\r\n\tIt has been established that energy/nutrient depletion, calcium flux injury, or oxidative stress disrupt endoplasmic reticulum homeostasis and even induce accumulation of misfolded/unfolded proteins leading to endoplasmic reticulum stress. Under endoplasmic reticulum stress conditions, an adaptive mechanism of coordinated signaling pathways, defined unfolded protein response (UPR), is activated to return the endoplasmic reticulum to its healthy functioning state. The aging causes a decrease of the protective adaptive response of the UPR and an increase of the pro-apoptotic pathway together with endoplasmic reticulum ultrastructural injury. Controlling endoplasmic reticulum stress response, maintaining the appropriate endoplasmic reticulum ultrastructure and homeostasis, and retaining mitochondria interplay are crucial aspects for cellular health.
\r\n
\r\n\tThis book presents a comprehensive overview of endoplasmic reticulum, including, but not limited to, endoplasmic reticulum ultrastructural anatomy, MAMs, endoplasmic reticulum stress, and their implication in health and diseases. Additionally, identifying perturbations in the endoplasmic reticulum stress response could lead to early detection of age-related disease and may help develop therapeutic approaches.
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She is currently engaged as a researcher for the Scientific-Disciplinary Sector BIO/16 Human Anatomy at the Anatomy and Pathophysiology Division, Department of Clinical and Experimental Sciences, University of Brescia (Italy).\r\nDr. Favero focuses on aging-related morphological dysfunctions as the prelude to various pathophysiological processes in her research programs. The central hypothesis is that natural antioxidants and, in particular, melatonin may act as molecular "switches" that modulate cells and tissues by suppressing, at various levels, oxidative stress and inflammatory signalling cascades. These research approaches represent powerful tools for developing innovative preventive strategies and identifying novel prognostic biomarkers for several diseases. 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1. Introduction
Sleep–wake disturbances play an important role in the clinical presentation of the disease in patients with PDD. Involvement of brainstem and diencephalic structures result in dysfunction of the reticular activating system and other structures related to sleep-wake homeostasis. The resultant sleep disruption may impair the clearance of these abnormal proteins through slow wave sleep-mediated glymphatic drainage. Thus, neurodegeneration and sleep disruption interact in a feedforward loop, increasing the progression of disease. PD is classically recognized for its motor symptoms of tremor, rigidity, bradykinesia, and postural instability. However, sleep disturbances, broadly classified as one of the nonmotor manifestations of the condition, are quite common and associated with significant morbidity. Nocturnal motor symptoms, nocturia, dopaminergic medications, comorbid medical, psychiatric, sleep disorders, and environmental factors can all contribute to poor sleep in these patients. On the other end of the clinical spectrum, disturbance of sleep–wake mechanisms is a core feature of DLB. There are fluctuations of attention and arousal manifesting as variability in the ability to organize thoughts throughout the day and frequent daytime lethargy and napping despite an adequate sleeping period at night. The presence of REM sleep behavior disorder (RBD), a sleep disorder characterized by the enactment of dream activity due to loss of the normal muscle paralysis associated with REM sleep, is highly prevalent in DLB and is often one of the first signs of the disease. DLB patients are also notably sensitive to neuroleptic medications, which can present as confusion, inattention, and somnolence upon exposure to this class of medication. Unfortunately, this is often discovered on the road to diagnosis in an attempt to manage the behavioral manifestations of the disease. These clinical features are often present in PD as well; however, they are often identified retrospectively or later in the course of the disease. PDD, on the spectrum of PD and DLB, is characterized by the onset of cognitive decline in patients with an established diagnosis of PD. All of the aforementioned sleep disturbances seen in PD and DLB can coexist in PDD and are often historically difficult to disentangle. While present as a core feature of DLB and a common nonmotor manifestation of PD, sleep disturbances are often underappreciated in these diseases, especially in the prodromal phase. The presence of disturbed sleep and/or daytime sleepiness in a patient with cognitive impairment or subtle signs of Parkinsonism should alert the clinician to the possibility of a neurodegenerative process. Sleep disruption often presents years prior to the motor or cognitive stigmata of these conditions; however, it may be overlooked as nonspecific or regarded as typical of age-related changes in sleeping patterns. A sleep-focused history, review of systems, standardized questionnaires, neurological examination, and polysomnography can aid in identifying sleep–wake disturbances in these patients. Early recognition is important for expectant disease management, counseling, and research into neuroprotective agents with the aim of halting or altering the trajectory of illness in these diseases.
2. Neurodegeneration of sleep–wake systems
Primary neurodegeneration in PDD causes excessive daytime sleepiness and disruption of sleep–wake architecture. Sleepiness and daytime napping are common in the older general population and not specific for the presence of a neurodegenerative condition. Clinical tools used to assess daytime sleepiness include quantitative questionnaires of subjective sleepiness like the Epworth Sleepiness Scale (ESS) as well as nocturnal polysomnography in combination with daytime nap testing, such as the multiple sleep latency test (MSLT). Patients with alpha-synuclein disease present with increased sleepiness by both subjective and objective measures compared to age-matched controls [1]. Within this population, daytime sleepiness also tends to increase with the presence of dementia with patients with PDD exhibiting increased sleepiness compared to patients with PD alone [2]. This daytime somnolence is in large part due to sleep disruption at night with associated alterations in underlying sleep architecture seen with polysomnography. Studies in patients with PD have shown poorer sleep quality with a delay in sleep onset, reduced N3 sleep, REM sleep, total sleep time, and sleep efficiency. This is similar to studies in patients with DLB that have also shown increased wake after sleep onset and N2 sleep [3]. These patients are also more frequently afflicted with EMG augmentation during REM sleep and confusional arousals from NREM sleep. These polysomnographic findings suggest that disruption of sleep–wake neural networks is common to both clinical conditions with overlap in their neuropathological spread. Posterior dominant slowing of background rhythms in REM sleep and wakefulness, temporal slowing with wakefulness, and impairment in spindle generation appear to be electrographic biomarkers for the development of PDD [4, 5]. This mirrors neurohistopathological findings that show increased Lewy body deposition in brainstem and limbic structures, which colocalize with the wake-promoting reticular activating system (RAS) and thalamocortical circuitry involved with spindle generation, respectively. The Unified Staging System for Lewy Body Disorders suggests that the involvement of these structures occurs early on in the disease course with brainstem involvement (IIa) characteristic of more parkinsonian-type features and limbic involvement (IIb) characteristic of a more dementing-type illness. It is at these stages of disease that sleep disturbances likely become apparent. PDD would be identified in the transition from mostly brainstem involvement (IIa) to both brainstem and limbic involvement (III). After both neuroanatomical regions become involved, there is subsequent spread to the neocortex (IV) (Figure 1) [6].
Figure 1.
Relevant neuroanatomical regions implicated in the pathogenesis of PDD.
Of course, it is important to note that the clinical picture is not always this clear. PD patients can also have Alzheimer’s and cerebrovascular disease. These patient populations also suffer from daytime sleepiness and sleep disturbances. Amyloid plaques, neurofibrillary tangles, and ischemic changes can be seen on pathological examination. Often, a detailed neurologic history can aid in distinguishing these entities. In summary, PDD patients have increased sleepiness compared to healthy elderly subjects and PD patients without dementia. This is in large part due to degradation of sleep architecture from Lewy body disease in the brainstem and limbic structures.
3. Impaired clearance and disease progression
Impaired clearance of abnormal proteins in the central nervous system has been hypothesized as a mechanism for disease progression in alpha-synuclein disease as well as other neurodegenerative conditions. Recent discovery of the glymphatic system, a perivascular pathway that promotes the clearance of waste products facilitated by aquaporin-4 (AQ4) astrocytic water channels, has shown to be associated with amyloid-beta clearance. The glymphatic system is most active during sleep, notably slow wave sleep, due to reduced cellular swelling, expansion of the extracellular space with flow driven by cardiac pulsations [7]. These findings have implications in the pathogenesis of Alzheimer’s disease and the role of sleep in general. While amyloid-beta levels have been shown to oscillate in CSF analysis of human subjects with the sleep–wake cycle, no such data exist with regard to alpha-synuclein. In a mouse model of PD, reduced glymphatic flow via ligation of deep cervical lymph nodes, yielded worsening of motor deficits and perivascular aggregation of alpha-synuclein [8]. Given sleep disturbances often occur early in alpha-synuclein disease and associated with more cognitive dysfunction, a feed forward mechanism for disease progression has been proposed. Primary neurodegeneration of sleep–wake structures in the brainstem leads to increased sleep disruption, which then impairs glymphatic clearance of abnormal proteins accumulated during wakefulness. This then causes worsening of the underlying neurodegenerative disease further disrupting sleep. Even if alpha-synuclein is not cleared by the glymphatic system, PD patients are not immune from developing Alzheimer’s disease (AD) pathology, which likely contributes to a subset of the PDD phenotype as mentioned previously. Reduction of comorbid amyloid-beta pathology could reduce central nervous system disease burden. Aging itself may be a risk factor for impaired glymphatic drainage as mouse models have demonstrated reduced beta-amyloid clearance with advancing age [9]. The importance of screening for comorbid sleep disorders in patients suspected of alpha-synuclein disease cannot be understated, as many of these disorders have established and effective therapies. Treatment of sleep disorders represents one of the significant modifiable risk factors in disease progression in patients who go on to develop PDD. In summary, the glymphatic system is hypothesized to play a role in alpha-synuclein accumulation through feed forward mechanisms. Comorbid AD pathology, advancing age, and sleep disorders can be contributory.
