**3. Chronic stress associated with COVID-19: multifaceted attack on the cardiovascular system**

The cardiovascular system is one of the direct targets of SARS-CoV2 made possible by the viral entry into the cells via ACE2 receptors expressed on endothelial cells and cardiomyocytes. The myocardial injury associated with the acute COVID-19 illness has been well documented [43–46]. The responsible mechanisms include direct cytotoxicity [47] and/or dysregulation of the renin angiotensin system (RAS) [33] and the immune response [33, 34]. The initial insights into myocardial injury came from autopsy studies which reported the presence of SARS-CoV2 pools located not in the cardiomyocytes but rather in interstitial cells and resident macrophages [48].

Perseverance of these viral reservoirs is still debatable and potentially insidious as they could play an important role in myocardial and vascular sequel in LH COVID-19. Furthermore, psychological or mental stress-related consequences of the COVID-19 pandemic are expected to contribute to the rising cases of cardiovascular disease. The link between mental stress and coronary artery disease, atrial fibrillation and stroke has been reported in various studies [49–52]. A large multicenter, multinational study, INTERHEART, reported after adjustment for covariates a more than 2-fold increase in the risk of myocardial infarction as a consequence of mental stress [53]. Although the long-term implications of COVID-19 pandemic on cardiovascular health are yet to be realized, previous work done in this area foreshadows a significant uptick of CVD globally, and independently of other comorbidities.

### **3.1 Chronic stress-associated effects on the central nervous and the cardiovascular systems**

In order to study stress as a risk factor, a proper definition must be set in place. The first distinction that must be made is between the stressor and the response of an individual to that stressor (i.e. how well they can cope with it). A stressor is not necessarily perilous *per se*, neither physiologically or psychologically, as there are many stressors that contribute to desirable outcomes. Much like physical exercise represents a stressor that leads to improved cardiovascular and musculoskeletal health, some psychological stress increases readiness and attention resulting in better outcomes in scholarly activities or sporting competitions. It is when individuals have unique perceptions of the stress and their inability to cope with it that creates a favorable milieu for psychological disturbance and new onset CVD. In fact, different personality types have been reported to be more susceptible to CVD as a consequence of mental stress, including Type A (hostile and angry outlook) [54] and Type D (tendency for pessimism and social inhibition) personalities [55]. Given that the COVID-19 pandemic has brought groups of stressors globally, the impact on psychological and cardiovascular well-being remains to be described.

The CMS rodent model is perhaps the most used in studying mental stress that humans endure and has the highest constructive, face and predicative validity [56]. It consists of exposing the animals to a series of mild, yet unpredictable stressors for at least 4 weeks. One could argue that this is a high-fidelity model of the pattern of stressful events that people experienced consistently during the COVID-19 pandemic, and in that view the data generated in CMS rats may necessitate a closer examination in terms of the CVD comorbidities after the chronic stress to inform future treatment strategies. Namely, the data has shown that rodents exposed to CMS develop depressive-like symptoms and behaviors with adverse cardiovascular symptoms including reduced heart rate variability, elevated resting heart rate, reduced baroreceptor function and increased sympathetic nervous system activity [6–8, 57]. The sympathetic drive has been shown to be mediated at least in part by the paraventricular nucleus (PVN), and via the vasopressinergic system rather than oxytocin [7, 8]. The CMS rats have also been shown to have increased expression of vasopressin receptors V1a and V1b in the PVN and that the simultaneous inhibition of both V1a and V1b receptors produced maximal inhibition of the neurocardiovascular responses to the exogenous vasopressin administration [7].

