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Subsyndromal Depression (SSD) and Dementia

Info: 6953 words (28 pages) Dissertation
Published: 10th Dec 2019

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Tags: Mental HealthPsychiatryAlzheimers



Statement of the Problem

Successful aging is strongly associated with optimal cognitive function. However, cognitive function declines with age, and the rate and severity of decline is variable due influences of socioeconomic status (SES), access to affordable healthcare, race/ethnicity, and comorbid conditions such as cardiovascular disease (CVD) and depression (Alter et al., 2006; Gamaldo et al., 2010; Mackin, Insel, Aisen, Geda, & Weiner, 2012; Wilson et al., 2005;).  Dementia is an umbrella term that represents many conditions that cause impairment in cognitive functions. Alzheimer’s disease (AD) and vascular dementia are the most common dementias. Alzheimer’s disease is a chronic irreversible condition characterized by problems with memory, thinking, and behavior that eventually interferes with one’s ability to engage in daily living tasks (McKhann et al., 1984).  AD is responsible for 60-70% of all dementia cases (Bobinski et al., 1999; Hamley, 2012; Weiner et al., 2010), and age is its greatest known risk factor, with most people with AD age 65 or older (Burns & Iliffe, 2009; Prince et al., 2014). There is no cure for AD and, as with many neurodegenerative diseases, successful treatment is not likely to result in full restoration of brain atrophy and associated functions (Bredesen et al., 2016; Mesulam, 2012).  The current goals of AD treatments and therapies focus on improving quality of life for affected individuals by stopping or slowing the rate of cognitive decline and providing support for affected individuals and their caregivers (Rabins & Black, 2007).  Early detection of AD symptoms can increase the likelihood of preserving cognitive functioning by creating access to available treatments, building a care team with family members and social groups, accumulating support services, and enrolling in clinical trials (Alz.org, 2015).

However, the early detection of AD has been difficult as the diagnosis of AD is typically given once cognitive impairment compromises activities of daily living (ADL) (Forstl & Kurz, 1999).  The goal of preventative AD research has been to identify a stage where the decline in cognitive functioning differs from that of the normal aging process and before people meet the full AD criteria (Mueller et al., 2005). This intermediate stage has been termed Mild Cognitive Impairment (MCI). MCI is characterized by the decline in cognitive functions that is not associated with the typical aging process (Petersen et al., 1999). Symptoms include problems with cognition, memory, or thinking but without functional impairment (National Institute on Aging, 2017).  The National Institute on Aging-Alzheimer’s Association (NIA-AA) and the Diagnostic and Statistical Manual for Mental Disorders (DSM-5) have classified MCI as a clinical condition. It includes cognitive impairment in one or more domains (e.g., memory, language, attention, visuospatial abilities, and executive functioning) when compared to appropriate normative data but not meeting criteria for dementia, preservation of functional independence, and clinical concern raised by patient, informant, or clinician observation (Albert et al., 2011; American Psychiatric Association, 2013).

Aging cohort studies have found that, when using the current definition of MCI, an estimated 10-20% of the elderly population aged 65 years of age or older have MCI (Albert et al., 2011). There are two subtypes of MCI: amnestic and non-amnestic (Petersen, 2004). People with amnestic MCI (aMCI) present with a primary concern of loss of memory, but other cognitive functions such as, executive functioning, language, visuospatial skills, and activities of daily living are not severely impacted (Petersen, 2011).  Individuals with aMCI are at greater risk for developing AD, as research has indicated that 90% of those affected by aMCI progress to dementia and show signs of AD (Petersen, Thomas, Grundman, Bennett, Dody, Ferris, et al., 2005). Non-amnestic MCI (naMCI) is defined as mild impairment in cognitive functioning that is not related to a decline in memory, language, visuospatial skills, or attention. This type of MCI is less common and is not considered to be a risk factor for developing AD (Peterson, 2011).

