**Nutrition for Brain Development**

Editor

**M. Hasan Mohajeri**

MDPI ' Basel ' Beijing ' Wuhan ' Barcelona ' Belgrade ' Manchester ' Tokyo ' Cluj ' Tianjin

*Editor* M. Hasan Mohajeri Department of Human Medicine University of Zurich Zurich Switzerland

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Nutrients* (ISSN 2072-6643) (available at: www.mdpi.com/journal/nutrients/special issues/Nutrition Brain Development).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

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© 2022 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

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## **Contents**



### **About the Editor**

#### **M. Hasan Mohajeri**

Professor M. Hasan Mohajeri is a Lecturer at the Medical Faculty of the University of Zurich, Switzerland. He has a solid publication record, with more than 110 contributions in topics related to brain development and aging, as well as the interactions between the nervous system and the gut microbiota (https://scholar.google.co.uk/citations?hl=de&user=OWHRJzEAAAAJ).

He is the co-inventor of 25+ patents. He is also a member of the Editorial board of Several journals. Dr. Mohajeri studied Biochemistry at the University of Zurich, Switzerland, and achieved his Ph.D. in Neurosciences from the Swiss Federal Institute of Technology (ETH) in Zurich, Switzerland, in 1994. He has more than 20 years of scientific and managerial working experience in large international life sciences enterprises, as well as start-up companies in Europe and the United States.

### **Preface to "Nutrition for Brain Development"**

This Special Issue focuses on factors that are implicated in the development of the brain, and thus have the potential to influence its functions. On the one hand, the brain depends on a steady and sufficient supply of oxygen and dietary ingredients for proper functioning. On the other hand, genetic predispositions and epigenetic and environmental factors may influence brain development and function. Imbalances in any of these factors may lead to the manifestation of developmental disorders at young ages, compromised daily capabilities, or age-associated brain disorders.

The goal of this Special Issue is to publish state-of-the-art contributions discussing the role of nutritional compounds, genetic factors, etc., on brain development, its functions, and aging. Mechanistic and epidemiological studies in vitro, in vivo, and in human subjects were considered for publication. The submission of original research articles, reviews, and meta-analyses was specifically encouraged. This book is of general interest to readership from medical and natural sciences interested in brain aging, the gut–brain axis, and nutrition.

> **M. Hasan Mohajeri** *Editor*

### *Editorial* **Nutrition for Brain Development**

**M. Hasan Mohajeri**

Department of Human Medicine, University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland; mhasan.mohajeri@uzh.ch; Tel.: +41-79-938-1203

This Special Issue focuses on the fundamental role of nutrition in brain development. A steady and sufficient supply of oxygen and dietary ingredients are indispensable for proper brain functioning but genetic and environmental factors may influence brain development and function. Imbalance in any of these factors may lead to the manifestation of developmental disorders of young ages, compromised daily capabilities, or age-associated brain disorders. This editorial will focus on important topics discussed in individual reports included in this Special Issue.

Granziera et al. [1] studied the associations between habitual food consumption, body mass index (BMI), and cognitive outcomes in 54 preschool children born in 2011–2014 and living in Tuscany, Italy. These authors showed, by using the Griffiths Mental Development Scales-Extended Revised (GMDS-ER) test, that adherence to the Mediterranean diet was associated with higher cognitive scores. Importantly, a high body mass index negatively impacted cognition. All associations were independent of maternal IQ, socioeconomic status, breastfeeding, actual age at cognitive assessment, and gender.

Nuthikattu and colleagues [2] showed, using a multi-omic approach, that a high glycemic diet (HGD) leads to differential expression of 608 genes in vivo. HGD affected gene expression of brain microvessels in memory centers by up-regulating the proteincoding and non-coding genes involved in mitochondrial function, oxidation, inflammation, and microvascular functioning. This report showed that inhibition of soluble epoxide hydrolase protects against cognitive decline by down-regulating the above-mentioned differentially expressed genes up-regulated by HGD.

The effects of spearmint extract (SME) and rosmarinic acid (the major component of SME) were examined on the amyloid fibril formation of αSyn, Aβ, and Tau proteins in vitro [3]. Utilizing thioflavin T (ThioT) binding assays and transmission electron microscopy (TEM), it was concluded that rosmarinic acid could disassemble preformed fibrils of αSyn, Aβ, and Tau. Given the fact that a successful therapy for neurodegenerative disorders has not been developed despite decades of intensive research [4], rosmarinic acid may be a promising candidate to be tested in disease models of amyloidosis and supports the notion that dietary ingredients may exhibit a realistic potential to improve brain functions in vivo [5].

Dietary restriction is known to profoundly affect fetal brain development. The report by Frapin et al. asked the question as to which are the cellular and molecular systems underlying the effects of maternal protein restriction (MPR) during fetal development [6]. Transcriptomic analysis of the fetal rat hypothalamus revealed that some genes encoding proteins of the mitochondrial respiratory chain were overexpressed and the mitochondrial metabolic activity in the fetal hypothalamus was altered. Collectively, this report suggests that MPR leads to early alterations of neuronal development and subsequent impaired hypothalamus function in vivo.

In their study, Gawli ´nski et al. evaluated how maternal diet determines the reinstatement of cocaine-seeking behavior and the expression of melanocortin-4 receptors in female rat offspring [7]. The authors showed that a maternal high-sugar diet is an important factor that triggers cocaine-seeking behavior in female offspring and the expression of

**Citation:** Mohajeri, M.H. Nutrition for Brain Development. *Nutrients* **2022**, *14*, 1419. https://doi.org/ 10.3390/nu14071419

Received: 25 February 2022 Accepted: 7 March 2022 Published: 29 March 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

melanocortin-4 (MC-4) receptors in the nucleus accumbens. Moreover, they suggested that an altered amount of macronutrients in the maternal diet disrupts the proper expression of MC-4 receptors in brain structures involved in cocaine relapse.

In their elaborated review, Kovacs et al. examined the potential beneficial effects of ketogenic supplements on the aging process and age-related neurodegenerative diseases. They concluded that exogenous ketogenic supplements (EKS), such as ketone salts and ketone esters, may mitigate aging processes, delay the onset of age-associated diseases and extend lifespan through ketosis. Consequently, the administration of EKS may be a potential therapeutic tool as an adjuvant therapeutics in combination with therapeutic drugs against age-related neurodegenerative diseases and increase the health span of the aging human population [8].

A collaborative effort of scientists in the United Kingdom and Italy [9] compared the test batteries, designed to monitor the effect of phenylketonuria (PKU) on cognitive performance. The parameter in the focus of this study included visual attention, visuomotor coordination, executive functions, sustained attention, verbal and visual memory, and learning. The results suggested that batteries with the same and/or matched tasks can be used to assess cognitive outcomes across countries allowing results to be compared and accrued.

Manganese (Mn) is a trace nutrient necessary for life but is toxic to the brain at high concentrations. McCabe and Zhao [10] provided an insight into the transport mechanisms of Mn through the blood–brain barrier (BBB) and the blood–CSF barrier (BCB) and its hemostasis in the brain by reviewing in vitro and in vivo models.

The potential effects of the human milk oligosaccharides (HMO) on cognitive functions were reviewed in mice, rats, and piglets [11]. The authors concluded that the administration of fucosylated (single or combined with Lacto-N-neoTetraose and other oligosaccharides) and sialylated HMOs results in marked age-dependent improvements in spatial memory and in an accelerated learning rate in operant tasks, which already become apparent during infancy. A combination of HMOs with other oligosaccharides yielded different effects on memory performance as opposed to single HMO administration, a topic that is being intensively researched.

Lastly, we evaluated in a systematic review [12] the available preclinical and clinical data on alterations of the gut microbiome, particularly on low taxonomic levels, and related them to the pathophysiology of major depressive (MDD) and bipolar disorder (BD). A discussion of diagnostic and treatment response parameters, their health-promoting potential, as well as novel adjunctive treatment options are also discussed. We also take on the task of systematically evaluating the role of the bacterial metabolites, beyond the short-chain fatty acids (SCFA), in brain development and different neurodegenerative diseases [13]. SCFA are extensively studied in various test systems, but the biology of other bacterial metabolites in health and disease is an overtly under-researched topic. Our data highlight the existence of altered bacterial metabolites in patients across various brain diseases and describe protective and detrimental effects of some bacterial metabolites in brain diseases such as autism spectrum disorder, affective disorders, multiple sclerosis, and Parkinson's disease. These findings could lead to further insights into the gut–brain axis and thus into potential diagnostic, therapeutic, or preventive strategies in brain diseases.

In conclusion, diet is of fundamental importance for the development of the brain. Given that diet directly or indirectly affects brain development and function, carefully planned and masterfully conducted basic and clinical research is needed to understand brain development better and to answer the question to which extent diet-related strategies can prevent brain disorders or be therapeutically exploited.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The author declares no conflict of interest.

#### **References**


### *Article* **Associations between the Mediterranean Diet Pattern and Weight Status and Cognitive Development in Preschool Children**

**Federico Granziera 1,2 , Maria Angela Guzzardi 1, \* and Patricia Iozzo 1**


**Abstract:** Cognitive dysfunctions are a global health concern. Early-life diet and weight status may contribute to children's cognitive development. For this reason, we explored the associations between habitual food consumption, body mass index (BMI) and cognitive outcomes in 54 preschool children belonging to the Pisa birth Cohort (PISAC). We estimated groups of foods, nutrients and calorie intakes through a food frequency questionnaire (FFQ) and Italian national databases. Then, we adopted the Mediterranean diet (MD) score to assess relative MD adherence. Cognition was examined using the Griffiths Mental Development Scales-Extended Revised (GMDS-ER). We found that higher, compared to low and moderate, adherence to MD was associated with higher performance scores. Furthermore, white meat consumption was positively related to BMI, and BMI (age–gender specific, z-scores) categories were negatively related to practical reasoning scores. All associations were independent of maternal IQ estimates, parents' socioeconomic status, exclusive/non-exclusive breastfeeding, actual age at cognitive assessment and gender. In conclusion, in preschool children, very high adherence to MD seemed protective, whereas BMI (reinforced by the intake of white meat) was negatively associated with cognition.

**Keywords:** early childhood; nutrition; Mediterranean diet; body mass index; cognitive development

#### **1. Introduction**

Cognitive decline and impairment are globally increasing health concerns, associating with a growing prevalence of metabolic diseases [1] and with population aging [2]. Effective treatment is lacking, and early prevention targeting modifiable determinants is warranted [3]. Life-course studies have shown that a lower intelligence quotient (IQ) at 11 years is already predictive of dementia seven decades later [4], suggesting that the risk is partially settled at 11 years and preventive actions should focus on younger children.