Stress can be categorized as acute, lasting seconds to weeks, and chronic, in the months to years range. COVID-19 pandemic-associated stress thus falls into the latter category, and further can be described as CMS. Chronic stressors associated with

#### *Chronic Mild Stress and COVID-19 Sequelae DOI: http://dx.doi.org/10.5772/intechopen.106578*

work and life related issues, such as injustice, effort-reward imbalance, marital stress at home, lack of life partnership, financial stress have all been shown to increase the risk of CVD [58–61]. Studies in humans have relied on measuring several parameters of the cardiovascular system function to assess the impact of mental stress, including cardiovascular reactivity, levels of catecholamines and inflammatory markers, heart and brain imaging, Holter monitoring and measures of endothelial function with flow-mediated dilatation [62–65]. It has been suggested that it is not the cardiac function but rather the vasculature, endothelium in particular, where the mental stress translates into CVD. Studies done in monkeys where they were exposed to a novel social environment showed increased endothelial damage in the thoracic aorta and coronary arteries [66]. Other studies in mice reported that both acute and chronic stress reduce the expression of nitric oxide synthase [67, 68], which is responsible for the synthesis of the vasodilatory molecule nitric oxide, leading to endothelial dysfunction. Stimulation of the sympathetic nervous system further increases local norepinephrine production and increase in the expression of adhesion molecules on the endothelium, and cytokine and chemokine production by macrophages and vascular smooth muscle cells. These feed forward cycles ultimately lead to leukocyte adhesion, vascular inflammation, atherosclerotic plaques instability, precipitating a cardiovascular event. Therefore, it is apparent that CMS endured during COVID-19 pandemic may cause similar vascular and endothelial dysfunction in humans as was shown in the above-described animal CMS models.

Chronic mild stress that individuals worldwide have endured during the COVID-19 pandemic has put them at a higher risk of developing anxiety and depression [69]. Lockdown policies instituted across the world resulted in isolation from human contact, worsening dementia and anxiety in individuals in long-term care facilities, exacerbation of conflict due to confinement and fear and confusion resulting from continuous bombardment with reporting information on all media, many of which were unreliable. Additionally, physical activity decreased partly due to the closure of fitness facilities as well as the lack of motivation and fear of SARS-CoV2 infection when leaving outside to obtain exercise. Some of the examples of reduced physical activity can be appreciated from the data from 30 million Fitbit activity tracker users, which showed a significant reduction in daily step counts by as much as 38% in Spain [70]. Similar data was obtained from analyzing step count trends from the app Argus in almost half a million users- a mean reduction in activity by 27.4% [71]. Some implied outcomes from these reductions in daily activity include exacerbation of hypertension. Several cross sectional studies indicated that reductions in step counts led to an increase of systolic blood pressure (SBP) of up to 7 mmHg [72, 73] and an increase of 4.5 mmHg for every additional hour of sitting every day [74]. Other behaviors during the pandemic that could have deleterious effects on blood pressure management include increase in body weight [75], increased sodium and decreased potassium intake [76, 77] which is particularly detrimental in the western countries where dietary intake of sodium is already high [78–81], and increase in alcohol consumption [82–84].

Most notably, CMS associated with the pandemic is expected to have adverse consequences on BP in both normotensive and hypertensive individuals. Although no study to date has reported direct associations related to COVID-19, published data indicate that chronic stress leads to an increase in the sympathetic drive as assessed with norepinephrine levels, changes in heart rate as well as via direct neurography [85–88]. Published clinical evidence repeatedly shows that depressed patients are at a higher risk of developing CVD which persists for a decade following the initial onset of depression [89–91]. This relationship is not unipolar, as patients with CVD have been

shown to develop depressive symptoms [90–93]. Some of the mechanisms explaining the co-occurrence of depression and CVD include neuroinflammation [94] and autonomic dysfunction [95], but they are by no means an exhaustive list (**Figure 1**).

