Haan et al., 2019). In contrast, gut-on-chip models recreate certain aspects of the intestinal microenvironment such as three-dimensional tissue architecture, epithelial cellular diversity and/or various biochemical and mechanical cues. These systems have been used in studies of intestinal barrier function and inflammatory conditions, as well as microbiota interactions, as previously reviewed (Taavitsainen et al., 2024).

The main advantages and limitations of the different *in vitro* digestion models discussed here are summarized in Table 1. Under physiological conditions, variables such as meal transit time, digestive fluid and enzyme secretions, and enzyme/substrate ratio influence digestion progress. While *in vitro* methods are based on vast amounts of *in vivo* data (e.g., INFOGEST), they cannot possibly capture all these complexities, and therefore always entail some level of simplification of the physiological conditions. Despite their limitations, *in vitro* digestion methods are especially useful for testing and comparing starch digestion patterns of differently formulated/processed foods and, as a result, steering of rational food (digestion) design (Duijsens et al., 2022). Hence, there is a need to validate the physiological relevance of *in vitro* digestion protocols for each research question, as previously highlighted within the INFOGEST research network (Egger et al., 2017).

In addition to the type of digestion model used, another aspect worth consideration is the sample analysis method. Recent work has illustrated the potential effect of different analytical methods. Three of the most commonly used methods have been compared: two spectrophotometric methods relying on the quantification of certain functional groups as well as the chromatographic quantification of starch metabolites via HPLC-ELSD. This study, confirmed that all three methods could distinguish at the level of starch digestion rates (and thus kinetics). However, absolute amyolysis levels depended on the applied quantification method and sample-specific metabolite formation patterns (Duijsens, Staes, et al., 2024).

Overall, researchers must weigh-up the suitability of different *in vitro* protocols in the context of their research questions. In some cases, adapted (or hybrid) approaches allow obtaining specific insights into the reactions occurring during digestion. Depending on the particular research interest, simulation conditions should be selected to be as simple as possible, but as complex as needed (Palchen et al., 2021). While static digestion simulations can provide valuable insight into substrate-enzyme interactions under fixed conditions, more accurate predictions could possibly be made when digestion is simulated under more physiologically relevant, dynamic conditions. Validated methodologies that capture broader aspects of the starch digestive-breakdown are needed to enable causal relationships and mechanisms by which different starch sources influence health to be fully uncovered. By providing mechanistic insights into starch digestion, these protocols can help develop our understanding of how different food matrices, formulations and processing techniques impact starch digestibility. Standardized, time-efficient, and cost-effective protocols enabling consistent results can therefore be useful tools to allow for rapid screening and optimization of food formulations before *in vivo* testing. This makes them valuable for the food industry in developing functional foods with targeted digestion profiles.

## 6.2. Future priorities

*In vitro* digestion models are evolving continuously. Priority development areas of particular importance for digestion studies of starch-rich food matrices include: improving simulation of the oral phase and intestinal brush-border, ensuring enzyme activity is physiologically representative and standardised, simulating the dynamic nature of gastro-intestinal processing, and integrating digestion models for the upper gastrointestinal lumen with cell absorption models and/or colonic fermentation models. Further to this, the need for methodological adaptations to adequately reflect the digestive conditions of different target population groups should be carefully considered on a case-by
case basis. Finally, there is also a need to evaluate the inter-laboratory reproducibility of digestion protocols focusing on starch digestion and to validate them against *in vivo* data.

### 6.2.1. *Simulation of the oral phase*

Due to the relatively short time the food resides in the mouth, and the small percentage of starch digested at that point in most foods, some models consider the enzymatic breakdown in the oral phase negligible when analysing the digestion of starch *in vitro*. This approach is particularly adopted when it is unlikely that the mastication and mechanical breakdown of the food that occurs in the mouth would cause the immediate release of starch “encapsulated” within a food matrix or when the time that the food would remain in the mouth, mixed with salivary α-amylase, is insufficient to have an impact in the overall digestion of starch. However, as described in sections 3a and 3b, an important proportion of starch hydrolysis may occur during the oral phase as well as in the gastric phase by salivary α-amylase because of the delay before this enzyme is inactivated by the increasing stomach acidity. To investigate the true contribution of the oro-gastric phase to starch digestion, it is thus critical to consider an oral step that generates physiologically representative particle size and bolus structure, followed by a gastric phase that includes a progressive acidification. This has been demonstrated by a series of *in vitro* works showing that the exact contribution of salivary α-amylase to the overall starch hydrolysis can vary from almost 0 up to 80%, depending on the characteristics of the food/meal digested (e.g. structural properties, particle size) as well as the model employed (e.g. pH profile during gastric digestion) (Duijsens, Verkempinck, et al., 2024; Edwards et al., 2021; Freitas, et al., 2018, 2023; Freitas & Le Feunteun, 2019b; Freitas, Souchon, & Le Feunteun, 2022; Nadia et al., 2021).

