**Cognitive Function and Atrial Fibrillation: From the Strength of Relationship to the Dark Side of Prevention. Is There a Contribution from Sinus Rhythm Restoration and Maintenance?**

#### **Emanuele Gallinoro , Saverio D'Elia, Dario Prozzo, Michele Lioncino, Francesco Natale, Paolo Golino and Giovanni Cimmino \***

Department of Translational Medical Sciences, University of Campania "Luigi Vanvitelli", 80131 Naples, Italy<EMAIL_ADDRESS>(E.G<EMAIL_ADDRESS>(S.D<EMAIL_ADDRESS>(D.P<EMAIL_ADDRESS>(M.L<EMAIL_ADDRESS>(F.N<EMAIL_ADDRESS>(P.G.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +39-081-706-4239

Received: 30 June 2019; Accepted: 10 September 2019; Published: 13 September 2019

**Abstract:** Atrial fibrillation (AF) is the most common chronic cardiac arrhythmia with an increasing prevalence over time mainly because of population aging. It is well established that the presence of AF increases the risk of stroke, heart failure, sudden death, and cardiovascular morbidity. In the last two decades several reports have shown an association between AF and cognitive function, ranging from impairment to dementia. Ischemic stroke linked to AF is a well-known risk factor and predictor of cognitive decline. In this clinical scenario, the risk of stroke might be reduced by oral anticoagulation. However, recent data suggest that AF may be a predictor of cognitive impairment and dementia also in the absence of stroke. Cerebral hypoperfusion, reduced brain volume, microbleeds, white matter hyperintensity, neuroinflammation, and genetic factors have been considered as potential mechanisms involved in the pathogenesis of AF-related cognitive dysfunction. However, a cause-effect relationship remains still controversial. Consequently, no therapeutic strategies are available to prevent AF-related cognitive decline in stroke-free patients. This review will analyze the potential mechanisms leading to cognitive dysfunction in AF patients and examine the available data on the impact of a sinus rhythm restoration and maintenance strategy in reducing the risk of cognitive decline.

**Keywords:** atrial fibrillation; cognitive decline; anticoagulation; rhythm control; microbleeds; cerebral ischemia

#### **1. Introduction**

Atrial fibrillation (AF) is a common chronic cardiac arrhythmia with an increasing prevalence over time mainly because of population aging, peaking at 10–17%incidence from the age of 80 years and older [1,2]. The presence of AF increases the risk of stroke up to five-fold [3], heart failure [4,5] and death [6,7]. Epidemiological evidence indicates an association between AF, cognitive impairment and dementia [8–13]. The great impact of this issue is demonstrated by the several articles that have been published only in the last 12 months from the present review paper by Heart Rhythm Associations [14] and others [10–12,15–22].Stroke-related AF is a well-known risk factor and predictor of cognitive impairment and dementia [23]. However, clinically recognized strokes represent only the tip of an iceberg. Some observations suggest that AF-induced brain ischemia and silent brain infarcts [24–26] detected by neuroimaging [27] are more frequent than clinical stroke and together with microinfarcts (beyond the power resolution of the conventional neuroimaging techniques) are associated with cognitive impairment and dementia [28,29]. Based on recent observations AF may also be a predictor of cognitive impairment and dementia in the absence of stroke [30,31]. Moreover, taking into account the different patterns of AF (paroxysmal, persistent, long-stand persistent, permanent, non-valvular, and incident [1,32]), it seems clear that the association between AF and cognitive function becomes more difficult to elucidate. In addition, a full understanding of the mechanisms by which AF may lead to cognitive impairment also in patients without any evidence of stroke remain not completely understood [33]. Cerebral hypoperfusion, chronic inflammation and endothelial dysfunction have been considered potentially involved in the pathogenesis of AF-cognitive impairment [11,20,34–36]. Currently, no therapeutic strategies are available to prevent cognitive dysfunction in stroke-free patients with AF; therefore, clarifying the potential underlying mechanisms of cognitive impairment in AF without stroke might be a critical issue.

This review, starting from the available literature, focuses on the relationship between AF and cognitive impairment, exploring both stroke and non-stroke related mechanisms that lead AF-patients to the development of progressive cognitive dysfunction. Moreover, the examination of the potential basic mechanisms provides an insight into the possible therapeutic implications. Finally, the potential benefit of a sinus rhythm restoration and maintenance strategy is explored.

#### **2. Current "Views" on Atrial Fibrillation-Related Stroke**

As reported by the majority of epidemiological studies the presence of AF implies up to five-fold increased risk of ischemic stroke [3,37,38], but the causal relationship of this correlation still remains not completely understood [33]. Moreover, this risk increases if other pathological conditions, such as hypertension [39], diabetes mellitus, valvular heart disease [6], heart failure [5], coronary heart disease [6,40], chronic kidney disease [6,32], inflammatory disorders [41,42], sleep apnea [43], and tobacco use [44] are present. To date some of these comorbidities are also included in the CHA2DS2-VASc score used to calculate the annual risk of stroke [1].

#### *2.1. Possible Mechanisms of AF-Related Stroke*

The current hypothesis postulates that uncoordinated myocytes activity could explain the impaired/loss of atrial contraction seen in AF patients, and the resulting blood stasis would cause the increased thromboembolic risk [33,45]. Despite a direct correlation between AF and stroke found in many studies, this is not consistent among all available data: according to some reports, the risk of embolic stroke seems not to be directly related to the duration of dysrhythmia [46–50]. This evidence seems to demonstrate the lack of a direct association between the burden of AF and the prevalence of stroke. Furthermore, it is important to note that a single brief episode of subclinical AF is associated with a 2-fold higher risk of stroke in older patients with vascular risk factors, whereas young and otherwise healthy patients with clinically apparent AF do not face a significantly increased stroke risk [51,52]. These data support the role of other concomitant risk factors apart from dysrhythmia in the determination of AF-related stroke. If AF causes thromboembolism, it should be specifically associated with embolic strokes [53]. However, almost 10% of patients with lacunar strokes have AF, and large-artery atherosclerosis is twice common in AF patients, suggesting a possible contribution from other factors [54]. Moreover, if dysrhythmia is the only cause of thromboembolism, maintaining a normal rhythm should eliminate stroke risk. However, in a meta-analysis of eight randomized clinical trials, a rhythm-control strategy had no effect on stroke risk (odds ratio, 0.99; 95% confidence interval, 0.76–1.30) [55], and it is unlikely that this result could reflect a failure to maintain sinus rhythm because rhythm-control strategies showed substantial success in maintaining normal sinus rhythm (odds ratio, 4.39; 95% confidence interval, 2.84–6.78). Atrial fibrillation coexists with other alterations, such as endothelial dysfunction [56], fibrosis [57], and mechanical dysfunction of left atrial appendage [58]. These factors have been associated to stroke. Some authors have proposed a novel up to date model of AF-related stroke, based on the severity of atrial cardiopathy rather than the duration of dysrhythmia [33]. According to this new hypothesis, AF and thromboembolism occur as separate downstream effects of atrial cardiopathy [33,38,59]. Briefly, this model highlights the interaction between systemic vascular risk factors, atrial substrate and rhythm suggesting that these factors with

the aging finally result in atrial cardiopathy, thus increasing the risk to develop AF and consequently thromboembolism. The role of atrial cardiopathy in thrombogenesis should be considered similar to the post myocardial infarction and heart failure related ventricular cardiopathy. In both of these diseases, thromboembolism can occur even in the absence of dysrhythmia. Once developed, AF causes contractile dysfunction and stasis because of dysrhythmia, which further increases the risk of thromboembolism [37,45]. In addition, long-standing persistent AF (a pattern that lasts at least a year without interruption) [1] causes atrium remodeling, thereby worsening atrial cardiopathy and increasing thromboembolic risk even further. On the other hand, systemic risk factors participate to increase risk of stroke via non atrium-related mechanisms, such as in situ cerebral small-vessel occlusion, atherosclerosis of the large-artery, and ventricular systolic dysfunction [60]. Finally, once stroke occurs, AF risk may transiently increase because of autonomic changes and post-stroke inflammation [61].