Important knowledge has been gleaned from reliable, validated rodent models of CMS [96], which are still utilized to tease apart the mechanistic links between CVD and stress/depression. Importantly, the new and ongoing investigations have been focusing on explaining the difference in vulnerability of individual animals to stress-associated CVD development [97–100], much like occurs in humans. Rodents exhibit two distinct coping styles when exposed to stress: [1] the proactive coping, which is characterized by more offensive, aggressive and impulsive behavior; and [2] and reactive coping, which is characterized by more cautious and fearful behavior [99, 101]. In addition to the behavioral differences, physiologically the two differ as well, where the proactive (active) copers exhibit heightened sympathetic activity and low HPA axis reactivity and the reactive (passive) copers show the opposite trends [99, 102]. The passive coping rats were also shown to have persistently elevated levels of pro-inflammatory cytokine IL-1β and oxidative stress [103], and it is thus plausible that neuroinflammation is at the intersection of depressive symptoms and CVD.

The sex-based dichotomy in the prevalence and severity of depression has been well-characterized [104, 105]. Furthermore, the efficacy of antidepressant pharmacotherapeutic agents also differs between men and women [106, 107]. Likewise, women are more likely to develop CVD that co-occurs with depression [108]. A growing body of evidence has emerged indicating that COVID-19 pandemic has increased the incidence of depression, with the meta-analysis of 12 community-based studies worldwide highlighting a prevalence of depression of 25% [109], with female gender emerging as a significant risk factor [110–112]. One study reported that women under 50 persist more devastating symptoms such as fatigue, myalgia, brain fog and fatigue after being hospitalized for COVID-19 [113]. Animal model studies of CMS that address this disparity in males and females are scarce, and some have shown differences in behavioral and hormonal profiles. Anhedonia associated with depressive-like state in CMS rodents is typically measured by an intake of 1–2% sucrose solution, and has been found to be more pronounced in females than in males [114]. The same study found no differences in the corticosterone levels however, indicating similar stress hormonal profiles. These findings are the extend of our understanding of sex-based differences in CVD susceptibility as a function of chronic stress thus representing a large gap in knowledge that future preclinical studies should address. Developing treatments that will target both the depressive symptomatology and the cardiovascular pathology, while also being titratable, will be of utmost importance since there may be a difference in the magnitude of effects caused by chronic stress associated with the pandemic between women than men.

#### **3.2 Long haul COVID-19: emerging effects on the brain, heart and vasculature**

During the acute phase of SARS-CoV2 infection, the viral entry into the CNS can be accomplished either directly or indirectly (via neuroinflammation) [115]. The direct viral entry, as mentioned previously, can occur via the olfactory [116] or terminal cranial nerves [117]. ACE2 expression has been recognized on endothelial cells, pericytes and astrocytes, allowing the viral invasion of the CNS via compromised BBB. Alternatively or even additionally, the virus could traverse the microvascular endothelial cells, as has been shown [118]. Consequently, the BBB leakage would allow the influx of the circulating pro-inflammatory cytokines, chemokines and

#### *Chronic Mild Stress and COVID-19 Sequelae DOI: http://dx.doi.org/10.5772/intechopen.106578*

mediators, further perpetuating neuroinflammation. *In vitro* studies also described the capabilities of SARS-CoV2 to initiate activation of astrocytes and microglia via its structural protein subunit (S1) [119]. This has been confirmed in autopsy studies of COVID-19 patients showing enlarged astrocytes and activated microglia [37]. Under normal physiological conditions, astrocytes play a crucial role in neurotransmission, as they control the synthesis of most essential neurotransmitters glutamate and GABA [120]. Additionally, astrocytes are involved in maintenance of synaptic plasticity via reuptake and recycling of neurotransmitters [121]. Under inflammatory conditions (i.e. SARS-CoV2 infection) astrocytes become reactive, which disrupts the glutamatergic balance, leading to excess extracellular glutamate contributing to dysfunction in both the CNS [58] and the cardiovascular systems [122]. Reactive astrocytosis is further supported by microglia via the NFkB pathway [123]. In the absence of the mechanisms that will shut down reactive astrocytosis (i.e. during COVID-19) the process could lead to the formation of astrocytic scars and in the long term neuronal death and neurodegeneration. Data describing the contribution of reactive astrocytosis in LH COVID-19 is lacking. One study so far has been published that measured plasma biomarkers of CNS injury in 100 COVID-19 survivors in Sweden. The biomarkers included nuerofilament light chain, glial fibrillary acidic protein (GFAP) and differentiation factor 15. In the acute phase, patients with severe symptoms had elevated neurfilament light chain compared to both age-matched controls and mild and moderate COVID-19, as well as higher GFAP than controls. However, after the median follow up of 225 days all CNS injury markers normalized and were indistinguishable from those found in healthy controls [124]. Since emerging data are pointing towards increased neuropathological manifestations one-year out compared to 6 months out [7], more studies are urgently needed to explain the mechanistic details and thus inform appropriate therapeutic strategies.