As discussed in Section 3a, the mastication and the breakdown of food in the mouth is very important but particularly difficult to reproduce in any model due to the big variation in size and consistency of the particles produced by individuals with different foods (Hoebler et al., 1998). In any case, despite the difficulties, it is a critical digestive step and omitting it could result in an underestimation of the true digestive behaviour and glycaemic impact of dietary starch sources. Indeed, it was observed that swallowing food without chewing reduced its glycaemic response in humans, resulting in a comparable effect to that of viscous polysaccharides or 'slow-release' carbohydrates (Read et al., 1986). When chewing is not conditioned, factors that reduce salivary α-amylase's activity, such as the co-ingestion of low pH foods, such as vinegar or lemon juice, can also lead to an attenuated glycaemic responses in humans (Brighenti, Castellani, et al., 1995; Freitas, Boue, et al., 2022; Freitas et al., 2021; Freitas & Le Feunteun, 2019a). Moreover, other studies have shown that the size of the particles after oral processing can further influence the extent of the digestion *in vitro* (Al-Rabadi et al., 2009; Canas et al., 2020; Nadia et al., 2021) as well as the glycaemic response *in vivo* (Ranawana et al., 2010).

As previously reviewed, extensive efforts have been directed toward the development of artificial mastication systems with varying degrees of complexity (Morell et al., 2014; Peyron & Woda, 2016). However, articulation with subsequent digestive steps is lacking. Instead, the oral phase is most often simulated by grinding the food using different mechanical methods (i.e. grinder, chopper, blender, sieves) and mixing it with human salivary α-amylase (Bajka et al., 2023; Brighenti, Pellegrini, et al., 1995; Mishra et al., 2008; Nadia et al., 2021) or with commercialised pooled human saliva (Duijsens, Verkempinck, et al., 2024; Freitas, et al., 2018, 2023). This approach enables the particle size/type to be controlled, characterised and standardised, but is unlikely to represent the full complexity of particle that arise from human mastication. Some studies have also used volunteers to chew and spit the food samples when they felt it had reached the right consistency in the mouth (Camelo-Mendez, Agama-Acevedo, Rosell, de, & Bello-Perez, 2018; Pallares Pallares et al., 2019). Although human mastication overcomes the issue of replicating physiological oral conditions, the downsides are

that the use of human masticated sample may be subject to ethical regulatory approval, and the large interindividual variations can introduce new challenges with regard to standardisation, reproducibility and interpretation. Thus, although there is growing awareness of the importance of the oral phase in digestion of starch-rich foods, there is currently no consensus as to how this step should be mimicked. Therefore further work is needed to develop the best-practice for simulating the oral phase for *in vitro* digestion experiments.

### 6.2.2. *Enzyme activities in intestinal lumen and brush-border*

Another key issue central to starch digestion studies is the selection and control of enzyme activities to represent physiological conditions. The current versions of INFOGEST's static (Brodkorb et al., 2019) and semi-dynamic (Mulet-Cabero et al., 2020) protocols end at the proximal small intestine (with the replication of luminal digestive conditions in the duodenum). However, as described above, brush border enzymes in the duodenum play an important role as well, and beyond that region, starch digestion may continue into the ileum (and large intestine). A number of reviews have compared the enzymes used in different *in vitro* models (Alminger et al., 2014; Dona et al., 2010; Kopf-Bolanz et al., 2012; Le Feunteun et al., 2021; Li et al., 2020; Martinez, 2021; Versantvoort et al., 2005; Zhong et al., 2018). The quantity and activities of pancreatic α-amylases used differs across various models, although ideally, they should reflect the amounts observed *in vivo* after the ingestion of a comparable meal. However, these quantities may vary considerably depending on the individuals, their health, age, status as well as the structure and composition of the meal, region, and flow through the gastrointestinal tract during the postprandial period. Adding another layer of complexity, is the fact that different, and sometimes not thoroughly described, methodologies are used to determine amylase activities in human fluids making it impossible to compare different studies. This raises numerous questions such as how to select an appropriate amylase activity for *in vitro* digestion protocols? To what extent should amylase activity be adjusted as a function of other factors such as meal properties (e.g., volume/mass, composition, particularly the starch content) and/or relative to other enzymes present within the digesta (in order to maintain a fixed ratio between the different digestive enzymes)?

