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Dissertations in Health Sciences
PUBLICATIONS OF THE UNIVERSITY OF EASTERN FINLAND
TATYANA SARYCHEVA
PREVALENCE, INCIDENCE AND SAFETYOF ANTIEPILEPTIC DRUG USE IN PERSONS
WITH ALZHEIMER’S DISEASE
PREVALENCE, INCIDENCE AND SAFETY
OF ANTIEPILEPTIC DRUG USE IN PERSONS
WITH ALZHEIMER’S DISEASE
TATYANA SARYCHEVA
PREVALENCE, INCIDENCE AND SAFETY
OF ANTIEPILEPTIC DRUG USE IN PERSONS
WITH ALZHEIMER’S DISEASE
To be presented by permission of the Faculty of Health Sciences, University of Eastern Finland for public examination in MS505 auditorium , Medistudia building,
Kuopio, on Monday, June 15th 2020, at 10 o’clock
Publications of the University of Eastern Finland Dissertations in Health Sciences
No 542
School of Pharmacy Faculty of Health Sciences
University of Eastern Finland Kuopio
2020
Series Editors Professor Tomi Laitinen, M.D., Ph.D.
Institute of Clinical Medicine, Clinical Physiology and Nuclear Medicine Faculty of Health Sciences
Associate professor (Tenure Track) Tarja Kvist, Ph.D. Department of Nursing Science
Faculty of Health Sciences
Professor Ville Leinonen, M.D., Ph.D. Institute of Clinical Medicine, Neurosurgery
Faculty of Health Sciences
Professor Tarja Malm, Ph.D. A.I. Virtanen Institute for Molecular Sciences
Faculty of Health Sciences
Lecturer Veli-Pekka Ranta, Ph.D. School of Pharmacy
Faculty of Health Sciences
Distributor: University of Eastern Finland
Kuopio Campus Library P.O.Box 1627
FI-70211 Kuopio, Finland www.uef.fi/kirjasto
Grano Oy Jyvaskyla , 2020
ISBN: 978-952-61-3262-4 (print) ISBN: 978-952-61-3263-1 (PDF)
ISSNL: 1798-5706 ISSN: 1798-5706
ISSN: 1798-5714 (PDF)
Author’s address: School of Pharmacy University of Eastern Finland KUOPIO FINLAND
Doctoral programme: Doctoral program in Drug Research
Supervisors: Professor Anna-Maija Tolppanen, Ph.D. School of Pharmacy University of Eastern Finland KUOPIO FINLAND Professor Sirpa Hartikainen, M.D., Ph.D. School of Pharmacy University of Eastern Finland KUOPIO FINLAND
Associate Professor Heidi Taipale, Ph.D. School of Pharmacy University of Eastern Finland KUOPIO FINLAND Assistant Professor
Department of Clinical Neuroscience Karolinska Institutet STOCKHOLM SWEDEN
Reviewers: Professor Kristina Johnell, Ph.D. Department of Medical Epidemiology and Biostatistics
Karolinska Institutet STOCKHOLM SWEDEN
Associate Professor Esa Jämsen, M.D., Ph.D. Faculty of Medicine and Health Technology University of Tampere TAMPERE FINLAND
Opponent: Jenni Ilomaki, M.Sc (Pharm.), Ph.D. Faculty of Pharmacy and Pharmaceutical Sciences Monash University MELBOURNE AUSTRALIA
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Sarycheva, Tatyana Prevalence, incidence and safery of antiepileptic drug use in persons with Alzheimer’s disease Kuopio: University of Eastern Finland Publications of the University of Eastern Finland Dissertations in Health Sciences No 542. 2020, 112 p. ISBN: 978-952-61-3262-4 (print) ISSNL: 1798-5706 ISSN: 1798-5706 ISBN: 978-952-61-3263-1 (PDF) ISSN: 1798-5714 (PDF) ABSTRACT
The prevalence of Alzheimer’s disease (AD) has increased rapidly all around the globe and it is among the leading causes of disability and death. AD also poses a high burden to families, the health care system and society. Persons with AD use more antiepileptic drugs (AEDs) than the general population. In addition to seizure control, AEDs are also used for other indications including neuropathic pain. Although AEDs have the potential to cause adverse drug reactions in older populations, little is known about their use and possible adverse outcomes in persons with AD.
The aims of this thesis were to investigate the incidence and prevalence of AED use in relation to the diagnosis of AD (Study I), to assess the risk of stroke (Study II) and the risk of death (Study III) associated with incident AED use among persons with AD.
The studies were based on the nationwide register-based Medication use in Alzheimer’s Disease (MEDALZ) cohort that has data on all community-dwelling persons with a clinically verified diagnosis of AD in Finland during 2005-2011 (70,718 people). Persons with AD were identified from the Special Reimbursement Register. Data on AED use was extracted from the Finnish Prescription Register and modelled with PRE2DUP (from prescription drug purchases to drug use periods) method. The prevalence and incidence of AED use were studied from nine years before to five years after the AD diagnosis, and compared to a matched cohort without AD. The risks of stroke and death were assessed among incident AED users compared to matched non-users with AD by applying Cox proportional hazards models and the inverse probability of treatment weighting (IPTW).
Persons with AD were more likely to use AEDs during the study period (4.3%) than the persons without AD (3.2%). The incidence and prevalence of AED use were higher among persons with AD and increased around the time of AD diagnosis. Compared with non-use, AED use was associated with a 37% increased relative risk of incident stroke (IPTW hazard ratio (HR) 1.37; 95% CI: 1.07‒1.74) and
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23% increased mortality (IPTW HR 1.23; 95% CI: 1.12‒1.36). With respect to both stroke and death, the risk was highest during the first 3 months of AED use. The use of older AEDs was associated with a 79% greater relative risk of death compared to use of newer AEDs, whereas no difference was observed in the risk of stroke among users of newer versus older AEDs.
Persons with AD used AEDs more frequently. The use of AEDs was associated with a higher risk of stroke and users of older AEDs had higher mortality, possibly explained by different indications. Therefore, caution is recommended in prescribing AEDs and in monitoring adverse effects in persons with AD. National Library of Medicine Classification: QV 56, QV 85, WA 950, WL 356, WT 155 Keywords: Alzheimer’s Disease; Antiepileptic Drugs; Prevalence; Incidence; Drug-related Side Effects and Adverse Reactions; Cerebrovascular Disorders; Stroke; Mortality; Risk; Cohort Studies, Longitudinal Studies; Follow-Up Studies; Registries; Finland
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Sarycheva, Tatyana Epilepsialääkeryhmän lääkkeiden käyttö sekä käyttöön liittyvät haittatapahtumat, Medication Use and Alzheimer’s Disease-tutkimus Kuopio: Itä-Suomen yliopisto Publications of the University of Eastern Finland Dissertations in Health Sciences No 542. 2020, 112 s. ISBN: 978-952-61-3262-4 (print) ISSNL: 1798-5706 ISSN: 1798-5706 ISBN: 978-952-61-3263-1 (PDF) ISSN: 1798-5714 (PDF) TIIVISTELMÄ
Maailmanlaajuisesti yleistynyt Alzheimerin tauti lisää merkittävästi sairastuneen riippuvuutta muista, huonontaa elämänlaatua ja on yleisimpien kuolemansyiden joukossa. Epilepsialääkeryhmän lääkkeitä käytetään epilepsian hoidon lisäksi enenevissä määrin myös muihin käyttöaiheisiin, kuten hermoperäisen kivun hoitoon. Vaikka näiden lääkkeiden käyttöön iäkkäillä liittyy suurentunut haittatapahtumien riski, niiden käytöstä ja yhteydestä haittatapahtumiin Alzheimerin tautia sairastavilla tiedetään hyvin vähän.
Tässä väitöskirjassa selvitettiin epilepsialääkeryhmän lääkkeiden käytön yleisyyttä ja ilmaantuvuutta suhteessa Alzheimerin taudin diagnoosiin ja verrattuna henkilöihin, joilla ei ollut Alzheimerin tautia (Osatyö I). Lisäksi tutkittiin näiden lääkkeiden käytön yhteyttä aivoverenkiertohäiriön ja kuoleman riskiin Alzheimerin tautia sairastavilla (Osatyöt II ja III).
Tutkimusaineistona käytettiin kansallista rekisteripohjaista Medication use in Alzheimer’s Disease (MEDALZ) kohorttia, johon kuuluivat Alzheimerin taudin lääkkeiden rajoitetun peruskorvausoikeuden Suomessa vuosina 2005-2011 saaneet, diagnoosihetkellä laitoshoidon ulkopuolella olleet henkilöt (N=70718). Tieto lääkekorvausoikeuksista poimittiin lääkkeiden erityiskorvausoikeusrekisteristä. Kelan reseptitiedostosta poimitut lääkeostot mallinnettiin käyttöjaksoiksi PRE2DUP-menetelmällä. Epilepsialääkeryhmän lääkkeiden käytön yleisyyttä ja ilmaantuvuutta tarkasteltiin ajanjaksolla, joka alkoi yhdeksän vuotta ennen Alzheimerin taudin diagnoosia ja päättyi viisi vuotta diagnoosin jälkeen. Käytön yleisyyttä verrattiin kaltaistettuun kohorttiin, jolla ei ollut Alzheimerin tautia. Käytön yhteyttä aivoverenkiertohäiriöiden ilmaantuvuuteen ja kuolemanriskiin Alzheimerin tautia sairastavilla tutkittiin uusien käyttäjien asetelmalla vertaamalla kaltaistettuihin henkilöihin, jotka eivät aloittaneet epilepsialääkeryhmän lääkkeiden käyttöä. Suhteellista riskiä tarkasteltiin hoidon käänteisellä todennäköisyydellä painotetulla Coxin suhteellisten riskitiheyksien mallilla.
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Tutkimusajanjakson aikana Alzheimerin tautia sairastavat käyttivät epilepsialääkeryhmän lääkkeitä hieman useammin (4,3 %) kuin ne, joilla ei ollut Alzheimerin tautia (3,2 %). Näiden lääkkeiden käyttö yleistyi selkeästi Alzheimerin taudin diagnoosin aikaan. Alzheimerin tautia sairastavien joukossa epilepsialääkeryhmän lääkkeiden käyttäjillä oli 37 % korkeampi suhteellinen riski aivoverenkiertohäiriöön verrattuna henkilöihin, jotka eivät käyttäneet näitä lääkkeitä (hoidon käänteisellä todennäköisyydellä painotettu hasardisuhde 1,37; 95 % luottamusväli 1,07‒1,74). Myös heidän suhteellinen kuolemanriskinsä oli 23 % korkeampi (hoidon käänteisellä todennäköisyydellä painotettu hasardisuhde 1,23; 95 % luottamusväli 1,12‒1,36). Molempien päätetapahtumien riski oli suurimmillaan kolmen kuukauden sisällä aloituksesta. Suhteellinen kuolemanriski oli 79 % suurempi vanhojen kuin uusien epilepsialääkeryhmän lääkkeiden käyttäjillä. Aivoverenkiertohäiriön riski oli samansuuruinen vanhojen ja uusien lääkkeiden käyttäjillä.
Epilepsialääkeryhmän lääkkeiden käyttö oli yleisempää Alzheimerin tautia sairastavilla ja niiden käyttöön liittyi korkeampi aivoverenkiertohäiriöiden ja kuoleman riski. Kuolleisuus oli korkeampi vanhojen kuin uusien epilepsialääkeryhmän lääkkeiden käyttäjillä, mutta tämä saattoi osittain selittyä erilaisilla käyttöaiheilla. Tulosten perusteella Alzheimerin tautia sairastavilla epilepsialääkkeen määräämistä ja käytön jatkamista tulisi arvioida säännöllisesti ja mahdollisia haittavaikutuksia tulisi seurata tarkasti.
Luokitus: QV 56, QV 85, WA 950, WL 356, WT 155 Avainsanat: Alzheimerin tauti; epilepsia; esiintyvyys; ilmaantuvuus; lääkkeet; haitat; aivoverenkiertohäiriöt; aivohalvaus; kuolleisuus; kohorttitutkimus; pitkittäistutkimus; seurantatutkimus; rekisterit; Suomi
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ACKNOWLEDGEMENTS
This doctoral study was completed in the University of Eastern Finland, School of Pharmacy during the period 2016-2019. Throughout this project, I have received a great deal of financial support and assistance. It is with immense gratitude that I would like to thank the Dean and all the staff and faculty members of the University of Eastern Finland, Department of Health Science and School of Pharmacy for their extended help, excellent cooperation and for all of the opportunities I have been given to conduct my research and finalize my dissertation.
Foremost, I owe my deepest gratitude to my mentor and supervisor, Professor Anna-Maija Tolppanen for the honour and opportunity to conduct my research and to be a part of the MEDALZ team. I wish to thank her for the continuous support throughout my doctoral study, for her patience, motivation, enthusiasm and immense knowledge. Her expertise was invaluable in the formulating of the research topic and methodology in particular. You provided me with all of the tools that were needed to make this study a success. It was a great privilege to work under her supervision.
I would particularly like to acknowledge my supervisors in the School of Pharmacy, Professor Sirpa Hartikainen and Associate Professor Heidi Taipale, for their expertise and competence predominantly in areas of geriatric clinical aspects and pharmacotherapy, pharmacoepidemiological studies and data management, insightful comments and generous suggestions, interesting discussions, collaborations and support during research work and thesis preparation despite their busy schedule.
I am indebted to my co-authors, Doctor Piia Lavikainen, Doctor Antti Tanskanen and Professor Jari Tiihonen, for their valuable guidance and support that made this project possible.
I would like to include special notes of thanks to the official reviewers of this dissertation, Professor Kristina Johnell and Professor Esa Jämsen for their constructive critique, comments and suggestions. It is a special honour to express my gratitude to Doctor Jenni Ilomaki who has accepted the invitation to be my opponent in the public examination of this dissertation in exceptional circumstances. Many thanks to Ewen MacDonald for revising the language of this thesis.
In addition, I would like to acknowledge my colleagues from the MEDALZ group and School of Pharmacy for their wonderful collaboration with regards to my research. You supported me greatly and were always willing to help me with all kind of problems.
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Finally, I am extremely grateful to all my family members for their love and guidance in whatever I pursue. This thesis would not have been possible without their spiritual support and encouragement.
Kuopio, 28 April 2020 Tatyana Sarycheva
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LIST OF ORIGINAL PUBLICATIONS
This dissertation is based on the following original publications:
I Sarycheva T, Taipale H, Lavikainen P, Tiihonen J, Tanskanen A, Hartikainen S and Tolppanen AM. Incidence and prevalence of antiepileptic medication use in community-dwelling persons with and without Alzheimer's disease. Journal of Alzheimer's disease. 2018;66:387-395.
II Sarycheva T, Lavikainen P, Taipale H, Tiihonen J, Tanskanen A, Hartikainen S
and Tolppanen AM. Antiepileptic drug use and the risk of stroke among community-dwelling people with Alzheimer disease: A matched cohort study. Journal of the American Heart Association. 2018;7:e009742.
III Sarycheva T, Lavikainen P, Taipale H, Tiihonen J, Tanskanen A, Hartikainen S
and Tolppanen AM. Antiepileptic drug use and mortality among community-dwelling people with Alzheimer disease. Neurology journal. 2020;94:1-e10.
The publications were adapted with the permission of the copyright owners.
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CONTENTS
ABSTRACT ............................................................................................................... 7 TIIVISTELMÄ ............................................................................................................ 9 ACKNOWLEDGEMENTS ....................................................................................... 11 1 INTRODUCTION ................................................................................................ 17 2 REVIEW OF THE LITERATURE ....................................................................... 19
2.1 Alzheimer’s Disease .................................................................................... 19 2.1.1 Pathophysiology ................................................................................ 20 2.1.2 Risk factors ....................................................................................... 20 2.1.3 Diagnosis .......................................................................................... 24 2.1.4 Clinical symptoms of AD ................................................................... 30 2.1.5 Treatment .......................................................................................... 30
2.2 Behavioral and Psychological Symptoms of Dementia ............................... 31 2.3 Antiepileptic drugs ....................................................................................... 36
2.3.1 Common AED indications ................................................................. 41 2.3.2 Incidence and prevalence of AED use .............................................. 43 2.3.3 Major adverse effects and events of AEDs ....................................... 46
3 AIMS OF THE STUDY ....................................................................................... 51 4 SUBJECTS AND METHODS ............................................................................ 52
4.1 Study cohort ................................................................................................ 52 4.2 Data sources ............................................................................................... 53 4.3 Drug exposure ............................................................................................. 54 4.4 Outcomes .................................................................................................... 56 4.5 Covariates ................................................................................................... 57 4.6 Settings and analyses ................................................................................. 64
4.6.1 Study I ............................................................................................... 64 4.6.2 Study II and Study III ......................................................................... 67
5 RESULTS ........................................................................................................... 70 5.1 Study I ......................................................................................................... 70
5.1.1 General characteristics of study cohorts ........................................... 70 5.1.2 Incidence and prevalence of AED use .............................................. 73 5.1.3 Incidence of epilepsy diagnosis ........................................................ 74
5.2 Studies II and III .......................................................................................... 75 5.2.1 General characteristics of study cohorts ........................................... 75 5.2.2 Association between AED use and stroke ........................................ 79 5.2.3 Association between AED use and mortality .................................... 80
6 DISCUSSION ..................................................................................................... 83 Summary of results ..................................................................................... 83
6.1 Discussion of Results .................................................................................. 83 6.1.1 Incidence and prevalence of AED use .............................................. 83
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6.1.2 Possible indications for AED use ...................................................... 83 6.1.3 Adverse outcomes associated with AED use ................................... 85 6.1.4 Duration of AED use in relation to adverse outcomes ...................... 87
6.2 Methodological considerations .................................................................... 89 6.2.1 Cohort profile and generalizability ..................................................... 89 6.2.2 Drug exposure assessment .............................................................. 89 6.2.3 Limitations ......................................................................................... 90
7 CONCLUSIONS ................................................................................................. 92 7.1 Clinical implications ..................................................................................... 92 7.2 Future directions ......................................................................................... 92
REFERENCES ........................................................................................................ 94
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ABBREVIATIONS
Aβ Amyloid β AChEI Acetylcholinesterase
inhibitor AD Alzheimer’s disease aHR Adjusted hazard ratio AED Antiepileptic drug ATC Anatomical Therapeutic Chemical APOE4 Apolipoprotein E ε4 allele APP Amyloid precursor protein BPSD Behavioral and psychological symptoms of dementia CBZ Carbamazepine CDR Clinical Dementia Rating CI Confidence interval CLZ Clonazepam COPD Chronic obstructive pulmonary disease CT Computed tomography CVD Cardiovascular disease CYP Cytochrome P450 DALYs Disability-adjusted life years DDD Defined daily dose DSM-IV Diagnostic and Statistical Manual of Mental Disorders, 4th edition EEG Electroencephalography GABA Gamma aminobutyric acid GBP Gabapentin HR Hazard ratio IADL Instrumental activities of daily living ICD International Classification
of Disease ILAE International League Against Epilepsy IR Incidence ratio IPTW Inverse probability of treatment waiting LEV Levetiracetam
LTG Lamotrigine MCI Mild cognitive impairment MEDALZ Medication Use in Alzheimer’s Disease MMSE Mini-Mental State Examination MRI Magnetic resonance imaging NINCDS-ADRDA National Institute
of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorder Association
NHI Finnish National Health Insurance NMDA N-methyl-D-aspartate receptor NPI Neuropsychiatric Inventory OTC Over-the-counter drugs OXC Oxcarbazepine PB Phenobarbital PET Positron emission tomography PGB Pregabalin PHT Phenytoin PIN Personal identification number PRE2DUP From prescription drug purchases to drug use periods PSEN1,2 Presenilin-1,2 SII The Social Insurance Institution of Finland SUDEP Sudden unexpected death in epilepsy patients TI Therapeutic index TPM Topiramate VPA Valproic acid Vnr Nordic Article number WHO World Health Organization
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1 INTRODUCTION
Alzheimer’s disease (AD) is the most common cognitive disorder and its prevalence is growing around the globe (Prince et al., 2013). In Finland, it is estimated that 14,500 new cases of cognitive disorders are diagnosed annually ("Memory disorders: Current Care Guideline," 2017). Although multiple risk factors have been associated with the development of AD, the greatest risk factor is aging (Exalto et al., 2014). It is predicted that the number of new cases of AD and other cognitive disorders will at least double by 2050 and substantially increase the socioeconomic burden worldwide ("2016 Alzheimer's disease facts and figures," 2016), although some studies from developed countries have reported a decreasing trend in the dementia incidence (Roehr, Pabst, Luck & Riedel-Heller, 2018; Satizabal et al., 2016).
Although acetylcholinesterase inhibitors (AChEI) and memantine are the main pharmacological treatment strategies in AD (Farlow & Cummings, 2007), other central nervous system drugs are also frequently used to treat behavioral and psychological symptoms of dementia (BPSD) (Bessey & Walaszek, 2019). It has been shown that the use of antidepressants, antipsychotics and benzodiazepines and related drugs increases around the time of AD diagnosis (Koponen et al., 2015; Saarelainen et al., 2016; Tolppanen et al., 2016), and therapy with antiepileptic drugs (AED) is also frequent in older people in institutionalized and community-dwelling settings (Callegari et al., 2016; Johnell & Fastbom, 2011; Oteri et al., 2010). In addition, previous studies have demonstrated that epileptic seizures (Baker, Libretto, Henley & Zeman, 2019; Friedman, Honig & Scarmeas, 2012; Horvath et al., 2018) and epileptiform activity (Vossel et al., 2013) are common in persons with AD (Vossel et al., 2016). However, specific patterns and possible risks of AED use in persons with AD have remained poorly investigated topics.
Approximately 1% of the general population needs AEDs for epilepsy control (National Institute for Health and Care Excellence, 2016). However, the use of AEDs for other indications is also common. Gabapentinoids (i.e., pregabalin and gabapentin) are mainly used for treatment of neuropathic pain (Cruccu & Truini, 2017), and certain AEDs can be used in BPSD treatment (Konovalov, Muralee & Tampi, 2008). Older AEDs like carbamazepine and valproic acid and newer AEDs like gabapentin have been suggested to be effective in the treatment of agitation and aggression (Gallagher & Herrmann, 2014).