4. REM sleep behavior disorder as a biomarker
REM sleep behavior disorder is often prodromal in PDD and may represent a unique subtype of PD patients. The early and often initially isolated presence of RBD provides a window into the prodromal stages of Lewy body disease. The perilocus coeruleus in the pontine tegmentum projects to medullary magnocellularis neurons via the lateral tegmental-reticular tract. These neurons then project to motor neurons in the anterior horn cells of the spinal cord to produce paralysis during REM sleep. Disruption of this pathway can lead to REM sleep without atonia (RSWA) and manifest clinically as RBD. Consistent with the hypothesis that Lewy body disease presents early in brainstem structures, the presence of RBD can predate the onset of parkinsonism or cognitive decline by years (Figure 2).
Figure 2.
Relative symptom onset with disease progression in Lewy body disease.
PD patients with RBD appear to have increased cognitive impairment than PD patients without RBD [10]. They also share a number of clinical features more characteristic of patients with DLB than classic tremor-predominant idiopathic Parkinson’s disease patients. An akinetic-rigid motor phenotype, hallucinations, and autonomic dysfunction have been more frequently described in PD patients with RBD than in those without. They also have more dense and diffuse pathology on autopsy [11]. These clinical findings are supportive of a specific pathological subtype among patients with PD. Prospective studies in patients with idiopathic RBD who have yet to develop symptoms of parkinsonism or dementia have yielded similar conclusions. A study in a cohort of presumably idiopathic RBD (iRBD) patients has uncovered a number of interesting findings. (1) The large majority of iRBD patients actually have prodromal alpha-synucleinopathy. (2) Quantitative motor evaluation was both predictive of phenoconversion to parkinsonism AND dementia. (3) The diagnoses of DLB and PDD may mostly be a clinical distinction in that phenoconversion to parkinsonism first versus dementia first was similarly predictive of DLB [12]. These findings suggest a predictable pattern of disease in patients with iRBD with a common endpoint, which can be assessed in the clinic. A patient with iRBD who develops motor features consistent with parkinsonism is at risk for PDD. Similarly, opposed to the notion that dementia precedes parkinsonian features in DLB, this did not seem to matter in patients with RBD with patients presenting with parkinsonism first having a similar risk for developing DLB. Of note, most of these studies focused on the prodromal or early stages of PD and dementia. Less is known about patients with longstanding PD who develop RBD later in the disease course. In summary, the mere presence of RBD may portend the development of PDD and DLB, which likely have the same pathological underpinnings with a more aggressive disease progress and poor prognosis.
5. Case study
History of present illness:
A 57-year-old left-handed retired salesman with a past medical history significant for restless legs syndrome (RLS), transient ischemic attack (TIA) with word-finding difficulties at the time, and concussion in his youth presents with a 10-year history of episodes of violent dream reenactment with sleep. Episodes occur monthly to a couple of times per week. They usually occur a couple of hours upon falling asleep. He will often kick off his covers, talk, yell, and curse. This is accompanied by dreams of trying to run away from an attacker or kick himself free. He may wake up in the midst of one of these episodes and realize they have occurred. It has also been witnessed by others. Alcohol and sweets prior to bedtime may precede an episode. Ropinirole and imipramine prescribed by a neurologist in the past seemed to be beneficial. Clonazepam was ineffective. He denies any associated symptoms, specifically, no difficulties with memory, complex tasks or judgment, visuospatial ability, language, or behavior. He also denies a loss of taste or smell, constipation, muscle stiffness, slowness of movement, tremor, or falls. He feels his mood is good and denies any hallucinations. He denies any loss or near loss of consciousness.
The patient presents with isolated violent dream enactment behavior suspicious for RBD. His age of onset is consistent with the usual initial presentation for Lewy body disease (>50 years old). Violent content, usually in the act of defense, is common. Time of onset in the night is usually in the latter half of the night due to more prolonged REM periods with successive sleep cycles; however, episodes can occur anytime the patient enters REM sleep. Waking in the midst of episodes and dream recall is common as REM sleep is a “lighter” stage of sleep with an EEG profile similar to wakefulness (low amplitude mixed frequency EEG). Some studies have reported an association with RBD and alcohol, however, only in the context of chronic and heavy consumption. Clonazepam is usually first line in the treatment of RBD; however, the patient felt this was subjectively ineffective. There is little data to support the use of dopamine agonists or tricyclic antidepressants in the treatment of RBD, with some concern that this may exacerbate the condition; however, the patient found these medications subjectively beneficial.
Medications: Aspirin, atorvastatin, vitamin D, and a multivitamin.
Allergies: No known drug allergies.
Past medical history: RLS, TIA, concussion, hypertension, hyperlipidemia, Gilbert’s syndrome, and vitamin D deficiency.
Past surgical history: Tonsillectomy.
Social history: No history of learning difficulties. Earned his bachelor’s degree. Recently retired due to work-related stress (not due to disability or job performance). Lives with his wife. Independent with his activities of daily living. Good social support. Rare alcohol. No smoking or recreational drug use.
Family history: Mother diagnosed with DLB, stroke in her 60s, and colon cancer. Died at 80 years old. Sister with breast cancer. Alive.
Of note, the patient is not on antidepressant or anticholinergic medications, which can mimic or “unmask” RBD. The patient has a family history of DLB in his mother. Having a family member with DLB may increase one’s risk of developing DLB; however, DLB is generally not considered a genetic disease with most mutations being sporadic. Genes implicated in DLB include APOE, GBA, SNCA, BIN1, and TMEM175 [13].
Exam: The patient is obese with stage 1 hypertension. All other vital signs were stable. General physical exam was normal. On neurological exam, he scored a 26/30 on the MOCA with points missed for copying the image of the cube (visuospatial), identifying the association between a train and bicycle (abstraction), and 3/5 on word recall (delayed recall). He also had difficulty with identification with the alcohol sniff test. Otherwise, his neurological exam was unremarkable.
The patient was within the lower end of the normal range on his MOCA with deficits in visuospatial, abstraction, and delayed recall. Cognitive deficits in Lewy body disease have attention, executive function, and visuospatial perception disproportionately involved. Nonspecific but possibly the most clinically relevant finding on exam was his impaired sense of smell with olfactory bulb involvement common in prodromal Lewy body disease and consistent with his largely isolated RBD presentation.
Neuropsychological testing: Evaluation revealed normal cognition with testing in the average to superior range in attention, processing speed, language abilities, memory, and executive functioning. His performance in visuospatial tasks was overall normal; however, this may possibly reflect area of very subtle weakness in comparison to other areas.
MRI of the brain: Mild generalized brain parenchymal volume loss without lobal predilection.
Diagnostic polysomnogram revealed the following (Figure 3):
Figure 3.
A 30-second epoch from a polysomnogram showing RSWA.
Phasic EMG activity in the bilateral arms and leg leads throughout as well as increased chin tone in the latter half of this 30-second epoch is observed. The background EEG is of low amplitude and mixed frequency with phasic “sail wave” eye movements observed in the EOG. The sleep technologist noted a behavioral manifestation in the corresponding video recording (not pictured here). These polysomnographic findings are consistent with RSWA and confirmatory of a diagnosis of RBD in the appropriate clinical setting.
6. Sources of sleep disturbance
There are a number of clinical symptoms associated with PDD that contribute to sleep disruption. Nocturnal motor and cognitive symptoms, pharmacologic (dopaminergic, acetylcholinesterase, antidepressant, and neuroleptic) therapy, circadian rhythm disturbances, mood disorder, nocturia, insomnia, obstructive sleep apnea (OSA), and RLS can all play a role.