Consideration of enzyme activity applies to oral and duodenal amylase as well as brush-border enzymes. Although starch intestinal digestion by α-amylase has been proven to be the rate-limiting factor for overall glycaemic response, starch oligomers and α-limit dextrins (from α-amylase digestion), and even gelatinized native starch molecules, can be converted into absorbable glucose by the brush border α-(1,4) exoglucosidases. Glycaemic response can therefore be affected by this often disregarded mucosal phase of starch digestion (Lim et al., 2021). The physiological resemblance of the entire glucogenesis from starch could become essential in certain matrices or starch products. For example, the digestibility of starch-derived products rich in α-linkages different from α-(1,4), such as pyrodextrins (Bai & Shi, 2016), or enzymatically modified starches with a much higher relative abundance of α-1,6 (Lee et al., 2013), would depend mostly, and in some cases exclusively, on the unusual α-linkage activity of the mammalian brush border exoglucosidase activity (Lee et al., 2016).

The action of the brush border enzymes has been more elusive to replicate due to the lack of commercial enzymes available for standardised work. Some authors have reported the use of brush border membrane vesicles extracted from pigs or intestine powders from rats (Ferreira-Lazarte et al., 2017; Garcia-Campayo et al., 2018; Hernandez-Hernandez, 2019; Kondrashina et al., 2020; Le Maux et al., 2013; Sun et al., 2022), the latter either used as is (Pico et al., 2019) or after further extraction of the mucosal enzymes (Bellmann et al., 2018). Rat intestinal powder is commercially available and the hydrolysing activity of human and rat small intestinal disaccharidases has been reported (Oku et al., 2011). Gallego-Lobillo et al. (2021) investigated the potential of incorporating rat small intestinal powder during the intestinal
digestion phase of the INFOGEST 2.0 model to account for mucosal
starch digestion. They observed that adding the rat small intestinal
powder resulted in a greater extent of starch digestion. However, the
standardization of the activity of one of the four brush border exoglu-
cosidases would entail the disharmony of the other three enzymes
involved in starch digestion. Standardizing maltase activity (the one
with the highest activity) seems the most logical approach, with mean
values reported in humans of 30–500 U/g protein in mucosa and with
increasing activities towards the distal small intestine (Auricchio et al.,
1963). The physiological matching of enzyme activity would be further
challenged by the presence of enterocyte-sensing products, such as
starch and starch degradation products, which mobilize and activate
brush border enzymes in the epithelial cells (Chegeni et al., 2018; Cheng
et al., 2014; Hasek et al., 2020).