It is important to note that the overall prevalence rates of MCI and AD vary by race/ethnicity (Katz, Lipton, Hall, Zimmerman, et al., 2012; Manly, Tang, Schupf, Stern et al., 2008; Plassman, Langa, Fisher, Heeringa, et al., 2008). Research has indicated that elderly African Americans and Hispanic Americans are two times more likely to meet criteria for AD and MCI (Katz et al., 2012). It has also been suggested that these populations are more likely to develop MCI and AD before the age of 70 and are diagnosed later in life as compared to their white counterparts (Chin, Negash, & Hamilton, 2011; Graff-Radford et al., 2002; Katz et al., 2012; Livney et al., 2011; Potter et al., 2009). Several factors may help explain the disparity in MCI and AD diagnosis among different populations. Research has found cognition to be negatively influenced by factors associated with of lower socioeconomic status (SES) such as less years of education, occupation, lack of access to quality healthcare insurance, and medical treatment (Alter et al., 2006; Barnes, DeLeon, Wilson, Benias, et al., 2004; Sudore, Mehta, Simonsick, Harris, et al., 2006). Additionally, these populations have higher prevalence of cardiovascular disease (CVD) risk factors, which is associated with increased risk for AD (Gamaldo et al, 2010; Knopman et al., 2001). Despite research that has demonstrated disparities in the prevalence of AD, the evidence is mixed with respect to actual incidence rates of MCI and AD in diverse populations (Tang, Cross, Andrews, Jacobs, Small, Bell et al., 2001). One explanation for this discrepancy indicates problems with the assessment of AD in diverse ethnic/racial populations (Schwartz, Glass, Bolla, Stewart, Glass, Rasmussen et al., 2004). Historically, cognitive performance tests have been used to diagnose people with AD and is considered the gold standard. However, minority populations are more likely on average to perform poorly on these types of assessments, which may contribute to greater incidences of AD (Schwartz et al., 2004). Other types of assessment measures are needed to better assess and or identify MCI and AD in diverse populations (Barnes & Bennett, 2014).

The need to identify MCI as an entity has been demonstrated in retrospective studies examining individuals’ progression to AD, given that physician medical charts often noted this intermediate state (Albert et al., 2011). Since MCI is now considered an intermediate stage of cognitive impairment, research has now focused on identifying and assessing MCI as early as possible with the goal of implementing evidence-based treatments and therapies that slow or stop the decline at this stage. By doing so, individuals remain functionally independent and can maintain their ability to care for themselves (Petersen & Morris, 2005; Rockwood, Chertkow, & Feldman, 2007; Wadley, Okonkwo, Crowe, & Ross-Meadows, 2008). Therefore, brief, accurate, and timely assessment of MCI, particularly in at-risk populations, has been the focus of recent studies (Rentz et al., 2013; Garcia-Casal et al., 2017).  However, when assessing elderly ethnic minorities, research has found that brief measures lose sensitivity leading to a disproportionate number of inaccurate classification and an overestimation of disease severity in this population (Bohnstedt, Fox, & Kohatsu, 1994; O’Bryant et al., 2008). Researchers have demonstrated that the compounding effects of education quality on literacy, socioeconomic status (SES), and cultural factors may help to explain the difficulty with using brief measures to assess MCI in older ethnic minority populations (Parker & Philp, 2004; Weissberger, Salmon, Bondi, & Gollan, 2013).

Albert et al. (2011) have suggested verbal and nonverbal memory tests and assessing other areas of cognition to supplement more accurate AD diagnosis (e.g., executive functioning, language, visuospatial skills, and attention).  For all assessments, particularly with ethnic minorities, clinicians aim to use measures whose reliability, validity, and comparable norms have been established for accurate comparison to the individual being tested (APA, 2002; Lucas et al., 2005).  Comprehensive neuropsychological assessments targeting MCI and AD provide a complete analysis of an individual’s cognitive abilities, foundations for differential diagnosis, considerations for psychosocial and functional capacities, and treatment response (Harvey, 2012).  However, the use of brief assessments can provide an efficient overview of cognitive functioning in health settings such as primary care, be used by a variety of health practitioners, and capture cognitive ability with few testing fatigue effects, which are common for elderly individuals (Geda et al., 2013; Lorentz, Scanlan, & Borson, 2002; Milne et al., 2008; Morris et al., 1993).  Brief and concise measures are most used in general practice providing the first component for referral, monitoring cognitive functioning, and further differential diagnostic considerations in dementia diagnosis (Boustani et al., 2003; Cullen et al., 2007).  For ethnic minorities, brief assessment must be as accurate as possible given the difficulties in healthcare access and the increased challenge of capturing the MCI window due to increased rate of cognitive decline (Barnes et al., 2006; Lee et al., 2012; Mehta et al., 2008; Sudore et al., 2006).