#### *2.2. AF and Stroke-Related Cognitive Impairment: The Visible Side of the "Moon"*

The relationship between AF and cognitive impairment/dementia has been reported in several studies [11,15–17,19,20,62,63]. The large cross-sectional Rotterdam Study was one of the first pieces of evidence to describe this association [64]. Of the 6584 participants, 635 (9.6%) had cognitive impairment without dementia, whereas 4.2% were diagnosed with dementia. In 75% of the affected patients, the most common form of dementia was Alzheimer's disease, whereas 15% had vascular dementia and 11% undefined dementia. Of the patients with Alzheimer's disease, almost 20% had concomitant cerebrovascular disease. Dementia was reported to be up to 2-fold more common in patients with AF than in those without it. A significant positive association between cognitive impairment and AF was also described, but this association was weaker [64]. Stratification for sex showed that these findings were restricted to women and patients younger than 75 years old [64]. Furthermore, a systematic review including more than 77,000 patients with normal baseline cognitive function and not suffering an acute stroke, showed that AF significantly increases the risk of incident dementia (HR 1.42, CI 1.17–1.72, *p* = 0.002) [30]. Three meta-analyses have shown a higher risk of dementia in patients with AF who have a stroke (RR 2.43–2.70) [30,65,66]. The risk of incident dementia and cognitive decline was more modest in those without stroke at baseline than in patients with AF and previous history of stroke. AF patients have up to a 2-fold higher risk of silent or subclinical strokes than those without AF [28]. In AF patients, subclinical stroke has been clearly associated to long-term rates of cognitive dysfunction and dementia compared to patients who do not have a stroke, and there is a direct correlation between the impairment of cognitive function and the number of silent cerebral lesions at MRI [26,28].

Of note, patients with persistent AF (defined as at least seven days of arrhythmia that may or may not end on its own [1]) have a significantly higher number of lesions than those with paroxysmal pattern, in which irregular heartbeat may last anywhere from several seconds to a week, but usually ends spontaneously within 24 h [1] (41.1 ± 28.0 vs. 33.2 ± 22.8, *p* = 0.04) [28]. Cognitive performance, assessed by well-validated tests, was significantly worse in patients with persistent and paroxysmal AF than in controls (Repeatable Battery for the Assessment of Neuropsychological Status scores 82.9 ± 11.5, 86.2 ± 13.8, and 92.4 ± 15.4 points, respectively, *p* < 0.01) [28].

Many of the previously cited studies were limited by the short duration of the follow-up. The Atherosclerosis Risk in Communities, a prospective cohort study with a 20-year follow-up, showed that participants who developed incident AF (defined as the first occurrence of hospitalization with a primary discharge diagnosis of AF or ≥2 ambulatory visits for AF [32])had greater cognitive decline over 20 years, compared to participants who did not develop AF. The AF-related decline in the global score was 16% greater and was augmented after accounting for attrition. In addition, incident AF was associated with 23% higher risk of dementia. Although adjustment for prevalent and incident ischemic stroke attenuated the associations slightly, they remained significant [8]. AF-related cognitive impairment was characterized by a greater decline in cognitive tests associated to language and executive function rather than memory tests [8]. While Alzheimer's disease is mainly characterized by memory deficits [67–69], AF shares with other vascular risk factors a preferential impairment of

visuospatial ability [28]. The study by Gaita et al. [28] evaluated the distribution of silent cerebral lesions in patients with paroxysmal or persistent atrial fibrillation, reporting bilateral distribution with cortical and subcortical areas of silent cerebral ischemia. These lesions showed a frontal spotted pattern which is in contrast with the hippocampal and temporal lobe involvement of Alzheimer's disease. The cardiac origin of the embolic particles was suggested by distribution and size of the embolic material. Emboli of cardiac origin are generally smaller than those due to atherothrombotic material and cause lesions widely distributed, on both sides, of the brain [70–72]. The cerebral MR pattern described in 50% and 67% of the patients with paroxysmal and persistent AF, respectively, was characterized by small, sharply demarcated lesions, often in clusters, with a bilateral distribution, prevalently in the frontal lobe, strongly supporting the non-atherothrombotic origin of the silent cerebral ischemia.

Finally, another cross-sectional study reported that AF was not associated to cognitive decline in patients without prevalent silent cerebral ischemia and/or subclinical cerebral infarct [73].

#### *2.3. E*ff*ects of Anticoagulation Therapy*

Despite the well-documented role of anticoagulants in cardioembolic stroke prevention [1,74], it is not clear if the risk of AF-related dementia can be significantly reduced by oral anticoagulation [11,16,75–77]. Prior studies have found that oral anticoagulation in stroke free patients was associated with dementia [78]. Intracranial hemorrhage has been considered the major concern with anticoagulation use, and the risk was higherin patientswithleukoaraiosis (whitematter changes) [79]. Inwarfarin-treated patients, themaintenance of an international ratio between 2 and 3 for most of the time-period (defined as time in therapeutic range [80]) is essential for stroke prevention [1]. It has been reported that chronic undercoagulation as well as overcoagulation might be linked to increased risk of cognitive impairment (HR 1.017 CI 1.007–1.027, *p* = 0.001 and HR 1.018 CI 1.006–1.031, *p* = 0.005; respectively) [81]. This trend was found significant only in younger patients (<80 year old), most probably because of a longer anticoagulation regimen overtime [81]. A retrospective study from a Swedish Patient Register showed lower incidence of dementia among patients with oral anticoagulation than patients without anticoagulants (1.14 vs. 1.78 per 100 patients/year at risk, *p* < 0.001) [82]. The use of anticoagulation at baseline was associated with 29% lower risk of dementia than in patients without anticoagulant drugs (HR 0.71, 95% confidence intervals 0.68–0.74 and 48% lower risk analyzed on treatment (HR 0.52, 95% CI 0.50–055) [82].

In the last decade, the use of novel anticoagulants (direct inhibitor of coagulation factor Xa or thrombin, named DOACs)that do not require lab monitoring has greatly improved the prevention of AF-related cardiac embolism [83], even in elderly [84,85] and in patients undergoing cardioversion [86,87], because of a better compliance and a uniform time in therapeutic range [59,83,88]. Based on the current literature, risk of undercoagulation as well as overcoagulation should be overcome [83]. The risk of dementia appeared to be lower with DOACs (HR 0.48, 95% CI 0.40–0.58) than warfarin (HR 0.62, 95% CI 0.60–0.64), but direct comparison showed no significant differences [76,82,89]. A recent meta-analysis including 471,057 AF patients under oral anticoagulants has shown that anticoagulation was associated with a significant reduction in cognitive impairment [90]. Moreover, comparison of DOACs with warfarin-based treatment showed that the novel agents-based group has a significantly lower occurrence of dementia with an increased risk of bleeding in warfarin group [89,90]. Furthermore, in the DOAC-treated group, a low combined risk of dementia and stroke was also reported [89].

However, pre-specified blind and randomized clinical trials are warranted to verify the role of oral anticoagulation in the prevention of dementia and resolved the current controversies. Actually, the Blinded Randomized Trial of Anticoagulation to Prevent Ischemic Stroke and Neurocognitive Impairment in AF (BRAIN-AF) (NCT02387229) is ongoing [91]. It is enrolling patients with non-valvular AF (defined as arrhythmia that is not caused by any moderate to severe heart valve disease [1]) that will be screened for dementia prior to randomization by mini-mental state examination and other tests. The efficacy and safety of rivaroxaban 15 mg will be evaluated for stroke reduction, transient

ischemic attack and neurocognitive decline [91]. Another trial entitled "Impact of Anticoagulation Therapy on the Cognitive Decline and Dementia in Patients with Non-Valvular Atrial Fibrillation (CAF—NCT03061006)", randomized, will compare the use of dabigatran vs. warfarin in 120 AF patients to assess the cognitive decline through neurological examination and cognitive testing [77].

Based on the current evidence, it has been suggested that DOACs would be a better choice for prevention of dementia than warfarin [92]. The lower rate of intracerebral bleeding has been suggested to be one possible mechanisms involved in this protective effect [92], but further studies are needed in order to investigate the role of DOACs in prevention of AF-related cognitive impairment and in the definition of a cause-effect relationship rather than a simple epidemiologic association.