6.2.3. *Simulating dynamic gastro-intestinal processes*

Semi-dynamic and dynamic digestion models can more accurately
replicate continuous changes in different aspects of digestion such as
mixing, inflow of secretions, gastric acidification kinetics, fluctuations
in enzyme concentrations, gastric emptying, among others. This is a
critical step towards the improvement of the physiological relevance
and accuracy of such methods. Indeed, as described above, the dynamic
nature of specific digestive processes (e.g., gastric acidification kinetics
and emptying rates) can directly affect the outcomes of digestion.
Therefore, even though certain static protocols can accurately replicate
or predict endpoint measurements observed *in vivo*, in order to investi-
gate temporal profiles of starch digestion the use of semi-dynamic or
dynamic approaches is needed. Another context where such approaches
may be required is when investigating the impact of composition (e.g.
protein content) or structural components on starch digestion. In such
studies it is essential to correctly reproduce the physiological order of
hydrolyses and breakdown processes. This can be impossible with static
approaches that start with gastric hydrolysis of protein by pepsin at
acidic pH and omit the oral and/or gastric amylolysis that, *in vivo*, would
have occurred first. An important challenge with more complex pro-
tocols however, is that for each dynamic aspect that is being reproduced,
there are methodological variations among protocols. Most often, these
methods will be able to replicate only part of the dynamic elements of
digestion (e.g., gradual flow of gastric secretions but not intestinal se-
cretions), or will replicate some aspects of it more accurately than others
(e.g., rotational mixing vs. mimicking gastric walls contraction). As the
complexity of the models increases, the better the physiological condi-
tions can be simulated, but the more complicated and expensive the
models become. With increased model sophistication, accessibility often
decreases and their translational use and comparison with the results
obtained from other models also becomes less straightforward. It is
therefore essential to understand how the variations in the different
dynamic elements influence the outcomes of starch digestion studies and
their comparison with *in vivo* data. Ultimately, to support the imple-
mentation of semi-dynamic and dynamic approaches, guidance on
critical parametrization factors, including acceptable set-up variations
and operating ranges for each dynamic element, should be available.
This could be achieved through the evaluation of the performance of
existing protocols and, if necessary, adaptation of the conditions to the
specificities starch digestion processes.

6.2.4. *Integration with other models*

Another challenge for studies of starch-digestion is the articulation
between models replicating luminal digestion within the upper digestive
tract and models that replicate other processes that occur within this
region (e.g., cell absorption models) and/or models of other gastroin-
testinal regions (e.g., colonic fermentation models).

Cell absorption is routinely studied using differentiated monolayers
of the human colonic epithelial cell line, Caco-2 (Asal et al., 2024;
Kondrashina et al., 2024). This is a simple, well established approach
which yields consistent results, generally well correlated with *in vivo*

(human) data (Khorasaniha et al., 2023). To recreate intestinal micro-
environments more closely, higher complexity models can be obtained
by incorporating different cell types as recently reviewed (Asal et al.,
2024). There can be challenges, however, with the exposure of such
models to food digesta. The digestive enzymes and bile salts present in
the digesta can be harmful to the cells used in these models. Therefore, it
is important to be aware of the potential complications and how they
can be managed so that this is taken into account when designing a
combined digestion and absorption study. For a critical review of
different approaches available, the readers are referred to the work of
Kondrashina et al. (2024) where the need for a harmonized consensus
protocol or framework for in vitro studies focusing on absorption of food
components has been highlighted.