Currently, there are at least sixteen different brief assessments instruments for MCI available for general practice with several computerized options in development (Snyder et al., 2011; Tierney et al., 2014).  Among these measures, there is variability in which domains of functioning are assessed resulting in differences in reporting cognitive functioning (Sachdev et al., 2015).  The Montreal Cognitive Assessment (MoCA), which was developed in 1996 and validated in 2005, has been found to efficiently assess the most cognitive domains and has been recently implemented in clinical settings to assess MCI due to AD (Freitas, Simões, Marôco, Alves, & Santana, 2012; Nasreddine et al., 2005).  It has been used previously to assess for MCI due to various medical conditions such as CVD, stroke, and Parkinson’s Disease but was only implemented to assess for MCI due to AD in 2010 (Dong et al., 2010; Gill, Freshman, Blender, & Ravina, 2008; Pendlebury, Cuthbertson, Welch, Mehta, & Rothwell, 2010; Roalf et al., 2013). The MoCA has been found to have strong psychometric properties, high sensitivity, and efficiency in clinic settings in Canada (Smith, Gildeh, & Holmes, 2007).  When using the MoCA in the US, studies have found similarly strong psychometric properties but have consistently indicated need for US population based norms and diagnostic scores, particularly for ethnically diverse populations and those with lower educational attainment to increase accuracy in MCI classification (Malek-Ahmadi et al., 2015; Roalf et al., 2013; Rossetti et al., 2017).  Due to differences in performance on cognitive measures between African Americans, Hispanic Americans, and whites on several neuropsychological measures; ethnic/racial specific norms are suggested to be used when available (Benuto, Soto, & Leany, 2015; Lichtenburg, 2009; Morgan, Marsiske, & Whitfield, 2007; Strauss, Sherman, & Spreen, 2006).  However, such specific norms are not yet available with adequate sample size for the MoCA (Goldstein et al., 2014; Malek-Ahmadi et al., 2015, Rossetti et al., 2017).

In addition to establishing a brief assessment measure for MCI, there is also a need to measure the presence and contributions of depressive symptoms in people who meet the diagnostic criteria for MCI (Goveas et al., 2011; Hsiao & Teng, 2013; Lyketsos et al., 2002; Mackin et al., 2012). Depressive symptoms are considered a significant risk factor for developing MCI and its subsequent conversion to AD (Butters et al., 2008; Elderkin-Thompson et al., 2007; Forsell, 2007; Mackin et al., 2012; Panza et al., 2010; Zahodne, Stern, & Manly, 2014). An estimated 20% of elderly individuals endorse subsyndromal depression (SSD), which is two or more depressive symptoms that do not meet criteria for clinical depression (Barua, Ghosh, Kar, & Basilio, 2011; Judd, Rappaport, Paulus & Brown, 1994).  Research has found measurable cognitive and functional difficulties that commonly occur with SSD such as, impaired executive functioning, attention, verbal recall, and working memory (Elderkin-Thompson, Mintz, Haroon, Lavretsky, & Kumar, 2007; Jessen et al., 2007; Steffens & Potter, 2008). Imaging studies of individuals endorsing SSD have found increased white matter lesions and decreased volume in the caudate nucleus, both are imaging findings associated with AD conversion (Hannestad et al., 2006).  Co-occurring SSD within MCI is estimated at a 50% prevalence rate and has been conceptualized as a part of the process of dementia development (Geda et al., 2008; Han, McCusker, Cole, Abrahamowicz, & Čapek, 2008; Mackin et al., 2012; Polyakova et al., 2014; Panza et al., 2010).  Despite evidence of cognitive difficulties and brain atrophy, few studies have examined SSD and their relationship with MCI and AD development in at-risk elderly ethnic minority populations (Hamilton et al., 2015).  Research finds elderly African Americans to have increased chronicity and severity of depressive symptoms (Williams et al., 2007).  In addition to increased CVD likelihood, increased chronicity and severity of depressive symptoms exacerbates MCI and AD risk for this population (Barnes, Alexopoulos, Lopez, Williamson, & Yaffe, 2006; Fillenbaum, Peterson, Welsh-Bohmer, Kukull, & Heyman, 1998; Obisesan et al., 2012; Ogunniyi et al., 2006; Tang et al., 2001).  Moreover, there has been little research on the effects of depressive symptoms on elderly Hispanic cognitive performance, despite this population endorsing the most depressive symptoms (Liveny et al., 2011; O’Bryant et al., 2013). These two elderly populations are more likely to endorse somatic symptoms of depression than mood symptoms, which is better captured in the construct of SSD (Brown et al., 1996; Costas & Gany, 2013; Lyness et al., 2006; Uebelacker, Strong, Weinstock, & Miller, 2008). Assessing for and examining SSD effects on brief screening tools, like the MoCA, can increase efficiency in monitoring for incipient MCI for these at-risk populations (Hamilton et al., 2015; Steffens et al., 2006).

