Associations of brain amyloid load with clinical characteristics in an older general population without dementia
Gazi Saadmaan Hossain Master’s Thesis Master’s in Public Health University of Eastern Finland Faculty of Health Sciences School of Medicine May 2020
Department of Public health
GAZI SAADMAAN, HOSSAIN: Associations of brain amyloid load with clinical characteristics in an older general population without dementia.
Supervisors: Associate Professor Alina Solomon, Anette Hall, Professor Tomi-Pekka Tuomainen
May 2020
Key words: Dementia, Brain Amyloid PET Scan, Cognitive Function.
Abstract
Alzheimer’s disease (AD) is the most common form of dementia. It affects memory, behavior, cognition and ultimately daily life activities. Pathological features of AD include cerebral neuronal loss, intraneuronal neurofibrillary tangles and extracellular amyloid plaques. These abnormal changes start to occur years before the symptoms begins to appear. Most studies have so far investigated the role of amyloid status on brain PET scans in individuals who already have cognitive impairment or dementia. The aim of this study was to evaluate associations between brain amyloid status and cognitive performance in an at-risk population without dementia or substantial cognitive impairment.
From the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) main study, 48 individuals participated in the 11C-Pittsburgh compound B (PiB)-PET exploratory sub-study. PET scans were assessed visually by two experienced readers based on the regional patterns of PiB retention. A comprehensive cognitive test battery was used to assess the cognitive performance.
Results from this thesis suggest that positive amyloid status is likely to reduce cognitive abilities over time. APOE genotype was strongly associated with amyloid status but no other factors had significant relation with amyloid positivity. High percentage of amyloid positive individuals showed successful recruitment of participants at risk for dementia. However, future studies with larger sample sizes and longer follow up period will be needed to further investigate the impact of amyloid accumulation in people with normal cognition.
Aβ: Amyloid Beta
Aβ42: 42-Amino Acid Form of Amyloid β Peptide ACR: Annual Conversion Rates
AD: Alzheimer’s Disease
ADD: Alzheimer’s Disease Dementia ADL: Activities of Daily Living APOE: Apolipoprotein E
APOE-ε2: ε2 Allele of Apolipoprotein E APOE-ε4: ε4 Allele of Apolipoprotein E APP: Amyloid Precursor Proteins BMI: Body Mass Index
CAIDE: Cardiovascular Risk Factors, Aging and Incidence of Dementia CERAD: Consortium to Establish a Registry for Alzheimer’s Disease CI: Confidence Interval
CRP: C-Reactive Protein CSF: Cerebrospinal Fluid CT: Computed Tomography CVD: Cardiovascular Disease DVR: Distribution Volume Ratio DLB: Dementia with Lewy Bodies DM: Diabetes Mellitus
DSM-V: Diagnostic and Statistical Manual of Mental Disorders, 5th Edition FDG: Fluorodeoxyglucose
FINGER: Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability
ICD-11: International Classification of Diseases, 11th Revision MCI: Mild Cognitive Impairment
MMSE: Mini Mental State Examination MRI: Magnetic Resonance Imaging NFTs: Neurofibrillary Tangles NTB: Neuropsychological Battery OR: Odds Ratio
PD: Parkinson’s Disease
PET: Positron Emission Tomography PiB: Pittsburgh Compound-B
P-tau: Phosphorylated Tau ROI: Region of Interest SD: Standard Deviation
SUVR: Standardized Uptake Value Ratio T-tau: Total Tau
VCI: Vascular Cognitive Impairment WHO: World Health Organization
WMS-R: Wechsler Memory Scale – Revised
“Think deeply about the wonders and creation of this universe” (Quran 3:191)
“Allah will raise those who have believed among you and those who were given knowledge, by degrees” (Quran 58:11)
“And certainly, did We create man from an extract of clay. Then We placed him as a sperm- drop in a firm lodging. Then We made the drop into an alaqah (leech-like, or suspended thing, or blood clot), then We made the alaqah into a lump (of flesh), and We made (from) the lump, bones, and We covered the bones with flesh; then We developed him into another creation”
(Quran 23:12-14)
I am most grateful to the Almighty for all His blessings.
Thank you Abbu and Ammu for everything. Your beliefs and prayers have kept me going. Ammu you are my inspiration and Abbu you are my role model. My siblings, Mohib and Tusi, thank you for supporting me and loving me throughout the journey from childhood. It has been an honor to be your elder brother but like a little brother. I would like to thank all my friends from Bangladesh and Kuopio, you guys are my family. You are one of the best things in my life. My warmest thanks to my beautiful wife for her respect, love, and, understanding towards me.
Keep inspiring, encouraging, and supporting me in bad and good times like you always do. I miss and love you all.
Dear Professor Alina Solomon and Anette Hall thank you from the bottom of my heart. Your guidance, support, and confidence in me have been extraordinary. Your valuable advice and concerns regarding my future progression left me in awe. Thank you for my introduction to the scientific world. My sincere thanks go to my secondary supervisor, Professor Tomi-Pekka Tuomainen, for the suggestions regarding the thesis. I could not ask for better supervisors.
I want to thank the Department of Public Health, specially Annika Männikkö and Assistant Professor Sohaib Khan, for every little suggestion you provided during whole the graduation period. My seniors and friends, Anna and Blair, thank you for your intellectual advices from the beginning till now. I sincerely thank my “co-strugglers”, specially Carlos, Sara and Nallely from MPH studies. You reduced the stress through your continuous support with your intellect, humor and helping hand.
Thank you, all my teachers. You are the reason for the knowledge what I have and without you, I could not be where I am today. Lastly, I am utterly thankful to the researchers worldwide working in dementia, Alzheimer’s disease, and cognition. For your valuable work, I learned and could pursue my thesis relating to this topic. I hope our analysis could also contribute to this community battling cognitive diseases.
1. INTRODUCTION ... 1
2. REVIEW OF THE LITERATURE ... 3
2.1 COGNITION... 3
2.1.1 Cognition between normal aging and dementia ... 3
2.2 COGNITIVE DISORDERS ... 4
2.2.1 Dementia ... 4
Types of dementia ... 5
Demographics ... 5
Diagnostic criteria for dementia ... 6
Risk factors ... 6
Protective factors ... 9
2.2.2 Alzheimer’s disease ... 10
Changes in the brain associated with Alzheimer’s disease ... 11
Stages of Alzheimer's disease ... 12
Therapeutic approaches in AD ... 13
Biomarkers ... 15
Diagnosis criteria for Alzheimer's disease ... 15
2.3 AMYLOID AND TAU PROTEIN... 18
2.4 POSITRON-EMISSION TOMOGRAPHY (PET)SCAN ... 19
2.4.1 PiB uptake in normal aging ... 19
2.4.2 PiB uptake in mild cognitive impairment ... 20
2.4.3 PiB uptake in Alzheimer’s disease ... 21
2.4.4 PiB uptake in Parkinson's disease dementia and Lewy body dementia ... 22
3. AIMS OF THE STUDY ... 23
4. MATERIALS AND METHODS ... 24
4.1 SELECTION CRITERIA FOR FINGER PARTICIPANTS ... 24
4.2 THE FINGERPIB-PET EXPLORATORY STUDY ... 25
4.3 COGNITIVE AND OTHER CHARACTERISTICS RELEVANT FOR THIS PROJECT ... 25
4.3.1 Cognitive tests ... 25
4.3.2 Subjective cognitive complaints ... 26
4.3.3 Zung depression scale ... 26
4.3.4 Medical history ... 27
4.3.5 APOE genotype ... 27
4.4 STATISTICAL ANALYSES ... 27
5. RESULTS ... 29
6.1 PREVALENCE OF AMYLOID POSITIVITY AND RELATION TO APOE Ε4 CARRIER STATUS ... 35
6.2 AMYLOID STATUS AND COGNITION ... 36
6.3 AMYLOID STATUS AND OTHER POPULATION CHARACTERISTICS ... 37
6.4 STRENGTHS AND LIMITATIONS OF THE STUDY ... 38
7. CONCLUSIONS ... 40
8. REFERENCES ... 41
1. INTRODUCTION
Dementias are chronic and progressive neurological disorders with the symptoms of cognitive impairment and loss of functional independence. Although consciousness is not altered, cognitive skills such as language, orientation, calculation, learning, and judgement are affected.
