Mild cognitive impairment is associated with falls among older adults findings from the irish longitudinal study on ageing (TILDA)

Stefanos Tyrovolas, Ai Koyanagi, Elvira Lara, Ziggi Santini, Josep Maria HaroDOI: doi: 10.1016/j.exger.2015.12.008 Reference: EXG 9756 To appear in: Experimental Gerontology Received dat

Stefanos Tyrovolas, Ai Koyanagi, Elvira Lara, Ziggi Santini, Josep Maria Haro

DOI: doi: 10.1016/j.exger.2015.12.008

Reference: EXG 9756

To appear in: Experimental Gerontology

Received date: 17 August 2015

Revised date: 15 December 2015

Accepted date: 16 December 2015

Please cite this article as: Tyrovolas, Stefanos, Koyanagi, Ai, Lara, Elvira, Santini, Ziggi, Haro, Josep Maria, Mild cognitive impairment is associated with falls among older adults:

findings from the Irish Longitudinal Study on Ageing (TILDA), Experimental Gerontology

(2015), doi: 10.1016/j.exger.2015.12.008

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Mild cognitive impairment is associated with falls among

older adults: findings from the Irish Longitudinal Study on

Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM,

Dr Antoni Pujadas, 42, 08830 – Sant Boi de Llobregat, Barcelona, Spain

Email: s.tyrovolas@pssjd.org

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Abstract

Introduction: The role of mild cognitive impairment (MCI) on falls among older

adults remains under-investigated The aim of this study was to evaluate the association between MCI and number of falls or occurrence of non-accidental falls

among older adults Methods: Data from the first wave of the Irish longitudinal Study

on Ageing (TILDA) was analysed The analytical sample consisted of 5364 individuals aged ≥50 years MCI was defined as: Montreal Cognitive Assessment (MoCA) score<26; presence of subjective cognitive complaints; Mini-Mental State Examination (MMSE) score≥14; and no limitations in activities of daily living (ADL) Multivariable poisson and logistic regression analyses were conducted to assess the association between MCI and number of falls or presence of non-accidental

falls in the past 12 months Results: The prevalence of MCI was 10.1% In the

fully-adjusted model, MCI was associated with a higher rate of falls (PR=1.41 95%CI=1.05-1.89) and odds for non-accidental falls in the past 12 months (OR=1.67 95%CI=1.07-2.61) Muscle strength and performance indicators, and medical health conditions were influential factors in the association between MCI and falls but did

not fully explain the association Conclusion: MCI is related with higher rates of falls

and the occurrence of non-accidental falls among older adults Future studies are warranted to clarify the underlying mechanism linking MCI and falls, and to establish

interventions targeting MCI to reduce the risk of falls

Keywords: Mild cognitive impairment; Falls; Gait speed; Muscle strength

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Introduction

The European commission has recognized population aging as one of the most challenging policy issues of this century in Europe (European Commission 2006) Advanced age is accompanied by various co-morbidities that affect health status and quality of life including falls (Janssen et al., 2002; Landi et al., 2013; Newman et al., 2006) Falls are a major health care problem for the elders Almost 30% of the older population have been reported to experience a fall accident at least once per year (Muir et al., 2012) Moreover, falls are associated with a higher risk of loss of independence, autonomy, and confidence Falls are one of the major contributors to the increased need for specialized care and hospitalization among older adults, while

it is also associated with higher rates of morbidity, mortality, and institutionalization (Tinetti et al., 1995; Tinetti and Williams, 1997) Additionally, the cost of falls for the public health services is high For example, in the UK, the cost of fall-related hospitalizations among older adults is almost £1 billion per year (Scuffham et al., 2003)

Various factors such as vision and hearing problems, abnormal blood pressure, mobility limitation, neuropsychiatric disorders, sarcopenia, and frailty have been associated with falls (Tinetti et al., 1986; Robbins et al., 1989; Shumway-Cook et al., 1997; Vellas et al., 1997; Mühlberg and Sieber, 2004) Among neuropsychiatric disorders, decline in cognitive function has been related with greater risk of falls in the older population Recent studies have reported an increased frequency of falls with lower Mini-Mental State Examination (MMSE) scores (i.e., loss of global cognitive ability) (Gleason et al., 2009) Impairments in attention (Amboni et al., 2013), processing speed (Chen et al., 2012), and executive functions (Banich, 2009) have been proposed as a set of interrelated factors in the pathway between cognitive

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impairment and falls Based on these previous findings, some researchers have proposed that fall and injury prevention strategies may benefit from focusing on the early prevention of cognitive decline (Montero-Odasso et al., 2009) In particular, in recent years, mild cognitive impairment (MCI), which is considered an intermediate state between normal aging and dementia, is gaining further attention from the viewpoint of prevention of dementia or cognitive decline However, despite the potentially important role that cognitive function plays in the occurrence of falls, the association between MCI and falls among older adults still remain under-investigated (Delbaere et al., 2012)