Older people and particularly persons with AD and other cognitive disorders have often been excluded from randomized clinical trials or the number of participants has been too small to specifically assess the risk of adverse effects in these individuals (Hilmer, Gnjidic & Abernethy, 2012). AED treatment of older persons is challenging since these individuals are more susceptible to the adverse effects evoked by AEDs due to ageing-related changes in pharmacokinetics, frequent co-morbid conditions and co-medications (Perucca et al., 2006), with
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persons with AD being particularly vulnerable (Cretin, 2018; Giorgi, Guida, Vergallo, Bonuccelli & Zaccara, 2017). Until now, one previous study has indicated that AEDs were more commonly used by persons with AD (Bell et al., 2011). In this thesis, I provide additional insights into this topic by investigating AED utilisation among persons with AD in greater detail (incidence and prevalence changes in relation to AD diagnosis as well as types of AEDs), as well as examining whether AED use is associated with the risk of stroke and mortality.
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2 REVIEW OF THE LITERATURE
2.1 ALZHEIMER’S DISEASE
Alzheimer’s disease (AD) is a complex neurodegenerative disorder characterised by a progressive and irreversible cognitive decline. It accounts for 60-80% of dementia cases (Cummings, Isaacson, Schmitt & Velting, 2015). Other forms of major cognitive disorders include vascular dementia (20%), dementia with Lewy Bodies (15%) and frontotemporal dementia (5%) ("Alzheimer's research UK. Different types of dementia," 2018). It has been shown that multiple pathologies are often overlapping in older persons and that a mixed form of dementia like AD and vascular dementia occurred most frequently (Custodio et al., 2017). AD is typically classified into the more common late-onset form which is mostly sporadic and occurs in persons aged over 65 years and the rarer early-onset form occurring in those younger than 65 years; the latter form displays a stronger genetic component (Atkins & Panegyres, 2011).
According to the World Health Organization, more than 50 million people aged 65 years or older around the world currently have a dementia diagnosis (WHO, 2017) and these numbers are estimated to climb to 152 million by 2050 ("Alzheimer’s Disease Facts and Figures, ebook (Chicago: Alzheimer’s Association, 2018)," 2018). The prevalence and number of disability-adjusted life years (DALYs) for Alzheimer’s disease and other dementias have almost doubled from 1990 to 2016 ("Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016," 2017) and this has substantially increased the associated global socioeconomic burden. In 2016, dementia was classified as the sixth leading cause of death according to the Center of Disease Control and Prevention (CDC) (Kochanek, Murphy, Xu & Arias, 2016); however, it has been proposed that this number derived from death certificates is an underestimation and AD could well be ranked as the third leading cause of death (Campbell-Taylor, 2014). In Finland, almost 200 000 persons are currently living with mild cognitive impairment, 100 000 people with mild or moderate dementia and 93 000 with moderate to severe dementia. It is estimated that over 14 500 new cases of cognitive disorder occur each year ("Memory disorders: Current Care Guideline," 2017). Therefore, it is crucial to understand and clarify the pathophysiological mechanisms, diagnostics and outcomes of different pharmacological and nonpharmacological treatments including symptomatic treatments of Alzheimer’s disease.
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2.1.1 Pathophysiology
Neurodegeneration is a process characterised by gradual neuronal damage, resulting in both structural and functional deteriorations of the nervous system (Przedborski, Vila & Jackson-Lewis, 2003). In AD, the general loss of neurons and synaptic connections starts in the basal neocortex, mostly in the entorhinal cortex; from there, it spreads further to the hippocampal area and with the progression of disease, it involves all areas of the cortex (Raskin, Cummings, Hardy, Schuh & Dean, 2015). The reduced volume of brain tissue leads to ventricular and sulcal enlargement (Raskin et al., 2015). Synaptic dysfunction and a selective loss of basal forebrain cholinergic neurons were associated with the functional abnormalities in AD (Sanabria-Castro, Alvarado-Echeverria & Monge-Bonilla, 2017). The pathological alterations in noradrenergic, dopaminergic, and serotonergic neurotransmitter systems also contribute to the cognitive impairment and the deterioration of the course of the disease (Kandimalla & Reddy, 2017). The triggering mechanisms of the neurodegeneration cascade in AD remain unclear. The neuropathological changes in the AD brain are characterized by the extensive distribution of extracellular amyloid plaques and intracellular neurofibrillary tangles (Selkoe & Hardy, 2016). According to the amyloid hypothesis, amyloid β (Aβ) protein accumulates and becomes aggregated into insoluble forms with the subsequent formation of plaques. The accumulation of Aβ proteins potentiates the hyperphosphorylation of tau proteins inside nerve cell bodies and drives the formation of neurofibrillary tangles, leading to changes in the cytoskeleton and disruption of the normal communication between nerve cells (Hardy & Selkoe, 2002). The neurotoxic effect of plaques induces the release of inflammatory factors (Heneka, O'Banion, Terwel & Kummer, 2010) and reactive oxygen agents which result in changes in the immune system and ultimately in neuronal cell death (Zhu et al., 2005). Findings of amyloid plaques in healthy people were associated with cognitive impairment and AD progression in case of further Aβ accumulation (Rodrigue et al., 2012). Other mechanisms of neurodegeneration involving non-neuronal cells such as microglia and astrocytes are currently topics of research interest.
2.1.2 Risk factors
AD is a gradually progressive process which starts years or even decades before the appearance of symptoms (Forlenza, Diniz & Gattaz, 2010). Various lifestyle, genetic and environmental factors have been associated with the lifetime risk of AD (Lindsay et al., 2002). These factors are divided into non-modifiable, such as age, gender, family history and genetic factors, or modifiable factors; these may either increase or decrease risk of AD as well as interacting with each other (Table 1).
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Non-modifiable risk factors Age is the most significant risk factor for AD. It has been demonstrated that the
incidence of cognitive disorders in persons aged 65-69 is 2.4 per 1000 person-years and it increases until 70.2 per 1000 person-years in persons aged 90 (van der Flier & Scheltens, 2005). Moreover, the incidence of dementia is higher in women than in men after the age of 85, suggesting that gender is also a significant risk factor.
Mutations in certain genes such as presenilin-1 (PSEN1), presenilin-2 (PSEN2) and amyloid precursor protein (APP) play a role in familial early onset AD (Dai, Zheng, Zeng & Zhang, 2018), whereas the presence of an apolipoprotein E ε4 allele (APOE4) potentiates the Aβ production and is a risk factor for late-onset AD (Liu, Kanekiyo, Xu & Bu, 2013). It has been suggested that carriers of two APOE4 copies may have a 20 times increased risk to develop AD (Uddin et al., 2019). However, the variant is nondeterministic, as persons aged 80-90 who are APOE4 homozygotes might be cognitively normal (Farlow, 1998) and in 30% of persons with AD, the APOE4 is absent (Uddin et al., 2019). Other genetic risk factors have been identified but their role is less prominent ("AlzGene - FIELD SYNOPSIS OF GENETIC ASSOCIATION STUDIES IN AD. Alzforum.," 2019)
Modifiable risk factors Modifiable risk factors are attractive targets for AD prevention (Kivipelto,
Mangialasche & Ngandu, 2018; Mangialasche, Kivipelto, Solomon & Fratiglioni, 2012; Serrano-Pozo & Growdon, 2019). Multiple factors have been associated with a higher risk of AD, and the effects of these factors may vary over the individual’s life-span (Baumgart et al., 2015). Several randomized controlled trials (e.g., the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER), the Multidomain Alzheimer Prevention Trial (MAPT) and the Prevention of Dementia by Intensive Vascular Care (PreDIVA) study) have been designed in order to identify effective preventive strategies (Kivipelto et al., 2013; Richard et al., 2009; Vellas et al., 2014).
Modifiable risk factors can be divided into socioeconomic, lifestyle and clinical factors (Ritchie et al., 2010). Low educational level and occupational status have been linked to an early cognitive decline and a higher risk of dementia (Sharp & Gatz, 2011). A good education and certain occupational activities have been considered as providing direct stimuli to brain structures, preserving the neuronal capacity (cognitive reserve) and delaying the onset of the cognitive decline (Soldan et al., 2017). The same lifestyle factors including a low level of physical activity, a diet with a high intake of saturated fat and salt, smoking and alcohol consumption that have been linked to several chronic cardiovascular and cerebrovascular diseases and diabetes, are associated with a higher risk of dementia (Table 1). There is consistent evidence that physical activity displays a protective association, but the optimal level of intensity and duration has not been determined (Baumgart et
22
al., 2015). Moderate alcohol consumption has been proposed to be a protective factor, however, this association is rather controversial (Serrano-Pozo & Growdon, 2019). It has been suggested that the Mediterranean diet which is characterized by a lower intake of saturated fatty acids and a higher consumption of fish, vegetables, fruits and nuts might be neuroprotective in middle-aged adults due to its antioxidant and anti-inflammatory constituents (Berti et al., 2018).
Various vascular risk factors including obesity, elevated levels of glucose, cholesterol and homocysteine and pre-existing diseases such as coronary artery disease, stroke, hypertension and diabetes are established risk factors for AD (Lindsay et al., 2002; Luchsinger et al., 2005). Both mid-life obesity and hypertension have been linked to a higher risk of AD and dementia in general (Serrano-Pozo & Growdon, 2019). Findings from an autopsy study examining the association between hypertension in mid-life and the increased number of Aβ plaques, neurofibrillary tangles and brain atrophy in late-life might explain the elevated risk (Petrovitch et al., 2000). In addition, having diabetes may accelerate the cognitive decline attributable to the associated metabolic changes, and potentially also due to a depletion of insulin-like growth factor (IGF-1) (Zhang et al., 2017).
The presence of a brain injury can cause both neuronal and vascular damage and is a risk factor for AD (Li et al., 2017). Depression has been linked to a higher risk of AD (Tapiainen, Hartikainen, Taipale, Tiihonen & Tolppanen, 2017). Some of these findings may be explained by protopathic bias, as depression has also been seen as a prodromal symptom of AD (Jorm, 2001). However, studies restricted to depression at mid-life have also reported the increased risk of developing dementia (Barnes et al., 2012). In addition, the chronic systemic hypoxia resulting from cardio- or respiratory disorders has been suggested to increase the risk of AD (Peers, Pearson & Boyle, 2007).
Prevention Non-pharmacological treatment strategies focus on reducing the risk factors and
include cognitive training, psychosocial interventions and diet modifications, as well as treatment with non-pharmacological compounds (Mendiola-Precoma, Berumen, Padilla & Garcia-Alcocer, 2016). A double-blind randomised controlled trial (FINGER study), which lasted 2 years, evaluated a multidomain intervention including restrictions on dietary intake of saturated fat, salt, sugar and alcohol consumption, in addition to exercise, cognitive training and vascular risk monitoring in persons aged 60-77 years with subtle symptoms of impaired cognition. That trial demonstrated the efficacy of a multidomain lifestyle intervention for improving and maintaining cognitive functioning as compared to the control group receiving general health advice (Ngandu et al., 2015). These results suggest that a multidomain intervention rather than a single domain strategy would be more effective in the prevention of cognitive decline.
23
With regards to aetiological theories, various pharmacological treatment strategies of AD have been proposed. These include agents which interfere with amyloid-β (vaccines, antibodies and inhibitors or modulators of γ- and β- secretase) and tau protein accumulation as well as agents affecting serotoninergic and histaminergic neurotransmission (antagonists of 5-HT6 and H3) (Godyn, Jonczyk, Panek & Malawska, 2016). Some Aβ monoclonal antibodies (e.g., solanezumab, crenezumab, and aducanumab) have been tested (Selkoe & Hardy, 2016).
Other pharmacological agents such as statins and hormone-replacement therapy have been tested with regard to risk factors for AD but these have failed to improve the symptoms of AD and were prone to adverse effects (Silva et al., 2019). Other components such as antioxidants and the B vitamins as agents acting to decrease homocysteine levels have been tested but they did not improve the cognitive symptoms in persons with AD (Rutjes et al., 2018).
Table 1. Risk factors of AD (Adapted from Memory disorders: Current Care Guidelines 2017, Serrano-Pozo & Growdon, 2019, Kivipelto et al., 2018, Mangialasche et al, 2012).
Non-modifiable factors Modifiable factors
Increasing risk Decreasing risk Increasing risk Decreasing risk
Age
Genetic factors APOE ε4 allele
and others
Familial aggregation
Gender female
Genetic factors APOE ε2 allele
and others
Social factors Low educational level and occupational attainment
Lifestyle factors
smoking, high alcohol intake, unhealthy diet (saturated fats,
homocysteine), sedentary lifestyle
Vascular factors
mid-life positive association (hypercholesterolemia, obesity,
hypertension), diabetes, cerebrovascular and
coronary artery diseases
Other factors head injury, depression,
sleep disorders, chronic inflammatory diseases,
infectious agents (Herpes Simplex Virus Type I, Clamydophila pneumoniae, Spirochetes)
Social factors high educational level
and occupational attainment, social engagement,
cognitive training
Lifestyle factors physical activity,
Mediterranean diet*
Pharmacological and other agents*
APOE ε4/ APOE ε2, Apolipoprotein E allele 4/2. *Low level of evidence
24
2.1.3 Diagnosis
The evolution of diagnostic criteria The history of AD started in 1906 from the first description of symptoms of
memory impairment together with specific changes in the brain found at autopsy (Hippius & Neundorfer, 2003), followed by discovery of amyloid protein in 1984 (Glenner & Wong, 1984) and neurofibrillary tangles in 1985 (Brion, Couck, Passareiro & Flament-Durand, 1985). At this historical point, the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorder Association (NINCDS-ADRDA) established the first diagnostic criteria for AD (McKhann et al., 1984). The main concept was based on the clinical representation of symptoms of memory impairment, which further gradually impacted on activities of daily living in the later stages. The diagnosis was based on reports from family members and caregivers and neurocognitive tests which assess cognitive functions such as episodic memory, executive function, language, visual and spatial skills. In addition, brain scanning and laboratory tests facilitated the differential diagnosis. Similar steps are still used in the current diagnostic procedures. As a result, a person would receive a possible or probable AD diagnosis but the definite diagnosis could only be established after an autopsy examination, revealing signs of neuronal loss, brain atrophy and the presence of amyloid plaques and neurofibrillary tangles. The fact that structural changes in the brain are seen in the absence of clinical symptoms (Nelson et al., 2012) has led to the revision of the first diagnostic criteria (Jack et al., 2010).
The current NINCDS-ADRDA criteria incorporating the Diagnostic and statistical manual of mental disorders 4th Edition criteria (DSM-IV) for a diagnosis of dementia distinguish between pre-clinical AD, mild cognitive impairment (MCI) due to AD, and dementia stages (American Psychiatric Association. Diagnostic and statistical manual of mental disorders (DSM-IV), 1994; McKhann et al., 2011). Within this new conceptual framework of diagnostic criteria, the AD diagnosis might be established before the onset of clinical symptoms i.e. during the preclinical or asymptomatic stage of AD. This stage is characterized by detectable changes in biomarkers levels without necessarily any changes in cognitive performance and it may last even as long as 20 years before the appearance of symptoms. MCI is a symptomatic stage, which requires signs of decreasing cognitive capacity compared to a previous level; compromising at least of two cognitive domains as memory impairment, aphasia, apraxia, agnosia and executive function with preserved instrumental activities of daily living (IADL, i.e., preparing meals, managing money, medications, and shopping) (Albert et al., 2011). During the dementia stage, more severe signs of cognitive impairment occur which interfere with the individual’s ability to function independently (Shaji, Sivakumar, Rao & Paul, 2018). The differences between former and current criteria are outlined in Table 2.
25
Other modifications of diagnostic criteria, based on biological and clinical features of AD, have been proposed by National Institute on Aging and Alzheimer’s Association (NIA-AA) and International Work Group (IWG). These recommendations have been devised in order to clarify the preclinical stage and facilitate the development of preventive strategies and drug trials, which incorporated biomarkers tests and characterized this stage rather as prodromal AD (Dubois et al., 2014; Sperling et al., 2011) (Table 2). In addition, the fifth edition of DSM criteria has been revised (American Psychiatric Association. Diagnostic and statistical manual of mental disorders (DSM-5®), 2013), where domains for cognitive impairment have been updated to learning and memory factors, language performance, perceptual-motor coordination, complex attention and social cognition. More importantly, the name “dementia” has been replaced by the term ‘major neurocognitive disorder and MCI by ‘mild neurocognitive disorder’. These criteria are based on the initial identification of clinical symptoms of a neurocognitive disorder and a further definition of its aetiology.
26 Tabl
e 2.
Com
paris
on o
f dia
gnos
tic c
riter
ia o
f AD
(Ada
pted
from
Mem
ory
diso
rder
s: C
urre
nt C
are
Gui
delin
es 2
017,
McK
hann
et a
l., 2
011,
Spe
rling
et a
l., 2
011
and
Dub
ois
et a
l., 2
014)
.
NIN
CD
S-A
DR
DA
cri
teri
a
(19
84
)�
NIN
CD
S-A
DR
DA
cri
teri
a
(20
11
)�
IWG
cri
teri
a
NIA
/AA
cri
teri
a�
Con
cept
C
linic
al-p
atho
logi
cal c
once
pt
Clin
ical
-pat
holo
gica
l con
tinuu
m
conc
ept
Clin
ical
-bio
logi
cal c
once
pt
Path
ophy
siol
ogic
al a
nd c
linic
al
conc
ept
AD s
tage
s 1
stag
e AD
-Dem
entia
3 st
ages
: -a
sym
ptom
atic
, pre
clin
ical
-s
ympt
omat
ic, p
re-d
emen
tia
-AD
dem
entia
3 st
ages
: -a
sym
ptom
atic
at-r
isk
for A
D
-pro
drom
al A
D
-AD
dem
entia
3 st
ages
: -p
recl
inic
al (a
sym
ptom
atic
am
yloi
dosi
s / +
sig
ns o
f neu
rona
l in
jury
/ +
subt
le c
ogni
tive/
beh
avio
ral
decl
ine)
-M
CI d
ue to
AD
(am
nest
ic, n
on-
amne
stic
) -A
D d
emen
tia (a
mne
stic
, non
-am
nest
ic)
Dia
gnos
tic c
riter
ia
Po
ss
ible
AD
: Sy
mpt
oms
of
mem
ory
impa
irmen
t, at
ypic
al
onse
t, ot
her c
ondi
tions
* ex
clud
ed.
Pro
ba
ble
AD
: Pro
gres
sion
of
sym
ptom
s m
ore
than
6 m
onth
s in
to 2
or m
ore
cogn
itive
dom
ains
co
nfirm
ed b
y ne
urop
sych
olog
ical
ex
amin
atio
n.
De
fin
ite
AD
: crit
eria
for p
roba
ble
AD c
onfir
med
via
pos
t-mor
tem
ex
amin
atio
n du
ring
auto
psy
Pre
cli
nic
al
AD
: lon
g as
ympt
omat
ic
stag
e de
tect
ed b
y po
sitiv
e AD
bi
omar
kers
† .
Key
crite
ria fo
r pos
sibl
e/pr
obab
le A
D
rem
aine
d un
chan
ged
with
mor
e in
volv
emen
t of b
iom
arke
rs fo
r AD
di
agno
sis
conf
orm
atio
n
As
ym
pto
ma
tic
at
ris
k f
or
AD
-
abse
nce
of c
linic
al s
ympt
oms
and
tau
or a
myl
oid
posi
tive
biom
arke
r P
rod
rom
al
AD
- as
ympt
omat
ic w
ith
posi
tive
gene
tic te
stin
g
AD re
fers
onl
y to
sym
ptom
atic
st
ages
, Pre
senc
e of
cog
nitiv
e im
pairm
ent a
ssoc
iate
d w
ith o
ne o
r m
ore
AD b
iom
arke
rs
Ty
pic
al
AD
-am
nest
ic s
yndr
ome
hipp
ocam
pal t
ype
Aty
pic
al
AD
-pos
terio
r cor
tical
at
roph
y, lo
gope
nic
and
front
al
varia
nts
Mix
ed
AD
-bot
h cl
inic
al fe
atur
es fo
r ty
pica
l and
aty
pica
l
MC
I d
ue
to
AD
: -u
nli
ke
ly (b
iom
arke
r tes
ting
nega
tive)
-i
nte
rme
dia
te o
r Alz
heim
er
path
olog
ic c
hang
e (ta
u or
am
yloi
d po
sitiv
e bi
omar
ker)
-hig
h (b
oth
tau
and
amyl
oid
biom
arke
rs p
ositi
ve)
P
os
sib
le A
D:
sign
s of
dem
entia
with
aty
pica
l cou
rse
(sud
den
onse
t or r
apid
cog
nitiv
e de
clin
e) o
r mix
ed a
etio
logy
P
rob
ab
le A
D:
-am
ne
sti
c v
ari
an
t: le
arni
ng a
nd re
call
cogn
itive
dom
ain
impa
ired
and
plus
1
or m
ore
othe
r dom
ains
. -n
on
an
mn
es
tic
va
ria
nt:
lang
uage
, vi
suos
patia
l and
/or e
xecu
tive
dom
ains
im
paire
d
27
N
INC
DS-
ADR
DA,
Nat
iona
l Ins
titut
e of
Neu
rolo
gica
l and
Com
mun
icat
ive
Dis
orde
rs a
nd S
troke
and
the
Alzh
eim
er’s
Dis
ease
and
Rel
ated
Dis
orde
r Ass
ocia
tion.
IW
G,
Inte
rnat
iona
l Wor
king
Gro
up.
NIA
/AA,
Nat
iona
l Ins
titut
e on
Agi
ng a
nd A
lzhe
imer
’s A
ssoc
iatio
n.