Around 50 million people worldwide are suffering from some form of dementia (World Health Organization 2017) and every 4 seconds, there is a new case (Duthey 2013). These numbers are expected to reach 82 million by 2030, and 152 million in 2050 (World Health Organization 2017). This not only concerns the social but also the economic burden to society.
Among others, Alzheimer’s disease (AD) is the most common cause of dementia contributing to 60-70% of cases. The disease not only shortens life but also has become one of the leading causes of death in older people worldwide (Ganguli et al. 2005a). Symptoms such as forgetting recent events, discussions or names reveal the clinical onset of the disease. Afterward, symptoms like behavior change, difficulty in speaking, swallowing and walking become prominent (Alzheimer’s association 2018). Other causes of dementia include e.g.
cerebrovascular disease, Parkinson’s disease (PD), frontotemporal dementia (FTD), dementia with Lewy bodies (DLB). Dementia can also be caused by e.g. brain injury, alcohol-related dementia, temporal lobe epilepsy, and prion diseases such as Creutzfeldt–Jakob disease.
AD is a multifactorial disorder having different risk factors intertwined with each other. These range from non-modifiable factors (age, genetics, etc.) to various modifiable factors (sedentary lifestyle, high blood cholesterol levels, etc.). Irreversible damage of the neurons in the brain prohibits the normal cognition, functioning and behavior (Alzheimer's Association 2018).
Although the exact causes are still not fully clear, multidisciplinary research associate amyloid plaques and neurofibrillary tangles in the brain with the disease (Ballard et al. 2011). Amyloid beta (Aβ) fibrils, which represent one of the primary changes in AD, can deposit in brain decades before the onset of dementia (Sperling et al. 2011).
Early prevention or diagnosis is crucial for diseases like AD which take many years before the symptoms start to appear. Biomarkers will play a vital role in diagnosis and potential novel treatments as well as monitoring their effectiveness (McKhann 2011). Various methods are being investigated to find out the appropriate biomarkers.
Computed tomography (CT) and magnetic resonance imaging (MRI) are used to investigate atrophy of Alzheimer’s disease-prone regions the brain (Braskie & Thompson 2014). It has been proposed that positron emission tomography (PET) of glucose metabolism in the brain may be used as one of the markers of disease development (Kadir et al. 2011). Scientists have been working on several tracers to detect tau deposition on PET scans as well.
Amyloid status is frequently ascertained from PET and cerebrospinal fluid (CSF) nowadays by both the researchers and the clinicians. PET has the advantage of specifying the regional burden of amyloid in the brain, thus providing more detailed information. On the other hand, CSF reveals the concentration of total tau (T-tau), phosphorylated tau (P-tau) and 42-amino acid form of amyloid β peptide (Aβ42) which are associated with the disease (Blennow 2010).
Recent studies among people without dementia have shown that different regions of the brain tend to show increased Aβ accumulation (Palmqvist et al. 2017, Villeneuve et al. 2015). The exact significance of brain Aβ accumulation in people without cognitive symptoms is not fully clear. This can be only be addressed with further studies of amyloid PET in cognitively normal individuals.
Development of PET amyloid ligands like 11C-Pittsburgh Compound B (11C-PiB-PET) has made it possible to visualize the cerebral amyloid deposition of living individuals. It has provided the opportunity in obtaining longitudinal information about Aβ deposition in the initial pathological processes in cognitively normal older individuals. Following the first 11C-PiB- PET study which revealed one positive case among the controls (Klunk et al. 2004), later studies also have been showing consistent results (Chételat et al. 2013). Even though most of the healthy individuals show low retention, a few of them particularly show elevated retention in the regions where usually substantial amount of Aβ develops in AD patients (Chételat et al.
2013). The focus of this thesis is on at-risk individuals without dementia. However, both dementia and AD will be discussed in detail since most amyloid PET studies have so far focused on people with cognitive impairment or dementia due to AD.
2. REVIEW OF THE LITERATURE 2.1 Cognition
Cognition is defined in the Oxford English Dictionary as “The mental action or process of acquiring knowledge and understanding through thought, experience, and the senses”. It comprises a set of mental processes that are required to perform in everyday life. Cognition includes activities such as thinking, analyzing, remembering, planning, judging carried out by the human brain through which one becomes conscious about the situations, required actions, and further goals and uses those information to apply problem-solving approaches for an ideal living (Borson 2010). These wide ranges of functions have been categorized within domains of cognition. Usually, most of the cognitive functioning involves various domains engaging simultaneously.
Cognition is subdivided into different domains such as memory, attention, language, perception, motor skills, executive functions, and visuospatial abilities (Harvey 2019). These domains and their elements can be influenced by normal aging in several ways. Although various domains of cognitive functioning have been described, they are only theoretical constructs and not measurable physical systems. However, there are tests available to examine specific cognitive functions in an individual. The score is then matched to the general population normative sample.
Cognitive screening is one of the initial steps in identifying dementia. For that purpose, a variety of standardized cognitive screening instruments are used, including for example the MMSE, Similarities, Verbal Fluency, Delayed Recall, and Trail Making (Shulman et al. 2006). Mini- Mental State Examination (MMSE) is used most commonly to assess cognitive status briefly (Gluhm 2013). The total score is 30 points, and 24 is the recommendation which is often used as the cut-off score for dementia. Although MMSE has been a popular tool in detecting dementia, cut-off points for MMSE in nondemented individuals or those with mild cognitive impairment (MCI) which is a much less severe form of impairment have not been established yet (Gluhm 2013).