Diet and body weight are accredited lifestyle determinants of cognitive (dys)function in adults and patients [5], but few studies have explored associations between nutrients or consumption of given foods or eating habits and cognitive outcomes in children from developed countries. Some have investigated the effects of individual micronutrients, such as vitamin B12, folic acid, zinc, iron, and iodine, but findings in non-IQ-deficient children are controversial [6]. In addition, studies investigating the association of plasma biomarkers of polyunsaturated fatty acids and cognition in children have provided inconsistent results [7]. For example, a study observed a direct relationship between the proportion of docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) in blood and working memory in children [8]; instead, Boucher et al. found no associations between the proportions of DHA, EPA, or other omega-3 fatty acids in blood and working memory in children [9]. Several other studies have focused on macronutrients and whole foods [10]. For example, an observational study in 586 European children aged 7–9 years documented that consumption of two fish (including one fatty fish) meals per week reduced social, attention, and

**Citation:** Granziera, F.; Guzzardi, M.A.; Iozzo, P. Associations between the Mediterranean Diet Pattern and Weight Status and Cognitive Development in Preschool Children. *Nutrients* **2021**, *13*, 3723. https:// doi.org/10.3390/nu13113723

Academic Editor: M. Hasan Mohajeri

Received: 7 August 2021 Accepted: 17 October 2021 Published: 22 October 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

behavioural problems [11]. Another study in 5200 Canadian children aged 10–11 showed that lower fat and higher fruit and vegetable intakes were associated with better reading and writing achievements [12]. Sugar intake did not emerge as a factor affecting behaviour or cognitive performance in children in meta-analysis studies [13], but consumption of sugar-sweetened beverages was recently reported as negative predictor of higher verbal scores in 3-year-old American children [14]. Other studies found that low-glycaemic index (GI) breakfasts predicted better attention and memory, and declarative-verbal memory and high-GI breakfasts were associated with better vigilance in 6–11 and 11–14-year-old children [15,16]. Taki et al. showed that brain grey and white matter volumes were greater in children eating rice than bread at breakfast, speculating that this may depend on the lower glycaemic index of rice [17]. However, a recent systematic review concluded that there is a lack of research comparing breakfast types, precluding recommendations for the size and composition of an optimal breakfast for children's cognitive function [18].

Dietary patterns and diet quality indices have been suggested to better reflect a reallife diet, where foods are the combination of various nutrients that act synergistically and are interrelated [19,20]. These diet quality indices mostly describe a diet high in vegetables, fruit and berries, non-refined cereals products and fish and low in meats and saturated fat. Among whole-diet scores, the Healthy Eating Index (HEI-2005), Dietary Approaches to Stop Hypertension (DASH) score, Baltic Sea Diet Score (BSDS) and the Finnish Children Healthy Eating Index (FCHEI) were linked to better reading skills or cognition among 6–9-year-old children [7].

This overview highlights that the current understanding of the diet–cognitive development relationship is limited, and a great majority of studies have been focused on children who have entered school, in whom the influence of academic education may represent a relevant confounder, whereas very sparse knowledge has been produced in preschool children.

The aim of this study was to explore, comparatively, the impact of food habits, amount of ingested daily calories, macro- and micronutrient intakes, and Mediterranean diet (MD) adherence in preschool children on cognitive outcomes while taking into account the most important known confounders, i.e., maternal IQ estimate, parental socio-economic status, exclusive or non-exclusive breastfeeding, actual age at cognitive assessment and children's gender.

#### **2. Materials and Methods**

#### *2.1. Study Population*

The study was conducted in a subgroup of *n* = 54, 5-year-old preschool children of the Pisa birth Cohort (PISAC). Overall, the PISAC cohort includes 90 families—father, mother and infants born in 2011–2014 and living in Tuscany, Italy—enrolled during pregnancy to investigate the effects of maternal obesity on offspring cardiometabolic and cognitive health. The cohort was intended to represent the general population and, therefore, the inclusion criteria were broad, namely, (1) mothers within the first trimester of pregnancy at the first visit or at delivery; (2) any parents' age; (3) any BMI; (4) willingness of mothers and fathers to participate and to actively collect questionnaires and samples; (5) capacity of mothers and fathers to understand the study and its implications; (6) signature of the informed consent by mothers and fathers; (7) absence of major diseases (mothers and fathers) and perinatal complications. Exclusion criteria were (1) history of major diseases in the mother and in the father (kidney failure, liver failure, cardiac failure, major lung disease, autoimmune disease, cancer, psychiatric illness, also including anorexia–bulimia nervosa and substance abuse); (2) major health complications during the perinatal period; (3) failure to understand the study's implications, comply with the study's schedule or sign the consent form. Follow-up visits were carried out from birth to the children's age of five years and consisted of anthropometric, echocardiographic and cognitive assessments and collection of biological samples (cord–blood, faeces and saliva) and of FFQs (at 5 years). Families were given the option to participate in all or only part of the assessment visits

(0, 12, 18, 24, 36 and 60 months of life); therefore, the sample size varied across age points and measurements. In particular, families not included in this analysis did not have time to attend all 60 months' visits and chose to have cardiac or anthropometric evaluations rather than cognitive assessments or no evaluation at all. At 60 months, children's body weight (in kg to the nearest 0.1 kg) and length (in cm to the nearest 0.5 cm) were measured by weight scale and stadiometer, with children wearing light clothes and standing straight without shoes and with heels close together [21]. Then, the children's BMI (kg/m<sup>2</sup> ) was calculated, and BMI-for-age (gender-specific, z-scores) categories were defined as follows: moderately underweight (>−3 to <−2 standard deviations), normal weight (>−2 to <+1 standard deviations), overweight (>+1 to <+2 standard deviations), obesity (>+2 standard deviations) [22]. Data on parents' jobs were transformed in socioeconomic classes using the European Socio-economic Classification (ESeC) [23].

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Massa and Carrara and the latest amendments by the Ethical Committee of the Area Vasta Nord-Ovest (CEAVNO), Pisa, Italy (Study ID 394, approval decree/document n. 75 and 71512). Parents gave their written informed consent before inclusion.

#### *2.2. Food Frequency Questionnaire (FFQ)*

Dietary assessment was conducted for 65 children throughout a validated, selfadministered, semi-quantitative FFQ, with minor modifications [24]. However, the current analyses pertained to the 54 children who also underwent the cognitive visit. The FFQ consisted of 53 commonly used food items (including 124 foods) classified into 22 groups (bread, pizza, crackers and breadsticks, pasta or rice, minestrone soup with pasta or rice, barley and spelled, polenta, couscous, potatoes, eggs, fresh and processed meats, fish, cheeses, milk, yogurt, vegetables, olives, fruit and nuts, legumes, cakes and snacks, sugar and honey teaspoons added to milk and drinks and beverages). Vegetable drinks, milkshakes, wine and beer were included in the FFQ but were not consumed in our population. The type of fat (extra virgin olive (EVO) oil, olive oil, seed oil, butter, margarine, cooking cream, bacon and lard) used for preparing, cooking and dressing food was also addressed.

Frequency response categories for foods items were the following: never, less than once a month, 1–3 times a month, once a week, 2–4 and 5–6 times a week, once a day and 2–3 and 4–5 times a day. Frequency response categories for cooking and dressing fats included: always (2–3 times per day), sometimes (twice per month) and never. The parents filled in the FFQ on the child's behalf. Data were checked for completeness and consistency, considering incompleteness in >25% items as grounds for a priori exclusion [25]. All FFQs were valid to be submitted into data processing and analysis. A total of 23 specific food groups were further grouped into 13 broader categories (cereals, potatoes, eggs, red and processed meats, white meat, fish, dairy products, legumes, vegetables, fruit and nuts, cakes and snacks, sugar-sweetened drinks and cooking–dressing fats ratio) based on their nutritional content. To estimate the weekly grams consumed for each food category, we first calculated the consumptions of the 53 food items, multiplying the frequency of consumption by the age-appropriate standard portion [26], and we summed the amounts consumed of each food item belonging to the specific category, thus obtaining relative consumption. To estimate daily nutrients and calories intakes, we started by obtaining the nutritional values of the original 124 foods using the Food Composition Tables for Epidemiological Studies in Italy (BDA) [27] or the Food Composition Tables (Council for Agricultural Research and Analysis of the Agricultural Economy, CREA) [28]. The nutritional content of each food was used to calculate the mean amount/portion for each nutrient in the 53 food items. Then, we obtained the daily intake of each nutrient through the weighted sum of the consumption frequency of each food item by its amount/portion. Furthermore, the daily caloric intake was estimated through the weighted sum of each macronutrient intake by its calories. Among the 65 children, eight participants had food allergies or intolerances (i.e., to gluten, milk or lactose, tomato, chickpeas, sesame, shellfish

and egg-white), and eighteen had taken food supplements (i.e., prebiotics, probiotics, vitamins and minerals) in the last month. None of the parents declared any other major food-related illnesses affecting their children.

#### *2.3. Mediterranean Diet (MD) Score*

The degree of children's adherence to the traditional Mediterranean diet was estimated according to the score proposed by Trichopoulou et al., with a minor modification [29]. Briefly, based on median consumption values, a score of 0 or 1 was assigned to each of the following 9 food categories: vegetables, legumes, fruits and nuts, cereals, fish, red and processed meats, white meat, dairy products and the ratio of unsaturated fatty acids to saturated fatty acids (cooking–dressing fats). For dietary components that are considered protective in the MD (vegetables, legumes, fruits and nuts, cereal, fish and a high ratio of unsaturated/saturated fatty acids), a score of 0 was attributed if consumption was below the median value of the population, and 1 point was given if it was equal or above the median value. The opposite was done for the other components (i.e., red/processed and white meats and dairy products). Thus, the total MD score ranged from 0 (reflecting no adherence at all) to 9 points (maximal adherence to the traditional MD). Finally, adherence to the MD score was categorised into 4 categories: low (score 0–2), moderate (score 3–4), high (score 5–6), and very high (score 7–9).

#### *2.4. Neuropsychological Assessment*

The children's cognitive development was evaluated by a trained psychologist in a dedicated hospital room using the GMDS-ER version [30–32] in *n* = 54 of the children, addressing the following 6 cognitive domains: locomotor, personal–social, hearing and language, hand–eye coordination, performance and practical reasoning. Maternal IQ was estimated in *n* = 52 women by the Raven's progressive matrices.

#### *2.5. Statistical Analysis*

SPSS for Windows (version 26, Chicago, IL, USA) was used for statistical analysis. Regression models, such as bivariate correlation analysis, were performed to assess associations between continuous variables, and partial correlation analyses to adjust for covariates (i.e., mothers' IQ estimate, parents' ESeC, exclusive or non-exclusive breastfeeding, actual age at cognitive assessment and gender). To avoid chance findings, *p*-values were corrected for multiple comparison, using the Benjamini–Hochberg false discovery rate (Q = 0.20). General linear models and *t*-tests or two-way ANOVAs (analysis of variance) were performed for two or more than two group comparisons, and ANCOVA (analysis of covariance) was used to incorporate covariates). The results are presented as the mean ± standard deviation (SD) or standard error (SEM), and *p*-values ≤ 0.05 were established as the threshold for rejecting the null hypothesis.

#### **3. Results**

#### *3.1. Description of the Study Population*

The characteristics of the study's population are reported in Table 1. The number of boys slightly prevailed over that of girls, and nearly half of the children were exclusively breastfed in the first 6 months of life [21]. According to WHO's child growth standards [22], half of the children were modestly underweight and <20% were overweight. The parents' mean age was 39.1 ± 4.2 years for mothers and 41.7 ± 4.6 years for fathers. With reference to the ESeC 3-class model [23], parents were well distributed between working and intermediate classes, and were less represented in the salariat class. Finally, children's mean age at cognitive assessment was 5.2 ± 0.1 and all children were cognitively healthy, like their mothers [31].


**Table 1.** Characteristics of the study population.

Population characteristics are given as the mean ± SD or number and (%), as appropriate. UW = moderately underweight, NW = normal weight, OW = overweight, OB = obesity, WK = working class, IN = intermediate class, SA = salariat class and MD = Mediterranean diet.

