domain: sdv scco

text:
Léonard Guillou

Valérie Durand

Michel Raymond

Claire Berticat
email: claire.berticat@umontpellier.fr

Guillou Léonard

Durand Valérie

Raymond Michel

Berticat Claire

Chronic refined carbohydrate

Keywords: Refined carbohydrates, diet evolution, cognition, sugars, high glycemic load, energy intake

Introduction

Each animal species is adapted to a specific diet (carnivore, herbivore, etc.) through a more or less specialized digestive process. Usually, a sudden and drastic change in diet leads to health problems due to inadequate nutrition, revealing that the change has driven the population outside of their adaptive peak.

For most Western people, substantial diet changes have occurred during the second half of the 20 th century, although some changes began earlier in the century. Industrialized foods were developed, with the introduction of highly processed refined products, in particular refined carbohydrates (primary sucrose, fiber-depleted gelatinous starches, high sugar corn syrup, and sugars; [START_REF] Cordain | Origins and evolution of the Western diet: Health implications for the 21st century[END_REF]. This massive dietary change has been associated with various medical problems, such as obesity, insulin resistance, type II diabetes, cardiovascular diseases, myopia, dental caries, Alzheimer's disease, hypertension, inflammatory diseases, and cancer. [START_REF] Carrera-Bastos | The western diet and lifestyle and diseases of civilization[END_REF][START_REF] Craft | Insulin resistance and Alzheimers Disease pathogenesis: potential mechanisms and implications for treatment[END_REF][START_REF] Gentreau | Refined carbohydrate-rich diet is associated with long-term risk of dementia and Alzheimer's disease in apolipoprotein E ε4 allele carriers[END_REF][START_REF] Jellinger | Metabolic consequences of hyperglycemia and insulin resistance[END_REF][START_REF] Lustig | Public health: The toxic truth about sugar[END_REF][START_REF] Myles | Fast food fever: Reviewing the impacts of the Western diet on immunity[END_REF][START_REF] Sofi | Adherence to Mediterranean diet and health status: Meta-analysis[END_REF][START_REF] Sofi | Accruing evidence on benefits of adherence to the Mediterranean diet on health: An updated systematic review and meta-analysis[END_REF][START_REF] Thorburn | Diet, metabolites, and "Western-lifestyle" inflammatory diseases[END_REF]. The physiological mechanisms involved in these pathologies are now thought to involve in particular repeated hyperglycemia/hyperinsulinemia and insulin resistance caused by the overconsumption of refined carbohydrates [START_REF] Cordain | Hyperinsulinemic diseases of civilization: More than just Syndrome X[END_REF][START_REF] Cordain | Origins and evolution of the Western diet: Health implications for the 21st century[END_REF]. Insulin resistance is defined as an impaired biologic response to insulin stimulation of target tissues, primarily liver, muscle, and adipose tissue, but also brain.

Insulin has a pivotal function in the brain. Insulin signaling not only regulates energy metabolism, cell survival, and cellular homeostasis but also has a neuroprotective role, regulates the concentration of several neurotransmitters and supports neuronal plasticity (Y. Sun et al., 2020). Consequently, insulin resistance may directly impact cognition, as shown by the association between insulin resistance (and high glycemia) and cognitive impairment (e.g., orientation, concentration and memory) and increased risk of dementia (e.g., [START_REF] Neergaard | Metabolic syndrome, insulin resistance, and cognitive dysfunction: Does your metabolic profile affect your brain?[END_REF][START_REF] Willette | Association of insulin resistance with cerebral glucose uptake in late middle-aged adults at risk for Alzheimer Disease[END_REF]. Chronic consumption of refined carbohydrates could therefore also promote cognitive impairment via insulin resistance. In obese individuals, often characterized by long-term consumption of high glycemic load meals and insulin resistance, reduced memory and executive functioning have been consistently demonstrated [START_REF] Gunstad | Obesity is associated with memory deficits in young and middle-aged adults. Eating and Weight Disorders -Studies on Anorexia[END_REF]. In adults with type 2 diabetes, also characterized by long-term consumption of high glycemic load meals and insulin resistance, it was demonstrated that the degree of glycemic control was related to cognitive performance [START_REF] Greenwood | Carbohydrate-induced memory impairment in adults with type 2 diabetes[END_REF]. In older people, high refined carbohydrate diets have recently been identified as a potential risk factor for Alzheimer's disease and cognitive impairment, with an underlying hypothesis involving the influence of insulin resistance [START_REF] Gentreau | Refined carbohydrate-rich diet is associated with long-term risk of dementia and Alzheimer's disease in apolipoprotein E ε4 allele carriers[END_REF]. Poor vascularization of the brain due to degradation of the capillary network by chronic hyperglycemia has also been proposed to explain this phenomenon [START_REF] Shigaeff | P3-115: Cognition and cerebral neurovascular coupling in the elderly with metabolic syndrome[END_REF]. Additionally, dammage from cardiovascular disease, one of the main risk factor of which is chronic high glycemic load diet [START_REF] Jenkins | Glycemic index, glycemic load, and cardiovascular disease and mortality[END_REF], may also contribute to cognitive decline [START_REF] Deckers | Coronary heart disease and risk for cognitive impairment or dementia: Systematic review and meta-analysis[END_REF][START_REF] Eggermont | Cardiac disease and cognitive impairment: a systematic review[END_REF]. Immediate refined carbohydrate consumption could also affect cognitive performance through blood glucose levels [START_REF] Greenwood | Carbohydrate-induced memory impairment in adults with type 2 diabetes[END_REF][START_REF] Papanikolaou | Better cognitive performance following a low-glycaemic-index compared with a high-glycaemic-index carbohydrate meal in adults with type 2 diabetes[END_REF][START_REF] Young | The glycemic load of meals, cognition and mood in middle and older aged adults with differences in glucose tolerance: A randomized trial[END_REF]. Particularly, it has been shown that the way carbohydrates raise blood glucose levels during breakfast may influence the cognitive performance of participants [START_REF] Edefonti | Breakfast and behavior in morning tasks: Facts or fads[END_REF][START_REF] Ingwersen | A low glycaemic index breakfast cereal preferentially prevents children's cognitive performance from declining throughout the morning[END_REF].