MC
I, m
ild c
ogni
tive
impa
irmen
t *O
ther
con
ditio
ns in
clud
e ot
her f
orm
s of
dem
entia
, maj
or d
epre
ssio
n, c
ereb
rova
scul
ar d
isea
se, t
oxic
, inf
lam
mat
ory,
and
met
abol
ic d
isor
ders
, all
of w
hich
may
requ
ire s
peci
fic
inve
stig
atio
ns, M
RI F
LAIR
or T
2 si
gnal
cha
nges
in th
e m
edia
l tem
pora
l lob
e th
at a
re c
onsi
sten
t with
infe
ctio
us o
r vas
cula
r ins
ults
† AD
bio
mar
kers
incl
ude
decr
ease
d Aβ
1–42
toge
ther
with
incr
ease
d T-
tau
or P
-tau
in C
SF, i
ncre
ased
trac
er re
tent
ion
on a
myl
oid
PET
and
AD a
utos
omal
dom
inan
t mut
atio
n pr
esen
t (in
PSE
N1,
PSE
N2,
or A
PP)
28
Diagnostic testings The diagnostic process of suspected AD is challenging and depends on the
exclusion of other causes to account for the AD-like symptoms. The role of imaging and biomarkers in new AD diagnostic criteria has become significant (Table 2). After evaluating the results of screening cognitive testing conducted within primary care, the subsequent steps in a standard diagnostic evaluation involve imaging tests. Magnetic resonance imaging (MRI) visualizes the decrease in the volume of the hippocampus, temporal lobe and other brain regions (Zhang, Yang, Lu, Yang & Zhang, 2017) and can provide information to help in the differential diagnosis of other forms of dementia and to identify pathological conditions as brain tumors, cerebrovascular diseases and brain injuries. In special cases, positron emission tomography (PET) is used to detect amyloid-β and tau proteins and functional MRI together with fluorodeoxyglucose (FDG)-PET can be used to assess cerebral metabolism (Forlenza et al., 2010).
In addition to imaging testing, biomarkers are used in clinical practice and for research purposes. The biomarkers can be categorized as biomarkers reflecting Aβ accumulation such as low cerebrospinal fluid (CSF) Aβ42 and tau biomarkers (e.g., elevated CSF tau (including phosphorylated tau)) (Jack et al., 2011). The evolution of some tests in accordance with AD progression is outlined in Figure 1.
Some laboratory tests including blood cell count, levels of glucose, thyroid hormone, sodium, potassium, calcium, vitamin B12, folate as well as markers of renal and liver function can help to differentiate AD from other causes of cognitive impairment ("Memory disorders: Current Care Guideline," 2017).
Genetic testing was also included in the revised IWG clinical guidelines (Dubois et al., 2007). Testing for the presence of mutations in APP, PSEN1 and/or PSEN2 can help to identify individuals with familial early-onset AD, whereas mutations in APOE4, SORL1, CLU, CR1, or PICALM may identify persons at risk of late-onset AD, although the possession of these mutations does not invariably lead to development of AD (Atkins & Panegyres, 2011).
.
29
Figure 1. Changes in cognition (MMSE score from 30 to 0), levels of biomarkers and brain structure with respect to the different AD stages (Adapted from Jack et al., 2010, Selkoe &Hardy, 2016, Memory disorders: Current Care Guidelines 2017, McKhann et al., 2011).
Diagnostic procedure in Finland According to the Finnish Care Guidelines ("Memory disorders: Current Care
Guideline," 2017), the clinical diagnosis process of probable AD begins with an interview where neuropsychological testing is used in the assessment of the cognitive decline. In case where there is an uncertainty in the diagnosis, additionally the Consortium to Establish a Registry for Alzheimer's Disease testing (CERAD) is performed in memory clinics ("Memory disorders: Current Care Guideline," 2017). CERAD includes also the commonly used screening tool the Mini-Mental Status Examination (MMSE). Other clinical tools in clinical settings are the Clinical Dementia Rating (CDR) and its scale-Sum of Boxes (CDR-SOB) (Balsis, Benge, Lowe, Geraci & Doody, 2015). CDR and CDR-SOB are used in defining the clinical stages of AD ("Memory disorders: Current Care Guideline," 2017). After the assessment of IADL and activities of daily living, imaging and laboratory tests are usually performed.
30
2.1.4 Clinical symptoms of AD
AD is a steadily progressive disease, and there is individual variation in the clinical symptoms during the course of disease.
The first symptoms of cognitive impairment occur during the symptomatic stage of AD ("Alzheimer’s Association: Stages of Alzheimer’s Disease," 2018). At this stage, a person might start having difficulties with remembering names, correct words, undertaking certain tasks and staying concentrated. These symptoms may be detected during the medical examination and should be distinguished from normal forgetfulness according to the age. In the mild stage, symptoms such as difficulties with remembering recent events and the correct current dates appeared. At this stage, a person may struggle with performing more complex tasks as calculations or managing his/her own finances. However, the ability to cope with daily activities is still preserved (Petersen et al., 2001).
During the moderate stage, a person needs assistance with performing IADL (for example, preparing meal, managing money, remembering personal information such as an address or a phone number as well as recognizing family members); and daily activities, such as dressing or choosing appropriate clothing. This stage is also associated with the appearance of various neuropsychiatric symptoms.
In the severe stage, a person needs assistance in all activities of daily living including toileting and eating. Primary reflexes and incontinence appear. At this stage, the person needs round-the-clock care (Volicer, 2001).
The course of disease may vary with an average time of 8.5 years from the disease diagnosis until the end point depending on age and health state of the AD person (Brodaty, Seeher & Gibson, 2012).
2.1.5 Treatment
AD is an incurable neurodegenerative disorder and the current treatment options focus on controlling its symptoms and improving the quality of life. Conventional symptomatic pharmacological treatment strategies include the use of acetylcholinesterase inhibitors (AChEIs) and the N-methyl-D-aspartate glutamate antagonist (NMDA antagonist), memantine (Mendiola-Precoma et al., 2016; Tan, Hilmer, Garcia-Ptacek & Bell, 2018).
AChEIs have remained the first-line pharmacotherapy for patients with mild to moderate AD (O'Brien et al., 2017). AChEIs reduce the synaptic degradation of acetylcholine, restore cholinergic neurotransmission and improve functional and cognitive symptoms. Currently three AChEIs, donepezil (reversible inhibitor activity), galantamine (irreversible inhibitor), and rivastigmine (pseudo-irreversible inhibitor) are available for the treatment of mild-to-severe AD. A systematic review of 21 randomized, double blind placebo controlled trials with durations from 12 to 24 months demonstrated their equivalent efficacy in the improvement of cognitive
31
functioning in persons with mild to moderate AD as measured by the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) (Kobayashi, Ohnishi, Nakagawa & Yoshizawa, 2016). Common adverse effects of AChEIs are linked to their cholinergic properties and include cholinergic cardiac effects (e.g., bradycardia and syncope), gastrointestinal effects (e.g., nausea, vomiting and diarrhea), muscle spasms, dizziness, headache and an impaired sleeping pattern (Tan et al., 2018).
NMDA receptors have been shown to be a promising target in reducing glutamate-mediated excitability (Paoletti, Bellone & Zhou, 2013). The first partial NMDA antagonist, memantine has been approved as a treatment option of moderate-to-severe AD and it is well-tolerated (Dou et al., 2018). Common adverse effects of memantine include somnolence, headache, confusion, dizziness and hypertension (Jiang & Jiang, 2015).
More evidence suggests that concomitant use of AChEIs and memantine is more beneficial, particularly in those with advanced stages of AD and such combined therapy is more common in clinical practice (Chen et al., 2017; Deardorff & Grossberg, 2016; Touchon et al., 2014). It has been shown that this treatment option improved cognition and activities of daily living in persons with moderate-to-severe AD (Chen et al., 2017; Deardorff & Grossberg, 2016; Gareri et al., 2014). In addition, adjunct treatment of memantine to AChEIs is effective in reducing overall behavioural and psychological symptoms (BPSD) and particularly in controlling certain symptoms such as anxiety and agitation/aggression (Chen et al., 2017; Gareri et al., 2014).
Persons with AD are more predisposed to epileptic seizures and antidementia drugs might play a role in this susceptibility (Liu, Yu & Lu, 2016). However, data on epileptogenic activity of AChEIs and memantine is limited and more research is needed.
2.2 BEHAVIORAL AND PSYCHOLOGICAL SYMPTOMS OF DEMENTIA
Behavioral and psychological symptoms (BPSD) is the collective term for various non-cognitive neuropsychiatric symptoms and behaviors (e.g., agitation, aberrant motor behavior, anxiety, elation of mood, irritability, depression, apathy, disinhibition, delusions, hallucinations, and sleep or appetite changes) occurring in persons with dementia (Cerejeira, Lagarto & Mukaetova-Ladinska, 2012). The prevalence of BPSD is elevated in accordance with the severity of dementia and is at its highest in persons in the advanced stage of the disease (Hessler et al., 2018). The BPSD incidence depends on its type, with apathy and hyperactivity being the more frequent and persistent than the other BPSD types (van der Linde et al., 2016). It has been shown that certain symptoms such as delusions, agitation, and aberrant motor behavior might accelerate the AD progression (Hallikainen et al., 2018). The
32
presence of BPSD affects the quality of life of persons with dementia and their caregivers(Cerejeira et al., 2012), and this frequently results in early nursing home placement (Toot, Swinson, Devine, Challis & Orrell, 2017), and an increased economic burden (Beeri, Werner, Davidson & Noy, 2002). BPSD have been explained by disease-related neurodegenerative processes in the frontal-subcortical circuits and the cortico-cortical networks which are connected to behavior, motivation, emotion and cognition (Tascone & Bottino, 2013). In addition to cholinergic deficits, an imbalance in other neurotransmitters i.e. serotonin, noradrenaline, and dopamine, has been also linked to BPSD development (Tible, Riese, Savaskan & von Gunten, 2017).
The risk factors for BPSD include patient-related factors, caregiver-related factors and environmental factors (Kales, Gitlin & Lyketsos, 2015). Previous psychiatric conditions, infections like pneumonia and urinary tract infections, constipation, sleep impairment as well as pain and its poor management can provoke BPSD (Bessey & Walaszek, 2019). Moreover, among family members and caregivers, stress-related health issues and depressive symptoms are frequent and may play a role in the incidence of BPSD and its severity (Porter et al., 2016). Several factors related to physical and safety conditions of the living environment (i.e., over- or under-stimulating housing conditions with modest safety features and distress of caregivers) and daily activities (i.e., lack of activity and few social interactions) have been linked to increased stress level and development of BPSD (Kales et al., 2015; Tible et al., 2017).
The clinical representation of BPSD in relation to AD progression is outlined in Table 3.
33
Table 3. Type and clinical representation of common behavioral and psychological symptoms of dementia (Adapted from Memory disorders: Current Care Guidelines 2017, Rabins et al., 2007, Kales et al., 2015),
BPSD Common clinical representation
Apathy
-Lack of interest -Less social interaction
-Less emotional responsiveness Less initiative
Anxiety -Excessive concern about upcoming events, financial problems -Fear of staying alone
Depression
-Decrease in mood -Lack of pleasure
-Self-deprecatory statements -Suicidal thoughts
Delusions -Misplaced things -Irrational suspicions
Hallucinations -Seeing non-existent people in the home
-Hearing deceased people -Smelling and testing something non-existent
Agitation Aggression
-Socially inappropriate verbal, vocal or motor activity -Restlessness
-Screaming -Wandering
Sleep disorders -Sleep difficulties
-Sleepy already in the early evening -Night-time wandering
Abberant motor behavior
-Hitting -Pincing -Kicking _-Biting
-Slapping
BPSD are a heterogeneous group of symptoms related to disturbed perception
(hallucinations), thought content (delusions), mood or behaviour (anxiety, depression and agitation) and apathy (Cerejeira et al., 2012). Compared to the gradual and progressive decline in cognition, these symptoms are periodic in nature with variability in persistence depending on its type (Helvik, Selbæk, Šaltytė Benth, Røen & Bergh, 2018). They vary within a group of symptoms, across the stages of dementia and between subjects with dementia. Certain symptoms such as anxiety and depression may be encountered in a prodromal stage; agitation and apathy might occur in mild and moderate stages, and symptoms as delusions, hallucinations and aggression are associated with disease progression and appear after the mild stage (Lyketsos et al., 2011).
34
The identification of BPSD is a challenging process as they can manifest as an individual symptom or as a mixed group with an overlapping clinical representation of symptoms (Cerejeira et al., 2012). If these symptoms are truly attributable to an underlying degenerative disease like AD, the problem in the diagnostic process is to distinguish them from other psychiatric disorders. A number of validated clinical instruments are in use for BPSD assessment in persons with AD, where a major role is given to reports from family members and caregivers (Lyketsos et al., 2011). These clinical measurements are grouped into those that assess all types of BPSD such as the Neuropsychiatric Inventory (NPI) and the behavioural pathology in Alzheimer’s disease rating scale (The BEHAVE-AD), and those that are focused on the assessment of some individual symptom such as the Geriatric depression scale (GDS), the Cornell scale for depression and the Cohen-Mansfield agitation inventory (Cerejeira et al., 2012). The NPI and its modified clinician-rated version (NPI-C) are the most extensively employed instruments to assess BPSD (de Medeiros et al., 2010). The NPI is a structured questionnaire, based on caregiver provided information and items assessing 12 BPSD symptoms (delusions, hallucinations, agitation, depression, anxiety, apathy, irritability, euphoria, disinhibition, aberrant motor behavior, night- time behavior disturbances, and eating behavior abnormalities) in terms of their frequency, severity and impact on the caregiver in the past month (de Medeiros et al., 2010).
Management of BPSD With regard to management, non-pharmacological strategies are considered as
the first line in the control of BPSD (Tible et al., 2017). It is recommended that non-pharmacological strategies should be employed regardless of the severity of BPSD and the stage of AD progression. With regard to the risk factors of BPSD, they include interventions oriented on persons with dementia, caregivers and environment (Kales et al., 2015). Various behavioural (regular exercise) and psychosocial (reminiscence, psychoeducation and counselling of caregivers) methods have become established in order to maintain the safe and friendly environment with routine settings for persons with dementia and to provide support for family members and caregivers.
Pharmacological treatment of BPSDs should be limited to situations which are hazardous for the patient and pose a risk to caregivers’ safety (Tible et al., 2017). The recommendation is that proper clinical investigation and management of underlying health related disorders including infections, constipation or diarrhea, urinary retention, dehydration and pain should be undertaken before initiating specific pharmacological treatment (Kales et al., 2015; Tible et al., 2017). Pharmacological interventions are considered as a second line and used in acute situations as well as in the treatment of severe types of BPSD (agitation, aggression, severe depression and psychosis) if the non-pharmacological strategies are not effective (Dyer, Harrison, Laver, Whitehead & Crotty, 2018). Most of the drugs have
35
no official indication for BPSD and are used off-label with the exception of the atypical antipsychotic, risperidone, which has an official indication for treatment of psychotic symptoms, agitation or aggression ("Memory disorders: Current Care Guideline," 2017). Although atypical antipsychotics have a more favourable profile in comparison to older antipsychotics, they are nonetheless associated with several adverse effects including anticholinergic effects, orthostatic hypotension, metabolic syndrome, extrapyramidal symptoms (e.g., parkinsonism, tardive dyskinesia and akathisia) and sedation (Tible et al., 2017). In view of the growing evidence that there is an association between antipsychotics with an increased stroke risk (Zivkovic, Koh, Kaza & Jackson, 2019), mortality (Koponen et al., 2017) and cognitive decline (Rosenberg et al., 2012), their use should be time-limited with an attempt made to withdraw within 6-12 weeks with strict monitoring of adverse effects in persons with AD (Masopust, Protopopova, Valis, Pavelek & Klimova, 2018).
AChEIs (donepezil, galantamine and rivastigmine) and memantine in mild and more severe stages are considered as preferable BPSD treatment options ("Memory disorders: Current Care Guideline," 2017). AChEIs might be effective in the treatment of anxiety and depression, and their combination with memantine might be beneficial in treatment of more severe symptoms (Masopust et al., 2018).
Other central nervous system acting drugs such as antidepressants, benzodiazepines and antiepileptic drugs have also been used in the treatment of various BPSD, however, their efficacy is contradictory and outweighed with the risk of adverse events (Kales et al., 2015) including the risk of hip fractures (Saarelainen et al., 2017; Torvinen-Kiiskinen et al., 2017), pneumonia (Taipale et al., 2019; Taipale et al., 2017); increased mortality has been associated with benzodiazepine use (Saarelainen et al., 2018). There is some evidence for the efficacy of antiepileptic drugs in BPSD treatment (Konovalov et al., 2008). Carbamazepine and valproic acid have been claimed to be effective in the treatment of agitation, aggression and abberant motor behavior; however, their use was associated with severe adverse effects including hyponatremia, dizziness, falls and cognitive impairment (Konovalov et al., 2008). Oxcarbazepine is a derivative of carbamazepine and it has a more favourable safety profile than the parent compound and therefore, it might be used in the treatment of agitation and aggression if other treatment options are not effective ("Memory disorders: Current Care Guideline," 2017). Pregabalin is a newer antiepileptic drug which is effective in the treatment of neuropathic pain (Cruccu & Truini, 2017), generalised anxiety disorder (Montgomery, Chatamra, Pauer, Whalen & Baldinetti, 2008) and might be used in the treatment of agitation and aggression where other treatment options are not effective (Supasitthumrong et al., 2019). However, it should be used with caution and in reduced doses, due to the age-related renal impairment (Guay, 2005; McGeeney, 2009).
36
2.3 ANTIEPILEPTIC DRUGS
Antiepileptic drugs are a group of medications with diverse mechanisms of action used for the treatment of epilepsy and other neurological indications including migraine, bipolar disorder and neuropathic pain. Up to 1% of the general population needs chronic antiepileptic treatment to control epilepsy (WHO, 2019b). Treatment with AEDs is a complex process, where a balance needs to be sought between the efficacy and adverse drug effects as well as with events correlated with tolerability and quality of life (Perucca & Tomson, 2011). Seizures occur as a result of impaired neuronal activity (Chang & Lowenstein, 2003) where neuronal excitation dominates over neuronal inhibition. AEDs have been designed primarily to prevent the occurrence of additional seizures (Goldenberg, 2010) by restoring the normal neuronal signalling through different mechanisms (Czapinski, Blaszczyk & Czuczwar, 2005; Knezevic & Marzinke, 2018), which explains why certain AEDs display differential efficacies in the treatment of specific types of seizures (Figure 2).
Figure 2. Mechanisms of action of Antiepileptic drugs (Adapted from Finnish Medicines Agency (Fimea.fi) 2019, Knezevic et al., 2018, Czapinski et al., 2005, Hanaya et al., 2016)
With regards to the chronological pattern of AED development, they are
classified as those approved before 1993 commonly named as older AEDs and
37
include phenobarbital, primidone, phenytoin, ethosuximide, carbamazepine, clonazepam and valproic acid and later approved AEDs as newer AEDs referring to pregabalin, gabapentin, lamotrigine, levetiracetam, oxcarbazepine, topiramate, lacosamide and vigabatrin (Hung & Shih, 2011). An overview of the available AEDs with their mechanisms of metabolism, indications and common adverse effects is given in Table 4.
38 Tabl
e 4.
Sum
mar
y of
ant
iepi
lept
ic d
rugs
(elim
inat
ion
rout
e, in
dica
tions
out
side
epi
leps
y, a
nd e
xam
ples
of p
ossi
ble
adve
rse
effe
cts
and
even
ts);
(Ada
pted
from
Fi
nnis
h M
edic
ines
Age
ncy
(Fim
ea.fi
, 201
9), E
urop
ean
Med
icin
es A
genc
y (e
ma.
euro
pe.e
u, 2
019)
, Epi
leps
ia: C
urre
nt C
are
Gui
delin
es 2
017,
NIC
E C
are
Gui
delin
es 2
016,
Kne
zevi
c et
al.,
201
8, J
acob
et a
l., 2
016,
Han
aya
et a
l., 2
016)
M
etab
olic
pat
hway
In
dica
tions
oth
er th
an e
pile
psy
Exam
ple
of p
ossi
ble
adve
rse
effe
cts
and
even
ts
Old
er A
EDs
Phe
noba
rbita
l C
YP
2C9,
CY
P2C
19
-Tre
atm
ent o
f tre
mor
s an
d se
izur
es
asso
ciat
ed w
ith a
brup
t with
draw
al fr
om
benz
odia
zepi
nes,
sub
stan
ce a
buse
Sed
atio
n, a
taxi
a, ri
sk o
f fal
ls a
nd im
paire
d co
gniti
ve p
erfo
rman
ce;
hepa
toto
xici
ty, s
kin
rash
; dru
g in
tera
ctio
ns
Prim
idon
e C
YP
2C19
N
A
Sed
atio
n, a
taxi
a, ri
sk o
f fal
ls; h
epat
otox
icity
, ski
n ra
sh; d
rug
inte
ract
ions
Phe
nyto
in
CY
P2C
9, C
YP
2C19
N
A
Nys
tagm
us, d
iplo
pia,
ata
xia,
sed
atio
n, ri
sk o
f fal
ls, i
mpa
ired
cogn
itive
pe
rform
ance
; im
paire
d A
-V c
ondu
ctio
n; d
rug
inte
ract
ions
Eth
osux
imid
e C
YP
3A4
N
A
Sed
atio
n; a
bdom
inal
pai
n an
d na
usea
; dru
g in
tera
ctio
ns
Car
bam
azep
ine
CY
P3A
4 -B
ipol
ar d
isor
der
-Trig
emin
al n
eura
lgia
Hyp
onat
rem
ia;
seda
tion,
risk
of f
alls
; im
paire
d co
gniti
ve p
erfo
rman
ce; a
ntic
holin
ergi
c ef
fect
s (i.
e., d
ry m
outh
, con
stip
atio
n, u
rinar
y im
pairm
ent o
r ret
entio
n,
impa
ired
card
iac
cond
uctio
n); l
euko
peni
a; a
plas
tic a
nem
ia;
hepa
toto
xici
ty; d
rug
inte
ract
ions
Clo
naze
pam
C
YP
3A
-NA
S
edat
ion;
risk
of f
alls
and
impa
ired
cogn
itive
per
form
ance
;
Val
proi
c ac
id
CY
P2A
6, C
YP
2B6,
C
YP
2C8/
9, C
YP
2C19
, C
YP
2E1
-Bip
olar
dis
orde
r -M
igra
ine
prop
hyla
xis
Hyp
onat
rem
ia;
seda
ttion
, fin
e tre
mor
, ris
k of
falls
; im
paire
d co
gniti
ve p
erfo
rman
ce;
naus
ea, h
epat
otox
icity
; dru
g in
tera
ctio
ns
(C
ontin
ued)
39
Ta
ble
4 (c
ontin
ued)
.