#### **3. Atrial Fibrillation and Non-Stroke-Related Cognitive Decline: The Submerged Part of the Iceberg?**

In the last decade, new evidence supported the role of AF as independent risk factor for cognitive impairment and dementia even in patients with no history of stroke as assessed by two meta-analysis including large samples of patients [30,66] as well as by a perspective post-hoc analysis of two randomized clinical trials: the ONTARGET and the TRASCEND [31]. Large longitudinal studies also provided data supporting this association. Chen et al. [8] analyzed the results from a cohort of more than 12,000 patients enrolled in the ARIC study and evaluated the association of incident AF with 20-year change in cognitive performance considering the incidence of dementia and the cognitive decline: In conclusion, AF increased the risk of cognitive impairment and dementia independently from ischemic stroke (global cognitive Z score = 0.115, 95% confidence interval, 0.014–0.215) [8]. Similarly, De Bruijn et al. [62] evaluated the association of incident and prevalent AF and incident dementia in 6514 dementia-free participants in the prospective population-based Rotterdam Study over a 20-year follow-up period showing that prevalent and incident AF increases the risk of dementia (HR 1.33; 1.02–1.7 for prevalent AF and 1.23 (0.98–1.56) for incident AF, 95% CI) especially in younger patients (<67 year old) and in those with longer duration of AF [62].

#### *Linking Mechanisms of AF to Cognitive Dysfunction in Stroke-Free Patients*

Despite this epidemiological evidence, the pathophysiological mechanisms correlating AF and cognitive dysfunction in stroke-free patients are not completely elucidated. It is widely known that microbleeds, which are often the result of hypertensive vasculopathy/fibrohyalinosis and cerebral amyloid angiopathy, are associated with cognitive impairment [93]. In some AF patients, anticoagulation therapy may favor the occurrence of microbleeds, a condition that, at least in part, could explain the progressive cognitive impairment observed in AF [94]. However, to date there is a lack of studies about microbleeds and cognitive function in stroke-free patients affected by AF.

Brain white matter hyperintensity detected by MRI evaluation are associated with AF and poor cognitive performance [95]. However, the pathogenesis of white matter hyperintensity remains not completely understood, and its occurrence may be associated to cerebral hypoperfusion, arterial hypertension, aging, and cerebrovascular disease [96]. Neuroinflammation may be another possible explanation of the cognitive impairment in AF [41,97]. Several inflammatory markers are elevated in patients with AF such as C-reactive protein, tumor necrosis factor-α, interleukin-2, interleukin-6, and interleukin-8 and they may trigger cerebral micro-infarction and subsequent cognitive dysfunction by inducing a prothrombotic state through endothelial activation/damage, production of tissue factor from monocytes, increased platelet activation, and increased expression of fibrinogen [98]. Lappegard et al. demonstrated that anti-inflammatory therapy through intensive lipid-lowering treatment with 40 mg atorvastatin and 10 mg ezetimibe can modify the deterioration of neurocognitive function, and the loss of volume in certain cerebral areas in older patients with AF [99]. Reduced brain volume has been considered another potential risk factor linking AF and cognitive function. In a cross-sectional analysis of 4252 participants without dementia, AF was associated with a lower volume of gray and white matter (*p* < 0.001 and *p* = 0.008, respectively) [100]. The association was reported to be even stronger in patients with persistent AF compared to paroxysmal AF [100]. A smaller hippocampal

volume, evaluated by structural MRI, has been associated with neurocognitive decline and progression towards Alzheimer disease in patients with mild cognitive impairment [101,102]. In a cross-sectional analysis, led by Knecht et al. on 122 patients, patients with AF without stroke showed worsening in tasks of learning and memory (*p* < 0.01) as well as attention and executive functions (*p* < 0.01) compared to subjects without AF; corresponding to the memory impairment, hippocampal volume was reduced in AF patients [103]. Genetic risk factors predisposing to dementia and cognitive impairment have been extensively studied but whether these factors may link AF and cognitive dysfunction is not well established. In the study by Rollo et al. 112 Caucasian patients with AF and dementia were matched 1:1 with patients with AF and without dementia resulting in an association between PITX2 loci, rs2200733, and dementia (OR = 2.15, *p* = 0.008) [104]. However further studies are warranted to confirm these results and clarify the role of genetic factors which may influence development of cognitive dysfunction in AF patient. Most of the mechanisms involved in AF-related cognitive dysfunction are summarized in Figure 1

**Figure 1.** View of the mechanisms involved in cognitive dysfunction in patients affected by atrial fibrillation.

#### **4. Rhythm Control Strategy and Cognitive Impairment: The Dark Side of the Prevention?**

The management of AF patients has been the subject of intensive investigations especially in the 1980s and early 1990s. It is well documented that compared with patients in sinus rhythm, the development of AF increases the risk of stroke [3] and worsen cardiovascular outcomes in patients with heart failure (HF) [5]. Nevertheless, it has to be taken into account that AF is a marker of more severe disease, and thus, evaluation of the coexisting comorbidities and their respective contribution to the worsening of long-term prognosis in AF patients should be carefully evaluated [5,32,39].

It seemed to be logical that restoration and maintenance of sinus rhythm might improve cardiovascular outcome. However, the analysis of the large clinical trials evaluating the impact of either rate or rhythm control strategy on mortality or combined end point of mortality and morbidity have demonstrated no benefits [105–109], resulting in a rethinking of the appropriate way in which to treat a patient with AF when the therapeutic options include both strategies, rate or rhythm control [110]. To date, this question still remains a matter of debate [111,112].

#### *4.1. AF, Cerebral Blood Flow, and Possible Contribution to Cognitive Impairment*

Among older adults, lower cardiac index is associated with reduced cerebral blood flow in the cerebral gray matter, especially in lobes [113]. This mechanism seems to be associated with incident dementia and Alzheimer's disease [114]. It is well documented that AF can reduce cardiac output [115,116]. Thus, by reducing cardiac output, AF could induce chronic brain hypoperfusion, which could be linked to AF-related cognitive impairment [104]. However, despite the association between AF and a reduction of almost 20% of the total cardiac output, cerebral blood flow has been reported by some evidence to be substantially unchanged, due to autoregulation mechanisms [117].

Currently, cardioversion of AF can be easily and safely performed because of DOACs [87,118–120]. A recent study has evaluated the impact of AF and sinus rhythm on cerebral blood perfusion [121] reporting that mean cerebral flow rates in AF and sinus rhythm are similar, even considering cerebral autoregulation (but not other associated pathologies). The authors concluded that a well-functioning cerebral autoregulating system is able to ensure a normal cerebral blood flow both during AF and sinus rhythm [121]. These findings are apparently in contrast with the hypothesis that AF is associated to chronic brain hypoperfusion. Flow variability is higher in AF compared to sinus rhythm, with a peak at arteriolar and capillary levels, thus resulting in local hypoperfusion [121]. Taking in to account these observations, the hemodynamic cerebral effect of AF could be a relevant mechanism into the genesis of AF-related cognitive impairment/dementia. In fact, deep white matter could undergo an ischemic damage because of two possible mechanisms: (1) the transient hypoperfusion as indicated above or (2) as a consequence of being exposed to transient hypertensive events (by arteriolosclerosis and capillary loss/bleeding), laying the basis for a potential AF-related vascular subcortical dementia [122]. On this matter, a cross-sectional study evaluating an unselected elderly cohort showed that AF is associated with decreased total cerebral blood flow compared to those who were in sinus rhythm, assessed by on phase-perfusion MRI and a reduction in total brain volume [123]. Brain perfusion was lowest in the persistent AF group compared to the paroxysmal AF group (46.4 mL/100 g/min vs. 50.9 mL/100 g/min; *p* < 0.05) and those with no AF (52.8 mL/100 g/min; *p* < 0.001) [123]. Although the hemodynamic effects on brain are complex, evidence suggests that a decreased cerebral blood flow may play a role in reducing brain volume and inducing decline in cognitive function seen in AF patients [100]. Of note, patients with paroxysmal AF who were in sinus rhythm at the time of MRI had higher cerebral blood flow and higher relative brain volume compared to those with permanent AF suggesting that as longer is the persistence in AF as low is the cerebral perfusion [123]. There is also evidence supporting that both cerebral blood flow and brain perfusion, assessed by phase-contrast MRI, improve after cardioversion [124,125]. This evidence further supports the hypothesis that also the time-period in AF may influence the risk to develop cognitive impairment [17].