To date, studies on the link between the long-term consumption of refined carbohydrates and cognition have mainly been carried out on older individuals or in a pathological context. Nevertheless, early life exposure to refined carbohydrates (i.e., prenatal, juvenile, and adolescence periods) may be particularly toxic to cognitive functioning, and neurocognitive deficits induced by a diet high in refined carbohydrates may manifest before overt obesity or metabolic disease onset (for a review see [START_REF] Hawkins | Carbohydrates and cognitive function[END_REF]. Here, we investigated whether refined carbohydrate consumption affects cognition in healthy young adults, men and women, taking into account several confounding variables and experimentally controlling for immediate carbohydrate consumption. Evaluation of refined carbohydrate consumption was based on the total glycemic load (a proxy of glycemic and insulinemic responses) of three mealtimes at higher glycemic risk (breakfast, afternoon snack and between-meal snack). Subjects were given an isocaloric high or low glycemia breakfast, they responded to a diet questionnaire and, at three different defined times, they performed a cognitive test, and their blood glucose was measured. We were thus able to address how chronic refined carbohydrate consumption can affect cognition in healthy adults.

Methods

Participants

Individuals between 20 and 30 years of age were recruited at the University of Montpellier, France, through an online form. The inclusion criteria used to participate in this experiment were the health status (nondiabetic, without food allergies) and the diet habit (usually eating breakfast). In addition, to limit cultural heterogeneity, only individuals declaring European ancestry were retained (95 participants: 48 female and 47 male).

Experimental design

The participants came in groups of three or four (one group/day) to the laboratory early in the morning and followed the procedure described below. At baseline (step 1), subject glycemia was measured (G1). Glycemia was read by a glucose meter (ACCU-CHEK Performa Nano Roche) into which a strip with a blood drop was inserted.

Blood drops were obtained with a single-use lancing pen that was self-operated by each subject. Then, they completed the Wechsler's digit symbol substitution cognitive test [START_REF] Wechsler | The psychometric tradition: Developing the Wechsler Adult Intelligence Scale[END_REF]. This test is sensitive to dementia, cortical damage and cognitive changes. It makes it possible to assess visual-motor coordination, mental and motor speed, executive functions and attention [START_REF] Hugonot-Diener | Grémoire: Tests et échelles de la maladie d'Alzheimer et des syndromes apparentés[END_REF]. Immediately following the test, one of two types of isocaloric breakfasts (500 kcal), B1 and B2, randomly chosen each day, was served to the whole group. B1 was composed of nonrefined carbohydrates (stone-ground wholemeal bread, butter, cheese, a raw fruit -orange or appleand a nonsweetened beverage -coffee, tea-without sugar available) and B2 was composed of refined carbohydrates (French baguette made from industrially milled flour, jam, fruit juice -orange or apple-and a nonsweetened beveragecoffee, tea-with sugar available). Once participants finished their breakfast, they completed a personal questionnaire and a quantitative diet questionnaire. Half an hour after the end of breakfast (step 2) and one and a half hours after breakfast (step 3), the participants' glycemia was again measured, and they undertook the Wechsler cognitive test.

Between step 2 and step 3, several morphometric variables (weight and height) were measured. Tests and questionnaires were completed on an online interface developed with PHP, HTML, MySQL and JavaScript. Time was recorded at each step, with t = 0 minutes at the first Wecshler's test. Over the two months of experimentation, breakfast B1 was served as many times as breakfast B2.

Cognitive test

A computer version of Wechsler's digit symbol substitution test was administered to participants (Supplementary Figure S1). This test has been validated in the WAIS-III (Wechsler Adult Intelligence Scale) with a sample of 1200 participants from 16 to 89 years [START_REF] Ryan | Wechsler adult intelligence scale-III[END_REF]. A list of 9 digit-symbol pairs (association rule) was displayed on the top of the screen. Below, a random digit was displayed with all the symbols in a random order. The participant had to click on the correct symbol corresponding to the displayed digit, according to the association rule above. The goal was to correctly associate as many symbols as possible with their corresponding digit during the allocated time (approximately 2 minutes). The number of correct clicks (CC; according to the specific association rule displayed on the screen) during the allocated time was recorded. To avoid learning effects, digitsymbol association rules were randomly changed between each test session.

Questionnaires

The personal questionnaire was used to collect possible confounding variables potentially affecting cognitive performance and diet: sex, age, socioeconomic status (parents homeowners, yes: 1, no: 0), and physical activity level (0 to 5).