Data from the prospective post-acute follow up studies focusing on cardiac events have been more abundant in the literature compared to those on the CNS abnormalities. Several large (n > 400) observational studies are still ongoing in 2022. Transthoracic echocardiography and cardiac magnetic resonance are the gold standard techniques used in the diagnosis of cardiac pathologies [125, 126]. The vast majority of the studies have reported the presence of pericarditis, right ventricular dysfunction and myocardial infarctions [127–133]. Persistent myocarditis was reported in a cohort of 100 patients [127], while in another study of healthcare workers matched for comorbidities and severity of infection showed no difference in cardiac abnormalities 6 months post-infection [128]. Studies in athletes [129–132] were undertaken within 1–2 months of infection, and the prevalence of myocarditis is generally considered to be low (0–3%), albeit studies beyond the 2 month mark are lacking. Echocardiographic studies have consistently reported right heart abnormalities [133–135] while the left systolic function is significantly less impaired [133, 136], even in patients with severe acute symptomatology. On the other hand, perhaps the emerging trend that will have to be closely monitored in LH COVID-19 is the diastolic dysfunction, as it was shown to be common in up to 60% of hospitalized patients [137]. It is thus plausible to speculate that the pathologic changes in diastology could manifest during LH COVID-19, given the time lapse from the initial infection. In terms of vasculature, one angiography study reported an association between vascular inflammation caused by the variant B1.1.7 (WHO label Alpha) and increased mortality risk [138]. Although multiple studies have been published so far that have highlighted or implied the development of cardiovascular pathologies in LH COVID-19, one common denominator of limitations in most of them is that the comparator

groups were either healthy individuals or individuals unmatched for comorbidities. Perhaps the most important consideration should be the lack of pre-COVID cardiac imaging studies, which makes it difficult to discern whether the pathologies observed were due to COVID-19 or other comorbidities, or perhaps both. Nevertheless, given the non-invasive nature of echocardiography, it likely behooves the cardiac clinicians to implement cardiac imaging in LH COVID-19 given the evidence from prospective studies and the available therapeutics for ensuing cardiac pathologies.

The concerning aspect of the known and implied consequences of COVID-19 discussed above and LH COVID-19 is that these pathological processes do not exist in isolation and ultimately lead to multisystem dysfunction. A multi-organ magnetic resonance imaging study on a small cohort of recovered COVID-19 patients and matched controls revealed some level of abnormalities in the lung (60%), heart (26%), liver (10%), kidneys (29%) and brain (11%) [18]. Recovery from multisystem damage has been shown to be impeded by the persistent pro-inflammatory state [139] as well as endothelial dysfunction [140–142] and perpetual prothrombotic state [143]. These studies highlight the importance of approaching treatment strategies from the multisystem perspective rather than treating isolated pathologies. For example, antithrombotic therapy may be beneficial for individuals who present with the prothrombotic phenotype and have persistent inflammation in their LH COVID-19 phase.