#### *4.2. Rhythm Maintenance Strategy: Do We Have Supporting Evidence?*

Data regarding the impact of rate control in AF and the incidence of cognitive dysfunction and dementia are still not conclusive. In the observational study from Bunch et al. the impact of effective AF ablation on the risk of cognitive decline and dementia was evaluated [126]. A total of 37,908 participants were enrolled from the large ongoing prospective Intermountain AF study and divided in three cohorts: (1) patients who underwent AF ablation (4212); (2) age/gender-matched controls with AF (no ablation, 16,848); and (3) age/gender-matched controls without AF (16,848). These cohorts were followed for at least 3 years. Authors reported a significant reduction of Alzheimer's dementia in AF-ablated patients (0.2%) compared to AF patients who did not underwent ablation (0.9%) and patients without AF (0.5%) [126]. Patients treated with catheter ablation for AF have long-term rates of death, stroke, and dementia similar to patients without AF. Other types of dementia occurred in 0.4% of the AF-ablated patients compared to 1.9% of the AF patients not undergone ablation and 0.7% of the control patients [126]. A recent report by Damanti et al. [22] seems to shed more light on this issue, supporting the protective role of rhythm control strategy on cognitive function. Specifically, in their retrospective analysis, 1082 individuals aged 65 and older with AF before hospital admission (for any cause) were enrolled. Logistic regression evaluation adjusted for age, sex, education, antithrombotic therapy, and comorbidities found that the rhythm control strategy and education were associated with less probability of cognitive impairment.

In Table 1 are reported the published data on the putative role of sinus rhythm of cognitive function. However, based on this available literature, the impact of sinus rhythm restoration and maintenance on the cognitive decline should be further investigated in pre-specified randomized clinical trials. The major limitation for such investigation will be the need of many thousands of patients to enroll and a longer follow-up to be run for at least 10 years. Therefore, because it is unlikely that such trials will ever be funded, the real role of sinus rhythm restoration and maintenance will remain a pathophysiological based strategy but with a dark side in prevention of cognitive impairment.

**Table 1.** Studies comparing impact of sinus rhythm vs. atrial fibrillation on cognitive function.

#### **5. Hypertension: A Non-Stroke-Related Mechanism of Cognitive Decline in Atrial Fibrillation?**

While hypertension is a known risk factor for AF [39,127] the contribution of hypertension on cognitive decline in combination with AF is not well defined in literature. Among patients with established AF, hypertension is present in ≈60% to 80% of individuals [128]. In the ARIC study (Atherosclerosis Risk in Communities), hypertension was the main contributor to the burden of AF, explaining ≈20% of new onset of dysrhythmia [129]. However, the effect of an intensive control of blood pressure on the risk of new onset of AF in hypertensive patients remains unclear [39]. As reported by the post-hoc analysis of the ONTARGET and TRANSCEND trials the association of AF and cognitive impairment was independent of treatment with antihypertensive drugs [31]. However, other studies seem to confirm the contribution of hypertension in the cognitive impairment [130]. In a 5-year longitudinal study of 353 community-dwelling persons, mean age 72 years, increased blood pressure variability was associated with poorer cognitive function [131]. In addition, another study involving 1373 French participants aged 59 to 71 years, the risk of cognitive impairment at 4-year assessment was increased 2.8-fold in hypertensive patients [132]. It has been reported that because of hypertension, the structure and function of cerebral blood vessels is impaired, leading to ischemic damage of white matter regions that is critical for cognitive function [130]. However, whether hypertension treatment might reduce the risk for AF and the associated cognitive impairment remains unclear [39]. It has to be considered that in AF patients, the improvement of cognitive function related to the restoration and maintenance of sinus rhythm might be partially lost by an uncontrolled hypertensive status [130]. To date, despite the lack of evidence in the relationship between hypertension treatment and cognitive impairment, the uncontrolled blood pressure remains a major epidemiological contributor to cardiovascular disease and neurological disorders, and thus, an aggressive approach, as suggested by the current guidelines [133], is highly recommended.

#### **6. Discussion**

Several studies have evaluated the association between AF and cognitive dysfunction, ranging from cognitive impairment to dementia. Although the strongest evidence supports the role of stroke as the principal risk factor for cognitive impairment, it has been also established that AF is a risk factor for cognitive dysfunction independently from stroke [8,63]. Various mechanisms linked to cognitive impairment in AF patients, apart from stroke, have been discussed, involving microbleeds, white matter hyperintensity, neuroinflammation, reduced brain volume, cerebral hypoperfusion and genetic factors (as shown in Figure 1). However, a clear cause-effect relationship between these putative mechanisms and cognitive dysfunction is still controversial. Some of these mechanisms (i.e., microbleeds, reduced brain volume and cerebral hypoperfusion) might be linked to a common pathophysiological substrate which might be the impact of rhythm control over a rate control strategy on brain damage. First, since AF, through the loss of atrial systole contribution in left ventricle filling, reduces cardiac output and cerebral blood flow, restoration of sinus rhythm could improve brain perfusion, thus resulting in better cognitive outcome. Second, cerebral hypo-perfusion might be related to beat-to-beat variation in stroke volume in AF [100,123]. Decreased cerebral perfusion has been associated with a reduction in both grey and white matter although the effect may be greater on the grey matter due to higher metabolic demand [123]. However, reduction in grey matter is heterogeneous in the brain, since some areas appears to have higher vulnerability to cerebral hypoperfusion [134]. In some studies, a correlation between volume reduction in specified brain area (such as hippocampus) in AF patients and neurocognitive impairment and dementia has been reported [103]. Other investigations have linked the cerebral hypoperfusion AF-related and the reduction of brain volume [123]. Of note, the reduction in brain volume, especially in grey matter volumes of temporal and hippocampal areas, has been clearly associated with the risk of dementia [135]. Moreover, also white matter hyperintensity, linked with AF and cognitive impairment, may lead to cerebral hypoperfusion [95]. Taken together, these data are consistent with the "hemodynamic hypothesis" of AF related dementia and, as a consequence, restoration and maintenance of sinus rhythm might be associated to an improved brain perfusion, thus potentially avoiding most of the "risk factors" associated to cognitive impairment.

Further evidence linking AF and cognitive impairment comes from the SWISS-AF trial, a prospective multicenter national cohort study of 2400 patients across 13 sites in Switzerland [136]. In this study, patients with documented AF underwent to extensive phenotyping and genotyping, repeated assessment of cognitive functions, quality of life, disability, electrocardiography and cerebral magnetic resonance imaging. Authors reported that four in ten patients with AF but no history of stroke or transient ischemic attack had clinically unrecognized 'silent' brain lesions or other structural brain abnormalities such as white matter lesions, microinfarctions or microbleeds that could serve a substrate for the cognitive decline [136].

The question whether a rapid restoration of sinus rhythm in AF patients may reduce the risk for AF-related cognitive impairment remains a matter of debate. Further studies specifically designed to compare patients in a rhythm control intervention vs. rate control strategy are warranted to define the role of these two therapeutic approaches in the cognitive outcome overtime. Moreover, evaluation over time of specific parameters, such as cerebral blood flow, brain mass, neuroinflammation and others, will be of great importance to identify the contributing factors involved in cognitive impairment in stroke-free patients and the putative role of a rhythm restoring/rate control strategy in the prevention of cerebral damage. However, the major limitation in testing the effect of sinus rhythm restoration and maintenance in preventing cognitive decline and dementia is the need of larger (with many thousands of patients to enroll) and longer (to run for more than 10 years) prospective randomized trials. Therefore, it is unlikely that such trials might be ever funded.

#### **7. Conclusions**

AF is an independent predictor for cognitive dysfunction ranging from cognitive impairment to dementia. Apart from stroke and ischemic substrate, other mechanisms have been studied to explain the risk for cognitive dysfunction in AF patients. By the pathophysiological point of view, restoration and maintenance of sinus rhythm might represent an additional intervention to reverse some of the pathological alterations that serve as substrate for cognitive impairment, thus with potential effect in prevention. However, this aspect remains to be better supported by stronger evidence.