The quantitative diet questionnaire concerning for breakfast, afternoon snack ["goûter" in French, corresponding to an after-school snack] and between-meals snack of the day before [START_REF] Slimani | Standardization of the 24-hour diet recall calibration method used in the European Prospective Investigation into Cancer and Nutrition (EPIC): General concepts and preliminary results[END_REF] was used to estimate participants' chronic refined carbohydrate consumption. The various daily meals have different nutritional compositions, and thus do not bring the same glycemic response. Indeed, carbohydrates are rarely ingested alone, and their degradation and absorption rates during digestion are modified by the other macronutrients. The glycemic response will be higher with a meal rich in refined carbohydrates and poor in fat, protein and fiber [START_REF] Hätönen | Protein and fat modify the glycaemic and insulinaemic responses to a mashed potato-based meal[END_REF][START_REF] Sun | Postprandial glucose, insulin and incretin responses differ by test meal macronutrient ingestion sequence (PATTERN study)[END_REF]. Moreover, the order of food macronutrient intake also changes glycemic and insulinemic responses (L. Sun et al., 2020). As a consequence, meals such as breakfast, afternoon snacks and between-meal snacks may be at higher glycemic risk than other meals because they are described as richer in refined carbohydrates and contain fewer food items [START_REF] Bellisle | Meals and snacking, diet quality and energy balance[END_REF][START_REF] Bellisle | Contribution of snacks and meals in the diet of French adults: A diet-diary study[END_REF][START_REF] Bellisle | Breakfast consumption in French children, adolescents, and adults: a nationally representative cross-sectional survey examined in the context of the International Breakfast Research Initiative[END_REF]. For each of these three meals, participants had to identify the foods consumed from a list of proposed items (Table S1) and their corresponding serving-size using the SU.VI.MAX cohort portion book, which allows an accurate estimation of the quantity consumed for each food with photos indicating several possible sizes [START_REF] Hercberg | The SU. VI. MAX Study: A randomized, placebo-controlled trial of the health effects of antioxidant vitamins and minerals[END_REF]. The different food groups chronically consumed during these 3 meals are shown in Table 1.

Daily Diet Variables

For each food item in the diet questionnaires, the glycemic index (GI) was calculated according to the International Tables of glycemic Index [START_REF] Foster-Powell | International table of glycemic index and glycemic load values: 2002[END_REF] and internet updates (www.glycemicindex.com), using glucose as the reference. The GI refers to the rate of glucose release by measuring the 2 h postprandial glycemia value after consumption of a food portion containing 50 g of available carbohydrates relative to 50 g of glucose consumption. Glycemic load (GL) reflects both carbohydrate quantity and quality. Compared with low-GL diets, high-GL diets elicit larger glycemic and insulinemic responses [START_REF] Foster-Powell | International table of glycemic index and glycemic load values: 2002[END_REF]. For each participant and each item, GL was calculated by multiplying the GI by the amount of available carbohydrates (g) per serving estimated from the photographs of SU.VI.MAX cohort catalog, divided by 100 [START_REF] Monro | Glycemic impact, glycemic glucose equivalents, glycemic index, and glycemic load: Definitions, distinctions, and implications[END_REF]. Foods with low carbohydrate content (e.g., meat, fats) were not assigned any glycemic load value [START_REF] Bakel | Dietary glycaemic index and glycaemic load in the European Prospective Investigation into Cancer and Nutrition[END_REF]. For each subject, the glycemic load for each item was summed, leading to a total glycemic load (Table 2) estimation for breakfast (GL1), afternoon snack (GL2) and between-meal intake (GL3).

Energy intake (EI) and macronutrients for each item were obtained from the Anses-Ciqual database (www.anses.ciqual.fr) and were calculated for each participant depending on the corresponding serving size estimated from the SU.VI.MAX cohort catalog. For each subject, they were summed, resulting in a total energy intake estimation for breakfast (EI1), afternoon snack (EI2) and between-meal intake (EI3), and corresponding macronutrient compositions (Table 2).

To measure GL independently of EI, linear models were used to produce regressions of EI as a function of GL for each meal (lm function from the R stats package version 3.6.3). These regression residuals were then used as new variables [START_REF] Willett | Adjustment for total energy intake in epidemiologic studies[END_REF]. In this way, each subject's refined residual carbohydrate consumption was described for breakfast (RGL1), afternoon snack (RGL2) and between-meal intake (RGL3). These variables represent the glycemic load part of food that is not explained by energy intake.

Statistical Analyses

Effects of immediate breakfast consumption on glycemia.

To assess the effect of breakfast type on glycemia, G1, G2 and G3 were compared across the B1 and B2 breakfast types (Table 3). At baseline (before breakfast), no significant difference in G1 glycemia was found between B1 and B2 (Wilcoxon-Mann-Whitney, P = 0.66). However, glycemia following breakfast (G2 and G3) was significantly different between B1 and B2 (Wilcoxon-Mann-Whitney: P < 10 -7 for G2, and P = 0.04 for G3).

Consumption of refined carbohydrates during breakfast thus had a significant immediate effect on glycemia (Figure 1).

Statistical analyses for cognitive performance.