New
er A
EDs
Gab
apen
tin
Ren
al e
limin
atio
n -N
euro
path
ic p
ain
Seda
tion,
ata
xia,
risk
of f
alls
; con
fusi
on a
nd e
mot
iona
l ins
tabi
lity;
hy
pona
trem
ia; d
ose
redu
ctio
n al
read
y in
mild
rena
l im
pairm
ent
Pre
gaba
lin
Ren
al e
limin
atio
n -N
euro
path
ic p
ain
-Anx
iety
dis
orde
r -T
reat
men
t of B
PS
D (a
gita
tion,
agg
ress
ion)
Seda
tion,
ata
xia,
risk
of f
alls
; irri
tabi
lity,
con
fusi
on, r
estle
ssne
ss; d
ose
redu
ctio
n al
read
y in
mild
rena
l im
pairm
ent
Oxc
arba
zepi
ne
AR
K1C
1, A
RK
1C2,
A
RK
1C3,
AR
K1C
4
-Bip
olar
dis
orde
r -T
reat
men
t of B
PS
D (a
gita
tion,
agg
ress
ion)
Hyp
onat
rem
ia;
seda
tion,
risk
of f
alls
; im
paire
d co
gniti
ve p
erfo
rman
ce; i
ncre
ased
risk
fo
r ant
icho
liner
gic
effe
cts
(i.e.
, dry
mou
th, c
onst
ipat
ion,
urin
ary
impa
irmen
t or r
eten
tion,
impa
ired
card
iac
cond
uctio
n); d
rug
inte
ract
ions
Lam
otrig
ine
M
etab
olis
m v
ia
gluc
uron
idat
ion
-Bip
olar
dis
orde
r S
edat
ion;
dip
lopi
a, ri
sk o
f fal
ls; i
rrita
bilit
y, ri
sk o
f agg
ress
ion;
hy
pona
triem
ia; s
kin
rush
Topi
ram
ate
Ren
al e
limin
atio
n m
ostly
un
chan
ged
-Mig
rain
e pr
ophy
laxi
s
Ris
k of
impa
ired
cogn
itive
per
form
ance
and
falls
; ant
icho
liner
gic
effe
cts
(i.e.
, dry
mou
th, c
onst
ipat
ion,
urin
ary
impa
irmen
t or r
eten
tion,
im
paire
d ca
rdia
c co
nduc
tion)
; orth
osta
tic h
ypot
ensi
on; h
ypok
alem
ia;
anor
exia
;
Leve
tirac
etam
R
enal
elim
inat
ion
-NA
Seda
tion,
risk
of f
alls
; Irri
tabi
lity,
agg
ress
ion;
hyp
onat
rem
ia
Briv
arac
etam
C
YP
2C19
-N
A
Seda
tion,
risk
of f
alls
; anx
iety
, dec
reas
ed a
ppet
ite; i
nfec
tions
Laco
sam
ide
CY
P2C
19�
NA
Se
datio
n, d
iplo
pia,
risk
of f
alls
; pro
long
ed P
R in
terv
al; c
onst
ipat
ion
CY
P, C
ytoc
hrom
e P
450
enzy
mes
; AR
K, a
rylk
eton
e re
duct
ase;
NA
, not
app
licab
le
40
The older AEDs have a long history of use in treating epilepsy and their
efficacy has been extensively evaluated. However, their safety profile is less favourable than the newer AEDs (Nevitt, Sudell, Weston, Tudur Smith & Marson, 2017) i.e. the newer AEDs are less prone to drug-drug interactions(Hanaya & Arita, 2016) and have wider indications for use (Jacob & Nair, 2016). Phenobarbital, carbamazepine and clonzepam are considered as potentially inappropriate medications for use and should be avoided in older persons ("American Geriatrics Society 2019 Updated AGS Beers Criteria(R) for Potentially Inappropriate Medication Use in Older Adults," 2019; Renom-Guiteras, Meyer & Thurmann, 2015).
Older AEDs are metabolized via the cytochrome P450 (CYP) enzyme system in the liver (Faught et al., 2018; Johannessen & Landmark, 2010). They are involved in clinically relevant (Perucca, 2006) drug-drug interactions with other drugs (Schmidt, 2009) metabolized by certain CYP isoenzymes i.e. CYP1A2, CYP2C9/10, CYP2C19 and CYP3A3/4 (Johannessen & Landmark, 2010) (Table 4). The induction of CYP is seen with carbamazepine, phenobarbital, phenytoin and primidone; this results in increased metabolism and in a decrease in the serum concentrations of several drugs e.g. dihydropyridine calcium-channel blockers (i.e., felodipine, amlodipine, nimodipine and nilvadipine), statins (i.e., atorvastatin, simvastatin, lovastatin and fluvastatin), warfarin and other oral anticoagulants (i.e., abixaban, rivaroxaban and dabigatran) and central nervous system drugs (i.e., quetiapine, mirtazapine, risperidone, diazepam and oxycodone) (Perucca, 2006; Zaccara & Perucca, 2014). One Finnish study investigating the risk of drug-drug interactions in older persons with newly diagnosed epilepsy (Bruun, Virta, Kalviainen & Keränen, 2017) reported that more than third of AED users; particularly those treated with carbamazepine, experienced drug-drug interactions, and the concomitant use with dihydropyridine calcium-channel blockers, statins, warfarin, and risperidone was associated with the highest risk of drug-drug interactions in this population. Other examples are interactions of oxcarbazepine with the oral anticoagulant, clopidogrel and with the AChEI, donepezil (Faught et al., 2018).
The CYP inhibitor, valproic acid, shows the opposite effect, where both the therapeutic and unwanted actions of the second drug such as warfarin, quetiapine and sertraline might be increased (Perucca, 2006).
In addition, other drugs (Zaccara & Perucca, 2014) including antidepressants (i.e., fluoxetine, fluvoxamine, trazodone and sertaline), antipsychotics (i.e., risperidone and quetiapine), antibiotics (i.e., fluconazole, isoniazid and ketoconazole) and cardiovascular drugs (i.e., verapamil and diltiazem) with inducing or inhibiting properties may alter serum concentrations of certain AEDs including carbamazepine and phenytoin and result in an increase in their toxicity (Johannessen & Landmark, 2010).
Most of the newer AEDs are eliminated through the kidney and, therefore, their doses must be reduced in older persons already with a mild decline in renal function (Guay, 2005; McGeeney, 2009).
41
Most of the AEDs have narrow therapeutic indices (TI), making them prone to evoke adverse drug effects (Greenberg et al., 2016). The TI is the ratio of the drug concentration effective in 50% of the population (EC50) in relation to the toxic concentration (TC50) associated with adverse events in 50% of the population (TI=TC50/EC50). A TI value equal to or less than 2 is considered as a narrow therapeutic range, i.e. it is the width of the dose range where the use of a medication is both safe and effective. However, the therapeutic range might be misleading, as both the efficacy and tolerability of AEDs vary between individuals.
AED treatment of older persons and particularly persons with AD is challenging due to their frailty and ageing related changes in pharmacokinetics, frequent co-morbid conditions and co-medications (Perucca et al., 2006). The prevalence of comorbid diseases among older persons is high and the polypharmacy rate may reach 50%, making them more susceptible to drug-drug interactions with the older AEDs and somatic and neurologic adverse effects (Perucca, 2007; Perucca et al., 2006). Physiological changes occurring in advanced age may alter the pharmacokinetic properties of certain AEDs and therefore, the risk of adverse effects might be increased (Perucca, 2007). These changes include a higher proportion of free drug because of malnutrition or some other reason, low plasma protein levels of highly protein bound AEDs like valproic acid, phenytoin, and carbamazepine (Klotz, 2009). The volume of distribution of lipophilic AEDs (i.e., phenytoin and carbamazepine) might be elevated due to the increased proportion of fat and the reduced proportion of body water (Giorgi et al., 2017).
2.3.1 Common AED indications
Epilepsy Alzheimer’s disease (AD) and epilepsy share some pathophysiological mechanisms and their incidence increases with age (Friedman et al., 2012). With regards to etiology, epileptic seizures can be classified as primary (idiopathic) and due to secondary causes including stroke and other cerebrovascular diseases, neurogenerative conditions, brain injuries and tumors (Fisher et al., 2014; Liu et al., 2016). Secondary mechanisms explained nearly 50% of new-onset epilepsy cases in older adults (Tanaka et al., 2013). Epilepsy is a frequent condition encountered in older persons, and it is more prevalent among persons with AD (Johnston & Smith, 2010). The prevalence of epilepsy increases with age with the maximum of 545.1/100 000 in people aged over 80 ("Global, regional, and national burden of epilepsy, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016," 2019). In a previous study, the prevalence of epileptic seizures in persons with AD was 4.7% and the incidence rate among them was doubled (11.9 per 1000 person-years) compared to a matched cohort without AD (5.7 per 1000 person-years) (Cheng et al., 2015). Another study found a lower prevalence rate (2.5%) of
42
epilepsy in persons with AD and a larger difference in the incidence of epileptic seizures between persons with AD (5.6 per 1000 person-years) and persons without AD (0.8 per 1000 person-years) (Imfeld, Bodmer, Schuerch, Jick & Meier, 2013). In a population-based Finnish study, the proportion of persons with AD diagnosed with epilepsy was 2.1% vs 1.3% in persons without AD (Bell et al., 2011). Differences in methodology including definitions of epilepsy and selected populations might explain the variation in epilepsy prevalence. It has been reported that persons in the severe stage of AD have a higher risk of seizures (Imfeld et al., 2013; Subota et al., 2017). However, epileptiform activity has been detected in 62% persons with amnestic MCI, suggesting that the seizures might occur at any disease stage (Vossel et al., 2013). Seizure diagnosis is particularly challenging in older persons with AD (Ferlazzo, Sueri, Gasparini & Aguglia, 2016; Hommet, Mondon, Camus, De Toffol & Constans, 2008) as postictal memory impairment lasts for a relatively short period of time, whereas the diagnosis of cognitive symptoms in persons with AD might takes months (Friedman et al., 2012). Moreover, persons with AD may have difficulties with recalling seizure episodes, as most of them are non-convulsive (Friedman et al., 2012). A routine 30-min EEG taken when the subject with AD was in an awakened stage, detected epileptiform activity in 2-6% of those tested (Liedorp, Stam, van der Flier, Pijnenburg & Scheltens, 2010; Vossel et al., 2013). It has been shown that individuals with AD having seizures underwent a faster decline in cognitive functionality as compared to AD persons without seizures (Vossel et al., 2016). More research is needed to clarify this possibility.
Neuropathic pain
Neuropathic pain has been defined as ‘pain caused by a lesion or disease of the
somatosensory system (Jensen et al., 2011). According to the anatomical localization of the underlying disorder, neuropathic pain is classified as either central due to brain disorders and/or spinal cord injuries, or as peripheral with generalized and focal distribution caused by diabetes, inflammatory and autoimmune disorders and infectious diseases (Colloca et al., 2017; Finnerup et al., 2016). Chronic neuropathic pain is a frequent condition which affects 7-10% of the general population (Colloca et al., 2017) and about 13.7% of older individuals (Fine, 2009; Roghani, Delbari, Asadi-Lari, Rashedi & Lokk, 2019). The prevalence of pain-related diagnosis is about 50% in persons with AD (Lin, Li, Chou, Chen & Lin, 2018).
Pain is one of the provocative factors of BPSD and its diagnosis is essential (Kales et al., 2015). Facial and behavioral expressions might be indicators of pain (de Tommaso et al., 2016). However, the assessment of pain is rather challenging in persons with AD and is based on observations, observational tools and information from caregivers, particularly in the more advanced stages of AD (de Tommaso et al., 2016). Difficulties in pain diagnosis might explain why about 46% of persons in community-dwelling settings (Shega, Hougham, Stocking, Cox-Hayley & Sachs,
43
2006) and 87% of AD persons in nursing home were reported to have poor pain control (Holmerova et al., 2018). According to guidelines from the International Association for the Study of Pain (IASP), newer antiepileptic drugs gabapentinoids and certain antidepressants (i.e., tricyclic antidepressant (TCA) like amitriptyline and the serotonin-norepinephrine reuptake inhibitor (SNRI) like duloxetine) are considered as first-line treatment options for neuropathic pain (Cruccu & Truini, 2017). The use of TCAs as potentially inappropriate medications should be avoided in older persons and particularly in those with AD ("American Geriatrics Society 2019 Updated AGS Beers Criteria(R) for Potentially Inappropriate Medication Use in Older Adults," 2019). The use of gabapentinoids has been associated with a significant reduction in pain scores regardless of their doses as compared to placebo (McGeeney, 2009). It seems that possible adverse effects of pregabalin and gapapentin are dose-dependent; these are described in Table 4. The use of gabapentinoids might increase the risk of falls and fractures (Carbone et al., 2010; Ensrud et al., 2008) in older persons and this has to be taken into consideration prior to the initiation of treatment with gabapentinoids.
Other indications
The use of AEDs for the treatment of BPSD and other indications is less common. Certain AEDs such as topiramate and valproic acid have been approved for migraine prophylaxis (Silberstein et al., 2012), however their use in persons with AD is limited due to their unwanted behavioral and cognitive adverse effects and events (Meador, Loring, Hulihan, Kamin & Karim, 2003). Carbamazepine, valproic acid and lamotrigine are also used in the treatment of bipolar disorder (Bialer, 2012). Valpoic acid and carbamazepine are effective in controlling manic and mixed episodes, whereas lamotrigine may delay the reoccurrence of depressive episodes (Bialer, 2012). It has been also suggested that oxcarbazepine might be equally effective with carbamazepine in the treatment of bipolar disorder and have a more favourable adverse effect profile (Muneer, 2016).
2.3.2 Incidence and prevalence of AED use
Numerous previous studies have investigated the use of AEDs in the general population (Hsieh & Huang, 2011; Johannessen, Landmark, Larsson, Rytter & Johannessen, 2009) demonstrating that the prevalence of their use has increased in the last decades and furthermore that there are large differences between European countries from 88 per 10,000 persons in the Netherlands to 144 per 10,000 in Denmark and Spain (de Groot et al., 2014). Fewer studies have examined the prevalence of AED use among older persons (Oteri et al., 2010; Perucca et al., 2006) but these also showed high variability in prevalence of use (2-10%), which might be explained by different settings (nursing home or community-dwelling) and
44
indications for use (Galimberti et al., 2006; Huber, Griener & Trinka, 2013; Huying, Klimpe & Werhahn, 2006; Johnell & Fastbom, 2011; Oteri et al., 2010; Rytter, Nakken, Mørch-Reiersen, Efjestad & Selvig, 2007) (Table 5).
A Norwegian study reported an 8.2% prevalence of AED use in older persons (30% with cognitive impairment) receiving home care and 7.7% in nursing home settings (80% with cognitive impairment) (Halvorsen, Johannessen, Landmark & Granas, 2016). In addition, concomitant use of other central nervous system drugs was more common among AED users than in non-users (Halvorsen et al., 2016). Another study (Oteri et al., 2010) investigated the incidence and prevalence of AED use in community-dwelling persons aged 65 years or more between 2004 and 2007 and found that the prevalence of older AEDs use had increased from 14.4 to 19.8 per 1000 and the use of newer AEDs from 22.5 to 41.0 per 1000, suggesting an elevated trend in the use of newer AEDs for treating neuropathic pain. A study investigating the use of AEDs in older persons with epilepsy between 2012 and 2016 also described the shift in prevalence from older AEDs to newer AEDs for epilepsy indications (Theitler, Brik, Shaniv, Berkovitch & Gandelman-Marton, 2017).
Nonetheless, there is only one previous study which has evaluated the use of
AEDs among persons with an AD diagnosis (Bell et al., 2011). In that study, the annual prevalence of AED use in persons with AD was 5.0% as compared to 3.4% in persons without AD, and the majority of AED users used AEDs for indications other than epilepsy.
45 Tabl
e 5.
The
pre
vale
nce
of a
ntie
pile
ptic
dru
g (A
ED) u
se a
mon
g ol
der p
erso
ns a
nd p
erso
ns w
ith A
D.
Ref
eren
ce,
coun
try
Popu
latio
n N
(num
ber)
St
udy
year
Pr
eval
ence
with
rega
rd to
stu
dy y
ears
≥1A
ED
Old
er A
EDs
(any
/mos
t fre
quen
t) N
ewer
AED
s (a
ny/m
ost f
requ
ent)
Bell
et a
l. 20
11
Finl
and
Com
mun
ity-
dwel
ling
with
AD
28
,089
20
06
5.0%
(ann
ual)
65%
C
arba
maz
epin
e 31
%*
Valp
roic
aci
d 38
.2%
35%
Pr
egab
alin
40.
5%*
Gab
apen
tin 3
8.1%
G
alim
berti
et a
l. 20
06
Italy
N
ursi
ng h
omes
2,
001
2000
4.
3%
94%
Ph
enob
arbi
tal 5
9%
Car
bam
azep
ine
28%
6%
Gab
apen
tin 1
00%
John
ell e
t al.
2011
Sw
eden
Com
mun
ity-
dwel
ling
N
ursi
ng h
ome
1,3
45,2
73
2008
2.2%
8.
5%
53%
C
arba
maz
epin
e 50
.0%
Va
lpro
ic a
cid
19.6
%
60%
C
arba
maz
epin
e 52
%
Valp
roic
aci
d 21
.4%
47%
G
abap
entin
43.
6%
Preg
abal
in 3
7.8%
40
%
Gab
apen
tin 4
8%
Preg
abal
in 3
8.3%
O
teri
et a
l. 20
10
Italy
GPs
el
derly
>65
17,0
71
2004
2007
33%
per
100
0 (a
nnua
l) 40
.2%
per
100
0 (a
nnua
l)
14.4
%
Phen
obar
bita
l 7%
19
.8%
Ph
enob
arbi
tal 9
.5%
22.
5%
Gab
apen
tin N
A 25
.5%
Pr
egab
alin
13.
1%
Hal
vors
en e
t al
2016
N
orw
ay
mix
ed w
ith h
ome
care
eld
erly
11
,254
20
09
7.8%
48
.5%
C
arba
maz
epin
e 40
%
Valp
roic
aci
d 19
.7%
51.5
%
Preg
abal
in 2
8.9%
La
mot
rigin
e 26
.7%
H
uyin
g et
al
2006
G
erm
any
Nur
sing
hom
es
525
2006
4.
96%
85
.2%
C
arba
maz
epin
e 37
.1%
Va
lpro
ic a
cid
25.9
%
14.8
%
N/A
Ryt
ter e
t al
2007
N
orw
ay
Nur
sing
hom
es
1053
20
05
11%
77
%
Car
bam
azep
ine
32%
Va
lpro
ic a
cid
18%
23%
G
abap
entin
15%
La
mot
rigin
e 7%
H
uber
et a
l 20
13
Aust
ria
Nur
sing
hom
es
828
2010
8.
5%
32.9
%
Valp
roic
aci
d 56
.5%
67
.1%
G
abap
entin
55.
3%
Leve
tirac
etam
36.
1%
AED
, ant
iepi
lept
ic d
rugs
; NA,
not
app
licab
le
*num
ber o
f cer
tain
AED
s w
as c
alcu
late
d fro
m to
tal n
umbe
r of o
lder
or n
ewer
AED
s
46
2.3.3 Major adverse effects and events of AEDs
Several previous studies have investigated the role of AEDs in the occurrence of major adverse events including stroke and increased mortality, and most of them have been performed in persons with epilepsy.
Stroke Several theories have been proposed to explain the putative association between
use of older AEDs and the risk of vascular events, including an elevated serum level of risk markers of vascular disease (i.e., homocysteine, C-reactive protein and cholesterol) (Mintzer et al., 2009) as well as the potential of older AEDs to be involved in drug-drug interactions (Faught et al., 2018). These hypotheses were built around the characteristics of the older AEDs, particularly phenobarbital, phenytoin carbamazepine and valproic acid (Table 4). Findings on reduced serum lipid and C-reactive protein levels within six weeks after switching from treatment with older AEDs to newer agents (i.e., lamotrigine, levetiracetam or topiramate) (Mintzer et al., 2016) supported the hypothesis that there would be an increased risk of stroke if the subject was using the older AEDs.
Drug-drug interactions involving AEDs (Perucca, 2006) and other concomitantly used drugs (Faught et al., 2018; Johannessen & Landmark, 2010) have been described in the previous chapter. Older AEDs, like carbamazepine, phenobarbital, phenytoin and primidone, due to their CYP enzyme-inducing effect, decrease serum concentrations of concomitantly used medications such as anticoagulants (i.e., warfarin, abixaban, dicoumarol and clopidogrel), which may lead to an increased risk of thromboembolic events (Perucca, 2006). In contrast, the CYP enzyme-inhibitor, valproic acid, exerts the opposite effect and might increase the serum concentrations of oral anticoagulants and elevate the risk of bleeding complications (Johannessen & Landmark, 2010). In addition, CYP inducers might decrease the concentration of cardiovascular medications including dihydropyridine calcium-channel blockers and beta-blockers (i.e., propranolol and metoprolol) up to 80-90% and result in a loss of antihypertensive control (Zaccara & Perucca, 2014). Similarly, concomitant use of CYP inducers may decrease statin concentrations below therapeutic levels (Zaccara & Perucca, 2014).
Previous studies on the association between AEDs use and the risk of stroke
have shown conflicting results (Table 6). Two population-based studies assessed the risk of stroke among incident users through the comparison between AED users according to the CYP metabolic route of AED (categorized as AED with CYP inducing or CYP inhibiting effects) in the general population who had at least one AED prescription (Patorno et al., 2013; Renoux, Dell'Aniello, Saarela, Filion & Boivin, 2015). The study of Patorno et al., found no robust evidence that strong CYP
47
enzyme inducers i.e. phenytoin, carbamazepine or phenobarbital, were associated with an increased risk of ischemic cerebrovascular events when compared against other AEDs (i.e., minimally inducing compounds such as lamotrigine, oxcarbazepine and topiramate, and non-inducing e.g. gabapentin, levetiracetam, pregabalin, tiagabine, valproic acid and zonisamide) (Patorno et al., 2013). In contrast, a UK study (Renoux et al., 2015) reported findings pointing to an elevated risk of ischemic strokes among users of inducing AEDs when they were compared with users of non-inducing AEDs. A Danish cohort study among AED users with epilepsy observed that, as compared to carbamazepine which was taken as the reference drug, oxcarbazepine use was associated with a higher risk of stroke, and valproic acid with a lower risk of stroke (Olesen et al., 2011). Conflicting results on risk of stroke among users of valproic acid in comparison to other AEDs were found in other studies (Dregan, Charlton, Wolfe, Gulliford & Markus, 2014; Hsieh, Lai, Yang & Lin, 2013).
