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Anticholinergic drug use is associated with episodic memory decline in older adults without dementia
Accepted Manuscript Anticholinergic drug use is associated with episodic memory decline in older adults without dementia Goran Papenberg, Lars Bäckman, Laura Fratiglioni, Erika J. Laukka, Johan Fastbom, Kristina Johnell PII:
S0197-4580(17)30080-5
DOI:
10.1016/j.neurobiolaging.2017.03.009
Reference:
NBA 9868
To appear in:
Neurobiology of Aging
Received Date: 12 June 2016 Revised Date:
2 March 2017
Accepted Date: 6 March 2017
Please cite this article as: Papenberg, G., Bäckman, L., Fratiglioni, L., Laukka, E.J., Fastbom, J., Johnell, K., Anticholinergic drug use is associated with episodic memory decline in older adults without dementia, Neurobiology of Aging (2017), doi: 10.1016/j.neurobiolaging.2017.03.009. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Anticholinergic drug use is associated with episodic memory
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decline in older adults without dementia
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Goran Papenberga, Lars Bäckmana, Laura Fratiglionia,b, Erika J. Laukkaa, Johan Fastboma, Kristina Johnella
a
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Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden b Stockholm Gerontology Research Center, Stockholm, Sweden
* Corresponding author:
Goran Papenberg
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Aging Research Center
Gävlegatan 16 SE-113 30 Stockholm
Email: [email protected] Tel:
+46-7-8 690 58 75
Fax : +46-7-8 690 68 89
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ACCEPTED MANUSCRIPT Abstract Anticholinergic drug use is common in older adults and has been related to increased dementia risk. This suggests that users of these drugs may experience accelerated cognitive decline. So far, however, longitudinal data on this topic are
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absent and the available evidence is inconclusive with respect to effects on specific cognitive domains due to suboptimal control of confounding variables.
We investigated whether anticholinergic medication use is associated with
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cognitive decline over 6 years in a population-based study of older adults (aged 60 to 90; n = 1473) without dementia. We found that users (n=29) declined more on
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episodic memory over 6 years compared to non-users (n=1418). These results were independent of age, sex, education, overall drug intake, physical activity, depression, cardiovascular risk burden, and cardiovascular disease. By contrast, anticholinergic drug use was unrelated to performance in processing speed, semantic memory, short-
Examination).
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term memory, verbal fluency, and global cognition (the Mini-Mental-State
Our results suggest that effects of anticholinergics may be particularly detrimental
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to episodic memory in older adults, which supports the assertion that the cholinergic
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system plays an important role in episodic memory formation.
(Abstract: 183 words)
Key words: anticholinergic drugs, cognition, episodic memory, aging, interindividual differences
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ACCEPTED MANUSCRIPT Introduction Use of anticholinergic medications is common in older adults (e.g., Haasum, Fastbom, & Johnell, 2012; Ness, Hoth, Barnett, Shorr, & Kaboli, 2006) and may have negative repercussions for cognitive functioning (Campbell et al., 2009; Cancelli,
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Beltrame, Gigli, & Valente, 2009; Moore & O'Keeffe, 1999). Cognitive aging is
characterized by large heterogeneity (Nyberg, Lövdén, Riklund, Lindenberger, &
Bäckman, 2012), which raises the question of the extent to which drug intake may
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contribute to this diversity, independent of diseases and pathologies treated by the
prescribed drug. Here we investigate whether anticholinergic medication is related to
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cognitive decline in a population-based sample of older adults without dementia. Anticholinergics comprise drugs from various classes, including incontinence drugs, low-potency antipsychotics, tricyclic antidepressants, and some antihistamines. Drugs with anticholinergic properties have been associated with increased risk for
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dementia (e.g., Bartus, Dean, Beer, & Lippa, 1982; Gray et al., 2015). In terms of cognition, anticholinergic drugs have been linked to impaired episodic memory – the memory for past events, mild-cognitive impairment, and different types of dementia
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(Fortin et al., 2011). This is in line with evidence suggesting that the cholinergic
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system is strongly implicated in cognition, in particular episodic memory (Bentley, Driver, & Dolan, 2011). Experimental studies show that, compared to placebo, anticholinergic drugs used to treat Parkinson’s disease are associated with episodic memory impairment in healthy younger (Sambeth, Riedel, Klinkenberg, Kahkonen, & Blokland, 2015) and older (Wezenberg, Verkes, Sabbe, Ruigt, & Hulstijn, 2005) adults. Moreover, positron imaging tomography data suggest that integrity of the cholinergic system is linked to interindividual variability in episodic memory functioning among older adults (Richter et al., 2014). However, despite observed
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ACCEPTED MANUSCRIPT links between the cholinergic system and cognition, particularly episodic memory, the specific effects of anticholinergic drug use on cognition remain unclear and longitudinal data are absent. One cross-sectional study showed that elderly people taking anticholinergic drugs had impairments in speed, attention, and episodic
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memory compared to non-users (Ancelin et al., 2006). However, this study did not control for some of the potential confounding variables (e.g., Parkinson’s disease, depression) that may drive the adverse effects of anticholinergics on cognition, as
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shown by another study (Lechevallier-Michel et al., 2005).
We examine the effects of anticholinergic drugs on cognitive decline over 6 years
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in a population-based sample of older adults without dementia (60-90 years; n = 1473). To overcome the shortcomings of previous studies, we compare cognitive performance between users and non-users of anticholinergic drugs and adjust for different background variables to assure that impairments in cognition are associated
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with drug use. In addition, we apply propensity-scores matching to minimize selection bias, which helps strengthen causal interpretations and provides a better estimation of treatment effects (Randolph et al., 2014). Given the work reviewed above, we were
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particularly interested in the association between drug intake and episodic memory.