6.1 Nocturnal motor symptoms
While the motor manifestations of parkinsonism classically abate with sleep, sleep is often disturbed by the presence of akinesia, rigidity, cramping, tremor, and dystonia. These motor manifestations of PD are caused by the loss of dopaminergic neurons in the substantia nigra. The degree to which these motor symptoms cause sleep disruption trends with the progression of the disease. The obvious challenge with assessing nocturnal motor symptoms is that treatment decisions are largely based on retrospective patient report and daytime motor assessment, which can be challenging in patients with PDD. There are a couple of tools that have been developed to address this need. The Parkinson’s disease sleep scale (PDSS) is a validated, short, 15-item inventory covering some of the most common sleep-related complaints in patients with PD with items 9–13 particularly focused on motor-related symptoms. However, this too relies on accurate patient or caregiver report [14]. More recently, and perhaps more objectively, actigraphy has emerged as an effective means of assessing nocturnal motor symptoms [15]. Patients in the early phase of the disease tend to show more frequent turning, similar to controls, however, with reduced speed and amplitude. More advanced PD patients show less turning, overall, with more time spent upright. This can be used to not only track disease progression but also response to therapy. Polysomnography would represent the “gold standard” assessment for these motor symptoms; however, it is not used clinically for this purpose outside of drug trials. It is mostly helpful in the identification of comorbid sleep disorders, such as RBD, OSA, and periodic limb movement disorder (PLMD). It would be impractical in monitoring the response to dopaminergic therapy. Long-acting preparations of levodopa, pramipexole, ropinirole, and a transdermal rotigotine patch have been developed. These medications have shown to be effective in treating nocturnal motor symptoms and reducing PDSS scores. Such preparations reduce the need for nocturnal medication administration. They also increase the amount of time the patient spends in the therapeutic range, without having to augment the dose, which can cause toxicity. In summary, motor symptoms of PDD can cause sleep disruption. Accurate assessment of these symptoms can be challenging in patients with cognitive impairment. Actigraphy offers an objective measure of motor symptoms that can be tracked over time and assessed in response to therapy. Long-acting dopaminergic preparations are helpful in alleviating motor symptoms causing sleep disruption.
6.2 Dopaminergic therapy
Proper timing and titration of dopaminergic therapy to address nocturnal motor symptoms specifically, as opposed to simply sleep disturbance in general, are critical. Bradykinesia, rigidity, and tremor can give way to dyskinesias in a hyperdopaminergic state. Dopamine also has direct wake-promoting properties and can cause insomnia. Most stimulant medications exert their wake-promoting effects through dopamine pathways. Rebound “sleep attacks” are also a well-known phenomenon, mostly related to dopamine agonists. While this may be less of a concern at night, it can be problematic for PDD patients who already exhibit fluctuations in attention and arousal. Dopamine-promoting medications have also been implicated in circadian rhythm disruption, causing increased melatonin production and an “uncoupling” of circadian phase and behavioral sleep onset (delayed relative to dim light melatonin onset [DLMO]) in PD patients on versus off therapy [16]. Lastly, PDD patients are also prone to complex visual hallucinations, which can be exacerbated by these drugs. In summary, while dopaminergic medications can treat motor symptoms causing sleep disruption, they can also disturb sleep through dyskinesias, insomnia, fluctuations in arousal, circadian uncoupling, and hallucinations. Caution is advised. Long-acting and levodopa-based medications are preferred. Short-acting dopamine agonists should be avoided.
6.3 Cognition
“Sundowning” or nocturnal delirium, hallucinations, and fluctuations in cognition, attention, and arousal can disturb sleep in patients with PDD. These symptoms can cause difficulty with sleep onset, long periods of wakefulness in the middle of the night, and disturb the homeostatic drive for sleep at night due to daytime somnolence (Figure 4).
Figure 4.
Sleep–wake disturbance with circadian and homeostatic sleep drive dyssynchrony.
Such symptoms can be seen across the spectrum of dementia. Nocturnal agitation can be one of the most disturbing and disabling symptoms for patients and their caregivers. This can lead to injury, particularly falls at night, in patients already prone to these issues. The mechanism for “sundowning” is unknown; however, it is felt to be multifactorial with circadian rhythm dysfunction and absence of environmental sensory cues hypothesized to play a driving role. Management can be challenging. Alternative causes should be excluded, such as toxic/metabolic encephalopathies, infection, and subdural hematoma. Nonpharmacologic interventions are considered first line despite the lack of randomized controlled trials in the PDD population due to their relative safety. Bright light therapy (on the order of 10,000 s of lux) in the morning for delayed sleep phase chronotype and in the evening for advanced sleep phase chronotype can be helpful. Daytime physical exercise can help entrain daytime wakefulness while consolidating sleep at night. It has also shown to improve multiple metrics in PD including: cognition [17], motor symptoms, and falls. Behavioral multicomponent interventions (cognitive behavioral therapy for insomnia) have shown to be effective in elderly adults with mixed results in patients with dementia. This can take the form of stimulus control, sleep restriction, relaxation exercises, paradoxical intention, psychotherapy, and sleep hygiene. However, such interventions may be challenging in PDD patients due to the degree of understanding and participation required. This is most useful early in the disease course and with caregiver support. Melatonin has shown to be helpful with “sundowning” in patients with Alzheimer’s dementia [18]. This agent has a dual benefit in PDD as melatonin is effective in suppressing the behavioral manifestations of RBD as well. In summary, nocturnal delirium, hallucinations, and fluctuations in arousal seen in PDD can cause sleep disturbance. Secondary causes should be considered. Nonpharmacologic strategies in the form of bright light and physical exercise are preferred as they carry little risk and show some benefit. Melatonin may help with delirium and improve RBD symptoms.
6.4 Medical management of behavioral symptoms
Pharmacological interventions in PDD are largely centered on improving cognitive symptoms and psychosis with improvements in sleep being of secondary benefit. Donepezil, rivastigmine, and memantine have been studied in PDD and DLB and have been shown to be effective in mitigating cognitive symptoms and hallucinations. Cognitive benefits seen in PDD and DLB may even exceed the benefits seen in AD [19] as patients with Lewy body disease have more relative cholinergic deficiency. However, similar to dopaminergic therapy, finding the minimum effective dose is important as acetylcholinesterase inhibitors (AChEIs) can cause nightmares due to their REM sleep-enhancing and consolidating effects. If hallucinations and delusions are distressing and dopaminergic therapy has been optimized, an atypical antipsychotic can be considered to facilitate sleep in psychotic patients. These typically have less D2 receptor blockade than typical antipsychotics to which PDD and DLB patients are particularly sensitive owing to the deficiency in striatal D2 receptors in these patients [20]. The lowest effective dose of quetiapine, clozapine, or aripiprazole dosed at night due to their sedating effects, is preferred. Pimavanserin, a selective serotonin 2A receptor inverse agonist, is a newer dopamine-sparing antipsychotic used in the treatment of PD psychosis. Its effects appear more robust in patients with greater cognitive impairment with concomitant AChEI use [21]. Of note, neuroleptic sensitivity and increased morbidity and mortality with antipsychotics are of concern in this particularly vulnerable population. Antipsychotics should be used sparingly for distressing hallucinations and delusions impacting the patient’s quality of life. As previously mentioned, excessive daytime sleepiness, daytime napping, and fluctuations in arousal are common in PDD. If behavioral interventions are insufficient, stimulants can be considered. These largely exert their effects through dopaminergic and adrenergic mechanisms. Modafinil, armodafinil, methylphenidate-based derivatives, and amphetamine salts can all help to promote wakefulness. Monitoring of cardiovascular parameters and symptoms of sympathetic hyperactivation is required. In summary, AChEIs, NMDAR antagonists, atypical antipsychotics, and stimulants are effective in managing cognitive fluctuations and psychotic features disturbing sleep in PDD with monitoring for adverse effects, which can contribute to morbidity and mortality.
6.5 Mood disorders
Depression and anxiety are common comorbidities in PDD and DLB that can impact sleep and neurocognitive outcomes. Despite evidence showing higher rates of depression in patients with Lewy body disease (~20%) compared to AD (~9%) [22], guidelines regarding treatment in this patient population are lacking. Depression can cause a delay in sleep onset, earlier REM sleep onset, poorer sleep efficiency, earlier waking, and increased time in bed. Medications in the selective serotonin reuptake inhibitor (SSRI) and serotonin norepinephrine reuptake inhibitor (SNRI) classes seem to be similarly effective. Tricyclic antidepressants (TCAs) are generally avoided owing to their anticholinergic properties. At least in the general population, treatment of depression appears to improve sleep quality, regardless of whether the medication used is activating or sedating. In PDD, caution must be given to worsening of RBD and RLS/PLMD with antidepressant medication initiation as these are known sleep-related side effects of these medications. In such cases, a reduction in dose or retiming of the medication is effective. If not, bupropion can be used as an alternative agent as it is the only antidepressant that has not been associated with these conditions. In summary, mood disorders are common in PDD and can disturb sleep. SSRIs and SNRIs are recommended if depression/anxiety is present. These can potentially worsen RBD and RLS/PLMD. Bupropion is a viable alternative.
6.6 Nocturia
Nocturia, or the need to urinate at night, significantly disrupts sleep in patients with Lewy body disease. Urinary urgency and frequency are well-known nonmotor symptoms in patients with PD and DLB with the highest prevalence in DLB [23]. Lewy body disease causes disinhibition of the micturition reflex causing an overactive bladder [24]. Obstructive uropathy in men and pelvic insufficiency in women are often contributory. While frequent trips to the bathroom may be tolerated during the day, these trips cause sleep fragmentation and can be dangerous at night. The combination of akinesia, postural instability, dementia, and autonomic instability in PDD patients is a recipe for falls. Additionally, patients may restrict fluid intake to mitigate symptoms that can cause dehydration and exacerbate orthostatic hypotension. Historically, neurologists have been limited in their interventions with nonpharmacologic therapies being the mainstay. Scheduled toileting (before bed), pelvic floor exercises, and adaptive techniques (bedside commode) have been used. Medications have generally been avoided due to the anticholinergic mechanism action of these drugs, which can worsen confusion. Mirabegron, a beta 3-adrenoreceptor agonist, can be used in those seeking to avoid this medication class. If an anticholinergic medication is to be chosen, trospium and darifenacin have shown to have less blood–brain barrier penetrance. Botulinum toxin injection into the detrusor muscle and percutaneous stimulation of the tibial nerve (PTNS) represent third-line therapies, which have been found to be effective in small studies of PD patients. In summary, nocturia in PDD can cause sleep disruption and precipitate falls. Behavioral modification, beta-adrenoreceptor agonists, quaternary amine antispasmodics, botulinum toxin, and PTNS can help treat symptoms.
