Subtle changes in daily functioning predict conversion from normal to mild cognitive impairment or dementia: an analysis of the NACC database

Abstract

Background

There are relatively small but observable changes in functional ability in those without Mild cognitive impairment (MCI) or dementia. The present study seeks to understand whether these individuals go on to develop MCI or dementia by assessing the association between baseline Functional Activities Questionnaire (FAQ) and conversion independent and after adjustment for cognitive tests.

Methods

The NACC database was used to conduct the analysis of which 7,625 participants were initially identified as having more than one visit and who were cognitively normal at their first visit. Cox proportional hazards were used to fit three models that controlled for executive and non-executive cognitive domains. A similar model was used to assess the effect of FAQ subcategories on conversion.

Results

Of these individuals, 1,328 converted to either MCI or dementia by visit 10. Converters had a total visit 1 FAQ score significantly higher than non-converters indicating more functional impairment at baseline. After adjustment for cognitive tests, the association between visit 1 FAQ and subsequent conversion was not attenuated. Doing taxes, remembering dates, and traveling were individually identified as significant predictors of conversion.

Conclusions

The FAQ can be used as an indirect measure of functional ability and is associated with conversion to MCI or dementia. There is a selective and significant association between changes in financial ability and conversion that is in accordance with other research of financial capacity.

Introduction

The number of Americans older than 65 is expected to more than double in the next 40 years, increasing from 40.2 million in 2010 to 88.5 million in 2050 (Alzheimer's, 2014). Of these older individuals, many will experience both cognitive decline and impairment in performing daily activities. Some of these declines may be signs of impending disease. While early cognitive changes often are recognized as the first symptoms of decline, impairment in daily functioning can also occur early and be quite distressing. This impairment may impact personal or environmental safety or ability to care and protect oneself. An important question is whether there are clinical characteristics of subtle yet observable functional changes before dementia that may anticipate future cognitive decline.

In elderly persons, subtle cognitive deficits occur within the normal range on standard testing protocols and predict the time to clinically relevant cognitive impairment before clinical symptoms are reported (Kawas et al., 2003; Visser et al., 2006). Similarly, there are subtle functional deficits, specifically with instrumental activities of daily living (IADLs) that can be identified and predate a diagnosis of MCI (Peres et al., 2006; Di Carlo et al., 2007; Reppermund et al., 2013). Mild cognitive impairment (MCI) is commonly referred to, as a state in which there is cognitive decline in older adults that is not of sufficient magnitude to impact daily life and so meet criteria for dementia (Albert et al., 2011). Because many persons with MCI are at increased risk of transitioning from normal aging to dementia it is of great public health importance to study this transition (Fischer et al., 2007; Marchesi, 2012). Dementia due to Alzheimer's disease (AD), for example, is most commonly preceded by a long prodromal period during which cognitive symptoms insidiously emerge though without significant impact to one's life (Nowrangi et al., 2011). The presence of brain amyloid (imaged by PET) in functionally “normal” older adults has been associated with subtle but significant cognitive deficits, which may reflect early neuropathological changes (Sperling et al., 2013). As impairments in daily functioning increase, a diagnosis of the dementia syndrome is made. The extent of this impairment and its measurement, however, is not well-understood. As our population ages such IADLs as financial management, driving an automobile, medication management, and other complex judgment and decision-making processes will become areas to carefully monitor as deficits in these can lead to unwanted or even dangerous outcomes (Harada et al., 2013). The development and course of functional changes that occur along the continuum towards dementia; however, are poorly defined and their relationship with the transition from no appreciable cognitive deficits to MCI or dementia has not been well-studied.

Because of both the clinical and scientific interest in identifying early stages of dementia, there is a growing body of research focused on better characterizing the transitions between those who appear cognitively normal and MCI or dementia (Vallotti et al., 2001; Kryscio et al., 2006; Artero et al., 2008; Oulhaj et al., 2009). Moreover, characterizing the earliest changes in daily functioning that might predict the onset of disease states might help identify individuals who could participate in secondary prevention trials that may halt, delay, or reverse disease progression.