Although the results concerning AEDs are mixed, it seems that certain types of AEDs increase the risk of stroke but these previous studies have not analyzed the risk in older persons with AD.
48 Tabl
e 6.
Stu
dies
on
antie
pile
ptic
dru
g us
e an
d ris
k of
stro
ke.
Ref
eren
ce,
coun
try
Stu
dy d
esig
n Y
ears
of d
ata
colle
ctio
n,
follo
w-u
p
Stu
dy p
opul
atio
n H
azar
d ra
tio (H
R) o
r ot
her
risk
mea
sure
(9
5% C
I) N
(num
ber)
A
ge, s
ex
Pop
ulat
ion
Dem
entia
Pato
rno
et a
l. 20
13
US
Coh
ort (
6mth
w
asho
ut p
erio
d)
2001
-200
6 16
6,03
1 40
-64
Gen
eral
po
pula
tion
not r
epor
ted
(adj
uste
d fo
r)
Indu
cing
CYP
vs
min
imal
ly o
r non
-in
duci
ng A
EDs
R
R 1
,48
(0.7
0-3.
13) i
sche
mic
stro
kes
Ren
oux
et a
l. 20
15
UK
Coh
ort w
ith n
este
d ca
se-c
ontro
l an
alys
is
1990
-201
3 25
2,40
7 m
ean
56,
fem
ale
60%
G
ener
al
popu
latio
n no
t rep
orte
d In
duci
ng C
YP v
s no
n-in
duci
ng C
YP A
EDs
IRR
1.1
6 (1
.02-
1.33
) isc
hem
ic s
troke
s
Ole
sen
et a
l. 20
11
Den
mar
k C
ohor
t 19
97-2
006
25,4
88 u
sers
with
ep
ileps
y m
ean
age
47,
fem
ale
54%
Pers
ons
with
ep
ileps
y di
agno
sis
not r
epor
ted
any
AED
s vs
non
use
HR
2.2
2 (2
.09-
2.36
)
OXC
vs
CBZ
HR
1.2
1 (1
.10-
1.34
)
VPA
vs C
BZ H
R 0
.86
(0.7
6-0.
96)
is
chem
ic s
troke
s
Dre
gan
et a
l. 20
14
UK
Nes
ted
case
-co
ntro
l 19
92-2
013
2,00
2 ca
ses
13,0
98 c
ontro
ls
mea
n ag
e 65
, fe
mal
e 50
%
All h
ad e
pile
psy
diag
nosi
s no
t rep
orte
d
Inci
dent
VPA
use
vs
nonu
se in
redu
cing
risk
of
stro
ke O
R 1
.01
(0.9
1-1.
12)
Dur
atio
n of
VPA
use
: lo
wes
t qua
rter O
R 1
.62
(1.3
7-1.
92)
high
est q
uarte
r OR
0.
59 (0
.46-
0.74
)
Hsi
eh e
t al.
2013
Ta
iwan
C
ohor
t 20
01-2
008
4,06
5 us
ers
with
ep
ileps
y:
Phen
ytoi
n n=
1,95
7
Valp
roic
n=3
93
C
arba
maz
epin
e n=
524
m
ean
age
51,1
fe
mal
e 36
.5%
m
ean
age
47
fem
ale
46.1
%
mea
n ag
e 47
,8
fem
ale
49.2
%
All h
ad e
pile
psy
diag
nosi
s no
t rep
orte
d
PH
T vs
CBZ
H
R 1
.72
(1.2
0-2.
47)
VP
A vs
CBZ
H
R 1
.27
(0.7
8-2.
07)
any
stro
kes
CYP
, Cyt
ochr
ome
P450
enz
ymes
;
O
XC, o
xcar
baze
pine
; CBZ
, car
bam
azep
ine;
VPA
, val
proi
c ac
id; P
HT,
phe
nyto
in;
IRR
, inc
iden
ce ra
te ra
tio, R
R, r
elat
ive
risk,
OR
, odd
s ra
tio;
49
Mortality Studies investigating mortality among AED users have focused on the risk of
sudden unexpected death in persons with epilepsy (SUDEP) (Table 7). In addition to the elevated risk of death due to epilepsy, potential respiratory and cardiac mechanisms of AEDs including an increased risk of arrhythmias and impaired cardiac conduction have been associated with the use of sodium channel blocking AEDs (i.e., carbamazepine, phenytoin and phenobarbital) (Aurlien, Gjerstad & Tauboll, 2016) and the use of AEDs modulating herg potassium currents (i.e., lamotrigine, gabapentine and topiramate) (Danielsson, Lansdell, Patmore & Tomson, 2005) might explain this association.
Several previous studies have explored the role of AEDs in the risk of SUDEP. A Swedish study (Nilsson, Farahmand, Persson, Thiblin & Tomson, 1999) examining the potential risk factors of the occurrence of SUDEP in users of phenytoin, carbamazepine and valproic acids speculated that concomitant use of these AEDs and a high frequency in their dose changes would be associated with an increased risk of SUDEP. The same study updated their results in 2001 and reported that the absence of therapeutic drug monitoring of AED concentrations might result in an elevated risk of SUDEP (Nilsson et al., 2001). There is only one case-control study (Bardai et al., 2015) which has assessed the risk of sudden cardiac death among AED users in the general population with the mean age at death being 72 years as compared with controls matched for age, gender and date of death. They identified an elevated risk of death in current users of AEDs and particularly among users with symptomatic seizures.
The overall risk of death might differ between the various AEDs; oxcarbazepine was associated with an increased risk of all-cause mortality when compared to carbamazepine (Olesen et al., 2011). In addition, a Dutch study suggested that the use of carbamazepine and gabapentin could elevate the risk of cardiac death (Bardai et al., 2015) as compared with non-use.
The higher mortality risk may also be a reflection of severe AED adverse events such as stroke (Patorno et al., 2013; Renoux et al., 2015), myocardial infarction (Renoux et al., 2015), pneumonia (Taipale et al., 2019) and hip fractures (Souverein, Webb, Weil, Van Staa & Egberts, 2006).
Previous studies reported conflicting results in terms of whether epilepsy or AED treatment associated with the risk of death, however, none of these previous studies assessed the risk of death in persons with AD, although these individuals use AEDs relatively often for various indications.
50
Tabl
e 7.
Stu
dies
on
antie
pile
ptic
dru
g us
e an
d ris
k of
dea
th.
Ref
eren
ce,
coun
try
Stu
dy
desi
gn
Yea
rs o
f dat
a co
llect
ion,
fo
llow
-up
Stu
dy p
opul
atio
n H
azar
d ra
tio (H
R) o
r ot
her
risk
m
easu
re (9
5% C
I) N
(num
ber)
A
ge, s
ex
Pop
ulat
ion
Dem
entia
Nis
son
et a
l. 19
99
Swed
en
Nes
ted
case
-con
trol
1989
-199
1 57
cas
es
171
cont
rols
Ag
e ra
nge
15-7
0,
fem
ale
40.4
%
All h
ad e
pile
psy
diag
nosi
s 6
case
s/ 3
co
ntro
ls
Use
of P
HT,
VA
or C
BZ 1
yea
r bef
ore
the
SUD
EP:
>12
seiz
ures
in a
yea
r R
R 4
.64
(1.2
2-17
.63)
C
onco
mita
nt u
se o
f 3 A
EDs:
R
R 1
0.23
(1.8
6-56
.45)
D
ose
chan
ge 3
-5 ti
me:
R
R 9
.32
(1.9
5-44
.50)
Nis
son
et a
l. 20
01
Swed
en
Nes
ted
case
-con
trol
1989
-199
1 57
cas
es
171
cont
rols
Ag
e ra
nge
15-7
0,
fem
ale
40.4
%
All h
ad e
pile
psy
diag
nosi
s 6
case
s/ 3
co
ntro
ls
Use
of P
HT,
VA
or C
BZ 1
yea
r bef
ore
the
SUD
EP:
No
TDM
vs
TDM
for 2
yea
r R
R 3
.74
(1.0
7-13
.12)
C
BZ le
vel:
Low
leve
l RR
2.3
1 (0
.56-
9.42
) H
igh
leve
l RR
4.0
6 (0
.48-
34.3
9)
Ole
sen
et a
l. 20
11
Den
mar
k C
ohor
t 19
97-2
006
25,4
88 u
sers
w
ith e
pile
psy
mea
n ag
e 47
, fe
mal
e 54
%
All h
ad e
pile
psy
diag
nosi
s no
t rep
orte
d
AED
trea
ted
epile
psy:
C
V de
ath
HR
1.6
4 (1
.57-
1.72
) Al
l cau
se d
eath
HR
1.9
2 (1
.86-
1.97
) O
XC v
s C
BZ
CV
deat
h H
R 1
.10
(1.0
2-1.
19)
All c
ause
dea
th H
R 1
.11
(1.0
5-1.
18)
PB v
sCBZ
CV
deat
h H
R 1
.08
(1.0
0-1.
17)
All c
ause
dea
th H
R 1
.18
(1.1
2-1.
25)
Bard
ai e
t al.
2015
N
ethe
rland
s C
ase-
cont
rol
2012
-201
4 92
6 ca
ses
9832
con
trols
m
ean
age
71.7
, fe
mal
e 38
%
98.4
% n
o ep
ileps
y 1.
5% w
ith e
pile
psy
2.6%
pas
t AED
use
2.
5% c
urre
nt A
ED u
se
not r
epor
ted
SCD
OR
AED
use
vs
non-
use:
Pa
st u
se O
R 1
.4 (0
.9-2
.3)
Cur
rent
use
OR
2.6
(1.5
-4.3
) U
se o
f CBZ
OR
3.2
(1.1
-9.2
) U
se o
f GBT
OR
5.7
(1.2
-27.
9)
SU
DEP
, Sud
den
unex
pect
ed d
eath
in e
pile
psy;
TD
M, t
hera
peut
ic d
rug
mon
itorin
g; C
V de
ath,
car
diov
ascu
lar d
eath
; SC
D, s
udde
n ca
rdia
c de
ath;
O
XC, o
xcar
baze
pine
; CBZ
, car
bam
azep
ine;
VPA
, val
proi
c ac
id; P
HT,
phe
nyto
in;
PB, p
heno
barb
ital;
GBT
, gab
apen
tin
RR
, rel
ativ
e ris
k; H
R, h
azar
d ra
tio; O
R, o
dds
ratio
;
51
3 AIMS OF THE STUDY
The purpose of this thesis was to investigate the patterns of AED use and the risk of adverse events associated with their use in persons with AD. The specific aims of this thesis were to study:
1. The incidence and prevalence of AED use from nine years before to five years after the diagnosis of AD by conducting a comparison between persons with and without AD (Study I).
2. The association between incident AED use and the risk of stroke in persons with AD according to type of AEDs and treatment duration (Study II).
3. The risk of death among incident AED users as compared to matched non-users with AD according to the cause of death, the duration of AED use and comparison between the type of AEDs being administered (Study III).
52
4 SUBJECTS AND METHODS
4.1 STUDY COHORT
All of the studies included in this thesis were based on an evaluation of data in the nationwide register-based Medication use and Alzheimer’s disease (MEDALZ) cohort study. MEDALZ has information about all community-dwelling persons residing in Finland who received diagnoses of AD during 2005-2011 (N=70,718) (Tolppanen et al., 2016). The age range of the study cohort was 35-105 years (mean age 80.1±7.1 years) and 65.2% of the study population were women. Clinically verified AD diagnoses were identified from the Special Reimbursement Register maintained by the Social Insurance Institution of Finland (SII). This register contains records of persons who are entitled to higher or restricted basic special reimbursement due to certain chronic diseases. All citizens and long-term residents who have been living in Finland for two years consecutively are covered under the Finnish National Health Insurance (NHI) scheme and are thus eligible for reimbursement of medical expenses for certain (chronic) disorders under the Health Insurance Act.
To be entitled for a special reimbursement (or restricted reimbursement in the case of certain medications, like those used in AD) due to a chronic disease, a person must meet predefined criteria and a diagnosis statement must be submitted to the SII for approval. The duration of the approval process is eliminated as the original date of application for Special Reimbursement is recorded as the date of diagnosis in that register. With respect to AD, the SII requires that the medical statement verifies that the patient has: (a) symptoms consistent with AD; (b) experienced a decrease in social capacity over a period of at least 3 months; (c) received a computed tomography (CT)/magnetic resonance imaging scan (MRI); (d) had possible alternative diagnoses excluded and (e) received confirmation of the diagnosis by a registered geriatrician or neurologist. The SII reviews all medical statements and confers special reimbursement status if the criteria are fulfilled. The diagnosis of AD is made according to NINCDS-ADRDA (McKhann et al., 2011) and DSM-IV criteria for Alzheimer’s disease. The positive predictive value of AD diagnosis in the register was 97.1% according to a previous validation (Solomon et al., 2014).
The MEDALZ study contains a matched comparison cohort of people without AD, which was also used as a comparison group in Study I. These community-dwelling people without AD were identified from a SII register containing people covered by
53
the NHI with incidence density sampling. Persons without AD were matched 1:1 according to age, gender and region of residence at the date of AD diagnosis. Comparison persons were not allowed to have AD (special reimbursement or antidementia drug dispensings) before the index date or during a 12 months time period after that date. If the comparison person was diagnosed with AD during the follow-up, he/she was censored from the persons without AD at that point.
4.2 DATA SOURCES
MEDALZ data have been linked to several Finnish registers (Special Reimbursement Register, Prescription Register, Care Register for Health Care, and Statistics Finland) with personal identification number (PIN) (Figure 3). The Special Reimbursement register is maintained by the SII, which stores data on special and restricted basic reimbursements for specific drugs and/or diseases since 1972 (KELA, 2019). From this register, we obtained data on AD diagnosis, epilepsy diagnosis and other chronic diseases. Data on purchased drugs since 1995 were extracted from the Prescription register. This register contains data on reimbursed prescription drugs dispensed in pharmacies to people receiving ambulatory care. The drugs are coded according to the World Health Organization (WHO) Anatomical Therapeutic Chemical (ATC) code. This register includes all reimbursed and dispensed medications, covered by the national health insurance system. Over-the-counter (OTC) drugs and drugs administered elsewhere than in pharmacies, e.g., hospitals and outpatient clinics, are not included in Prescription register (Furu et al., 2010). Data on diagnosis in inpatient hospital care since 1972 and durations of hospital stays were retrieved from the Care Register for Health Care (previously Hospital Discharge Register) maintained by the National Institute of Health and Welfare. The register is based on mandatory records and covers both public and private hospitals. The validity of most diagnosis codes has been confirmed previously (Sund, 2012). Data on long-term institutionalization were provided by the SII. Information regarding socioeconomic status, dates and underlying causes of death were obtained from the Statistics Finland. Coding of causes of death for mortality statistics has been validated previously (Lahti & Penttilä, 2001).
54
Figure 3. Data sources utilized in the MEDALZ dataset used in this thesis. 4.3 DRUG EXPOSURE
Data on reimbursed prescription drug purchases since 1995 were extracted from the Prescription register. As the Prescription register does not cover drugs used in public nursing homes and hospitals, the date of long-term institutionalization for each participant during the follow-up was obtained from the SII. In addition, durations of hospital stays have been extracted from the Care Register for Health Care.
Antiepileptics were defined as all drugs with the ATC code N03A. AEDs were sub-categorized into older and newer AEDs (Table 8) (Johannessen, Landmark et al., 2009):
A. Special Reimbursement
Register (SII)
B. PrescriptionRegister
(SII)
C. Care Register for Health Care
D. Statistics Finland
Alzheimer's disease diagnosis (2005-2011)
Comorbidity diagnoses (1972-2015)
Purchased prescription drugs (1995-2015)
Outcome diagnosis (stroke), Comorbidity diagnoses,
hospitals days (1972-2015)
Socioeconomic status, dates and causes of death
(2005-2015)
MEDALZCOHORT
(70718 persons)
55
Table 8. Categorisation of antiepileptic drugs (based on their ATC codes) in this thesis.
Older AEDs Newer AEDs
Valproic acid (N03AG01) Pregabalin (N03AX16)
Carbamazepine (N03AF01) Gabapentin (N03AX12)
Clonazepam (N03AE01) Lamotrigine (N03AX09)
Primidone (N03AA03) Oxcarbazepine (N03AF02)
Phenytoin (N03AB02) Topiramate (N03AX11)
Levetiracetam (N03AX14)
ATC=Anatomical Therapeutic Chemical classification
Drug purchases recorded in the Prescription register do not indicate whether the drugs have actually been used. Moreover, the register contains information on the dosage of medication in Defined Daily Dose (DDD), which is the average maintenance dose per day for adults when the drug is used for its main indication (WHO, 2019a). This fixed average dose does not account for variations in drug doses in other indications, due to aging related changes or in case of adverse events, which may occur during long-term use of medications. Therefore, we have applied a mathematical modeling from prescription drug purchases to drug use periods (PRE2DUP) method to calculate AED drug use periods, i.e. when continuous drug use started and ended, for each drug and each person with regards to individual purchasing behavior (Tanskanen et al., 2015). The method constructs continuous drug use periods by calculating a sliding average of dose in DDDs (Figure 4). This method combines purchases of the same drug into use periods by taking into account stockpiling, purchases regularity, dose changes and periods of hospital care when drug use is not recorded in the Prescription register (Taipale et al., 2016; Tanskanen et al., 2015). After modelling each drug substance, overlapping periods of AEDs were combined to retrieve the time when any AED was used vs. non-use comparisons and similarly time on old versus new AED use. The joining of purchases within the method is controlled with parameters designed for each AED package, based on the number of units and strength, general dosing instructions for the drug among older persons, whether tablets can be divided etc. retrieved from the Nordic Article number (vnr-number). These parameters, in addition to other features such as considering the regularity of drug purchases, possible stockpiling
56
of drugs and stays at hospital or nursing home care, ensure that drug use is continuous during modelled drug use periods. The validity of this method has been evaluated previously in different populations (Forsman, Taipale, Masterman, Tiihonen & Tanskanen, 2018; Taipale et al., 2016)
Figure 4. PRE2DUP AED use modelling. 4.4 OUTCOMES
Study II
The main outcome was hospitalization or death (direct or underlying cause) due to stroke. Incident strokes were identified from the Care Register for Health Care and Causes of Death register with ICD-10 codes I60‒I64 and further divided into ischemic strokes (I63), hemorrhagic strokes (I60‒62) and unspecified strokes (I64). Unspecified strokes were not analyzed separately because of the small number of events in the dataset. Previous strokes were identified also according to corresponding ICD-8 (430, 431, 432, 433, 434) and ICD-9 (430, 431, 432, 4330A,
57
4331A, 4339A, 4349A, 4340A, 4341A, 4360) codes. Persons with previous strokes were excluded from the analyses. The validity of the stroke diagnosis in registers has been evaluated previously (Leppälä, Virtamo & Heinonen, 1999; Tolonen et al., 2007) and the positive predictive value for incident strokes was 86%.
Study III The primary outcome was all-cause mortality. Dates and causes of death were
obtained from the Causes of Death register maintained by Statistics Finland. The register is compiled on the basis of death certificates, which are issued by physicians. Autopsy is requested in cases in which the cause of death is not fully verified by clinical history and premortal findings (14.5% of deaths of AD cases in the MEDALZ population led to medicolegal or forensic autopsy).
Death certificates are delivered to the regional unit of the National Institute for Health and Welfare of the region where the deceased resided. There, the provincial medical officer evaluates and confirms the correctness of the certificates before they are sent to Statistics Finland for registration. Causes of death are reported according to ICD-10 codes. In this study, cause-specific mortality was based on underlying causes of death which are determined according to the selection and application rules of ICD-10 maintained by the WHO. The cause-specific mortality was categorized into diseases of the circulatory system (ICD-10 codes I*), dementia and Alzheimer’s disease (ICD-10 codes F01-F03, G30-G31) and other diseases due to the small number of other causes of deaths.
4.5 COVARIATES
In all of the studies, data on hospitalization-based covariates since 1996 until the beginning of follow-up starting from the index date (date of AD diagnosis for Study I and AED initiation for studies II-III) were retrieved on the basis of ICD-10 codes. Diseases based on entitlements to special reimbursements were defined as those occurring after 1972 until the index date. Detailed definitions and classifications of covariates are provided in Table 9.
The selection of covariates was based on their association with AED use and the outcomes. Time since AD diagnosis, socio-demographic characteristics, comorbidity and concomitant use of other drugs were selected as a proxy for AD and general health status severity and possible indications for AED use in our cohort.
For a diagnosis of epilepsy to be verified and recorded in the Special Reimbursement Register, a neurologist provides a medical statement to the SII indicating that the person has (i) been examined by a neurologist or at a neurology clinic, (ii) received relevant examinations including electroencephalography, a CT
58
or MRI scan, and relevant laboratory tests for diagnosis according to the ICD-10, and (iii) has a care plan in accordance with good clinical practice.
Diagnoses of schizophrenia were restricted to those that were diagnosed at least 5 years before the diagnosis of AD based on possible misclassification of symptoms occurring closer to AD diagnosis, as illustrated by the accumulation of mental and behavioural disorder diagnosis three to five years before AD diagnosis (Tapiainen et al., 2017). Substance abuse was defined as alcohol-induced chronic pancreatitis, mental and behavioral disorders due to psychoactive substance abuse and/or substance abuse as a reason for admission.
Use of concomitant medications was identified from the PRE2DUP modelled drug use data.
59 Tabl
e 9.
Def
initi
ons,
cat
egor
isat
ions
and
dat
a so
urce
s fo
r cov
aria
tes
in S
tudi
es I-
III.