Linear mixed models were used to analyze cognitive performance in relation to the chronic refined carbohydrate glycemic load of the three meals. The response variable was the number of correct clicks (CC). Subjects completed the same cognitive test several times, and so participant was a random-effect variable. The maximum random effects structure (intercept and slope) was included according to [START_REF] Barr | Random effects structure for confirmatory hypothesis testing: Keep it maximal[END_REF]. RGL1, RGL2 and RGL3

were integrated into the model as the variables of interest, and several control variables potentially affecting cognitive performance and diet were added: the type of breakfast served (B1 or B2), energy intake (EI1, EI2 and EI3), a quadratic effect of time since the first test (t and t2), sex, age, BMI (calculated as weight divided by the squared height; [START_REF] Craft | Insulin resistance and Alzheimers Disease pathogenesis: potential mechanisms and implications for treatment[END_REF][START_REF] Kim | Insulin resistance as a key link for the increased risk of cognitive impairment in the metabolic syndrome[END_REF], parents' homeowner status and physical activity level [START_REF] Donnelly | Physical activity, fitness, cognitive function, and academic achievement in children: a systematic review[END_REF][START_REF] Mikus | Lowering physical activity impairs glycemic control in healthy volunteers[END_REF]. Interactions with sex were included in the model. P < 0.10 was considered statistically significant for interaction terms. All statistical analyses were performed using R software version 3.6.3 using the package lme4 (v1.1-26; [START_REF] Bates | Fitting linear mixed-effects models using lme4[END_REF]. All quantitative variables used in the models were centered and scaled. The significance of each term was assessed from the model including all of the other variables. The variance inflation factor was computed using the vif.mer function adapted from the vif function of the R package rms (v6.2-0; [START_REF] Harrell | Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis[END_REF][START_REF] Zuur | A protocol for data exploration to avoid common statistical problems[END_REF].

Ethical Statement

The protocol used to recruit participants and collect data was approved by the French Committee of Information and Liberty (CNIL #1783997V0) and the Committee for the protection of persons (CPP IDRCB 2018-A00505-50). For each participant, the general purpose of the study was explained ("Effects of diet on major phenotypic traits"), and a written voluntary agreement was obtained for a statistical use of data (private information).

Data were analyzed anonymously.

Results

Descriptive statistics of the physical characteristics of the 95 participants (48 female and 47 male) are given in Table 4. The proportions of individuals eating an afternoon snack were 40% and 54% for males and females, respectively, and those eating a between-meal snack were 25% for both sexes (Table 1). Mean glycemic load (GL) and energy intake (EI) for each meal are indicated in Table 2. GL items are defined as "low" when the GL value is below 10 and as "high" when the value is above 20 [START_REF] Eleazu | The concept of low glycemic index and glycemic load foods as panacea for type 2 diabetes mellitus; prospects, challenges and solutions[END_REF]. GL values were within the same range of variation as those from other studies on the French population [START_REF] Berticat | Refined carbohydrate consumption and facial attractiveness[END_REF][START_REF] Gentreau | Dietary Glycemic Load and Plasma Amyloid-β Biomarkers of Alzheimer's Disease[END_REF].

Chronic refined carbohydrate consumption, chronic energy intake and the other control variables had different effects on cognitive performance (Table 5). Only the interaction term between sex and BMI had a P-value under the threshold of 0.10 and was thus kept in the final model. Between-meal refined carbohydrate consumption significantly decreased cognitive performance (RGL3, β = -1.64, se = 0.71, P = 0.02). Cognitive performance was also significantly affected by energy intake at the afternoon snack (EI2, β = -1.46, se = 0.71, P = 0.04), and a trend in the same direction was observed for energy intake at breakfast (EI1, β = -1.15, se = 0.66, P = 0.08). No effect of immediate breakfast on cognitive performance was detected (P = 0.67). A significant negative effect of BMI (β = -3.04, se = 0.89, P = 0.004) was observed which appeared to be mainly driven by men (BMI.sex, β = 2.75, se = 1.53, P = 0.07; β men = -3.04; β women = -0,29). The linear and quadratic effects of time were both statistically significant (both P < 10-11). The performance of individuals on Wechsler's digit symbol substitution test improved over time (t, β = 6.35, se = 0.60, P < 10 -15 ), as expected, due to habituation of the participants, although the association rules were changed between each test session. All other variables studied were nonsignificant. Removing an outlier for GL3 variable from initial dataset (GL3 = 110, greater than 2 standard deviations from mean, Table 2) did not qualitatively change the results.

The full model explained 31% of the total variance. The variance inflation factors (VIFs) were less than 1.9 (except for the related variables, time and squared time). The VIF values indicated that the multicollinearity between covariables was weak and not of concern [START_REF] Zuur | A protocol for data exploration to avoid common statistical problems[END_REF].

Discussion

In this study, we investigated whether chronic refined carbohydrate consumption estimated by the glycemic load of three mealtimes at higher glycemic risk (breakfast, afternoon snack and between-meal snack) was related to cognitive performance in healthy women and men. We found that cognitive performance as measured by Wechsler's digit symbol substitution test was not independent of chronic between-meals consumption of refined carbohydrates.

The higher the consumption of refined carbohydrates between meals, the lower the cognitive performance. This effect was maintained when controlling for potential confounding effects such as age, sex, BMI, physical activity, parental home ownership, chronic energy intake of the three mealtimes, and immediate consumption of refined carbohydrates.

How refined carbohydrates could affect cognition ?

