*Article* **Dysmetabolism and Sleep Fragmentation in Obstructive Sleep Apnea Patients Run Independently of High Caffeine Consumption**

**Sílvia V. Conde 1,\*, Fátima O. Martins 1, Sara S. Dias 2,3, Paula Pinto 4, Cristina Bárbara <sup>4</sup> and Emília C. Monteiro <sup>1</sup>**


**Abstract:** Daytime hypersomnolence, the prime feature of obstructive sleep apnea (OSA), frequently leads to high coffee consumption. Nevertheless, some clinicians ask for patients' caffeine avoidance. Caffeinated drinks are sometimes associated with more severe OSA. However, these effects are not consensual. Here we investigated the effect of caffeine consumption on sleep architecture and apnea/hypopnea index in OSA. Also, the impact of caffeine on variables related with dysmetabolism, dyslipidemia, and sympathetic nervous system (SNS) dysfunction were investigated. A total of 65 patients diagnosed with OSA and 32 without OSA were included after given written informed consent. Polysomnographic studies were performed. Blood was collected to quantify caffeine and its metabolites in plasma and biochemical parameters. 24 h urine samples were collected for catecholamines measurement. Statistical analyses were performed by SPSS: (1) non-parametric Mann-Whitney test to compare variables between controls and OSA; (2) multivariate logistic regression testing the effect of caffeine on sets of variables in the 2 groups; and (3) Spearmans' correlation between caffeine levels and comorbidities in patients with OSA. As expected OSA development is associated with dyslipidemia, dysmetabolism, SNS dysfunction, and sleep fragmentation. There was also a significant increase in plasma caffeine levels in the OSA group. However, the higher consumption of caffeine by OSA patients do not alter any of these associations. These results showed that there is no apparent rationale for caffeine avoidance in chronic consumers with OSA.

**Keywords:** caffeine; obstructive sleep apnea; apnea/hipopnea index; sleep architecture catecholamines; dysmetabolism

#### **1. Introduction**

Obstructive sleep apnea (OSA), the most common sleep-disordered breathing, is a highly prevalent disease [1] characterized by repetitive episodes of airflow cessation (apnea) or airflow reduction (hypopnea) during the sleep. These obstructive apneas during sleep result into recurrent arousals during sleep and in repetitive episodes of hypoxia, hypercapnia and apneas, which result in chemoreflex activation and consequent activation of the sympathetic nervous system (SNS) [2,3]. Frequent arousal can cause relevant changes on sleep architecture, namely sleep fragmentation, and may be responsible for diurnal hypersomnolence [3], as well as several adverse safety and health consequences including diurnal hypertension [4], cardiovascular disease, stroke, motor vehicle accidents, and diminished cognitive capacities and quality of life [5].

**Citation:** Conde, S.V.; Martins, F.O.; Dias, S.S.; Pinto, P.; Bárbara, C.; Monteiro, E.C. Dysmetabolism and Sleep Fragmentation in Obstructive Sleep Apnea Patients Run Independently of High Caffeine Consumption. *Nutrients* **2022**, *14*, 1382. https://doi.org/10.3390/ nu14071382

Academic Editor: Maria Luz Fernandez

Received: 18 February 2022 Accepted: 24 March 2022 Published: 25 March 2022

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

**Copyright:** © 2022 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/).

Daytime hypersomnolence and fatigue are regular symptoms in OSA, being frequently considered the main responsible for the high coffee consumption. It was previously described that OSA patients drink, on average, nearly three times more coffee than control subjects [6]. However, a recent meta-analysis did not show enough evidence to confirm the association between OSA and caffeine consumption [7]. Also, caffeinated drinks have been associated with more severe OSA [8] and some physicians ask for its avoidance, however these effects are not consensual as it has been found that coffee or tea do not seem to impact on OSA severity [8] and that caffeine seemed to improve cognitive function in people with OSA [9].

Several authors have correlated high caffeine consumption with an elevated blood pressure (BP) [10], suggesting that caffeine augments BP and total peripheral resistance in response to stressors [11–13]. Additionally, it has been previously described that caffeine increases SNS activity by increasing catecholamine levels, down-regulating betareceptors [14] and blocking adenosine receptors [15]. In contrast, several studies have showed beneficial effects of long-term coffee intake on glucose metabolism and insulin action [16,17]. Moreover, some studies have associated coffee chronic consumption with a slower cognitive decline and with a reduced risk of cognitive dysfunction, dementia, and Alzheimer disease [18,19]. In particular, in moderate and severe OSA patient's, daily caffeine intake was associated with less cognitive impairment [9]. However, despite these positive impacts of caffeine on glucose homeostasis in metabolic disease patients and on cognitive dysfunction in OSA patients [9] most of the clinicians remain asking for patient caffeine avoidance.

Besides, all of the literature that can be found regarding the association of OSA and sympathetic activity and BP, the effect of caffeine consumption in other variables closely associated with OSA, such as sleep parameters (e.g., rapid eye movement (REM) sleep, non-REM sleep and arousals), dyslipidemia (e.g., triglycerides (TGs), cholesterol, high-density lipoproteins (HDL) and low-density lipoproteins (LDL) and dysmetabolism (e.g., body mass index (BMI) and glycemia), has not been evaluated so far. Thus, the aim of the present study was to investigate if caffeine plasma levels affect OSA severity and OSA association with dysmetabolism and sympathetic nervous system dysfunction.

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

#### *2.1. Patients and Ethics*

Eligible male patients with or without OSA (65 vs. 32) were recruited from the Respiratory Diseases Service of Hospital Pulido Valente (Lisbon). The diagnosis of OSA was established by the presence of typical clinical features of this disorder and confirmed by overnight polysomnography. Patients were included independently of their hypertension condition, hypertension ongoing medication and body mass index (BMI) and excluded if they had any psychiatric disorder, smoking habits, or inability to understand the information required for an informed consent. The study was approved by the Centro Hospitalar Lisboa Norte's Ethics Committee (Ethical approval: 11st November 2009) and by the Ethics Committee of the NOVA Medical School and registered at ClinicalTrials.gov accessed on 17 February 2022 (NCT01803815). It was performed in accordance with the Helsinki Declaration. All volunteers gave their written informed consent.

#### *2.2. Study Design*

Sociodemographic and anthropometric data, comorbidities and ongoing medication profile were documented. Weight, and abdominal circumference using standardized protocols were assessed.

Polysomnographic studies were performed using a polysomnographer (Medcare, Somnologica, Portugal). Polysomnography report included sleep parameters (e.g., sleep staging, sleep time, sleep latency, sleep efficiency, REM and non-REM sleep and arousals), respiratory events such as number and duration of apneas and hypopneas (apnea is the cessation of airflow at the nose and mouth for more than 10 s associated with oxygen desat-

uration of 3% and hypopnea is a discernible reduction in respiratory effort accompanied by a decrease of more than 4% of oxygen saturation) and apnea/hypopnea index (AHI). OSA was categorized according to current AHI cut-offs of less than 5 (nondiagnostic), 5≥ and <15 (mild), 15≥ and <30 (moderate), and at least 30 (severe).

In all patients, blood and 24 h urine samples were collected, before and after polysomnographic studies and stored at −80◦ until analyzed. Xanthine's concentrations, including caffeine, theobromine, theophylline and paraxanthine, glycemia total cholesterol, triglycerides (TG), HDL and LDL were measured in blood and epinephrine (E), norepinephrine (NE) and dopamine were measured in the 24 h urine. Quantifications of xanthines and catecholamines were carried by HPLC with UV and electrochemical detection, respectively, as previously described [20,21].

#### *2.3. Statistical Analysis*

Data were presented as median (interquartile range) with nonparametric Mann-Whitney test between two independent samples. Spearman correlations were performed between caffeine concentrations and sleep-related parameters in all subjects or in OSA patients and it was considered significantly correlated at *p* < 0.05. We have used nonparametric tests since variables did not follow a normal distribution.

Exploring the OSA patients we have performed an exploratory data analysis by binary logistic regression for each measured parameter for the dependent variable OSA and data were presented as odds ratio (OR), interval of confidence of 95% (IC95%) and correspondent value of p, with *p* < 0.05 being considered statistically significant. Afterwards we have grouped the significantly different variables between the 2 groups in 4 different models [dysmetabolism (glycemia and BMI), dyslipidemia (TGs, HDL and LDL), SNS (dopamine and NE) and sleep (arousals)] and tested its association with OSA when caffeine concentration in blood is present in each model. Each logistic regression was tested for goodness of fit by the Hosmer and Lemeshow test. Statistical analyses were performed using SPSS statistical software (vs 26.0 for Mac) (IBM, NY, US).

#### **3. Results**

Male patients diagnosed with OSA (65) and male patients without OSA (32) were recruited. The study and the control groups included volunteers aged 50 ± 8 and 34 ± 7 years, respectively.

Table 1 summarizes findings regarding metabolic and sympathetic nervous system variables studied in OSA patients and control subjects. OSA patients showed significantly higher BMI, glycemia, total cholesterol, LDL, TG and dopamine and NE levels in comparison to control patients. OSA patients also showed a significantly lower HDL levels than control subjects (Table 1).

**Table 1.** Comparison between metabolic and sympathetic nervous system variables in patients with and without obstructive sleep apnea (OSA).



**Table 1.** *Cont.*

Data are presented as median (interquartile range). LDL-c, low density lipoproteins; HDL-c, high density lipoproteins; TG, triglycerides; E, epinephrine; NE, norepinephrine (\* *p* < 0.05; \*\* *p* < 0.01; \*\*\* *p* < 0.001, median Mann-Whitney statistic test for independent samples, corresponding to the difference between patients with and without OSA).

In Table 2 are summarized the levels of caffeine plasmatic concentrations and sleep parameters for both groups of patients. OSA patients showed significantly higher caffeine plasmatic concentrations than control subjects. As expected, AHI and the number of arousals were also higher for patients included in the study group. As it can be observed in Table 2, no statistical differences between the REM and non-REM sleep period in patients with or without OSA were found.

**Table 2.** Comparison of caffeine and total xanthine plasmatic concentrations, Apnea-hypopnea index (AHI), sleep architecture (REM and non-REM) and arousals events in patients with or without obstructive sleep apnea (OSA).


Data are presented as median (interquartile range). AHI, apnea-hypopnea index; REM, rapid eye movement sleep; NREM, non-rapid eye movement sleep (\*\* *p* < 0.01; \*\*\* *p* < 0.001, median Mann-Whitney statistic test for independent samples, corresponding to the difference between patients with and without OSA).

Aiming to evaluate the impact of caffeine consumption on the prediction of OSA development and on disease severity we correlated caffeine plasma levels and sleep architecturerelated parameters for the OSA patients' group. We found that there is no statistically significant correlation for any of the tested parameters, showing that caffeine is not associated with disease severity (Table 3).

To deeply understand if OSA development is associated with dyslipidemia, dysmetabolism, SNS dysfunction and/or sleep fragmentation we performed a binary logistic regression within both groups, OSA and control patients, for all the covariables significantly different in the previous nonparametric test analysis. Blood pressure was not considered as a covariable for the statistical analysis since OSA patients presented normotensive values due to the use of anti-hypertensive medication. Also, apart from this medication no relevant drugs for the present study were taken by patients.

We found that all the tested covariables were associated with the development of OSA, showed by the *p* value in Table 4 for the differences between OR for an interval of confidence of 95% present. The binary logistic regression of each model followed the goodness of the fit by the Hosmer and Lemeshow test.


**Table 3.** Correlation of caffeine concentration in OSA patients with sleep-related parameters: apneahypopnea index (AHI), arousals, rapid-eye movement (REM) and non-rapid eye movement (NREM).

Data are presented as Spearman correlation coefficient between caffeine concentrations and sleep-related parameters and correspondent value of *p*, considered significantly correlated at *p* < 0.05; AHI, apnea-hypopnea index; REM, rapid eye movement; NREM, non-rapid eye movement.

**Table 4.** Binary logistic regression for BMI, glycemia, cholesterol, LDL, HDL, TGs, dopamine, NE, caffeine concentration and arousals with OSA as dependent variable.


Data are presented as odds ratio (OR), interval of confidence of 95% (IC95%) and correspondent value of *p*; BMI, body-mass index; LDL, low-density lipoprotein; HDL, high-density lipoprotein; TG, triglycerides.

Coffee and other beverages and foods rich in caffeine consumption has been reported to be increased in OSA condition due to the sleep privation and daily sleepiness, as shown herein in the present study by the higher caffeine concentration in blood in patients diagnosed with OSA. As such, we had interest in investigating if the associations between metabolic and sleep parameters with OSA pathology are influenced by caffeine levels.