2.1.1 Cognition between normal aging and dementia
Several concepts have been introduced to distinguish the grey area between cognitively normal and dementia. This grey area is especially important given that different studies involving
cognitively normal at-risk individuals have linked gradual cognitive impairment with positive amyloid status. Currently, MCI is commonly used referring to those persons with normal Activities of Daily Living (ADL) but objective memory or cognitive impairment, and subjective memory or cognitive symptoms (Burns & Iliffe 2009, Werner & Korczyn 2008). Over time, from an exclusive focus on memory (Petersen et al. 1999) to including also other cognitive domain impairments, MCI has been defined in various ways. People with MCI do not fulfil diagnostic criteria for dementia. They usually preserve the basic activities of daily living or have minimal difficulties in complex activities (Winblad et al. 2004). However, they suffer from cognitive decline which is reported either by them or their relatives and decline or impairment is observed in objective cognitive tasks. The incidence and prevalence of MCI varies from study to study depending on whether it is in general or clinic-based populations.
Progression of individuals with MCI to AD dementia is more common than in individuals without any evidence of cognitive impairment. Nevertheless, the annual conversion rates among studies fluctuate from 3% to above 50% in populations characterized in accordance with Mayo Clinic MCI diagnostic criteria, averaging 8.1% in clinical populations and 6.8% in general population studies (Mitchell & Shiri‐Feshki 2009). Individuals with amnestic MCI also tend to develop dementia due to AD more than other dementia types (Winblad et al. 2004).
The Vascular Cognitive Impairment (VCI) concept refers to the effects of cerebrovascular disease (CVD) on cognition (Erkinjuntti & Gauthier 2009). As vascular and Alzheimer pathology frequently exist together, it can be difficult to differentiate between individuals having VCI or MCI (Snowdon et al. 1997).
2.2 Cognitive disorders 2.2.1 Dementia
Dementia is a syndrome characterized by a progressive deterioration in memory and impairment in different cognitive functions. In addition, the cognitive impairment is serious enough to hamper social or occupational functioning compared to an individual’s past grade of functioning (American Psychiatric Association 1994). The severity of the cognitive impairment is beyond age-specific decline. Cognitive along with functional decline or functional dependence leave the patients reliant on caregivers to carry out daily activities.
With aging, chances for both general and cerebral multimorbidities rise significantly (Jellinger
& Attems 2015). This applies to dementia as well. Proteinopathies responsible for dementia are strongly associated with advancing age (Jellinger & Attems 2015). The disease is highly prevalent in the elderly population over 60 years worldwide.
Types of dementia
Dementia is a general term used to represent several neurological disorders related to memory and cognition. Several pathologies contribute to the development of dementia. While different sets of diagnostic criteria exist, they have many similarities as well. Due to the overlapping of signs and symptoms of the various dementia types, and co-existing pathologies especially at older ages, identifying a single cause may be difficult in some cases (LoGiudice & Watson 2014). AD, Vascular dementia, Dementia from PD and similar disorders, DLB, FTD (Pick's disease), Creutzfeldt-Jakob disease, Mixed dementia are the most common types of dementia.
Demographics
With the increase of aging population worldwide, the numbers of people with dementia are increasing as well. Until now, there have been no effective disease-modifying drugs for AD and other types of dementia though the delayed onset of dementia would prove to be an advantage in the oldest population (Tom et al. 2015). Compared to high-income countries the number is quite higher in low and middle income countries (Alzheimer’s Disease International 2013, Prince et al. 2013). While countries like the USA, Canada, UK, Netherlands, and Sweden noticed a surprising reduction in age-related dementia incidence/prevalence, increasing incidence in China and growth in prevalence rate in Japan have also been reported (Livingston et al. 2017). At the same time, a study about the Nigerian population showed stability in both the prevalence and incidence (Gao et al. 2016). Worldwide meta-analyses and systematic reviews imply that the prevalence of dementia may be greater in Latin America and lower in Sub-Saharan Africa compared to the whole world (Hugo & Ganguli 2014). Data from these sources indicate that health and lifestyle might affect the total dementia risk in a population.
There are countries with 65+ years old populations showing healthier cognition than their ancestors either due to increasing exposure to protective factors or decreasing exposure to risk factors (Wu et al. 2017).
Diagnostic criteria for dementia
Most recent diagnostic criteria are defined in the World Health Organization’s (WHO) ICD-11 (World Health Organization 2018), the National Institute on Aging and the Alzheimer’s Association (NIA/AA) (McKhann et al. 2011) and the American Psychiatric Association’s DSM-V (American Psychiatric Association 2013). Although ICD 11 still uses the term
‘dementia’, DSM-V has adopted the terminology ‘major neurocognitive disorder’. They both have similar diagnostic criteria. DSM-V in particular emphasized the different etiological factors of dementia and broadened the disease staging to cover both major and mild neurocognitive disorders. The newer NIA/AA guidelines focus more on AD and represent a framework to be used primarily in interventional and observational research studies.
The 2011 NIA-AA diagnostic recommendations represented a major revision of previous criteria for the diagnosis of dementia published in 1984. The main reasons for this update were deeper understanding of the entire disease course including stages before dementia onset, and methodological/technical advances especially regarding biomarkers. A person is diagnosed with dementia if cognitive or behavioral or neuropsychiatric symptoms: any interference in functional abilities of daily work, reduced functional capacity from previous level, impairment cannot be described by delirium or any major psychiatric disorder, combined information about cognitive decline (history from patients or knowledgeable individual and objective assessment like bedside neuropsychological test). Lastly, the impairment should involve at least 2 of the domains: gaining and recalling new information is reduced (getting lost in a known place, forgetting events, etc.), difficulty performing complex activities (unable to manage finance, inability to plan sequential events, etc.), visuospatial inabilities (dressing inappropriately, failure to recognize faces, etc.), lingual inabilities (writing errors, difficulty in speaking common words, etc.), personality or behavioral changes (loss of drive, mood fluctuations, etc.).
The presence or absence of significant interference in the daily life activities of an individual makes the difference between dementia and MCI. The judgement is essentially made by skilled physicians based on the information provided by the patient or informant who is eligible to provide the details (McKhann et al. 2011).
Risk factors
Risk factors are related to higher chance of having a disease, early development of a disease or an elevated incidence rate of any disease. Various lifestyle, demographical and systemic factors
influence the overall risk of dementia. Some of these factors often interact with each other during the whole life span and contribute to cognitive impairment either individually or together through several pathological pathways. These risk factors can be divided into two categories:
modifiable (smoking, physical activity, etc.) and non-modifiable risk factors (age, family history, etc.). Complicated pathological pathways of dementia and lack of appropriate treatment have influenced researchers to study these modifiable factors to identify preventable approaches (Sindi et al. 2015). Protective factors, on the other hand, have the opposite effect by reducing risk. Risk factors do not certainly cause diseases and similarly, protective factors do not certainly prevent diseases.
Demographic risk factors
The strongest risk factor for dementia is age (Hugo & Ganguli 2014). The majority of the cases develop after the 6th decade of life or after that (Kalantarian et al. 2013). Between 65 and 90 years, the incidence develops further exponentially and doubles almost every 5 years (Corrada et al. 2010). While reports suggest that incidence among 60 to 64 years is 3.1 cases per 1000, it increases to 175.0 cases per 1000 among 95 years or above (World Health Organization 2012).
In ages over 65 years, prevalence is 5–10% and generally higher in females compared to males in the high-income countries (Hugo & Ganguli 2014).