6.7 Insomnia
Insomnia is common in PDD as well as the general population, affecting up to 30% of the industrialized world. It also increases with age, cognitive impairment, and medical and psychiatric illness. Given its prevalence in the general population, it is not unsurprising that insomnia and daytime sleepiness do not appear to be biomarkers for the development of neurodegenerative disease in RBD patients [25]. The quandary in PDD lies in identifying the contributions of insomnia to sleep disruption from secondary causes and underlying neurodegeneration. Insomnia is conventionally a state of hyperarousal characterized by difficulty with sleep onset, maintenance, and early waking. Excessive daytime sleepiness and sleep maintenance out of proportion to sleep onset difficulties should alert the clinician to secondary causes for the patient’s insomnia suggestive of sleep deprivation due to sleep fragmentation. However, in PDD, especially as the disease progresses, daytime hypersomnolence and disruption of sleep–wake architecture usually occur. Comorbid sleep disorders like OSA, RLS, RBD, and circadian rhythm disturbances are common. It is for these reasons that primary insomnia should be considered a diagnosis of exclusion in PDD patients with disturbed sleep. Behavioral measures (see recommendations for “sundowning” above), such as bright light, physical exercise, and cognitive behavioral therapy for insomnia are recommended. Patients with objectively shorter sleeping periods (<6 hours) may be less responsive to behavioral therapy [26]. Pharmacotherapy should be reserved for patients whose insomnia is refractory to these measures and cause distress and impairment in daytime functioning. PDD patients are particularly vulnerable to the side effects of sedative/hypnotic medications as these can cause morning after sedation, cognitive and motor slowing, amnesia, and parasomnias. Clinical practice guidelines for the pharmacologic treatment of chronic insomnia for elderly and cognitively impaired patients do not exist. Choice of therapy should take into account a number of factors: (1) type of problem (sleep onset, maintenance, or both), (2) duration of therapy, (3) adverse effects, (4) medical/psychiatric comorbidities, (5) medication interactions, (6) secondary indication, (7) patient preference, and (8) cost. For isolated sleep onset difficulties, dual orexin receptor antagonists (suvorexant and lemborexant), a melatonin agonist (ramelteon), and a short-acting benzodiazepine receptor agonist (zaleplon) could be considered. Suvorexant, lemborexant, and ramelteon have been studied in patients with dementia [27]. Benzodiazepines should generally be avoided due to their amnestic effects. Some nonbenzodiazepine receptor agonists (BzRAs) have been associated with an increase in parasomnias. For sleep onset and maintenance difficulties, a dual orexin receptor antagonist (DORA) is recommended. For patients with comorbid depression, low-dose trazodone or mirtazapine can be effective. TCAs are generally avoided due to their increased anticholinergic effects. For patients with comorbid RBD, high-dose melatonin or low-dose clonazepam (with caution and if RBD refractory to melatonin) is used. For patients with comorbid, disabling, and refractory psychosis, quetiapine can be considered. In summary, insomnia is common in PDD as well as the general population. Secondary causes for insomnia should be investigated. Behavioral interventions are first line in the treatment of insomnia in PDD. If refractory, there are a number of medication options available based on insomnia subtype and safety profile.
6.8 Obstructive sleep apnea
High rates of OSA have been observed in polysomnographic studies in patients with PD and DLB, affecting up to one-third of these patients [28]. Bulbar symptoms related to parkinsonism are presumed to contribute to airway collapse. This is partially offset by weight loss, often observed as the disease progresses. Patients with loud snoring, witnessed apneas, excessive daytime sleepiness, hypertension, obesity, large neck circumference, older age, and male gender are at increased risk for OSA. Sleep disruption and hypoxia from this condition contribute to neurocognitive decline, affecting attention, concentration, and mood. Additionally, OSA is associated with increased cerebrovascular disease, which can lead to vascular dementia. Motor outcomes in PD patients with OSA are also poorer compared to PD patients without OSA [29]. OSA can also be associated with REM sleep fragmentation with arousal-related motor manifestations out of REM sleep simulating RBD, or so-called pseudo-RBD. Due to REM atonia, the upper airway becomes more relaxed and accessory muscles of respiration become paralyzed, worsening OSA during this stage of sleep. Due to the effects OSA has on these aforementioned PDD-related symptoms, it is important to screen all patients for this condition. There are a number of OSA screening questionnaires that have been developed; however, it is important to note that these have not been validated in the PDD population. Polysomnography and increasingly, home-based sleep apnea testing, are used to diagnose this condition. In-lab testing is preferred in PDD patients as it is monitored by a sleep technologist who can assist with the setup and maintain the quality of the recording. Patients with PDD also often have other sleep disorders, such as RBD and PLMD, which home testing is usually not powered to detect. Continuous positive airway pressure (CPAP) remains the gold standard therapy for OSA. While studies have showed mixed results, CPAP appears to improve cognitive, motor, mood, and sleep quality outcomes in PD [30]. It also improves excessive daytime sleepiness. The cardiometabolic benefits of CPAP remain controversial. As with any behavioral therapy, but especially in PDD patients, adherence and tolerance to CPAP therapy are challenges. The cognitive impairment, trips to the restroom due to nocturia, dryness, and lack of dexterity to apply and adjust the mask are frequently reported issues. Patient education, frequent follow-up, and heated humidification have shown to improve compliance. In mild–moderate cases of OSA, a mandibular advancement device (MAD), a dental appliance that protrudes the jaw and increases the anterior to posterior upper airway diameter at the level of the tongue base, can be used as an alternative therapy. Upper airway surgery can be considered in patients with moderate–severe OSA in which CPAP is not tolerated; however, the risks of surgery and likely more prolonged postoperative course outweigh the benefits in the PDD population. Neuromodulation with a hypoglossal nerve stimulator (HGNS) that promotes tongue protrusion and airway patency represents an interesting less invasive option with minimal postoperative discomfort. Lifestyle measures including weight loss (if obese), positional therapy (side sleeping or head of bed elevation), and myofunctional therapy (mouth and throat exercises) are supplemental and recommend for all patients. It is worth noting that PDD patients spend more time in the supine sleeping position in which OSA is shown to be worse. In summary, OSA contributes to the symptomatology of PDD through different mechanisms. CPAP can improve the impact of OSA on these symptoms; however, adherence and tolerance are issues in this population. MAD, HGNS, and lifestyle measures are alternative and complementary options.
6.9 Restless legs syndrome/periodic limb movement disorder
RLS and PDD pathogenesis, clinical presentation, and therapy significantly overlap. In PD, there is a loss of dopaminergic neurons in the substantia nigra resulting in reduced striatal dopamine. In RLS, there is dopaminergic dysregulation and reduced iron in the basal ganglia with increased striatal dopamine, suggesting reduced sensitivity as opposed to deficiency. Interestingly, as opposed to RLS that shows reduced brain iron transport, PD patients show brain iron accumulation. It is unclear whether iron has a toxic effect in PD or whether this is a by-product of neurodegeneration. RLS is characterized by an urge to move the legs, sometimes associated with an unpleasant sensation, worse with rest, better with movement, and predominant in the evening. Akinesia, tremor, and dystonia may be misinterpreted as restless legs symptoms leading to overdiagnosis. It is important to make sure patients meet all clinical criteria before making the diagnosis of RLS. Periodic limb movements of sleep (PLMS) are common in RLS and PD, which may or may not be of clinical significance. PLMS must cause significant sleep fragmentation with resultant daytime symptoms to make a diagnosis of PLMD. This phenomenology can overlap with the nocturnal motor manifestations of PD and are often difficult to distinguish. Ultimately, RLS is a clinical diagnosis based on patient or caregiver history, which can be challenging in patients with dementia. The symptoms of RLS and motor manifestations of PD are treated similarly, making the diagnostic distinction between the two entities less critical. Oftentimes, PD patients are already on dopaminergic therapy, which can be adjusted to address evening and nighttime RLS symptoms. While generally not first line in RLS-only patients due to high rates of augmentation, levodopa is the preferred choice in PDD due to its dual benefit and lower rates of augmentation [31] in this population. This difference may best be explained by the dopamine deficiency seen in PD. Long-acting oral dopamine agonists and transdermal preparations can also be used. Alpha-2-delta ligand medications, such as gabapentin encarbil and pregabalin, are also considered first line in RLS. These can help with neuropathic pain and sleep induction but do not have activity on the motor manifestations of PD. Serum ferritin is checked systematically and iron supplemented if <50 ng/ml. A trial of oral iron supplementation is reasonable; however, due to constipation, which is already an issue in PD, an iron infusion may be required. Lifestyle measures include light exercise, stretching, avoidance of alcohol and sleep deprivation, and various forms of counter stimulation (vibrating pad, compression stockings, etc.). If medically refractory, a low-potency opioid could be considered with precautions. In summary, RLS and PDD both involve dysfunction in basal ganglia dopaminergic pathways, have overlapping symptoms that can make them difficult to distinguish, and benefit from dopaminergic therapy. Iron, alpha-2-delta ligand medications, behavioral interventions, and opioids are additionally used in RLS.