Therefore, we performed a multivariate logistic regression with OSA as dependent variable and tested the association of the pathology with dyslipidemia (TG, HDL-c, LDL-c), dysmetabolism (BMI and glycemia), SNS dysregulation (dopamine and norepinephrine) and sleep impairment (arousals) taking in account caffeine concentration in all models. Each model followed the goodness of fit represented by a non-significative Hosmer and Lemeshow test. The results for each model tested are presented in Table 5.

We found that caffeine association with OSA is lost when all the other variables related with OSA comorbidities are taken in account. Moreover, we also observed that all the variables tested maintain their association with OSA even in the presence of caffeine. Regarding the dysmetabolism model we observed that BMI and glycemia associates statistically significantly with OSA independently of the higher caffeine intake in these patients (Table 5a). Similarly, in the dyslipidemia model we found that TGs do not associate with OSA when caffeine factor is present, but HDL and LDL maintains its statistically significantly association with the pathology, with an expected protective association (OR < 1) and risk factor association (OR > 1), respectively (Table 5b). Regarding SNS function association with OSA we found that NE is statistically significantly associated with the pathology and that caffeine levels seem to have a role on SNS association with OSA (Table 5c). Finally, as expected, arousals are positively statistically significantly associated with OSA, but caffeine do not contribute for this association (Table 5d).

**Table 5.** Multivariate logistic regression for four different models with OSA as dependent variable and testing the effect of caffeine on variables association with the development of the pathology. Variables for testing caffeine effect on the association of OSA with: (a) dysmetabolism-related comorbidities; (b) dyslipidaemia; (c) sympathetic nervous system (SNS) dysfunction; and (d) sleep fragmentation.


#### **4. Discussion**

In the present study, we showed that the group of OSA patients studied in the present manuscript exhibit significant dysmetabolic condition with higher BMI and glycemia, dyslipidemia with high total cholesterol, LDL, TG and lower HDL levels and deregulation of the SNS assessed as high dopamine and NE levels. Moreover, we showed, as expected, that OSA patients consume more caffeinated products to compensate daytime sleepiness, this being reflected in the high caffeine plasma levels in these patients. We also found that no correlation exists between caffeine plasma levels and OSA and its severity. Finally, we have shown that metabolic dysfunction, dyslipidemia, and sleep disturbances are associated with OSA independently of caffeine plasma levels.

Caffeine is one of the most widely consumed psychoactive substances in the World and it is known that this xanthine disrupts sleep, diminishing total sleep time and sleep onset in healthy volunteers [15,22]. Apart from some controversy on the effects of caffeine on REM sleep [23,24], it is consensual that caffeine affects arousals [25] and NREM sleep [15,26]. However, our results are not in this line of evidence since we found that caffeine plasma levels lose association with OSA when arousals are taken in account and do not correlate with any sleep-related parameters when this association is analyzed only in OSA group. When subjects from both groups are analyzed for the association of caffeine plasma levels with sleep parameters, both AHI and arousals have a statistically significant correlation, showing that OSA patients indeed drink more caffeine due to sleep fragmentation. Moreover, no correlation has been found between caffeine consumption and the severity of OSA. These results are in line with the findings of the authors in [8], showing that coffee intake is not associated with sleep disorder breathing, a wide spectrum of sleep-related

conditions that includes OSA. Interestingly, and in complete contrast with the idea that caffeine may contribute to sleep disruption in OSA or even to its absence of effects, a recent study by Takabayashi et al. [27] showed in a cohort of Japanese men, that individuals who were overweight exhibited a significant inverse association between coffee consumption and oxygen desaturation index, a parameter that correlate with AHI [28,29]. While this study of Takabayashi et al. [27] did not provided causality between coffee consumption and sleep disorder breathing it clearly highlights the importance of further research on the associations between coffee consumption in sleep disorder breathing and especially in OSA. In fact, it was recently emphasized in a meta-analysis that there are insufficient data in the literature to determine whether and how, OSA is associated with caffeine consumption [7].

Some studies in the past have suggested that caffeinated coffee consumption was associated with a lower metabolic control and therefore with an increased risk of type 2 diabetes and metabolic syndrome [17,30], although nowadays is becoming consensual that drinking coffee (whether caffeinated and decaffeinated) may actually decrease the risk of developing metabolic syndrome and type 2 diabetes [16,31]. These results were supported by animal work showing that long-term caffeine intake prevent and reverse fat deposition, insulin resistance and glucose intolerance in hypercaloric animal models [21,32,33]. However, the results for the effect of caffeine on dysmetabolism do not seem to be extrapolated to dyslipidemia as a recent meta-analysis suggested that coffee consumption may be associated with an elevated risk for dyslipidemia [34]. Nevertheless, these results were not supported by previous clinical studies showing that coffee consumption has been positively associated with blood lipid concentration in humans [35,36] and by the findings that caffeine intake in hypercaloric animal models decrease non-esterified fatty acids [21]. Our results show that caffeine-enriched products consumption in OSA patients do not influence OSA metabolic comorbidities. Moreover, we also observed that all of the variables related with dysmetabolism and dyslipidemia tested herein, maintain their association with OSA even in the presence of caffeine, suggesting that caffeine intake in OSA is incapable of prevent or improve dysmetabolic features. Therefore, we can suggest that the mechanisms behind the development of dysmetabolism in OSA might differ from the common dysmetabolic states in where chronic caffeine intake have shown to have beneficial effects [21,32,33].

Regarding SNS-related parameters and following the previous findings of Bardwell et al. [6], where a positive association between NE levels and caffeine consumption in OSA patients was observed, or with the previous observations of Benowitz [14] where an increase in catecholamines circulating levels elicited by caffeine was found, herein we observed that caffeine is directly associated with NE levels in OSA. Therefore, we can suggest that higher caffeine intake in OSA patients can contribute to the overactivity of the SNS activity observed in OSA patients [37] and in here manifested by an increase in NE levels.

In conclusion, caffeine consumption in OSA patients is not associated with OSA severity, as well as with dysmetabolism and sleep fragmentation although we cannot discharge a contribution of high caffeine levels to the overactivation of the SNS observed in OSA patients. While there is no apparent rationale for caffeine avoidance in chronic consumers with OSA, we believe that more studies regarding coffee/caffeine consumption in OSA patients should be performed.

**Author Contributions:** Conceptualization, S.V.C. and E.C.M.; methodology, S.V.C., P.P. and C.B.; formal analysis, S.V.C.; statistical analysis, F.O.M. and S.S.D.; writing—original draft preparation, S.V.C. and F.O.M.; writing—review and editing, S.V.C., F.O.M. and S.S.D.; funding acquisition, E.C.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Portuguese Science and Technology Foundation (FCT) with the grant number CEECIND/04266/2017.

**Institutional Review Board Statement:** The study was approved by the Centro Hospitalar Lisboa Norte's Ethics Committee (Ethical approval: 11st November 2009) and by the Ethics Committee of the NOVA Medical School and registered at ClinicalTrials.gov accessed on 17 February 2022 (NCT01803815). It was performed in accordance with the Helsinki Declaration. All volunteers gave their written informed consent.

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

**Data Availability Statement:** Data available on request due to restrictions (ethical reason). The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical concerns.

**Acknowledgments:** The authors would like to thank Eunice Silva for technical support.

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

#### **References**


## *Review* **Non-Pharmacological Treatments for Insulin Resistance: Effective Intervention of Plant-Based Diets—A Critical Review**

**Michalina Banaszak 1, Ilona Górna 2,\* and Juliusz Przysławski <sup>2</sup>**


**Abstract:** Plant-based diets are becoming increasingly popular. Vegetarian diets are better for the environment and exhibit health benefits. A correctly balanced plant-based diet is appropriate at every stage of life. Compared to omnivores, vegetarians consume more fruits and vegetables, more fibre, vitamins C and E, magnesium and less saturated fats. In general, they have better nutrition knowledge, and they are slimmer, healthier and live longer than omnivores. It also seems that following a plant-based diet prevents the onset of chronic diseases such as cardiovascular diseases, hypertension, type 2 diabetes, obesity and some cancers. Food intake has a key influence on insulin resistance. Consumption of calorie-rich and highly processed foods, meats and sweetened beverages is a characteristic element of Western diets. They promote and elevate insulin resistance and type 2 diabetes. In contrast, intake of pulses and exclusion of meats as well as animal products bring significant benefits to vegetarian diets. According to studies, vegetarians and vegans have better blood parameters, including better glucose, insulin, total cholesterol, and LDL cholesterol levels. Their homeostatic model assessment for insulin resistance (HOMA-IR) test results are also better. More plant-based foods and fewer animal foods in a diet result in lower insulin resistance and a lower risk of prediabetes and type 2 diabetes. The aim of the study was to investigate the effect of plant-based diets on insulin resistance. In this review, we focused on presenting the positive effects of vegetarian and vegan diets on insulin resistance while showing possible clinical applications of plant-based diets in the treatment and prevention of modern-age diseases. Current and reliable publications meeting the requirements of Evidence-Based Medicine (EBM) were taken into account in this review.

**Keywords:** vegetarian diet; vegan diet; insulin resistance; insulin sensitivity

#### **1. Introduction**

Insulin resistance (IR) is a pathological condition in which cells fail to respond normally to insulin. As a result, glucose remains in the bloodstream. It may be defined as a state where normal or elevated insulin levels result in a poor biological response. Insulin resistance is often accompanied by hyperinsulinemia, a condition where pancreatic β-cells release excessive amounts of insulin to maintain normal glycaemia [1–3]. Unfortunately, over time, the pancreas is unable to secrete adequate amounts of the hormone. This leads to the development of type 2 diabetes. Apart from diabetes, IR is also associated with the development of metabolic syndrome and cardiovascular diseases [4]. It is estimated that insulin resistance precedes the development of diabetes type 2 by 10 to 15 years [5].

The exact causes of insulin resistance have not yet been fully understood. Increased intramuscular fat and fatty acid metabolites are amongst the suspected risk factors. Excess body weight is considered to be the main cause of insulin resistance. It was suggested that IR starts in myocytes due to immune-mediated inflammatory changes and an excess

**Citation:** Banaszak, M.; Górna, I.; Przysławski, J. Non-Pharmacological Treatments for Insulin Resistance: Effective Intervention of Plant-Based Diets—A Critical Review. *Nutrients* **2022**, *14*, 1400. https://doi.org/ 10.3390/nu14071400

Academic Editors: Silvia V. Conde, Fatima O. Martins and Rosa Casas

Received: 23 February 2022 Accepted: 25 March 2022 Published: 27 March 2022

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

**Copyright:** © 2022 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/).

of free fatty acids resulting in ectopic lipid deposition. In the muscles, the process of glucose uptake is disturbed, so that excess glucose returns to the liver, where it increases lipogenesis and the levels of circulating free fatty acids. Increased levels of free fatty acids exacerbate insulin resistance. There are also genetic causes of insulin resistance, including genetic diseases (myotonic dystrophy, Rabson–Mendenhall syndrome, Werner syndrome) and abnormalities in the function of insulin antibodies and insulin receptors. The gold standard for diagnosing insulin resistance is the hyperinsulinemic-euglycemic clamp. This method involves the continuous infusion of insulin at high speed to inhibit the production of glucose by the liver. Simultaneously, 20% dextrose solution is administered at different rates to reduce blood glucose levels in the euglycemic range. Blood glucose is monitored frequently during the test. The amount of glucose required to achieve normoglycemia reflects the excretion of exogenous glucose required to compensate for hyperinsulinemia. However, as this technique is labour-intensive and costly, indices are used to quantify IR. The homeostatic model assessment for insulin resistance (HOMA-IR) is the prevalent method. It is calculated by multiplying fasting insulin and fasting glucose levels and then dividing that figure by 22.5 [4–6].

The primary non-pharmacological treatment for insulin resistance should entail lifestyle approaches. Dietary changes should include a modification of eating habits, reduction in energy intake (for excessive body weight) and avoidance of carbohydrates that excessively stimulate insulin secretion, known as high glycaemic index carbohydrates and high glycaemic load carbohydrates. Regular exercise improves muscle insulin sensitivity [1,5,7,8].

The aim of this review was to show that diets that emphasise whole grains, vegetables, fruits and legumes and exclude animal products are able to reduce insulin resistance and improve tissue insulin sensitivity.

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

Forty-four publications from the PubMed database, the Web of Science and the Cochrane Library were used for this literature review. The following inclusion criteria were applied for the review: papers in English and only research conducted on adults (over the age of 18). The following keywords were used to search for papers: 'insulin', 'insulin secretion', 'insulin resistance', 'insulin sensitivity', 'vegan diet', 'vegetarian diet', 'plant-based diet', 'vegetarian cuisine', 'vegan cuisine'. The literature included comparative studies, cross-sectional studies and randomised controlled trials (Figure 1).

**Figure 1.** Full search strategy.

#### **3. Plant-Based Diets**

People are showing increasing interest in plant-based diets. Ethical, environmental and social considerations are some of the primary reasons for changing to a plant-based diet. Health issues are also playing an increasingly significant role [9]. Compared to traditional diets, those based on plants are more environmentally friendly. They use fewer natural resources and cause less pollution [10].