There have been reports that gender also impacts the overall risk of dementia. The prevalence of dementia is consistently greater in women, but not incidence. It has been suggested that the reason might be higher life expectancy and hormonal changes after menopause (Rocca et al.
2014). Association of prevalence with lower education levels have been also reported. Findings from the USA revealed a higher prevalence rate among African American and Latino communities credited results to lower education levels and increased CVD morbidity in those communities (Hugo & Ganguli 2014).
Education often represents the socioeconomic condition of an individual and thus reflects the status of the surrounding environmental aspects such as nutrition or lifestyle. Despite these factors, higher education level has been linked with decreased prevalence of dementia (Meng
& D’arcy 2012). Studies found out bilingualism influence the delayed onset of dementia regardless of the level of education (Alladi et al. 2013) and might act as a protective factor against executive function and attention impairment (Craik et al. 2010).
Genetic factors
Different genes have been reported to increase the risk of dementia. Risk increases around 25- 50% if an individual has a positive family history according to different studies (Milne et al.
2008). The presence of the ε4 allele of Apolipoprotein E (APOE-ε4) on Chromosome 19 is considered as the strongest genetic risk factor specially for AD. Around 15% to 20% of all cases of dementia are credited to it. APOE-ε4 is reported to be related to CVD and hypercholesterolemia as well. It has also been associated with vascular dementia, dementia due to PD, FTD, and DLB in men (Chuang et al. 2010, Roses 1996, Srinivasan et al. 2006, Tsuang et al. 2013, Yin et al. 2012). Homozygotic APOE-ε4 persons have a greater risk than the heterozygotic carriers but this is not a diagnostic marker. Furthermore, the effect on dementia risk wears off during the 8th decade.
Cardiovascular and metabolic factors
Cardiovascular diseases increases the risk of vascular dementia and also neurodegenerative dementias specifically AD (Hugo & Ganguli 2014). Hypertension, hypercholesterolemia, high body mass index (BMI), and diabetes mellitus (DM) in midlife carry risk for late-life dementia, emphasizing the significance of early exposures (Kivipelto et al. 2005, Schnaider Beeri et al.
2004). Studies have also shown the impact of obesity on dementia risk (Miller & Spencer 2014, Ylilauri et al. 2017). In vascular dementias and AD, inflammation as well as changes in various inflammatory indicators (C-reactive protein, cytokines, interleukins) have been observed (Ravaglia et al. 2007, Schmidt et al. 2002). Studies exploring inflammatory pathways have suggested several mechanisms playing a role in AD (Akiyama et al. 2000, Schott & Revesz 2013, Tuppo & Arias 2005). Obstructive sleep apnea has been related to white matter changes (Kim et al. 2013) and stroke, which elevate the risk of dementia (Pendlebury & Rothwell 2009, Van Kooten & Koudstaal 1998).
DM has been reported to increase the risk of vascular dementia around 60%. It has been shown that cerebrovascular abnormalities caused by DM can explain this risk association. Some studies have also linked DM with amyloid accumulation, making patients with DM vulnerable to develop AD (Chung et al. 2018). More studies are ongoing to elucidate the exact pathways, as various mechanisms like mitochondrial dysfunction caused by DM have been linked to increased risk for AD (Yang & Song 2013). Furthermore, metabolic impairment due to DM contributes to the harmful impact on cognition (Hardigan et al. 2016).
Lifestyle and other risk factors
There are several environmental and occupational exposures that have been associated with impairment in cognition. Smoking has been shown to be a risk factor (Anstey et al. 2007).
Current smokers have a greater risk of developing dementia than former and non-smokers.
Cessation of smoking decreases the risk of dementia, but former smokers and non-smokers have similar risks (Zhong et al. 2015). Heavy alcohol consumption elevates the risk (Anttila et al. 2004, Mukamal et al. 2003) through possible pathways of neurotoxicity, impairment of cerebrovascular function and depression of the central nervous system (Wilhelm et al. 2015).
Dementia and depression share a bi-directional and complex relationship. Depression has been linked with dementia due to some similarities in cerebral changes, but the exact mechanisms are not yet fully known (Bennett & Thomas 2014). Risk of dementia was reported to increase in later life with a history of recurrent major depression in early adulthood (Dotson et al. 2010).
Depression may also occur during the prodromal stages of dementia. Post-traumatic stress disorder has been found to elevate dementia risk as well (Yaffe et al. 2010).
Another risk factor that has been associated with dementia, particularly AD (Fleminger et al.
2003), is head injury. This relation was reported to be important especially in circumstances where consciousness was lost. After any traumatic brain damage or impairment of consciousness, there is a possibility to develop neurocognitive impairment immediately (American Psychiatric Association 2013). However, repeated minor injuries that change cerebral structure compounded over time have been linked with developing dementia (Hugo &
Ganguli 2014).
Protective factors
Protective factors lower the risk or incidence rate or delay the onset of dementia. Besides studying risk factors, exploring potential protective factors is important for formulating preventive measures. Studies of Apolipoprotein E (APOE) gene have shown that the ε2 allele (APOE-ε2) may have a protective effect (Hugo & Ganguli 2014). There have been reports that increased physical activity protects against dementia (Hamer & Chida 2009). Although heavy alcoholism was showed to increase the risk of cognitive impairment, mild to moderate intake of alcohol has been reported to reduce the incidence of dementia and cognitive decline (Anttila et al. 2004, Ganguli et al. 2005b, Kim et al. 2016, Mukamal et al. 2003, Ruitenberg et al. 2002, Sabia et al. 2018). Having larger social networks (Crooks et al. 2008) and taking part in different
intellectual or cognitive activities have been associated with a reduced risk of dementia (Wang et al. 2002). There has been much research on the potentially protective effects of different drugs on dementia risk. Some studies reported potential benefits of non-steroidal anti- inflammatory drugs (in ’t Veld et al. 1998, Szekely et al. 2008, Yip et al. 2005), although a meta-analysis emphasized several methodological limitations among such studies (De Craen et al. 2005). Another meta-analysis of lipid-lowering HMG Co-A reductase inhibitors (statins) suggested protective effects, although these have not been confirmed in randomized controlled trials (Swiger et al. 2013, Wong et al. 2013).
2.2.2 Alzheimer’s disease
AD is a degenerative brain disease. Over time, the condition of the patient worsens. The changes in the brain can begin 20 or more years before the symptoms start to appear. An individual starts to experience noticeable changes with symptoms like memory loss or language problems only after years. These symptoms occur due to the damaged nerve cells responsible for different cognitive functions such as thinking, memory, and learning. Dementia due to AD is defined when a person’s ability to perform daily activities independently is affected by these symptoms.
In the end, the impairment is severe enough to prevent basic bodily functions; difficulty in speaking, swallowing and walking become prominent (Alzheimer’s association 2018).
Eventually, the patients are bed-ridden and require continuous help from caregivers.
AD is a multifactorial disorder having different risk factors intertwined with each other. These range from non-modifiable factors (age, genetics, etc.) to various modifiable factors (sedentary lifestyle, high blood cholesterol levels, etc.). Putative risk and protective factors that have been associated with the AD summarized in Table 1.