The purpose of this study is to provide US-population based norms and threshold scores for the MoCA.  Following the methodology for the Mayo Clinic’s Older American Norm Studies (MOANS) and the available studies on the MoCA published by the American Academy of Neurology, normative data will be stratified by cognitive state, age, and education and grouped by ethnicity (Goldstein et al., 2014; Lucas et al., 2005; Paulker, 1988; Rossetti et al., 2017).  Reporting in this manner allows the clinician to best identify their group of interest for comparison (Committee on Psychological Testing, 2015; Mitrushina, 1999).  Receiver operating characteristic (ROC) analyses will examine sensitivity and specificity of current and population based threshold scores used to identify presence of MCI.  Similar methods of threshold score analyses have been conducted examining the MoCA in detecting MCI in Parkinson’s Disease and for other population-based studies for brief cognitive measures (Davis et al., 2015; Ling et al., 2013; Trzepacz et al., 2015).  To further understand the effects of SSD on cognitive functioning and progression in MCI, a comparison MoCA scores of those endorsing SSD, MCI, and co-occurring SSD and MCI will be compared at three intervals.  Group comparisons have been essential to understanding elderly cognitive presentations affected by mood symptoms and have been done on other brief cognitive measures (Elderkin-Thompson et al., 2007; Lyketsos et al., 2002).  Examining the effects of SSD on MoCA scores can aid in monitoring those at higher risk of AD conversion (Albert et al., 2011; Petersen et al., 2001).  At the time of this proposal, no studies on the effects of depression or depressive symptoms on the MoCA performance are available.  Through a conceptual framework that highlights the contributions of biopsychosocial factors on elderly cognitive presentation, this research seeks to contribute US population based norms and threshold scores with consideration for age, education and ethnicity as covariates; and to examine the effects of SSD on MCI presentation in US populations.


Health Outcomes

As of 2017, the direct cost of Alzheimer’s Disease (AD) and other dementias are estimated to be $259 billion in the United States, with out of pocket costs making up 22% and Medicare covering $179 billion (Karlawish, Jack, Rocca, Snyder, & Carrillo, 2017).  Prevalence of AD is projected to rise from 4.7 million in 2010 to 13.8 million in 2050 if preventative measures are not enacted (Hebert, Weuve, Scherr, & Evans, 2013).  AD is the 6th leading cause of death in the US in Americans age 65 and older (Alzheimer’s Association, 2013).  Given these staggering cost, and the growing public health concern; research in the field of gerontology and geropsychology has focused on prevention, diagnosis and care. Current literature has termed the targeted intermediate stage of cognition as Mild Cognitive Impairment (MCI). However, previous research has referred to MCI by other nomenclature for example, age-associated memory impairment, cognitive impairment no dementia, and prodromal AD. The earliest reported condition similar to MCI was Age-Associated Memory Impairment (AAMI), which presented with gradual memory loss over the age of 50, proof of memory loss compared to relevant means on standardized cognitive measures, and evidence of adequate intellectual functioning (Crook et al., 1986).  Cognitive Impairment No Dementia (CIND) was defined as deficits in memory or cognitive domains that were not severe enough to meet criteria for dementia but were distinctly different from cognitively normal individuals of the same age (Ebly, Hogan, & Parhad, 1995).  Prodromal AD was the competing term for MCI but included associated brain lesions in the mesial temporal regions (Dubois, 2000; Hodges, 1998).  It is now a concept used in the neuroimaging of AD specifying hippocampal damage and amnestic features (Colliot et al., 2008; Dubois & Albert, 2004).  Because there has been a wide-range of reported prevalence rates from 0-45% in older individuals, it has become necessary to standardize the identification of this intermediate stage of cognitive deterioration in older adults (Albert et al., 2011; Ward, Arrighi, Michels, & Cedarbaum, 2012). Since the research on MCI and its prevalence has yielded mixed findings, a task-force group was formed to develop specific criteria to be used to diagnose MCI (Petersen et al., 2014; Petersen et al., 1999). Currently, the National Institute on Aging-Alzheimer Association (NIA-AA) working group and the Diagnostic and Statistical Manual for Mental Disorders (DSM-5) acknowledges the predementia condition as Mild Neurocognitive Disorder (NCD) with the following criteria:

  1. Clinical concern raised by patient or informant, or observations made by the clinician;
  2. Cognitive impairment in one or more cognitive domains preferably relative to appropriate normative data for the individual;
  3. Preservation of functional independence; and
  4. No dementia (Albert et al., 2011; American Psychiatric Association, 2013).

For the purposes of this research and the associated background literature, I refer to the target condition as MCI.