#### *3.2. Children's Food, Calories and Nutrients Intake*

The consumption of the 13 food categories and nutritional and caloric intake are reported in Table 2. We found no difference between boys and girls, and the average weekly food consumption was mostly in line with the Guidelines for Healthy Italian Food Habits, with the exception of a lower consumption of eggs (<100 g/week) [26]. The estimated calories and (micro-)nutrients amounts were in accordance with the Nutrient and Energy Reference Intake Levels for the Italian population aged 4–6, except for vitamin D, the consumption of which was almost 90% below the recommended amount (10 µg/day) [33]. Among cooking and dressing fats, the intake of unsaturated (over saturated) fats prevailed, reflecting a predominant use of EVO, olive and seed oils.

**Table 2.** Children's weekly food groups and daily energy and nutrients intakes.



**Table 2.** *Cont.*

Continuous data are reported as the mean ± SD.

#### *3.3. Correlations between Food Categories and BMI and Cognitive Outcomes*

Relevant associations between weekly consumption of foods included in the MD score and children's BMI or cognitive outcomes are reported in Table 3. Bivariate analysis showed that the consumption of white meat was related to BMI (*p* = 0.005), unsaturated/saturated fats ratio (cooking–dressing) was related to hand–eye coordination (*p* = 0.005), dairy products were related to performance scores and vegetable consumption was associated with personal–social scores (*p* = 0.015). In addition, BMI categories were negatively related to practical reasoning score (*p* = 0.010) (Table 3). The other variables in Table 2 did not show associations with cognitive scores or BMI.

**Table 3.** Correlations between MD food categories and BMI (including BMI categories) and cognitive outcomes.


The table provides the results of the regression analysis for both the bivariate and adjusted models. Bivariate analyses were performed in *n* = 53 (list-wise deletion) and partial correlation analyses were performed in *n* = 43 (list-wise deletion); *p*-values were corrected for the false discovery rate using the Benjamini–Hochberg (Q = 0.20). \* *p* < 0.05, \*\* *p* < 0.01. Adjustment for confounders in multivariate analyses includes mothers' IQ estimate, parents' ESeC, exclusive/non-exclusive breastfeeding, actual age at cognitive assessment and gender.

Among potential confounders, we found that some intakes in children were correlated with maternal IQ and parents' ESeC or exclusive/non-exclusive breastfeeding in the first 6 months of life. In particular, maternal IQ estimate was related to children's unsaturated/saturated fats ratio intake (r = 0.367, *p* = 0.007), to hand–eye coordination scores (r = 0.305, *p* = 0.035) and practical reasoning scores (r = 0.376, *p* = 0.008); parents' ESeC was related to children's cereals intake (r = 0.320, *p* = 0.014) and to unsaturated/saturated fats ratio intake (r = 0.437, *p* = 0.001). Moreover, exclusively breastfed children consumed more potatoes than non-exclusively breastfed children (*t*-test, *p* = 0.017). In addition, the actual age (in months) at cognitive assessment was negatively related with all but two cognitive domains, hearing and language and hand–eye coordination (r = −0.326, *p* = 0.016 locomotor; r = −0.441, *p* = 0.001 personal–social; r = −0.327, *p* = 0.016 performance; r = −0.498, *p* < 0.001 practical reasoning domains). For this reason, the above correlative analyses were adjusted for these variables and for children's gender. Instead, mothers' BMI during pregnancy was not related to any dietary or cognitive outcome.

After adjustment, partial correlation analyses showed that white meat consumption was significantly related to BMI, and BMI categories remained negatively related to practical reasoning scores. Instead, associations between food categories and cognitive outcomes did not remain significant (Table 3).

#### *3.4. Adherence to MD and Cognitive Outcomes*

Continuous data did not show significance. Children were further stratified into tertiles of cognitive scores (gender-specific high, medium and low tertiles), and univariate and adjusted analyses were performed. In the unadjusted model, children with maximum adherence to the MD had higher performance scores than those with low and moderate adherence to the MD (*p* = 0.003, *p* = 0.014, respectively; *p* trend = 0.008). In the other five cognitive domains, no difference was seen between the four MD categories (data not shown). Adjustment for the relevant covariates confirmed the above results, as shown by significance levels given in Figure 1. According to Ivens et al. [31], children in the second tertile were within the average range of performance scores, and children in the third tertile were above average to the very high range. The grouping in tertiles reduced the interindividual variability effects and resulted in the detection of very high MD adherence as a significant thresholding range.

**Figure 1.** The children's performance in tertiles (low = 1<; medium = >1 to 2<; high = >2) stratified by the Mediterranean diet score categories (*n* = 44). Data are shown as mean ± SEM. \* *p* < 0.05.

#### **4. Discussion**

In the present study, we found that stricter MD adherence was positively related with scores in the performance domain in preschool children. Furthermore, children's BMI was associated with white meat consumption (positively) and with practical reasoning scores (inversely).

Among cognitively healthy school children, two cohort studies have shown that a higher quality diet was associated with better cognitive tasks. Khan et al. explored the association between inhibitory control (Kaufman Brief Intelligence Test or the Woodcock-Johnson Tests of Cognitive Abilities) and overall diet quality (HEI-2005 score) among 65 American 7–9-year-old children using three-day dietary records. They found that the HEI-2005 scores were negatively associated with response accuracy interference, suggesting greater cognitive flexibility [34]. Haapala et al. suggested that diet quality favours precocious non-verbal fluid intelligence and abstract reasoning in 428 Finnish children aged 6–8 years, in whom DASH and BSDS scores, calculated by a four-day food record, were directly associated with Raven's Coloured Progressive Matrixes scores (i.e., higher cognitive performance) [35]. In the present study, we evaluated diet quality through the use of the MD score. To our knowledge, these findings are the first evidence for the associations of adherence to the MD and cognition in preschool children, even if limited to a small population. The expectation when adopting this score was to reinforce the impact of the single components by their pooling and establish the level of MD adherence that would result into a clinically significant difference. Interestingly, a two-point MD adherence score difference has been shown to lower overall and cardiovascular mortality, and the incidence of Parkinson's and Alzheimer's diseases in the general population [29]. Though neural mechanisms underlying cognitive benefits of the MD have not been clarified [36], there is evidence that circulating levels of glucose, choline, tyrosine and tryptophan, polyunsaturated fatty acids, vitamins/minerals, antioxidants and the gut microbiota affect neurochemistry, neurotransmission and neuroprotection in the human brain [37–39].

In addition, due to the sample size's limitation, the ability to control for confounders was limited. Other important confounders may include cognitive stimulation, parental educational attainment, etc.

We aimed to dissect single food categories or macro- and micronutrients that could contribute to explaining cognitive scores in children. However, it may be difficult to separate the specific effects of single nutrients/foods because of the interactive/synergistic nature of nutrients, high intercorrelation among nutrients and foods, potential small effect of a single food or nutrient and the residual confounding by dietary patterns [8]. We applied false discovery rate corrections to our analyses and then adjusted for confounders, maximising rigor and minimising chances for significant findings. Therefore, we consider that bivariate associations surviving false discovery rate corrections retain the value of hypothesis generating observations. They suggest positive effects of high intakes of vegetables (vs. personal–social scores), dairy products (vs. performance scores) and unsaturated/saturated fats ratios (vs. hand–eye coordination scores). These correlations were not significant after introducing parental and children's confounders, which may be due to the inter-correlation, limited sample and/or lack of a direct relationship and, overall, the correlations must be interpreted in light of the risk of type II error (i.e., false negative).

We noted that the intake of vitamin D in children seemed very low. Though food is not the main source and indicator of circulating vitamin D, there are recommended daily vitamin D intake ranges, and the observation of a 90% reduction below this range in our children is consistent with the estimated 80% prevalence of vitamin D deficiency in children living in developed countries, including countries with ample sunrays [40].

As a secondary study outcome, we found an inverse association between weight status and practical reasoning scores. Childhood overweight and obesity rates have risen dramatically over the past few decades. Although obesity has been linked to poorer neurocognitive functioning in adults, much less is known about this relationship in children and adolescents [41,42]. Our finding is in line with a recent meta-analysis showing a

negative relationship between BMI and various aspects of neurocognitive function such as executive function, attention, visuo-spatial performance and motor skills in healthy children [43]. Our results strongly support these authors' statement, that longitudinal studies are urgently needed to determine the directionality of such relationships and identify the critical intervention time periods in order to develop effective treatment programs [43].

The present findings should be interpreted within the context of the study's strengths and limitations. Strengths of the present study include the assessment of children's diet with a validated dietary questionnaire, well-established outcome measures and control for several family, maternal and children's characteristics. We opted for the FFQ to reflect usual eating habits rather than short-lasting dietary exposures [44], and cognitive development was objectively measured by a trained psychologist by using the GMDS-ER test. Dietary estimations were based on a total of 124 foods using validated Italian food databases; however, given the FFQ format, we recognise that adopting standard portion sizes may be less accurate than actually measuring portions. Another important limitation is the small sample size. Therefore, our findings should be interpreted as hypothesis generating observations. We also acknowledge that there are possibly more comprehensive tests to assess cognition at the age of 5 years, but our children were followed from the age of 6 months, and we chose to preserve the same (though age-adapted) test longitudinally. We recognise that comparison with other studies is rather complex, mainly because of different methodological approaches, including dietary records, type of dietary scores/indices, control of confounding factors, type of cognitive subtests, age range of children, with very few studies addressing preschool children. Although our results incorporated information on the most known relevant confounders, we acknowledge that the ability to control for confounders could be reduced because of the sample size, and that residual confounding effects related to other unmeasured variables, such as cognitive stimulation, parental educational attainment and physical activity, may still occur. Moreover, the age at cognitive assessment had a negative association with cognitive outcomes, but we can only speculate that other unaccounted factors could be involved. Finally, the cross-sectional design cannot establish causal relationships.

#### **5. Conclusions**

The present study provides limited but novel evidence linking children's diet, weight status and cognition, suggesting potentially positive impacts of the Mediterranean diet and negative impacts of a high BMI. Our data are compatible with the hypothesis that an optimal intake of key foods, acting in synergy with the MD, may confer protection. Our hypothesis-generating findings on single foods support the conduct of larger prospective and mechanistic studies to confirm the positive relationship observed between intake of dairy products, vegetables or unsaturated (vs saturated) fats and cognitive outcomes.

**Author Contributions:** Formal analysis, writing—original draft, F.G.; data collection, supervision of analyses, writing—review and editing of the manuscript, M.A.G.; conceptualisation, funding acquisition, writing—review and editing of the manuscript, P.I. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was partly supported by the EU-FP7-HEALTH DORIAN Project: Development Origins of Healthy and Unhealthy Aging: The Role of Maternal Obesity (grant agreement No. 278603), the JPI-HDHL-INTIMIC Knowledge Platform of Food, Diet, Intestinal Microbiomics, and Human Health (sub-project No. KP-778 MISVILUPPO, Italian Ministry of Agricultural, Food, and Forestry Policies, Ministry Decree 23092/7303/19), and the JPI-HDHL-INTIMIC Joint Transnational Research program (project No. INTIMIC-085 GUTMOM, Italian Ministry of Education, University and Research, Ministry Decree No. 946/2019). Projects supported by the Joint Action "European Joint Programming Initiative: A Healthy Diet for a Healthy Life" (JPI HDHL) were funded by the respective national/regional funding organisations: Fund for Scientific Research (FRS–FNRS, Belgium); Research Foundation—Flanders (FWO, Belgium); INSERM Institut National de la Santé et de la Recherche Médicale (France); Federal Ministry of Food and Agriculture (BMEL) represented by

the Federal Office for Agriculture and Food (BLE, Germany); Ministry of Education, University and Research (MIUR), Ministry of Agricultural, Food, and Forestry Policies (MiPAAF), National Institute of Health (ISS) on behalf of the Ministry of Health (Italy); National Institute of Health Carlos III (Spain); The Netherlands Organisation for Health Research and Development (ZonMw, The Netherlands); Austrian Research Promotion Agency (FFG) on behalf of the Austrian Federal Ministry for Education, Science, and Research (BMBWF); the Ministry of Science and Technology (Israel); Formas (Sweden). The funders had no role in the study's design, data collection and analysis or preparation of the manuscript.