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However, to investigate the specificity of the influence of anticholinergic drugs on cognitive functioning, we also examined their effects on speed, verbal fluency, and semantic memory.
Methods
Participants Data were collected within the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K). The original SNAC-K population consisted of 4590 alive and eligible persons who lived in Kungsholmen/Essingeöarna in central Stockholm, 4
ACCEPTED MANUSCRIPT belonged to prespecified age strata, and were randomly selected to take part in the study. Between 2001 and 2004, 3363 persons participated in the baseline assessment. They belonged to the age cohorts 60, 66, 72, 78, 81, 84, 87, 90, 93, 96 years, and 99 years and older. The examination consists of three parts: a nurse interview, a medical
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examination, and a neuropsychological testing session. Altogether, the examination
takes about 6 hours. Participants are reexamined each time they reach the age of the
next age cohort. All parts of the SNAC-K project have been approved by the regional
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ethical review board in Stockholm. Informed consent was obtained from all
impaired), from next-of-kin.
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participants, or, if the person was unable to answer (e.g. severely cognitively
Out of 3363 participants, 1724 persons underwent cognitive testing at both baseline and follow-up. At baseline, 390 participants refused cognitive testing, 106 had very low MMSE scores (< 10), 10 had died, and 9 had other reasons for not being tested.
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558 persons did not participate in the follow-up assessment after 6 years, because they themselves or a relative refused participation, (n=168), they had moved or could not be contacted (n=73), or had died (n=317). From the older participants, who were also
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assessed after 3 years, 89 refused participation, 19 had moved or could not be
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contacted, and 257 had died. Among those who participated in the follow-up after 6 years, 82 refused cognitive testing, 29 had very low MMSE scores, and 5 could not be tested due to other reasons. From this cognitive sample, we excluded persons with dementia (n = 118), Parkinson’s disease (n = 17), and schizophrenia (n = 6) at baseline or follow-up. Only participants with complete data with respect to cognition, drugs, and confounding variables were included in the analyses, resulting in a final sample of 1473 individuals.
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ACCEPTED MANUSCRIPT Dementia Diagnosis Diagnosis of dementia was made according to the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM–IV; American Psychiatric Association, 1994), using a three-step procedure. The preliminary diagnosis made by the examining
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physician was compared with a second independent diagnosis based on computerized data. In cases of disagreement, a supervising physician made a third and final
diagnosis. The cognitive assessment used for diagnostic purposes included the MMSE
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(Folstein et al., 1975), the Clock test (Manos & Wu, 1994), and questions regarding
solving.
Anticholinergic Drugs
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memory, orientation, executive functioning, interpretation of proverbs, and problem
Information about drug use was collected by a physician during the medical
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examination. Before the interview, participants were instructed to bring a list of currently used drugs, including both regularly and as needed used drugs. Both prescribed and over-the-counter drugs were recorded. When the older person could
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not provide information (e.g., due to cognitive impairment), a relative or a close
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informant was asked instead or the information was collected from medical records (Haasum, Fastbom, Fratiglioni, Kareholt, & Johnell, 2011). Drugs were classified according to the Anatomical Therapeutic Chemical (ATC) classification system, as recommended by the World Health Organization (http://www.whocc.no/atcddd/). We identified anticholinergic drugs in accordance with the Swedish National Board of Health and Welfare indicators for drug therapy in the elderly (Sköldunger, Fastbom, Wimo, Fratiglioni, & Johnell, 2015): urinary and gastrointestinal antispasmodics, anticholinergic antiemetics, class Ia antiarrhythmics, anticholinergic antiparkinsonian
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ACCEPTED MANUSCRIPT drugs, low-potency antipsychotics, tricyclic antidepressants, and first-generation antihistamines (Table 1). Our analyses focused on participants that used anticholinergic drugs at both baseline examination and at six-year follow-up (n = 29). However, we also report
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effects of anticholinergic drugs on cognition where older adults that used anticholinergic drugs at any time point were included (n= 55).
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Propensity score matching
In addition to the total sample, we compared cognitive performance of
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anticholinergic drug users with a matched sample of non-users to reduce selection bias and get a better estimation of treatment effects. Using the MatchIt toolbox in R 3.2.1 (Randolph et al., 2014), participants were matched on age, sex, education, total number of drugs used, physical inactivity, cardiovascular risk burden, cardiovascular
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disease burden, and depression. Here we employed the matching algorithm “nearest”, which uses a distance measure to select the best control match for each individual in the treatment group. Two aggregated sum scores were used for cardiovascular burden,
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namely cardiovascular risk factors (CRFs) and diseases (CVDs). The aggregated CRFs included stage-2 hypertension, high cholesterol (i.e., fasting cholesterol level ≥
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6.5 mmol/L), obesity (≥30 kg/m2), diabetes, and smoking. The aggregated score for CVDs included occurrence of ischemic heart diseases, atrial fibrillation, and heart failure (see, Welmer, Angleman, Rydwik, Fratiglioni, & Qiu, 2013, for details on assessment of CRFs and CVDs). Diagnosis of depression was based on ICD-10 criteria (Pantzar et al., 2014). Participation in physical activities was self-reported for the past 12 months and categorized according to level of intensity (light exercise such as short bike rides or usual paced walks or moderate to intense exercise such as brisk
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ACCEPTED MANUSCRIPT walks or jogging) and frequency (never, <2-3 times/month, 2-3 times/month, several times/week, and every day). Participants were considered physically inactive if they engaged less or equal to 2–3 times per month into light and/or moderate/intense exercise (Ferencz et al., 2014; Rydwik et al., 2013).