A vegetarian diet involves abstaining from meat, poultry, fish and seafood [11]. There are different types of vegetarian diets. Lacto-vegetarians consume dairy products, ovovegetarians consume eggs, whereas lacto-ovo-vegetarians consume eggs and dairy products [12]. On the other hand, vegans abstain from all animal products [13]. Such a diet consists exclusively of plant foods such as cereals, vegetables, fruits, legumes, nuts, seeds and vegetable oils [10,14–16]. The most prevalent types of plant-based diets are shown in Table 1.

**Table 1.** Characteristic of plant-based diets.


There is also a semi-vegetarian diet (flexitarian). This eating pattern limits the consumption of meat or fish to a few portions per week. However, it is not considered vegetarian per se [17].

According to the Academy of Nutrition and Dietetics, well-balanced vegetarian diets, including vegan diets, are suitable at all stages of life, including during pregnancy, lactation, infancy, childhood, adolescence and old age. Such direst is also suitable for athletes [10].

Generally speaking, numerous studies indicate that vegetarians are more aware of the health consequences of poor nutrition that they are slimmer and healthier than people who do not follow such restrictions [11]. Compared to non-vegetarian diets, a plant-based diet may prevent the onset of chronic diseases such as cardiovascular diseases, hypertension, type 2 diabetes, obesity and lower overall cancer risk, especially colorectal cancer, breast cancer and prostate cancer [10]. According to research, for vegans, the risk of hypertension is reduced by 75%, the risk of type 2 diabetes is reduced by 47–78% and the risk of cancer is reduced by 14% [18]. Furthermore, plant-based diets are associated with reduced mortality rates for humans [19,20].

Unfortunately, not every diet based on fruit and vegetables is healthy. A poorly balanced vegetarian diet can be just as harmful as an unbalanced traditional diet. Plantbased diets can be deficient in B vitamins (especially B12), iron, calcium, zinc, omega 3 fatty acids and protein. It is, therefore very important to consume fortified foods and dietary supplements [15,17]. There is some concern as to whether a vegan diet can meet the protein requirements of the human body [21]. A well-balanced vegan diet provides all essential amino acids and an adequate amount of total protein without the need to supplement the diet with special foods [10].

The number of people around the world who follow a vegetarian diet is not precisely known. However, every year that number is growing. It is estimated that there may be 7.3 million vegetarians in the USA, 3.6 million in the UK and 30,000 in Portugal [17]. According to estimates, vegans account for as many as 46% of vegetarians [10].

#### **4. Impact of a Vegetarian Diet on Insulin Resistance**

The foods we eat have a key impact on insulin resistance. This is especially true for the elderly and those who are not very physically active. The increase in the consumption of highly processed, calorie-rich foods such as fast foods, meats and meat products, refined cereal products and soft drinks is estimated to play an important role in the worldwide growth of type 2 diabetes [22]. Balanced and well-composed diets can play a significant role in reducing insulin resistance and the risk of type 2 diabetes (Table 2) [23].


#### **Table 2.** The influence of a vegetarian diet on insulin resistance.


#### **Table 2.** *Cont.*

Those following vegetarian diets consume more monounsaturated fatty acids (MUFA), more polyunsaturated fatty acids (PUFA) and less saturated fatty acids (SFA) as compared to non-vegetarians [42]. Saturated fatty acids and palmitic acid, in particular, interfere with insulin signalling in muscle cells due to the accumulation of free fatty acid intermediates, ceramides and diacylglycerol [43,44]. Diets that include animal products contain much more SFAs than plant-based diets. This means that vegetarians and vegans have less insulin resistance [41]. In addition, replacing SFAs in a diet with MUFAs and PUFAs has anti-inflammatory effects and improves insulin sensitivity [45].

Studies have shown that legumes, which are an important source of protein in a vegetarian diet, reduce insulin resistance, which is associated with protection against the onset of metabolic syndrome [24,25,46,47]. Pittaway et al. [26] demonstrated an improvement in tested blood parameters after 12 weeks of consuming chickpeas (minimum 728 g per week). In addition to a reduction in total cholesterol, there was a 0.75 μIU/mL decrease in fasting insulin (*p* = 0.045), and the HOMA-IR insulin resistance index fell by 0.21 compared with the start of the study (*p* = 0.01).

Animal products, in particular red meat, show a tendency to elevate insulin resistance [27,28,48–51]. A study by Tucker et al. [27] on a group of 292 females without diabetes found that women who consumed large and moderate amounts of meat had significantly higher HOMA-IR insulin resistance index scores than those with a lower meat intake (F = 7.4; *p* = 0.0070). They put forward the idea that meat consumption could lead to insulin resistance. This is associated with a reduced risk of type 2 diabetes for vegetarians compared to omnivores.

Numerous studies show a positive effect of a vegetarian diet on insulin resistance compared to traditional, non-vegetarian diets [29–38].

In their study, Kahleova et al. [35] compared a vegetarian diet (∼60% energy from carbohydrates, 15% proteins and 25% fats) with a conventional diabetic diet (50% energy from carbohydrates, 20% proteins and <30% fats). A group of 74 patients with type 2 diabetes was randomly assigned to 2 groups: an experimental group (*n* = 37) that followed a vegetarian diet and a control group (*n* = 37) that followed a conventional diabetic diet. The results from the experimental group showed that as many as 43% of those on a vegetarian diet were able to reduce the doses of their diabetes medication (compared with 5% in the control group; *p* < 0.001). Bodyweight also decreased more for those in the experimental group than in the control group (−6.2 kg (95% CI −6.6 to −5.3) vs. −3.2 kg (95% CI −3.7 to −2.5)). Furthermore, there was a significant increase in insulin sensitivity in the experimental group (30%; 95% CI 24.5–39) compared to the control group (20%; 95% CI 14–25) (*p* = 0.04). A vegetarian diet was also shown to reduce visceral adipose tissue volume and positively affect adiponectin and oxidative stress markers. The results indicate that a vegetarian diet alone or in combination with exercise is more effective in reducing insulin resistance than a conventional diabetic diet.

Metabolic abnormalities are a factor that can trigger age-related diseases. This may be more intense for obese individuals. With that in mind, Valachovicová et al. [36] evaluated insulin resistance in relation to diet. Fasting glucose and fasting insulin concentrations, as well as insulin resistance (HOMA) values, were analysed in normal-weight adults. The group included 95 vegetarians and 107 non-vegetarians on a Western diet. The results were significantly lower in the vegetarian group (glucose concentration 4.47 ± 0.05 vs. 4.71 ± 0.07 mmol/L; insulin concentration 4.96 ± 0.23 vs. 7.32 ± 0.41 mU/L; HOMA-IR index 0.99 ± 0.05 vs. 1.59 ± 0.10). Vegetarians were significantly more likely to consume whole grain products, pulses, oat and barley products, as well as fruit and vegetables. Studies show the beneficial effects of long-term use of a plant-based diet in the prevention of metabolic syndrome, diabetes and cardiovascular disease.

On the other hand, Chiang et al. [37] found that vegetarianism was associated with better lipid profiles, insulin resistance and lower risks of metabolic syndrome. The study comprised 391 female vegetarians (80% lacto-vegetarians) and 315 non-vegetarian Buddhist women from Taiwan. The vegetarians had a lower BMI (22.9 ± 2.7 vs. 23.8 ± 3.2 kg/m2), smaller waist circumference (72.9 ± 6.9 vs. 75.3 ± 7.6 cm), lower fasting glucose (4.98 ± 0.89 vs. 5.15 ± 0.97 mmol/L) and fasting insulin (41.67 ± 37.50 vs. 52.09 ± 41.67 pmol/L) levels, lower total cholesterol (4.68 ± 0.83 vs. 5.21 ± 0.90 mmol/L) and lower LDL cholesterol (3.01 ± 0.74 vs. 3.43 ± 0.81 mmol/L) compared to non-vegetarians (*p* < 0.001). These vegetarians also had better insulin sensitivity, which was associated with lower HOMA levels (vegetarians 1.3 ± 1.2 vs. non-vegetarians 1.7 ± 1.5; *p* < 0.001). According to the study, a lacto-vegetarian diet was associated with reduced insulin resistance and risk of metabolic syndrome.

Vuˇci´c Lovrenˇci´c et al. [38] reached similar conclusions. Female vegetarians had significantly lower fasting insulin levels (38.4 (35.1–46.4) vs. 49.9 (37.7–63.8) pmol/L) and lower HOMA index values (0.80 (0.75–0.95) vs. 1.10 (0.80–1.30)) than omnivorous women (*p* = 0.02) Additionally, pancreatic β-cell function as estimated by HOMA2 (HOMA2%B) was significantly higher in subjects on a plant-based diet than in those consuming meat products (115.5 ± 42.9 vs. 91.0 ± 35.0, *p* = 0.04), indicating that a vegetarian diet had a positive effect on improving β-cell function.

Ellsworth et al. [39] focused on verifying whether lifestyle changes would improve IR in individuals with different severities of metabolic dysfunction. Participants with type 2 diabetes, ischaemic heart disease or at risk from these diseases were allocated to two groups: an intensive non-randomised programme with a strict vegetarian diet (90 participants in the study group, 90 in the control group) or a moderately randomised trial on a Mediterranean diet (89 participants in the study group, 58 in the control group). The research carried out over one year showed that both interventions resulted in weight loss (−8.9% (95% CI, −10.3 to −7.4)—intensive programme; −2.8% (95% CI, −3.8 to −1.9) moderate programme; *p* < 0.001). Insulin resistance was measured using the Lipoprotein IR Index (LPIR) score. LPIR are six lipoprotein parameters that exhibit the strongest association with insulin resistance. This index may be useful for identifying patients at risk of developing type 2 diabetes and ischaemic heart disease [52]. Insulin resistance significantly decreased for subjects in both groups (LPIR −13.3% (95% CI, −18.2 to −8.3) intensive; −8.8% (95% CI, −12.9 to −4.7)—moderate; *p* < 0.01). Based on the results, one may conclude that both a strict change to a vegetarian diet as well as a moderate change to a Mediterranean diet is effective in improving IR as defined by LPIR [39].

On the other hand, Garousi et al. [40] compared the effects of a lacto-vegetarian diet with a standard weight loss diet in a group of 75 adults with nonalcoholic fatty liver disease (NAFLD). The nutritional make-up for both diets was similar: 50–55% carbohydrates, 15–20% protein and 25–30% fats. After 3 months of research, alanine aminotransferase (ALT) levels were significantly lower for the lacto-ovo-vegetarian group (−21.32 ± 19.77 vs. −10.15 ± 20.30 IU/L; *p* = 0.04) as compared with the standard weight loss diet. In addition to improving liver function, the lacto-vegetarian diet resulted in significant reductions in body weight, BMI, waist circumference and lipid profile parameters. Additionally, compared to the group on a weight loss diet, the lacto-vegetarian diet resulted in a significant reduction in insulin (−4.94 ± 5.40 vs. +0.81 ± 8.35 μU/mL; *p* = 0.006) and lower HOMA-IR index values (−1.62 ± 1.48 vs. +0.02 ± 2.14; *p* < 0.001). According to research, following a lacto-vegetarian diet had a more beneficial effect on improving the health of NAFLD patients than following a standard reduction diet.

The association of insulin resistance, prediabetes and type 2 diabetes with vegan and vegetarian diets was also analysed. Chen et al. [41] carried out research on a group of 6798 subjects. They showed that more plant-based foods and fewer animal products in a diet result in lower insulin resistance and a lower risk of prediabetes and type 2 diabetes. This suggests that plant-based products may reduce the risk of insulin resistance and its negative effects.

#### **5. Impact of a Vegan Diet on Insulin Resistance**

Compared to other vegetarian diets, vegans generally consume less saturated fat, less cholesterol and more fibre. Higher intakes of antioxidants and magnesium also elevate insulin sensitivity (Table 3) [53].


**Table 3.** The influence of a vegan diet on insulin resistance.


**Table 3.** *Cont.*

The composition of dietary proteins influences glucagon and insulin activity in the body [64], which in turn affects body composition and insulin resistance [65]. It was found that a high intake of branched-chain amino acids (leucine, isoleucine and valine) can increase insulin resistance. Kahleova et al. [54] conducted a study to determine the effects of plant protein on body weight, body composition and insulin resistance in overweight individuals. A group of 75 participants with excessive body weight was randomly assigned to a group on a plant-based diet (*n* = 38) or following a control diet (*n* = 37). The vegan group achieved significant reductions in body weight (−6.5 kg (95% CI −8.9 to −4.1 kg); *p* < 0.001), fat mass (−4.3 kg (95% CI −5.4 to −3.2 kg); *p* < 0.001) and HOMA-IR (treatment effect −1.0 (95% CI −1.2 to −0.8); *p* = 0.004). Interestingly, it was noted that a decrease in fat mass was associated with an increase in plant protein intake and a decrease in animal protein intake (r = −0.30, *p* = 0.011; r = +0.39, *p* = 0.001, respectively). Proportionally reduced dietary leucine intake was positively correlated with decreased fat mass (r = +0.40; *p* < 0.001), and proportionally reduced histidine intake was associated with decreased insulin resistance (r = +0.38; *p* = 0.003). To conclude, plant protein as a component of a plant-based diet was associated with improved body composition, reduced body weight and reduced insulin resistance.