Though cognition and functioning are related to each other, the overall objective of the present study is to understand the transition from apparently normal cognition to MCI or dementia from the perspective of daily functioning. Because there are relatively small but observable and measurable deficits in daily functioning in a subset of those who cognitively test as normal, we intend to investigate whether these individuals go on to develop MCI or dementia when examined independent of cognitive measures. In order to accomplish this goal, we will measure the independent utility of the Functional Activity Questionnaire (FAQ) in predicting conversion to MCI or dementia, with and without adjustment for cognitive performance. We first hypothesize that relatively small changes in the FAQ are associated with an increase in the risk of conversion to MCI or dementia. We underscore here that we are not asserting causation. Secondarily, we hypothesize that those primarily with deficits in IADLs specific to financial activities will go on to convert from normal aging to MCI or dementia compared with other IADLs. We will do this by individually examining the FAQ's component parts.

Methods

Participants and study design

National Alzheimer Coordinating Center (NACC): Volunteers were recruited from 34 past and present NIA-funded Alzheimer's Disease Centers (ADC) across the USA. NACC has developed and maintains a large database of standardized clinical and neuropathological research data (Beekly et al., 2004). Data included in this study occurred between September 2005 and January 20015. All ADCs were overseen by local IRBs, and after complete description of the study to the subjects, written informed consent was obtained. Volunteers were evaluated annually by trained clinicians either in the clinic or at home. At each annual visit, a Uniformed Data Set (UDS) (Beekly et al., 2007) was collected, which included demographics, medical history, family history, behavioral, and functional assessments, as well as a neuropsychological battery. Race and ethnicity were based on the participant's report. Diagnoses were made by a clinician or consensus conference using all available data (Morris et al., 2006; Morris, 2008; Weintraub et al., 2009). MCI diagnoses were made using modified Petersen criteria (Petersen et al., 2001; Petersen et al., 2009) and AD diagnoses were made using NINCDS/ADRDA criteria (McKhann, 2011). Normal participants were not diagnosed with either MCI or any type of dementia.

Measures

The Functional Activities Questionnaire (FAQ) was administered to the informant at each visit by a trained and certified NACC staff member (Morris et al., 2006; Morris, 2008; Weintraub et al., 2009). The FAQ (Pfeffer et al., 1982), is a 10-item clinical measure of independence for completing daily tasks necessary for independent living that can be administered to either patient or caregiver. The FAQ is a consistently accurate instrument with good sensitivity (85%) and also has good reliability (exceeding 0.80) (Pfeffer et al., 1982). Tests of validity have been performed on the FAQ and have established it as both a bedside as well as a research instrument. The abilities assessed in the FAQ are: (1) writing checks, paying bills, or balancing a checkbook; (2) assembling tax records, business affairs, or papers; (3) shopping alone for clothes, household necessities, or groceries; (4) playing a game of skill or working on a hobby; (5) heating water, making a cup of coffee, or turning off the stove after use; (6) preparing a balanced meal; (7) keeping track of current events; (8) paying attention to or understanding and discussing a TV, book, or magazine; (9) remembering appointments, family occasions, holidays, medications; (10) traveling out of the neighborhood, driving, arranging to take a bus. For each activity, four levels ranging from dependence (scored 0) to independence (scored 3) are specified. Sum scores range from 0 to 30 with higher scores reflecting greater dependency. A cut-point score of 9 (dependent in 3 or more activities) is recommended to indicate impaired function and possible cognitive impairment. If the subject had never done one of these activities, a coding of “not applicable” was given and was excluded from this analysis.

Several tests of memory and executive function were selected for inclusion in the analysis because performance of daily functional tasks requires effective recruitment and execution of cognition specifically in these domains (Royall et al., 2007; Hughes et al., 2012; Monsell et al., 2014). While other cognitive domains such as language and visuospatial ability are also necessary, most evidence suggests episodic memory and executive control are essential (Royall et al., 2002; Royall et al., 2007; Reinvang et al., 2012). Therefore, we included the following cognitive tests to assess executive function: (1) Trail Making Test (TMT) A and B jointly assess attention, visual scanning and search skills, and psychomotor speed and coordination (Reitan, 1955). Independently, TMT A assesses processing speed whereas TMT B assesses set switching therefore, both were included in the analysis and the difference between TMT B and A as a measure of executive function. (2) Category (animals and vegetables) fluency evaluated semantic and phonemic verbal fluency (Lezak, 2004c). (3) Digit span forwards and backwards (Jensen and Figueroa, 1975) are also measures of working memory and often fall under the auspices of executive function. Memory function was primarily evaluated by use of: (3) Mini-Mental State Exam (MMSE) (Folstein et al., 1975) which includes screening estimates of episodic recall but also of orientation, attention, language, and visual construction. (4) Current Logical Memory 1A Story Units Recalled is an age-, sex-, education-adjusted z-score for the total number of items recalled (Abikoff et al., 1987). This test examines the ability to recall a short story and is used as an approximation of episodic memory. The Boston naming test (Kaplan et al., 1983; LaBarge et al., 1986) was also included in the analysis to assess effect of language function. It assesses confrontational word retrieval commonly in patients with aphasia due to a variety of neurological conditions. The Wechsler Adult Intelligence Scale (WAIS-R) digit symbol test was added to provide an estimate of processing speed (Wechsler, 1939). Finally, we included the Geriatric Depression Scale (GDS), which is a 15-item screening measure for depression in older adults. Scores range from 0 to 15, with scores greater than 5 indicating a positive screen (Yesavage, 1988).