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The matching of participants was successful, as there were no differences in demographic, cardiovascular risk or drug-related variables between users and
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matched non-users of anticholinergic drugs (Table 2).
Cognitive Measures
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The cognitive test battery assessed six cognitive domains: episodic memory, semantic memory, perceptual speed, letter fluency, and category fluency (Laukka et al., 2013) and short-term memory (Pantzar et al., 2014; Papenberg et al., 2017). Below we briefly describe the tasks that were used to represent the cognitive constructs.
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Episodic memory. Free recall and recognition were assessed with a word list comprised of 16 unrelated concrete nouns. Words were presented both orally and visually, and the presentation rate was one new word every 5 s. Immediately after
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presentation, participants were given two minutes for oral free recall. Following free
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recall, participants were given a self-paced recognition task, where the 16 target words were presented randomly intermixed with 16 distracters. If participants responded “yes” on a recognition trial, their memory of the item was further probed by asking if (a) they remember hearing and/or seeing the presented word (recollection), (b) they recognize it, but have no clear recollection of the presented word (familiarity), or (c) neither of the two (guess) (Gardiner & Java, 1990; Gardiner et al., 1998). Recognition scores in this study were based solely on recollection
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ACCEPTED MANUSCRIPT responses (i.e., familiarity and guess responses were not included). Unit-weighted composite scores were computed based on the free recall and recognition scores. Semantic memory. Semantic memory was assessed with a vocabulary (SRB:1) and a general knowledge task. The SRB:1 is a 30-item vocabulary test (Dureman 1960;
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Nilsson et al. 1997), where each target word is presented together with 5 other words. Participants were instructed to underline the words representing the synonyms to the
target words (maximum time = 7 min). The general knowledge task (Dahl, Allwood,
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and Hagberg 2009) consisted of 10 questions (e.g., “What is the capital of
Uruguay?”). Participants were instructed to select the correct answer out of two
and general knowledge tasks.
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alternatives. Unit-weighted composite scores were computed based on the vocabulary
Short-term memory. In Digit Span forward, participants repeated lists of one digit numbers (starting with list length 3). In Digit Span backwards, participants repeated
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the lists of numbers in reversed order (starting with list length 2). If a participant failed to repeat two trials of a certain list length, the task was terminated. The outcome variables were number of correct repetitions for forward and backward digit
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span, which were combined into a unit-weighted composite score.
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Perceptual speed. The perceptual speed tasks comprised two paper-and-pencil tests, the digit cancellation (Zazzo 1974) and pattern comparison (Salthouse and Babcock 1991) tests. For digit cancellation, participants were instructed to sequentially go through 11 rows of random digits as quickly as possible and cross out every “4” they encountered during 30 sec. For pattern comparison, they were asked to sequentially go through line-segment patterns as rapidly as possible in two trials, each 30 seconds long, and mark whether the patterns were “same” or “different”. Unit-weighted
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ACCEPTED MANUSCRIPT composite scores were computed based on the digit cancellation and pattern comparison tests. Letter fluency. For the letter fluency tasks, participants were asked to orally generate as many words as possible within 60 sec, beginning with the letters F and A,
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respectively. They were instructed that proper names, numbers, or words with a
different suffix were not credited. Unit-weighted composite scores were computed based on the two letter-fluency measures.
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Category fluency. For category fluency, participants were asked to orally generate as many words as possible within 60 sec, belonging to the categories animals and
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professions. As before, unit-weighted composite scores were computed based on the two measures.
Statistical Analyses
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Behavioral and demographic data were analyzed using SPSS for Windows 22 (SPSS, Chicago, IL, USA). We conducted a repeated-measures analysis of covariance (ANCOVA) with treatment group (users, non-users) as a between-subjects factor and
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time (baseline, 6-year follow-up) as a within-subjects factor for the cognitive
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measures. Analyses were conducted both in the total sample and in the matched sample. The following potential confounders were included as covariates in all analyses: age, sex, education, CRFs, CVDs, physical inactivity, total number of drugs, and depression. For all analyses, the alpha level was set to p < .05. Effect sizes are indicated by partial eta squared. In addition, treatment effects are indicated by Cohen’s d. Results Mean scores on the cognitive tests for anticholinergic drug users and non-users are 10
ACCEPTED MANUSCRIPT presented in Table 3. In the total sample, we found a main effect of time for episodic memory, F(1,1437) = 142.2, p < .05, partial eta-squared = .090, indicating decline over 6 years (Fig. 1a). Moreover, the main effect of treatment group was reliable, F(1,1437) = 5.76, p < .05, partial eta-squared = .004, Cohen’s d= .42, reflecting worse
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overall memory performance for individuals taking anticholinergic drugs. Critically, the treatment group × time interaction was significant, F(1,1437) = 4.39, p <.05,
partial eta-squared = .003. Follow-up comparisons showed no differences in episodic-
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memory performance between treatment groups at baseline (p > .10). However, at follow-up, persons using anticholinergic drugs performed worse than non-users,
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F(1,1436) = 7.64, p <.05, partial eta-squared = .006, Cohen’s d = .59. By contrast, anticholinergic drug use was unrelated to perceptual speed, semantic memory, letter fluency, category fluency, short-term memory, and MMSE (ps >.10), suggesting a unique association between anticholinergics and decline in episodic memory. When
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including persons who took anticholinergic medication at only one time point (n = 55), the main effect of anticholinergic drug use and the treatment group × time interaction did not approach conventional significance (p < .10). Thus, the drug
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effects became diluted when including individuals that used anticholinergics at either
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baseline or follow-up.