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Massa and Carrara and the latest amendments by the Ethical Committee of the Area Vasta Nord-Ovest (CEAVNO), Pisa, Italy (study ID 394, approval decree/document n. 75 and 71512). Parents gave their written informed consent before inclusion.

**Informed Consent Statement:** Informed consent was obtained from all parents involved in the study.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Acknowledgments:** We wish to acknowledge the families participating in the study.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Article* **Inhibition of Soluble Epoxide Hydrolase Is Protective against the Multiomic Effects of a High Glycemic Diet on Brain Microvascular Inflammation and Cognitive Dysfunction**

**Saivageethi Nuthikattu 1 , Dragan Milenkovic 1,2,3 , Jennifer E. Norman 1 , John Rutledge 1 and Amparo Villablanca 1, \***


**Abstract:** Diet is a modifiable risk factor for cardiovascular disease (CVD) and dementia, yet relatively little is known about the effect of a high glycemic diet (HGD) on the brain's microvasculature. The objective of our study was to determine the molecular effects of an HGD on hippocampal microvessels and cognitive function and determine if a soluble epoxide hydrolase (sEH) inhibitor (sEHI), known to be vasculoprotective and anti-inflammatory, modulates these effects. Wild type male mice were fed a low glycemic diet (LGD, 12% sucrose/weight) or an HGD (34% sucrose/weight) with/without the sEHI, trans-4-[4-(3-adamantan-1-yl-ureido)-cyclohexyloxy]-benzoic acid (t-AUCB), for 12 weeks. Brain hippocampal microvascular gene expression was assessed by microarray and data analyzed using a multi-omic approach for differential expression of protein and non-proteincoding genes, gene networks, functional pathways, and transcription factors. Global hippocampal microvascular gene expression was fundamentally different for mice fed the HGD vs. the LGD. The HGD response was characterized by differential expression of 608 genes involved in cell signaling, neurodegeneration, metabolism, and cell adhesion/inflammation/oxidation effects reversible by t-AUCB and hence sEH inhibitor correlated with protection against Alzheimer's dementia. Ours is the first study to demonstrate that high dietary glycemia contributes to brain hippocampal microvascular inflammation through sEH.

**Keywords:** multi-omics; microvascular; brain; dementia; high glycemic diet; soluble epoxide hydrolase inhibitor; maless

#### **1. Introduction**

Dementias are the seventh leading cause of death globally and contribute significantly to health care costs [1]. Several studies suggest that a high-fat diet or Western diet (high fat and high glycemic content) can lead to reduced cognitive function [2–6]. It has also become increasingly recognized that vasculature plays an important role in the development of dementias [7]. Our group has previously demonstrated the multi-omic and lipotoxic effect of a Western diet on the brain microvasculature and its negative consequences on cognitive function in male and female mice [8–11]. While there have been numerous mechanistic studies focusing on cognitive function with high-fat diets, few studies have explored the impact of high glycemia in the absence of high levels of dietary fat.

There is compelling epidemiological data to suggest that the effects of a high glycemic diet (HGD) on cognitive function and the brain are an important area of study. High blood glucose and high dietary glycemic load were both found to be related to poorer

**Citation:** Nuthikattu, S.; Milenkovic, D.; Norman, J.E.; Rutledge, J.; Villablanca, A. Inhibition of Soluble Epoxide Hydrolase Is Protective against the Multiomic Effects of a High Glycemic Diet on Brain Microvascular Inflammation and Cognitive Dysfunction. *Nutrients* **2021**, *13*, 3913. https://doi.org/ 10.3390/nu13113913

Academic Editor: M. Hasan Mohajeri

Received: 30 September 2021 Accepted: 29 October 2021 Published: 1 November 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

performance in perceptual speed and spatial ability [12]. An HGD has also been associated with a greater cerebral amyloid burden [13]. Further, consumption of a high glycemic index afternoon snack was associated with cognitive decline in apolipoprotein E4 allele carriers [14]. However, there is relatively sparse animal data on the effects of an HGD on the brain and cognitive function. In rats, an HGD had detrimental effects on memory [15,16], disrupted hypothalamic redox homeostasis [17], and increased hippocampal endoplasmic reticulum stress [18]. In a mouse model of Alzheimer's disease, an HGD was found to increase neuroinflammation and cortical levels of Amyloid-β [19]. These studies suggest that an HGD is associated with cognitive impairment in animal models.

The impact of high glycemia on cognition may be reversible. One study in rats found that a high sucrose diet increased brain unesterified arachidonic acid and the activity of enzymes facilitating the release of arachidonic acid from phospholipids [20]. Arachidonic acid is metabolized by cytochrome P450 enzymes to epoxyeicosatrienoic acid isomers (EETs), which are short-lasting and locally active neuroprotective, vasodilatory, and antiinflammatory [21] signaling molecules. Soluble epoxide hydrolase (sEH) is an enzyme that converts EETs into dihydroxyeicosatrienoic acids (DHETs), which have less biological activity [21,22]. Inhibiting sEH activity thus increases the amount of beneficial EETs [22]. Studies have implicated soluble epoxide hydrolase (sEH) in many disorders of the central nervous system, including Parkinson's disease, white matter hyperintensities, vascular cognitive decline/impairment, and Alzheimer's disease [22–26]. Inhibitors of sEH (sEHI) have been shown to be protective in animal models of stroke [27,28]. Inhibition of sEH is of great clinical interest as it has also been shown to reduce neuroinflammation and cognitive impairment in animal models of cerebral hypoperfusion and type 1 and type 2 diabetes mellitus [29–33]. Further, the use of sEHI and genetic knockout of the sEH gene reduces cognitive impairment in animal models of age-related cognitive decline and Alzheimer's disease [26,34,35].

The hippocampus is central to the formation of memory [36], and dysfunction of the microvasculature can contribute to the development of dementia [37]. To our knowledge, no studies have been published to date examining the effects of an HGD on brain hippocampal microvascular gene expression. The objectives of this study were to use a male murine model to comprehensively characterize the effect of an HGD on neurovascular function through hippocampal microvascular multi-omics and to assess the impact of an HGD on cognitive function. We hypothesized that an HGD would result in injurious differential gene expression changes characterized by brain hippocampal microvascular oxidation, inflammation, and blood–brain barrier disruption. Further, we aimed to determine whether the deleterious genomic effects of an HGD could be mitigated by inhibiting sEH.

#### **2. Materials and Methods**

#### *2.1. Experimental Animals and Soluble Epoxide Hydrolase Inhibitor (sEHI) Treatment*

19-week-old C57BL/6J wild type (WT) male mice (Jackson Laboratories, stock 000664) were fed a standard chow diet (catalog no 0915 from Envigo Teklad Diets, Madison, WI, USA) and allowed to acclimate for one week prior to beginning the study procedures. Mice were then fed for 12 weeks either a Low Glycemic Index diet (LGD, catalog no TD.08485 from Envigo Teklad Diets, Madison, WI, USA) composed of 13% fat, 19.1% protein, 67.9% carbohydrate, as percent kcal, containing 12% sucrose by weight, or a High Glycemic Index diet (HGD, catalog no. TD.05230, Envigo Teklad Diets, Madison, WI, USA) composed of 12.6% fat, 18.7% protein, 68.7% carbohydrate, as percent kcal, containing 34% sucrose by weight. Mice receiving each diet were given 10mg/L of soluble epoxide hydrolase inhibitor (sEHI), trans-4-[4-(3-adamantan-1-yl-ureido)-cyclohexyloxy]-benzoic acid (t-AUCB) (Cayman Chemical, Ann Arbor, MI, USA) containing 1% *v/v* polyethylene glycol 400 (PEG400) (Millipore, Burlington, MA, USA) in the drinking water for 12 weeks at which point mice were 32 weeks of age and sacrificed. Mice consumed approximately 7 to 7.5 mL of water each day, consistent with previously published work [38], and 2.5 to 3 mg of t-AUCB (sEHI) per kg per day. There were a total of four experimental treatment

groups (*n* = 7 mice/gp): LGD alone, LGD with sEHI (LGD+sEHI), HGD alone, and HGD with sEHI (HGD+sEHI). Mice were randomly assigned to the dietary groups.

Animals were housed one mouse per cage in a temperature- and humidity-controlled environment with a 12 h light/dark cycle in the University of California, Davis Mouse Biology Program. Body weight was measured at baseline and at the completion of the dietary intervention period, and activity, water, and food intake were monitored daily by vivarium staff. The research was conducted in conformity with the Public Health Service Policy on Humane Care and Use of Laboratory Animals and all protocols approved by the Institutional Animal Care and Use Committee of the University of California, Davis.

#### *2.2. Serum Lipid, Glucose, and Insulin Assays*

Mice were fasted overnight for 8 hours, and blood obtained by submandibular nick blood draw for the pre diet samples, and by ventricular puncture at the time of sacrifice following completion of the dietary feeding period for the post diet samples. Blood samples were stored at −80 ◦C. Lipid, glucose and insulin levels were measured in fasted serum samples. Total cholesterol (TC), high-density lipoprotein cholesterol (HDL), and lowdensity lipoprotein cholesterol (LDL) were measured using enzymatic assays from Fisher Diagnostics (Middleton, VA, USA), and precipitation separation from AbCam (Cambridge, MA, USA) adapted to a microplate format. Glucose was measured using enzymatic assays from Fisher Diagnostics (Middleton, VA, USA), and insulin was determined by electrochemiluminescence from Meso Scale Discovery (Rockville, MD, USA) according to the manufacturer's instructions. All serum assays were performed by the UC Davis Mouse Metabolic Phenotyping Center (MMPC) on non-pooled serum samples.

#### *2.3. Isolation and Cryosection of Murine Brain Hippocampus*

Following completion of the 12 week dietary feeding period, mice were anesthetized by intraperitoneal xylazine/ketamine and euthanized by exsanguination during the light phase of their light/dark cycle. Intact brains were rapidly removed under RNAse free conditions, cut into regions including the temporal lobe segment containing the hippocampus, and embedded using HistoPrep Frozen Tissue Embedding Media (Fisher Scientific, Pittsburgh, PA, USA). To identify the hippocampus and hippocampal neurons, brain sections in the medial aspect of the temporal lobe were stained with hematoxylin and visualized with microscopy as previously described [8]. The hippocampus was then coronally cryosectioned (8 µm, Leica Frigocut 2800n Cryostat, Leica Biosystems, Buffalo Grove, IL, USA) and placed on charged RNA-free PEN Membrane Glass slides, treated with RNAlater®-ICE (Life Technologies, Grand Island, NY, USA) to prevent RNA degradation, and stored at −80 ◦C until use. When ready for use, cryosections from the hippocampal segments were submerged in nuclease-free water and dehydrated in desiccant.