It has been suggested that carbohydrate-rich diet-related alterations in metabolic processes may cause neurological injury prior to overt obesity or metabolic disease development, and that early-life exposure to refined carbohydrates may be particularly toxic to cognitive functioning (for a review, see [START_REF] Hawkins | Carbohydrates and cognitive function[END_REF]. This phenomenon could explain the relationship between the between-meal snack GL and the cognitive performance observed in our study with healthy adult participants. The decrease in cognitive performance observed could be a first biological consequence of chronic hyperinsulinemia and insulin resistance. Indeed, as mentioned earlier, betweenmeal snack is a meal with high GL foods (Table 1), and the greater the GL is, the greater the spike of glycemia and insulinemia. Thus, chronic exposure to a high-GL diet, such as between-meal snacks, could promote chronic hyperinsulinemia and insulin resistance [START_REF] Cao | Intake of sucrose-sweetened water induces insulin resistance and exacerbates memory deficits and amyloidosis in a transgenic mouse model of Alzheimer Disease *[END_REF][START_REF] Ludwig | The Carbohydrate-insulin model of obesity: Beyond "calories in, calories out[END_REF][START_REF] Mckeown | Carbohydrate nutrition, insulin resistance, and the prevalence of the metabolic syndrome in the Framingham Offspring Cohort[END_REF].

The disturbance of brain cognitive functions by chronic hyperinsulinemia and insulin resistance could be due to an alteration of brain glucose metabolism and of blood brain barrier permeability, and to a generation of oxidative stress and inflammation [START_REF] Arnold | Brain insulin resistance in type 2 diabetes and Alzheimer disease: Concepts and conundrums[END_REF][START_REF] Butterfield | Elevated risk of type 2 diabetes for development of Alzheimer disease: A key role for oxidative stress in brain[END_REF][START_REF] Ma | Insulin resistance and cognitive dysfunction[END_REF][START_REF] Monnier | Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes[END_REF]. Cerebral glucose hypometabolism has been identified in prediabetic and diabetic elderly individuals compared to nondiabetic subjects [START_REF] Baker | Insulin resistance and Alzheimer-like reductions in regional cerebral glucose metabolism for cognitively normal adults with prediabetes or early type 2 diabetes[END_REF], which can be explained either by a partial deregulation of insulin signaling decreasing glucose entry into cells produced by chronic peripheral hyperinsulinemia [START_REF] Benomar | Insulin and leptin induce Glut4 plasma membrane translocation and glucose uptake in a human neuronal cell line by a Phosphatidylinositol 3-Kinase-dependent mechanism[END_REF] or by insulin resistance at the level of the blood brain barrier leading to a decrease in brain insulin concentration and insulinmediated brain cell activity [START_REF] Arnold | Brain insulin resistance in type 2 diabetes and Alzheimer disease: Concepts and conundrums[END_REF]. In both hypotheses, the absorption and use of glucose by brain cells is altered. In parallel, insulin resistance leads to mitochondrial dysfunction that promotes the formation of free radicals, which produce excess oxidative stress [START_REF] Butterfield | Elevated risk of type 2 diabetes for development of Alzheimer disease: A key role for oxidative stress in brain[END_REF]. The damage caused by free radicals is pivotal, as insulin resistance impairs the antioxidant defense capacities of cells [START_REF] Kolb | Insulin: Too much of a good thing is bad[END_REF]. Chronic hyperglycemia can also alter proteins through a process of glycation, leading to the formation of a diverse group of modified proteins known as AGEs (Advanced Glycation End products), which are able to attack other molecules [START_REF] Butterfield | Elevated risk of type 2 diabetes for development of Alzheimer disease: A key role for oxidative stress in brain[END_REF]. In diabetic individuals, it is suggested that acute blood glucose fluctuations over daily period exhibited a more specific triggering effect on oxidative stress than sustained chronic hyperglycemia estimated by hemoglobin A1c [START_REF] Monnier | Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes[END_REF]. Finally, insulin resistance could promote inflammation. It has been shown that a high GL diet was associated with a higher concentration of CRP (C-reactive protein), a marker of systemic inflammation [START_REF] Liu | Relation between a diet with a high glycemic load and plasma concentrations of high-sensitivity C-reactive protein in middle-aged women[END_REF], and that chronic hyperglycemia, induced by a high GL diet, led to an increase in the plasma concentration of free fatty acids, which, in turn, stimulated the secretion of proinflammatory cytokines [START_REF] Butterfield | Elevated risk of type 2 diabetes for development of Alzheimer disease: A key role for oxidative stress in brain[END_REF][START_REF] Litwiniuk | Inflammasome NLRP3 potentially links obesityassociated low-grade systemic inflammation and insulin resistance with Alzheimer's Disease[END_REF].

Poor vascularization of the brain due to degradation of the capillary network by chronic hyperglycemia has also been proposed to explain the disruption of brain cognitive function in relation to refined carbohydrate consumption [START_REF] Shigaeff | P3-115: Cognition and cerebral neurovascular coupling in the elderly with metabolic syndrome[END_REF].

Why do the three chronic meals affect cognition differently?