The Cohort Studies of Memory in an International Consortium (COSMIC) is a comprehensive review that examined 11 worldwide aging cohorts and reported estimated prevalence rates of MCI from 5.0%-36.7% (Sachdev et al., 2015). In the US, the Einstein Aging Study is a COSMIC study that followed a sample of 1944 participants from the Bronx community of New York.  Katz and colleagues (2012) reported a prevalence of 21.5% MCI in this community sample with comparable rates in men (22.2%) and women (21.0%).  This study also found that 15-20% of the patients that met criteria for MCI would convert to AD (Karas et al., 2008; Katz et al., 2012; Petersen et al., 2001; Schmidtke & Hermeneit, 2008).  Research studies have examined risk factors that explain the conversion from MCI to AD (Biessels, Staekenborg, Brunner, Brayne, & Scheltens, 2006; Chen, Lin, & Chen, 2009; Knopman et al., 2001; Mackin et al., 2012; Obisesan et al., 2012; Sattler, Toro, Schönknecht, & Schröder, 2012). These include: biological factors such as age, genetic presentation, and poor cardiovascular health; psychological factors for example depression and SSD, and social factors such as socioeconomic status, education level, and access to healthcare.

Biological Risk Factors

Age is the major contributing factor for MCI and AD, as the risk of developing these conditions doubles every five years over the age of 65 (Mayo Clinic, 2017).  It is estimated that dementia affects one in 14 people over the age of 65 and one in six people over the age of 80 (Prince et al., 2014).  The number of Americans over the age of 65 is projected to rise from 46 million to 98 million but 2060 meaning that over 6 million individuals will develop dementia and an estimated 19 million will meet MCI criteria (Mather, 2017).  Though decline in cognition can begin as early at 45, many studies focus on ages 65 due to the ability to receive benefits in the US, like the standardization of annual wellness visits within Medicare (Cordell et al., 2013).  Hypotheses around why age is a significant risk factor for MCI and AD include reduced integrity of nerve cells and cell structure, less effective immune and structural repair system, and increased risk of cardiovascular diseases with age (Prince et al., 2014; Yazdanyar & Newman, 2009).  With such estimates, accurate assessment of cognitive functioning is essential to differentiate cognitive impairments from normal aging.

The current pattern for norming cognitive measures includes reducing sample size from 200 to 100 over the age of 65 (Albert et al., 2011; Wechsler, 2008; Wechsler, 2009; Wechsler, 2011).  This decrease in norm sample comes with an associated lack of diverse representation which is concerning as the population over 65 is becoming more culturally and ethnically diverse.  For example, the percentage of non-Hispanic whites is projected to drop from 78% to 54% by 2060 (Mather, 2017).  African Americans and Hispanic Americans develop MCI at a rate two times higher than whites across age bands of 5 years starting at age 70 (Katz et al., 2012; Tang et al., 2001).  Despite previous reports of elderly African Americans declining slower and living longer than whites, recent research finds higher mortality rates and faster rates of decline in this population (Barnes et al., 2006; Lee et al., 2012; Mehta et al., 2008).  African American and Hispanic elderly have increased likelihood of conversion to AD if initially meeting MCI upon assessment (Early et al., 2013; Tang et al., 2001).  Even when given the option to complete brief measures in English or Spanish, Hispanic elderly were found to meet criteria for MCI at a younger age and had higher prevalence of AD with age compared to whites (O’Bryant et al., 2013).  With the smaller sample for norms at older ages in cognitive measures, there is also a need for increased representation of at risk minorities in norming samples to increase accuracy in differentiating cognitive states.  The current study will attempt to provide adequate sample size to examine differences in elderly ethnic groups.

There are notable differences in cognitive presentation and aging between men and women.  Women make up over 60% of AD patients, which has been attributed to findings that women live longer than men (Lin et al., 2015; Mielke, Vemuri, & Rocca, 2014).  In examining disease progression, MCI is more prevalent in men than women but among women with MCI progression to AD at faster rate and more abrupt (Lin et al., 2015; Petersen et al., 2010).  While genetics, low education, and less instrumental activities of daily living were associated with AD conversion, depressive symptoms were found to be a more significant contributing factor in women than men (Artero et al., 2008).  There is very little data examining gender differences in ethnic minorities, particularly for Hispanic women.  Katz and colleagues (2012) reported no difference in prevalence of MCI and AD between African American men and women, nor did they report interaction effects of race and gender.  With MCI being the point of intervention for dementia research, available data shows that the window of MCI in women is brief and difficult to identify.  Little evidence is available on the influence of interaction effects of race for women, or other variables of influence, in the development and conversion of MCI and AD.  With such a small window for intervention, accurate diagnostic scores on the MoCA should be evaluated for women.