#### *2.4. Laser Capture Microdissection (LCM) of Hippocampal Microvessels*

For analysis of gene transcriptome of hippocampal brain microvessels, endothelial microvessels (<20um) were first identified in the hippocampal brain cryosections by alkaline phosphatase staining utilizing 5-bromo-4-chloro-3-indolyl phosphate/nitro blue tetrazolium chloride (BCIP/NBT) substrate as previously described [39]. Laser capture microdissection (LCM) was then used to isolate the microvascular endothelium in hippocampal cryosections by capture of the entire vessel wall under direct microscopic visualization using a Leica LMD6000 Laser Microdissection Microscope (Leica Microsystems, Wetzlar, Germany). Microvessels were not categorized by hippocampal region or subregion, although they primarily corresponded to endothelial enriched sections in hippocampus dorsal segments that would have included CA1 and CA3 regions.

#### *2.5. RNA Extraction from Laser Captured Brain Microvessels*

Total RNA was extracted from the laser-captured hippocampal brain microvessels (300 per mice, 3 mice per experimental group) using an Arcturus PicoPure™ RNA Isolation Kit (Thermo Fisher Scientific, Santa Clara, CA, USA) according to the manufacturer's instructions. The quality of the RNA from the LCM-derived vessels was assessed by Nanodrop. RNA quantification was performed according to Affymetrix RNA quantification kit with SYBR Green I and ROX™ Passive Reference Dye protocol (Affymetrix, Santa Clara, CA, USA).

#### *2.6. Microarray Hybridization and Transcriptome Analysis*

For transcriptomics analysis, we used Clariom D Mouse Array (one array per mouse), containing more than 7 million probes for protein-coding and protein non-coding genes such as micro RNAs (miRNAs), small nucleolar RNAs (snoRNAs), and long non-coding RNAs (LncRNAs) (Thermo Fisher, Santa Clara, CA, USA). RNA (122.3 pg) was used to prepare cRNA and sscDNA using GeneChip®WT Pico Kit (Thermo Fisher, Santa Clara, CA, USA). SscDNA (5.5 µg) was fragmented by uracil-DNA glycosylase (UDG) and apurinic/apyrimidinic endonuclease 1 (APE 1) and labeled by terminal deoxynucleotidyl transferase (TdT) using the DNA Labeling Reagent that is covalently linked to biotin. Fragmented and labeled sscDNA samples were then submitted to the UC Davis Genome Center shared resource core for hybridization, staining, and scanning using Thermo Fisher Scientific WT array hybridization protocol following the manufacturer's protocol. Hybridization of fragmented and labeled sscDNA samples was performed using GeneChip™Hybridization Oven 645, and samples were then washed and stained using GeneChip™ Fluidics Station 450. The arrays were scanned using GeneChip™ Scanner 3000 7G (Thermo Fisher Scientific, Santa Clara, CA, USA). Quality control of the microarrays and data analysis was performed using Thermo Fisher Scientific Transcriptome Analysis Console software version 4.0.2. We have deposited the microarray data in GEO, and the accession number is GSE185057.

#### *2.7. Bioinformatic Analysis*

Bioinformatics analysis of differentially expressed genes was performed by two of the study investigators (SN and DM) using multiple software tools. We compared the following study groups: (A) HGD to LGD (to determine the effect of the HGD diet) and (B) HGD+sEHI to HGD (to determine the effect of the inhibitor on the HGD diet) as shown in the flow chart in Figure 1.

The Principal Component Analysis (PCA) plot of identified differentially expressed genes (DEG) was obtained through ClustVis [40]. miRNA targets of DEG were identified using Mienturnet [41]. LncRNAs of DEG were identified using LncRRIsearch [42] and Rtools CBRC [43]. Canonical pathway analysis was conducted using GeneTrial2 online database [44,45]. Networks were constructed and visualized using Cytoscape software (version 3.7.1) [46–49]. Data preparation was performed with the use of several R packages including splitstackshape [50], data.table [51], dplyr [52,53] and string [54,55]. Pathway networks were built for pathways enriched from a global pathway analysis, considering all omic layers components together.

Transcription factor analyses were performed using Enrichr [56–58]. Hierarchical clustering and heat map representations of differentially expressed genes (DEG) were performed using PermutMatrix software [59,60].

We performed Pearson's correlation analysis between the genes differentially expressed by the HGD vs. LGD and the HGD+sEHI vs. HGD. We used the ggpubr package in R [61] to obtain the correlation coefficient and the significance level, as well as the scatter plot with regression line and confidence interval. We then performed prediction of neurodegenerative disease trait by performing correlation analysis between the obtained changes in the expression of genes following the HGD vs. LGD or the HGD+sEHI vs. HGD and the gene expression profiles observed in patients with Alzheimer's disease or cognitive disorders. The human genome data were extracted from the gene expression profile database, which was deposited in the GEO (gene expression omnibus) database. The identification of differentially expressed genes between patients and healthy volunteers

was performed using GEO2R [62], an NCBI web tool that allows comparisons between two or more groups of samples in the GEO series to identify differentially expressed genes across the experimental conditions. The differentially expressed genes were screened according to *p*-values < 0.05. Pearson's correlation analysis between the genes identified as differentially expressed, following the HGD vs. LGD or the HGD + sEHI vs. HGD, and patients with neurogenerative diseases was performed using ggpubr package in R [61].

**Figure 1.** Research methodology flow chart. The flow chart shows the steps in the genomic analysis for: (A) the high glycemic diet compared to the low glycemic diet (HGD vs LGD), and (B) the high glycemic diet with the soluble epoxide hydrolase inhibitor (sEHI) compared to without the sEHI (HGD + sEHI vs HGD).

Interaction of diet and inhibitor effects was performed using Thermo Fisher Scientific Transcriptome Analysis Console software version 4.0.2. Interaction was defined by a ≥2 or ≤−2 delta fold change and a *p*-value < 0.05 when comparing HGD+sEHI vs. LGD+sEHI to HGD vs. LGD.

#### *2.8. Statistical Methods*

For microarray, ANOVA ebayes (Thermo Fisher Scientific Transcriptome Analysis Console software, Santa Clara, CA, USA) were used for statistical analysis of microvessel transcriptomes. All genes from the microarray with *p* < 0.05 and ±2.0-fold change were considered as differentially expressed. Mean body weight and plasma lipid levels were expressed as means ± standard error of the mean (SEM), and significance was determined at *p* ≤ 0.05 using unpaired Student's *t*-tests (GraphPad software, La Jolla, CA, USA).

#### **3. Results**

The dietary treatment resulted in the expected weight gain in the study mice as follows: The mean weight for male mice at 20 weeks of age, on the chow diet, prior to initiation of the study diets, was 30 g and increased significantly (*p* < 0.05) after 12 weeks in both diet groups (LGD mean 36 g, HGD mean 34g). The soluble epoxide hydrolase inhibitor (sEHI) had no effect on body weight.

Total cholesterol (TC) levels at the end of the feeding period increased significantly (*p* < 0.05) in all the groups when compared to the baseline measurement at 20 weeks of age and did not statistically differ between the LGD (154.9 mg/dL) and HGD (146.2 mg/dL). The sEHI had no significant effect on total cholesterol levels.

Glucose and insulin levels also increased significantly (*p* < 0.05) in both the LGD (416.1 mg/dL) and HGD (374.7 mg/dL) treatment groups but did not statistically differ between them. The sEHI had no significant effect on glucose and insulin levels.

#### *3.1. Effect of the High Glycemic Diet on the Hippocampal Microvascular Genome*

#### 3.1.1. Global Gene Expression and Hierarchical Clustering

To define the molecular mechanisms in brain hippocampal microvessels in response to the HGD, we began by assessing global gene expression using principal component analysis (PCA), a genetic distance visualization tool that shows relatedness between populations. PCA plot analysis showed that the global gene expression profiles of mice on the HDG and LGD were distinctly different from each other (Figure 2A). Using loading plot analysis, we further defined that the genes relevant to the separation of the PCA had opposite expression patterns with the HGD vs the LGD diet. Plots for a few genes (*Higd2a*, *Gm24400*, *Snora30*, *Cox5b*, *Mir5125*, *Acta2*, and *Map3k7cl*) that contributed to separation of the two dietary groups are provided as examples in Figure 2B.

We then performed hierarchical clustering of global gene expression profiles. Hierarchical clustering groups similar data points together and then organizes the clusters into a hierarchy. Using this strategy, we further confirmed that genes with higher levels of expression with the HGD had lower levels of expression with the LGD, and vice versa (Figure 3). Thus, the effect of the HGD on global hippocampal microvascular gene expression was fundamentally opposite compared to the LGD.

#### 3.1.2. Differential Gene Expression

To study the effect of the HGD on gene expression, we compared the HGD to the LGD (flow chart Figure 1A). Statistical analysis of microarray data revealed 608 differentially expressed genes (DEGs) in hippocampal microvessels following the HGD, with the majority of the DEGs being up-regulated by the HGD (468 genes up-regulated vs. 140 genes downregulated) when compared to the LGD, Supplemental Figure S1. The fold-change varied from 2.0 to 25.94 for up-regulated genes and from −16.65 to −2.01 for down-regulated genes (see Supplemental Table S1 for a complete listing of the DEGs). To our knowledge, we also show for the first time that the HGD regulates the expression of protein-coding genes (221) and non-coding genes (78) in male murine hippocampal microvessels (Supplemental Figure S2), including 32 long non-coding RNAs (lncRNAs), 25 microRNAs (miRNAs), and 21 small nucleolar RNAs (snoRNAs). The remaining 309 DEGs were other genes (pseudogenes, ribosomal RNAs (rRNAs), unassigned genes, and multiple-complex genes).

**Figure 2.** Principal Component Analysis (PCA) and loading plot of genes expressed in hippocampal microvessels of the high glycemic diet (HGD) and the low glycemic diet (LGD). (**A**) PCA scatter plot of the microarray data shows the trends of the expression profiles of the hippocampal microvasculature in the high glycemic diet (HGD, red circles) and low glycemic diet (LGD, green circles), respectively. The PCA plot captures the variance in a dataset in terms of principal components and displays the most significant of these on the x and y axes. The percentages of the total variation that are accounted for by the 1st and 2nd principal components are shown on the x- and y-axes labels. The data are shown for three biological replicates for each dietary group. (**B**) Loading plot of genes (violet circles) relevant to the separation of PCA. Blue arrows show box plots with expression levels of a few genes (*Higd2a, Gm24400, Snora30, Cox5b, Mir5125, Acta2, Map3k7cl, Snora30* and *Map3k7cl*) that contribute to separation of the two dietary groups (HGD, LGD). In the box plots, the black dots represent gene expression levels, the notch indicates 95% confidence interval around the median of each group, and yellow diamond indicates the average gene expression of each group.

**Figure 3.** Hierarchical clustering of differentially expressed genes in hippocampal microvessels of the low glycemic diet (LGD) and the high glycemic diet (HGD). Upregulated genes are in red and downregulated genes are in blue. The data are shown for three biological replicates for each dietary group.

3.1.3. Pathways and Networks for Coding and Non-Coding Differentially Expressed Genes

− − Next, we performed bioinformatic analysis to find cellular pathways involving differentially expressed (DE) protein-coding genes. Among the 56 cellular pathways we identified were those involved in the regulation of neurodegenerative diseases (e.g., Alzheimer's disease), pathways involved in the regulation of cellular energy pathways (e.g., oxidative phosphorylation), and cellular metabolism (e.g., fatty acid metabolism). We also observed several cellular signaling pathways, cell adhesion pathways, as well as other pathways such as cell cycle or protein processing in endoplasmic reticulum (Supplemental Figure S3). Therefore, the HGD led to genomic modification in brain microvasculature pathways primarily by modulating the expression of genes involved in neurodegeneration, metabolism, and cell signaling. We also performed bioinformatics analyses of DEGs to identify potential transcription factors (TFs) whose activity could be modulated by the HGD and result in the observed genomic effects. Enrichr database analysis of the top 25 TFs and the DEGs regulated by them is shown in Supplemental Figure S4. The most statistically significant transcription factors were STAT3 (Signal Transducer and Activator of Transcription 3) involved in focal adhesion, DNMT1 (DNA Methyl Transferase 1), and PPARA (Peroxisome Proliferator-Activated Receptor Alpha) involved in Alzheimer's disease (Supplemental Table S2).