A relationship between refined carbohydrate content and cognitive performance was observed from only one (between-meal snack) of the three meals examined. This meal is not the one with the higher mean GL and is not particularly notable for its macronutrient content, carbohydrates, proteins, fats or fibers (Table 2). A possible explanation is that these three types of meals could correspond to different ecological food habits that affect subjects differently with different physiological consequences. Between-meal snacks, relative to the other meals, are often not associated with physiological hunger and are mainly motivated by social or other external stimuli, with few resulting effects on satiety and compensation mechanisms [START_REF] Bellisle | Meals and snacking, diet quality and energy balance[END_REF]. Therefore, this meal category could better reflect chronic and acute refined carbohydrate consumption. Interestingly, chronic energy intake during the afternoon snack and breakfast also decreased cognitive performance. In the model, the effect of energy intake of each meal was estimated at equal residual glycemic load and thus represented mainly the effects of fat and protein intake. One possibility is that a large proportion of saturated fat is involved in these two meals, such as those found in pastries or dairy; Fat is known to be an antagonist of insulin and a contributor to hyperinsulinemia and insulin resistance [START_REF] Kennedy | Saturated Fatty Acid-Mediated Inflammation and Insulin Resistance in Adipose Tissue: Mechanisms of Action and Implications[END_REF], thus mimicking the potential negative effects of refined carbohydrates.

How the other control variables could affect cognition?

We controlled for several other variables potentially affecting cognition in our study, such as BMI and immediate intake of refined carbohydrates.

BMI was significantly associated with a decrease in cognitive performance in both sexes, although this effect may be driven mainly by men (Table 5). The link between BMI and cognition is well established [START_REF] Craft | Insulin resistance and Alzheimers Disease pathogenesis: potential mechanisms and implications for treatment[END_REF][START_REF] Kim | Insulin resistance as a key link for the increased risk of cognitive impairment in the metabolic syndrome[END_REF]. Studies on obese people show that a high BMI is associated with a smaller brain, which negatively affects cognition [START_REF] Raji | Brain structure and obesity[END_REF]. Other studies have demonstrated an increased risk of cognitive dysfunction or dementia in obese individuals even after adjustment for metabolic and vascular factors such as hypertension, hyperlipidemia, metabolic syndrome, insulin resistance and diabetes [START_REF] Rosengren | Body mass index, other cardiovascular risk factors, and hospitalization for dementia[END_REF][START_REF] Whitmer | Body mass index in midlife and risk of Alzheimer Disease and vascular dementia[END_REF][START_REF] Whitmer | Obesity in middle age and future risk of dementia: A 27 year longitudinal population based study[END_REF]. The mechanisms involved have not yet been elucidated, but impaired vascularization of the brain has been proposed to explain this phenomenon [START_REF] Shigaeff | P3-115: Cognition and cerebral neurovascular coupling in the elderly with metabolic syndrome[END_REF]. The lower impact of BMI on women's cognition has already been discussed [START_REF] Karlsson | The dynamic association between body mass index and cognition from midlife through late-life, and the effect of sex and genetic influences[END_REF], and there is evidence that being overweight may contribute to negative health outcomes differently in men and women [START_REF] Bohn | Body mass index predicts cognitive aging trajectories selectively for females: Evidence from the Victoria Longitudinal Study[END_REF][START_REF] Gannon | Sex differences in risk factors for vascular contributions to cognitive impairment & dementia[END_REF]. One explanation could reside in the difference in body fat distribution between men and women, which is of importance, as the location and type of fat stored affect the association between overweight and negative health outcomes [START_REF] Sulc | Heterogeneity in obesity: genetic basis and metabolic consequences[END_REF].

The immediate intake of refined carbohydrates with a high or low glycemic breakfast influenced blood sugar levels but had no impact on cognitive performance, whereas in the literature, an association between a low-GL breakfast and better cognitive performance is generally reported (for a review see [START_REF] Edefonti | Breakfast and behavior in morning tasks: Facts or fads[END_REF]. There are several explanations for this result. First, it is possible that Wechsler's digit symbol substitution test is sensitive to hypoglycemia: the brain is not supplied with optimal energy at a time when it is particularly in demand [START_REF] Gold | Role of glucose in regulating the brain and cognition[END_REF].

The duration of our experiment (1hour and 30 minutes after the end of breakfast) may have not allowed the cognitive test to be performed during the period of hypoglycemia expected for high-GL breakfast B2 at t = 1h30 -2h (consecutive to an acute insulin response to B2-induced hyperglycemia). Second, the lack of effect may be related to the fact that postprandial blood glucose levels affect the cognitive performances of young adults less, as found by Sanchez-Aguadero et al., 2018 on several cognitive domains, such as attention, and executive functions also evaluated by the Weschler cognitive test.

How can the influence of diet on health and cognition be evolutionary triggered?

The adaptive landscape of the past human environments selected for multiple genetic, behavioral and physiological traits. The transition to the industrial era rapidly reshaped these environments, resulting in evolutionary mismatch, defined here as the phenomenon by which previously adaptive traits become maladaptive in a new environment. The current mismatch between the way human physiology has evolved and Western industrialized lifestyles has been proposed to explain the current epidemic of numerous diseases. Generally, traditional food (i.e., preindustrial or nonrefined) does not generate hyperglycemia or insulin resistance. This is the case for fresh fruits, vegetables, legumes and traditionally prepared cereals. Traditional foods with a high level of sugar contents are energetically rewarding, but they are typically seasonal or scarce, such as ripened fruit and honey. In the current industrial dietary environment, foods that generate hyperglycemia, hyperinsulinemia and ultimately insulin resistance, such as refined carbohydrates, are not quantitatively limited. In addition, humans did not evolve in such a dietary environment. It is thus not surprising that this massive consumption of refined carbohydrates generates deregulation of the insulin response system [START_REF] Gunn-Moore | Alzheimer's disease in humans and other animals: A consequence of postreproductive life span and longevity rather than aging[END_REF], which now affects many aspects of health and well-being.