Table 1: Potential risk and protective factors associated with Alzheimer`s Disease
Risk Factors Protective Factors
Age >65 Years APOE-ε4 Hypertension Diabetes mellitus Cardiovascular disease
Smoking Depression Head injury
APOE-ε2 Cognitive activity Intellectual activity
Social activity Mediterranean diet
Changes in the brain associated with Alzheimer’s disease
Although the exact causes are still not fully clear, amyloid plaques and neurofibrillary tangles are considered the hallmarks of AD (Ballard et al. 2011). The amyloid plaques accumulate outside the neurons and tau proteins accumulate inside. While amyloid deposits may contribute to the neuronal death by blocking cell to cell signaling at synapses, tau pathology prevents the nutritional and other essential molecular transport (National Institutes of Health 2018). These two also activate microglia, immune system cells in the brain that clear the debris produced by the widespread dying cells, leading to chronic inflammation. The functional ability of the cells is compromised, and the brain loses the ability to mobilize the main source of energy, glucose.
Aβ fibrils, which represent one of the primary changes in AD, may deposit in the brain decades before the onset of dementia (Sperling et al. 2011). One recent study (Gordon et al. 2018) reported that amyloid deposition started 22 years, decreased glucose metabolism 18 years and atrophy of the brain 13 years before the anticipated start of the symptoms.
The human brain tries to compensate for the early changes made by these pathological processes. An individual usually maintains a normal or asymptomatic living during this period.
With time, the brain cannot keep up with the changes and subtle symptoms start to appear.
Furthermore, amyloid and tau pathology starts to spread to other parts of the brain (Alzheimer’s association 2019). The obvious symptoms due to cognitive decline then start to appear gradually as a result of marked neuronal damage.
Stages of Alzheimer's disease
Recent diagnostic criteria have described 3 stages of AD: preclinical AD, MCI due to AD and dementia due to AD (Albert et al. 2011, Jack Jr et al. 2011, McKhann et al. 2011, Sperling et al. 2011). While preclinical AD is asymptomatic, later stages are categorized by varying degrees of AD symptoms.
A. Preclinical Alzheimer's disease
The preclinical disease stage has become an important target for the prevention of progression to later, clinically manifest stages. Asymptomatic status, however, makes it difficult to identify individuals with preclinical AD. Researchers have been trying to use biomarkers as the earliest tools to predict AD conversion. Although few of them have been successful identifying some of the early pathological changes in the brain (Alzheimer’s association 2019), more studies are required to diagnose preclinical AD accurately before making them available for widespread use. It is important to mention that many individuals with the presence of AD biomarkers progress to MCI or dementia but not all (Bennett et al. 2006, Knopman et al. 2003).
B. MCI due to Alzheimer's disease
In MCI, individuals suffer from mild cognitive deterioration greater than anticipated for age.
The degree of impairment does not hamper daily activities but there is a noticeable change in the brain identified with AD biomarkers (Roberts & Knopman 2013). Usually, family members or friends who live in close contact with an individual with MCI also notice the decline. Around 15-20% of people aged 65 years and older suffer from MCI due to different causes (Roberts &
Knopman 2013). These MCI individuals are likely to develop AD especially if they are suffering from memory impairment compared to cognitively normal individuals (Kantarci et al.
2009, Mitchell & Shiri‐Feshki 2009). While one recent study with 2 years of follow-up found that 15% of MCI developed dementia (Petersen et al. 2018), one systemic review and meta- analysis with 5 or more years of follow-up period saw the percentage increased to 32% (Ward et al. 2013) and 38% (Mitchell & Shiri‐Feshki 2009) respectively.
C. Dementia due to Alzheimer's disease
In dementias, a patient suffers from obvious memory, thinking and behavioral abnormalities that hinder daily activities. AD patients also have similar characteristics besides evidence of AD pathology in the brain. These patients experience over the years worsening in multiple symptoms. The progression time from mild to moderate and then to severe symptoms varies from one person to another.
The mild stage does not usually fully incapacitate an individual from activities like driving, work or other regular recreational activities. Most people can function relatively independently but may need help in several activities.
Moderate stage is frequently the longest among the three dementia stages. Difficulty in communication and performing routine chores like dressing or bathing, incontinence at times, changes in personality or behavior along with agitation and suspiciousness are some of the common features during this stage.
In the severe dementia stage, an AD patient requires continuous monitoring from care providers for assistance with daily activities. The severity of AD in an individual is physically noticed during this stage. Damage in areas of the brain connected to mobility causes a patient to be bed- ridden which brings conditions such as clots, sepsis and skin infections triggering inflammatory reactions throughout the body and ultimately can trigger multi-organ failure. Drinking and eating is affected due to damaged brain areas related to swallowing, with the risk that food particles or water may enter the trachea and further to the lung. This can cause lung infection known as aspiration pneumonia which is one of the causes of death in AD patients.
Therapeutic approaches in AD A. Pharmacological approaches
There are no effective disease modifying medications available to halt or slow down the progression of AD. Galantamine, rivastigmine, memantine, donepezil are some of the common drugs approved, that are used to ease the symptoms or delay the severity of the symptoms.
Memantine is a N-Methyl-D-aspartate receptor antagonist while others are acetylcholinesterase inhibitors. The user response of these drugs is variable, and they have a restricted duration of action. Knowledge gaps about the underlying specific molecular pathology, a long trial period to observe the outcome of the drugs and issues regarding recruiting participants for clinical
trials are some of the factors that have so far made it difficult to find effective drugs (Alzheimer’s association 2019).
Due to the long duration of AD pathology development, current drug development research in AD is increasingly focused on testing treatments early in the preclinical or MCI stage, which may efficiently delay or halt AD progression and maintain cognition. For this reason, research on identifying accurate biomarkers and more accessible methods for measuring them during early disease stages has increased (Bloudek et al. 2011). These could detect the early stages as well as will be useful to measure the effectiveness of the drugs. Researchers have already started using biomarkers to assess AD brain changes after the administration of drugs in different trials (McKhann et al. 2012). Most trials are still mainly targeting amyloid pathology, although tau pathology is also targeted in several trials.
B. Non-pharmacological approaches
Lack of effective medication has emphasized the importance of non-pharmacological approaches to prevent or treat AD. Non-pharmacological trials have been conducted in both patients with AD dementia and cognitively unimpaired adults, to prevent or reduce cognitive impairment. In the case of AD dementia, the primary aim is to improve the overall quality of life. Additional aims are to decrease behavioral manifestations such as aggression, depression, sleeping disorder, agitation, apathy, etc. For instance, computerized programs are used for memory training, specialized lighting is used to prevent sleeping disorders and music is used as an instrument to stimulate recall. These methods, nevertheless, do not stop or slow down the undergoing pathological process that causes the symptoms.
One meta-analysis (Groot et al. 2016) and systematic reviews (Aguirre et al. 2013, Farina et al.
2014) of randomized controlled trials involving non-pharmacological approaches reported some beneficial effects to AD patients suffering from dementia. These randomized controlled trials were assessing different physical exercises (Farina et al. 2014, Groot et al. 2016) and cognitive stimulation (Aguirre et al. 2013). According to the meta-analysis, aerobic exercise and with a mixture of non-aerobic and aerobic exercise were beneficial for cognitive function.