Varia
bles
D
efin
ition
/Cat
egor
isat
ion
Sour
ce
Mea
sure
men
t tim
e po
int
Soci
o-de
mog
raph
ic c
hara
cter
istic
s
Age
, yea
rs
-64
65-7
4 75
-84
85-
Pre
scrip
tion
Reg
iste
r S
tart
of fo
llow
-up
Gen
der
Mal
e Fe
mal
e P
resc
riptio
n R
egis
ter
Sta
rt of
follo
w-u
p
Uni
vers
ity h
ospi
tal
catc
hmen
t are
a
Hel
sink
i Tu
rku
Tam
pere
K
uopi
o O
ulu
Sta
tistic
s Fi
nlan
d S
tart
of fo
llow
-up
Occ
upat
iona
l so
cioe
cono
mic
pos
ition
Man
ager
ial/p
rofe
ssio
nal
Offi
ce
Farm
ing/
fore
stry
S
ales
, ind
ustri
al, c
lean
ing
Unk
now
n an
d th
ose
with
mis
sing
dat
a at
S
tatis
tics
Finl
and)
Sta
tistic
s Fi
nlan
d H
ighe
st p
ositi
on re
cord
ed s
ince
197
2 un
til 3
yea
rs p
rior t
o A
D d
iagn
osis
Use
of c
onco
mita
nt m
edic
atio
ns a
ccor
ding
to A
TC c
odes
Ace
tylc
holin
este
rase
in
hibi
tors
* N
06D
A
Pre
scrip
tion
Reg
iste
r O
ne y
ear p
rior t
o th
e st
art o
f fol
low
-up
Mem
antin
e*
N06
DX
01
Pre
scrip
tion
Reg
iste
r O
ne y
ear p
rior t
o th
e st
art o
f fol
low
-up
Ant
idep
ress
ants
N
06A
P
resc
riptio
n R
egis
ter
One
yea
r prio
r to
the
star
t of f
ollo
w-u
p
(
Con
tinue
d)
60 Tabl
e 9
(con
tinue
d).
Ant
ipsy
chot
ics
N05
A e
xclu
ding
lith
ium
N05
AN
01
Pre
scrip
tion
Reg
iste
r O
ne y
ear p
rior t
o th
e st
art o
f fol
low
-up
Ben
zodi
azep
ines
an
d re
late
d dr
ugs
Ben
zodi
azep
ines
N05
BA
, N05
CD
and
/or Z
-dr
ugs
N05
CF
Pre
scrip
tion
Reg
iste
r O
ne y
ear p
rior t
o th
e st
art o
f fol
low
-up
Ant
ithro
mbo
tic
agen
ts
B01
A
Pre
scrip
tion
Reg
iste
r O
ne y
ear p
rior t
o th
e st
art o
f fol
low
-up
Sta
tins*
C
10A
A, C
10B
A
Pre
scrip
tion
Reg
iste
r O
ne y
ear p
rior t
o th
e st
art o
f fol
low
-up
Pro
ton
pum
p in
hibi
tors
* A
02B
C
Pre
scrip
tion
Reg
iste
r O
ne y
ear p
rior t
o th
e st
art o
f fol
low
-up
Non
ster
oida
l ant
i-in
flam
mat
ory
drug
s M
01A
(exc
ludi
ng M
01A
X05
) P
resc
riptio
n R
egis
ter
One
yea
r prio
r to
the
star
t of f
ollo
w-u
p
Com
orbi
dity
dia
gnos
is a
ccor
ding
to IC
D c
odes
Car
diov
ascu
lar d
isea
ses
Hyp
erte
nsio
n H
ospi
tal d
iagn
osis
(IC
D-1
0: I1
0-I1
5) o
r spe
cial
re
imbu
rsem
ent (
clas
sific
atio
n nu
mbe
r 205
)
Car
e R
egis
ter f
or H
ealth
Car
e
Sin
ce 1
994
(Stu
dy I)
/ 19
96 (S
tudy
II)
until
the
star
t of f
ollo
w-u
p
Spe
cial
Rei
mbu
rsem
ent R
egis
ter
Sin
ce 1
972
until
the
star
t of f
ollo
w-u
p
Cor
onar
y ar
tery
di
seas
e
Hos
pita
l dia
gnos
is (I
CD
-10:
I20-
I25)
or s
peci
al
reim
burs
emen
t (cl
assi
ficat
ion
num
bers
206
, 213
, 28
0, 2
11)
Car
e R
egis
ter f
or H
ealth
Car
e S
ince
199
4 (S
tudy
I) /
1996
(Stu
dy II
) un
til th
e st
art o
f fol
low
-up
Spe
cial
Rei
mbu
rsem
ent R
egis
ter
Sin
ce 1
972
until
the
star
t of f
ollo
w-u
p
His
tory
of s
troke
*
Hos
pita
l dia
gnos
is (I
CD
-10:
I60-
64, I
69; I
CD
-9:
430-
432,
436
0, 4
380A
, 433
0A, 4
331A
, 433
9A,
4349
A, 4
340A
, 434
1A; I
CD
-8: 4
30-4
34))
C
are
Reg
iste
r for
Hea
lth C
are
Sin
ce 1
972
until
the
star
t of f
ollo
w-u
p
(C
ontin
ued)
61 Tabl
e 9
(con
tinue
d).
Chr
onic
hea
rt fa
ilure
Hos
pita
l dia
gnos
is (I
CD
-10:
I43-
43, I
50, I
110)
or
spe
cial
reim
burs
emen
t (cl
assi
ficat
ion
num
ber
201)
Car
e R
egis
ter f
or H
ealth
Car
e S
ince
199
4 (S
tudy
I) /
1996
(Stu
dy II
) un
til th
e st
art o
f fol
low
-up
Spe
cial
Rei
mbu
rsem
ent R
egis
ter
Sin
ce 1
972
until
the
star
t of f
ollo
w-u
p
Car
diac
arr
hyth
mia
H
ospi
tal d
iagn
osis
(IC
D-1
0: I4
6-I4
9) o
r spe
cial
re
imbu
rsem
ent (
clas
sific
atio
n nu
mbe
r 207
)
Car
e R
egis
ter f
or H
ealth
Car
e S
ince
199
4 (S
tudy
I) /
1996
(Stu
dy II
) un
til th
e st
art o
f fol
low
-up
Spe
cial
Rei
mbu
rsem
ent R
egis
ter
Sin
ce 1
972
until
the
star
t of f
ollo
w-u
p
Per
iphe
ral v
ascu
lar
dise
ase
Hos
pita
l dia
gnos
is (I
CD
-10:
I70,
I712
, I71
4, I7
16,
I719
, I73
, I77
, I79
, K55
1, K
559,
Z95
8, IC
D-9
: 430
-43
2, 4
360,
438
0A, 4
330A
, 433
1A, 4
339A
, 434
9A,
4340
A, 4
341A
, IC
D-8
: 430
-434
) C
are
Reg
iste
r for
Hea
lth C
are
Sin
ce 1
994
(Stu
dy I)
/ 19
87 (S
tudy
II)
until
the
star
t of f
ollo
w-u
p
Men
tal d
isor
ders
Sch
izop
hren
ia
Hos
pita
l dia
gnos
is (I
CD
-10:
F20
-F29
(s
chiz
ophr
enia
, sch
izot
ypal
or d
elus
iona
l di
sord
ers)
) C
are
Reg
iste
r for
Hea
lth C
are
Sin
ce 1
994
(Stu
dy I)
/ 19
96 (S
tudy
II)
until
5 y
ears
prio
r to
AD
dia
gnos
is
Dep
ress
ion
or b
ipol
ar
diso
rder
Hos
pita
l dia
gnos
is (I
CD
-10:
F30
-F34
, F38
-F39
, IC
D-9
: 296
1, 2
968,
301
1, 3
004,
ICD
-8: 3
004,
301
1,
2960
) C
are
Reg
iste
r for
Hea
lth C
are
Sin
ce 1
994
(Stu
dy I)
/ 19
72 (S
tudy
II)
until
the
star
t of f
ollo
w-u
p
Sub
stan
ce a
buse
Hos
pita
l dia
gnos
is (I
CD
-10:
K86
0 (a
lcoh
ol-
indu
ced
chro
nic
panc
reat
itis)
, F10
-19
(men
tal a
nd
beha
vior
al d
isor
ders
due
to p
sych
oact
ive
subs
tanc
e ab
use)
and
/or s
ubst
ance
abu
se a
s re
ason
for a
dmis
sion
), IC
D-9
: 291
, 292
, 294
0A,
2948
X, 3
03, 3
04, 3
05, 5
770D
-F, 5
771C
, 577
1D,
5710
A, 5
711A
, 571
2A, 5
713X
, IC
D-8
: 291
, 303
, 30
4, 5
7700
-577
08)
Car
e R
egis
ter f
or H
ealth
Car
e S
ince
199
4 (S
tudy
I) /
1972
(Stu
dy II
) un
til th
e st
art o
f fol
low
-up
(Con
tinue
d)
62 Tabl
e 9
(con
tinue
d).
Oth
er d
isea
ses
His
tory
of c
ance
r*
Hos
pita
l dia
gnos
is (I
CD
-10:
C00
-C97
, Z85
, IC
D-9
: 14
0-19
5, 2
730,
273
3, V
1046
, 200
-208
)
Car
e R
egis
ter f
or H
ealth
Car
e
S
ince
198
7 un
til th
e st
art o
f fol
low
-up
Dia
bete
s
Dia
bete
s m
edic
atio
n (A
TC: A
10),
Hos
pita
l di
agno
sis
(ICD
-10:
E10
-E14
, E89
.1) o
r spe
cial
re
imbu
rsem
ent f
or d
iabe
tes
(cla
ssifi
catio
n nu
mbe
r 10
3)
Pre
scrip
tion
Reg
iste
r
One
yea
r prio
r to
the
star
t of f
ollo
w-u
p
Car
e R
egis
ter f
or H
ealth
Car
e
Sin
ce 1
994
(Stu
dy I)
/ 19
96 (S
tudy
II)
unt
il th
e st
art o
f fol
low
-up
Spe
cial
Rei
mbu
rsem
ent R
egis
ter
S
ince
197
2 un
til th
e st
art o
f fol
low
-up
Ren
al fa
ilure
*
Hos
pita
l dia
gnos
is (I
CD
-10:
I13.
1, N
18, N
19, Z
94.0
, Z9
9.2,
Z49
; IC
D-9
: 403
11, 4
0391
, 404
12, 4
0492
, 58
5, 5
86, V
420,
V45
1, V
560,
V56
8) o
r spe
cial
re
imbu
rsem
ent (
clas
sific
atio
n nu
mbe
r 137
, 138
)
Car
e R
egis
ter f
or H
ealth
Car
e
S
ince
198
7 un
til th
e st
art o
f fol
low
-up
Spe
cial
Rei
mbu
rsem
ent R
egis
ter
Sin
ce 1
972
until
the
star
t of f
ollo
w-u
p
Ast
hma/
CO
PD
H
ospi
tal d
iagn
osis
(IC
D-1
0: I2
6, J
41, J
42, J
44,
J439
, J45
, J47
) or s
peci
al re
imbu
rsem
ent
(cla
ssifi
catio
n nu
mbe
rs 2
03, 2
10)
Car
e R
egis
ter f
or H
ealth
Car
e S
ince
199
4 (S
tudy
I) /
1996
(Stu
dy II
) un
til th
e st
art o
f fol
low
-up
Spe
cial
Rei
mbu
rsem
ent R
egis
ter
Sin
ce 1
972
until
the
star
t of f
ollo
w-u
p
Pne
umon
ia*
Hos
pita
l dia
gnos
is (I
CD
-10:
J10
0, J
110,
J12
, J13
, J1
4, J
15, J
16, J
18, J
690)
C
are
Reg
iste
r for
Hea
lth C
are
S
ince
199
6 un
til th
e st
art o
f fol
low
-up
Rhe
umat
oid
arth
ritis
H
ospi
tal d
iagn
osis
(IC
D-1
0: M
05, M
06, M
45) o
r sp
ecia
l rei
mbu
rsem
ent (
clas
sific
atio
n nu
mbe
r 202
)
Car
e R
egis
ter f
or H
ealth
Car
e S
ince
199
4 (S
tudy
I) /1
996
(Stu
dy II
) un
til th
e st
art o
f fol
low
-up
Spe
cial
Rei
mbu
rsem
ent R
egis
ter
Sin
ce 1
972
until
the
star
t of f
ollo
w-u
p
(C
ontin
ued)
63 Tabl
e 9
(con
tinue
d).
Epi
leps
y S
peci
al re
imbu
rsem
ent (
clas
sific
atio
n nu
mbe
r 111
) S
peci
al R
eim
burs
emen
t Reg
iste
r S
ince
197
2 un
til th
e st
art o
f fol
low
-up
Hea
d in
jurie
s H
ospi
tal d
iagn
osis
(IC
D-1
0: S
0* (I
njur
ies
to th
e he
ad))
C
are
Reg
iste
r for
Hea
lth C
are
Sin
ce 1
994
(Stu
dy I)
/199
6 (S
tudy
II)
until
the
star
t of f
ollo
w-u
p
Hip
frac
ture
H
ospi
tal d
iagn
osis
(IC
D-1
0: S
72.0
-S72
.2)
Car
e R
egis
ter f
or H
ealth
Car
e S
ince
199
4 (S
tudy
I) /1
996
(Stu
dy II
) un
til th
e st
art o
f fol
low
-up
ATC
, Ana
tom
ical
The
rape
utic
Che
mic
al; I
CD
, Int
erna
tiona
l Cla
ssifi
catio
n of
Dis
ease
s;
AD
, Alz
heim
er’s
dis
ease
; CO
PD
, chr
onic
obs
truct
ive
pulm
onar
y di
seas
e;
*cov
aria
tes
wer
e ut
ilize
d ad
ditio
nally
in S
tudy
III
64
4.6 SETTINGS AND ANALYSES
4.6.1 Study I
In this study, the incidence and prevalence of antiepileptic drug use were compared
between persons with AD (N=68,355) and matched persons without AD (N=68,355)
selected with the exclusion criteria described in Table 10. A one-year washout
period 9-10 years before the AD diagnosis was applied to define incident use
(Figure 5). The information on the drug use during
hospitalization/institutionalization is not recorded in the Prescription register,
therefore persons hospitalized or institutionalized for more than 182 days during
the washout period, or >90 days at the end of the washout period were excluded.
The overview of the study design is shown in Figure 5.
65
Table 10. Formation of study cohorts
Selection criteria
Study I Study II Study III
Persons with AD
Persons without
AD
AED users with AD
Non-users with AD
AED users with AD
Non-users with AD
Initial numbers of persons: 70718 70718 70718 70718
Initial numbers of AED users: NA NA 7491 NA 7491 NA
Exclusion criteria:
AED use (during 1-year washout period): 1310 974 648 648
Long-term hospitalization/ institutionalization (1-year
washout period)*: 52 44 654 664
Acute cancer (1-year washout period)†: NA NA 182 181
Previous stroke (since 1972)‡: NA NA 744 NA
Unmatched persons: 1001 1345 96 360
Final sample size: 68355 68355 5167 5167 5638 5638
NA,not applied *Ongoing hospitalization/ institutionalization ≥90 days at the end of the washout period or over 50% of the washout period. †Acute cancer ICD-10 (C00 – C97); NOMESCO (AAG50, AX, HA0, PJO, QA0, QB0, QC0, QD0, QW0, QX0, WA, WB, WC, WD, WE, WF0, WFO, ZX0); use of antineoplastic or immunomodulating agents ATC (L01 (excluding persons using L01BA01 and special reimbursement for rheumatoid arthritis), L02, L03AA, L03AB01, L03AB04, L03AB05, L03AC, L03AX (excluding L03AX13), L04AA10, L04AA34, L04AA18, L04AX02, L04AX03 (excluding persons with special reimbursement for rheumatoid arthritis)). ‡History of any stroke (ICD-10: I60-I64; ICD-9: 430, 431, 432, 4330A, 4331A, 4339A, 4349A, 4340A, 4341A, 4360; ICD-8: 430, 431, 432, 433, 434
66
Figure 5. Design of the Study I
The index date was set to the date of AD diagnosis or the corresponding date for
matching person without AD. Follow-up for incident use started from nine years
before AD diagnosis and was censored on the date of AED initation (allowing only
one initiation for each person). Additionally, persons were censored in case of their
long-term hospitalization/institutionalization more than 90 days, death, AD
diagnosis for comparison persons without AD or the end of the data linkage
(December 31, 2015). Definitions for measurement/calculation incidence rates and prevalence proportions:
Time points for estimation = each six-month interval during follow-up period
Incidence rate = Number of incident AED users during 6 months/
Person-years of included persons during 6 months X 100
Incidence rates with 95% confidence intervals of AED use per 100 person-years
were calculated separately among persons with AD and without AD.
Incidence rate ratio = Incidence rate of AED use for persons with AD/
Incidence rate of AED use for persons without AD
Prevalence proportion = Number of current AED users within 6 months/
Number of included persons within 6 months X 100
Current use included new and prevalent AED use. Similarly, prevalence rates
(proportions) were calculated separately for persons with and without AD.
67
Additional analyses on the incidence of epilepsy diagnoses were conducted to
demonstrate the impact of new epilepsy cases on the incidence of AED use with the
similar design as with respect to the incidence of AED use, by excluding persons
with epilepsy more than nine years before AD diagnoses and follow-up of incident
diagnoses until five years after the index date.
Analyses were performed with Stata (Version 14; StataCorp) and R 3.4 (R Core
Team 2017).
4.6.2 Study II and Study III
Both Study II and Study III were restricted to persons with AD. To avoid
prevalent user bias (Ray, 2003), a new user design was selected. In this design, the
follow-up time for outcome investigation starts at the time of treatment initiation.
The study is restricted to persons with a period of nonuse (washout period) prior to
the beginning of follow-up.
Study designs for main analyses (users vs nonusers) of studies II and III are
outlined in Figure 6. As active cancer and its treatment might potentiate the
occurence of death and stroke, we excluded persons with acute cancer (definition
explained in Table 10) during the one-year period prior to AED initation. In
addition, in Study II persons with a history of stroke were excluded (Table 10). We
considered only newly diagnosed or incident cases of stroke in order to avoid bias
caused by possible impairments persisting from past stroke, and their possible
impacts on drug use. Further, only incident strokes can be reliably identified from
the Care register of Health Care (Tolonen et al., 2007).
68
Figure 6. Main design of Study II and Study III
For these studies, the index date was set to the date of AED initiation for AED
users or the corresponding date for matching non-users. Non-users matched 1:1
were identified based on the same exclusion criteria (Table 3) applying incidence
density sampling without replacement. The matching criteria were age (±730 days),
sex and time since Alzheimer’s disease diagnosis (±90 days). Persons without a
match were excluded from further analyses. Drug-drug comparisons were
performed with a similar design, except that they were restricted to users, and
switching to another AED was considered as an additional reason for censoring in
these analyses.
A one-year washout period prior to AED initiation was applied to define
incident use. In these studies, the follow-up time was set from the index date (date
of AED initiation) until outcome or censoring occurred. In both studies, persons
were censored at the time continuous hospitalization/institutionalization more than
three months, AED discontinuation for AED users or AED initiation for matched
non-users or the end of the study (31 December, 2015). In Study II, the occurrence of
death due to other causes than stroke was an additional reason for censoring.
Hazard ratios (HR) with their corresponding 95% confidence intervals (CI)
produced by Cox proportional hazard model were used for comparing the
incidence of outcome among AED initiators and matched nonusers. AED use was
modelled as a time-dependent exposure.
We used a robust variance estimator in the models for AED user and non-user
comparisons to account for the matched nature of our sample. Proportional hazards
69
assumptions were confirmed by exploring parallelism of log negative and log
estimated survival curves for each covariate. In each type of analysis, three Cox
models were performed. Initially, a crude, unadjusted HR was estimated. Secondly,
the model was adjusted for age, sex, use of concomitant medications (i.e.,
antidepressants, antipsychotics, benzodiazepines and related drugs, antithrombotic
agents and nonsteroidal anti-inflammatory drugs) and comorbidity diagnosis (i.e.,
hypertension, coronary artery disease, chronic heart failure, cardiac arrhythmia,
peripheral artery disease, diabetes, epilepsy and head injuries). A description of the
covariates selected for adjustment is provided in Table 9. The results of Study III
were adjusted for use of AChEIs, memantine, statins and proton pump inhibitors;
history of stroke, cancer, renal failure and pneumonia in addition to covariates
included in Study II. Lastly, a HR was weighted with an inverse probability of
treatment (IPT) weights based on the propensity score for controlling confounding
by indication. The propensity score was estimated with a logistic regression model
as the conditional probability of AED use conditioned on the covariates measured
at the baseline. The balancing properties of the IPT weighting between the AED
users and matched non-users were ascertained by comparing covariate
distributions before and after IPT weighting using the standardized difference.
Standardized difference >10% was considered as an indication of a meaningful
difference (Austin, 2008).
Subsequently, all three models were re-estimated in accordance to type of
strokes (ischemic, hemorrhagic and unspecified) in Study II and to cause-specific
mortality (deaths from cardiovascular and cerebrovascular diseases, deaths from
dementia and AD and deaths from other diseases) in Study III. To investigate the
time-dependent association, the analyses were performed for the following periods:
1‒90 days, 91‒180 days, 181‒365 days and 366‒1095 days.
In drug-drug comparisons, a user only-design was applied and users of multiple
AEDs were excluded. Valproic acid was selected as a reference AED. For these
analyses, the weights for IPT weighting were re-calculated.
Additionally, in Study II, we performed competing risks sensitivity analyses
applied Fine&Gray sub-distribution hazard models (Fine & Gray, 1999). In these
analyses, we evaluated the instantaneous rate of stroke in subjects who are still
alive or who have previously died due to other causes than stroke. Sensitivity
analyses after excluding persons with epilepsy were also conducted in Study III in
order to exclude potential influence of epilepsy on mortality risk.
All analyses were performed with SAS (Version 9.4; SAS Institute Inc., Cary.,
North Carolina, USA) and Stata (Version 14; StataCorp).