Given the rapid aging occurring in Europe, the scarcity of studies on MCI and falls, and a complete lack of studies on this topic from Ireland, the aim of the present work was to evaluate the associations between MCI and frequency of falls or occurrence of non-accidental falls in a large, nationally-representative sample of non-institutionalized older Irish individuals

Methods

Study design and sample

Data from the first wave of the Irish Longitudinal Study on Ageing (TILDA) was analyzed The full description of the survey and the sampling procedures can be found elsewhere (Cronin et al., 2013) Briefly, TILDA was an Irish nationally-representative, cross-sectional study on the economic, health, and social status of the non-institutionalized population, and was conducted between 2009 and 2010 by Trinity College in Dublin (Cronin et al., 2013) The sample included a total of 8504 people [individuals aged ≥50 years (n=8175) and their spouses or partners younger than 50 years (n=329)] Of these individuals, 5895 completed a health assessment

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Information was obtained by face-to-face interviews conducted by trained professionals using Computer Assisted Personal Interviewing (CAPI) The response rate was 62% (Whelan and Savva, 2013)

The Trinity College Dublin approved the design and procedures of the study Informed consent was obtained from all participants Individuals were not eligible for inclusion if they reported a doctor’s diagnosis of dementia Furthermore, individuals who were not able to consent personally because of severe cognitive impairment (at interviewer’s discretion) were also excluded

Number of falls and the presence of non-accidental falls

The number of falls in the past 12 months was assessed by the question “How many times have you fallen in the last year?” Information on the presence of non-accidental falls in the past 12 months was assessed by the question “Were any of these falls non-accidental, i.e., with no apparent or obvious reason?” among those who had fallen in the past 12 months The answer options were “Yes” or “No”

Mild cognitive impairment

The case definition of MCI was based on the core criteria outlined by the National Institute on Aging-Alzheimer´s Association (Albert et al., 2011):

a) Concern about a change in cognition: Subjective cognitive complaints were assessed by the question “How would you rate your day-to-day memory at present time?” with answer options: excellent, very good, good, fair, and poor Those who replied fair or poor were considered to have subjective cognitive complaints

b) Objective evidence of impairment in one or more cognitive domains, typically including memory: Cognitive function was assessed with the Montreal Cognitive

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Assessment (MoCA) (score range: 0–30) This tool has been demonstrated to be sensitive to mild cognitive deficits when applied in cognitively intact older adults (Kenny et al., 2013), and includes measures of executive function, language, memory, attention, orientation, calculation, and visuospatial ability Cognitive impairment was defined as a MoCA score<26 (Freitas et al., 2013)

c) Preservation of independence in functional abilities: The participants were presented with a list of six basic standard ADLs on dressing, walking, bathing, eating, getting in or out of bed, and using the toilet (Katz et al., 1963), and were asked if they have difficulty with these activities They were also asked to exclude any difficulties that are expected to last for less than three months Those who claimed to have difficulty with any of the six abovementioned ADLs were excluded from the analysis d) Not demented: Individuals who obtained a score <14 on the MMSE were excluded from the analytical sample (Shigemori et al., 2010)

Sociodemographic and lifestyle characteristics

Sociodemographic and lifestyle characteristics included age (50-59, 60-69, 70-79, ≥80 years), gender, education (primary, secondary, tertiary), wealth, living arrangement (alone or not), residence [urban (Dublin city or county/another town or city) or rural], physical activity, and problem drinking Wealth (financial strain) was assessed by the statement “shortage of money stops me from doing the things I want to do” with answer options never, rarely, sometimes, and often Physical activity was measured using the short form of the International Physical Activity Questionnaire, which converts levels of physical activity of various domains into predicted kilocalories expended per week (Craig et al., 2003) Problem drinking was assessed by the CAGE

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screening test with scores of ≥2 being used as a cut-off for problem drinking (Mayfield et al., 1974)

Muscle strength and performance

Handgrip strength and gait speed were considered indicators of muscle strength and performance respectively (Tyrovolas et al., 2015) Grip strength was assessed using a dynamometer Two readings from the dominant hand were taken, and the mean strength was calculated Gait speed was measured using the GAITRite portable electronic walkway system (CIR Systems, Inc., Havertown, PA) Participants walked

at their usual pace along a 4.88-m (16-foot) walkway with an extra 2.5 m at each end

to allow for acceleration and deceleration Gait speed was then calculated as meters per second and then transformed to centimeters per second

Obesity and medical health conditions

Weight and height were measured using standard procedures Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared Obesity was defined as BMI≥30kg/m2

The presence of medical conditions was assessed by asking the respondents about whether they were ever told by a doctor that they have angina, arthritis (including osteoarthritis and rheumatism), congestive heart failure, diabetes or high blood sugar, heart attack (including myocardial infarction and coronary thrombosis), stroke (cerebral vascular disease), or Parkinson's disease Heart disease referred to having at least one of: angina, congestive heart failure, and heart attack Depression was measured with the 20-item Center for Epidemiologic Studies Depression (CES-D) (Radloff et al., 1977) based on symptoms experienced in the past week, and was defined as a CES-D score of ≥ 16 (Beekman et al., 1997)