Further, we repeated the analyses using a propensity-score matched sample to
achieve better control of selection bias and more precise estimation of anticholinergic effects on episodic memory. When considering the total sample of individuals taking anticholinergic drugs (n=55), the main effect of anticholinergic drugs was reliable, F(1,101) = 6.99, p <.05, partial eta-squared = .065 (Cohen’s d = 0.41). When focusing on continuous users only, as before both the main effect of anticholinergic drug use, F(1,48) = 6.13, p =.05, partial eta-squared = .111 (Cohen’s d = .60), and the treatment
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ACCEPTED MANUSCRIPT group × time interaction, F(1,48) = 3.56, p <.05, partial eta-squared = .068, were significant (Fig. 1b). Again, the effects were driven by episodic-memory differences at six years, F(1,48) = 9.21, p <.05, partial eta-squared = .158 (Cohen’s d = .66), as
Discussion
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group differences were not reliable at baseline (p >.10).
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We investigated the effects of anticholinergic drugs on cognitive decline over 6
years in a population-based sample of older adults without dementia. Our results show
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that older adults using anticholinergics both at baseline and follow-up declined more on episodic memory than non-users. These effects were replicated in a propensityscore matched control sample (n = 29), suggesting that the effects are independent of several potential confounders, including age, sex, education, depression status, as well as cardiovascular risk and diseases.
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Our findings are in accordance with evidence from different lines of research, implicating the cholinergic system in episodic memory. First, cholinergic activity and
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protein expression is highest in the hippocampus (Mesulam, Volicer, Marquis, Mufson, & Green, 1986), a brain region critical to successful episodic memory.
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Second, formation of episodic memories relies on the cholinergic system, which enhances long-term potentiation and facilitates encoding without interference from previously stored information by suppressing excitatory connections (Hasselmo, 1999, 2006). Moreover, a receptor imaging study showed that individual differences in the integrity of the cholinergic system are associated with episodic memory functioning in older adults (Richter et al., 2014). Our data extend these results and show that continuous use of anticholinergic drugs is associated with episodic memory decline in older adults. Importantly, the
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ACCEPTED MANUSCRIPT detrimental effects of anticholinergic drug use were confined to episodic memory; there were no negative effects for speed, verbal fluency, semantic, and short-term memory. This pattern is in line with a review implicating the cholinergic system particularly in episodic memory (Bentley et al., 2011).
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Previously reported effects of anticholinergic medication on processing speed might rather be associated with the different comorbidities of individuals taking
anticholinergic drugs (Ancelin et al., 2006), such as Parkinson patients (Karayanidis,
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1989) and depressed persons (Brewster, Peterson, Roker, Ellis, & Edwards, 2016; Pantzar, Atti, Bäckman, & Laukka, 2015). Similarly, past research has reported
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associations between anticholinergic drugs and verbal fluency (Uusvaara, Pitkala, Kautiainen, Tilvis, & Strandberg, 2013). This association may also be related to other confounders, as the analyses were only adjusted for age, sex, and education in that particular study. Similarly, another cross-sectional study reported an association
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between anticholinergic load and impairments in verbal fluency among older women, in addition to impairments in episodic memory (Koyama et al., 2004). However, this study did not adjust for Parkinson’s disease, which also impairs verbal fluency (Henry
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& Crawford, 2004). Our data strongly suggest that anticholinergics may accelerate
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cognitive decline in older adults without dementia. In terms of effect sizes, the observed differences are of medium size and, as such, should be clinically relevant. An interesting avenue for future research is to examine whether the observed effects on episodic memory are reversible or permanent. Some limitations of the present investigation should be acknowledged. Notably, only about 2% of the participants used anticholinergic drugs over 6 years, which is a low number compared to the point prevalence of anticholinergic medication use in previous reports (e.g., Haasum et al., 2012; Ness et al., 2006). This low number is
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ACCEPTED MANUSCRIPT likely due to the fact that we focused on participants that took the drug at both time points only and excluded participants with different pathologies (e.g., dementia, Parkinson’s disease). Given the association between dementia and depression (e.g., Hermida, McDonald, & Steenland, 2012), the fact that we excluded participants with
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dementia likely resulted in an exclusion of participants taking antidepressants. However, we should also acknowledge that our sample is in general slightly
positively selected (Laukka et al., 2013), which may also contribute to the relatively
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small number of drug users. Whereas we controlled for the most important
confounders, we cannot rule out the possibility that other individual factors associated
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with long-term anticholinergic drug use may underlie the faster episodic memory decline in users. Another limitation is that self-reported data on drug use can be subject to recall bias. However, compared with refill register data, our data were based on medications that were actually used and not only dispensed (Sköldunger,
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Fastbom, Wimo, Fratiglioni, & Johnell, 2016).
Taken together, our results support evidence regarding the influence of anticholinergic drugs on episodic memory: Older adults taking anticholinergic drugs
Acknowledgments
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declined more on episodic memory over six years than non-users.