Microarray analysis also revealed that the HGD could induce differential expression of non-coding RNAs (miRNAs, lncRNAs, and snoRNAs). Of the 25 DE miRNAs in HGD vs. LGD (Supplemental Table S3), 12 were down-regulated (fold change −5.81 to −2.01) and 13 up-regulated (fold change 2.01 to 25.94). Using MIENTURNET software and database interrogation, we identified 442 potential target genes for 17 of the 25 miRNAs. The network of interactions between these miRNAs and their target genes is presented in Supplemental Figure S5. While most genes were the target of a single miRNA, we showed redundancy in that some genes were targets of two or three different miRNAs. Pathway analyses of miRNA target genes revealed they were involved in pathways regulating cell

transduction, cell–cell adhesion, permeability, and neurofunction (Supplemental Figure S6). Eleven of the miRNA target genes pathways were in common with the protein-coding DEG pathways, such as Hypoxia-inducible factor 1 (HIF-1) signaling involved in endothelial cell function. Pathways unique to miRNA targets were primarily related to inflammation and cell signaling, whereas protein-coding DEGs specific pathways were primarily involved in cellular metabolism.

Together with miRNAs, our analysis also revealed 32 DE lncRNAs (Supplemental Table S4) following the HGD. Among these, 4 were down-regulated (fold-change −16.65 to −2.02), and 28 were up-regulated (fold-change 2.02 to 5.34). Using LncRRIsearch and Rtools CBRC databases, we were able to identify 458 potential target genes for 5 of the 32 lncRNAs (Supplemental Figure S7). Pathway analysis of these target genes showed that they were involved in pathways such as nitric oxide signaling, N-cadherin that regulate vascular endothelial function, as well as Alzheimer's disease (Supplemental Figure S8), which was also one of the 7 pathways in common with the miRNA targets pathways.

We also studied the expression of snoRNAs in the HGD compared to LGD. Among the 21 DE snoRNAs, 9 were up-regulated (fold change 2.36 to 19.44), and 12 were downregulated (fold change −10.37 to −2.07) (Supplemental Table S5). A literature review did not identify any known target genes for the DE snoRNAs.

#### 3.1.4. Integrated Analysis of Differentially Expressed Genes, Key Pathways and Networks

Following the individual omic analysis, we performed integrated analysis of all the identified DEGs including mRNAs, miRNAs and their targets, lncRNAs and their targets, and the identified potential transcription factors. This analysis allowed us to obtain networks of interactions (Figure 4A), and showed that in comparison to the LGD, the HGD significantly impacted the expression levels of different RNA types which interact and form a large molecular network. This molecular network can have a significant effect on cellular functions. Therefore, in order to determine if there was a pattern of functional coordination in the molecular differential expression pattern of the HGD on brain microvessels, we performed integrated pathway and network analysis using DE mRNAs, miRNAs and lncRNAs targets for the HGD compared to LGD. This analysis indeed revealed differential regulation of 5 key cellular pathways including for neurodegenerative diseases (such as Alzheimer's disease), cell signaling pathways (such as PPAR signaling and phosphoinositide-3-kinase-protein kinase B (PI3K-Akt) signaling), cell adhesion and mobility (including focal adhesion), cellular metabolism (including oxidative phosphorylation and electron transport chain), and other cellular pathways (such as mRNA processing and oxidative damage) (Figure 4B and 4C). Integrated pathways for DEGs, transcription factors, miRNAs and their targets, and lncRNAs and their targets are shown for the focal adhesion pathway (Figure 4D) and the Alzheimer's disease pathway (Figure 4E). Pathways are discussed in further detail in the Discussion section.

#### *3.2. Effect of the Soluble Epoxide Hydrolase Inhibitor (sEHI) on the Hippocampal Microvascular Genome of Mice fed the High Glycemic Diet*

In order to determine whether the soluble epoxide hydrolase inhibitor (sEHI) could inhibit the seemingly deleterious molecular effects of the HGD on hippocampal microvessels (upregulation of genes in pathways such as PPAR signaling, PI3K-Akt signaling that play an important role in oxidative stress, inflammation and Alzheimer's disease), we again performed hierarchical clustering of global gene expression profiles for the LGD and the HGD in the presence of the inhibitor (Figure 5). Since the gene expression profile of the LGD group did not substantially differ from the LGD with the inhibitor (Figure 5), the effect of the inhibitor was primarily analyzed in reference to the HGD in our analytic comparisons. Interestingly, in the presence of the sEHI, the gene expression profile of the HGD on hippocampal microvessels was nearly completely reversed and similar to that on the LGD.

Using PCA analysis, we were further able to show that the gene expression profile of hippocampal microvessels following the HGD was distinctly different from that of the **A)**

HGD+sEHI (Figure 6A). We used loading plot to identify genes important to the separation of PCA (Figure 6B). Genes such as *Gm24400, Resp18, Rheb, Brms1l, Ndufa13,* and *Ighv5-12-4* showed opposite expression in the HGD with inhibitor when compared to the HGD alone.

Statistical analysis of microarray data showed that there were a larger number of DEGs (1701) for the comparison of the HGD+sEHI vs. HGD (Supplemental Table S6) than there were for the HGD vs. LGD comparison. The sEHI primarily down-regulated both proteincoding and non-coding DEGs in the HGD (Figure 7A, and Supplemental Figures S9 and S10). We then performed correlation analysis between the DEGs of the HGD+sEHI vs. HGD and the HGD vs. LGD and identified a highly significant negative correlation with the sEHI (Figure 7B). This suggests that the inhibitor counteracts the effects of the HGD on differential gene expression.

**Figure 4.** *Cont*.

26

**Figure 4.** *Cont*.

**C)** 

**Figure 4.** *Cont*.

**Figure 4.** Effect of the high glycemic diet (HGD) on cellular pathways of differentially expressed protein-coding genes, miRNA targets, and LncRNA targets in the hippocampal microvasculature. (**A**) Network of differentially expressed protein-coding genes (grey circles), transcription factors (red hexagons), miRNAs (green diamonds) and their targets (blue circles), LncRNAs (purple rectangles) and their targets (blue circles) in the hippocampal microvessels for the high glycemic diet (HGD) compared to the low glycemic diet (LGD). (**B**) Histogram of a subset of the relevant pathways of differentially expressed protein-coding genes, miRNA targets, and LncRNA targets in the hippocampal microvasculature for the HGD vs LGD. The data are shown for three biological replicates for each dietary group. Statistically significant pathways (*p* < 0.05) were identified using Genetrial2 online database and grouped by cellular function. (**C**) Network of important cellular pathways shown in (**B**) and their genes. Pathways are shown in boxes and color coded based on cellular function such as neuro-related (red), cell signaling (green), cell adhesion and mobility (purple), cellular metabolism (yellow), and other cellular pathways (grey). White circles are differentially expressed genes (DEGs) or target genes of miRNAs and lncRNAs. Integrated analysis of (**D**) Focal adhesion and (**E**) Alzheimer's disease pathways. Blue=DEGs with potential transcription factors (TFs); Red= differentially expressed miRNAs and their targets; Green= differentially expressed lncRNAs and their targets.

**Figure 5.** Hierarchical clustering of differentially expressed genes in hippocampal microvessels for the low glycemic diet (LGD) and the high glycemic diet (HGD) with and without soluble epoxide hydrolase inhibitor (sEHI). Hierarchical clustering of differentially expressed gene profiles in hippocampal microvessels of four experimental treatment groups: low glycemic diet (LGD), LGD with soluble epoxide hydrolase inhibitor (LGD+sEHI), high glycemic diet (HGD), and HGD with sEHI (HGD+sEHI). The data are shown for three biological replicates for each dietary group. Up-regulated genes are in red, and down-regulated genes are in blue. In the presence or absence of inhibitor, LGD groups had similar gene expression profiles while HGD groups had opposite gene expression profiles.

Most of the protein-coding genes down-regulated by the sEHI were involved in similar pathways (such as Alzheimer's disease, oxidative phosphorylation, and fatty acid metabolism) activated by the HGD alone (Supplemental Figure S11), suggesting that the inhibitor may offset the effect of the HGD on differential gene expression by targeting similar pathways. We also found 17 TFs in common between the HGD with and without inhibitors such as NEUROG3 (Neurogenin 3), MECP2 (Methyl-CpG Binding Protein 2), KLF9 (Kruppel Like Factor 9), and PPARA (Supplemental Table S2). Target genes of these TFs were up-regulated by the HGD but down-regulated by the inhibitor (Supplemental Figure S12). These target genes were involved in neurological disease pathways (e.g., Alzheimer's disease), oxidative phosphorylation (e.g., electron transport chain), apoptosis-related pathways (e.g., proteasome degradation), and cell signaling pathways (e.g., HF-1 signaling).

**Figure 6.** Principal Component Analysis (PCA) and loading plot of genes expressed in hippocampal microvessels for the high glycemic diet (HGD) with and without soluble epoxide hydrolase inhibitor (sEHI). (**A**) PCA scatter plot of the microarray data shows the trends of the expression profiles of the hippocampal microvasculature of the high glycemic diet with (HGD+sEHI, green circles) and without (HGD, red circles) the soluble epoxide hydrolase inhibitor compared to the low glycemic diet (LGD, blue circles). The PCA plot captures the variance in a dataset in terms of principal components and displays the most significant of these on the x and y axes. The percentages of the total variation that are accounted for by the 1st and 2nd principal components are shown on the x- and y-axes labels. The data are shown for three biological replicates for each dietary group. (**B**) Loading plot of genes (violet circles) relevant to the separation of PCA. Blue arrows show box plots with the expression levels of a few genes (*Gm24400, Resp18, Rheb, Brms1l, Ndufa13, Ighv5-12-4*) that contribute to separation of the three dietary groups (HGD, HGD+sEHI, LGD). In the box plots, the black dots represent gene expression levels, the notch indicates 95% confidence interval around the median of each group, and the yellow diamond indicates the average gene expression of each group.

**Figure 7.** *Cont*.

**Figure 7.** The effect of the high glycemic diet with and without soluble epoxide hydrolase inhibitor (sEHI) on differential gene expression in the hippocampal microvasculature. (**A**) Heat map showing individual differentially expressed genes (DEGs) in rows, and the two different experimental dietary comparison groups in columns, as follows: column 1: high glycemic diet compared to low glycemic diet (HGD vs LGD); column 2: HGD with soluble epoxide hydrolase inhibitor compared to without inhibitor (HGD+sEHI vs HGD). The data are shown for three biological replicates for each dietary group. Up-regulated genes are shown in red, and down-regulated genes in green. The rectangular box insert shows the list of genes with opposite gene expression patterns in the two comparisons (HGD vs. LGD compared to HGD+sEHI vs. HGD). (**B**) Pearson's correlation analysis of the DEGs between the comparisons, HGD vs. LGD and HGD+sEHI vs. HGD.