To study the relationship between the microbiota and human health,
in vitro, ex/in vivo, in silico and animal gut model systems have been used
(Williams et al., 2015). In light of the practical challenges of collecting
human gastrointestinal samples and the limitations of volunteer studies,
considerable efforts have been made to develop in vitro models. In vitro
colon models are seeded with human faecal samples or synthetic mi-
crobial communities (SynComs) to establish the microbiota of the
gastrointestinal tract (van Leeuwen et al., 2023). Using ‘microcosm’
models provides a unique opportunity to probe microbiota changes in a
controlled experiment, in real-time and over-time through dynamic
monitoring and repeated sampling. While stool offers advantages in
terms of accessibility, dietary reflection (facilitating investigation of
colonic fermentation responses), and rich microbiome information, its
limitations require consideration. However, many of these systems still
lack physiological relevance to the human body and exclude epithelial
mucosa/human derived cells, physiological shear stress, host immuno-
logical interactions, and simulated multi-organ interactions (Costa &
Ahluwalia, 2019; Williams et al., 2015). Faecal microbiota can be
viewed as the "end product" of the entire gut microbial community. The
relative abundance of microbes within faeces more closely resembles the
distal colon compared to the proximal colon (Prochazkova et al., 2023).
This raises a crucial question: are the absolute numbers of saccharolytic
bacterial groups truly similar between the proximal colon and faeces
(mirroring the distal colon), or do they only appear to differ in relative
abundance due to the enrichment of slow-growing and proteolytic
bacteria in the distal region (Isenring et al., 2023; Prochazkova et al.,
2023)? Despite these limitations, strategies exist to enhance the in vitro
representation of the proximal colon. Careful control of the initial
fermentation phase and operating conditions within continuous models
can partially rectify discrepancies (Isenring et al., 2023). Alternatively,
utilizing artificial human gut microbiota produced through intestinal
fermentation technology from a donor inoculum can offer a more ac-
curate representation (Demuth et al., 2021). The required inoculum
volume can be substantial depending on the model and experimental
design. This necessitates large amounts of starting faecal material, which
can be difficult or impossible to collect from a single donor. Conse-
quently, pooling faecal microbiota from multiple donors has been
employed to increase the inoculum volume. However, this approach
eliminates interindividual variations, creating an "artificial" community
with unpredictable competition and taxonomic balance (Isenring et al.,
2023). For a detailed review and comparison of intestinal tract models,
see Williams et al. (2015). There is currently no consensus on the
appropriate selection and operation of human gut microbiota models
and several studies have failed to include an upper-gastrointestinal
pre-digestion when studying foods or components that are known to
be digested and absorbed mainly in the upper digestive tract leading to
questions on the validity of the results produced (Isenring et al., 2023).
To avoid the risk of deficiencies in study design and result interpreta-
tion, there are number of crucial aspects that should be considered in
terms of the application, setup and operation as well as data validation
and result interpretation (Isenring et al., 2023). A physiologically rele-
vant pre-digestion step is essential to ensure that the in vitro digesta
subjected to colonic fermentation (particularly its starch content and
characteristics such as degree of starch hydrolysis and levels of struc-
tural breakdown of the food matrix) is comparable to the ileal effluent
that would reach the colon in vivo. The models that do attempt to
simulate all regions of the gastrointestinal tract, for example the SHIME,
are complex, costly, and time-consuming to run, meaning that they
cannot be as widely adopted. However, even when such models are not
accessible, a complete simulation of the upper digestive tract can be
done separately using in vitro digestion protocols such as INFOGEST's
consensus methods (Brodkorb et al., 2019; Menard, et al., 2018, 2023;
Mulet-Cabero et al., 2020) which can be followed by protocols specif-
ically designed to be used after in vitro gastrointestinal digestions
(Pérez-Burillo et al., 2021).

6.2.5. Differences between population groups

Another consideration is to what extent the digestion model repre-
sents the target population group. Starch digestion research conducted
so far has been largely based on the (generally) healthy adult model.
Widely used simplified protocols, such as the amylolysis assay proposed
by Goñi's to predict the glycaemic index in vitro (Goñi et al., 1997), have
been validated against in vivo data from healthy adults. The most
commonly used digestion protocols, e.g., INFOGEST's static and
semi-dynamic models (Brodkorb et al., 2019; Mulet-Cabero et al., 2020)
were also based on physiological data from healthy adults. However, the
digestive function of specific cohorts may deviate from this. For that
reason, digestion models are being adapted to reflect differences in
gastrointestinal conditions of different population groups according to,
for example, sex (Lajterer et al., 2022) or age, e.g., infants (Menard et al.,
2018) and older adults (Menard et al., 2023). Future work should be
carried out to compare the performance of such targeted methods in
starch digestion studies and evaluate their correlations with in vivo data
from the target populations.

6.2.6. Validation

Before any *in vitro* results could be extrapolated towards predictions
of the *in vivo* case, they should be validated by investigating the corre-
lation with *in vivo* data (Alongi & Anese, 2021; Bohn et al., 2018). In
most cases, however, this relation is difficult to find, since *in vitro* bio-
accessibility data (e.g., release of reducing sugars upon amyolysis) and
*in vivo* data (e.g., blood sugar levels) are obtained at different stages of
digestion. Nonetheless strong correlations have been found in some
cases. The inter-laboratory reproducibility of INFOGEST's static pro-
tocols applied to (dairy) proteins has been previously investigated
(Egger et al., 2016). Subsequently, validation studies have been under-
taken, with confirmation of the protocol's suitability to study protein
digestibility based on comparisons with endpoints of animal (Egger
et al., 2017) and human (Sousa et al., 2023) studies. For starch digestion,
at present, and as described above, relevance to glycaemic responses has
been demonstrated for simpler enzyme-kinetic approaches (Bellmann
et al., 2018; Edwards, et al., 2014, 2019; Goni et al., 1997; Jenkins et al.,
1982). Similarly, digestion profiles of different starch-rich foods, ob-
tained with adapted versions of INFOGEST's semi-dynamic protocol to
different models (INRAE's DiDGI and a commercially available
auto-titrator) have also shown good agreement with glycaemic re-
sponses *in vivo* (Freitas, et al., 2021, 2023; Freitas & Le Feunteun, 2019a,
2019b; Freitas, Souchon, & Le Feunteun, 2022). However,
inter-laboratory comparisons of the performance of INFOGEST's pro-
tocols applied to starch digestion are lacking. More research is war-
ranted to build an evidence base that enables a greater understanding of
the relationships between methodological specificities, results, and
physiological data. Ultimately, this will enable evidence-informed
decision-making when designing digestion studies and guide the inter-
pretation of the physiological implications of *in vitro* starch digestion
profiles.