The positive effects of a low-fat vegan diet on insulin resistance and insulin sensitivity index (PREDIM) were shown by numerous studies [55–60]. Kahleova et al. [61] used a randomised controlled trial to examine the effects of a low-fat vegan diet on body weight, insulin resistance as well as hepatic and intracellular lipid levels in overweight adults. The study group comprised 244 people, 122 of whom were allocated to an intervention group on a low-fat vegan diet (approximately 75% energy from carbohydrates, 15% from proteins and 10% from fats), with the remaining 122 eating as before during the 16-week study period. Based on the results, participants in the intervention group recorded a weight reduction of 5.9 kg (95% CI, 5.0–6.7 kg; *p* < 0.001). The HOMA insulin resistance index was also lower (−1.3; 95% CI, −2.2 to −0.3; *p* < 0.001) and the predicted insulin sensitivity index (PREDIM) increased (0.9; 95% CI, 0.5–1.2; *p* < 0.001) for the vegan group. The low-fat vegan diet also had a positive effect on lipid levels, with hepatic cell lipid amounts down by 34.4% and intracellular lipids down by 10.4%. The above variables did not change significantly for the control group. The results confirm the positive effects of a plant-based diet on both body weight, insulin resistance and body lipid levels.

Barnard et al. [62] carried out a similar study. A group of 64 postmenopausal women with excessive body weight were randomly assigned to a group on a low-fat vegan diet (10% energy from fats, 15% from protein, 75% from carbohydrates) or a control diet (fat 30%, protein about 15%, carbohydrate 55%). An analysis of these results showed that mean body

weight in the intervention group decreased by 5.8 ± 3.2 kg as compared with 3.8 ± 2.8 kg in the control group (*p* = 0.012). On the other hand, the insulin sensitivity index, calculated using a quotient of fasting blood glucose and fasting insulin, mean blood glucose and mean insulin concentration, increased from 4.6 ± 2.9 to 5.7 ± 3.9 (*p* = 0.017) in the intervention group; however, differences between groups were not significant (*p* = 0.17). Studies have shown that despite not specifying recommended portion sizes or amounts of food, women switching to a low-fat vegan diet lost significant amounts of weight.

Sli ´ z et al. [ ˙ 63] compared a vegan diet (*n* = 44) to an omnivorous diet (*n* = 54) among Polish athletes. The vegan group had significantly better HOMA-IR (0.96 ± 0.49 vs. 1.17 ± 0.44; *p* < 0.01), fasting glucose (4.3 ± 0.6 vs. 4.8 ± 0.5 mmol/L; *p* = 0.01) and C-peptide (1.2 ± 0.4 vs. 1.5 ± 0.35 ng/mL; *p* < 0.01) results than the group consuming animal products. Vegan athletes who consumed sufficient macronutrients with their diet had better insulin sensitivity and lower cholesterol levels than omnivorous athletes. However, the content of dietary protein, EPA and DHA may have been insufficient, and supplementation should be considered for vegan athletes.

#### **6. Discussion**

Research indicates that people decide to change to a plant-based diet mainly for health reasons, then for satisfaction and well-being. The environmental and ethical factors were reported less frequently [66,67]. In addition to many positive effects on human health, plant diets can also show negative features. It was suggested that plant-based diets are suitable for people at all stages of life, as long as they are well-balanced and planned [68]. One of the main objections to using plant-based diets is that they are deficient in protein. Research shows that currently, there are no significant differences in covering protein requirements, provided that plant sources of protein are used: legumes, soy products, nuts and seeds [10,67,69,70]. The nutritional value of legume protein is slightly lower than that of meat. Due to the limiting amino acids in plant protein sources, it is necessary to combine the legumes with cereal products, which supplement the missing amino acids [71,72]. Incorrect selection of plant foods may result in deficiencies of micronutrients, in particular B vitamins (B2 and B12), vitamin D, iron, zinc and calcium. Vitamin B12 is present mainly in animal products (liver, meat, milk, dairy products, eggs); therefore, it is essential to choose products enriched with this vitamin or take dietary supplements [68,73]. It is important to seek health and dietary advice based on scientific evidence [74].

Some people cannot decide to exclude animal products from the diet completely, and therefore they limit their consumption [68]. The maintenance of plant-based diet habits is influenced by many factors, including personal factors, friends and family and the availability of vegetarian and vegan products [68,75]. Questionnaire research indicates that the main barrier to changing eating habits is the pleasure of eating meat and the difficulty in giving up eating it [67,76]. For many people, the difficulty adapting plant diets is the complicated process of preparing meals, the availability of ready-made products and dishes, high prices, lack of variety and unpalatable meals [67,77–80]. Research shows that replacing animal proteins with legumes is difficult. The respondents indicate that dishes based on legumes are unattractive and unpalatable, which is a severe barrier to plant-based diets [71,81–84].

In addition, vegetarians can experience discrimination and social constraints. Individuals may feel rejected and judged by other family members due to plant-based diets. Moreover, established eating habits and attitudes constitute a barrier to changing the diet to a plant-based, mainly for the elderly [67,68].

The main limitations of our study include the extensive literature on plant-based diets. Unfortunately, among many articles, few of them focused on the impact of plant-based diets on insulin resistance, which makes the presented results favourable and unambiguous. We did not find any study showing a negative effect of plant-based diets on insulin resistance. Many of the 44 references used for the review were comparative studies. Few randomised controlled trials limit the possibility of unequivocally establishing the effect of plant-based

diets on insulin resistance. We believe that more research of this type is needed, setting future research directions. Thanks to the PRISMA diagram, we were able to conduct the above literature review. It made our work easier and systematised the analysis of the topic of the influence of plant-based diets on insulin resistance.

#### **7. Conclusions**

Vegetarian diets show beneficial effects not only on insulin resistance but also on other health parameters, including body weight, body fat, BMI and lipid profile parameters. Meat-free diets are suitable for everyone, regardless of age or health. Unfortunately, improperly balanced plant-based diets may carry a risk of nutritional deficiencies, in particular deficiencies in protein, B vitamins, iron, zinc and omega 3 fatty acids.

This review has such limitations as studies in languages other than English, children studies, no abstract, limited data and no data on plant-based diet and insulin resistance. However, more research is needed to determine whether plant-based diets can be used to treat diseases.

Based on available research, it may be concluded that vegetarian diets deliver good results, that more plant-based foods and fewer animal products in a diet result in lower insulin resistance and a lower risk of prediabetes and type 2 diabetes.

**Author Contributions:** Conceptualisation, M.B. and I.G.; formal analysis, M.B.; writing—original draft preparation, M.B.; writing—review and editing, M.B., I.G. and J.P.; supervision, I.G. and J.P. All authors have read and agreed to the published version of the manuscript.

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

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

#### **References**


## *Review* **Dysmetabolism and Neurodegeneration: Trick or Treat?**

**Adriana M. Capucho †, Ana Chegão †, Fátima O. Martins, Hugo Vicente Miranda and Sílvia V. Conde \***

CEDOC, NOVA Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, 1150-082 Lisboa, Portugal; adriana.capucho@nms.unl.pt (A.M.C.); ana.chegao@nms.unl.pt (A.C.); fatima.martins@nms.unl.pt (F.O.M.); hmvmiranda@nms.unl.pt (H.V.M.)

**\*** Correspondence: silvia.conde@nms.unl.pt

† These authors contributed equally to this work.

**Abstract:** Accumulating evidence suggests the existence of a strong link between metabolic syndrome and neurodegeneration. Indeed, epidemiologic studies have described solid associations between metabolic syndrome and neurodegeneration, whereas animal models contributed for the clarification of the mechanistic underlying the complex relationships between these conditions, having the development of an insulin resistance state a pivotal role in this relationship. Herein, we review in a concise manner the association between metabolic syndrome and neurodegeneration. We start by providing concepts regarding the role of insulin and insulin signaling pathways as well as the pathophysiological mechanisms that are in the genesis of metabolic diseases. Then, we focus on the role of insulin in the brain, with special attention to its function in the regulation of brain glucose metabolism, feeding, and cognition. Moreover, we extensively report on the association between neurodegeneration and metabolic diseases, with a particular emphasis on the evidence observed in animal models of dysmetabolism induced by hypercaloric diets. We also debate on strategies to prevent and/or delay neurodegeneration through the normalization of whole-body glucose homeostasis, particularly via the modulation of the carotid bodies, organs known to be key in connecting the periphery with the brain.

**Keywords:** insulin signaling; metabolic disorders; hypercaloric diets; neurodegeneration

#### **1. Introduction**

Aging is broadly defined as the time-dependent functional decline that affects most living organisms. It is characterized by a progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death [1]. Aging is pointed as the main risk factor for major human pathologies, including cancer, diabetes mellitus (DM), cardiovascular disorders, and neurodegenerative diseases. The hallmarks of aging are genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular communication [1]. Aging is therefore accompanied by a variety of physiological changes that make the organisms highly prone to so-called aged-related disorders. On the other hand, several diseases such as cardiometabolic and metabolic disorders can accelerate the aging and senescence process.

The metabolic syndrome is defined as a cluster of disorders including obesity, DM, and cardiovascular disease, and is reaching pandemic levels worldwide. Several studies have shown that the prevalence of metabolic diseases increases up to 50% in people aged over 65 years [2,3]. Consequently, as the global population ages, there will be a higher prevalence of obesity, DM, and cardiovascular disease [4,5].

Additionally, neurodegenerative diseases, which are characterized by the progressive loss of the structure or function of neurons, are classified as an age-related cluster of pathologies that were shown to be increased in the set of metabolic disorders [6,7]. Age and metabolic disorder-related neurodegenerative diseases, such as Alzheimer's (AD) and

**Citation:** Capucho, A.M.; Chegão, A.; Martins, F.O.; Vicente Miranda, H.; Conde, S.V. Dysmetabolism and Neurodegeneration: Trick or Treat? *Nutrients* **2022**, *14*, 1425. https://doi.org/10.3390/ nu14071425

Academic Editor: Paolo Brambilla

Received: 24 February 2022 Accepted: 25 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 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/).

Parkinson's (PD), are increasing worldwide, and it is not clear whether metabolic syndrome is the cause or the consequence of these neurodegenerative diseases [7–10].

Changing lifestyles and eating habits opened a door to pronounced alterations in the aging process. In this review, we mainly focus on the relationship between metabolic syndrome and neurodegenerative diseases, exploring the role of hypercaloric diets in brain function and the physiological role of insulin and insulin resistance in the central nervous system (CNS).

#### **2. Insulin and Insulin Signaling Pathways**

Around one hundred years ago, in 1921, an important hormone—insulin—was discovered by Frederick G. Banting, Charles Best, and John MacLeod in Canada. This event completely changed the lives of mankind, and since then thousands of lives have been saved [11].

Insulin is a 51 amino acids peptide hormone, organized in 2 polypeptides chains, A (21 amino acids) and B (30 amino acids), which are connected by disulfide bonds. Insulin production and release is carried out by pancreatic β-cells. This hormone plays a fundamental role in regulating fat and carbohydrate metabolism, promoting their conversion into storage macromolecules such as glycogen, proteins, and lipids [12–14].

Insulin is the most important anabolic hormone in the organism and plays a critical role in the regulation of different physiological processes, including metabolism, cell growth and differentiation [12,15]. The effects of insulin are highly pleiotropic and differ according to the target tissue. Concerning the peripheral regulation of metabolism, the major insulin-sensitive tissues include the skeletal muscle, adipose tissue, and liver [16]. Insulin stimulates the uptake of glucose in the skeletal muscle and adipose tissue, inhibits hepatic glucose production, and promotes the storage of substrates in the adipose tissue, liver, and muscle by triggering lipogenesis, glycogen, and protein synthesis, and by inhibiting lipolysis, glycogenolysis, and protein breakdown [15–17]. Insulin also plays a role in the CNS, mainly controlling appetite, by acting in the hypothalamus, one of the most important neuronal centers to the control of satiety and feeding through negative feedback to ensure balanced energy homeostasis [18].

Although insulin plays several functions in the organism, it is widely known as a glucose homeostasis regulating hormone. The tight control of the plasma levels of glucose is driven by the balance between glucose absorption from the intestine after feeding, production by the liver and uptake and utilization by peripheral tissues. An increase in the plasma glucose levels after a meal triggers insulin production in the pancreatic islets by β-cells.