A systematic review (Farina et al. 2014) reported a positive impact of exercise on cognitive function. Another systematic review (Aguirre et al. 2013) concluded that cognitive stimulation benefits cognitive function.
A systematic review (Butler et al. 2018b) of the benefit of over-the-counter supplements on cognitive function, MCI or AD found little to no effect. Another systematic review (Butler et al. 2018a) assessing cognitive training programs in cognitive impairment reported that the performance improved in individual domains such as executive function but there was a lack of evidence on the effect on preventing or delaying cognitive impairment or dementia.
Similarly, another systematic review (Brasure et al. 2018) lacked evidence while associating physical activity with the prevention of AD dementia progression or slowing down of cognitive decline.
More recent dementia prevention trials have been testing multi-domain lifestyle interventions that combine multiple non-pharmacological approaches to target several risk factors at the same time. The importance of this multi-domain approach has also been emphasized in the 2019 World Health Organization Guidelines on risk reduction of cognitive decline and dementia.
Biomarkers
Biological markers are naturally occurring molecules produced during physiological or pathological pathways indicating the risk, progression, and presence or absence of disease.
There are many biomarkers that are used for a variety of purposes. For instance, blood cholesterol level is used to determine the risk of cardiovascular diseases and C-reactive protein (CRP) is a well-known marker for inflammation. Regarding AD, abnormal tau and beta- amyloid levels (e.g. in the CSF or on PET scans) are the most investigated biomarkers, which are also used in clinical trials. Discovering an easy and inexpensive method for accurately measuring such biomarkers for example in blood could additionally help to diagnose AD or measure the disease progression.
Diagnosis criteria for Alzheimer's disease
In the clinic, AD is diagnosed based on a series of assessments including, but not limited to, family and medical history, informant interview about behavioral, cognitive changes and daily life activities, physical and cognitive tests, blood and imaging investigations. Medical professionals are usually successful in diagnosing dementia, but the cause can be difficult to detect. NIA/AA and International Working Group (IWG) have been developing a diagnostic framework to identify AD already in asymptomatic stages. Table 2 below summarizes recent updates from both these groups to compare the criteria. While it focuses on the biomarkers defining different pathological AD states of an individual, Table 3 is based on the NIA/AA 2018 criteria merging clinical features and biomarkers.
Table 2: Comparison of diagnostic criteria between research groups Research group
Diagnostic criteria
IWG-2, 2014 Proposed changes to IWG-2, 2016
NIA-AA, 2018
AD in general
AD starts with first clinical symptoms
AD starts with first pathological
changes
AD starts with first pathological
changes
Preclinical AD
Preclinical states of AD defined as:
• Absence of clinical symptoms
• Plus: A or B
Preclinical AD defined as an asymptomatic disease stage with both amyloid + and tau +
Preclinical AD defined as an asymptomatic disease stage with both amyloid + and tau +
A Asymptomatic at-risk state:
1. ↓ Aβ1–42 + ↑ T-tau or P-tau in CSF
OR
2. ↑ amyloid retention on brain PET scan
B Presymptomatic state:
Proven AD
autosomal dominant mutation in PSEN1, PSEN2, or APP, or other proven genes (including Down's syndrome trisomy 21)
Asymptomatic at- risk state for AD defined as either amyloid + or tau + A distinction is thus made between AD as a disease, and an at-risk state characterized by presence of a risk factor (either amyloid or tau).
An Alzheimer’s continuum is defined. In addition to preclinical AD, this continuum also includes:
• Alzheimer’s pathologic change (amyloid+ and tau- )
• Alzheimer’s and concomitant
suspected non- Alzheimer’s
pathologic change (amyloid+, tau-, and other neuronal injury marker(s)+
Individuals with amyloid- and tau+
are considered as having non-AD pathologic change, which is not part of the Alzheimer’s continuum.
Table 3: Syndromal cognitive staging combined with biomarkers, based on the most recent research diagnostic criteria (NIA-AA 2018)
Cognitive stage
Cognitively Normal Mild Cognitive Impairment
Dementia
Biomarker Profile
A− T− (N)− AD biomarkers:
Normal.
+
Cognitively normal
AD biomarkers:
Normal.
+ MCI
AD biomarkers:
Normal.
+
Dementia A+ T (N) Preclinical AD with
pathologic change.
AD pathologic change.
+ MCI
AD pathologic change.
+
Dementia A+ T+ (N)− Preclinical AD AD with MCI
(Prodromal AD)
AD with dementia A+ T+(N)+
A+ T (N)+ Alzheimer’s and concomitant suspected non- Alzheimer’s
pathologic change.
+
Cognitively normal
Alzheimer’s and concomitant suspected non- Alzheimer’s pathologic change.
+ MCI
Alzheimer’s and concomitant suspected non- Alzheimer’s
pathologic change.
+
Dementia A− T+(N)− Non-Alzheimer’s
pathologic change.
+
Cognitively normal
Non-Alzheimer’s pathologic change.
+ MCI
Non-Alzheimer’s pathologic change.
+
Dementia A− T− (N)+
A−T+(N)+
Abbreviations: AD, Alzheimer disease; A: Aggregated Aβ or associated pathologic state (CSF Aβ42, or Aβ42/Aβ40 ratio or Amyloid PET), T: Aggregated tau (neurofibrillary tangles) or associated pathologic state (CSF phosphorylated tau or Tau PET), (N): Neurodegeneration or neuronal injury, MCI: mild cognitive impairment.
2.3 Amyloid and tau protein
The sequential proteolytic degradation of amyloid precursor protein (APP) leads to the formation of amyloid plaques. Various tissues and synapses of neurons have APP as an integral membrane protein. In the synapses, APP are concentrated with abundance. The balance of APP processing shifts towards the amyloidogenic pathway in AD (Evin & Barakat 2014). Three enzymes: α-, β- and γ-secretases usually degrade APP. β- and γ-secretases degrade APP to form (38- 43)-amino-acid Aβ peptides. Around 10% of all Aβ in the brain are represented by longer Aβ42 which tends to aggregate as extracellular accumulations in older plaques (Vetrivel &
Thinakaran 2006). Aggregated amyloids, which causes amyloid angiopathy, are detected in blood vessel walls of the brain too. During APP cleavage by α-secretase, it also yields non- amyloidogenic products which show neuroprotective functions (Adeniji et al. 2017)
The other important histopathological trademark of AD is the production of intracellular neurofibrillary tangles (NFTs). These NFTs are produced from hyperphosphorylation of tau, a microtubule related protein, prompting its cellular oligomerization and microtubule destabilization which further leads to neuronal apoptosis (Avila 2006). These distinctive pathological developments proceed in an ordered manner as they are observed first usually in the medial temporal lobe region and gradually in the neocortex (Braak & Braak 1995).
Therefore, loss of neurons is quite noticeable, and gradually the brain is atrophied in the same order, usually from temporal lobe region to central and cortical area (Duyckaerts 2011).