7. Conclusions
PDD is a clinical subtype on the spectrum of Lewy body disease. It is characterized by multidimensional and multifactorial sleep disturbances, which occur early and contribute significantly to the overall disease burden. Daytime sleepiness and disturbed sleep at night are merely symptomatic endpoints. Predictable brainstem, limbic, and subsequent neocortical spread of Lewy body pathology is responsible for early sleep disruption, followed by motor and then cognitive symptoms. This prodromal sleep disruption is hypothesized to perpetuate disease progression through a feed forward mechanism involving impaired glymphatic clearance. RBD is an example of sleep’s role as a biomarker for the alpha-synucleinopathies. The convergence of PD and DLB symptoms in PDD causes further deterioration in sleep. Treatment of these symptoms is a double-edged sword. Medications can ameliorate but also exacerbate sleeping difficulties. Trial data on therapeutics in PDD are lacking with most recommendations being inferred from studies in PD and AD. Sleep disorders, such as circadian rhythm disturbances, insomnia, OSA, and RLS, are common. These often go unrecognized due to attribution to the underlying neurodegenerative process. Most of these conditions have established treatment protocols that can be adapted to the PDD population.
Acknowledgments
I would like to thank Drs. Lin Zhang and John Olichney for inviting me to be a part of this book. I would also like to thank the Department of Neurology at the University of California, Davis, for its support.
Conflict of interest
The authors declare no conflict of interest.
Thanks
I would also like to thank my family for their love and support.
\n',keywords:"Parkinson’s disease dementia, Lewy body dementia, glymphatic system, REM sleep behavior disorder, circadian rhythm disturbance, insomnia, obstructive sleep apnea, restless legs syndrome, nocturia",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/77809.pdf",chapterXML:"https://mts.intechopen.com/source/xml/77809.xml",downloadPdfUrl:"/chapter/pdf-download/77809",previewPdfUrl:"/chapter/pdf-preview/77809",totalDownloads:49,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:0,totalAltmetricsMentions:0,impactScore:0,impactScorePercentile:0,impactScoreQuartile:0,hasAltmetrics:0,dateSubmitted:"April 19th 2021",dateReviewed:"June 25th 2021",datePrePublished:"February 1st 2022",datePublished:"April 20th 2022",dateFinished:"August 3rd 2021",readingETA:"0",abstract:"Sleep disruption and daytime somnolence are common in Parkinson’s disease dementia (PDD). In this condition, the clinical features of Parkinson’s disease (PD) and dementia with Lewy bodies (DLB) converge. Both PD and DLB populations have different sleep disturbances that are amplified when combined. Hence, sleep disruption is often significant and multifactorial in PDD. It is proposed that sleep–wake neural networks are affected early in the neurodegenerative process. The resultant lack of sleep results in impaired clearance of toxic metabolites, hastening disease progress. As the motor and nonmotor symptoms of PDD worsen, sleep becomes more disturbed. Medications used to control these symptoms can be sedating or cause insomnia. Comorbid sleep disorders are also often present. All of these factors contribute to poor sleep in these patients. Management is centered on symptom control, quality of life, and treatment of comorbidities.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/77809",risUrl:"/chapter/ris/77809",book:{id:"10334",slug:"dementia-in-parkinson-s-disease-everything-you-need-to-know"},signatures:"Matthew Chow",authors:[{id:"346202",title:"Dr.",name:"Matthew",middleName:null,surname:"Chow",fullName:"Matthew Chow",slug:"matthew-chow",email:"mchowza@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"UC Davis Health System",institutionURL:null,country:{name:"United States of America"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Neurodegeneration of sleep–wake systems",level:"1"},{id:"sec_3",title:"3. Impaired clearance and disease progression",level:"1"},{id:"sec_4",title:"4. REM sleep behavior disorder as a biomarker",level:"1"},{id:"sec_5",title:"5. Case study",level:"1"},{id:"sec_6",title:"6. Sources of sleep disturbance",level:"1"},{id:"sec_6_2",title:"6.1 Nocturnal motor symptoms",level:"2"},{id:"sec_7_2",title:"6.2 Dopaminergic therapy",level:"2"},{id:"sec_8_2",title:"6.3 Cognition",level:"2"},{id:"sec_9_2",title:"6.4 Medical management of behavioral symptoms",level:"2"},{id:"sec_10_2",title:"6.5 Mood disorders",level:"2"},{id:"sec_11_2",title:"6.6 Nocturia",level:"2"},{id:"sec_12_2",title:"6.7 Insomnia",level:"2"},{id:"sec_13_2",title:"6.8 Obstructive sleep apnea",level:"2"},{id:"sec_14_2",title:"6.9 Restless legs syndrome/periodic limb movement disorder",level:"2"},{id:"sec_16",title:"7. Conclusions",level:"1"},{id:"sec_17",title:"Acknowledgments",level:"1"},{id:"sec_20",title:"Conflict of interest",level:"1"},{id:"sec_17",title:"Thanks",level:"1"}],chapterReferences:[{id:"B1",body:'Monaca C, Duhamel A, Jacquesson JM, Ozsancak C, Destée A, Guieu JD, et al. Vigilance troubles in Parkinson\'s disease: A subjective and objective polysomnographic study. 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Analysis of video-polysomnographic sleep findings in dementia with Lewy bodies. Movement Disorders. 2013;28(10):1416-1423. DOI: 10.1002/mds.25523'},{id:"B29",body:'Elfil M, Bahbah EI, Attia MM, Eldokmak M, Koo BB. Impact of obstructive sleep apnea on cognitive and motor functions in Parkinson\'s disease. Movement Disorders. 2021;36(3):570-580. DOI: 10.1002/mds.28412'},{id:"B30",body:'Kaminska M, Mery VP, Lafontaine AL, et al. Change in cognition and other non-motor symptoms with obstructive sleep apnea treatment in Parkinson disease. Journal of Clinical Sleep Medicine. 2018;14(5):819-828. DOI: 10.5664/jcsm.7114'},{id:"B31",body:'Stefani A, Högl B. Sleep in Parkinson\'s disease. Neuropsychopharmacology. 2020;45(1):121-128. DOI: 10.1038/s41386-019-0448-y'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Matthew Chow",address:"mtchow@ucdavis.edu",affiliation:'
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1. Introduction
Chronic hyperglycemia can lead to complications involving damage to the kidneys, retina, nervous system and cardiovascular system. In this chapter, we discuss the causes of hyperglycemia, including drug-induced hyperglycemia, highlighting the importance and approaches to prevention and management of hyperglycemia. We focus on the role and rationale for the use of metformin for the prevention of hyperglycemia, presenting the evidence that supports its use for this indication.
2. Hyperglycemia
Hyperglycemia, which literally means ‘high blood glucose’ levels, refers to the elevation of blood glucose concentrations above the normal range. Specifically, it refers to fasting blood glucose levels greater than 7.0 mmol/L (126 mg/dl) or 2-hour postprandial blood glucose levels greater than 11.0 mmol/L (200 mg/dl) [1].
2.1 Symptoms and complications
Mild, transient hyperglycemia is largely asymptomatic. However, prolonged uncontrolled hyperglycemia is associated with various symptoms including the classic hyperglycemic triad of polyuria, polydipsia, and polyphagia, as well as blurred vision, dehydration, weight changes (gain or loss), generalized fatigue, abdominal discomfort, nausea, vomiting and muscle cramps [1, 2]. Complications arise when the hyperglycemia is severe and/or persists over an extended period. Frequent infections, erectile dysfunction and poor wound healing are associated with prolonged hyperglycemia. Chronic hyperglycemia can also lead to many serious life-threatening complications involving damage to the kidneys (nephropathy), retina (retinopathy), nervous system (peripheral neuropathy) and cardiovascular system (myocardial infarction, stroke) [1, 2, 3, 4, 5].
2.2 Causes of hyperglycemia
Blood glucose levels reflect the dynamic balance between, on the one hand, dietary glucose absorption and hepatic glucose production and, on the other hand, glucose uptake and utilization by peripheral tissues. Except for dietary glucose absorption, these complex and interrelated processes are under the control of the hormone insulin and, to a lesser extent, other counter-regulatory hormones such as glucagon, catecholamines, cortisol and growth hormone [1, 6]. Hyperglycemia arises from an imbalance in these processes that determine blood glucose levels.