**Author Contributions:** Conceptualization, G.C. and P.G.; resources, S.D.; data curation, F.N.; writing—original draft preparation, E.G., M.L. and D.P.; writing—review and editing, G.C. and P.G.

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

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

#### **References**

© 2019 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 (http://creativecommons.org/licenses/by/4.0/).


**3. Multibody Formalism**

Among the modeling options presented in Section 2.1, we have opted for a relative coordinate formalism (Section 3.1) and a symbolic implementation of multibody equations (Section 3.2). This combination offers the advantage of producing models that can be easily interfaced, either with other disciplines (in a multiphysics perspective) or within a middelware platform (to couple devices and software in a real-time process).

#### *3.1. Modeling MBS Using Relative Coordinates*

When choosing relative coordinates, a MBS can be built on the basis of its topology characterized by the successive bodies and joints, as shown in Figure 2, on the left for a tree-like MBS (such as a serial robot) and on the right for a MBS containing kinematic loops (such as a multi-link car suspension). The relative angular (*rad*) or linear (*m*) displacements within the joints are called the generalized coordinates, *q*, of the MBS (also called *joint coordinates*). These are independent for a tree-like MBS but not for a closed-loop one, each loop of bodies leading to geometrical constraints, denoted as *h*(*q*) = 0, between the joint coordinates present in the loop. The distinction between tree-like and closed-loop MBS is essential because it is at the root of the relative coordinate formalisms as synthetically explained below.

**Figure 2.** Tree-like (**left**) and closed-loop (**right**) MBS topologies.

#### Tree-Like MBS Modeling

Basically (see in [8] for more details), in the case of a tree-like MBS containing *n* joint coordinates, these confer *n* motion degrees of freedom (DOF) to the system, leading to a set of *n* equations of motion.

Various techniques can be envisaged to obtain them (d'Alembert principle, Lagrange equations, Newton–Euler equations recursively computed on the topology [8]). In any case, one ends up with a form equivalent to the following one ("." stands for *d dt* and ".." for *d*2 *dt*2 ):

$$M(q)\,\ddot{q} + c(q, \dot{q}, frc, trq) = Q(q, \dot{q}),\tag{1}$$

in which *q*˙ and *q*¨, respectively, stand for the generalized relative velocities and accelerations; *M*(*q*) represents the system mass matrix; *c*(*q*, *q*˙) gather the centripetal, Coriolis, gyroscopic terms as well as the external forces *f rc*(*q*, *q*˙) and torques *trq*(*q*, *q*˙); and *Q*(*q*, *q*˙) denotes the efforts within the joints (actuators or passive elements).

Equation (1) can be straightforwardly solved using linear algebra to obtain the relative accelerations *q*¨: this refers to *Direct Dynamics*. This computation has a *O*(*n*3) computational complexity due to the factorization of the mass matrix. Among other things, direct dynamics allows, via the time integration of the accelerations, to determine the time history of the positions *q*(*t*) and velocities *q*˙(*t*) and thus to predict the motion of the MBS subjected to internal or external forces, starting from initial conditions *q*(0), *q*˙(0).

As an illustration, direct dynamics is widely used for road and railway vehicles to predict, analyze, or optimize their dynamic performances. Direct dynamics is also used in various driving simulators to make haptic feedback more accurate and realistic (see Section 5.2).

*Inverse Dynamics* is another way of using Equation (1) for applications where the MBS trajectory is known (*q*(*t*), *q*˙(*t*), and *q*¨(*t*)) and for which the unknowns are now the joint forces and torques *Q*(*t*) and not the accelerations anymore. Equation (1) can then be written as follows:

$$Q(q, \dot{q}) = \phi(q, \dot{q}, \ddot{q}, frc, trq) \tag{2}$$

with

$$\phi = M(q)\ddot{q} + c(q, \dot{q}, frc, trq)$$

As the explicit computation of the mass matrix *M* is not necessary anymore to compute *Q* via (2), *φ* can be generated without explicitly computing it, which leads to an efficient *O*(*n*) computational complexity of the formalism.

For instance, inverse dynamics is useful to compute the actuator torques of robots for evaluating a feedforward component in their motion controller.

#### Closed-Loop MBS Modeling

A multibody system can be subject to different types of constraints between its generalized coordinates, *q*, which can be holonomic (i.e., algebraic, like assembly constraints), non-holonomic (expressed at the level of velocities, like for a slip-free rolling), and/or rheonomic (when they depend explicitly on time *t*). In the present context of haptic devices modeling, we will limit ourselves to the holonomic case, focusing on kinematic loop constraints; this does not exclude the case of other algebraic constraints that would be treated in the same way by the following formalism.

When dealing with closed-loop MBS (as depicted on the right of Figure 2), *m* loop constraints between the generalized coordinates *q*, denoted *h*(*q*) = 0, must be solved in parallel to the equations of motion (1) or (2), in which an additional term is required to take the constraint forces and torques into account, according to the well-established Lagrange multipliers technique. For the direct dynamics, the final form reads

$$\begin{array}{rcl}M(q)\;\ddot{q} + c(q, \dot{q}, frc, trq) &=& Q(q, \dot{q}) + f^t(q)\;\lambda\\h(q) = 0 \; ; \; \dot{h}(q, \dot{q}) = I(q)\; \dot{q} &=& 0 \; ; \; \ddot{h}(q, \dot{q}, \ddot{q}) = 0\end{array} \tag{3}$$

And in a similar way, for the inverse dynamics the equations becomes

$$\begin{array}{rcl}Q(q,\dot{q})&=&\Phi(q,\dot{q},\ddot{q},\text{frc},\text{tr}q)-\dot{J}^{t}(q)\,\lambda\\h(q)&=&0\,\text{; }h(q,\dot{q})=\dot{J}(q)\,\dot{q}=0\,\text{; }\dot{h}(q,\dot{q},\ddot{q})=0\end{array} \tag{4}$$

in which *J*(*q*) *∂h ∂qt* denotes the constraint Jacobian matrix and *λ* are the Lagrange multipliers associated with the constraints *h*(*q*). In both cases, the number of DOF equals *n* − *m*, that is the number of generalized coordinates *q* minus the number of independent constraints *h* (The case of redundant constraints is treated within Robotran, but only for "classical" multibody simulations, and not for the proposed haptic coupling. As there were no redundant constraints for the applications treated in this paper, this topic has not been discussed.).

Let us illustrate closed-loop morphologies in the field of robotics. An anthropomorphic serial robot with 6 actuated joints (Figure 3 (left)) possesses 6 DOFs. On the other hand, a parallel structure such as the Stewart platform (Figure 3 (right)) has 18 joints embedded into multiple loops of bodies, but its upper-plate only possesses 6 DOFs and its motion is ensured by 6 actuators forming the legs of the platform.

**Figure 3.** Serial (**left**) versus parallel (**right**) manipulators.

Equations (3) or (4) form a DAE system, as they are made of differential and algebraic equations, whose direct solving is not trivial. A practical and elegant way to solve them is to transform them into ordinary differential equations (ODE), using the so-called *Coordinate Partitioning Method* briefly presented below (see in [4,8] for more details).

The starting point of the method refers to the number of degrees of freedom (*n* − *m*) of the system, to which (*n* − *m*) generalized coordinates can match. They are called *independent* coordinates, denoted *u*, while the others, denoted *v*, are *dependent*. Therefore, we can partition the column vector *q* as follows:

$$
\eta = \begin{pmatrix} u \\ v \end{pmatrix} \tag{5}
$$

By applying this partitioning to all the matrices and vectors appearing in Equation (3) or (4), and by expressing the dependent coordinates via the solving of the constraints at position, velocity and acceleration levels,

$$\begin{aligned} h(q) &= 0 \quad \Rightarrow \quad v = f(u), \\ \dot{h}(q, \dot{q}) &= 0 \quad \Rightarrow \quad \dot{v} = g(q, \dot{u}), \\ \dot{h}(q, \dot{q}, \ddot{q}) &= 0 \quad \Rightarrow \quad \ddot{v} = h(q, \dot{q}, \ddot{u}), \end{aligned} \tag{6}$$

it is possible, after a few matrix manipulations, to end up with a reduced set of equations of motion in terms of the independent coordinates *u*:

$$\left(M\_{\mathbf{r}}(\mathbf{u}), \ddot{\mathbf{u}} + \mathbf{c}\_{\mathbf{r}}(\mathbf{u}, \dot{\mathbf{u}}, \mathrm{frc}, \mathrm{trq})\right) \\ \quad = \quad Q\_{\mathbf{r}}(\mathbf{u}, \dot{\mathbf{u}}) \tag{7}$$

$$\mathcal{Q}\_r(\mathfrak{u}, \mathfrak{u}) \quad = \quad \mathfrak{\phi}\_r(\mathfrak{u}, \mathfrak{u}, \mathfrak{u}), \tag{8}$$

for the direct and inverse dynamics, respectively.