Family history of dementia of cognitive dysfunction is also considered a risk factor in developing MCI. In relatives of those with dementia, there is an increased presence of AD biomarkers, such as brain inflammation, genetic predisposition to amyloid deposition, and tau pathology, that contribute to differences in brain structure integrity in areas associated with AD, like the hippocampus (Bendlin et al., 2010; Honea, Vidoni, Swerdlow, & Burns, 2012).  Genetics studies in AD are examining individuals with the presence of apolipoprotein E4 allele (ApoE4), a variation on the principal cholesterol carrier to the brain via ApoE receptors, which has been identified as a risk factor MCI and cardiovascular disease (Stolerman, 2010).  ApoE4 may play a role in the beta-amyloid hypothesis of AD, which posits that the brains of individuals with AD have beta-amyloid plaque buildup disrupting cell-to-cell communication, incorrect immune response, and brain cell death due to inflammation (Beyreuther & Masters, 1991; Hardy & Allsop, 1991; Hardy & Higgins, 1992; Selkoe, 1991; Selkoe & Hardy, 2016). The ApoE4 variant is not as effective as other isoforms in breaking down the beta-amyloid peptide resulting in increased AD risk (Jiang et al., 2008).  Several studies have found the genetic variation to have a significant effect on converting from MCI to AD (Blom et al., 2009; Fei & Jianhua, 2013).  In participants that meet criteria for MCI, the associated genotype has been associated with a 1.4-fold increased risk incident AD development (Boyle, Buchman, Wilson, Kelly, & Bennett, 2010).  Gender differences in decline were more disinct for female ApoE4 carriers (Lin et al., 2015).  Where genotyping is available, the ApoE4 gene variation has been seen in 34% of preMCI, 15% of nonamnestic MCI, and 39% of amnestic MCI (Duara et al., 2011).

Presence of the ApoE4 gene greatly increases MCI and AD likelihood for non-Hispanic whites, accounting for 20-50% of the estimated risk (Breitner et al., 1999; Tang et al., 1996).  However, prevalence of ApoE4 is higher in African Americans than whites, but is not always associated with AD or MCI in this population (Evans et al., 2003; Logue et al., 2011).  A recent study finding the co-occurrence of ApoE4 and another specific gene involved in transporting cholesterol (ABCA7) is specifically related to increased AD risk in African Americans (Reitz et al., 2013). Such findings have prompted research examining specific genes in this population, but when comparing families and first-degree relatives of African Americans and individuals in Yoruba, AD rates are twice as high in African Americans (Ogunniyi et al., 2006).  This difference suggests that while genetics can provide answers, there is also a contribution from the environment specific to African Americans suggesting the need for a biopsychosocial understanding of MCI and AD (Obisesan et al., 2012).  One study reports a 21% of their sample of 28 Mexican Hispanics had the ApoE4 allele but it was not associated with increased conversion to AD risk, however there is little genetic data available for Hispanic populations (Campos, Edland, & Peavy, 2013; Reitz & Mayeux, 2014).  While interests in the research community lie in genetics for preventative endeavors, accurate assessment of cognitive functioning in ApoE4 carriers will help in diagnosis and treatment for those currently affected.

Several health conditions have been identified as risk factors for MCI and AD.  Diabetes mellitus type 2 has been associated with increased risk of MCI, AD, and vascular dementia affecting learning, memory, mental flexibility, and processing speed (Biessels, Staekenborg, Brunner, Brayne, & Scheltens; Cheng, Huang, Deng, & Wang, 2012). Cognitive deficits are likely due to poorly controlled blood sugars in type 2 diabetes, which leads to nerve and cell damage in the brain (Roberts et al. 2008).  Imaging studies find white matter lesions, hippocampal and amydala atrophy and presence of infarcts inversely related to cognitive functioning in this population (den Heijer et al., 2003; Manschot, Brands, van der Grond, Kessels, et al., 2006).  Reported prevalence of type 2 diabetes in MCI individuals is about 14% in adults aged 55-59 and 17% in individuals aged 60 and over (Tiwari et al., 2012).  The increased risk of cognitive impairment in elderly populations with type 2 diabetes may be from a combination of reduced structural integrity in the brain and diabetes-related brain atrophy.  Type 2 diabetes has been found to accelerate progression from MCI to other dementias (Xu et al., 2010).  The ApoE allele association with glucose and lipid metabolism and total serum cholesterol is also a risk factor for developing type 2 diabetes (Carmel et al., 2009; Chene et al., 2016). In examining those who convert to AD, there evidence of high total serum cholesterol (Anstey, Lipnicki, & Low, 2008; Solfrizzi et al., 2004).  Findings suggest that the presence of the ApoE allele increases likelihood of developing metabolic disorders like type diabetes and hypertension.  Furthermore, presence of the allele means that there is an inefficient breakdown of lipids increasing likelihood of MCI and AD via the buildup of amyloid deposits (Chene et al., 2016).  The detection of MCI in type 2 diabetes in brief measures like the MoCA have been promising, suggesting their effectiveness to identify windows of intervention (Alagiakrishnan et al., 2013; Lee et al., 2014).