We thank all the participants and staff who were involved in the collection and
management of the data for their contribution to the study. The Swedish National study on Aging and Care, SNAC, (www.snac.org) is financially supported by the Ministry of Health and Social Affairs, Sweden, the participating County Councils and Municipalities, and the Swedish Research Council. In addition, grants were obtained from the Stiftelsen för Gamla Tjänarinnor and the Swedish Research Council. LB was supported by grants from the Swedish Research Council, the Swedish Research
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ACCEPTED MANUSCRIPT Council for Health, Working Life, and Welfare, an Alexander von Humboldt
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Research Award, and a donation from the af Jochnick Foundation.
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ACCEPTED MANUSCRIPT Table 1 Frequency of Different Anticholinergic Drugs. Continuous and Discontinuous Users n=55
1 2 23 2 1 9
n=29 1
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2
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Synthetic anticholinergics, quarternary ammonium compounds A04AD Other antiemetics C01BA Antiarrhythmics, class Ia G04BD Drugs for urinary frequency and incontinence N05AA Antipsychotics N05AB04 Prochlorperazine N05BB01 Hydroxyzine N06AA Non-selective monoamine reuptake inhibitors (tricyclic antidepressants) R06AA02 Diphenhydramine R06AB Substituted alkylamines R06AD Phenothiazine derivatives Note. ATC= Anatomical Therapeutic Chemical code.
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ATC code A03AB
Continuous Users
1 1 5
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15 1 1 4 5 1 3
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Table 2 Demographic, Drug-related, and Vascular Risk Variables for Anticholinergic Medication Users and Non-Users. Users
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Age, M ± SD Women, % Years of education, M ± SD Depression, % Number of drugs, M ± SD Cardiovascular risk factor burden, M ± SD Cardiovascular disease burden, M ± SD Physical inactivity, %
Anticholinergic Medication Non-Users Matched Non-Users
n = 29
n = 1418
n = 29
71.1 (7.2) 65.5 12.4 (3.4) 6.8 8.1 (4.5) 1.2 (.86)
68.9 (8.4) a 61.0 b 13.02 (4.0) a 1.6 b 3.05 (2.8) c .95 (.85) a
70.8 (8.5) a 62.1 b 13.1 (2.9) a 6.8 b 7.7 (4.0) a 1.2 (.76) a
.52 (.68)
.26 (.39) c
.41 (.73) a
27.6
18.8 b
24.1 b
a
ANOVAs = n.s. Chi-square test = n.s. c ANOVAs: Number of drugs, F(1,1438) = 87.5, p < .05, partial eta-squared = .057; Cardiovascular disease burden, F(1,1438) = 5.5, p < .05, partial eta-squared = .019 b
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Table 3 Cognitive Measures (M ± SD) for Anticholinergic Medication Users and Non-Users. Anticholinergic Medication Non-Users Matched Non-Users n = 1418 n = 29 7.4 (2.8) 8.2 (3.1) 6.5 (2.9) 7.2 (3.4) 15.6 (2.5) 15.9 (2.3) 15.2 (2.6) 15.9 (2.1) 6.7 (1.8) 6.7 (1.8) 6.7 (1.9) 6.5 (1.9) 20.2 (5.0) 20.2 (5.4) 19.7 (5.2) 18.9 (4.3) 14.8 (4.6) 15.1 (4.6) 14.6 (4.8) 15.0 (5.0) 16.9 (3.2) 16.3 (2.9) 16.2 (3.5) 15.8 (3.3) 29.3 (.9) 29.3 (.9) 28.4 (2.0) 28.5 (1.3)
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n = 29 Episodic Memory Baseline 7.0 (3.1) Episodic Memory Follow-Up 4.9 (2.5) Semantic Memory Baseline 15.9 (2.7) Semantic Memory Follow-Up 15.7 (2.1) Short-Term Memory Baselinea 6.5 (1.5) b Short-Term Memory Follow-Up 6.2 (1.7) Category Fluency Baseline 19.0 (4.4) Category Fluency Follow-Up 17.7 (4.5) Letter Fluency Baseline 15.9 (5.2) Letter Fluency Follow-Up 14.9 (4.4) Processing Speed Baseline 16.0 (2.2) Processing Speed Follow-Up 14.7 (2.4) MMSE Baseline 29.2 (.9) MMSE Follow-Up 28.0 (1.4) Note. MMSE= Mini-Mental-State Examination. a Sample size for users: n=28; Sample size for non-users: 1384. b Sample size for users: n=29; Sample size for non-users: 1409.
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Users
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Figure 1. (a). Episodic memory composite score at baseline and 6-year follow-up for non-users (n=1418) and users (n=29) of anticholinergic drugs in the total sample. (b). Episodic memory performance at baseline and 6-year follow-up for users of anticholinergic drugs (n=29) and propensity-score matched controls (n=29). Error bars represent standard errors around the means.