¨

The coordinate partitioning technique amounts to projecting the equations of motion in the MBS motion manifold, with the advantage of getting rid rigorously of the algebraic constraint equations. Therefore, one ends up with a ODE system of minimum size (n-m) which exactly corresponds to the number of DOF of the MBS (ex. *n* − *m* = 6 for the Stewart platform of Figure 3).

It is worth mentioning that the resolution of the constraints (6) can be very accurate using an iterative (for *h*) or an algebraic (for ˙ *h* and ¨ *h*) algorithm, at each evaluation of the dynamic equations.

#### Driven Motion

When dealing with MBS in which the time evolution of some of the generalized coordinates *u* is fully imposed (such as for instance in robotics when motion control is achieved), the corresponding joints are qualified as *driven*. The time history of the associated coordinates is thus a prescribed function of time *t*. To take them into account in the above formalism, we can apply a second partitioning, but on *u* in the present case, that is,

$$
u = \begin{pmatrix} u\_{\tt n} \\ u\_d \end{pmatrix}'\tag{9}$$

in which *uu* represents the new subset of independent coordinates and *ud* stands for the driven coordinates viewed, from the MBS point of view, as known functions of time at position, velocity, and acceleration levels:

$$
\mu\_d = f(t) \; ; \; \dot{u}\_d = \frac{d u\_d}{d t} \; ; \; \dot{u}\_d = \frac{d^2 u\_d}{d t^2} \tag{10}
$$

Note that driven variables can also be used to deal with rheonomic constraints. Indeed, they allow to describe an explicit time dependence of any function in which they appear, as in an algebraic constraint for example.

Introducing the partitioning (9) into the equations of motion (7) gives

$$
\begin{pmatrix} M\_r^{\text{mu}} & M\_r^{\text{mu}} \\ M\_r^{\text{du}} & M\_r^{\text{dd}} \end{pmatrix} \begin{pmatrix} \ddot{u}\_{\text{ll}} \\ \ddot{u}\_{\text{d}} \end{pmatrix} + \begin{pmatrix} c\_r^u \\ c\_r^d \end{pmatrix} = \begin{pmatrix} Q\_r^u \\ Q\_r^d \end{pmatrix} \tag{11}
$$

The first line of (11) can be solved with respect to *u*¨*u* and represents the final form of a *direct dynamics* problem (to be time integrated with respect to the *uu* variables for instance):

$$M\_r^{uu}\ddot{u}\_{\iota} + \mathfrak{c}\_r^u = Q\_r^u - M\_r^{ud}\ddot{u}\_d \tag{12}$$

Let us note that this equation can also be formulated in an inverse dynamics form, in a similar way to Equation (8).

The second line of (11) can be put aside if we are only interested in analysing the motion of the MBS Equations (12). However, those equations can be very useful to compute efforts (*Qd r* ) associated with the driven coordinates (ex. a motor torque, a force in an mechanical assembly or a reaction force in a bearing):

$$\mathbf{Q}\_r^d = \mathbf{M}\_r^{du}\mathbf{i}\_u + \mathbf{M}\_r^{dd}\mathbf{i}\_d + \mathbf{c}\_r^d \tag{13}$$

In a certain manner, this equation represents an inverse dynamics computation carried out in parallel with—or as a postprocess of—the direct dynamics (12).

#### Implementation in a Haptic Context

The multibody computation workflow varies slightly whether direct dynamics or inverse dynamics are considered (Figure 4). For direct dynamics simulations, a numerical integrator computes the time evolution of the independent variables. Trajectory sensors (i.e., position, velocity, or acceleration sensors) define the motion of *driven joints* (Step A), such as the angular position of a steering wheel for a driver simulator or the displacement of a piano key (see Section 5). When the trajectories of all independent joints are measured, the inverse dynamics formulation is used. Filtering, differentiating or integrating the sensor signal may be necessary.

The algebraic constraints (6) of closed-loop MBS are used to compute dependent positions and velocities (step B). Once the configuration of the system is fully defined, external and internal forces are computed, for instance, the tyre/ground interaction for a car or the contact interaction between the parts of a piano action (step C). At this stage, inputs coming from force/torque sensors can be considered in the dynamics. The dynamic Equations (3) or (4) are computed and, for closed-loop systems, the coordinate partitioning technique is applied (step D) to get the reduced set of Equation (7). In the case of direct dynamics, a linear system of equations must be solved (step E) to compute the independent

acceleration that will be handled by the time integrator. For the inverse dynamics, joint forces/torques are derived directly from the dynamics (step F) using (8).

Forces or torques associated with driven joints are computed (step F) using (13), which enables computing a feedback that takes the dynamics into account, like the wheel steering torque on the driver or the reaction of the piano key on the musician finger. In addition, any kinematic information of the system, such as the position or velocity of any point, can also be computed (step G) and be used for the system feedback using so-called *multibody sensors*.

Direct dynamics Inverse dynamics

**Figure 4.** Flow diagram of direct and inverse dynamics in view of coupling them with haptic devices.

### *3.2. Symbolic Multibody Model*

The aim of the symbolic generation of multibody models is to take advantage of both the genericity inherent to a numerical generation of multibody equations and of the simplification potentialities of their manual writing. So-called *symbolic* multibody programs manipulate arithmetical operators (+, −, \*, /) and alphanumeric strings representing the data (*m*, *c*, *K*, etc., see Figure 5) to generate analytical equations in C, Python, or MATLAB, making the most of arithmetical and trigonometric simplifications. For a generic 3D MBS, the symbolic generation is performed only once (in less than one second with ROBOTRAN for MBS containing a hundred DOF) prior to any numerical analysis.

**Figure 5.** Symbolic Generation Process.

The computational superiority of the symbolic vs. the numerical approach is particularly attractive since it gives the opportunity to

These two advantages are widely exploited in the applications underlying this work as detailed in the next sections.

#### ROBOTRAN Symbolic Generation

This section summarizes the key elements of the symbolic generation underlying the multibody software ROBOTRAN, the details of which can be found in reference [10], dedicated to this precise topic. There do obviously exist commercial general-purpose symbolic packages: why do we not use these to generate multibody equations?

The first reason relates to the amount of computer memory required to generate large symbolic models (up to 400 DOFs in our case). The combined use of dynamic memory allocation (via the use of C-pointers) and of the linked lists technique, allows us to finely control the ROBOTRAN memory requirement during the generation process.

The second reason concerns the possible simplifications of mathematical alphanumeric expressions. Although the simplification capabilities of commercial packages are extremely powerful, the symbolic condensation of multibody equations relies on a set of rules and tricks which are specific to MBS, namely, the simplification of complex trigonometric formulae and kinematic expressions, the pure and lasting elimination of unnecessary recursive equations and the parallelization of the latter, among others.