Type 2 diabetes disproportionally affects ethnic minorities in the US.  In comparing African Americans and non-Hispanic whites, African American women are two times more likely to develop diabetes and white men are at one fifth the risk compared the African American men (Baptiste-Roberts et al., 2007; Lipton, Uao, Cao, Cooper, & McGee, 1993).   Presentation of diabetes in African Americans has atypical features, such as diabetic ketoacidosis at initial diagnosis or spontaneous remission after about three months of intensive therapy (Marshall, 2005).  Some have suggested that increased prevalence of the ApoE4 allele provides a genetic explanation for this increased proportion due to the gene’s key interactions with lipid breakdown (Chene et al., 2016).  Type 2 diabetes also disproportionally affects Hispanic populations.  For example, California has one of the largest Hispanic populations and reports a 6% prevalence of diabetes statewide among Hispanics (Chukwueke & Cordero-MacIntyre, 2010).  The risk of MCI  attributable to diabetes is significantly higher in African Americans (8.4%) and Hispanics (11.0%) compared to non-Hispanic whites (4.6%) which suggests interaction effects of health conditions and ethnicity in cognition (Luchsinger et al., 2007).  The MoCA’s role in monitoring individuals with diabetes will be essential to identifying MCI as managing one’s own medications is a key aspect functional independence (Lawton & Brody, 1969).  Maintaining this function is essential for elderly individuals with chronic diseases. With such difference in prevalence rates these populations contributing to an increased risk in MCI presentation, norms and accuracy of diagnostic tools is needed for these populations.

Diabetes is often linked to hypertension or high blood pressure.  Overall prevalence of the co-occurring diseases ranges from 60-86% of diabetes cases increasing up to 93% above age 80 (Kabakov et al., 2006).  Diabetes will weaken arteries often causing them to harden, requiring that the heart pump blood at greater force to maneuver blood cells (Barhum, 2017).  Risk of developing hypertension increases with age affecting one third of the US population over age 18 (Nwankwo, Yoon, Burt, & Gu, 2013).  Hypertension has been associated with increased risk of vascular dementia and stroke, but it has also been associated with increased risk of MCI and AD as those with hypertension have higher Aβ deposits in the brain (Carnevale, Perrotta, Lembo, & Trimarco, 2016; Sharp, Aarsland, Day, Sønnesyn, & Ballard, 2011).  There are several models that hypothesize relationships of hypertension and beta Aβ deposits, but one theory posits hypertension interference in the receptor for advanced glycation end products (RAGE) regulation which is involved in transporting Aβ through the blood brain barrier (Carnevale et al., 2016).  Review of pharmacological antihypertensive therapy in people with MCI finds a slower rate of cognitive decline and a 36% reduction in rate of conversion to AD (Duron et al., 2009; Schneider et al., 2011).  Cognitive deficits can also be seen in brief screening measures for individuals with hypertension.  For example, average MoCA scores for a CVD population were 22.8 (McLennan et al., 2011).

One of the largest public health concerns related to this condition is the high prevalence of undiagnosed and unmanaged hypertension in ethnic minority populations (CDC, 2013).  Hypertension is significantly present in African Americans compared to other ethnic groups, with a reported 42% prevalence continuing into older adulthood meaning that this condition is two times more likely to develop in this population more than any other group in the US (Nwankwo et al., 2013). Hispanic elderly are more likely than whites to have uncontrolled hypertension (Ostchega, Dillon, Hughes, Carroll, & Yoon, 2007).  Prevention, education, and awareness efforts have been made to address such differences in prevalence resulting in overall increased awareness for all US population (Nwankwo et al., 2013).  Despite this increased awareness, prevalence of uncontrolled hypertension and antihypertensive medication non-adherence is still higher for African American and Hispanic populations (Keenan, Rosendorf, Control, & Prevention, 2011).  Such fluctuations in medication adherence and lack of awareness of hypertension status can affect cognitive performance.  As previously mentioned, MoCA scores were three points below the current threshold score for individuals with CVD (McLennan et al., 2011).  If hypertension status is unknown, clinicians will not know to consider contributions of CVD to cognitive presentation.  Just as the MoCA can be used in the clinical trials to track changes in cognition due to medication, it can be used as an ancillary tool related to medication non-adherence to ensure that intervention and education are communicated to at-risk populations.