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ACCEPTED MANUSCRIPT References
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Ancelin, M. L., Artero, S., Portet, F., Dupuy, A. M., Touchon, J., & Ritchie, K. (2006). Non-degenerative mild cognitive impairment in elderly people and use of anticholinergic drugs: longitudinal cohort study. BMJ, 332(7539), 455459. doi: 10.1136/bmj.38740.439664.DE Bartus, R. T., Dean, R. L., 3rd, Beer, B., & Lippa, A. S. (1982). The cholinergic hypothesis of geriatric memory dysfunction. Science, 217(4558), 408-414. Bentley, P., Driver, J., & Dolan, R. J. (2011). Cholinergic modulation of cognition: insights from human pharmacological functional neuroimaging. Prog Neurobiol, 94(4), 360-388. doi: 10.1016/j.pneurobio.2011.06.002 Brewster, G. S., Peterson, L., Roker, R., Ellis, M. L., & Edwards, J. D. (2016). Depressive Symptoms, Cognition, and Everyday Function Among Community-Residing Older Adults. J Aging Health. doi: 10.1177/0898264316635587 Campbell, N., Boustani, M., Limbil, T., Ott, C., Fox, C., Maidment, I., . . . Gulati, R. (2009). The cognitive impact of anticholinergics: a clinical review. Clin Interv Aging, 4, 225-233. Cancelli, I., Beltrame, M., Gigli, G. L., & Valente, M. (2009). Drugs with anticholinergic properties: cognitive and neuropsychiatric side-effects in elderly patients. Neurol Sci, 30(2), 87-92. doi: 10.1007/s10072-009-0033y Ferencz, B., Laukka, E. J., Welmer, A. K., Kalpouzos, G., Angleman, S., Keller, L., . . . Backman, L. (2014). The benefits of staying active in old age: physical activity counteracts the negative influence of PICALM, BIN1, and CLU risk alleles on episodic memory functioning. Psychol Aging, 29(2), 440-449. doi: 10.1037/a0035465 Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975): Mini-Mental State: A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res, 12,189 –198. doi: 10.1016/0022-3956(75)90026-6 Fortin, M. P., Rouch, I., Dauphinot, V., Gedeon, C., Genthon, S., Bonnefoy, M., & Krolak-Salmon, P. (2011). Effects of anticholinergic drugs on verbal episodic memory function in the elderly: a retrospective, cross-sectional study. Drugs Aging, 28(3), 195-204. doi: 10.2165/11586580-00000000000000 Gardiner, J.M, & Java, R.I. (1990). Recollective experience in word and nonword recognition. Mem Cognit. 18:23-30. Gardiner, J.M, Ramponi, C., & Richardson-Klavehn, A. (1998). Experiences of remembering, knowing, and guessing. Conscious Cogn. 7:1-26. doi:10.1006/ccog.1997.0321 Gray, S. L., Anderson, M. L., Dublin, S., Hanlon, J. T., Hubbard, R., Walker, R., . . . Larson, E. B. (2015). Cumulative use of strong anticholinergics and incident dementia: a prospective cohort study. JAMA Intern Med, 175(3), 401-407. doi: 10.1001/jamainternmed.2014.7663 Haasum, Y., Fastbom, J., Fratiglioni, L., Kareholt, I., & Johnell, K. (2011). Pain treatment in elderly persons with and without dementia: a population-
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Pantzar, A., Laukka, E. J., Atti, A. R., Fastbom, J., Fratiglioni, L., & Backman, L. (2014). Cognitive deficits in unipolar old-age depression: a populationbased study. Psychol Med, 44(5), 937-947. doi: 10.1017/S0033291713001736 Papenberg, G., Becker, N., Ferencz, B., Naveh-Benjamin, M., Laukka, E., Bäckman, L., & Brehmer, Y. (2017). Dopamine receptor genes modulate associative memory in old age. Journal of Cognitive Neuroscience, 20, 1-9. doi: 10.1162/jocn_a_01048 Randolph, Justus J., Falbe, Kristina, Manuel, Austin Kureethara, & Balloun, Joseph L. (2014). A Step-by-Step Guide to Propensity Score Matching in R. Practical Assessment, Research & Evaluation, 19(18). Available online: http://pareonline.net/getvn.asp?v=19&n=18 Richter, N., Allendorf, I., Onur, O. A., Kracht, L., Dietlein, M., Tittgemeyer, M., . . . Kukolja, J. (2014). The integrity of the cholinergic system determines memory performance in healthy elderly. Neuroimage, 100, 481-488. doi: 10.1016/j.neuroimage.2014.06.031 Rydwik, E., Welmer, A. K., Kareholt, I., Angleman, S., Fratiglioni, L., & Wang, H. X. (2013). Adherence to physical exercise recommendations in people over 65--the SNAC-Kungsholmen study. Eur J Public Health, 23(5), 799-804. doi: 10.1093/eurpub/cks150 Sambeth, A., Riedel, W. J., Klinkenberg, I., Kahkonen, S., & Blokland, A. (2015). Biperiden selectively induces memory impairment in healthy volunteers: no interaction with citalopram. Psychopharmacology (Berl), 232(11), 1887-1897. doi: 10.1007/s00213-014-3822-9 Skoldunger, A., Fastbom, J., Wimo, A., Fratiglioni, L., & Johnell, K. (2015). Impact of Inappropriate Drug Use on Hospitalizations, Mortality, and Costs in Older Persons and Persons with Dementia: Findings from the SNAC Study. Drugs Aging, 32(8), 671-678. doi: 10.1007/s40266-015-0287-4 Sköldunger, A., Fastbom, J., Wimo, A, Fratiglioni, L, & Johnell, K. (2016). The impact of dementia on drug costs in older people: results from the SNAC study. BMC Neurol. 29, 16:28. doi: 10.1186/s12883-016-0547-x. Uusvaara, J., Pitkala, K. H., Kautiainen, H., Tilvis, R. S., & Strandberg, T. E. (2013). Detailed cognitive function and use of drugs with anticholinergic properties in older people: a community-based cross-sectional study. Drugs Aging, 30(3), 177-182. doi: 10.1007/s40266-013-0055-2 Welmer, A. K., Angleman, S., Rydwik, E., Fratiglioni, L., & Qiu, C. (2013). Association of cardiovascular burden with mobility limitation among elderly people: a population-based study. PLoS One, 8(5), e65815. doi: 10.1371/journal.pone.0065815 Wezenberg, E., Verkes, R. J., Sabbe, B. G., Ruigt, G. S., & Hulstijn, W. (2005). Modulation of memory and visuospatial processes by biperiden and rivastigmine in elderly healthy subjects. Psychopharmacology (Berl), 181(3), 582-594. doi: 10.1007/s00213-005-0083-7 WHO Collaborating Centre for Drug Statistics Methodology, Oslo, Norway. About the ATC/DDD system [online]. Available from URL: http://www.whocc.no/atcddd/
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ACCEPTED MANUSCRIPT Highlights • • •
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Cholinergic system plays an important role in episodic memory Anticholinergic drug use is common in older adults Drug users declined more on episodic memory over 6 years compared to nonusers Drug use was unrelated to speed, fluency, MMSE, short-term, and semantic memory