Limitations

Our study presents some limitations. First, the chronic effect of refined carbohydrate consumption on cognitive performance observed here could be confounded by several variables not considered in this work. The main and more complex meals (i.e., lunch and dinner), were not included, precluding the computation of an overall diet quality index, which could have captured other aspects of diet influencing cognition (e.g., high fruit and vegetable consumption, polyunsaturated fats). However, diet quality indices are correlated with low glycemic load food (higher values for an increase in low glycemic load food; [START_REF] Azadbakht | The association between dietary glycemic index, glycemic load and diet quality indices in Iranian adults: Results from Isfahan Healthy Heart Program[END_REF][START_REF] Jones | Dietary glycaemic index and glycaemic load among Australian children and adolescents: Results from the 2011-2012 Australian Health Survey[END_REF]. Thus, diet quality consumption was partially described by glycemic load measures. Second, even if the effect of diet on brain health is well documented [START_REF] Firth | Food and mood: how do diet and nutrition affect mental wellbeing?[END_REF][START_REF] Bremner | Diet, stress and mental health[END_REF], the converse is also possible as mental health, for exemple the development of psychiatric disorders, can lead to changes in eating habits [START_REF] Bremner | Diet, stress and mental health[END_REF]. Thus direction of causal effect are not straightforwardly determined. Third, the sample size of participants was relatively small, in particular that of consumers of between-meal snacks (N = 24, Table 2), with the possibility that outliers have a determinant influence. However, removing an outlier did not change qualitatively the results for RGL3. In addition, repeatedly (1000 times) randomly removing 10 % of the total dataset, or 12.5 % (N = 3) of the pool of between-meal snacks consumers, led to 94 % or 78 %, respectively, significant P-values (< 0.05) for the RGL3 variable, suggesting that the effect detected is strong enough to be detected in a smaller sample size. Nevertheless, a small sample size remains here a limitation of type 1 error. Fourth, concerning the method used to measure blood glucose for the assessment of the effect of breakfast type on glycemia: although the glucose meter was conform to the ISO 15197:2013 norm, a direct measurement of blood glucose would have been more relevant leading to more accurate results. Finally, we used a single cognitive test to assess cognitive performance. Even if the Wechsler's digit symbol substitution covers many different cognitive domains (visual-motor coordination, mental and motor speed, executive functions and attention; [START_REF] Hugonot-Diener | Grémoire: Tests et échelles de la maladie d'Alzheimer et des syndromes apparentés[END_REF], the use of other tests would have contributed additional dimensions of cognition, for example, verbal episodic memory, immediate visual memory or global cognitive function, currently used for the assessment of cognitive health and the diagnosis of neurodegeneration.

Conclusion

The recent Western dietary change, characterized mainly by the massive increase in refined carbohydrate consumption, has well-known detrimental health consequences. Given the increasing number of people affected by these pathologies and the repeated failure of many medical treatments, our study reinforces the belief that the most promising research should focus on prevention in healthy persons. Indeed, we have shown that cognition in healthy individuals is impacted by chronic refined carbohydrate consumption, indicating that neurocognitive deficits induced by this type of diet may manifest before overt obesity, metabolic disease onset or dementia. Thus, the identification of modifiable factors that could be targeted in interventions to prevent future health impairment is pivotal. Further studies are needed to investigate what other potential health-related traits might be affected by refined carbohydrate consumption in healthy people.

Figure 1 .

 1 Figure 1. Effects of immediate breakfast consumption on glycemia. Glycemia from the low (B1) and high (B2) glycemic load breakfasts are represented in blue and red, respectively.

Figure 2 .

 2 Figure 2. Forest plot of the model of the impact of RGL1, RGL2 and RGL3 and control variables on the performance on Wechsler's digit symbol substitution cognitive test. For categorical data, the estimates are for one category compared to the reference category (underlined term). * P < 0.05 ** P < 0.01 *** P < 0.001

Table 1 .

 1 Number of individuals consuming the different food groups for each meal. N indicates the number of consumers.

			Women (N = 48)		Men (N = 47)
	Food group	Breakfast (N = 48, 100%)	Afternoon snack (N = 26, 54%)	Between-meal snack (N = 12, 25%)	Breakfast (N = 47, 100%)	Afternoon snack (N = 19, 40%)	Between-meal snack (N = 12, 25%)
	Cereals and bread	36	9	2	38	7	3
	Biscuits, cakes, and pastries	9	11	4	8	11	3
	Sweets and chocolate	20	13	5	29	8	9
	Sweetened beverages	17	5	0	15	4	3
	Dairy products	31	6	3	24	8	3
	Fruits	16	13	3	13	6	4
	Nuts	5	5	2	2	0	0

Table 2 .

 2 Descriptive statistics of food consumption for the three meals. Means and standard deviations (SDs) are given for consumers only. GL1, GL2 and GL3 are the three variables representing chronic refined carbohydrate consumption.