Psychological Contributors

There is a low prevalence of major depressive disorder (MDD) in elderly populations, but when present it is associated with impaired social functioning, increased risk of suicide, and self-neglect (Alexopoulos, 2005; Blazer, 2003). These populations are likely to endorse somatic symptoms and more severe apathy compared to younger individuals (Fiske, Wetherell, & Gatz, 2009).  Research has demonstrated the adverse cognitive effects of depression in the elderly, notably impaired executive functioning, verbal recall, and working memory (Elderkin-Thompson et al., 2007; Jessen et al., 2007; Steffens & Potter, 2008). This confound has resulted in depression as an exclusion criterion in many studies examining cognitive performance (Mueller et al., 2005; Rabinovici et al., 2015; Rosenbloom et al., 2014).  However, depression and depression history has been identified as a risk factor for overall development of dementia and specifically so in AD (Alexopoulos, 2005; Luchsinger, Honig, Tang, & Devanand, 2008; Ownby et al., 2006, Speck et al., 1995). For example, Luchsinger and colleagues (2008) found the relationship between AD and depressive symptoms was not accounted for by CVD suggesting depressive symptoms have their own relationship.  Meta-analytic studies found increased risk ratios for AD for subjects currently with or with history of depression (Ownby et al., 2006; Speck et al., 1995).

Endorsement of two but less than five symptoms of depression, but not necessarily mood symptoms, has been defined as subsyndromal depression (SSD; Judd et al., 1994).  Prevalence of geriatric depression increases to 20% when including SSD among individuals aged 65 and above (Barua, Ghosh, Kar, & Basilio, 2011; Han et al., 2008; Steffens, Fisher, Langa, Potter, & Plassman, 2009).  Furthermore, findings indicate that 21.1% of individuals with MCI endorse SSD suggesting that there is a relationship between the two conditions (Goveas, Espeland, Woods, Wassertheil‐Smoller, & Kotchen, 2011; Polyakova et al., 2014).  For example, Goveas and colleagues (2011) that women over 65 who endorsed depressive symptoms at baseline were twice as likely to develop MCI or probably dementia after controlling for sociodemographic status, CVD status, and antidepressant use.  Many studies have found increased risk of MCI conversion to dementia when individuals report a history of depression, endorse SSD, or indicate geriatric depression (Dotson, Beydoun, & Zonderman, 2010; Mackin et al, 2013; Palmer et al., 2007).  For example, Dotson and colleagues (2010) examined 1239 older adults from the Baltimore Longitudinal Study of Aging over about 25 years and found a risk of developing dementia increased by 14% per reported depressive episode in individuals with a history of depression.   Brain imaging and genetic studies find elderly with both MCI and SSD have more ApoE4 alleles, lower overall white matter, lower hippocampal volume, and are nearly two times more likely to have functional impairment compared to non-depressed individuals (Mackin et al., 2013).  Biomarker studies examining Aβ clusters in the brain find higher concentrations for MCI individuals who endorse SSD than non-depressed increasing likelihood of AD conversion according to the amyloid hypothesis (Brendel et al., 2015).

Several hypotheses have surrounded the idea of depressive symptoms and mild cognitive impairment. Such hypotheses include whether depression is either a prodrome or risk factor for dementia or whether the conditions share a neurological pathway of decline (Butters et al., 2008; Panza et al., 2010; Wetherell, Gatz, Johansson, & Pedersen, 1999).  Panza and colleagues (2010) suggests several potential interactions of MCI and depressive symptoms:

1.      That there is a significant overlap of depressive symptoms with MCI

2.      Individuals experiencing some level of cognitive decline develop a depressive reaction

3.      Hippocampal atrophy results in mood and memory impairment

4.      Depressive symptoms co-occurring with the ApoE genotype are found to be more resistant to antidepressant treatment

As with MCI progression, research also finds a mood progression. Initial mood reaction to mild cognitive difficulty has been found to be apathy, measured by endorsement of the associated three items on the GDS (Bertens et al., 2016; Panza et al., 2010).  SSD apathy symptoms have been more frequently associated with amnestic MCI and associated with double to risk of developing AD than non-apathetic individuals with MCI (Robert et al., 2006).  With research connecting brain imaging biomarkers, significant increased risk, and indication of increased disease severity to depressive symptoms; there is a need to standardize mood screening for depressive symptoms when assessing for MCI (Forsell, 2007).  Examining the effects of SSD on the MoCA will aid in identifying the window of MCI due to associated increased rate of conversion with both conditions.

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