70
5 RESULTS
5.1 STUDY I
5.1.1 General characteristics of study cohorts
Altogether 4.3% (n=3,058) persons with AD and 3.2% (n=2,255) without AD used
AEDs at the time of AD diagnosis, and 34.9% of users with AD and 26.6% without
AD had a diagnosis of epilepsy (Table 11). AED users with AD (mean age 78.3±8.0)
were slightly younger than AED users without AD (mean age 80.5±6.8). At the time
of AD diagnosis, 46.5% (n=1,422) in AD and 55.1%, (n=1,243) in non-AD population
used newer AEDs, with pregabalin being the most frequently used AED. However,
the use of older AEDs was more frequent among persons with AD (44.3%, n=1,356)
than without AD (39.6%, (n=892); the most commonly used older AED was valproic
acid followed by carbamazepine and clonazepam. AED users with AD and users of
older AEDs were more likely to be using antipsychotics and have had a history of
epilepsy, stroke or mental and behavioral disorders. In contrast, users of newer
AEDs were more likely to have cardiovascular diseases, diabetes and
asthma/COPD in the AD cohort.
71
Tabl
e 11
. Gen
eral
cha
ract
eris
tics
of th
e st
udy
coho
rt ac
cord
ing
to A
D d
iagn
osis
and
AED
use
.
A
D
(n=
70,7
18)
p-va
lue
Non
-AD
(n
= 70
,718
) p-
valu
e A
D A
ED
use
rs*
(n=
2,77
8)
p-va
lue
Non
-AD
AE
D u
sers
* (n
= 2,
135)
p-
valu
e
N
on-u
sers
(n
= 67
,660
)
AE
D
user
s (n
= 3,
058)
Non
-use
rs
(n=
68,4
63)
AE
D
user
s (n
= 2,
255)
Old
AE
D-
user
s (n
= 1,
356)
New
AE
D-
user
s (n
= 1,
422)
Old
AE
D-
user
s (n
= 89
2)
New
AE
D-
user
s (n
= 1,
243)
Age
(yea
rs),
mea
n±SD
80
.1±7
.0
78.3
±8.0
<0
.001
80.0
±7.1
80
.5±6
.8
0.99
077
.05±
8.2
80.0
±7.1
<0
.001
79
.67±
7.0
81.3
±6.7
<0
.001
Wom
en, n
(%)
44,2
86
(65.
4)
1,83
0 (5
9.8)
<0
.001
44,6
13
(65.
2)
1,50
0 (6
6.5)
0.
180
770
(56.
8)
901
(6
3.4)
<0
.001
536
(60.
1)
897
(7
2.2)
<0
.001
Use
of c
onco
mita
nt m
edic
atio
ns, n
(%)
Antid
epre
ssan
ts
13,0
01
(19.
2)
994
(3
2.5)
<0
.001
5,35
6 (7
,8)
541
(24.
0)
<0.0
01
388
(2
8.6)
54
4
(38.
3)
<0.0
01
179
(2
0,1)
33
3
(26.
8)
<0.0
01
Benz
odia
zepi
nes
14,5
06
(21.
4)
1,04
6 (3
4.2)
<0
.001
14,1
71
(20.
7)
801
(3
5.5)
<0
.001
393
(2
9.0)
57
0
(40.
1)
<0.0
01
261
(29.
3)
500
(4
0.2)
<0
.001
Antip
sych
otic
s 6,
013
(8
.9)
477
(1
5.6)
<0
.001
1,89
8
(2.8
) 18
2
(8.1
) <0
.001
257
(1
9.0)
17
2
(12.
1)
<0.0
01
97
(10.
9)
78
(6.3
) <0
.001
Opi
ods
2,45
0
(3.6
) 38
4
(12.
6)
<0.0
01
2,92
7
(4.3
) 40
6
(18.
0)
<0.0
01
61
(4.5
) 29
8
(21.
0)
<0.0
01
66
(7.4
) 31
9
(25.
7)
<0.0
01
NSA
IDs†
5,49
1
(8.1
) 37
3
(12.
2)
<0.0
01
6,82
9 (1
0.1)
35
1
(15.
6)
<0.0
01
125
(9
.2)
206
(1
4.5)
<0
.001
115
(1
2.9)
21
7
(17.
5)
<0.0
01
C
omor
bidi
ty d
iagn
osis
, n(%
)
(C
ontin
ued)
72
Tabl
e 11
(con
tinue
d).
Car
diov
ascu
lar
dise
ases
Cor
onar
y ar
tery
di
seas
e 19
,451
(2
8.8)
1,
005
(32.
9)
<0.0
01
18,9
27
(27.
6)
741
(3
2.9)
<0
.001
427
(3
1.5)
50
1
(35.
2)
0.03
727
3
(30.
6)
425
(3
4.2)
0.
081
Stro
ke
6,56
2
(9.7
) 64
3
(21.
0)
<0.0
01
5,56
2
(8.1
) 43
6
(19.
3)
<0.0
01
322
(2
3.8)
24
2
(17.
0)
<0.0
01
203
(2
2.8)
20
7
(16.
7)
<0.0
01
Men
tal
diso
rder
s
Dep
ress
ion
2,12
1
(3.1
) 24
8
(8.1
) <0
.001
1,78
4
(2.6
) 15
5
(6.9
) <0
.001
126
(9
.3)
99
(7.0
) 0.
024
78
(8.7
) 70
(5
.6)
0.00
5
Bipo
lar d
isor
der
2,21
5
(3.3
) 28
0
(9.2
) <0
.001
1,84
5
(2.7
) 17
9
(7.9
) <0
.001
155
(1
1.4)
10
2
(7.2
) <0
.001
95
(10.
7)
76
(6.1
) <0
.001
Schi
zoph
reni
a 97
1
(1.4
) 10
8
(3.5
) <0
.001
863
(1
.3)
70
(3.1
) <0
.001
75
(5.5
) 23
(1
.6)
<0.0
01
50
(5.6
) 17
(1
.4)
<0.0
01
Oth
er d
isea
ses
Epile
psy
390
(0
.6)
1,06
6 (3
4.9)
<0
.001
342
(0
.5)
601
(2
6.6)
<0
.001
668
(4
9.3)
19
9
(14.
0)
<0.0
01
388
(4
3.5)
14
6
(11.
8)
<0.0
01
Dia
bete
s 12
,227
(1
8.1)
63
5
(20.
8)
<0.0
01
10,3
51
(15.
1)
437
(1
9.4)
<0
.001
228
(1
6.8)
36
0
(25.
3)
<0.0
01
141
(1
5.8)
28
2
(22.
7)
<0.0
01
Asth
ma/
CO
PD‡
6,87
5 (1
0.2)
40
9
(13.
4)
<0.0
01
6,80
0
(9.9
) 31
7
(14.
1)
<0.0
01
156
(1
1.5)
22
0
(15.
5)
0.00
212
9
(14.
5)
174
(1
4.0)
0.
762
P-va
lues
est
imat
ed b
y AN
OVA
or C
hi S
quar
e te
sts.
*P
erso
ns w
ith p
olyt
hera
py e
xclu
ded.
† N
SAID
s, n
on-s
tero
idal
ant
i-inf
lam
mat
ory
drug
s.
† CO
PD, c
hron
ic o
bstru
ctiv
e pu
lmon
ary
dise
ase.
73
5.1.2 Incidence and prevalence of AED use
The incidence and prevalence of AED use had an upward trend from the beginning of the follow-up until the time of AD diagnosis in both persons with and without AD (Figure 7-8). The incidence of AED use started to increase two years before AD diagnosis. After AD diagnosis, the AED initiation rate peaked during the first 6 months from approximately 1.9 initiations to 2.5 per 100 person-years, whereas among persons without AD, the incidence rate increased up to 1.5 initiations per 100 person-years during the same period. The difference between cohorts was less evident after two years since AD diagnosis until the end of follow-up (Figure 7). The prevalence rate increased until the end of follow-up and reached the maximum of 7.4% in the AD cohort and 5.1% in the persons without AD (Figure 8).
Figure 7. Incidence of antiepileptic (AED) use in relation to Alzheimer’s disease (AD) diagnosis. Reproduced with permission from IOS Press. The publication is available at IOS Press through http://dx.doi.org/10.3233/JAD-180594.
74
Figure 8. Prevalence of antiepileptic (AED) use in relation to Alzheimer’s disease (AD) diagnosis. Reproduced with permission from IOS Press. The publication is available at IOS Press through http://dx.doi.org/10.3233/JAD-180594.
5.1.3 Incidence of epilepsy diagnosis
The incidence of epilepsy diagnosis among persons with AD increased in parallel with the incidence of AED use, reaching a maximum of 0.5 per 100 person-years at 6 months after AD diagnosis; this value was higher as compared to persons without AD (0.15 per 100 person-years). However, this increase in epilepsy diagnosis was much lower and only partially explained the increase in the incidence and prevalence of AED use.
75
5.2 STUDIES II AND III
5.2.1 General characteristics of study cohorts
The cohort of the Study II involved 5167 incident users and 5638 incident AED users who were included in Study III (Table 12). As compared to matched non-users, AED users in both studies had more often chronic cardiovascular diseases, mental and behavioral disorders such as depression and bipolar disorders; and almost all diagnoses of epilepsy were observed among them (Table 12). AED users were also more likely to use antidepressants, antipsychotics, benzodiazepines and related drugs as well as NSAIDs. These differences were balanced after IPT weighting.
In Study II, mean follow-up time was shorter among users compared to non-users (351.7 days vs 728.6 days respectively) with the median time of AED use being 181 days (1-1095 days). AED discontinuation (54.7%) was the most common reason for censoring among AED users whereas non-users censored most often after three years of follow-up (39.3%). In Study III, 48.7% had discontinued AED use within six months.
76 Tabl
e 12
. Gen
eral
cha
ract
eris
tics
of S
tudi
es II
and
III c
ohor
ts o
f per
sons
with
AD
acc
ordi
ng to
AED
use
.
S
tudy
II (n
=10,
334)
S
tudy
III (
n=11
,276
)
A
ED
use
rs
(n=5
,167
) N
on-u
sers
(n
=5,1
67)
IPT
wei
ghte
d S
tand
ardi
zed
Diff
eren
ce*
AE
D u
sers
(n
=5,6
38)
Non
-use
rs
(n=5
,638
)
IPT
wei
ghte
d S
tand
ardi
zed
Diff
eren
ce*
Age
(yea
rs),
mea
n (S
D)
80.7
±7.6
2 80
.7±7
.54
0.1
81.4
±6.7
3 81
.4±6
.67
1.2
Age
(yea
rs),
n(%
)
N
A
0.
8
< 65
21
4 (4
.1)
211
(4.1
) 2.
5 96
(1.7
) 99
(1.8
) N
A65
‒74
830
(16.
1)
806
(15.
6)
2.7
838
(14.
9)
802
(14.
2)
NA
75‒8
4 2,
543
(49.
2)
2,56
8 (4
9.7)
0.
3 2,
927
(51.
9)
2,97
6 (5
2.8)
N
A≥
85
1,58
0 (3
0.6)
1,
582
(30.
6)
0.6
1,77
7 (3
1.5)
1,
761
(31.
2)
NA
Wom
en, n
(%)
3,46
8 (6
7.1)
3,
468
(67.
1)
0.6
3,74
4 (6
6.4)
3,
744
(66.
4)
0.7
Tim
e si
nce
AD
diag
nosi
s† , mea
n (S
D)
1,02
1.9±
801.
6 1,
022.
2±80
0.7
1.0
1,77
3.2±
726.
2 1,
773.
2±72
5.7
1.6
Occ
upat
iona
l so
cioe
cono
mic
pos
ition
‡ , n(
%)
NA
1.3
Man
ager
ial/p
rofe
ssio
nal
2,24
4 (4
3.4)
2,
158
(41.
8)
3.7
2,49
7 (4
4.3)
2,
400
(42.
6)
NA
Offi
ce
838
(16.
2)
919
(17.
8)
2.0
951
(16.
9)
1,03
1 (1
8.3)
N
A(C
ontin
ued)
77 Tabl
e 12
(con
tinue
d).
Farm
ing/
fore
stry
44
0 (8
.5)
480
(9.3
) 1.
4 48
5 (8
.6)
481
(8.5
) N
ASa
les,
indu
stria
l, cl
eani
ng
1,20
0 (2
3.2)
1,
185
(22.
9)
1.8
1,24
4 (2
2.1)
1,
254
(22.
2)
NA
Unk
now
n, n
o re
spon
se
445
(8.6
) 42
5 (8
.2)
0.2
461
(8.2
) 47
2 (8
.4)
NA
Use
of c
onco
mita
nt m
edic
atio
ns, n
(%)
Antid
epre
ssan
ts
2,22
8 (4
3.1)
1,
488
(28.
8)
2.9
2,44
1 (4
3.3)
1,
644
(29.
2)
1.8
Antip
sych
otic
s 1,
968
(38.
1)
1,19
2 (2
3.1)
3.
4 2,
098
(37.
2)
1,26
6 (2
2.5)
0.
7
Benz
odia
zepi
nes
and
rela
ted
drug
s 2,
324
(45.
0)
1,34
3 (2
6.0)
2.
2 2,
509
(44.
5)
1,50
9 (2
4.8)
3.
7
Antit
hrom
botic
age
nts
1,33
0 (2
5.7)
1,
157
(22.
4)
0.1
1,73
6 (3
0.8)
1,
490
(26.
4)
2.3
Non
-ste
roid
al a
nti-
infla
mm
ator
y dr
ugs
1,22
4 (2
3.7)
70
1 (1
3.6)
2.
0 1,
307
(23.
2)
797
(14.
1)
3.4
Com
orbi
dity
dia
gnos
is, n
(%)
Car
diov
ascu
lar
dise
ases
Hyp
erte
nsio
n 2,
305
(44.
6)
2,12
1 (4
1.1)
1.
3 2,
757
(48.
9)
2,50
6 (4
4.5)
0.
2
Cor
onar
y ar
tery
dis
ease
1,
511
(29.
2)
1,34
7 (2
6.1)
4.
1 1,
802
(32.
0)
1,60
5 (2
8.5)
0.
8
Chr
onic
hea
rt fa
ilure
68
1 (1
3.2)
64
0 (1
2.4)
0.
2 84
6 (1
5.0)
72
8 (1
2.9)
1.
6
(Con
tinue
d)
78 Tabl
e 12
(con
tinue
d).
Atria
l fib
rilla
tion
901
(17.
4)
821
(15.
9)
2.3
1,00
0 (1
7.7)
90
5 (1
6.0)
1.
0
Men
tal d
isor
ders
Dep
ress
ion
or b
ipol
ar
diso
rder
30
7 (5
.9)
177
(3.4
) 1.
4 47
4 (8
.4)
274
(4.9
) 2.
0
Schi
zoph
reni
a 34
(0.7
) 27
(0.5
) 0.
7 15
7 (2
.8)
97 (1
.7)
2.4
Subs
tanc
e ab
use
166
(3.2
) 10
6 (2
.1)
5.5
274
(4.9
) 18
8 (3
.3)
1.1
Oth
er d
isea
ses
Dia
bete
s 1,
080
(20.
9)
978
(18.
9)
0.6
1,21
5 (2
1.6)
1,
080
(19.
2)
0.6
Asth
ma/
CO
PD
642
(12.
4)
573
(11.
1)
0.9
648
(11.
5)
547
(9.7
) 2.
9
Rhe
umat
oid
arth
ritis
25
5 (4
.9)
225
(4.4
) 0.
8 28
7 (5
.1)
264
(4.7
) 1.
1
Epile
psy
442
(8.5
) 17
(0.3
) 6.
8 50
0 (8
.9)
19 (0
.3)
3.1
Hea
d in
jurie
s 36
1 (7
.0)
269
(5.2
) 4.
9 47
1 (8
.4)
366
(6.5
) 1.
2
Hip
frac
ture
35
9 (7
.0)
270
(5.2
) 1.
2 46
7 (8
.3)
348
(6.2
) 1.
7
His
tory
of c
ance
r N
AN
AN
A62
9 (1
1.2)
52
1 (9
.2)
0.2
AD, A
lzhe
imer
Dis
ease
; AED
, ant
iepi
lept
ic d
rug;
CO
PD, c
hron
ic o
bstru
ctiv
e pu
lmon
ary
dise
ase.
N
A, n
ot a
pplie
d *IP
T, in
vers
e pr
obab
ility
of tr
eatm
ent w
eigh
ting
(mea
ning
ful d
iffer
ence
> 1
0.0)
† Ti
me
from
AD
dia
gnos
is to
the
date
of a
ntie
pile
ptic
dru
g in
itiat
ion
of th
e co
rresp
ondi
ng u
ser.
‡ H
ighe
st o
ccup
atio
nal s
ocia
l cla
ss p
rior A
D d
iagn
osis
.
79
5.2.2 Association between AED use and stroke
Risk of stroke overall and sub-divided by type of stroke: In total, 328 cases of stroke occurred during the follow-up, resulting in
incidence rates of 2.75 (95% confidence interval (CI): 2.33‒3.26) per 100 person-years among users and 1.85 (95% CI: 1.61‒2.14) per 100 person-years among non-users. After applying IPT weighting, the use of AEDs was associated with a 37% increased relative risk of stroke as compared to non-use (IPTW HR: 1.37; 95% CI: 1.07‒1.74). In addition, in analyses stratified by type of stroke, the AED use was related to an increased risk of ischemic strokes (IPTW HR: 1.34, 95% CI: 1.00‒1.79), and hemorrhagic strokes (IPTW HR: 1.44, 95% CI: 0.86‒2.43). The number of hemorrhagic strokes (n=30) was considerably smaller than the number of ischemic strokes (n=104); this may explain the wider confidence intervals for hemorrhagic stroke.
Risk of stroke according to duration of AED use: The risk of stroke was highest during the first 90 days after AED initiation
(adjusted HR: 2.36, 95% CI: 1.25‒4.47), and decreased in longer-term use (adjusted HR for 6 months use: 1.80, 95% CI: 1.00‒3.24) (Figure 9).
80
Figure 9. Relative risk of stroke with 95% confidence intervals according to the duration of AED use.
Risk of stroke according to type of AED: Differences between individual AEDs in comparison to valproic acid were not
statistically significant. Users of older AEDs did not differ with respect to the risk of stroke from users of newer AEDs (Figure 10).
Figure 10. Relative risk of stroke with 95% confidence intervals by individual AEDs (reference; valproic acid) and type of AEDs (older vs newer).
5.2.3 Association between AED use and mortality
Risk of death overall and cause-specific: In total, 2182 persons died during the 3 years of follow-up (mortality rate, 95%
CI); 14.5, 13.5‒15.5 among users and 11.2, 10.7‒11.8 per 100 person-years among non-users. After applying IPT weighting, AED use was associated with a 23% increased relative risk of death compared to non-use (IPTW HR: 1.23; 95% CI: 1.12‒
81
1.36). In addition, when the analyses were stratified by causes of death, AED use was related to an increased risk of death from dementia and Alzheimer’s disease causes (IPTW HR: 1.62, 95% CI: 1.42‒1.86), but not to death due to cardiovascular and cerebrovascular or other causes (IPTW HR: 1.01, 95% CI: 0.86‒1.19).
Risk of death according to duration of AED use: The association between AED use and all-cause mortality was strongest during
the first three months after AED initiation (IPTW HR: 2.40, 95% CI: 1.91‒3.03), and diminished during the next 90-day interval (IPTW HR for 3-6 months use: 1.58, 95% CI: 1.22‒2.06) (Figure 11).
Figure 11. Relative risk of all-cause mortality with 95% confidence intervals according to the duration of AED use.
Risk of death according to type of AED: Individual AEDs differed in all-cause mortality (Figure 14). Users of pregabalin
(IPTW HR: 0.56, 95% CI: 0.40‒0.78), gabapentin (IPTW HR: 0.31, 95% CI: 0.13‒0.72) and clonazepam (IPTW HR: 0.48, 95% CI: 0.26‒0.89) had a lower risk of death in comparison to valproic acid users. Differences between other AED substances were
82
not statistically significant. The use of older AEDs was associated with a 79% higher relative risk of death (IPTW HR: 1.79, 95% CI: 1.52‒2.16) compared to the use of newer AEDs (Figure 12).
Figure 12. All-cause mortality with 95% confidence intervals by individual AEDs (reference; valproic acid) and type of AEDs (older vs newer).
83
6 DISCUSSION
Summary of results
The incidence and prevalence of AED use steadily increased around the time of AD diagnosis and this was not explained by new diagnoses of epilepsy. Persons with AD used more AEDs and their use of older AEDs was more common when they were compared with persons without AD. In addition, persons with AD using AEDs had a 37% increased relative risk of stroke and a 23% increased relative risk of death compared with matched nonusers. The risk was particularly elevated during the first six months of AED use. Discontinuation of AED treatment was frequent. Both older and newer AEDs were associated with a similar risk of stroke, while mortality was considerably higher among users of older AEDs. These are the first studies that have evaluated the pattern and possible risks of AED use in people with Alzheimer’s disease.
6.1 DISCUSSION OF RESULTS
6.1.1 Incidence and prevalence of AED use
The incidence and prevalence of AED use increased from 5 years before AD diagnosis until the end of follow-up. The same increasing pattern was observed in persons without AD but persons with AD had a higher increase in the incidence and prevalence of AED use at the time of AD diagnosis. These findings are in accordance with a previous Finnish study on community dwellers (Bell et al., 2011), the only previous study that investigated the annual prevalence of AED use among persons with AD. In that previous study, the use of AEDs was more prevalent in persons with AD compared to persons without AD (Bell et al., 2011). However, the overall number of prevalent AED users has increased and was higher in our study.
6.1.2 Possible indications for AED use
The main indication for older AEDs is epilepsy (National Institute for Health and Care Excellence, 2016, February), and both older and newer AEDs are also used for other indications including treatment of BPSD and neuropathic pain as well as migraine prophylaxis (Bartolini et al., 2005; Bialer, 2012; Yeh & Ouyang, 2012).
In Study I, the incidence of diagnosed epilepsy increased in persons with AD, but the increase was significantly less pronounced as compared to the incident AED use. Therefore, epilepsy does not explain our findings for the increased incidence
84
and prevalence of AED use in persons with AD. However, it has been shown that persons with AD are more predisposed to seizures than persons without AD, and the incidence of seizures has almost doubled in AD persons (Cheng et al., 2015). The occurrence of seizures had been detected even in the early stage of AD (Beagle et al., 2017) i.e. in the absence of an established AD diagnosis and cognitive symptoms (Amatniek et al., 2006; Sarkis, Dickerson, Cole & Chemali, 2016). The diagnosis of seizures in AD persons is challenging, as most of the unprovoked seizures are non-convulsive (Sarkis et al., 2016), therefore, it is possible that in our study, there are some unidentified epilepsy cases as well as some misdiagnoses of epilepsy.