**4.1.4 Dendritic cells and mast cells**

218 Breast Cancer – Focusing Tumor Microenvironment, Stem Cells and Metastasis

c-Myc, MMP, and VEGF, which are all involved in promoting tumourigenesis. In BCA, NOS2 expression within tumour cells themselves is correlated with increased tumour grade

T cells orient their cytoskeleton and migrate towards sites of inflammation, such as those present in a tumour microenvironment (TME), in a PKC-dependent manner as a direct result of CD44 crosslinking by HA (Fanning *et al*., 2005). In BCA, CD8+ T cells are most predominant in advanced cancer stages where their presence in proliferating tumours is a good prognostic indicator. T cells are able to participate in either a Th1 or Th2 polarized immune response and, when polarized to a Th1 response, they express and secrete IFNγ, TGFβ, TNFα, IL-2, resulting in cytotoxic cooperation (T cells and M1). Th2 polarized CD4+ T cells secrete IL-4,5,6,10,13 which leads to an increase in B cell mediated immunity (DeNardo and Coussens, 2007). Because of the anti-tumour effects of T cells, the activation of cytotoxic T cells against HA receptors as immunotherapy in leukemias is currently undergoing clinical trials and will be discussed later in this chapter. On the other hand, the presence of CD4+ T cells correlates with disease progression and metastasis; however, it has been shown by different groups that CD4+ T cells are crucial for mounting an immune response against cancer. For example, tumour growth of EL4 lymphoma cells inoculated into mice is inhibited by the presence of dendritic cells primed against RHAMM protein. This interaction, however, is dependent on CD4+ T cells, as the effect of DC killing of the tumour is significantly reduced with a reduced CD4+ T cell population (Fukui *et al*., 2006). Furthermore, Rakhra *et al.* (2010) showed that in ALL and B-cell leukemia, CD4+ cells were necessary for sustained tumour regression. In mouse models, inhibition of MYC or BCR-ABL rescues tumours from oncogene addiction; however, tumours regress in the presence of TSP-1 induced CD4+ T cells, and knockdown of TSP-1 impairs this ability (Rakhra *et al*.,

Regulatory T cells (Treg; CD4+/CD25+/FOXP3+) play controversial roles in tumour progression and can have both anti- and pro-tumourigenic effects, depending on the chemokines or cytokines produced and the type of solid tumour. Treg cells may be activated in an immunosuppressive manner, preventing cytotoxic immune responses, and allowing the tumours to evade immune attack. For example, in CLL, a large Treg population dampens specific CD8+ T cell responses against tumour associated antigens (Giannopoulos *et al*., 2010). The same may be true for solid tumours. When coordinated, however, with a high T cell density, they may indicate good prognosis and inhibition of metastasis (Camus *et*

Immunoglobulin deposition by B cells in BCA stroma can be detrimental to disease progression and the accumulation of autoantibodies produced by B cells and deposited in the stroma correlates with poor prognosis (Fernandez Madrid *et al*., 2005). An increase in serum IgG correlates with an increase in TAM numbers which, in turn, promotes angiogenesis in mouse mammary carcinoma, a process associated with poor clinical outcome. A proposed mechanism for the involvement of TAMs in B cell processes is the phagocytosis of IgG by macrophages. IgG engages Fcγ receptors, which stimulates VEGF secretion, increases angiogenesis and promotes tumour growth rate (Barbera-Guillem *et al*.,

and angiogenesis (Ambs and Glynn, 2011).

**4.1.2 T Cells**

2010).

*al*., 2009, Carreras *et al*., 2006).

**4.1.3 B Cells**

Dendritic cells (DC) can also exhibit HA dependent characteristics that either promote or inhibit tumourigenesis. HA or chondroitin sulphate, in conjunction with CSF-1, activate DC from an immature to differentiated state via an NFκB regulated process, illustrating the importance of HA in eliciting an immune response (Yang *et al*., 2002). Pedroza-Gonzalez *et al* (2011) recently showed that human BCA produces thymic stromal lymphopoietin (TSLP) which induces expression of OX40L on DCs, polarizing them towards a Th2 inflammatory response. *In vitro* this drives the production of IL-13 and TNF by Th2 polarized T cells (Pedroza-Gonzalez *et al*., 2011). DC also become tumour insensitive and, as a result, do not mature and differentiate into cytotoxic cells. Furthermore, HA fragment build ups are at least partly responsible for preventing DC maturation in tumour bearing animals (Kuang *et al*., 2008).

In BCA, c-kit expression by mast cells, a protein which is usually only present in specific tissue types, such as germ cells, predicts primary tumour recurrence (Khazaie *et al*., 2011). However, an abundance of stromal mast cells in invasive BCA is associated with good prognosis (Rajput *et al*., 2008). The mast cell line HMC-1 expresses high levels of CD44s and, through an interaction with HA, adheres to stromal tissue (Fukui *et al*., 2000). Therefore, in both mast cells and DC, a CD44-HA interaction may result in anti-tumour responses.


**2. Literature review**

#### **2.1 Sustainable agricultural practices**

The green revolution as it was called brought with it increased productivity enabling the world to meet its food needs. As of the mid-1960s, Asia was faced with a food crisis that threatened millions. This situation was salvaged by the green revolution, which made Asia self-sufficient with essential food products within 20 years [6]. This self-sufficiency that was the trend during the green revolution can be said to be coming to an unfortunate end given the looming food crisis and the prediction by the World Bank that the world will need to feed 8.9 million persons by 2050 (which has been reiterated by many researchers)**.** The agricultural practices that characterized the green revolution can be described as a quick fix that came at a very high cost. The positive effects of high yield were doused by negative impacts on the environment, such as erosion, water pollution, eutrophication of water bodies, water wastage, and the growth of resistant varieties of weeds and pests [7].

The transition from traditional agricultural practices to modern agricultural practices characterized by the use of machines, inorganic fertilizers, pesticides, and herbicides among others can be linked first to the Industrial Revolution in the eighteenthcentury discovery of fossil fuels in the twentieth century, which made the industrial production of agrochemicals possible [8]. Consequent to the green revolution, crops

### *Sustainable Agricultural Practices: Value Chain of Egusi, A Local Soup Condiment… DOI: http://dx.doi.org/10.5772/intechopen.109854*

were cultivated with little or no consideration for the depleting nutrients and the destruction of the soil structure. This, however, has polluted the entire agricultural value chain [9]. The use of pesticides destroys both pest and useful soil organisms, inorganic fertilizers cause water pollution when washed into water bodies, monocropping depletes soil nutrients without replenishing them, and heavy ridging destroys the soil structure making it more susceptible to erosion among others remain the major non-sustainable agricultural practices. Consequently, the need for sustainable agricultural practices cannot be overstated.

One of the earliest definitions of sustainable agricultural practices states that it is a set of management strategies that address the concerns of society about the quality of food or the protection of the environment [10]. Later, the focus was on the ability of agricultural practices to productivity over a long period and the capacity to adapt to changes in future. Currently, the criteria for sustainable agricultural practices are economic viability, environmental protection, and social consideration.

Franklin King [11] in his publication titled Farmers of Forty Centuries: Permanent Agriculture in China, Korea, and Japan brought to light how farmers in some parts of East Asia cultivated their fields for four centuries without depleting the soil. It was around the same time that industrialization was emerging. With machines, more efficiency and effective use of resources could be guaranteed. This led to higher productivity and profitability; hence, the long-term effect was not considered.

By adopting sustainable agricultural practices, it is not implied that farmers should return to pre-Industrial Revolution practices. Sustainable agricultural practices involve adopting a system of crop cultivation with the use of fertilizers, pesticides, and herbicides among others. Emphasis is rather placed on combining traditional methods and modern innovations that are sustainability-centric. These practices include crop rotation, water and soil conservation, use of organic fertilization methods, little or zero tilling, cover cropping, and many other practices that put into consideration the long-term implication of their application.

The International Federation of Agricultural Movement (IFOMA) opined that organic agriculture, which is sometimes used as a synonym for sustainable agricultural practices, is a method of production that can preserve the soil nutrients, ecosystem, and health of the members of the value chain. Such practices could be said to be the best agricultural system to adopt given the challenges of the present day [12]. Sustainable agricultural practices are been promoted because of their ability to enrich environmental biodiversity through the use of animal manure, compost manure, organic waste materials, crop rotation, mixed cropping, and biological pests and weed control methods.