The greatest quantitative determinant for hyperglycemia is dysfunction in pancreatic islet cell activity which affects insulin release from the pancreas in response to. The pathophysiology of hyperglycemia also entails a resulting degree of insulin resistance and impairment in homeostatic glucose regulation. Insulin resistance results in decreased uptake of glucose by insulin-sensitive tissues as well as a consequential increase in endogenous glucose production. This all leads to hyperglycemia [7]. The elevation of blood glucose levels during the fasting state is directly proportional to the increase in hepatic glucose production while that of the postprandial state is connected to insufficient suppression of glucose output plus a defect in the stimulation of insulin hormone on recipient tissues like skeletal muscle [8].
The progression of this imbalance in blood glucose homeostasis over time leads to the development of diabetes, a chronic disease affecting glucose metabolism that occurs due to either insufficient production of insulin by the pancreas, or inadequate response by tissues to insulin [9]. The development of diabetes can be delayed or prevented by targeting the early prevention and/or reversal of hyperglycemia, as well as by inhibiting the development of hyperinsulinemia-induced insulin resistance [10]. This would also delay progression of prediabetic states to diabetes [11].
In addition to diabetes, there are a myriad of other causes of hyperglycemia, i.e., non-diabetic hyperglycemia. Disorders that compromise pancreatic function (pancreatic cancer, cystic fibrosis, chronic pancreatitis, etc.) or affect the glucose counter-regulatory hormones (pheochromocytoma, acromegaly, Cushing syndrome) cause hyperglycemia. Transient hyperglycemia may arise consequent to abnormally high carbohydrates in the diet, dextrose infusion and total parental nutrition. Acute or serious illness or injury may also bring about transient hyperglycemia referred to as stress hyperglycemia or hospital-related hyperglycemia [1, 12].
Medicines may also induce hyperglycemia [1, 6, 13].
2.3 Drug-induced hyperglycemia
Drug-induced hyperglycemia refers to the clinically relevant elevation of blood glucose levels caused by drugs [13]. Whereas drug-induced hyperglycemia is often mild and asymptomatic, severe hyperglycemia may occur particularly in predisposed patients, such as those with pre-existing pancreatic dysfunction or insulin resistance. Drug-induced hyperglycemia can occur in adults and children alike, and certain patient factors are known to increase the risk of drug-induced hyperglycemia, such as obesity, sedentary lifestyle, stress, illness, history of gestational diabetes, or a family history of diabetes [6, 14].
Many classes of drugs have been implicated in causing hyperglycemia via various mechanisms. Some drugs cause hyperglycemia by reducing insulin production/secretion (glucocorticoids, β-receptor antagonists, thiazide diuretics, calcium-channel blockers, phenytoin, pentamidine, calcineurin inhibitors, protease inhibitors), including by direct damage to pancreatic cells (glucocorticoids, pentamidine, statins). Glucocorticoids, β-receptor antagonists and thiazide diuretics also promote hepatic glucose production and reduce insulin sensitivity. Other classes of drugs that reduce peripheral tissue sensitivity to insulin include atypical antipsychotics, antidepressants, oral contraceptives, statins, nucleoside reverse transcriptase inhibitors and protease inhibitors [1, 6, 14, 15, 16]. Hyperglycemia is one of the common adverse effects of the anticancer agent L-asparaginase, which inhibits insulin synthesis by depleting available asparagine in pancreatic cells in addition to impairing insulin receptor activity and promoting peripheral tissue resistance to insulin [14]. Monoclonal antibodies such as nivolumab and pembrolizumab may cause severe hyperglycemia by triggering the autoimmune-mediated destruction of pancreatic cells [17, 18]. β2-receptor agonists cause hyperglycemia by promoting hepatic and muscle glucose production [19]. The various mechanisms of drug-induced hyperglycemia and the classes of drugs implicated are shown in Figure 1.
Figure 1.
Mechanisms of drug-induced hyperglycemia and implicated classes of drugs.
The overall occurrence of drug-induced hyperglycemia is not known and would obviously vary between individual drugs. There is a lack of data on the burden of drug-induced hyperglycemia for specific drugs, and a few studies have attempted to address this gap. For example, the incidence of corticosteroid-related hyperglycemia in patients treated with high dose corticosteroids has been estimated to be in excess 50% [20, 21]. Comparably high prevalence has been reported for clozapine [22]. These and other similar findings strongly suggest that the risk of drug-induced hyperglycemia (alongside the risk of new-onset diabetes) is real.
The onset of drug-induced hyperglycemia varies on the medication administered. At the time of or shortly after initiating corticosteroids, blood glucose levels may be altered, whereas patients on hydrochlorothiazide may not experience altered levels for weeks or longer, depending on the dose given. In regard to second generation antipsychotics (SGAs), a consensus statement developed by the American Diabetes Association (ADA) in conjunction with other medical organizations recommends monitoring fasting blood glucose for 12 weeks after initiation of therapy and annually thereafter in those without diabetes. However, cases involving hyperglycemic crises have been reported within weeks of starting SGAs [23].
3. Prevention and management of hyperglycemia
The common medical occurrence of hyperglycemic states has yet to be given the due attention it deserves, considering the numerous consequences it bears to patients and the healthcare fraternity. The existing reality of numerous patients suffering from hyperglycemia of varied cause provides an overwhelming patient load, unmatched by the number of specialized providers. However, the management of hyperglycemia has continually posed a great challenge mainly from a lack of standardized protocols [24]. Currently, lack of knowledge and consensus on strategies of management play a significant role in its mismanagement.
Insulin resistance and the resulting compensatory hyperinsulinemia is considered to preclude the development of type 2 diabetes. Hyperglycemia prophylaxis is thus highly attractive based on the numerous socio-economic benefits it confers to patients and the healthcare system. Several studies have demonstrated the advantages gained from preventing elevations of blood glucose levels across a divergent patient portfolio. Research has broadly focused on management of hyperglycemia regardless of the cause, which underlies the common pathways involved in the development of hyperglycemia.
3.1 The role of insulin
The primary strategy employed in hyperglycemia management is insulin [25]. Consensus arrived at by ADA and European Association for the Study of Diabetes (EASD) outline the management of hyperglycemia in type 2 diabetes patients. These guidelines have also been adopted in the prevention of hyperglycemia from other causes, including drug-induced hyperglycemia. The guidelines recommend the use of insulin in all hospitalized patients, with discontinuation of oral hypoglycemic medication [26, 27]. Stoppage of the drugs is on the basis that majority of hospitalized patients present with concurrent conditions and/or physiological dysfunctions that tend to contraindicate continued use of these medications if already prescribed. The pharmacokinetics of oral medication, which tend to have a slow onset of action, disallows for rapid dose adjustment to changing patient needs [28]. Therefore, it is recommended that critically ill patients be treated with a continuous insulin infusion while non-critically ill patients are initiated on subcutaneous (SC) insulin. An individualized dose adjustment for insulin is advised across major studies [26, 29]. Resumption of oral diabetic agents (ODA) when transitioning from inpatient to outpatient setting, with careful consideration given to previous insulin dosing, is advised upon successful treatment. A study involving patients without diabetes recommended the administration of intravenous (IV) insulin infusion in patients with serum blood glucose level values of greater than 10 mmol/L, with a target of achieving serum blood glucose levels of 7.8–10 mmol/L in non-critical settings and less than 7.8 mmol/L in an outpatient setting [30].
Despite numerous recommendations, challenges faced by providers during insulin administration cannot be overlooked. The biggest impediment to insulin use in management of drug-induced hyperglycemia in the affected population is the unavoidable side effect of hypoglycemia [31]. Unfortunately, insulin treatment is the most common risk factor for inpatient hypoglycemia. The incidence of hypoglycemia is approximately 30% in elderly patients, in spite of using low dose insulin and oral diabetic agents [28]. This is associated with increased mortality rate and prolonged hospital stays. Hence, constant monitoring of blood glucose levels is necessary.
Dose adjustments using patients’ weight is perceived to be safe and effective as long as close monitoring is done. However, this is not always feasible, let alone practical with many patients. So too is the recommendation of individualizing glycemic targets for patients based on clinical status, risk of hypoglycemia and patient comorbidities, no matter the benefit it confers. This is because the number of patients with drug-induced hyperglycemia cannot be matched to the number of specialized health care workers required to meet this need.
Herein lies the difficulty as many patients are unable to achieve the close monitoring desired, let alone manage the expected side effects in a home-based set up. Even in hospitalized patients, lack of protocols for dose adjustment poses a hindrance in adequate control of elevated blood glucose levels. Hypoglycemia presents a consequential effect that should be carefully considered in hyperglycemia management. Any chosen medication, in addition to lifestyle interventions, should ideally be one that is safe, effective, economical and with minimal side effects.
3.2 The role of oral antidiabetic medications
Non-insulin medications provide a practical alternative to achieving glycemic control. These agents may also confer a non-glycemic benefit whilst regulating the fluctuations in blood glucose levels. Alternatives among non-insulin medication include metformin, sulphonylureas, glinides, thiazolidinediones, glucagon-like peptide-1 (GLP-1) receptor agonists, and sodium–glucose cotransporter2 (SGLT2) inhibitors.