<figure><img src="image_5.png" /><figcaption>Fig. 3. Differences in gastrointestinal conditions within and among individuals (or different population groups), as well as often disregarded physicochemical/ microbiological environments that challenge the development and use of standardized *in vitro* starch digestion models (A). Relationships between *in vitro* starch digestion models and biomarkers with potential repercussions on physiological response (B).</figcaption></figure>
## 7. General conclusion and future perspectives

Starch is a major component of the human diet but in addition to the amounts consumed, the rate of digestion and the degree to which it is digested are equally relevant from nutrition and health standpoints. Starch digestion is relevant for understanding and predicting the effect of starch-rich foods on glycaemic and endocrine responses, as well as its potential to be fermented by the lower gut microbiota. Because *in vivo* studies are often impossible due to logistical, financial or ethical constraints, *in vitro* models are frequently preferred. In addition to their simplicity and reproducibility, *in vitro* models have the additional advantage of enabling a deeper understanding of the mechanisms that characterize the digestive process of starch. In this context, this review has provided an overview of the *in vitro* methodologies available to simulate starch digestion, and an analysis of their applications, advantages and limitations as well as priority areas for future work. A key finding of this review, is that there is a wide variability in the *in vitro* protocols used and they do not always reflect physiological reality and sometimes omit elements that are an essential part of starch digestion (e. g., oral processing).

Differences in gastrointestinal conditions within and among individuals, as well as often disregarded physicochemical/microbiological environments that challenge the development and use of standardized *in vitro* starch digestion models are summarized in Fig. 3A. It is important that *in vitro* methods are sensitive enough to identify differences in the digestive profiles that, although potentially more subtle, would still be physiologically meaningful (Fig. 3B).

The enzymatic processes of starch digestion in humans are well-reported, but *in vitro* methods vary in their approach to simulating enzyme activities. Even though digestion by pancreatic α-amylase is often regarded as the most relevant step of starch digestion, starch digestion is a dynamic and complex process accomplished by the combined action of different enzymes throughout all the compartments of the digestive tract. Growing understanding of the complexity of the starch digestion process makes clear that salivary α-amylase and brush border enzymes also play a significant role, yet these enzymes are not consistently accounted for in *in vitro* digestion models. Another key challenge is the simulation of meal transit through gastro-intestinal regions, which each provide different digestive conditions and relate to different physiological effects. For instance, in the upper-small intestine, the arrival of digestion products from salivary α-amylase activity into the duodenum, together with further release of duodenal starch digestion products, drives glucose absorption kinetics and is relevant for predicting blood glucose responses. The digestive breakdown of starch in the ileum (end of small intestine), where digestive breakdown of starch transitions from being driven by mammalian to microbial enzymes, is far less understood, and an important knowledge gap with regard to studying the effects on gut hormone responses or microbial metabolite production. Although a high proportion of starch in the human diet is readily digested and absorbed in the upper small intestine, there is interest in the role of resistant starch as a fermentable fibre. The ileal stage is critical to establishing what proportion of starch has the potential to be fermented. Placing starch substrates into colonic models without upper-gastrointestinal pre-digestion and simulated absorption is unlikely to give physiologically relevant data, and there is a clear need for protocols detailing the integration between upper-gastrointestinal and colonic and/or cell-culture models.

Furthermore, the contribution of each digestive stage can be greatly influenced by numerous factors pertaining to characteristics of the individuals (e.g., level of particle size reduction reached during mastication, the amount of saliva incorporated into the food bolus, and the interindividual variability in salivary α-amylase activity saliva have all been shown to impact the oral digestibility of starch. It is important to be aware that consensus protocols that standardise digestive conditions enable reproducibility and thereby scientific progress, but will not capture interindividual diversity. The development of new models that

better represent specialised population sub-groups, for example the modification of INFOGEST to better model digestion in ageing and infant groups, provides a solution.