A higher number of DE non-coding RNAs (171 miRNAs, 127 snoRNAs, and 80 lncRNAs) were modulated by the sEHI (Supplemental Tables S3, S4, and S5, respectively) than the HGD alone. Most of the DE miRNAs that were up-regulated by the HGD were downregulated by the inhibitor by targeting 300 genes (Supplemental Figure S13) that were involved in apoptosis-related pathways (such as PTEN dependent cell cycle arrest and Ubiquitin mediated proteolysis) (Supplemental Figure S14) and pathways related to cell degradation and death. The majority of DE lncRNAs that were up-regulated by the HGD were down-regulated by the inhibitor and again targeted a greater number of genes (785) compared to the HGD alone (Supplemental Figure S15) and were mainly involved in cell signaling (Supplemental Figure S16).

We then performed an integrated analysis of all the identified DEGs, including mR-NAs, miRNAs and their targets, lncRNAs and their targets, and the potential TFs. We obtained a large molecular network of interactions of the different types of RNAs modulated by the inhibitor (Figure 8A). Integrated pathway analysis of this molecular network (Figure 8B) revealed that the inhibitor regulated similar pathways as the HGD, namely neuro-related pathways (such as Alzheimer's disease), cell signaling, cell adhesion (such as focal adhesion), and cellular metabolism, but once again, in opposite directions to the HGD. In toto, these data suggest that the inhibitor may counteract the effect of an HGD by robustly reversing the deleterious pattern of differential gene expression on cellular function and on the operative TFs, particularly by down-regulating the protein-coding and non-protein-coding DEGs that are up-regulated by the HGD.

#### *3.3. Correlation of Genomics with Human Alzheimer's Disease Data*

To evaluate the genomic modifications induced by the HGD on the development of neurodegenerative diseases, we analyzed the correlations between the genes modulated by the HGD and the genes associated with Alzheimer's disease. We analyzed the available GEO datasets with GEO2R and differentially expressed mRNAs were correlated with those extracted from the analyzed publications using the R program. These analyses showed that the gene expression profile in the brain of patients with Alzheimer's disease (GSE118553) was correlated, with high statistical significance, to the gene expression profile identified in our mice after the consumption of the HGD (Figure 9A). The gene expression profile of mice on the HGD in our study was also correlated with the gene expression profile obtained in another study (GSE132903) of brains of patients presenting with Alzheimer's disease (Figure 9B). This analysis suggests that consumption of the HGD induces changes in the expression of genes in murine hippocampus that correlates with the development of Alzheimer's disease in humans. Using the same approach, we also tested correlations between the genes modulated in our study by the HGD in the presence of the sEHI and the gene expression profiles obtained in both of the above noted genomic studies using the brains of patients with Alzheimer's disease. Interestingly, we observed a negative correlation between the human AD data and the gene expression profile of our brain microvasculature for animals on the HGD with the sEHI (Figure 9C,D). These results suggest that the inhibitor can prevent, at least partially, changes in the expression of genes induced by the HGD that are associated with the development of Alzheimer's disease in humans and can therefore exert a potentially protective effect against this neurological disorder.

**Figure 8.** *Cont*.

**B)**

**Figure 8.** Effect of the high glycemic diet (HGD) with soluble epoxide hydrolase inhibitor (sEHI) on cellular pathways of differentially expressed protein-coding genes, miRNA targets, and LncRNA targets in the hippocampal microvasculature. (**A**) Network of differentially expressed protein-coding genes (grey circles), transcription factors (red hexagons), miRNAs (green diamonds) and their targets (blue circles), LncRNAs (purple rectangles) and their targets (blue circles) in the hippocampal microvessels for the high glycemic diet (HGD) with soluble epoxide hydrolase inhibitor (sEHI) when compared to HGD without sEHI. (**B**) Histogram of subset of relevant pathways of differentially expressed protein-coding genes, miRNA targets, and LncRNA targets in the hippocampal microvasculature for the HGD+sEHI vs HGD. The data are shown for three biological replicates for each dietary group. Statistically significant pathways (*p* < 0.05) were identified using Genetrial2 online database and are grouped by cellular function such as neuro-related, cell signaling, cell adhesion and mobility, cellular metabolism, and other.

**Figure 9.** Correlation of the high glycemic diet (HGD) with and without inhibitor to Alzheimer's disease. Pearson correlation analysis between the genes modulated by the HGD vs LGD and the gene expression profiles observed in patients with Alzheimer's disease extracted from GEO database number (**A**) *GSE118553* and (**B**) *GSE132903*. Correlation between genes modulated by the HGD+sEHI vs LGD and patients with Alzheimer's disease extracted from GEO database number (**C**) *GSE118553* and (**D**) *GSE132903*.

#### *3.4. Interaction of Effects of the Glycemic Diet and the Soluble Epoxide Hydrolase Inhibitor*

In order to more clearly define how the sEHI modulated the response of hippocampal microvessels to an HGD, we used two-factor analysis to identify the DEGs with a significant interaction between the effects of the diet and the effects of the inhibitor. For this analysis, the LGD+sEHI group was included as it was necessary to use this additional control group for the two-factor analysis and to determine the delta fold change [(HGD+sEHI vs. LGD+sEHI) vs. (HGD vs. LGD)]. A positive delta fold change indicates a gene that is "more up-regulated" or "less down-regulated" by HGD in sEHI treated mice (compared to those with no inhibitor). A negative delta fold change indicates a gene that is "more downregulated" or "less up-regulated" by an HGD in sEHI treated mice (compared to those with no inhibitor). We identified a total of 1543 DEGs (Supplemental Table S7) that had a significant interaction between diet and inhibitor, the majority of which had a negative delta fold change. In many cases, the direction of the regulation of the DEGs by the HGD was opposite in direction in the presence of the inhibitor when compared to the effects of the HGD on that same gene in the absence of the inhibitor. The DEGs with a negative delta fold change were diversified between RNA types, including coding, non-coding, multiple complex, and pseudogenes. The 583 DEGs with a positive delta fold change were primarily non-coding genes. Pathway analyses using the KEGG and WikiPathway databases for all of the coding DEGs (positive and negative delta fold change) demonstrated that the sEHI modulated the effects of the HGD for pathways involved in the electron transport chain, oxidative phosphorylation, focal adhesion, HIF-1 signaling pathway, and pathways active in neurological diseases (Alzheimer's pathway) (Figure 10A).

**Figure 10.** Analysis of DEGs with a significant interaction of diet and inhibitor. (**A**) Relevant enriched WikiPathways and KEGG Pathways for DEGs with an interaction between diet and inhibitor effects. (**B**) Signal plots of selected DEGs with an interaction between diet and inhibitor effects. Data shown are from three biological replicates (mean +/− standard deviation). The DEGs displayed are cytochrome c oxidase subunit Vb (Cox5b), NADH dehydrogenase (ubiquinone) 1 alpha subcomplex 2 (Ndufa2), and Rho-associated coiled-coil containing protein kinase 1 (Rock1).

An additional finding of the two-factor analysis was that when the individual DEGs of the relevant pathways shown in Figure 10A (77 DEGs) were examined, it again became apparent that the sEHI reverted the gene expression profile of brain hippocampal microvascular endothelium of mice on the HGD to a profile similar to that of the LGD. Examples of three genes (*Rock1, Ndufa2, and Cox5b*) are shown in Figure 10B. These DEGs were selected because they were each represented in multiple relevant pathways including focal adhesion (*Rock1*), electron transport chain (*Ndufa2* and *Cox5b*), and oxidative phosphorylation (*Ndufa2* and *Cox5b*).

#### **4. Discussion**

In this study, we characterized the effect of a high glycemic diet (HGD) on murine neurovascular function and cognition using hippocampal microvascular multiomics of mice exposed to high or low glycemic index diets. Utilizing similar techniques of largescale transcriptome gene profiling, we have previously shown the effect of lipid stress and a Western diet (WD) on hippocampal microvascular injury and cognitive decline in male and female mice ApoE-/- and LDL-R-/- mice [8–11]. However, the molecular footprint of the glycemic component of the WD on the brain microvasculature, and whether and how the HGD may contribute to microvascular injury, was up to now unknown. A HGD is a risk factor for dementia [14] and is associated with poor cognitive performance [15,16], but whether the effect can be reversed has also been unknown. Given that in murine models of diabetes, soluble epoxide hydrolase inhibitors (sEHI) reduce neuroinflammation and cognitive decline [32–34], we also aimed to examine their role in our model.

We used a relevant model of glycemic index and demonstrated the expected significant differences in weight, cholesterol, glucose, and insulin levels consistent with what has

been published previously for these experimental models [19,63–65]. To our knowledge, the present study shows for the first time that the HGD has distinct differential effects on gene expression that are different from the LGD and characterized by up-regulation of differentially expressed protein-coding genes, transcription factors, as well as non-proteincoding genes (miRNAs, snoRNAs, and lncRNAs), that are involved in five major cellular pathways: neurodegenerative diseases (Alzheimer's disease), endothelial cell function (focal adhesion), cell signaling (PPAR signaling, PI3K-Akt signaling), cellular metabolism (oxidative phosphorylation, electron transport chain), and other cellular pathways (mRNA processing, oxidative damage). Below we discuss our multi-omics integrative analysis for the focal adhesion and Alzheimer's disease pathways to illustrate the complexity and characterize the molecular regulation in response to the HGD.

Integrative analyses allowed us to identify the protein-coding genes, TFs, and the non-coding genes modulated by the HGD in the focal adhesion pathway. *Cdc42* (Cell Division Control Protein Homolog 42) is a protein-coding DEG up-regulated by the HGD and targeted by STAT3 (Signal Transducer and Activator of Transcription 3) transcription factor. *Cdc42* is up-regulated in aging endothelial cells via the focal adhesion pathway [66,67] and mediates the activation of proinflammatory genes [68]. STAT3 transcription factor is activated by oxidative stress [69,70] and is associated with neurovascular inflammation and Alzheimer's disease [71]. The HGD also up-regulated differentially expressed miRNA *miR-1902* that targets *ITGB* (Integrin β). Activation of ITGB induces a reduction in endothelial barrier function by destabilizing intercellular junctions and abnormal remodeling of extracellular matrix (ECM) [72]. Another example of differentially expressed miRNAs up-regulated by the HGD is *miR-5125* and *miR-692* which target *PKC* (Protein Kinase C). PKC is associated with endothelial dysfunction, insulin resistance [73], and neuroinflammation [74]. The HGD also up-regulated differential expression of LncRNA *Gm6117* that targets *MLCK* (Myosin Light Chain Kinase). Activation of MLCK induces endothelial vascular hyperpermeability, microvascular barrier dysfunction, and inflammation [75]. Another interesting pathway activated by the HGD was the vascular endothelial growth factor (VEGF) signaling pathway. VEGF is associated with an increase in microvascular permeability by activating PKC [76], which then signals the activation of MLCK [75]. Taken together, this molecular cascade suggests that an HGD may promote endothelial cell dysfunction by microvascular hyper permeability and activating proinflammatory TFs, protein, and non-protein-coding genes.