Insulin acts by binding to its receptor, the insulin receptor (IR). IR is a glycoprotein that belongs to the tyrosine kinase receptor family, being composed by an α extracellular and β transmembrane subunits. When insulin binds to the α subunit, it triggers the dimerization of the receptor, forming an α2β2 complex, leading to the autophosphorylation of β subunit at Tyr1158, 1162, and 1163, the so-called activation of IR phosphorylation cascade (Figure 1) [15].

Activation of IR signaling pathway leads to the recruitment and phosphorylation of several proteins such as the insulin receptor substrates (IRS 1-4), that will induce the activation of intracellular pathways as phosphatidylinositide-3-kinase (PI3K) and the mitogen-activated protein kinase (MAPK) cascades [15,19]. Insulin-induced activation of Ras-MAPK promotes the regulation of cell growth and mitogenesis, whereas PI3K activation generates phosphatidylinositol (3,4,5)-triphosphate (PIP3) that will activate phosphoinositide dependent protein kinase-1 and -2 (PDK1 and PDK2), which regulate the effect of insulin on metabolism and pro-survival. Also, PDK1 and 2, have an important function in the activation of Protein Kinase B (AKT/ PKB) [15,17,20]. AKT has a key function in phosphorylating several downstream proteins, as glycogen synthase kinase b (Gsk3b), that inhibits glycogen synthesis [20,21]. AKT also phosphorylates other mediators such as the activation of AKT substrate 160 kDa (AS160) and activates Rab10 GTPase that leads to the translocation of the glucose transporter 4 (GLUT4) to the plasma membrane to promote glucose uptake [22]. Phosphodiesterase 3B (PDE3B), an enzyme that catalyzes the degradation of cyclic adenosine monophosphate (cAMP), is also activated by AKT. Additionally, AKT inhibits cAMP response element-binding protein (CREB)-regulated transcription coactivator 2 (CRTC2), important to increase hepatic gluconeogenesis. AKT also triggers liver lipogenesis by phosphorylating sterol regulatory element-binding protein 1 (SREBP1) [23], and phosphorylates Forkhead box protein O1 (Foxo1), inhibiting its transcriptional activity and leading to a suppressed liver glucose production [15,21].

**Figure 1.** Schematic representation of insulin signaling pathways. When insulin binds to its receptor (IR), autophosphorylation of tyrosine kinase residues occurs and various downstream regulatory proteins are recruited. The insulin signal is transduced among the target proteins/enzymes ending with the fusion of the glucose transporter 4 (GLUT4) vesicle with the cell plasma membrane and the placement of GLUT4 transporters in the plasma membrane leading to the uptake of glucose. IRS, insulin receptor substrate; PI3K, phosphatidylinositide-3-kinase; PIP2, phosphatidylinositol (4,5) biphosphate; PIP3, phosphatidylinositol (3,4,5)-triphosphate; PDK1, phosphoinositide-dependent protein kinase-1; AKT, protein kinase B; AS160, Akt substrate of 160 kDa; aPKC, atypical protein kinase C; SOS/RAS, son of sevenless; MEK, mitogen-activated protein kinase; AMPK, mitogenactivated protein kinase; PFKFB3, 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3; ATGL, adipose triglyceride lipase; GSK3b, glycogen synthase kinase b; FoxO, forkhead box protein O1; mTORC1, mammalian target of rapamycin complex 1; GLUT4, glucose transporter type 4.

#### **3. Metabolic Syndrome: Insulin Resistance and Diabetes**

Failure in insulin production, insulin tissue-sensitivity, and/or insulin signaling is a pathophysiological hallmark of pre-diabetic states, resulting in hyperglycemia due to impaired uptake of glucose by cells and deregulation of hepatic glucose production [24]. Furthermore, insulin resistance together with obesity, hypertension, hyperlipidemia, dyslipidemia and hyperglycemia are key features of metabolic syndrome. DM is one of the most prevalent metabolic disorders, characterized by chronic hyperglycemia.

DM has reached pandemic levels, affecting more than 463 million people and being among the top 10 causes of deaths in adults worldwide [25]. DM is a complex, chronic condition that has a major impact on the lives and well-being of individuals, families, and a vast associated economic burden for societies [26]. DM refers to a group of chronic metabolic diseases characterized by hyperglycemia and dysfunction or destruction of pancreatic β-cells, causing defects in insulin secretion, insulin action, or both [27–29]. The heterogeneous etiopathology mechanisms leading to the decline in function, or the complete destruction of β-cells includes genetic predisposition and abnormalities, epigenetic processes, insulin resistance, autoimmunity, concurrent illnesses, inflammation, and environmental factors [27,30]. Moreover, DM is also associated with disturbances of carbohydrate, fat, and protein metabolism.

The World Health Organization (WHO) has recently proposed the classification of DM into six subtypes: type 1 diabetes mellitus (T1D); type 2 diabetes mellitus (T2D); hybrid forms of DM; other specific types; unclassified DM; hyperglycemia during pregnancy [31]. T1D is characterized by the destruction of pancreatic β-cells, resulting in the inability of the body to produce insulin. Consequently, individuals suffering from T1D are insulin dependent. In contrast, T2D which accounts for 90% of all diabetic cases, is characterized by insulin resistance. The pathogenesis of T2D involves abnormalities in both peripheral insulin action and insulin secretion by pancreatic β-cells, resulting in hyperglycemia. Insulin resistance, in an initial phase called prediabetes, is usually compensated by hyperinsulinemia, that, although it might be tolerated in the short term, will lead to a vicious cycle in which chronic hyperinsulinemia exacerbates insulin resistance and contributes directly to β-cells exhaustion and the definitive settlement of T2D.

DM predisposes to long-term complications including retinopathy, nephropathy, and neuropathy. DM increases the risk for cardiovascular diseases, obesity, cataracts, erectile dysfunction, and non-alcoholic fatty liver disease (NAFLD) [31].

Furthermore, insulin resistance syndrome negatively impacts the CNS function and DM is associated with an increased risk to develop neuropsychiatric conditions and neurodegenerative disorders [6]. As such, the control of peripheral and central insulin levels can be an important approach to prevent the development of those pathologies.

#### *Impact of Hypercaloric Diets on Insulin Resistance and Metabolic Syndrome*

The consumption of hypercaloric diets and overnutrition is a common trigger for the development of obesity, which is the major driver to insulin resistance, prediabetes and T2D. In recent years, the role of nutrition and lifestyle in the development of metabolic diseases has been extensively explored. Due to the fast-paced and sedentary lifestyles of modern society, the intake of hypercaloric and unhealthy food has risen, increasing the incidence of metabolic diseases in the population, and negatively impacting the aging process, aggravating the aging-associated consequences [25,31].

It is well known that the amount and nature of macro and micronutrient consumption is associated with the development of obesity and T2D, since the dietary composition and volume could be implicated in insulin resistance states, being in the genesis of T2D [32]. More than the hypercaloric composition, overnutrition is also one of the causes to the development of dysmetabolism [33]. Excessive or incorrect nutrients consumption, both carbohydrates or fat, promotes a general state of inflammation, which has an important role in insulin dysfunction as well as in energy production and catabolism of those nutrients [34].

Studies, both in animal models and humans, showed that hypercaloric diets, independently of their composition, lead to dysmetabolic states. For example, diets enriched in fat, free sugars, and fructose lead to whole-body insulin resistance [32,35]. However, different diets composition originates different metabolic features [36]. Melo et al. [36], by submitting different groups of Wistar rats to a high-fat diet (HF), high-fat–high-sucrose diet (HFHSu) and high-sucrose diet (HSu) for different periods of time, showed that the animals submitted to a HF diet for 19 weeks exhibited increased weight gain when compared to the other groups. On the other hand, animals fed with an HSu diet for 16 weeks did not exhibit a significant increase in weight gain, while animals under HFHSu diet showed an intermediate increase in weight gain between the HSu and HF groups. In fact, Melo et al. showed that the HF diet feeding model was the hypercaloric diet model that presented more phenotypic features related to T2D and obesity in humans, since they developed insulin resistance, impaired lipid and glucose metabolism, elevated sympathetic activity, hypertension, hypertriglyceridemia, and increased lipid deposition in the liver resembling NAFLD [37]. In the case of the HFHSu diet, animals exhibit significant alterations in insulin and C-peptide levels, which can be associated with impaired pancreatic insulin secretion and/or impaired insulin clearance. This finding suggests that the combination of sucrose with fat has a greater impact in glucose metabolism and insulin secretion [36,38]. These differences in the effects of diet nutrient composition over metabolism needs more clarification for a full understanding of the mechanisms in physiological and pathophysiological conditions, as well as for developing new therapies for these types of pathologies.

The adipose tissue is the most important organ for fatty acids storage. However, adipocytes have a limited capacity to accumulate fat, and when they over-expand, they create a hypoxic environment. This environment triggers the overactivation of hypoxic inducible factor 1 (HIF-1) protein creating an inflammatory state that leads to insulin resistance in the adipocytes [39–41]. Additionally, insulin has a role as anti-lipolytic hormone, therefore inhibiting the release of adipose tissue fatty acids to the circulation [42]. However, in a state of excessive fat accumulation within the adipose tissue and a state of insulin resistance, the levels of free fatty acids in the circulation increase. This will stimulate the uptake of fatty acids by other tissues, such as the liver, which does not have the capacity to store high amounts of fat. This increased uptake of free fatty acids by the liver contributes to liver insulin resistance and consequent hepatic glucose production and glycogen synthesis deregulation, aggravating the whole-body insulin resistance and glucose homeostasis impairment [43,44]. This event is one of the first consequences of dysmetabolism, leading to the development of NAFLD, which is present in 90% of obese T2D patients [45].

Regarding the role of sugar on metabolism and dysmetabolism, it is known that the different types of sugars are additive, palatable, and rewarding compounds from food [46]. Excessive sugar consumption leads to several metabolic disorders, and some related brain pathologies, impairing reward systems which may lead to compulsive eating [47,48].

Sugar refers to a category of carbohydrates which includes fructose and glucose (monosaccharides), and lactose and sucrose (disaccharides), having different roles in the organism. Many people consider fructose as a healthy sugar because it is naturally present in fruit and other vegetables. However, our body does not respond similarly to the fructose that is present in the fruit and the fructose that is added on the diet [49–51]. In fact, the added fructose, mainly in the form of corn syrup, is one of the major causes of insulin resistance and impaired lipid metabolism associated to T2D and its comorbidities [49–53]. Additionally, recent studies have shown that when fructose is replaced by glucose in starch form in the diet these pathological features were alleviated [54,55]. The differences in the metabolic features promoted by glucose and fructose indicate that the metabolic pathways involved are different. In fact, fructose absorption in the gut promoted by GLUT5 is independent of insulin as well as its usage in the liver. In the liver, fructose is converted into glucose, lactate, and fatty acids, that will be released to the blood, oxidized, and used by other tissues [51,56,57]. Glucose is taken by muscle and the adipose tissue to be used as energy source. In contrast, these tissues do not uptake fructose since this sugar is not a primary source of energy [46]. In opposition to glucose and sucrose, fructose promotes smaller effects in plasma glucose levels, satiety hormones and insulin action, leading to overconsumption of calories to promote satiety [58].

#### **4. The Role of Insulin in the Brain**

As stated before, insulin acts in insulin-sensitive peripheral tissues such as the liver, the skeletal muscle, the pancreas, and the adipose tissue to regulate glucose homeostasis. However, insulin has been also described to play a key role in the CNS [19,20]. Insulin action in the brain regulates several processes including energy expenditure, glucose homeostasis, feeding behavior and satiety, reward pathways, reproduction, cell proliferation and differentiation. Moreover, insulin has neuroprotective and neuromodulatory properties and plays a crucial role in neuronal transmission and survival, neurogenesis, plasticity, and memory and cognition [19,20,59,60].

Insulin reaches the brain from circulation by crossing the blood–brain barrier (BBB) through a selective, saturable transport [19,61,62]. Although insulin levels in cerebrospinal fluid (CSF) are much lower than in plasma, these levels are correlated, indicating that most insulin in the brain may derive from circulating pancreatic insulin [63,64]. Some authors demonstrated the presence of relatively high concentrations of insulin in brain extracts, suggesting that insulin could also be synthetized in the CNS and released locally, as already established for other hormones [65,66]. In fact, some data supports that neurons and astrocytes may produce insulin in relative higher amounts [67–69]. Insulin synthesis was described to be independent of its peripheral concentrations, since insulin levels in the hypothalamus were not related to pancreatic insulin concentrations during fasting [70]. Additionally, it was found that GABAergic neurons synthetize insulin in the rat cerebral cortex, as well as in the hypothalamus and hippocampus [71]. However, insulin production by neurons and glial cells is still under debate.