Statistical analysis

Conversion was defined as change in diagnostic class from normal to MCI or dementia. We compared demographics, baseline cognitive measures (as described above), FAQ total (0–30) and individually (0–3) between converters and non-converters using t-tests or χ² tests (p < 0.05). All tests were two-sided; t-tests assumed unequal variances and used Satterthwaite's approximation for degrees of freedom (Satterthwaite, 1946).

In order to test our hypothesis, we fit three statistical models that systematically adjusted for cognitive variables. In the base model, (1) we fit Cox proportional hazards models and included for baseline age, sex, years of education, GDS, and total FAQ. To further investigate the effect of cognition, we fit two other models additionally controlling for, (2) non-executive (memory and naming): MMSE, logical memory, Boston naming, and (3) combined executive function and nonexecutive: TMT B–A, animal and vegetable category fluencies, and digit span forward and backward, WAIS-R (executive measures) with nonexecutive as in (2). Time to conversion or censor was measured in years.

Secondarily, we fit Cox proportional hazards models to FAQ subcategories, as referenced earlier, to investigate which abilities, if any, predicted conversion. To control for cognitive variables as well as age, sex, years of education, we applied the same three-model procedure as described above in the primary analysis. All computations were done using STATA version 11.0 (StataCorp, College Station, TX).

Results

Participant characteristics

Our data request included all visits for all 7,625 participants with more than one visit and who were cognitively normal at their first visit. Of these individuals, 1,328 converted to either MCI or dementia by visit 10. Table 1 summarizes demographic, cognitive, and functional data by conversion status. Converters were more likely to be older, female, and have slightly less education over all. On a scale of independence with 1 indicating complete independence and 4 complete dependence, converters, at baseline, were somewhat more dependent. Not surprisingly, converters tended to live less independently than their non-converter counterparts and a greater percentage lived in a retirement community or other assisted living environment. Cognitively, converters had lower MMSE, and episodic memory scores (logical memory) and tests of executive function were lower than those of non-converters.

Table 1. Mean participant characteristics at baseline (SD).

		non-converters (N = 6,297)	converters (N = 1,328)	p value
Age (SD)		70.53 (9.91)	76.85 (8.75)	p < 0.001
Female		4,257 (67.6%)	810 (61.0%)	p < 0.001
Years of education (SD)		15.77 (2.90)	15.18 (3.17)	p < 0.001
Level of Independence*	1	6,188 (98.3%)	1,261 (95.0%)	p < 0.001
	2	90 (1.4%)	47 (3.5%)
	3	19 (0.3%)	17 (1.3%)
	4	0 (0.0%)	3 (0.2%)
Type of residence				p < 0.001
Single family residence		5,694 (90.7%)	1,116 (84.2%)
Retirement community		436 (6.9%)	169 (12.7%)
ALF/boarding home/adult family home		17 (0.3%)	10 (0.8%)
SNF/NH		1 (<1%)	0 (0.0%)
Other		133 (2.1%)	31 (2.3%)
MMSE		29.01 (1.32)	28.48 (1.63)	p < 0.001
Logical memory		13.81 (3.79)	11.02 (4.01)	p < 0.001
Boston naming		27.32 (3.09)	25.91 (3.94)	p < 0.001
Category fluency (animals)		20.68 (5.65)	17.86 (5.18)	p < 0.001
Category fluency (vegetables)		15.15 (4.26)	12.76 (4.03)	p < 0.001
Trails B–A		52.96 (39.71)	74.76 (51.86)	p < 0.001
Digit span forward		8.66 (2.03)	8.28 (2.06)	p < 0.001
Digit span backward		6.94 (2.25)	6.37 (2.10)	p < 0.001
WAIS		48.87 (12.33)	40.92 (11.56)	p < 0.001
FAQ total (0–30)		1.92 (6.11)	3.15 (6.74)	p < 0.001

^*Level of Independence: 1: able to live independently; 2: requires some assistance with complex activities; 3: requires some assistance with basic activities; 4: completely dependent.