S0197-4580(17)30080-5
DOI:
10.1016/j.neurobiolaging.2017.03.009
Reference:
NBA 9868
To appear in:
Neurobiology of Aging
Received Date: 12 June 2016 Revised Date:
2 March 2017
Accepted Date: 6 March 2017
Please cite this article as: Papenberg, G., Bäckman, L., Fratiglioni, L., Laukka, E.J., Fastbom, J., Johnell, K., Anticholinergic drug use is associated with episodic memory decline in older adults without dementia, Neurobiology of Aging (2017), doi: 10.1016/j.neurobiolaging.2017.03.009. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Anticholinergic drug use is associated with episodic memory
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decline in older adults without dementia
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Goran Papenberga, Lars Bäckmana, Laura Fratiglionia,b, Erika J. Laukkaa, Johan Fastboma, Kristina Johnella
a
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Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden b Stockholm Gerontology Research Center, Stockholm, Sweden
* Corresponding author:
Goran Papenberg
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Aging Research Center
Gävlegatan 16 SE-113 30 Stockholm
Email: [email protected] Tel:
+46-7-8 690 58 75
Fax : +46-7-8 690 68 89
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ACCEPTED MANUSCRIPT Abstract Anticholinergic drug use is common in older adults and has been related to increased dementia risk. This suggests that users of these drugs may experience accelerated cognitive decline. So far, however, longitudinal data on this topic are
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absent and the available evidence is inconclusive with respect to effects on specific cognitive domains due to suboptimal control of confounding variables.
We investigated whether anticholinergic medication use is associated with
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cognitive decline over 6 years in a population-based study of older adults (aged 60 to 90; n = 1473) without dementia. We found that users (n=29) declined more on
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episodic memory over 6 years compared to non-users (n=1418). These results were independent of age, sex, education, overall drug intake, physical activity, depression, cardiovascular risk burden, and cardiovascular disease. By contrast, anticholinergic drug use was unrelated to performance in processing speed, semantic memory, short-
Examination).
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term memory, verbal fluency, and global cognition (the Mini-Mental-State
Our results suggest that effects of anticholinergics may be particularly detrimental
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to episodic memory in older adults, which supports the assertion that the cholinergic
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system plays an important role in episodic memory formation.
(Abstract: 183 words)
Key words: anticholinergic drugs, cognition, episodic memory, aging, interindividual differences
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ACCEPTED MANUSCRIPT Introduction Use of anticholinergic medications is common in older adults (e.g., Haasum, Fastbom, & Johnell, 2012; Ness, Hoth, Barnett, Shorr, & Kaboli, 2006) and may have negative repercussions for cognitive functioning (Campbell et al., 2009; Cancelli,
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Beltrame, Gigli, & Valente, 2009; Moore & O'Keeffe, 1999). Cognitive aging is
characterized by large heterogeneity (Nyberg, Lövdén, Riklund, Lindenberger, &
Bäckman, 2012), which raises the question of the extent to which drug intake may
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contribute to this diversity, independent of diseases and pathologies treated by the
prescribed drug. Here we investigate whether anticholinergic medication is related to
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cognitive decline in a population-based sample of older adults without dementia. Anticholinergics comprise drugs from various classes, including incontinence drugs, low-potency antipsychotics, tricyclic antidepressants, and some antihistamines. Drugs with anticholinergic properties have been associated with increased risk for
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dementia (e.g., Bartus, Dean, Beer, & Lippa, 1982; Gray et al., 2015). In terms of cognition, anticholinergic drugs have been linked to impaired episodic memory – the memory for past events, mild-cognitive impairment, and different types of dementia
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(Fortin et al., 2011). This is in line with evidence suggesting that the cholinergic
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system is strongly implicated in cognition, in particular episodic memory (Bentley, Driver, & Dolan, 2011). Experimental studies show that, compared to placebo, anticholinergic drugs used to treat Parkinson’s disease are associated with episodic memory impairment in healthy younger (Sambeth, Riedel, Klinkenberg, Kahkonen, & Blokland, 2015) and older (Wezenberg, Verkes, Sabbe, Ruigt, & Hulstijn, 2005) adults. Moreover, positron imaging tomography data suggest that integrity of the cholinergic system is linked to interindividual variability in episodic memory functioning among older adults (Richter et al., 2014). However, despite observed
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ACCEPTED MANUSCRIPT links between the cholinergic system and cognition, particularly episodic memory, the specific effects of anticholinergic drug use on cognition remain unclear and longitudinal data are absent. One cross-sectional study showed that elderly people taking anticholinergic drugs had impairments in speed, attention, and episodic
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memory compared to non-users (Ancelin et al., 2006). However, this study did not control for some of the potential confounding variables (e.g., Parkinson’s disease, depression) that may drive the adverse effects of anticholinergics on cognition, as
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shown by another study (Lechevallier-Michel et al., 2005).