Nevertheless, it is widely accepted that most insulin in the brain originates in the pancreas. The rate of insulin transport across the BBB is influenced by physiological factors such as exposure to hypercaloric diets, hyperglycemia, or a diabetic state. These conditions lead to a decreased transport of insulin to the brain, and/or to a disruption of the BBB. Rhea et al., showed that IR inhibition with the selective antagonist S961, does not modify insulin transport across the BBB, suggesting that the hormone is able to cross this barrier in an IR signaling-independent manner [72].

In the brain, two different isoforms of IR can be found: a long isoform, IR-B, and a short predominant isoform [73], IR-A, with insulin having similar affinity and potency for both isoforms [74–76]. IR are expressed at high levels in different brain regions such as the hypothalamus, olfactory bulb, hippocampus, striatum, cerebral cortex, and cerebellum, where they are involved in different functions. The olfactory bulb is the region of the brain that contains the highest amount of IR, although its function in this area is not fully understood. The cortex and the hippocampus also have high levels of IR with very important functions in terms of synaptic plasticity and cognitive processes. The third region that contains the highest levels of this receptor is the hypothalamus with a central role in the regulation of glucose homeostasis and regulation of satiety pathways [77]. Most of these actions seem to be independent of glucose utilization [62,78].

#### *4.1. Role of Insulin in Brain Glucose Metabolism and Feeding*

Glucose is the main source of energy in the brain, reaching this organ through the GLUTs, which have numerous isoforms [6]. Most glucose uptake to brain and by neuronal and glial cells is insulin-independent and relies on the insulin-insensitive GLUT1 and GLUT3, among other less abundant region-specific GLUTs [79–81]. However, in brain regions related to cognition and metabolic control, such as the basal forebrain, hippocampus, amygdala, cortex, cerebellum, and hypothalamus, the inducible insulin-sensitive GLUT4 is reported to be co-expressed with GLUT3 [82–84]. Activation by insulin induces the translocation of GLUT4 to the membrane and is thought to improve glucose flux into neurons during periods of high metabolic demand, such as learning [85,86]. The effect of insulin and its relevance on glucose uptake in the brain is not consensual and for a long time was thought to be exclusively insulin independent. In fact, several studies from the 80 s that focused on the effect of insulin in brain glucose uptake showed that brain glucose metabolism was unaffected by insulin. Goodner et al. measured glucose uptake in fasting rats 30 min after 0.1U insulin administration [87] and observed that the brain did not increase the rate of glucose uptake, concluding that the brain was "insensitive to insulin" [88]. However, subsequent studies using 18-fluorodeoxyglucose positron emission tomography showed that insulin increases brain glucose uptake in humans, mostly marked in cortical areas [89]. In agreement with the role of insulin in regulating glucose uptake in the brain, GLUT4 colocalizes with IR in several brain regions [90].

Another important role of insulin in the indirect control of glucose homeostasis is related with the mesolimbic reward pathways on the dopaminergic neurons [91]. Insulin can alter food choices and preferences, as it increases dopamine release in the reward pathways after food restriction, an effect not seen in animals submitted to hypercaloric diets. Therefore, it has been shown that dopaminergic pathways are negatively regulated by insulin, as insulin causes the desire to consume high-calorie foods rich in sugar and fat, promoting a feeling of satiety and reward [91,92].

Beyond its role on controlling glucose homeostasis, insulin in the brain also impacts insulin sensitivity at the periphery. Heni and colleagues have found in lean men that central insulin action, achieved by an intra-nasal insulin spray application, improved peripheral insulin sensitivity measured by hyperglycemic clamp [93]. When they explored the mechanisms involved, they discovered that the vagus nerve and the parasympathetic system activation is involved in the peripheral insulin sensitivity regulation by CNS insulin. The hypothalamus appears to be one of the most important brain regions in this link [94]. Moreover, they have found an impairment in the central regulation of peripheral insulin signaling in obese people, showing the tight link between brain and peripheral insulin resistance in dysmetabolism conditions [94].

Apart from glucose homeostasis regulation, insulin and its receptors have implications in the regulation of energy balance where the hypothalamus also plays a major role [18]. Together with insulin, leptin, an adipokine produced by the adipose tissue, plays a role in the regulation of feeding behavior. Both hormones act on the hypothalamus affecting the expression of neuropeptides involved in satiety pathways [18,95]. In the hypothalamus, these hormones act in the arcuate nucleus (ARC) which is composed by two antagonistic types of neurons: the anorexigenic neurons or the appetite-suppressing neurons known as proopiomelanocortin-expressing neurons (POMC) neurons, and the orexigenic or appetitestimulating neurons known as neuropeptide Y (NPY) and agouti-related peptide (AgRP) expressing neurons [18,95] (Figure 2). Both orexigenic and anorexigenic neurons express insulin and leptin receptors.

The POMC neurons project to the second order neurons in the paraventricular nucleus (PVN) but also to the lateral hypothalamus (LH), the ventromedial hypothalamus (VMN) and the dorsomedial hypothalamus (DMH) [96]. The PVN is responsible, in part, for the secretion of a wide range of regulatory neuropeptides and by the control of sympathetic nervous system activity to peripheral organs [18]. After a meal, POMC is cleaved in α-melanocyte- stimulating hormone (α-MSH) that is released by the POMC neurons to activate melanocortin 3 and 4 receptors (MC3/4R) in both hypothalamic and extra-hypothalamic neurons to suppress feeding and food intake and stimulate energy expenditure [97]. In general, insulin and leptin, by acting on POMC neurons, suppress feeding and promote energy expenditure (Figure 2). In fact, these hormones in POMC neurons also have a role in the regulation of peripheral glucose homeostasis, although the mechanisms involved are not so clear. The disruption of hypothalamic insulin and leptin pathways in POMC neurons, driven by insulin and leptin receptors deletion in these neurons, leads to a state of systemic insulin resistance and deterioration of glucose homeostasis [18]. This suggests that intact insulin and leptin hypothalamic pathways are required for a correct peripheral insulin and glucose homeostasis.

In contrast, in AgRP/NPy neurons, insulin regulates hepatic glucose production by inducing hyperpolarization and decreasing firing between AgRP neurons, which leads to reduced gluconeogenesis. Also, leptin directly acts on AgRP neurons at the hypothalamus to exert an inhibitory effect. As such, the crucial effect of leptin in the hypothalamus is to promote energy expenditure and inhibit food intake [18,97].

Apart from the regulation of feeding behavior and glucose homeostasis, insulin has been shown to have other functions in the brain as the regulation of cognitive functions, particularly memory. Notably, defects in insulin signaling in the brain may contribute to neurodegenerative disorders and damage of the cognitive system leading to dementia states.

**Figure 2.** Role of the hypothalamus in the regulation of energy balance. Signals as leptin and insulin act antagonistically in the two antagonistic neurons of the arcuate nucleus (ARC): the orexigenic (appetite-stimulating) neuropeptide Y (NPY) and agouti-related peptide (AgRP)-expressing AgRP/NPY neurons and the anorexigenic (appetite-suppressing) proopiomelanocortin (POMC) expressing POMC neurons. By one way, insulin and leptin stimulate POMC neurons, in another way they inhibit AgRP/NPY neurons. Both AgRP/NPY neurons and POMC neurons project to second-order neurons in the paraventricular nucleus (PVN), leading to an integrated response on energy intake and expenditure. The intestine also secretes peptide YY (PYY) and the stomach ghrelin that act on AgRP/NPY neurons to stimulate hunger. Dopamine modulates both AgRP/NPY and POMC neurons.

#### *4.2. Insulin and Cognitive Function*

The role of insulin in the brain was first studied in the context of energy homeostasis, a process mainly regulated by the hypothalamus [68]. More recently, the role of insulin in other brain functions such as memory and cognition, neuronal development, and plasticity have been explored [6,98]. Insulin has a key role in the hippocampus, a brain region that expresses high levels of IR and that is involved in learning and memory [99].

The hippocampus is a complex structure in the brain located in the temporal lobe. It forms part of the limbic system, being an extension of the cerebral cortex [100]. The hippocampus is a very plastic region, with a key role in memory formation, organization, and storage [100]. The hippocampus converts short-term memory into long-term memory, solves spatial memory, and recollects the past experiences of places. It also plays a pivotal role in emotions and behavior [101]. Different parts of the hippocampus have distinct functions in certain types of memory: spatial memory, mainly processed by the rear part of the hippocampus; memory consolidation, a process by which the hippocampus organizes the stored information in the neocortex; and memory transfer, since long term memories are not stored in the hippocampus [101]. Information in the hippocampus travels along a unidirectional trisynaptic circuit originating from the entorhinal cortex and projecting to the dentate gyrus (DG), then to area CA3, and finally to area CA1 of the horn of Ammon [102,103]. These areas of the hippocampus have different functions, being composed of different types of neurons: the granular cells in the DG and pyramidal cells in the areas CA1 and CA3 of the horn of Ammon with a vast network of interneurons [102]. Special attention has been paid to the CA3 region for its specific role in memory formation and neurodegeneration [104]. CA3 receive excitatory inputs from the pyramidal cells and then inhibitory feedback that inhibit the pyramidal cells. This recurrent inhibition is a simple feedback circuit that can dampen excitatory responses in the hippocampus, being involved in memory formation processes [105,106].

The hippocampus has also an important role in brain plasticity. Brain plasticity underlies learning and memory and depends both on the activity and the number of synapses. Synapses may be modulated via potentiation or depression, processes that regulate the formation of new dendritic spines promoting tasks, learning, and consolidating behavioral alterations [6,107]. It has been postulated that insulin may be involved in the regulation of synaptic plasticity mechanisms and memory formation, since the mRNA levels of IR in dendrites and synapses at the hippocampus is high [99,108]. In fact, hippocampal neurons treated with insulin for 48 h show an increase in the frequency of miniature excitatory postsynaptic currents (mEPSCs), whilst downregulation of IR with short hairpin RNAs (ShRNAs) leads to the formation of few dendritic spines and therefore to reduced frequency of mEPSCs [6,109]. Insulin was also shown to regulate neuronal plasticity by controlling long-term potentiation (LTP) and long-term depression (LTD) [110]. Moreover, IR substrate p53 (IRSp53) interacts with the postsynaptic density protein 95 (PSD-95), present at excitatory synapses, therefore regulating a variety of receptors and channels, and increasing dendritic spine formation [6,111]. Insulin further plays a role on glutamatergic response by increasing the recruitment of the N-methyl-D-aspartate receptors (NMDARs) to the membrane and by enhancing the phosphorylation of NR2A and NR2B subunits [112]. In agreement, IRS2 knockout mice showed lower activation of NR2B subunits and a decrease in the LTP at CA3-CA1 synapses, however with higher density of CA1 dendritic spines [112–114]. Additionally, downregulation of a-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) activity of CA1 neurons in the hippocampus is crucial to insulin-induced LTD, an important feature to process memory formation [115].

The hippocampus is not the single player in the generation and regulation of memory and cognition. The prefrontal cortex is known to mediate decision making being involved in the retrieval of remote long-term memory and supporting memory and consolidation in a time scale ranging from seconds to days [116]. In fact, the hippocampus and prefrontal cortex work together to support the rapid encoding of new information, consolidation, and organization of memory networks. The cerebral cortex is known to process information about objects and events that we experience, and about the places where they occurred. Additionally, the ventral hippocampus (in the rat) and the anterior hippocampus (in humans) sends information to the medial prefrontal cortex (mPFC), suggesting that the mPFC could accumulate interrelated memories. The information of mPFC is sent back to other cortical regions, however the mPFC may bias or select the retrieval of event information in the 'what' stream. Therefore, interactions between these two brain regions can support the ability to create contextual representations that are associated with recent memories and use them to remember memories that are appropriate within a given context [117].

Considering the key role of the cortex in memory and decision making, it was reported that patients with frontal lobe damage exhibit inappropriate social behaviors and memory decline [89], suggesting that these dysfunctions can result from an impairment of memory storage in the prefrontal cortex. In fact, insulin has also an important role in the cortex, specially the IGFs since they are involved in the process of dendritic elaboration and to the stimulation of neurite outgrowth from dissociated neurons [118].

Therefore, the impairment of insulin signaling in the brain will have a negative impact in some neuronal functions additionally to the ones related with metabolism. These defects in insulin action in the CNS and specially in the hippocampus and prefrontal cortex represent a possible relationship between metabolic and cognitive disorders [119,120].

#### **5. Metabolic Syndrome and the Neurodegenerative Process**

It is consensual that hypercaloric diet consumption leads to obesity and several comorbidities such as insulin resistance, which can be in the genesis of T2D. The defects in insulin signaling also affects the CNS, since obesity and T2D are clear risk factors for several pathologies related with the CSN, like AD and PD [120]. It was observed that patients with T2D and/or obesity, show decreased concentrations of insulin in the cerebrospinal fluid, despite having high insulin levels in plasma [121,122]. Moreover, obesity and inflammation are reported to decrease the transport of insulin to the brain through the BBB, and T2D patients show a decreased expression of IR in the brain [121].