The use of older AEDs was more prevalent among persons with AD than in persons without AD. The most frequently used older AED among persons with AD was valproic acid, whereas carbamazepine was being used more commonly in the non-AD cohort. In a previous Finnish study, the older AEDs (i.e., valproic acid and carbamazepine) were also the primary choice among older persons with newly diagnosed epilepsy (Bruun, Virta, Kalviainen & Keränen, 2015). Older AEDs including carbamazepine and valproic acid are recommended as a first line therapy for generalized and potentially for focal seizures in older adults (Glauser et al., 2013; National Institute for Health and Care Excellence, 2016). The use of older AEDs for indications other than epilepsy is less common. It has been postulated that carbamazepine and valproic acid can be used in BPSD treatment (i.e., agitation and aggression) (Rabins et al., 2007), however, their use is limited due to their less favourable profile as compared to other treatment options (Yeh & Ouyang, 2012).
Pregabalin was the most frequently used AED in our cohort. Previous studies on AED use in older persons have also described a shift from the use of older AEDs towards newer AEDs in the last decades (Oteri et al., 2010; Theitler et al., 2017). Gabapentinoids (i.e., pregabalin and gabapentin) are mainly used for neuropathic pain and occasionally for generalised anxiety disorder treatment in older populations (Ben-Menachem, 2004; Hitiris & Brodie, 2006; Tassone, Boyce, Guyer & Nuzum, 2007; Wettermark, Brandt, Kieler & Bodén, 2014). Neuropathic pain is a prevalent condition in older persons (Fine, 2009; Roghani et al., 2019), which is often poorly managed due to the complexity of its diagnosis in persons with AD (de Tommaso et al., 2016). Untreated pain is a provocative factor for BPSD occurrence (Ahn & Horgas, 2013), therefore, its effective control is essential in persons with AD. When compared to other types of pain e.g. due to arthritis, osteoporosis, fractures, peripheral vascular and musculoskeletal diseases, neuropathic pain is characterized by different mechanisms and requires special management (Colloca et al., 2017). This disorder can occur due to injuries after mechanical trauma or have diabetic, toxic and inflammatory origins resulting in impaired afferent transmission of normal somatosensory stimuli from the peripheral nervous system to the spinal cord and onwards to thalamic and cortex
85
brain regions. These alterations in normal neurotransmission characterized by imbalance in excitatory and inhibitory processes together with other pathophysiological processes (i.e., inflammatory mediator release and glial cell activation) lead to neuronal hyperexcitability and the development of neuropathic pain (Colloca et al., 2017). Gabapentinoids bind to the α2δ-1 subunit of voltage-gated Ca2+ channels and inhibit excitabilty and, thus restore normal signal functioning contributing to pain relief (Patel & Dickenson, 2016). Although both gabapentinoids (i.e., pregabalin and gabapentin) are considered as first-line treatment options for neuropathic pain (Cruccu & Truini, 2017), pregabalin has a more favourable pharmacokinetic and dose-response profile, explaining its prevalent use in our cohort (Bockbrader et al., 2010). In addition, pregabalin and oxcarbazepine can be used for the treatment of agitation in the current Finnish guidelines as alternative therapeutic options in BPSD treatment ("Memory disorders: Current Care Guideline," 2017). Various BPSD might occur during the prodromal stage of AD, and use of other psychotropic drugs (Koponen et al., 2015; Puranen et al., 2017) at this stage is very frequent. Thus, these reasons might explain our findings on the increased incidence and prevalence of AED use in persons with AD around AD diagnosis.
6.1.3 Adverse outcomes associated with AED use
Regarding the possible risks of AED use in persons with AD, Study II showed that when these individuals were compared with non-users, the AED users had a 37% higher relative risk of stroke and a 34% increased relative risk of ischemic stroke, while there was no robust evidence to suspect an association with hemorrhagic strokes. However, the smaller number of hemorrhagic strokes means that we had less power to detect an association with this type of stroke. The incidence rate for all types of stroke was 2.75 (CI: 2.33 – 3.26) per 100 person-years among AED users and 1.85 (CI: 1.61 – 2.14) per 100 person-years meaning that AED use contributed to 9 extra strokes for each 1000 person-years in persons with AD. In association with the AED use, there was a prevalence estimate of 7.4% after AD diagnosis; this indicates that there were 45 excess strokes in AD persons using AEDs which is of clinical relevance.
None of previous studies investigated the risk of stroke among AED users with AD. A doubled risk of ischemic strokes was observed in persons with epilepsy treated with AEDs as compared with non-users in the general population (Olesen et al., 2011). In our study, the association with the risk of stroke was weaker compared to that previous study, however, our population was older and the epilepsy diagnosis was rather rare in our cohort. Newer and older AEDs have different safety profiles and the risk of stroke may vary between them. In our study, the users of older and newer AEDs had a similar relative risk of stroke, meaning that both AED types might result in an increased risk of stroke in persons with AD. The previous study by Renoux et al. (Renoux et al., 2015) investigated the risk of
86
vascular events between different AED types in the general population and found that the increased risk of ischemic strokes was present among users of CYP inducing AEDs like phenytoin, carbamazepine or phenobarbital as compared with users of non-inducing AEDs (i.e., gabapentin, levetiracetam, pregabalin, tiagabine, valproic acid and zonisamide).
CYP enzyme-inducing AEDs are known to increase the risk of thromboembolic events due to their potential to elevate serum levels of atherogenic markers (Mintzer et al., 2009) and to be involved in drug-drug interactions resulting in reduced serum concentrations of oral anticoagulants like warfarin, apixaban, dicoumarol and clopidogrel (Perucca, 2006) and drug interactions with dihydropyridine calcium-channel blockers and certain beta-blockers leading to a decrease in their antihypertensive effects (Zaccara & Perucca, 2014). In our cohort, the use of CYP-inducing older AEDs was less common and valproic acid, which has enzyme-inhibiting properties, was the most frequently used older AED in our cohort, therefore, these findings might only partly explain our results. The use of valproic acid was associated with a decreased risk of incident ischemic stroke compared with the use of carbamazepine in persons with epilepsy (Olesen et al., 2011) and recurrent ischemic strokes in persons with previous history of stroke (Brookes et al., 2018). However, the concomitant use of valproic acid with certain oral anticoagulants might result in an increased risk of bleeding complications and elevate risk of stroke (Johannessen & Landmark, 2010). In addition, valproic acid possesses a proatherogenic property as it induces insulin resistance, metabolic syndrome and increases oxidative stress (Belcastro, D'Egidio, Striano & Verrotti, 2013). Newer AEDs might also contribute to an increased risk of stroke and explain our results. Pregabalin as a newer AED was used most frequently in our study. Pregabalin is excreted renally and has no CYP activity (Bockbrader et al., 2010). However, despite its favourable profile, some investigators have claimed that pregabalin might elevate the risk of vascular events due to bodyweight gain and the increased risk of peripheral edema, contributing to the progress of heart failure (Hoppe, Rademacher, Hoffmann, Schmidt & Elger, 2008; Murphy, Mockler, Ryder, Ledwidge & McDonald, 2007; Zaccara, Gangemi, Perucca & Specchio, 2011).
In study III, incident AED use was associated with a 23% higher relative risk of death. The difference in incident rates between AED users (IR 14.5 (CI: 13.5-15.5) per 100 person-years) and non-users (IR 11.2 (CI: 10.7-11.8) per 100 person-years) indicates that there were 33 excess deaths per 1000 person-years and more than 150 deaths in the entire cohort of AED users with AD in our study. Previous studies (Duble et al., 2019; Nilsson et al., 1999) have investigated AED use in relation to sudden unexpected death in persons with epilepsy (SUDEP). Thus, none of them specifically assessed the risk of mortality in older persons with AD. In previous studies, the elevated risk of death in persons with epilepsy was explained by the use of multiple AEDs (Duble et al., 2019; Nilsson et al., 1999) and the mechanism of action of individual AEDs (Danielsson et al., 2005; Ishizue et al., 2016). In our study, the risk of death was still elevated after exclusion of persons with epilepsy, as only
87
a small proportion of persons had received an epilepsy diagnosis in our cohort. Thus, epilepsy did not explain our finding of the increased mortality among AED users. However, it is possible that in our cohort of AD persons where the diagnosis of epilepsy is challenging (Vossel et al., 2016), some seizures remained undiagnosed.
Several mechanisms have been proposed to link the use of AEDs with higher mortality. Previous studies have noted the pro-arrhythmic mechanisms of individual AEDs via herg potassium currents (i.e., lamotrigine, gabapentin and topiramate) (Danielsson et al., 2005) or due to sodium channel-blocking properties i.e. carbamazepine, phenytoin and phenobarbital (Ishizue et al., 2016; Timmings, 1998) which could potentially explain our results. Although an association between AED use and stroke was observed in Study II, when cause-specific mortality according to underlying causes of death was assessed, the increased risk of death was mainly explained by deaths from dementia and was not related to cardiovascular and cerebrovascular deaths in Study III. These causes were ascertained according to international classification rules, however, it is possible that in some cases, the immediate causes of death were cardiovascular and cerebrovascular diseases, while the underlying cause of death was either dementia or AD. Thus, the impact of other causes may have been underestimated.
In our study the use of older AEDs was associated with an almost doubled relative risk of death when compared with the use of newer AEDs. These results might be explained by different indications for AED use. Older AEDs such as valproic acid and carbamazepine might be selected for BPSD treatment in persons at a more advanced stage of AD in a situation where other treatment options are not effective (Yeh & Ouyang, 2012). Poor management of BPSDs together with concomitant use of antipsychotics for BPSD treatment may lead to increased mortality and explain our results of increased death from dementia-related causes (Koponen et al., 2017; Stella, Laks, Govone, de Medeiros & Forlenza, 2016). In contrast, newer AEDs and gabapentinoids, which were the most commonly used AEDs in our cohort, are often prescribed in the treatment of neuropathic pain (Marcum, Duncan & Makris, 2016). It is possible that they were administered to persons in a better health state, as the use of gabapentinoids is limited in persons with renal insufficiency (Italiano & Perucca, 2013; Perucca, 2007). However, in our study, the number of users for specific drug substances was not sufficient to allow us to confirm this suspicion and more research on this topic is needed.
6.1.4 Duration of AED use in relation to adverse outcomes
The risk of both stroke and mortality were at their highest during the first 180 days after the initiation of treatment with a doubling of the relative risk during the first 3 months. The risks of life-threatening cardiovascular and neurotoxic complications might be particularly elevated as a response to AED initiation due to altered age-related pharmacokinetic and pharmacodynamics of AEDs and the high
88
frequency of polypharmacy and comorbidity in older persons with AD (Perucca, 2007; Perucca et al., 2006). There was a rather frequent use of gabapentinoids (i.e., pregabalin and gabapentin) in our cohort. It has been shown that the initiation of gabapentinoids would be associated with a tripled relative risk of atrial fibrillation and thus a risk of stroke during the first 90 days of their use as compared with the initiation of analgesic opioids or benzodiazepines in older persons (Ortiz de Landaluce et al., 2018). The use of gabapentin at higher doses (>600 mg) as compared to its initiation at lower doses (≤600 mg) during the first 30 days of use has been reported to increase the risk of hospitalizations due to impaired mental status in older persons (Fleet et al., 2018), and these findings might partly explain the increased risk of death from dementia in our study. As with most nervous system drugs, the use of both older and newer AEDs was associated with elevated risk of falls and injury-related deaths in older persons (Haasum & Johnell, 2017; Maximos, Chang & Patel, 2017), and the the stronger association with the current or recent AED use (Tsiropoulos et al., 2008) is supporting our results.
The low short-term tolerability and high AED discontinuation rate may explain the lack of association between the use of AEDs and the risk of stroke and mortality after 6 months of AED use, as only around 50% of AED users continued AED treatment beyond six months. The low tolerability of AED treatment due to adverse effects has been demonstrated in previous studies, where only every second AED user remained on AED treatment after 12 months of AED use (Zeber, Copeland & Pugh, 2010) with the majority of persons experiencing adverse effects of AEDs within the first of 3 to 11 months of AED use (Arif et al., 2010). In addition, it is also possible that AEDs were withdrawn due to an improvement of the clinical conditions for which they had been prescribed (i.e., BPSD symptoms and neuropathic pain), and those who continued AED treatment might be a somewhat selected population represented by people in a better health stage and tolerance to AED treatment. Although the register-based data lacks the information on exact reasons for AED termination, the discontinuation of AED use was accounted for in the analyses by censoring users at the time of discontinuation. Although it is possible that abrupt discontinuation of long-term AED use may lead to severe adverse effects (even death) particularly in terms of epilepsy indications, it is likely that many of those who discontinued the use had a very short duration of use before discontinuation and were likely receiving a very low starting dose for indications other than epilepsy.
89
6.2 METHODOLOGICAL CONSIDERATIONS
6.2.1 Cohort profile and generalizability
The strength of this study is that our cohort is nationally representative and included all community-dwellers who received a clinically verified diagnosis of AD. The Finnish Special Reimbursement register covers all citizens without limitation to the residential area or socioeconomic status (Tolppanen et al., 2016). The accuracy (97.1%) and sensitivity (63.5%) of AD diagnosis in this register have been validated previously (Solomon et al., 2014). In that validation study, the diagnosis of AD was performed with a three-stage screening method, meaning that the persons could be diagnosed with AD even in early disease stages, which explains the low sensitivity compared to accuracy. The Finnish current care guidelines recommend acetylcholinesterase inhibitor/ memantine treatment for persons with mild/moderate Alzheimer’s disease ("Memory disorders: Current Care Guideline," 2017). Thus, our cohort includes all persons for whom the acetylcholinesterase inhibitor or memantine initiation was considered and it is not restricted to users only. The number of persons receiving the special reimbursement for acetylcholinesterase inhibitor/memantine corresponds well with the national estimate of incident AD cases ("Memory disorders: Current Care Guideline," 2017), especially after 2007 (Tolppanen et al., 2016).
Persons with the mixed form of dementia (AD with vascular lesions or AD with signs of Lewy body dementia) are also eligible for a special reimbursement and thus our study includes also persons with mixed dementia, given that the main cause is AD. As our study was restricted to persons who were community-dwelling at the time of AD diagnosis, the results may not be generalizable to institutionalized persons. We excluded institutionalized persons as their drug exposure status could not be reliably ascertained (Solomon et al., 2014). For the same reason, long hospital stays were used as an exclusion criterion and as a reason for ending the follow-up in order to avoid misclassification of the AED exposure. However, informative censoring (i.e. ending the follow-up due to long hospitalization and outcome event occurring during long hospital care) might lead to an underestimation of our results in Studies II and III. We excluded persons with previous AED use (during one year) to avoid a prevalent user bias (Ray, 2003), because a person who had stayed on AED treatment for a longer time before the follow-up was more likely to tolerate the drug and his/her inclusion would have biased the results. However, with the incident user design approach, in general, the majority of new users stay on the treatment for a limited period of time which restricts the assessment of the effect of long-term treatment on the outcome of interest (Lund, Richardson & Sturmer, 2015).
6.2.2 Drug exposure assessment
AED use was identified by utilizing the data on purchased drugs from the
90
Prescription Register instead of self- /or proxy-reported data, or data on merely prescribed drugs. Thus, a recall bias was avoided. The drug purchases were modelled to AED use periods with the PRE2DUP method which has been shown to have good validity for most of the central nervous system medications (Taipale et al., 2016) and a higher accuracy in correctness of drug use periods compared with other published methods (Tanskanen et al., 2017). However, the higher validity of the PRE2DUP method was shown for the drug used on a regular basis and it might be that the short-term use of AEDs for some indications other than epilepsy may not be correctly estimated. The robustness of the PRE2DUP modelled AED use was evaluated by comparison with postmortem toxicological findings and showed a good agreement between two methods (overall AED use Cohen’s kappa 0.63) (Forsman et al., 2018). In the study of Forsman et al., the actual adherence at the time of death and differences in pharmacokinetic properties of the investigated drugs (i.e. biological half-life) might impact on their postmortem detection and explain their results.
In longitudinal studies, the drug exposure is time-dependent and the valid estimation of duration of drug use in relation to the outcome occurrence is essential. In addition, a general limitation of the prescription register is that the information on the actual drug use time period, the exact date of the treatment discontinuation and adherence to the treatment (i.e., willingness to take medications as instructed) is lacking. However, instead of fixed assumption methods, AED use was modeled with the PRE2DUP method. This method constructs continuous drug use periods and calculates the sliding average of daily dose in DDDs based on personal drug purchase history, accounting for purchase regularity, doses change, stockpiling and periods of hospitalizations and/or institutionalization (Tanskanen et al., 2015). Nonetheless, the approximation of the average dose per day may not represent the actual used dose, which may vary over the time and the limited our ability to perform dose-dependent analyses in Studies II and III. In addition, the definition of actual adherence would require data on dosing instructions stated by the prescriber (and possible changes to them after the prescription was written) and this information is not recorded in the Prescription register. However, in our study population persons with AD very likely have assistance in drug administration (by home care or a caregiver) as they are incapable of managing these kinds of tasks any longer due to cognitive decline and, therefore, their drug use is less impacted by non-adherence than in the general population.
6.2.3 Limitations
Like most register-based data, our data lacks information about symptoms and the severity of AD. In order to minimize this limitation, we selected the matched non-user comparison persons based on age, sex and time since AD diagnosis, which was used as a proxy for the AD stage. Data on comorbidity diagnoses obtained from the Care Register for Health Care and Special Reimbursement
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Register, as well as information on concomitant use of other medications obtained from the Prescription Register allowed us to account for different diagnoses. Although register-based data lack information on lifestyle factors such as smoking, BMI and nutritional status, the extensive covariate adjustment and IPT weighting applied in the design of our models likely minimized these limitations.
In Study II, we limited the study to incident strokes to avoid bias caused by possible impairments persisting from a past stroke, and their possible impacts on drug use (Ray, 2003). Although the validity of stroke diagnoses in the Care Register for Health Care is high (Tolonen et al., 2007), there is a risk that some cases of stroke were misdiagnosed or undiagnosed among persons with AD and therefore, not included in our analyses. Non-differential diagnostic misclassification of outcome might potentially mask a true association and lead to some underestimation but it would not affect the relative risk point estimate.
Coding of causes of death for mortality statistics has been validated previously and was appropriate (Lahti & Penttilä, 2001). However, in Study III, it might be that some causes of death from cardiovascular or cerebrovascular diseases were limited to underlying causes from AD and therefore the association with deaths from vascular causes could be diluted. Study III was restricted to underlying causes of death, as their classification was based on the international standards established by WHO.
To describe the possible indication for AED use, we reported the incidence of epilepsy in relation to the AD diagnosis date in Study I. The epilepsy diagnoses were based on Special Reimbursement register data and were validated in terms of the steps required to verify the diagnosis with the SII. The analyses conducted in Study II were adjusted for epilepsy diagnoses whereas in Study III, subgroup analyses were conducted among those without epilepsy. However, we were not able to describe the use of AEDs in relation to types and severity of epilepsy, nor could we analyse the risk of adverse outcomes in persons with epilepsy due to the small sample size.
In addition, we lacked data on other possible AED indications, particularly in terms of the use of the newer AEDs. Therefore, some confounding by indication may not be excluded and could have led to an overestimation of our results in Studies II and III. It is possible that symptoms for which AEDs were initiated may reflect disease severity and also be associated with an increased risk of stroke and mortality. We addressed these differences by extensive covariate control, which accounts for measured confounders. As registers do not contain data on certain issues, for example the severity of AD or BPSDs, we used antidepressants, benzodiazepines and related drugs as well as antipsychotics as a proxy for BPSDs. In addition, matching with time since AD diagnosis was performed to account for duration of AD. These factors, in addition to other measured comorbidities and medications were balanced between users and matched non-users with IPT weighting and thus, controlled for in the analyses.
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7 CONCLUSIONS
Summarizing the results of this thesis, the following conclusions can be drawn: 1. The higher incidence and prevalence of AED use in persons with AD,
particularly around the time of AD diagnosis, was not explained by epilepsy. 2. In persons with AD:
- Initiation of AED use was associated with an increased risk of stroke and mortality, and the risk was highest during the first six months after the initiation of AED therapy.
- Users of older and newer AEDs did not differ in their risk of stroke, although the mortality was higher among users of older AEDs as compared to users of newer ones, and different indications might play a role in explaining these differences. These findings highlight the need for careful consideration prior to AED initiation for some indications other than epilepsy in this vulnerable population.
7.1 CLINICAL IMPLICATIONS
1. Older AEDs have a narrow therapeutic window and potentially multiple pharmacokinetic drug-drug interactions, and persons with AD, may be susceptible to the adverse effects and events associated with the older AEDs. Therefore, their use in this population should be avoided, if possible.
2. If AED treatment is necessary, newer AEDs should be preferred if possible.
3. The occurrence of major complications such as stroke and mortality even with short-term AED use, should be borne in mind before prescribing AEDs to older individuals with cognitive disorders.
7.2 FUTURE DIRECTIONS
1. The indications of AED prescriptions in persons with AD should be studied.
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2. The risks of stroke and mortality associated with the incident AED use in persons with AD should be confirmed in further studies. More studies are needed to clarify the mechanisms behind these associations.
3. It is necessary to assess the risks of major adverse events between individual AEDs.
4. The efficacy, effectiveness and safery of specific AEDs in the treatment of BPSD should be clarified.
5. It would be important to determine whether unrecognized and untreated seizures are associated with a more rapid cognitive decline in persons with AD.
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ISBN 978-952-61-3262-4ISSN 1798-5706
Antiepileptic drugs are used for other indications
than epilepsy including behavioural and psychiatric symptoms of dementia. The trend of use and safety of antiepileptics in people with
Alzheimer’s disease remain poorly investigated.This nationwide register-based cohort study
explored the incidence and prevalence of antiepileptic drug use through the comparison between matched community-dwellers with and
without Alzheimer’s disease. The study also evaluated associations between antiepileptic drug use and the risk of stroke and mortality
in this vulnerable population.
TATYANA SARYCHEVA