In terms of daily living activities, converters had a total FAQ score significantly higher than non-converters though mean scores did not meet FAQ cut point of 9, which recommends loss of independence. Within each subscale, converters also scored slightly higher than their counterparts.

Functional activity hazard models

We fit Cox proportional hazard models with conversion to MCI or dementia as the outcome. Table 2 reports Hazard Ratios with standard errors for all three models (as described above). Most notably, the hazard ratio estimate for total FAQ did not change between model 1 (1.02, (0.003), p < 0.01), model 2 (1.01, (0.003), p < 0.01) and model 3 (1.01, (0.004), p = 0.01). Though the association between total FAQ at baseline and conversion-free survival is small, these models suggest that there is no attenuation of the association after adjusting for the cognitive tests. A separate analysis (not shown) was conducted to include various covariates into the statistical model. These included history of heart attack, stroke, seizure, traumatic brain injury, use of anxiolytics including benzodiazepines, prior diagnosis of major psychiatric disorder, and alcohol use. The association between FAQ and hazard of conversion was not altered across all three models when including these covariates.

Table 2. Cox proportional hazard models for conversion to MCI or dementia.

		hazard ratio (SE) p
		model 1†	model 2‡	model 3§
Age		1.06 (0.003) < 0.01	1.05 (0.003) < 0.01	1.04 (0.003) < 0.01
Male		1.29 (0.07) < 0.01	1.17 (0.07) 0.004	1.01 (0.07) 0.92
Education		0.96 (0.01) < 0.01	1.02 (0.01) 0.107	1.03 (0.01) < 0.01
Level of independence	2	2.13 (0.32) < 0.01	1.83 (0.28) < 0.01	1.62 (0.25) < 0.002
	3	1.89 (0.48) 0.01	2.07 (0.53) < 0.01	1.78 (0.46) 0.02
	4	2.10 (1.25) 0.21	2.40 (1.43) 0.14	1.65 (0.99) 0.40
FAQ total		1.02 (0.003) < 0.01	1.01 (0.003) < 0.01	1.01 (0.004) 0.01
GDS		1.11 (0.003) < 0.01	1.10 (0.004) < 0.01	1.10 (0.13) < 0.01
MMSE			0.94 (0.02) 0.001	0.97 (0.02) 0.17
Logical memory			0.93 (0.01) < 0.01	0.94 (0.01) < 0.01
Boston			0.97 (0.01) < 0.01	1.09 (0.01) 0.44
Category fluency (animals)				0.98 (0.01) 0.01
Category fluency (vegetables)				0.95 (0.01) < 0.01
Trails B–A				1.00 (0.001) 0.115
Digit span forward				1.02 (0.02) 0.286
Digit span backward				0.99 (0.02) 0.709
WAIS				0.98 (0.003) < 0.01

^†Model 1: adjusted for baseline age, sex, years of education, and total FAQ.

^‡Model 2: model 1 + MMSE, logical memory, Boston naming.

^§Model 3: model 1 + model 2 + TMT B–A, animal and vegetable category fluencies, and digit span forward and backward.

Subanalysis: Hazard estimation of each FAQ subscales

Table 3 reports sub analyses in which we examine the association between each FAQ subtest and conversion-free survival. The subtests associated with the greatest hazard of conversion were ability to do taxes, remember dates, and travel.

Table 3. Cox proportional hazard models for conversion to MCI or dementia using FAQ components.