It was described by García-Cáceres and colleagues that alterations in insulin signaling in the brain promotes several alterations not only in neural but also in glial cell function [123]. In fact, insulin action in the brain has a direct effect on neurodegenerative and psychiatric disorders, which include alterations in dopaminergic signaling, hippocampal synaptic plasticity, expression of some proteins, and BBB function, among others [123,124].

In addition, changes in insulin action at the level of the hippocampus affect molecular mechanisms involved in synaptic formation and plasticity, having a negative impact in the maintenance of mental abilities and being a great risk factor for dementia [125]. In states of insulin resistance, IR-dependent molecular cascades may start to be insensitive to this hormone, leading to brain insulin resistance (BIR), and in this situation, insulin lose its ability to improve plasticity [122]. It is well described by different authors that rodents submitted to HF diet is a well-established animal model to study metabolic disorders since it induces metabolic features that resemble T2D in humans. Moreover, it was also described that HF diet leads to alterations in functional and structural brain plasticity, a characteristic similar to other models of insulin resistance [126]. Insulin signaling impairment promoted by a HF diet leads to the decreased expression of a post-synaptic protein, PSD-95, which has a role in promoting maturation and regulation of synaptic strength and plasticity [127]. Moreover, studies performed in Zucker rats, which are known to be insulin resistant, also showed altered hippocampal insulin signaling that was negatively associated with synaptic activity, since those animals presented impairment of LTP at CA3-CA1 synapses [128].

Several studies explored the relationship between obesity and/or T2D and neurodegenerative disorders such as PD and AD, since it was already established that people who develop T2D at an early age have an increased risk to develop neurodegenerative disorders (Figure 3) [6,122,129–131].

**Figure 3.** Metabolic syndrome increases the risk of developing Alzheimer's and Parkinson's diseases. **Left** and **right** panels show, respectively, the pathophysiology of Alzheimer's (AD) and Parkinson's (PD) diseases. The **left** panel, in violet, shows the physiological structure of a healthy brain and an Alzheimer's disease brain, presenting extracellular accumulation of Aβ plaques and intraneuronal accumulation of neurofibrillary tangles of hyperphosphorylated tau protein. The **right** panel, in green, shows the physiological structure of a healthy substantia nigra versus a substantia nigra of PD patient. In PD there is the aggregation and accumulation of aSyn in Lewy bodies which are toxic leading to dopaminergic neuronal loss.

Neurodegeneration is the progressive loss of structure or function of neurons, which may ultimately involve cell death and is considered an age-related process. Neurogenerative diseases are often associated with the misfolding and aggregation of given proteins. AD is the most common neurodegenerative disease, and its pathological hallmark is

the extra-neuronal accumulation of beta-amyloid (Aβ) plaques and intra-neuronal hyperphosphorylation of tau protein and formation of tau neurofibrillary tangles [132,133]. Synucleinopathies comprise a wide group of neurodegenerative disorders with a broader spectrum of clinical presentations, which includes PD, dementia with Lewy bodies, multiple system atrophy, and pure autonomic failure. These disorders are believed to result from the pathological accumulation of neuronal or glial insoluble aggregates of alpha-synuclein (aSyn) [134–139]. Although several genetic mutations and polymorphisms are described to be associated with familiar forms of neurodegenerative diseases, the etiology of these diseases remain largely elusive and molecular mechanisms poorly characterized.

In human studies, AD, cognitive impairment, and other forms of dementia have also been linked to a HF diet, obesity, DM, and metabolic syndrome [7,140–143]. Prospective studies have shown that a diet enriched in saturated fats and refined carbohydrates prime middle-aged and elderly adults to an increased risk for neurological disorders such as AD and to a faster rate of normal age-related cognitive decline [141,144,145]. Additionally, intake of this type of diet in adolescents negatively correlates with visual-spatial learning and memory performance later on and with school performance problems, especially with self-reported difficulties in mathematics [146,147]. Moreover, in children, body mass index negatively correlates with visual-spatial intelligence [148]. Even more, healthy adults exposed to a high saturated fat diet for one week had worse performance on tasks measuring attention and speed of retrieval than they had prior to the diet [149,150]. Epidemiologic reports demonstrate not only an association between obesity caused by dietary fat intake and an increased risk for AD, but also that patients with DM have a 50 to 75% increased risk of developing AD compared to age and gender matched control groups (Figure 4) [151–154]. Furthermore, T2D or impaired fasting blood glucose are reported in 80% of AD patients [155]. Accumulating data suggests that AD is closely related to insulin resistance and the dysfunction of both insulin signaling and glucose metabolism in the brain. The evidence of systemic insulin resistance and defective insulin signaling in the brain being common features of AD transformed T2D as an important risk factor for this neurological disorder. In fact, evidence suggests that insulin resistance and T2D aggravate AD pathology and cognitive deficits [156–159]. Moreover, post-mortem studies have demonstrated central insulin signaling dysregulation in hippocampal and cortical samples from patients with both mild cognitive impairment and early AD [160,161].

The hippocampus and cerebral cortex are the brain regions most vulnerable to aggregation and accumulation of Aβ and Tau, pathological hallmarks of AD. These brain regions express high levels of IRs; however, under AD conditions it has been reported that the levels of insulin mRNA are decreased [162]. In fact, impaired insulin signaling leads to decreased PI3K/AKT signaling, leading to the overactivation of GSK-3β [163]. Consequently, the over-activation of GSK-3β results in the hyperphosphorylation of tau and to a higher production of Aβ peptides, contributing to cognitive impairment (Figure 4) [163–165]. Additionally, insulin deficiency leads to a decrease in GLUTs levels, leading to an impairment in glucose uptake/metabolism in the brain [22]. Decreased intraneuronal glucose uptake causes a reduction of intraneuronal generation of ATP, impairing synaptic activity and cognitive function. It also leads to a decrease in the levels of uridine diphosphate (UDP)- β-N-acetylglucosamine (GlcNAc) via the hexosamine biosynthetic pathway and, consequently, decreasing tau O-GlcNAcylation, a process that inversely regulates tau phosphorylation. An increase of tau phosphorylation induces the formation of tau oligomers, which are neurotoxic, contributing to neuronal loss and degeneration [166].

**Figure 4.** Type 2 diabetes accelerates Alzheimer's disease pathology. Brain insulin resistance leads to alterations in the insulin signaling pathway that inactivate AKT triggering the inactivation of GSK3-β leading to tau hyperphosphorylation and higher production of Aβ peptides. AKT, Protein kinase B; GSK-3β, Glycogen synthase kinase 3 beta; IR, insulin receptor; IRS, insulin receptor substrate.

The unequivocal link between AD and T2D prompted the concept of type 3 diabetes or insulin-resistant brain state to refer to AD [142]. Importantly, epidemiologic studies point long-term hyperinsulinemia as a risk factor for dementia, while insulin administration to AD patients improves memory formation, by keeping glucose levels in the brain constant [167].

The risk for PD has been shown to increase in metabolic syndrome, with T2D and obesity being important risk factors for PD [130,168–171]. Epidemiological studies revealed that 80% of PD patients have impaired glucose metabolism. Moreover, T2D can increase the risk of developing PD up to 50% in the general population and significantly accelerates the progression of both motor and cognitive deficits [9,131,172]. Impressively, this risk increases up to 380% for young diabetic individuals [130,131]. Predictably, obesity also affects motor control capabilities, degrading daily functions and health [171]. Children who are obese or overweight are poorer in gross and fine motor control and have delayed motor development [173–179]. PD is characterized by motor and non-motor symptoms, being a disease deeply associated with aSyn aggregation, accumulation and toxicity, and dopaminergic neuronal loss in the substantia nigra pars compacta [180].

Following the strong evidence of a link between metabolic syndrome and neurodegenerative diseases dissected in clinical and epidemiological studies, animal models of human diseases have been largely used to scrutinize the molecular mechanisms and pathways underling this association. An extensive description of these studies will be provided below.

#### *5.1. The Impact of Diet in Neurodegeneration: Evidence from Animal Models*

Evidence provided by exhaustive research using animal models strongly suggests that diet plays a crucial role in brain physiology and pathology. Currently, the research field is based on both pharmacological and genetic model of AD and PD, both in rats and mice, that have been crucial to disclose molecular mechanisms and dysregulated pathways associated to these neurodegenerative disorders. In the case of metabolic syndrome, as previously described, researchers can also count with diet-induced and genetic models of the disease.

#### 5.1.1. Parkinson's Disease Animal Models

The most accepted and used pharmacological models of PD use 6-hydroxydopamine (6-OHDA) or 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) to induce dopaminergic degeneration and to induce parkinsonism. Most of the work that aimed to study the impact of diet on neurodegenerative diseases has been performed by submitting these PD models to hypercaloric diets. As it can be seen in Table 1, several studies pointed towards an exacerbation of neurotoxic/neurodegenerative mechanism in the presence of hypercaloric diets, and in particular HF diet.

In pharmacological PD-like models, several studies reported that the HF diet potentiated the decrease of tyrosine hydroxylase (TH) and dopamine depletion in the substantia nigra and/or striatum, suggesting that both MPTP and 6-OHDA exacerbate dopaminergic neurodegeneration. Furthermore, a few studies correlated these alterations with motor abnormalities [181–185]. In transgenic models of PD, the same trend for the impact of HF diet in neurodegeneration features was observed (Table 1). In the transgenic mouse models, HF diet accelerated the onset of aSyn pathology, locomotor impairment, and enhanced dopaminergic degeneration [186,187].

In recent years, more physiological studies, based on challenging the animal models to hypercaloric diets, have been the gold standard in the field. Although beyond insulin resistance, DM, and obesity phenotypes, several studies have been reporting the effects of non-standard diets on brain function and physiology, on motor performance and cognition, and on neurodegenerative-associated processes. In more physiological models using wild type (WT) rats and mice, HF diets reduced the levels of TH in the substantia nigra and striatum and attenuated dopamine release and clearance, accompanied by a decrease in movement together with abnormal motor behavior and other behavior alterations [180,188–190]. In a study where no alterations were observed in the levels of aSyn, the authors claimed that the reduction of TH levels in the nigrostriatal axis occurs through an aSyn-independent pathway and can be attributed to brain inflammation, oxidative stress and metabolic syndrome induced by obesity [180].

However, even if these few studies point towards a link between hypercaloric dietsassociated defects in insulin signaling and brain glucose metabolism and the neuropathological features related with PD, the mechanisms by which the hypercaloric diets impact in these neuropathological features deserves further investigation. Also, we would like to highlight, that contrary to what happens for other neurodegenerative diseases as dementia and AD, there are not so many studies evaluating the impact of hypercaloric diets per se and dysmetabolism on the neuronal function and degeneration leading to motor alteration conducting to PD, as well as the impact of different diet composition on these neuropathological features.


**Table 1.** Effects of hypercaloric diets on Parkinson's disease-like and wild-type rodent models.

6-OHDA, 6-hydroxydopamine; aSyn, alpha-synuclein; HF, high-fat; MPTP, 1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine; PD, Parkinson's disease; TH, tyrosine hydroxylase; WT, ild type.

#### 5.1.2. Alzheimer's Disease Animal Models

As stated before, there is an unequivocal link between AD and T2D/impaired insulin signaling, which prompted the concept of type 3 diabetes to refer to AD. This link comes from the extensive literature reporting the deregulation of insulin signaling in AD animal models and patients. Valuable insights on the role of hypercaloric diets in cognitive function were provided by several studies, associating their intake with pathological markers of AD (see Table 2).


**Table 2.** Effects of hypercaloric diets on Alzheimer's disease-like and wild-type rodent models.

AD, Alzheimer's disease; HF, high-fat; HFHSu, High-fat–high-sucrose; NC, normal chow; WT, wild-type.

Velazquez and collaborators reported central insulin dysregulation and energy dyshomeostasis in two transgenic mouse models of AD, the Tg2576 and the triple transgenic AD (3xTg-AD) mice [194]. Moreover, HF diet in transgenic AD-like mice enhanced memory impairment and cognitive decline without affecting the levels of Aβ and phospho-tau protein. The exacerbated phenotype seems to be mediated by increased inflammation and oxidative stress [195,196]. Moreover, both HF or HFHSu diets in WT mice and rats are reported to induce significant cognitive alterations, mainly short- and long-term, contextual, and auditorycued fear memory impairment. These behavioral phenotypes were associated with insulin resistance, increased neuroinflammation, Aβ deposition and neurofibrillary tangle formation, and decreased synaptic plasticity [197–204]. In agreement, Abbott and co-workers conducted a meta-analysis of the results from rodent studies using different diets (HF, HSu, or HFHSu diets) and reported that each type of diet and task adversely affected performance, with the largest effect produced by exposure to a combined HFHSu diet, as assessed by the radial arm maze to evaluate the effect of such diets on cognition [205].