However, the side effects of each of these agents must also be considered. For example, SGLT2 inhibitors reduce blood glucose levels by preventing proximal tubular reabsorption in the kidney. This has been shown to effectively reduce glycated hemoglobin A1c (HbA1c) levels by 0.6–1.0%. They are also associated with a low risk of hypoglycemia. However, the dehydration side effects make these agents contraindicated in renal dysfunction. They also bear an increased risk of urinary and genital tract infections and are related with the development of diabetic ketoacidosis among diabetic patients [32]. Such a profile tends to limit the use of these agents. Metformin use is contraindicated in the presence of any possible indication for iodinated contrast media and in renal insufficiency while thiazolidinediones are associated with fluid retention. On the other hand, sulfonylureas and glinides result in hypoglycemia in most patients while GLP-1 receptor antagonists can cause nausea and hence need to be withheld in critical patients. In spite of the many side effects of oral diabetic agents and the recommendation of using insulin as first line, recent studies have leaned towards the adoption of the oral diabetic agents. The drug most endorsed based on clinical evidence has been metformin [33].
4. Metformin for the prevention of hyperglycemia
4.1 Introduction and rationale
The pathophysiology of hyperglycemia entails a degree of insulin resistance and results in decreased uptake of glucose by insulin-sensitive tissues as well as a consequential increase in endogenous glucose production [7]. Dysfunction in the activity of pancreatic islet cells affects insulin release in response to rising blood glucose levels. Targeting the prevention and/or reversal of dysglycemia and insulin resistance is the principal behind preventing the development of hyperglycemia [11]. Any agent used in prevention of hyperglycemia must therefore target these pathways, thereby partially or completely eliminating its development.
Metformin can rightfully be considered for hyperglycemia prevention and treatment in cases of insulin resistance. Metformin is a first-line agent in treatment of type 2 diabetes mellitus. Recent studies have shown it confers a greater benefit to patients than the other oral diabetic agents, which has led to its recommendation for use in the prevention of hyperglycemia and prediabetes in at risk patients [34, 35, 36].
4.2 Mechanisms of action/pharmacodynamics
Metformin prevents hyperglycemia by hastening the clearance of glucose [37, 38]. It causes a reduction in hyperglycemia and hyperinsulinemia [39]. This facilitates a consequent decline in high insulin and high blood glucose levels, with no effect on insulin secretion. The primary mechanism involved in lowering blood glucose levels is through improving hepatic and peripheral tissue sensitivity to insulin [40]. It inhibits the production of glucose by the liver whilst enhancing uptake of circulating glucose and its utilization in peripheral tissues such as muscle and adipose tissue.
Hepatic gluconeogenesis is an energy-demanding process in which synthesis of one molecule of glucose from lactate or pyruvate requires four molecules of ATP and two molecules of GTP. Metformin suppresses hepatic gluconeogenesis by causing a reduction of cellular ATP levels [41]. Molecularly, metformin appears to inhibit mitochondrial respiration. The resulting shift in cellular energy balance increases the activity of AMP-activated protein kinase (AMPK), which promotes the action of insulin and reduces hepatic gluconeogenesis [42]. AMPK acts as a cell energy sensor: it plays a role in energy balance at the cellular and body level by adapting to changes in the concentration of AMP/ADP relative to ATP [43]. Upon activation by a decrease in cellular energy levels, AMPK initiates a change from anabolic to catabolic pathways that consume ATP. This stimulates the uptake and use of glucose and oxidation of fatty acids, in addition to the suppression of hepatic glucose production. Metformin’s’ inhibition of the mitochondrial complex is the basis of its effect as observed through the change in the ratios of AMP/ATP or ADP/ATP after its administration [44]. Multiple studies have demostrated that one of the mechanisms of action of metformin is the disruption of mitochondrial complex I [45, 46].
Metformin may also modulate the gut-brain-liver axis through the activation of a duodenal AMPK-dependent pathway, as has been demonstrated in rats. This effect involves activation of protein kinase A (Pka) by GLP-1 in duodenal enterocytes, and results in suppression of hepatic glucose production [47]. It has been shown that glucocorticoid therapy leads to changes in the activation of AMPK in Cushing’s syndrome patients and in vitro in human adipocytes, effects that were reversed with metformin in human adipocytes. These indicate the likelihood of converse effects of steroids and metformin in the AMPK signaling pathway, as well as the overriding of steroid effects by metformin [44, 48]. Supporting studies demonstrate that steroid-related increase in glucose levels can be prevented with an AMPK activator [49].
Another postulated mechanism of action for metformin is by causing an increase in circulating cyclic adenosine monophosphate (cAMP) which in turn opposes the hyperglycemic action of glucagon [42, 50]. Metformin has also been postulated to increase the concentration of Glucagon-like peptide-1 (GLP-1) by enhancing site production as well as subsequently decreasing its degradation in circulation and specific tissues via inactivation of the enzyme dipeptide peptidase-4 (DPP-4). Additionally, metformin may induce up regulation of GLP-1receptors on beta cell surfaces of the pancreas. This can aid in ameliorating the beta cell dysfunction associated with hyperglycemia via the enhancement of the role of GLP-1 on glucose dependent release of insulin [11].
4.3 Metformin prevents hyperglycemia and hyperinsulinemia
Metformin can rightfully be considered for hyperglycemia prevention and treatment in cases of insulin resistance. Metformin has been identified as a first line agent in treatment of type 2 diabetes mellitus. Recent studies have shown that it confers a greater benefit to patients than the other oral diabetic agents, which has led to its recommendation for use in the prevention of prediabetes in at risk patients [34, 35, 51]. Presently though, only a few nations have formally adopted this proposal such as Poland, Philippines and Turkey but many may adopt it in the near future based on the emerging evidence [11]. Metformin overrides most of the factors that contribute to poor glycemic management like inaccessibility to medicine and fear of developing hypoglycemia. This improves patient perception on its use regardless of the minimal side effects. In addition, it has been demonstrated to confer long term benefit to those who use it prophylactically. A study that followed up patients from a diabetes prevention program after 15 years found that the metformin treatment arm had a 17% lower incidence for developing type 2 diabetes than the placebo arm. This was determined using the HbA1c parameter, in which 36% of the patients had a risk reduction for diabetes development [34].
In a prospective observational study in persons with normal glucose tolerance and hyperinsulinemia, a dose of 2.55 ± 0.2 g/day of metformin restored physiological insulin secretion by decreasing fasting and post-glucose load hyperinsulinemia in the oral glucose tolerance test (OGTT). Over the observation period, the effect of metformin on the reduction of hyperinsulinemia increased over time, peaking after 1 year of treatment. The ability to lower fasting blood glucose levels also improved with time. Fasting blood glucose levels reached normoglycemic range at 3 months and remained so until the end of the 1 year observation period, with no development of hypoglycemia [39]. A substantial decrease in hyperinsulinemia from high blood glucose levels has also been reported in metformin-treated patients based on an increase in the uptake of glucose [52]. The enhancement of insulin action reduces the load on the beta cells in insulin secretion thus can aid in ameliorating the beta cell dysfunction to an extent; this confers an advantage to patients predisposed to developing hyperglycemia.
In addition, a randomized controlled study showed that there was no significant difference in blood glucose levels between critically ill patients receiving 1000 mg of metformin daily versus a similar spectrum of patients receiving 50 International Units (IU) of regular insulin. Furthermore, metformin-treated patients had blood glucose levels subside to near-normal range [40]. The targeted desired blood glucose levels were achieved with metformin after three days while insulin failed to do the same.
4.4 Metformin for drug-induced hyperglycemia
In acute lymphoblastic leukemia patients with drug-induce hyperglycemia, metformin monotherapy controlled blood glucose in 12 out of 17 patients, without the need for insulin using a median dose of 1000 mg/day for a median of 6 days. Blood glucose levels never exceeded 11.1 mmol/L in 8 of the 12 patients. The one patient who developed hyperglycemia during relapse re-induction for leukemia treatment was effectively controlled using metformin alone [53]. Three of the patients given insulin therapy due to high blood glucose levels were eventually weaned off insulin to metformin alone. Additionally, in a controlled trial consisting of non-diabetic patients on glucocorticoids, metformin prevented an increase of 2-hour glucose AUC with, signifying glucose tolerance preservation. No changes in baseline and after 4 weeks metformin treatment was seen with the 2-hour glucose AUC whereas this parameter increased in the placebo group [54].