**Meet the editor**

Felicia M. Ferreri graduated summa cum laude from the University of Messina, Italy, in 1998 and completed her ophthalmology residency at the Policlinico Universitario, Messina, in 2002. She was interned at San Raffaele Hospital in Milan (corneal section) and at the Hospital Careggi in Florence (pediatric ophthalmology diseases). She spent research periods in Seville (Virginio del Rocio

Hospital), Madrid (San Carlos Hospital), Manchester (Bolton Hospital), and Rio de Janiero (Universidade Fluminense).

She has served as coinvestigator for many national and international clinical trials. Since 2002, she has been an assistant professor in ophthalmology at the University of Messina. Her research interests are in the areas of glaucoma, neuro-ophthalmology, pediatric ophthalmology, and cataract. She has authored more than 50 scientific papers.

Contents

**Preface VII**

**Development 1**

**Function 11**

**Section 1 Anatomy of the Optic Nerve and Anomalies in its**

Felicia M. Ferreri and Giuseppina Ferreri

Chapter 2 **Anatomy of the Human Optic Nerve: Structure and**

Rojas, Alberto Triviño and José M. Ramírez

Chapter 4 **Optic Nerve Changes in Diabetic Retinopathy 85**

**Optical Coherence Tomography 107** Bilyana Mihaylova and Galina Dimitrova

Chapter 6 **Clinical Assessment of Lesions Compressing the**

**Visual Pathway 125**

Lawan Abdu

Hind Alkatan, Daniah Alshowaeir and Tariq Alzahem

**Section 2 Clinical Challenges in Optic Nerve Disorder Management 105**

**Layers in Primary Open-Angle Glaucoma with Spectral-Domain**

Chapter 5 **Evaluation of Retinal Nerve Fiber Layer and Inner Macular**

Chapter 3 **Optic Nerve: Developmental Anomalies and**

**Common Tumors 57**

Andi Arus Victor

Chapter 1 **Introductory Chapter: The Dysthyroid Optic Neuropathy 3**

Juan J. Salazar, Ana I. Ramírez, Rosa De Hoz, Elena Salobrar-Garcia, Pilar Rojas, José A. Fernández-Albarral, Inés López-Cuenca, Blanca

## Contents

**Preface XI**

Lawan Abdu

#### **X** Contents

## Chapter 7 **Contribution to the Optic Nerve 139**

Preface

researchers and clinicians.

and, ultimately, surgeons.

ders and making a diagnosis.

for the more experienced clinicians.

flow, glial barriers, and the lamina cribrosa.

The study of the optic nerve, its structure, functioning, and disorders is a key topic for both

Many pathologies in fact can affect the optic nerve: some of them are congenital (think of myelinated nerve fibers, morning glory syndrome, choristoma, and rare anomalies to name a few), while others can be classified as tumors both in primary form (i.e., if neoplasia sur‐ rounds the optic nerve sheath) and secondary form (relevant examples are medulloepithe‐ lioma, optic nerve meningioma, and others). Optic nerve examination is arguably the most important component of the evaluation of a glaucoma patient. The appearance of the optic

Due to the variety and complexity of disorders affecting the optic nerve, both researchers and physicians need a comprehensive reference that starts from detailing the anatomy of the optic nerve, illustrates how the most modern diagnostic procedures (think of imaging tech‐ niques) can be effectively employed for clinical purposes, and, last but not the least, shows how to manage complex clinical cases that may occur in the daily life of an ophthalmologist. This book aims to elevate itself as an authoritative reference that can help broad and hetero‐ geneous audiences, ranging from resident students to clinicians, neuroscientists, researchers,

The book is divided into two main sections: it starts by reviewing in detail the anatomy of the optic nerve with special emphasis on the optic nerve head and chiasm. Organization of the optic nerve into regions is explored in detail with a thorough discussion of axoplasmic

This book then concentrates on embryology, physiology, and pathology of the optic nerve and introduces clinical features and imaging findings that help clinicians in detecting disor‐

The book will explore in detail the relationship between neuroendocrine structures and functional/structural modifications occurring in the optic nerve: a first example is drawn by diabetic retinopathy and we will pay a special attention to changes to the optic nerve it gen‐

We will also discuss diagnostic tests (e.g., optical coherence tomography and visual evoked

We will finally shift to analysis of optic nerve head drusen, a class of congenital anomalies that may produce a progressive optic neuropathy but are hard to detect and manage even

> **Felicia M. Ferreri** University of Messina

> > Messina, Italy

potential) that allow us to detect structural and functional changes to the optic nerve.

erates such as diabetic papillopathy and neovascularization of the optic disc.

nerve is therefore crucial to diagnose glaucoma and detect its progression.