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Statistical analysis

A descriptive analysis was conducted to characterize the study sample by the presence

of MCI The differences in sample characteristics by the presence of MCI were tested

by chi-squared tests and student’s t-tests for categorical and continuous variables

respectively Poisson and logistic regression analyses were done with number of falls and presence of non-accidental falls in the past 12 months as the outcome respectively MCI was the main covariate of interest Since it is possible that the inclusion of different blocks of control variables in the model affects the association between MCI and falls in different ways, we conducted hierarchical analyses where three different models were constructed for each outcome: Model 1 - adjusted for sociodemographic and lifestyle characteristics; Model 2 - adjusted for covariates in model 1 and grip strength and gait speed; Model 3 - adjusted for covariates in model 2 and obesity and medical health conditions All variables were included in the models as categorical variables with the exception of grip strength and gait speed (continous variables) The selection of the covariates was based on past literature (Muir et al., 2012; Tinetti et al., 1995; Tinetti et al., 1986; Robbins et al., 1989) In order to assess the influence of multicolinearity, we calculated the variance inflation factor (VIF) value for each independent variable The highest VIF was 2.44, which is much lower than the commonly used-cut off of 10 (O'Brien RM, 2007), indicating that multicolinearity was unlikely to be a problem in our analyses The analyses were done with Stata version 13.1 (Stata Corp LP, College Station, Texas) In order to generate nationally-representative estimates, in all analyses, the sample weighting and the complex study design were taken into account with Taylor linearization methods Prevalence ratios (PR) and odds ratios (OR) and 95% confidence intervals (95%CI) are reported The level of statistical significance was set at P<0.05

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Results

The analytical sample consisted of 5364 individuals aged ≥50 years with no limitations in ADL and a MMSE score of ≥14 The prevalence of MCI was 10.1% Overall, 18.9% had fallen at least once and 4.6% had non-accidental falls in the past

12 months The characteristics of the study participants by MCI status are summarized

in Table 1 The following characteristics were significantly associated with MCI:

older age, lower levels of education, higher levels of financial strain, living alone, rural residence, low physical activity, weaker hand grip strength, slower gait speed, obesity, and presence of medical conditions (arthritis, stroke, heart disease, and depression) The association of MCI and other covariates with number of falls in the

past 12 months is shown in Table 2 In the model adjusted for sociodemographic and

lifestyle characteristics (Model 1), MCI was associated with a higher rate of falls 1.51 (95%CI 1.15-1.97) After the addition of hand grip strength and gait speed, the PR (95%CI) became 1.47 (95%CI 1.11-1.95) This association remained significant even after further adjustment for obesity and other medical health conditions [PR 1.41 (95%CI 1.05-1.89)] In the fully-adjusted model (Model 3), gender, grip strength, and arthritis were also significantly associated with falls

The association of MCI and other covariates with non-accidental falls in the

past 12 months is shown in Table 3 MCI was associated with 1.93 (95%CI

1.28-2.93) times higher odds for non-accidental falls in the model adjusted for sociodemographic and lifestyle factors (Model 1) Further adjustment for grip strength and gait speed (Model 2), or obesity and medical health conditions in addition to grip strength and gait speed (Model 3) attenuated the ORs [Model 2: OR 1.68 (95%CI 1.09-2.60); Model 3: OR 1.67 (95%CI 1.07-2.61)] when compared to Model 1, but results were still statistically significant in both Model 2 and 3 In the fully-adjusted

Our findings on the association between MCI and falls are in line with previous studies For example, Liu-Ambrose et al (2008), in a sample of 158 older Canadians, reported that females with MCI had higher physiological risk of falling and increased postural sway compared to females without MCI Additionally, Dealbaere et al (2012) analyzed a sample of 419 non-demented community-dwelling adults in Sydney, and reported that MCI was associated with a 1.72 (95%CI 1.03-2.89) greater risk for falls Also, Borges et al (2015), in a sample of 104 community-dwelling elders in Brazil, showed that the prevalence of falls in MCI was higher than in cognitively healthy older adults Finally Uemura et al., (2014) analyzed

to be related with balance control and consequently with falls (Alexander and Hausdorff, 2008) Differences in brain structures between those with and without MCI have also been reported For example, individuals with MCI may have reduced integrity of the posterior regions of the brain, and their medial temporal lobe, insula, and thalamus may constitute of reduced gray matter (Medina et al., 2006) Since these regions of the brain are known to be associated with attention and balance control (Zimmerman et al., 2006), individuals with MCI may be more likely to fall due to impairment in attention and equilibrium

Apart from MCI, factors such as handgrip strength, gait speed and arthritis, were also associated with falls Muscle strength and performance have been reported

to be significant predictors of falls (Mühlberg and Sieber, 2004) Furthermore, an interrelated pathway among gait speed, balance control, cognitive decline and falls in older adults has been proposed In addition, falls commonly occur in patients with arthritis (Kaz Kaz et al., 2004), and the role of arthritis on the epidemiology of falls has previously been reported Since falls are associated with high healthcare expenditures (Ambrose et al., 2013), the prevention of MCI and other co-morbidities