Similarly, the effect of metformin on prednisone-induced hyperglycemia (PIH) was observed on fasting and 2-hour post prandial glucose levels in hematological cancer patients. The fasting blood glucose readings indicated a proportion of prednisone-induced hyperglycemia of 72.7% and 14.3% in the control and treatment groups respectively. The proportion was slightly lower while using the 2-hour post prandial glucose, in which 54.5% of participants in the control group developed prednisone-induced hyperglycemia while none developed prednisone-induced hyperglycemia in the treatment group. Patients in the control group had 16 (95% CI 1.3–194.6) times the odds of developing prednisone-induced hyperglycemia compared to patients in the treatment group. Double daily dosing (1700 mg twice daily) was more effective in preventing prednisone-induced hyperglycemia [21]. This is supported by other studies that show that that a daily dose of metformin 1500 mg contributes to 80–85% glucose lowering effects [55].
4.5 Metformin for hyperglycemia: risks and benefits
The limitations attached to the full exploitation of metformin use include its relative contraindications in many hospitalized patients who present with comorbidities like renal insufficiency or unstable hemodynamic status. Metformin is contraindicated if serum creatinine is ≥133 mmol/L in men or ≥ 124 mmol/L in women. Emerging evidence shows that the established cut-off points for renal safety may be overly restrictive [56]. It has been argued that there is a need to relax these cut-offs and policies to allow use of this drug to patients with stable chronic kidney disease characterized by mild–moderate renal insufficiency [57, 58, 59].
The associated risk of lactic acidosis tends to deter the use of metformin in majority of the comorbid patients on drugs that predispose to the development of hyperglycemia. However, the studies that made such recommendations used a small percentage of the patient population, thus limiting the extrapolation of these recommendations to the greater public [60]. Fortunately, the incidence of metformin-induced lactic acidosis is rare and can be significantly reduced in at-risk patients by observing the necessary precautions [27, 56]. Other factors may also play a greater role in in being predictors of acidosis, such as dehydration, severe heart and renal failure. Thus, its benefits for use outweigh the potential risk of lactic acidosis.
Supporting evidence on avoidance of metformin use in certain cases is poor and inconsistent such as in patients undergoing radio-contrast imaging which theoretically predisposes patients to media-induced nephropathy, increasing the risk of lactic acidosis [56].
The benefits of metformin in the prevention of hyperglycemia are unmatched despite its list of contraindications. This has facilitated its expanded use based on its well-founded glycemic effects as well as numerous benefits conferred such as the beneficial effect on reduction of development of cardiovascular risk factors [61]. It confers good glycemic management that yields a substantial and enduring decrease in the onset and progression of micro vascular complications [60].
Moreover, large based clinical trials and systematic reviews have shown its beneficial effect of enhancing weight loss, even the weight loss associated with medicaments like antipsychotic agents [62, 63].
5. Conclusions
In summary, the suppression of glucose production by metformin’s direct effect plus the enhancement of hepatic insulin signaling will curb the development of drug-induced hyperglycemia. Metformin has been shown to reduce the incidence of hyperglycemia-related complications such as diabetes and risk factors for cardiovascular disease in patients with impaired glucose tolerance and fasting blood sugar [11, 64, 65]. This has led to its endorsement of use in patients with high risk of developing the aforementioned conditions [36].
\n',keywords:"hyperglycemia, hyperinsulinemia, insulin, metformin, glucose",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/78058.pdf",chapterXML:"https://mts.intechopen.com/source/xml/78058.xml",downloadPdfUrl:"/chapter/pdf-download/78058",previewPdfUrl:"/chapter/pdf-preview/78058",totalDownloads:137,totalViews:0,totalCrossrefCites:0,dateSubmitted:"July 3rd 2021",dateReviewed:"July 9th 2021",datePrePublished:"October 4th 2021",datePublished:"December 15th 2021",dateFinished:"August 12th 2021",readingETA:"0",abstract:"Hyperglycemia is the elevation of blood glucose concentrations above the normal range. Prolonged uncontrolled hyperglycemia is associated with serious life-threatening complications. Hyperglycemia arises from an imbalance between glucose production and glucose uptake and utilization by peripheral tissues. Disorders that compromise pancreatic function or affect the glucose counter-regulatory hormones cause hyperglycemia. Acute or serious illness or injury may also bring about hyperglycemia, as can many classes of drugs. Metformin lowers blood glucose levels by inhibiting the production of glucose by the liver whilst enhancing uptake of circulating glucose and its utilization in peripheral tissues such as muscle and adipose tissue. Metformin suppresses hepatic gluconeogenesis by inhibiting mitochondrial respiration and causing a reduction of cellular ATP levels. Metformin may also modulate the gut-brain-liver axis, resulting in suppression of hepatic glucose production. Metformin also opposes the hyperglycemic action of glucagon and may ameliorate pancreatic cell dysfunction associated with hyperglycemia. Metformin is therefore recommended for use in the prevention of hyperglycemia, including drug-induced hyperglycemia, in at risk patients. The benefits of metformin in the prevention of hyperglycemia are unmatched despite its contraindications.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/78058",risUrl:"/chapter/ris/78058",signatures:"Lucy A. Ochola and Eric M. Guantai",book:{id:"10735",type:"book",title:"Metformin",subtitle:"Pharmacology and Drug Interactions",fullTitle:"Metformin - Pharmacology and Drug Interactions",slug:"metformin-pharmacology-and-drug-interactions",publishedDate:"December 15th 2021",bookSignature:"Juber Akhtar, Usama Ahmad, Badruddeen and Mohammad Irfan Khan",coverURL:"https://cdn.intechopen.com/books/images_new/10735.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83969-606-0",printIsbn:"978-1-83969-605-3",pdfIsbn:"978-1-83969-607-7",isAvailableForWebshopOrdering:!0,editors:[{id:"345595",title:"Prof.",name:"Juber",middleName:null,surname:"Akhtar",slug:"juber-akhtar",fullName:"Juber Akhtar"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"418482",title:"Dr.",name:"Lucy A.",middleName:null,surname:"Ochola",fullName:"Lucy A. Ochola",slug:"lucy-a.-ochola",email:"lucyochola@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"420882",title:"Dr.",name:"Eric M.",middleName:null,surname:"Guantai",fullName:"Eric M. Guantai",slug:"eric-m.-guantai",email:"eguantai@uonbi.ac.ke",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of Nairobi",institutionURL:null,country:{name:"Kenya"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Hyperglycemia",level:"1"},{id:"sec_2_2",title:"2.1 Symptoms and complications",level:"2"},{id:"sec_3_2",title:"2.2 Causes of hyperglycemia",level:"2"},{id:"sec_4_2",title:"2.3 Drug-induced hyperglycemia",level:"2"},{id:"sec_6",title:"3. Prevention and management of hyperglycemia",level:"1"},{id:"sec_6_2",title:"3.1 The role of insulin",level:"2"},{id:"sec_7_2",title:"3.2 The role of oral antidiabetic medications",level:"2"},{id:"sec_9",title:"4. Metformin for the prevention of hyperglycemia",level:"1"},{id:"sec_9_2",title:"4.1 Introduction and rationale",level:"2"},{id:"sec_10_2",title:"4.2 Mechanisms of action/pharmacodynamics",level:"2"},{id:"sec_11_2",title:"4.3 Metformin prevents hyperglycemia and hyperinsulinemia",level:"2"},{id:"sec_12_2",title:"4.4 Metformin for drug-induced hyperglycemia",level:"2"},{id:"sec_13_2",title:"4.5 Metformin for hyperglycemia: risks and benefits",level:"2"},{id:"sec_15",title:"5. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'Mouri Mi, Badireddy M. Hyperglycemia. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2021 [cited 2021 Jun 17]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK430900/'},{id:"B2",body:'Utiger RD. 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Use of metformin in the setting of mild-to-moderate renal insufficiency. Diabetes Care. 2011;34(6):1431-7'},{id:"B60",body:'Davies MJ, D’Alessio DA, Fradkin J, Kernan WN, Mathieu C, Mingrone G, et al. Management of hyperglycemia in type 2 diabetes, 2018. A consensus report by the American Diabetes Association (ADA) and the european association for the study of diabetes (EASD). Diabetes Care. 2018;41(12):2669-701'},{id:"B61",body:'Strack T. Metformin: a review. Drugs Today (Barc). 2008 Apr;44(4):303-14'},{id:"B62",body:'Diabetes N, Clearinghouse I. Diabetes Prevention Program (DPP)'},{id:"B63",body:'Björkhem-Bergman L, Asplund AB, Lindh JD. Metformin for weight reduction in non-diabetic patients on antipsychotic drugs: a systematic review and meta-analysis. J Psychopharmacol. 2011 Mar;25(3):299-305'},{id:"B64",body:'Temprosa M. 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He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. 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Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}},{id:"351158",title:"Prof.",name:"David W.",middleName:null,surname:"Anderson",slug:"david-w.-anderson",fullName:"David W. Anderson",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Calgary",country:{name:"Canada"}}}]}},subseries:{item:{id:"22",type:"subseries",title:"Applied Intelligence",keywords:"Machine Learning, Intelligence Algorithms, Data Science, Artificial Intelligence, Applications on Applied Intelligence",scope:"This field is the key in the current industrial revolution (Industry 4.0), where the new models and developments are based on the knowledge generation on applied intelligence. The motor of the society is the industry and the research of this topic has to be empowered in order to increase and improve the quality of our lives.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11418,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. 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