Sustainable agricultural practices have a positive impact on the environment as well as the farm. These practices help soil microorganisms thrive. Soil microorganisms such as rhizobacteria produce bioactive substances, which in turn enhance plant growth and offer protection against pathogens [13]. The presence of soil microorganisms also promotes the aeration of the soil. Another noteworthy action of soil microorganisms is the replenishing of soil nitrogen through their action on the roots of legumes. Given this myriad of benefits offered to the soil by microorganisms, it is in place to protect and promote their existence in the soil. The consequence of the neglect is the increasing use of harmful chemicals in farming systems. In the bid to safeguard and enhance the existence of soil microorganisms, the use of eco-friendly agricultural chemicals, which contains effective microorganisms, has proven to enhance plant growth and environmental sustainability [13].

In 2012, a research by Chhetry and Mangang, the French Bean Rust was studied for 3 years. During the course of the study, farmers who adopted sustainable agricultural practices and those who adopted intensive methods were understudied. Their research revealed that intercropping beans with maize lessened the occurrence of beans rust as the maize served as a sort of barrier between the spores that cause beans rust and the leaves of the beans plant. Indications were also made that maize plants could have a negative effect on rust pathogens. The application of plant extract was found to be beneficial as it reduces the severity of bean rust although it did not reduce the occurrence of the disease. These findings led to the conclusion that sustainable agricultural practices are more effective in reducing bean rust compared to inorganic methods.

#### **2.2 Dimensions of sustainable agriculture**

The misconception would be to think that sustainable agricultural practices are targeted just to conserve the environment while negating the advantages of machinery, pesticides, and herbicides to improve yield and profitability for the farmer. In some corners, it is discussed as an ineffectual method of food production [14]. Contrary to the foregoing, it is a blend of science, traditional agricultural methods, and innovation [15] while putting into consideration the relationship between the environment and quality of life. Agricultural practices can be described as sustainable if they maintain productivity and profitability over a long period of time without compromising the ability of future generations to do the same. Simply put, the farm has to be viable economically, ensure environmental protection, and be socially fair [7]. Sustainability in agriculture has dimensions that encompass all areas of concern to the stakeholders in the agricultural value chain. These dimensions are economic, social, and ecological.

#### **2.3 Economic dimension**

Considering the overall economic viability of a farm is key to promoting sustainable agricultural practices. A farm can be said to be sustainable if it can cater to the financial needs of the farm and create value and profit throughout the value chain among other criteria. This brings to light the fact that sustainable agriculture is not just about environmental protection. It is pertinent that the farm is profitable. To incur a loss on account of the use of sustainable agricultural practices would be a contradiction and a poor application of sustainable practices. Economic profitability is a key criterion in ascertaining a farm practice of sustainable agriculture [15]. Economic sustainability can be achieved through the use of fewer inputs without putting productivity at risk.

When a farm decides to adopt sustainable practices, its dependence on input relating to intensive agriculture is reduced if not eradicated completely. Consequently, the production cost is reduced [16]. The argument is that shifting to sustainable agricultural practices will result in poor yield. However, it has been brought to light in practice that relying on crop rotation, mixed cropping, minerals-carrying rocks, planting cover crops, and biological means of pest control among other methods could result in high yield while maintaining soil fertility and productivity [17]. It is common for farmers to adopt cropping systems that enhance output, profitability, and sustainability [18]. Therefore, bringing to light the benefits of sustainable agricultural

*Sustainable Agricultural Practices: Value Chain of Egusi, A Local Soup Condiment… DOI: http://dx.doi.org/10.5772/intechopen.109854*

practices will serve to the advantage of ecological sustainability given the high level of ignorance among farmers in developing countries like Nigeria.

Intercropping has been considered a practice that is low input and energy-efficient [19]. Intercropping is an agricultural practice where two or more crops are grown together. These crops could be planted at different times and in most cases harvested at different times resulting in efficient use of resources, enhancing productivity and income as well as giving support to each other. Cultivating two or more crops simultaneously promotes the existence of insect pollinators, which in turn improves yield and also suppresses the growth of weeds because of plant population [19]. Leguminous plants have the ability to host predators, which results in a simpler pest management practice [20].

#### **2.4 Social dimension**

The social dimension of sustainable agriculture advocates for the welfare and safety of the players in the value chain. The prevailing practices in the past years have failed to meet the aspirations of a large part of society who seek good working conditions and dignified lives (www.ilo.org). This has led to the advocacy for practices that puts the welfare of people and the environment at the heart of its affairs. The employment relationship in the agricultural value chain has proved to be complicated. This is due to the nature of the contract, which is wage-based. This complicated relationship gives little or no room for proper benefits that ensures welfare. This is because the contract ends with the completion of each task with no guarantee of renewal.

This unclear relationship thrives because the employers hide the true legal status of engagement. The resulting effect denies the worker labor protection and other benefits. Sustainable agriculture promotes the proper treatment of employees and a better relationship among all members of the value chain, a relationship where trust and respect are evident. The adoption of sustainable agricultural practices results in increased labor demand, improved productivity, and higher reverse migration.

#### **2.5 Ecological dimension**

This dimension of sustainable agricultural practices stems from the rising cases of the pollution of both surface and groundwater, which is caused by the heavy application of chemical fertilizers, and pesticides and also the erosion of surface soil due to repeated tilling, which poses a great environmental challenge [21]. Agricultural practices and the environment are so closely linked in such a way that for agriculture to thrive, environmental processes need to be functioning properly, and the health of the environment depends on a balanced agricultural system [22]. From this closely knitted relationship, intensive agricultural practices can be referred to as self-sabotage.

The environmental degradation caused by intensive agricultural practices needs to be critically analyzed [23] and its long-term effect needs to be brought to the knowledge of the stakeholders in the agricultural value chain. Realizing that environmental pollution caused by intensive agricultural practices threatens the livelihood of millions could lead to the modification of agricultural systems that promotes both environmental sustainability and economic sustainability [24]. Putting the harmful effect of intensive agriculture in the open is not enough, innovative, and sustainable practices should be promoted. The importance of promoting sustainable agricultural practices goes without saying considering the harmful effect of chemicals on beneficial

microorganisms, which are responsible for fixing soil nitrogen and decomposition of organic wastes suppressing plant diseases among others. From the perspective of environmental protection and the vitality of soil microorganisms (which further impacts the overall health of the soil), intensive agricultural practices can be said to be a menace.

Such practices are mixed cropping, crop rotation, zero-till practice, use of compost, green manure and biopesticides, planting of cover crops, and effective water management among others. Intensive agricultural practices, such as mechanization use of chemicals, monoculture, and intensive tilling, led to the simplification of the different parts interacting in the agricultural systems. This has led to erosion, making the agricultural system prone to abiotic stress and thus, putting the agricultural system at risk of being unsustainable. In order to safeguard the sustainability of the cropping system, restoration of the biodiversity of farms is integral. This restoration can be achieved through the adoption of mixed cropping and other farming systems that guarantee sustainability [25].

The success of sustainable agricultural practices in promoting biodiversity and environmental production hinges on the effectiveness of the diversification of crops, the inclusion of leguminous crops in the cropping system, organic restoration of soil fertility, and the no-till practice. Notable among the benefits of mixed cropping to the environment is its ability to reduce runoff water because the crops tend to cover more ground. As insignificant as this may seem, less run-off water means less soil erosion and nutrient loss [26].

#### **2.6 Strategies for sustainable agricultural practices**

As earlier seen, the practice of sustainable agriculture has been heavily debated. Hence, in order to fully achieve sustainability, it is necessary for a workable framework to be followed. Mac Rea et al. [27] suggested substitution and agroecological and global strategies.

In substitution, the farming practices are not fundamentally altered but are slightly modified. The use of toxic pesticides, herbicides, and fertilizers is replaced with less toxic chemical compounds and the cultivation of genetically modified plants [7]. An agroecological strategy is a holistic approach. Here, ecological principles and concepts are applied to the planning and execution of all farm practices. Practices such as mixed cropping, crop rotation, and the use of compost manure are used to foster biodiversity [7].

The global strategy as the name implies seeks to find solutions to the adoption and application of sustainable agricultural practices at a global scale. In the global strategy, an interdisciplinary effort involving scientists, economists, politicians, and agriculturists is called for in order to fine-tune the current rough edges of sustainable agricultural practices, economic growth, and food security. The global strategy, therefore, implies that in order to achieve sustainability, the relationship between the players in the agricultural value chain has to be cemented as farm systems without the collaboration of the food systems cannot achieve sustainability [28].