Altogether, hypercaloric diets prompt to neurogenerative processes that are in the basis of cognitive dysfunction and that different diet composition conduce to different pathological mechanisms originating different neuropathological features. Moreover, several mechanisms contribute to these neurogenerative processes, namely insulin signaling dysregulation, altered brain glucose homeostasis, neuroinflammation, and oxidative stress, among others. Therefore, strategies aiming to prevent and regulate the hypercaloric diets associated pathological mechanisms, namely the control of dysmetabolic states might be extremely useful for the prevention and delay of neurodegenerative processes.

#### *5.2. Sex Differences in the Link Dysmetabolism-Neurodegeneration*

Gender or sex is a significant variable in the prevalence and incidence of neurodegenerative disorders, such as AD and PD. In fact, sex dimorphisms have a cardinal role in the pathogenesis, progression, age of onset, and treatment response in AD and PD. The prevalence of AD has been reported to be higher in women than in men (1.6–3:1 female/male ratio) [206–213]. Several studies showed that the extent of cognitive deficits is higher in AD females, with enhanced clinical expression of disease pathology due to more neurofibrillary tangles [214]. In contrast, the prevalence of PD is reported to be higher in men than women (1:1.5–3 female/male ratio) [215–218]. Studies reveled that PD symptoms like rigidity, rapid eye movement behavior disorder, sleep disturbances, deficits in verbal communication, and lack of facial emotions are more prevalent in males. On the other hand, the onset of PD is delayed in females, that exhibit a tremor-dominant form of the disease with greater impairment of postural stability, depression, and reduced ability to conduct daily activities, and in which reduction in visuospatial cognition is more frequent [214].

Although the biological factors and molecular mechanisms underlying these gender disparities are still poorly understood, sex steroid hormones, including estrogens, progestogens, and androgens, have been implicated in these sexual dimorphisms. Notably, sexual steroid hormones are known to regulate energy metabolism [219–223] and exert neuroprotective effects on the adult brain, increasing neural function and resilience and promoting neuronal survival [224–227]. The aging process is characterized by a shift in the hormonal profile both in men and women, with an abrupt loss of estrogens during menopause in women and a gradual but significant decline of testosterone during andropause in men [219,224]. Despite the brain levels of steroid hormones depend on both endocrine gland production and on the local synthesis of neurosteroids, the brain also accounts with a decline in the levels of sex steroids [228]. Thus, reproductive senescence has been implicated in metabolic alterations and negatively impacts neural function and represents a significant age-associated risk factor for AD and PD.

#### **6. Regulation of Metabolic Function as a Prevention of Neurodegeneration**

The existing therapies for neurodegenerative disorders are not disease-modifying. For the case of PD, they are only able to alleviate the symptoms. They are mostly dopaminereplacing therapies (levodopa-carbidopa, dopamine agonists), able to improve PD-motor features in the initial stages of the disease. Tackling non-motor features require nondopaminergic approaches as for example, the selective serotonin reuptake inhibitors [229]. For the case of AD, current treatments also aim to attenuate symptoms (e.g., NMDA receptor antagonists and drugs that target cholinergic transmission) such as cognitive impairment, aggression, and seizures, and do not target disease pathology [230].

Considering the increased risk for neurodegenerative diseases among patients with T2D and the remarkable pathophysiological mechanisms shared, there is mounting interest in the potential of antidiabetic agents for the treatment of these neurological disorders. Hundreds of pre-clinical and clinical studies have explored the potential of antidiabetic medications both for lowering the risk of and as a novel therapeutic avenue for neurodegenerative diseases such as PD and AD.

In recent years, several anti-diabetic drugs have been investigated in PD and AD patients, including metformin, glucagon like peptide (GLP-1) analogues, dipeptidyl peptidase 4 (DPP-4) inhibitors or gliptins, among others. For example, a study performed in a Taiwanese cohort evaluated the usage of metformin, known to reduce blood glucose levels and to decrease insulin resistance, in PD patients and reported that the combination of metformin with sulfonylurea therapy was able to reduce the risk for PD [231,232]. Indeed, findings from human studies are recapitulated in animal models. Metformin administered to MPTP-induced PD mice was shown to improve locomotor activity in these animals [233]. Metformin was also demonstrated to be able to cross the BBB and to act as a neuroprotective agent by reducing tau phosphorylation in primary cortical neurons of a transgenic mouse with the lack of microtubule-associated protein tau (MAPT), a known mechanism of AD development [234]. Moreover, metformin is also reported to have anti-inflammatory properties, to lower reactive oxygen species (ROS) production, and to act as methylglyoxal scavenger and an advanced glycation end products (AGEs) inhibitor [235–239]. Metformin was shown to prevent amyloid plaque deposition and memory impairment in APP/PS1 mice, to restore neuronal insulin signaling and prevent AD-associated pathological changes in neuronal cultures and to protect against Aβ-induced mitochondrial dysfunction [240–242]. Metformin prevents aSyn accumulation, aggregation, and phosphorylation, and prevents or attenuates dopaminergic neurodegeneration, ameliorates pathology, and improves locomotor activity and coordination in acute MPTPand 6-hydroxydopamine (6-OHDA)-induced Parkinsonism rodent models [233,243–247]. Additionally, a Singapore longitudinal aging study showed that long-term treatment with metformin is associated with reduced risks of cognitive decline in T2D patients. [248]. Controversially, studies conducted by Imfeld and Moore reported a slightly higher risk of AD and cognitive impairment in patients with T2D under metformin therapy [249,250]. Furthermore, Kuan, Ping, and Qin showed overall lack of correlation between metformin therapy and the prevention of PD and reported that long-term metformin exposure in patients with T2D may increase the risk for PD and dementia [251–253]. These conflicting results may derive from the high heterogeneity among clinical studies in terms of population, treatment plan, and follow-up. Furthermore, the effect of metformin likely depends on complex neurodegeneration-associated pathological processes and may not be beneficial for all patients with PD or AD, rather than for patients with evidence of metabolic disease.

Thiazolidinediones (TZDs) are insulin sensitizers that primarily reduce insulin resistance in insulin-sensitive tissues in the periphery. Promising results from preclinical studies reported that pioglitazone and rosiglitazone are able to ameliorate memory and cognitive impairment and decrease AD-related pathology in several rodent models of AD, including the 3xTg-AD mice and Tg2576 mice, by enhancing AKT signaling and attenuating tau hyperphosphorylation, neuroinflammation, and AD pathology [254–257]. Furthermore, pioglitazone, rosiglitazone and mitoglitazone were also shown to prevent dopaminergic neurodegeneration and locomotor deficits in MPTP-, STZ-, and 6-OHDA-lesioned rodent models of PD, by modulating the neuroinflammatory response in a neuroprotective fashion [258–264]. These findings are consistent with data from clinical studies. TZDs lead to an improvement in memory and selective attention in patients with AD and amnestic mild cognitive impairment [265–267].

Another class of antidiabetic drugs used to manage the neurodegenerative process are the sulfonylureas, which act to increase insulin release from the beta cells in the pancreas. In fact, several studies support the idea that sulfonylureas could be used as therapeutics to AD and PD. For example, the use of glimepiride was shown to protect against Aβ induced synaptic damage in neuronal cultures [268]. Also, T2D patients treated with sulfonylureas exhibit low risk of dementia, and a combination of metformin with sulfonylureas decreased the risk by 35% over 8 years [269].

Sodium-glucose cotransporter 2 (SGLT2) inhibitors, the newest class of anti-diabetic drugs used in T2D treatment, act via the inhibition of renal glucose reabsorption therefore promoting glucose excursion [270]. SGLT2 inhibitors have neuroprotective actions in T2D mouse models, particularly by reducing inflammation and oxidative stress [271–273]. In agreement, empagliflozin reduced cerebral oxidative stress and impairment of cognitive function in db/db animals [271]. Moreover, dapagliflozin improved cognitive decline in HF diet animals [273].

Another class of anti-diabetics are the modulators of incretins, that include GLP-1 receptor agonists and DPP-4 inhibitors (vildagliptin or sitagliptin) used for T2D and obesity (in the case of GLP-1 agonists). Dulaglutide, a GLP-1 receptor agonist, ameliorated STZ-induced AD-like impairment of learning and memory ability by modulating hyperphosphorylation of tau and neurofilaments [274]. Liraglutide, also a GLP-1 receptor agonist, prevented the accumulation of Aβ plaques, decreased tau hyperphosphorylation and neurofilament proteins, prevented the loss of brain IR and synapses, and reversed

cognitive and memory impairment in several mouse models of AD-like pathology and in a non-human primate model of AD [275–277]. In relation to PD, a huge amount of evidence supported the repurpose of the use of GLP-1 agonists for the treatment of this pathology [278,279]. Exenatide and Ex4 both in animal models and humans was shown to promote neuroprotection (for a review see [278,279]). For example, these drugs in human trials led to an improvement of motor symptoms, to benefits in cognitive performance, as well as to better results in tremor-dominant phenotype [280–283]. Liraglutide and lixisenatide ameliorated locomotor function and prevented dopaminergic neurons degeneration, by suppressing neuroinflammation in the substantia nigra in the MPTP-induced PD-like mice and rotenone-lesioned PD-like rats [284–286]. Also, semaglutide in chronic MPTP mouse model of PD showed to promote neuroprotection by decreasing aSyn levels and neuroinflammation with consequent improvement in motor function [287,288].

Sitagliptin, saxagliptin and vildagliptin, DPP-4 inhibitors, improves learning and memory deficits through decreasing abnormal phosphorylation of tau and neurofilaments (NFs), reducing intercellular Aβ accumulation in 3xTg-AD and STZ-lesioned mice [289–291]. Sitagliptin reversed nigrostriatal degeneration, improved motor performance, and rescued the memory deficits in rotenone-lesioned PD-like rats [286,292]. The use of DPP4 inhibitors and GLP-1 mimetics is associated with a lower risk for PD and AD among patients with T2D, even compared to the use of other oral antidiabetic drugs [293,294]. All together, these clearly state that the decrease in insulin resistance and the regulation of whole-body glucose homeostasis, and therefore a better metabolic control, is associated with better cognitive function and slower deterioration of brain capacities related to the neurodegenerative process.

Another way to improve metabolic control is by targeting the carotid bodies (CBs), metabolic sensors deeply involved in peripheral insulin action and glucose homeostasis [295–297]. The CBs are peripheral chemoreceptors located near the bifurcation of the common carotid artery, classically defined as oxygen, carbon dioxide, and pH sensors [298,299]. They are surrounded by a dense net of capillaries and penetrated by the sensory nerve ending of the carotid sinus nerve (CSN) a tiny branch of the glossopharyngeal nerve which does the connection from the CBs to the brainstem where their information is integrated [298,300]. In recent years, the CBs have been shown to have a key role in the control of peripheral glucose metabolism and insulin action, since it was shown that: (1) in animal models of dysmetabolism and in prediabetic patients, the CBs were overactivated, this being correlated with a state of insulin resistance and glucose intolerance [295,301,302]; (2) insulin, leptin, and GLP-1, known metabolic mediators involved in metabolic regulation, are capable of activating the CBs [295,296,303,304]; and (3) the resection or neuromodulation of the CSN prevents and reverses the pathological features associated with dysmetabolic states [295,297,301]. Considering the close association between metabolic and neurodegenerative disorders, and that the therapeutics that provide metabolic control have a huge impact on neurodegenerative processes, we anticipate that the modulation of the CBs might be a useful therapeutic strategy to improve metabolism, and therefore prevent and/or delay the progression of neurodegenerative disorders.

#### **7. Conclusions**

The association between dysmetabolic pathologies, like T2D or obesity, and neurodegenerative disorders is unequivocal. We have compiled the most important literature discussing the pathological mechanisms behind this relationship. Altogether it shows that the increasing incidence of both types of diseases is somehow related with worldwide aging and life habits changing, with special attention to overnutrition and low quality of nutrient intake. Insulin also has a clear central role in the relationship between metabolic and neurodegenerative disorders since it joins important peripheral and central actions for glucose homeostasis, cognitive function regulation, and food behavior control. However, it is still unclear which disease is cause or consequence. We reason that it is instead a vicious cycle. The covered literature also opens doors for possible new targets of therapeutic interventions, both by pharmacological (antidiabetic drugs) or non-pharmacologic (CBs modulation) approaches, to control these epidemics of metabolic and neurodegenerative diseases.

**Author Contributions:** A.M.C., A.C. and S.V.C. wrote the manuscript. F.O.M. and H.V.M. made a critical review. All authors have read and agreed to the published version of the manuscript.

**Funding:** AC and FOM were supported by grants and contracts from the Portuguese Foundation for Science and Technology, PD/BD/136863/2018 and CEECIND/04266/2017, respectively.

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

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

#### **References**