Starch susceptibility to digestive conditions is strongly influenced by the surrounding food matrix and other meal components, which may limit its bioaccessibility. *In vitro* models must therefore simulate the structural and biochemical breakdown of the food matrix to provide a physiologically representative indication of starch digestion. This requires both understanding of food transformation and breakdown through each physiological region, obtained for example, from human gastro-enteric intubation and/or imaging studies, as well as development of models capable of simulating both mechanical and biochemical actions. All current models have some limitations, but the inadequate and inconsistent simulation of mastication stands out as a major weakness, given its transformative effects on food structure, which is critical to subsequent digestion.

The key priorities for further development of *in vitro* protocols relevant to starch digestion are as follows.

- Development of a standardised protocol for oral-phase digestion of different food types that captures the structural and biochemical transformations that occur *in vivo*.

- Ensure that key digestive enzymes are accounted for and that transitions between digestive steps enable to accurately mimic the physiological order of enzymatic events.

- Improved understanding of human ileal conditions and develop models to simulate this region.

- Development of adapted model systems to represent the unique physiology of specific sub-populations.

- Integration between upper-gastrointestinal and lower gastrointestinal (colonic) and/or brush-border (cell-culture) models.

These future directives must be supported by continued efforts to validate *in vitro* approaches adapted to starch studies against human study data, to test their inter-laboratory reproducibility, and to develop harmonized standard protocols and recommendations that can improve the value and impact of *in vitro* starch digestion studies.

### CRediT authorship contribution statement

Daniela Freitas: Investigation, Writing – original draft, Writing - Review & Editing, Project administration. Athina Lazaridou: Investigation, Writing – original draft. Dorine Duijsens: Investigation, Writing – original draft. Kali Kotsiou: Investigation, Writing – original draft. Kendall R. Corbin: Investigation, Writing – original draft. Marilisa Alongi: Investigation, Writing – original draft. Natalia Perez-Moral: Investigation, Writing – original draft. Sebnem Simsek: Investigation, Writing – original draft. Sedef Nehir El: Investigation, Writing – original draft. Shannon Gwala: Investigation, Writing – original draft. Sibel Karakaya: Investigation, Writing – original draft. Steven Le Feunteun: Investigation, Writing – original draft. Tara Grauwet: Investigation, Writing – original draft. Mario M. Martinez: Investigation, Writing – original draft, Writing - Review & Editing. Cathrina H. Edwards: Investigation, Writing – original draft, Writing - Review & Editing.

### Funding acknowledgement

Daniela Freitas is funded by Research Ireland (previously Science Foundation Ireland and Irish Research Council) (grant number 21/ PATH-S/9418).

Athina Lazaridou and Kali Kotsiou are funded by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH—CREATE—INNOVATE (project code: T2EDK-00468).

Drs Cathrina Edwards and Natalia Perez-Moral (Quadram Institute
Bioscience, Norwich, UK) are funded by Biotechnology and Biological Sciences Research Council, UK (BBSRC) Institute Strategic Programme grants Food Innovation and Health BB/R012512/1 and its constituent project BBS/E/F/000PR10343; and BBS/E/F/00044427; BB/X011054/1 Food Microbiome and Health; and BB/X011003/1 Delivering Sustainable Wheat.

Mario M. Martinez would like to thank financial support from Independent Research Fund Denmark - Danmarks Frie Forskingsfond (Sapere Aude Starting Grant, project number 1051-00046B).

Dorine Duijsens is a fellow funded by Research Foundation Flanders, Belgium (FWO - Grant no. 12A0225N). The authors acknowledge the financial support of the KU Leuven internal fund.

Kendall R. Corbin thanks the Hatch capacity program, Accession no. 7003575, from the United States Department of Agriculture's National Institute of Food and Agriculture, for support.

## Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

The Graphical abstract and Figures were created with Biorender.com. The authors of this review are members of the INFOGEST research network "Improving health properties of food by sharing our knowledge on the digestive process". The authors would like to acknowledge Teagasc (the Irish Agriculture and Food Development Authority) for supporting the publication fees of this article.

## Data availability

No data was used for the research described in the article.

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