	hazard ratio (SE) p
	model 1**	model 2††	model 3‡‡
FAQ total
Bills	0.993 (0.020) 0.75	0.987 (0.019) 0.54	0.973 (0.020) 0.18
Shopping	0.974 (0.034) 0.46	0.970 (0.034) 0.40	0.961 (0.034) 0.26
Taxes	1.05 (0.016) < 0.01	1.04 (0.016) 0.019	1.042 (0.016) 0.006
Games	1.02 (0.014) 0.14	1.016 (0.015) 0.26	1.008 (0.014) 0.58
Stove	0.972 (0.044) 0.53	0.959 (0.042) 0.35	0.972 (0.042) 0.51
Meal prep	1.01 (0.015) 0.97	1.00 (0.015) 0.817	0.991 (0.016) 0.57
Events	0.991 (0.056) 0.88	0.982 (0.055) 0.75	1.001 (0.056) 0.978
Pay attention	0.860 (0.072) 0.07	0.906 (0.071) 0.21	0.944 (0.071) 0.443
Remember dates	1.178 (0.040) < 0.01	1.183 (0.040) <0.01	1.176 (0.040) <0.01
Travel	1.125 (0.038) 0.001	1.121 (0.038) 0.001	1.093 (0.037) 0.008

^**Model 1: adjusted for baseline age, sex, years of education, and total FAQ.

^††Model 2: model 1 + MMSE, logical memory, Boston naming.

^‡‡Model 3: model 1 + model 2 + TMT B–A, animal and vegetable category fluencies, and digit span forward and backward.

Discussion

In this analysis, we sought to examine the association between baseline functioning and conversion to MCI or dementia. We tested the hypothesis that changes in functional ability as measured by the FAQ-predicted conversion from normal to either MCI or dementia, even after adjustment for cognitive tests. We first found that indeed there was variability among total FAQ scores in older adults who tested cognitively within normal range. While FAQ scores were understandably low (since these participants were in the normal NACC cohort) and did not meet the cut point to indicate functional impairment, we did find that those who converted to MCI or dementia had higher baseline total FAQ (Table 1). We found a statistically significant association between baseline FAQ total score and risk of conversion to MCI or dementia. To our knowledge, this association has not been previously identified in a large sample. Additionally, the association between baseline FAQ and risk of conversion was not attenuated after adjustment for cognitive test scores (Table 2). Finally, we showed that the ability to do taxes, remember dates, and to travel were associated with increased risk of conversion (Table 3).

As individuals advance in age, the ability to maintain an independent and good quality of life is dependent not only on their health status but also on their cognitive and functional abilities. To date there is a consistent body of literature supporting a general pattern of age-related changes in cognitive function in which crystallized abilities (general knowledge and acquired skills) are relatively preserved with increasing age, while fluid abilities (reasoning, pattern recognition, and novel problem solving) gradually decline after they peak, typically in one's 20's–30's (Salthouse et al., 2010; Harada et al., 2013). Specific cognitive domains that are typically investigated in regards to normal age-related changes; namely cognitive (psychomotor) speed, attention, executive functions, and memory have also been attributed to MCI and dementia. It is often inferred, then, that declines in cognitive performance as measured by neuropsychological tests closely mirrors or even causes loss of functional abilities that ultimately define the dementia syndrome (Reppermund et al., 2011; Shankle et al., 2013; Caselli et al., 2014).

To our knowledge, this is the first study of the FAQ as an indirect measure of functional ability to predict conversion to MCI or dementia. Other studies have examined conversion from normal to MCI or dementia in relation to functioning but have not explored association directly (Wadley et al., 2007; Artero et al., 2008; Jefferson et al., 2008). Because the neurodegenerative process leading to dementia begins early in adulthood and progresses over decades (Braak and Braak, 1995; Bookheimer et al., 2000), more research is seeking to examine earlier pre-morbid stages to identify individuals that may be more amenable to intervention. A number of studies have now shown that daily functioning becomes impaired in the early stages of cognitive decline before a diagnosis of dementia is warranted and that these individuals go on to develop MCI or dementia (Barberger-Gateau et al., 1993; Tabert et al., 2002; Burton et al., 2006; Peres et al., 2006; Mariani et al., 2008). This is set in the context that those with MCI are at increased risk of progressing to dementia (DeCarli, 2003; Bruscoli and Lovestone, 2004; Panza et al., 2005; Pinto and Subramanyam, 2009). As a result, the revised criteria for MCI as put forward by Albert et al. (2011), suggests that while those with MCI may have mild problems performing complex functional tasks, they are able to live independently. Similarly, a number of studies have shown that early impairment in activities of daily living increased risk of conversion from normal to MCI to dementia (Perneczky et al., 2006; Bangen et al., 2010; Marshall et al., 2014). In the Sydney Memory and Ageing Study (Reppermund et al., 2013), the investigators showed that participation in highly cognitively demanding activities specifically predicted amnestic MCI but not non-amnestic MCI whereas those with low cognitive demand did not predict MCI or dementia (Reppermund et al., 2013). Similarly, Peres et al. (2006), Di Carlo et al. (2007), and DeCarli (2003) found that in those with normal cognition, self-reported IADL restrictions predicted progression to dementia after 2 and 4 years, respectively.