			Women (N = 48)			Men (N= 47)
		Range	Mean	SD	Range	Mean	SD
	Breakfast
	GL1	0-92	30.4	21.8	1-143	45.3	30.5
	EI1	4-1038	377.3	245.1	8-1451	495.3	310.4
	Carbohydrates (g)	0-144	42.8 (67%)	32.2	1-224	62 (70%)	41.4
	Fat (g)	0-39	9.5 (17%)	9.4	0-55	13.4 (14%)	12.4
	Protein (g)	0-27	6.7 (11%)	5.7	0-36	9.1 (10%)	7.7
	Fiber (g)	0-17	3.7 (5%)	3.5	0-22	4.9 (5%)	4.1
	Afternoon Snack
	GL2	0-62	13.1	16.7	0-128	16.4	29.4
	EI2	0-985	184.9	244.6	0-1065	177.2	295.4
	Carbohydrates (g)	0-103	19.6 (65%)	26.4	0-126	17.9 (74%)	30.9
	Fat (g)	0-72	7.7 (19%)	13.6	0-42	3.5 (14%)	7.4
	Protein (g)	0-23	3.2 (8%)	5.0	0-30	2.5 (8%)	5.8
	Fiber (g)	0-16	2.3 (7%)	3.4	0 -10	1.3 (4%)	2.6
	Between-Meal Snack
	GL3	0-42	3.8	9.2	0-110	7.5	19.4
	EI3	0-622	63.6	149.0	0-1010	85.1	201.1
	Carbohydrates (g)	0-66	4.7 (60%)	11.5	0-111	8.7 (71%)	21.4
	Fat (g)	0-39	2.3 (24%)	6.7	0-23	1.9 (16%)	5.4
	Protein (g)	0-19	1.1 (11%)	3.3	0-22	1.1 (7%)	3.6
	Fiber (g)	0-8	0.6 (5%)	1.7	0-7	0.7 (6%)	1.7

Table 3 .

 3 Effects of breakfast type on the subject'sglycemia of the participants. The type of breakfast, mean glycemia for each, Wilcoxon test statistic (W) and corresponding P-value are given. Bold characters indicate significant (P < 0.05) effects. B1 was composed of nonrefined carbohydrates, and B2 was composed of refined carbohydrates.

	Glycemia	Type of Breakfast	Mean glycemia (mg.dL -1 )	W	P
	G1 (before breakfast)	B1	85.8	1186	0.66
		B2	85.8
	G2 (30 minutes after breakfast)	B1	117.5	1846	< 10 -7
		B2	144.3
	G3 (90 minutes after breakfast)	B1	94.5	1401	0.04
		B2	99.9

Table 4 .

 4 Descriptive statistics of the subjects' physical characteristics

			Women (N = 48)			Men (N= 47)
		Range	Mean	SD	Range	Mean	SD
	Age (years)	20.0-28.1	22.7	2.1	20.0-29.8	23.1	2.1
	BMI (kg/m²)	16.4-28.8	21.7	2.6	16.5-37.4	23.3	3.9

Table 5 .

 5 Effects of different variables studied on Wechsler's digit symbol substitution cognitive test performances. RGL1, RGL2 and RGL3 represent chronic refined carbohydrate consumption during breakfast, afternoon snack and between-meal, respectively. E1, E2 and E3 refer to energy intake during breakfast, afternoon snack and between-meal, respectively. For categorical data, the estimates are given for one category compared to the reference category (underlined term). The estimate (β), standard error of the mean (se), χ² statistic, and corresponding P-value are given.

	Bold characters indicate significant (P < 0.05) effects.
		β	se	χ²	P(>χ²)χ²)
	Intercept	62.049	1.866
	t	6.347	0.598	112.6	< 10 -15
	t 2	-3.975	0.591	45.25	< 10 -10
	RGL1	0.267	0.642	0.173	0.677
	RGL2	0.535	0.675	0.627	0.428
	RGL3	-1.639	0.706	5.389	0.020
	EI1	-1.150	0.658	3.057	0.080
	EI2	-1.457	0.712	4.183	0.040
	EI3	0.736	0.656	1.256	0.262
	Breakfast type (B1/B2)	0.561	1.309	0.184	0.668
	Age	-0.668	0.642	1.081	0.298
	BMI	-3.042	0.892	8.432	0.003
	Physical activity	0.364	0.687	0.281	0.596
	Parental homeownership (yes/no)	-0.372	1.810	0.042	0.837
	Sex (Women/Men)	1.657	1.364	0.918	0.338
	BMI.sex (women with BMI/men with BMI)	2.750	1.532	3.222	0.073
					29

Data Availability

The data and R script associated with this research are available on Zenodo repository 10.5281/zenodo.7031099.

Competing interests statement

The author(s) have declared no potential competing interests with respect to the research, authorship, and/or publication of this article.

Appendix A. Supplementary data

Supplemental data for this article can be found online.

References Supplementary Figure S1. Example from the

Wechsler's digit symbol substitution cognitive test. The participant had to click on the correct symbol corresponding to the displayed digit, according to the association rule above (Règle). Upon a choice, another randomly chosen digit is proposed, with a shuffled list of symbols (but with the same association rule). The goal was to correctly associate as many digits as possible with their corresponding symbol during the allocated time (blue bar).