Neither amyloid plaque production nor hyperphosphorylation of tau are exclusive in AD. For example, in the case of tau, various neurodegenerative diseases like cortico-basal degeneration (CBD) and progressive supranuclear palsy (PSP), are known as tauopathies and have features of intraneuronal filamentous deposits made of hyperphosphorylated tau (Goedert & Jakes 2005). In the case of amyloid plaques, some of them called diffuse plaques can develop during normal aging, although they structurally vary from the typical AD neuritic plaques.
Furthermore, mixed pathology is commonly seen in clinically identified AD individuals, making AD a pathologically heterogenous disorders (Schneider et al. 2009). These proteinopathies responsible for dementias are strongly associated with advancing age (Jellinger
& Attems 2013, 2015).
2.4 Positron-emission tomography (PET) Scan
PET is a nuclear medicine functional imaging method. It is widely used both in clinical practice and in research. PET helps to monitor metabolic processes in the body aiding in the detection of various diseases, including diseases associated with amyloid. Even though CSF and PET evaluate different amyloid forms, studies have reported a good degree of consistency between these 2 biomarkers (Jansen et al. 2015). PET presents in vivo quantitative cross-sectional impressions of various biochemical and physiological mechanisms like metabolism, blood circulation, and oxygen consumption. Artificially created positron-emitting radionuclides called tracers are introduced inside a biologically active molecule which is later injected usually into the bloodstream of an individual. 11C-labelled 2-[4′-(methylamino) phenyl]-6- hydroxybenzothiazole known as Pittsburgh Compound-B (11C-PiB) and 2-[18F]fluoro-2- deoxy-d-glucose (FDG) are some of the commonly used tracers. PiB-PET is utilized to identify Aβ deposition in the brain while FDG-PET aids in the measurement of metabolic rates of glucose in the brain (Berti et al. 2010).
So far there have been no standard guidelines to decide the positivity of PiB-PET scans for pathological amyloid accumulation. Observers classify the scans based on the uptake and retention threshold of PiB. Cerebral PET scans can be assessed by two methods. One focuses on the whole brain with the voxel-based method and another one focuses on specific regions with a region-of-interest (ROI) method (Ashburner & Friston 2000). Usually, neuro-anatomical image skilled professionals manually analyse two-dimensional scans of specific domains in case of ROI method. However, newer voxel-based methods are automated and are proving to be more effective than conventional ROI approach. There are several ways to measure PiB retention namely standardized uptake value ratio (SUVR) and distribution volume ratio (DVR).
SUVR estimates the activities in the ROI and compares it with a normalized period while DVR compares the distribution ratio between ROI and reference area. Researchers have also been studying the time interval between the injection of PIB and acquisition in the cerebral domains (He et al. 2015).
2.4.1 PiB uptake in normal aging
There have been inconsistent results concerning differences in Aβ status between subjective cognitively impaired and cognitively normal individuals (Amariglio et al. 2012, Chételat et al.
2010). Furthermore, Aβ accumulation on PET scans has been observed in both symptomatic
AD and asymptomatic elderly individuals. Around one-fourth or more of the nondemented persons aged over 75 years have been documented with a moderate amount of neuritic plaques (Bennett et al. 2006). Healthy older, neurologically normal persons can show important neuropathology, as amyloid deposition has been observed during autopsy (Dickson et al. 1992).
PiB imaging for amyloid has shown uptake in the same brain areas as reflected in the earliest autopsy studies (Braak & Braak 1997).
Amyloid PET scanning has offered the possibility of determining cerebral amyloid load, and exploring the timeline of its development among nondemented, MCI and AD individuals. Like AD, the sequence of PiB uptake seems to be quite similar in the cognitively normal individuals.
The prefrontal cortex, lateral temporal cortex, striatum, and lateral and medial parietal regions are involved. The uptake can be more focal, as indicated by several studies reporting frequent deposition in prefrontal cortex and precuneus / posterior cingulate (Rowe et al. 2007, Villemagne et al. 2008) implying the necessity of further investigation.
2.4.2 PiB uptake in mild cognitive impairment
Prediction of the conversion from MCI to AD dementia with elevated PiB uptake has been reported (Forsberg et al. 2008, Pike et al. 2007), but approximately 40 percent of the people who fulfil the clinical characteristics of MCI do not advance to clinical dementia (Busse et al.
2006). However, the progression from MCI to dementia is more likely in individuals with positive amyloid status and apparent (Petersen et al. 2016) if the decline is persistent with time.
The amnestic form of MCI has been most predictive for the progression to dementia. An association between PiB uptake and impaired episodic memory performance has been seen among healthy and MCI individuals in several studies (Pike et al. 2007).
Studies have shown that AD patients have PiB uptake in frontal, parietal and temporal regions in comparison to a healthy aging population (Klunk et al. 2004, Scheinin et al. 2009). MCI population has also shown a similar trend (Jack et al. 2008, Kemppainen et al. 2007). Elevated uptake in posterior cingulate has also been reported (Forsberg et al. 2008, Kemppainen et al.
2007) but region-specific pattern was reported only in MCI non-converters. Nevertheless, no substantial difference was found between the non-converters and the converters (Villain et al.
2012).
2.4.3 PiB uptake in Alzheimer’s disease
In AD research and clinical trials, PiB-PET scanning is starting to be utilized more widely, and it has also become available in specialized clinics. PET scans comprise various analysis monitoring the non-specific metabolic developments in AD patients targeting several neurotransmitter mechanisms as well as pathological developments including the accumulation of Aβ (Rinne & Någren 2010). To explore the development of the AD pathology, it is important to assess Aβ deposition within the disease process longitudinally (Hatashita et al. 2019).
According to a few longitudinal studies, PiB-PET scanning has demonstrated that amyloid accumulation rises as healthy individuals develop dementia but reduces during advanced stages of AD (Jack et al. 2013, Villemagne et al. 2013) Furthermore, it has been possible to differentiate AD from FTD with PiB imaging (Drzezga 2008, Rabinovici et al. 2007) which is important for the prognosis and symptomatic treatment.
Assessment regarding the amyloid-PET scan sensitivity compared to autopsy has been done.
Clear relations have been observed between PiB uptake in vivo and the outcomes of region- specific investigations of amyloid deposition from post-mortem examinations (Ikonomovic et al. 2008, Kadir et al. 2011). However, region-specific investigations of PiB uptake have also provided some conflicting findings in AD patients. Some of the studies have shown no clear variations between regions (Villain et al. 2012) while few showed there was significant solitary uptake in the medial prefrontal cortex (Scheinin et al. 2009). Aβ accumulation was reported within the frontal, parietal, temporal, and cingulate cortex in some of the PiB-PET studies (Rinne et al. 2010, Villemagne et al. 2011).
According to a systematic review by Zhang et al. (2014), PiB showed high sensitivity but lower specificity for identifying people with MCI who developed AD dementia. The false-negative outcomes might be due to the fact that PiB-PET may not be efficient to identify some types of amyloid deposits (Leinonen et al. 2008). On the other hand, the false-positive results implied that PiB-PET imaging might also act as a biomarker for additional neurodegenerative conditions or neurological diseases in individuals without any symptoms (Chen et al. 2014).