While clinicians frequently use neuropsycho-logical measures to account for differences in functional status among older adults these tests are designed to evaluate specific cognitive abilities rather than functional abilities, and there is little evidence to support such test use in this manner (Chaytor and Schmitter-Edgecombe, 2003; Farias et al., 2003). Research does suggest, however, that global cognition assessed by such measures as the MMSE as well as measures of executive functioning such as the Delis–Kaplan Executive Function System (Delis et al., 2004; Homack et al., 2005), account for the greatest proportion of variance of functional ability (Duda et al., 2014). Several studies have shown specific associations between executive functions (Mariani et al., 2008; Pereira et al., 2010), memory and psychomotor speed (Barberger-Gateau et al., 1993; Tuokko et al., 2005). In a study by Reppermund et al. (2011), significant associations were observed between an instrument of functional ability, the Bayer-Activities of Daily Living (B-ADL) and five cognitive domains (Memory, Attention/processing speed, visuospatial, language, and executive functions) in tasks that designated to be “high cognitive demand” by factor analysis. Other studies including a review of 68 papers by Royall et al. (2005), however, showed only modest association between cognitive measures and functional outcomes. Results from this analysis show the independent association of the FAQ and hazard of conversion to MCI or dementia. This is of potential importance given that neuropsychological batteries take a long time and may be expensive to administer while the FAQ is relatively simple and take a shorter time to administer. Taken together, these results with those from other studies point to a complex interaction between cognition and daily functioning that more likely to be reciprocating rather than linear or simple causation.

The results of this analysis demonstrate a striking association between changes in financial management (taxes) with conversion to MCI or dementia. Financial ability has been the focus of insightful research in normal and pathological cognitive aging. Financial capacity comprises a broad range of conceptual, pragmatic, and judgment and decision-making abilities ranging from basic to more complex skills such as bill payment, balancing a bank account, and exercising complex financial judgment (Marson et al., 2000; Farias et al., 2006). It has been systematically shown that impairment in financial capacity is one of the earliest IADL changes seen in MCI. Studies by Marson et al., have shown impaired performance on a measure of financial capacity called the financial capacity instrument (FCI) (Marson et al., 2000; Marson et al., 2009). The neurocognitive predictors of financial ability in people with MCI and AD include measures of attention and working memory (executive function), short-term episodic memory, visuomotor tracking, and written arithmetic ability (Royall et al., 2007; Okonkwo et al., 2008). These cognitive abilities as they pertain to financial capacity have been associated with brain changes including the medial frontal cortex and decreases in posterior cortical volumes (Stoeckel et al., 2013). Results of this analysis support the assertion that financial capacity is a significant and early indicator of future functional decline and potentially conversion to MCI or dementia. Additionally, this analysis also shows an association between conversion and remembering dates and traveling. A study by Marshall et al. (2014) showed that the remembering shopping items as well as appointments and events discriminated between cognitively normal individuals and MCI here suggesting agreement with other research related to memory-based functional abilities. Traveling as a specific instrumental activity to our knowledge does not appear to have been studied and so represents a potential area of focus for future investigation.

This study over-all has several strengths including large sample, which enabled analysis of the FAQ by subcategories rather than by total score as is most commonly done. Moreover, the choice of the FAQ reflects an informant-based rather than self-reported instrument, which is a common weakness in other studies. Finally, the statistical approach of the study sought to examine functioning in relation to cognition rather than in addition to it provides a useful perspective in disentangling the two. While it is desirable to develop predictive models in a participant-by-participant fashion, there are challenges to doing this, which includes needing to develop it in a training sample and test it in a separate one. The strength of this these present data is that an association has been shown between functioning and conversion independent of cognition and this may be the basis of developing separate predictive models in the future. Weaknesses included potential selection biases since the sample included typically highly educated volunteers who were mostly female. Additionally, sex differences in functioning as well as relative sensitivity and specificity of the FAQ in determining conversion was not investigated but we identify them as potential topics for future research. As our population ages, it will become important to understand both cognitive and functional transitions because of the potential for delaying the development of pathological states such as AD but also for identifying candidates for disease modifying therapies or identifying and developing effective compensatory measures and employing assistive technologies.