We also performed an integrated analysis of the Alzheimer's disease pathway and identified TFs, protein-coding, and non-coding RNAs modulated by the HGD. Mitochondrial *complex IV* protein-coding DEG was up-regulated by the HGD and targeted by DNMT1 (DNA Methyl Transferase 1) transcription factor. Increased expression of *complex IV* has been observed in the hippocampus of AD patients [77], and DNMT1 expression is up-regulated in late-onset AD [78]. Alterations in mitochondrial function are also associated with diabetes [79]. Another example of a DEG up-regulated by the HGD was *LRP1* (LDLR-related protein). Expression of *LRP1* is increased in the hippocampus of AD patients [80], particularly in the microvasculature close to amyloid plaques in AD brains [81,82]. DE miRNA, *miR-5125* was activated by the HGD and is known to target *DKK1* (Dickkopf-1). Increased expression of *DKK1* has been detected in the plasma and brain of AD patients and AD transgenic mice [83,84]. Another target of *miR-5125* was *LPL* (Lipoprotein lipase). LPL accumulates in senile plaques [85], and its expression has been shown to be increased in the hippocampus of AD mice [86]. LncRNAs *Gm6117* and *Gm16339* were also up-regulated by the HGD and target *NMDAR* (N-methyl-D-aspartate receptor). An increase in NMDAR activity induces apoptosis and neurodegeneration in AD [87]. Yet another interesting pathway modulated by the HGD was the PI3K-Akt signaling pathway. Activation of the PI3K/Akt pathway promotes oxidative stress-mediated cell death [88]. Thus, our findings suggest that an HGD may be involved in previously defined Alzheimer's disease-related pathways via deleterious effects on the brain microvasculature by promoting mitochondrial dysfunction, apoptosis, and neurodegeneration, and similar to

the focal adhesion pathway, via up-regulation of pathway-associated TFs, protein-coding, and non-coding genes.

Our work with the soluble epoxide hydrolase inhibitor (sEHI) trans-4-[4-(3-adamantan-1-yl-ureido)-cyclohexyloxy]-benzoic acid (t-AUCB) showed several novel findings. sEH enzyme degrades anti-inflammatory and neuroprotective fatty acid epoxides (EETs) to biologically less active diols (DHETs) which are known to be involved in vascular cognitive impairment and Alzheimer's disease [24,25]. In animal models of stroke, sEH inhibitors have been shown to be neuroprotective [27,28]. In our study, we show for the first time that the sEHI reversed the deleterious phenotype of the HGD by down-regulating the differentially expressed genes up-regulated by the HGD and their TFs, and also by differential expression of non-coding RNAs (miRNA, snoRNA, and lncRNA) and that this proceeds by targeting the same pathways up-regulated by the HGD. When looking at specific DEGs targeted by the inhibitor, we identified interesting DEGs. For example, the presence of the sEHI down-regulated the expression of *Cdc42*, and the deletion of *Cdc42* is known to have protective effects against chronic inflammation in endothelial cells [67]. The sEHI also down-regulated the expression of the genes targeted by the PPAR (Peroxisome Proliferator-Activated Receptor) transcription factor, which is known to play a protective role against neuroinflammation in AD [89]. Furthermore, the sEHI down-regulated DE miRNAs, *miR-5125,* and *miR-692*, and their targets, *DKK1* and *PKC*, respectively. Inhibition of DKK1 has been shown to improve cognitive impairment [90] and protect against neurotoxicity [91]. PKC inhibition also protects against vascular injury of the blood–brain barrier [74]. Additionally, the sEHI also down-regulated DE lncRNA *Gm6117* and its target, *MLCK*. MLCK inhibitors play a protective role against vascular hyperpermeability and microvascular dysfunction [75]. These findings indicate that the sEHI served to protect against the neurotoxicity, endothelial- and neuro-inflammation, and blood– brain barrier permeability injury associated with the HGD.

Furthermore, our correlation analysis of the DEGs modulated by the HGD and inhibitor with the gene expression profiles obtained from the brains of Alzheimer's disease patients showed that the HGD is positively correlated, and the inhibitor is negatively correlated with the genes associated with Alzheimer's disease.

Our 2-factor interactive analysis served to further identify DEGs having a significant interaction between diet and the soluble epoxide hydrolase inhibitor. These DEGs were involved in Alzheimer's disease, electron transport chain, oxidative phosphorylation, and focal adhesion. The sEHI also was shown to restore the gene expression profile of hippocampal microvessels of mice on the HGD to a profile similar to that of the LGD, including for genes such as *Rock1* (Rho-associated protein kinase 1), which was increased by an HGD and decreased by the sEHI. *ROCK1* is activated in Alzheimer's disease, and reduction of *ROCK1* protects against AD by depleting amyloid-β levels in the brain [92]. In vitro exposure of brain microvascular endothelial cells to EETs, which generally increase after inhibition of sEH, has been shown to inhibit rho kinase (ROCK) activity [93]. Taken together, our molecular results suggest that the soluble epoxide hydrolase inhibitor may offset the effect of an HGD by robustly reversing the deleterious pattern of differential gene expression to that similar to an LGD and likely thereby protecting against vascular injury, neuroinflammation, and neurodegeneration. Furthermore, the sEHI was associated with improved cognition by down-regulating the TFs, protein-coding, and non-protein-coding DEGs that were up-regulated by the HGD.

#### **5. Conclusions and Clinical Implications**

In alignment with our hypothesis, our results show that an HGD has deleterious effects on differential gene expression of brain microvessels in memory centers in the brain by up-regulating the protein-coding and non-coding genes involved in alterations of mitochondrial function, oxidation, inflammation, and microvascular dysfunction. These processes play an important role in the pathophysiology of dementia. We also showed that inhibition of sEH protects against neuroinflammation, apoptosis, vascular injury, and cognitive decline by down-regulating the DEGs up-regulated by an HGD. The clinical role of sEHIs has been reviewed [22]. In clinical trials, sEHIs play a protective role in hypertension [94], and in animal models, they demonstrate neuroprotection in stroke [27,28], cerebral hypoperfusion, and in diabetes type 1 and type 2 [29–33]. Our studies show that sEHIs may also be promising therapeutic targets in the microvascular endothelial dysfunction that accompanies Alzheimer's and vascular dementias.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/nu13113913/s1, Figure S1: Volcano plot of differential gene expression changes in hippocampal microvessels for the high glycemic diet (HGD) compared to the low glycemic diet (LGD), Figure S2: Distribution of differentially expressed RNAs in hippocampal microvessels for the high glycemic diet (HGD) when compared to the low glycemic diet (LGD), Figure S3: Histogram of differentially expressed protein coding genes pathways in hippocampal microvessels for the high glycemic diet (HGD) when compared to the low glycemic diet (LGD), Figure S4. Target gene networks of differentially expressed transcription factors (TFs) in hippocampal microvessels for the high glycemic diet (HGD) compared to the low glycemic diet (LGD), Figure S5. Target gene networks of differentially expressed miRNAs in hippocampal microvessels with the high glycemic diet (HGD) compared to the low glycemic diet (LGD), Figure S6: Histogram of differentially expressed miRNA targets pathways in hippocampal microvessels with the high glycemic diet (HGD) when compared to the low glycemic diet (LGD), Figure S7. Target gene networks of differentially expressed LncRNAs in hippocampal microvessels with the high glycemic diet (HGD) compared to the low glycemic diet (LGD), Figure S8: Histogram of differentially expressed lncRNA targets pathways in hippocampal microvessels with the high glycemic diet (HGD) when compared to the low glycemic diet (LGD), Figure S9: Volcano plot of differential gene expression changes in hippocampal microvessels for the high glycemic diet (HGD) with soluble epoxide hydrolase inhibitor (sEHI) compared to without sEHI treatment, Figure S10: Distribution of differentially expressed RNAs in hippocampal microvessels from the high glycemic diet (HGD) with soluble epoxide hydrolase inhibitor (sEHI) compared to without sEHI treatment, Figure S11: Histogram of protein coding differentially expressed genes pathways in hippocampal microvessels for the high glycemic diet (HGD) with soluble epoxide hydrolase inhibitor (sEHI) compared to without sEHI treatment, Figure S12: Target gene networks of differentially expressed transcription factors (TFs) in hippocampal microvessels for the high glycemic diet (HGD) with and without soluble epoxide hydrolase inhibitor (sEHI), Figure S13: Target gene networks of differentially expressed miRNAs in hippocampal microvessels for the high glycemic diet (HGD) with and without soluble epoxide hydrolase inhibitor (sEHI), Figure S14: Histogram of differentially expressed miRNA targets pathways in hippocampal microvessels for the HGD with soluble epoxide hydrolase inhibitor (sEHI) compared to without sEHI treatment, Figure S15: Target gene networks of differentially expressed LncRNAs in hippocampal microvessels for the high glycemic diet (HGD) with and without soluble epoxide hydrolase inhibitor (sEHI), Figure S16: Histogram of differentially expressed lncRNA targets pathways in hippocampal microvessels for the high glycemic diet (HGD) with soluble epoxide hydrolase inhibitor (sEHI) compared to without the sEHI; Table S1: Differentially expressed genes for the high glycemic diet (HGD) compared to the low glycemic diet (LGD), Table S2: Top 30 transcription factors (TFs) differentially expressed by a high glycemic diet (HGD) with and without soluble epoxide hydrolase inhibitor (sEHI) in hippocampal microvascular endothelium, Table S3: Effect of the high glycemic diet (HGD) with and without soluble epoxide hydrolase inhibitor (sEHI) on the expression of microRNAs (miRNAs) in hippocampal microvessels, Table S4: Effect of the high glycemic diet (HGD) with and without soluble epoxide hydrolase inhibitor (sEHI) on the expression of long non-coding RNAs (lncRNAs) in hippocampal microvessels, Table S5: Effect of the high glycemic diet (HGD) with and without soluble epoxide hydrolase inhibitor (sEHI) on the expression of small nucleolar RNAs (snoRNAs) in hippocampal microvessels, Table S6: Differentially expressed genes for the high glycemic diet (HGD) with soluble epoxide hydrolase inhibitor (sEHI) compared to without sEHI, Table S7: Differentially expressed genes with a significant interaction of diet and inhibitor.

**Author Contributions:** Individual contributions of the authors were as follows. Conceptualization, A.V. and J.R.; methodology, S.N., A.V.; software, S.N., A.V., D.M., J.E.N.; validation, A.V., S.N., D.M. and J.E.N.; formal analysis, S.N., D.M., A.V., J.E.N.; investigation, S.N.; resources, A.V. and J.R.; data curation, A.V., D.M., S.N., J.E.N.; writing—original draft preparation, A.V. and S.N.; writing—review

and editing, A.V., S.N., D.M., J.E.N.; visualization, A.V., S.N., D.M., J.E.N.; supervision, A.V., J.R., D.M.; project administration, S.N.; funding acquisition, A.V. and J.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by an award from the Richard A. and Nora Eccles Foundation (A20-0111), the Richard A. and Nora Eccles Harrison Endowed Chair in Diabetes Research (J.C.R.), and the Frances Lazda Endowed Chair in Women's Cardiovascular Medicine (A.C.V). Laser Capture Microdissection and Cryo-Sectioning were conducted at the Cellular and Molecular Imaging core facility at the University of California Davis (UC Davis) Center for Health and the Environment. The laser capture microscope was funded by NIHS10RR-023555. Microarray Scanning and Hybridization was done by the Genomics Shared Resource at the UC Davis Medical Center. We are also grateful for the technical support and/or services provided to our research by the UC Davis Mouse Metabolic Phenotyping Center which assisted with the metabolic assays supported by U24 DK092993.

**Institutional Review Board Statement:** The institutional review board of the University of California, Davis, the Institutional Animal Care and Use Committee (IACUC) approved this project protocol number 20943 on 18 April 2019.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** We have deposited the microarray data in GEO and the accession number is GSE185057.

**Acknowledgments:** We thank Nikita Patel, Anthony Pham, Taarini Hariharan, Nejma Wais, Ryan Vinh, and Corey Buckley for technical assistance in this project.

**Conflicts of Interest:** The authors declare no conflict of interest.

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