Furthermore, PiB attaches with β-amyloid in the vascular surface specifically concerning to cerebral amyloid angiopathy (Zhang et al. 2014).
2.4.4 PiB uptake in Parkinson's disease dementia and Lewy body dementia
PD patients possess six times greater risk of progression to dementia compared to healthy controls (Edison et al. 2008). Patients with PD take a few years to many years to develop dementia (Lim et al. 2019). Studies have explored potential common pathophysiological pathways between PD and AD. Prevalence of amyloid positivity from different PET studies varied between 0%-38% in non-demented PD patients, while 16.6-33% were reported in demented patients with PD (Lim et al. 2019).
Edison et al. study in 2008 suggested that PiB uptake is detected besides the usual Lewy body deposition in patients with DLB as well as in PD patients, but amyloid deposition was lower compared to DLB patients. Reports from studies revealed that most of the DLB individuals show cortical Aβ plaques (Jellinger & Attems 2006) in contrast to patients with PD with dementia (Jendroska et al. 1996, Mastaglia et al. 2003). In a study by Kantarci et al. in 2010, DLB patients showed significantly lower global PiB retention compared to AD patients but higher than cognitively normal individuals. However, while DLB patients had frontal Aβ plaques, temporoparietal deposition was less than in AD. This might be due to the topographical amyloid pathophysiology of these diseases.
3. AIMS OF THE STUDY
The unclear significance of amyloid positive status in at-risk individuals and individuals without dementia has made it difficult to identify the early stages of AD accurately. There have been several attempts to link different combinations of clinical characteristics, neuropsychological tests and other assessments to brain amyloid deposition in pre-dementia disease stages. For example, characteristics like functional decline have been connected to AD with increased amyloid retention (Hsu et al. 2017). This kind of association has yet to be fully elucidated in individuals without dementia. Furthermore, there is a lack of detailed description of the clinical characteristics of cognitively normal individuals with positive amyloid scans.
The literature on this topic shows variable results. Some studies reported that amyloid status did not have an association with cognition (Lin et al. 2009, Rowe et al. 2010), while others found a significant relation (Farrer et al. 1997, Jack et al. 2014, Matthews et al. 2005, Morris et al. 2010, Suri et al. 2013). This thesis aims to investigate the relationship between brain amyloid load and clinical characteristics in an older at-risk general population without substantial cognitive impairment or dementia. Clinical characteristics to be studied include objective measures of cognition (global cognition and cognitive domains), and subjective cognitive complaints. The association of amyloid status with changes in cognition over time is assessed as well.
4. MATERIALS AND METHODS
The thesis used data from the exploratory PET sub-study of the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER). FINGER trial was a multi- center lifestyle intervention among 1260 older at-risk individuals. It was the first randomized controlled trial showing the possibility to prevent cognitive impairment with a multi-domain lifestyle intervention (Winblad et al. 2016). The two-year intervention involved nutritional guidance; exercise; cognitive training and social activity; and management of metabolic and vascular risk factors. Control group participants were provided with regular health recommendations. The principal result of the intervention was cognitive scores which was assessed through a modified Neuropsychological Test Battery (mNTB) (Ngandu 2014).
4.1 Selection criteria for FINGER participants
The individuals were selected among the survey participants of the Finnish type 2 diabetes prevention program in 2004 or 2007 (Saaristo et al. 2007) or the National FINRISK study in 1972, 1977, 1982, 1987, 1992, 1997, 2002 or 2007 (Ngandu 2014). These cross-sectional observational surveys were organized to study risk factors of chronic non-communicable diseases. The rate of participation in these surveys was ranging from 70% to above 96%
(Vartiainen 2010). The invitation to participate in the FINGER study was sent to the people aged 60–77 years old in 2009, and having Cardiovascular Risk Factors, Aging and Incidence of Dementia (CAIDE) Dementia Risk Score (Kivipelto 2006) of 6 points or more at the start of the study. CAIDE scoring encompasses easily measurable factors (sex, age, education, physical inactivity, hypercholesterolemia, hypertension as well as obesity) which are related to the risk of dementia. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) neuropsychological battery (Moms et al. 1989) was used to evaluate the cognition of the participants during screening appointments. Everyone had to fulfil at least one of these criteria to be included in FINGER: (a) Mini-mental state examination (MMSE) ≤26/30 points; or (b) Word List Recall ≤75%; or (c) Word List Learning task (10 words × 3) ≤19 words. Participants having cognitive scores at average level or a little less than anticipated for age (according to Finnish population norms) were selected by these criteria (Ngandu 2014). Factors influencing safe participation in the intervention (especially physical exercise part), major depression, current malignant diseases, MMSE < 20, dementia/substantial cognitive impairment in accordance with a clinical interview, hearing or communicative ability, severe loss of vision, symptomatic cardiovascular disease, re-vascularisation within a year, factors hindering co-
operation as evaluated by the study physician, in addition to coincidental engagement in any other intervention trial comprised the exclusion criteria. Randomization was done in sections of 4 participants (2 individuals randomly assigned to either intervention or control group) by the study nurse with computer software at each study site (Ngandu 2014).
4.2 The FINGER PiB-PET exploratory study
During the FINGER trial, brain 11C-PiB-PET imaging was available at the Turku PET Centre.
The PiB-PET exploratory study was conducted only in the FINGER cohort in Turku. When PET resources became available and if there were no contraindications, the most recently recruited FINGER trial participants were selected for the PET sub-study. In total, 48 individuals (22 female, 26 male, mean age 71.4 years, SD 5.2 years) from the Turku cohort of the FINGER primary study sample were able to engage in this PET sub-study. The evaluation methods for amyloid deposition and methods for the PiB-PET scan have been previously described in detail (Kemppainen 2018). Two experienced readers analysed the scans visually. The assessment was visually positive or negative following consensus agreement. Participants categorised as PiB positive presented cortical retention of 11C-PiB-PET in at least one cortical region classically affected in AD. In contrast, individuals categorised as PiB negative had nonspecific 11C-PiB- PET retention in white matter. With the automated ROI analysis, the scans were quantitatively assessed as well. A composite PiB retention score was determined as the average of the prefrontal, parietal, precuneus, anterior cingulate, posterior cingulate, and lateral temporal ROIs amyloid deposition (Kemppainen 2018). In this thesis, data on brain amyloid status collected in connection to the baseline FINGER visit were used.
4.3 Cognitive and other characteristics relevant for this project 4.3.1 Cognitive tests
Trained study psychologists carried out a series of standardized neuropsychological tests, i.e. a modified and extended form of the neuropsychological test battery, NTB (Harrison 2007) for a thorough cognitive evaluation. The total composite mNTB score involving 14 tests was the principal outcome measure. Other cognitive measures were determined as follows: the memory domain involved Logical Memory immediate recall (assessment range, 0–25 points) and delayed recall (assessment range, 0–25 points) of the Wechsler Memory Scale-Revised (WMS- R) (Wechsler 1997); Visual Paired Associates immediate recall (assessment range, 0-18 points)