Marija Radenković


*3.6. Sequential Feature Selection Results*

We used backward sequential feature selection under a linear model and a decision tree regression (no linear model). However, implementation of the sequential feature selection through the decision tree may have generated overfitting; thus, a 10-fold cross-validation allowed the recognition of the accepted characteristics by modifying the tree depth from 2 to 5.

3.6.1. Sequential Feature Selection with Linear Regression Model

Table 7 presents the results obtained for the linear regression model (Section 2.8.1) determining the feature elimination sequence, which used as attributes the admitted range from 1 to 6. The table follows the logic of Figure 5, showing each color in the elimination ranking with the variance threshold.

**Table 7.** *Cont.*

3.6.2. Sequential Feature Selection with Decision Tree Regression Model

The feature recognition through the decision tree regression model used the variance in a specific feature, which ranged from 1 to 6, allowing the elimination order for each input variable (Tables 8–11) to be obtained from the tree depth configuration. A color image of each feature, following the structure for variance threshold selection, is shown in Figure 5.

**Table 8.** Sequential feature selection by decision tree for depth = 2.

**Table 9.** Sequential feature selection by decision tree for depth = 3.

**Table 10.** Sequential feature selection by decision tree for depth = 4.

**Table 11.** Sequential feature selection by decision tree for depth = 5.

#### **4. Discussion**

The results obtained require division into linear and nonlinear model selection algorithms. The division generated makes it possible to analyze the results according to the model type and to identify the sequence of each feature. Tables 12 and 13 show the algorithms by group, the feature selection order, and the mean.

**Table 13.** Elimination order for the nonlinear model group.

The averages calculated and reported in Tables 12 and 13 indicate two different behaviors depending on the model performance (linear or nonlinear). Figure 8 shows an alternative way to visualize the performance between linear and nonlinear models.

**Figure 8.** Feature elimination order distributions with algorithms from the two models used.

After dividing the models into linear and nonlinear groups, we validated the ordinal elimination variables on a scale from 1 to 7 and tested the distributions with the Kruskal– Wallis test (Table 14) [61]. The sequence elimination distribution for the proposed models is shown in Figures 9 and 10 (linear and nonlinear models, respectively).

**Table 14.** Values obtained with the Kruskal–Wallis test.

**Figure 9.** Order of elimination for features in linear models.

The linear model indicated that the essential characteristic was intensity, while the least significant was the duty cycle (Figure 9 and Table 14). If the appropriate sequence for any variable is required, the mean value can be found in Table 12. This means that the elimination order for the linear models was duty cycle, G, R, W, B, frequency, and intensity.

The nonlinear model found that the most crucial characteristic was intensity, while the least important was W (white color), with sufficient significance *p* < 0.05. If the correct sequence of the other variables is required, we can rely on the mean values for the feature distribution (Table 13). Overall, the elimination sequence was W, G, B, duty cycle, frequency, R, and intensity.

The elimination order for the duty cycle and R in the linear and nonlinear models suggests that they are nonlinear features, mainly because several linear algorithms selected them as the first variables to eliminate but nonlinear algorithms selected them as the most important ones.

**Figure 10.** Order of elimination for features in nonlinear models.


**4. Results and Discussion**

The detailed discussions of the results obtained from the various tests performed are presented in this section. The results obtained include the tensile test, bending test, microstructure and SEM analysis.