We examine the effects of anticholinergic drugs on cognitive decline over 6 years
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in a population-based sample of older adults without dementia (60-90 years; n = 1473). To overcome the shortcomings of previous studies, we compare cognitive performance between users and non-users of anticholinergic drugs and adjust for different background variables to assure that impairments in cognition are associated
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with drug use. In addition, we apply propensity-scores matching to minimize selection bias, which helps strengthen causal interpretations and provides a better estimation of treatment effects (Randolph et al., 2014). Given the work reviewed above, we were
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particularly interested in the association between drug intake and episodic memory.
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However, to investigate the specificity of the influence of anticholinergic drugs on cognitive functioning, we also examined their effects on speed, verbal fluency, and semantic memory.
Methods
Participants Data were collected within the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K). The original SNAC-K population consisted of 4590 alive and eligible persons who lived in Kungsholmen/Essingeöarna in central Stockholm, 4
ACCEPTED MANUSCRIPT belonged to prespecified age strata, and were randomly selected to take part in the study. Between 2001 and 2004, 3363 persons participated in the baseline assessment. They belonged to the age cohorts 60, 66, 72, 78, 81, 84, 87, 90, 93, 96 years, and 99 years and older. The examination consists of three parts: a nurse interview, a medical
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examination, and a neuropsychological testing session. Altogether, the examination
takes about 6 hours. Participants are reexamined each time they reach the age of the
next age cohort. All parts of the SNAC-K project have been approved by the regional
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ethical review board in Stockholm. Informed consent was obtained from all
impaired), from next-of-kin.
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participants, or, if the person was unable to answer (e.g. severely cognitively
Out of 3363 participants, 1724 persons underwent cognitive testing at both baseline and follow-up. At baseline, 390 participants refused cognitive testing, 106 had very low MMSE scores (< 10), 10 had died, and 9 had other reasons for not being tested.
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558 persons did not participate in the follow-up assessment after 6 years, because they themselves or a relative refused participation, (n=168), they had moved or could not be contacted (n=73), or had died (n=317). From the older participants, who were also
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assessed after 3 years, 89 refused participation, 19 had moved or could not be
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contacted, and 257 had died. Among those who participated in the follow-up after 6 years, 82 refused cognitive testing, 29 had very low MMSE scores, and 5 could not be tested due to other reasons. From this cognitive sample, we excluded persons with dementia (n = 118), Parkinson’s disease (n = 17), and schizophrenia (n = 6) at baseline or follow-up. Only participants with complete data with respect to cognition, drugs, and confounding variables were included in the analyses, resulting in a final sample of 1473 individuals.
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ACCEPTED MANUSCRIPT Dementia Diagnosis Diagnosis of dementia was made according to the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM–IV; American Psychiatric Association, 1994), using a three-step procedure. The preliminary diagnosis made by the examining
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physician was compared with a second independent diagnosis based on computerized data. In cases of disagreement, a supervising physician made a third and final
diagnosis. The cognitive assessment used for diagnostic purposes included the MMSE
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(Folstein et al., 1975), the Clock test (Manos & Wu, 1994), and questions regarding
solving.
Anticholinergic Drugs
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memory, orientation, executive functioning, interpretation of proverbs, and problem
Information about drug use was collected by a physician during the medical
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examination. Before the interview, participants were instructed to bring a list of currently used drugs, including both regularly and as needed used drugs. Both prescribed and over-the-counter drugs were recorded. When the older person could
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not provide information (e.g., due to cognitive impairment), a relative or a close
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informant was asked instead or the information was collected from medical records (Haasum, Fastbom, Fratiglioni, Kareholt, & Johnell, 2011). Drugs were classified according to the Anatomical Therapeutic Chemical (ATC) classification system, as recommended by the World Health Organization (http://www.whocc.no/atcddd/). We identified anticholinergic drugs in accordance with the Swedish National Board of Health and Welfare indicators for drug therapy in the elderly (Sköldunger, Fastbom, Wimo, Fratiglioni, & Johnell, 2015): urinary and gastrointestinal antispasmodics, anticholinergic antiemetics, class Ia antiarrhythmics, anticholinergic antiparkinsonian
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ACCEPTED MANUSCRIPT drugs, low-potency antipsychotics, tricyclic antidepressants, and first-generation antihistamines (Table 1). Our analyses focused on participants that used anticholinergic drugs at both baseline examination and at six-year follow-up (n = 29). However, we also report
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effects of anticholinergic drugs on cognition where older adults that used anticholinergic drugs at any time point were included (n= 55).
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Propensity score matching
In addition to the total sample, we compared cognitive performance of
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anticholinergic drug users with a matched sample of non-users to reduce selection bias and get a better estimation of treatment effects. Using the MatchIt toolbox in R 3.2.1 (Randolph et al., 2014), participants were matched on age, sex, education, total number of drugs used, physical inactivity, cardiovascular risk burden, cardiovascular
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disease burden, and depression. Here we employed the matching algorithm “nearest”, which uses a distance measure to select the best control match for each individual in the treatment group. Two aggregated sum scores were used for cardiovascular burden,
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namely cardiovascular risk factors (CRFs) and diseases (CVDs). The aggregated CRFs included stage-2 hypertension, high cholesterol (i.e., fasting cholesterol level ≥
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6.5 mmol/L), obesity (≥30 kg/m2), diabetes, and smoking. The aggregated score for CVDs included occurrence of ischemic heart diseases, atrial fibrillation, and heart failure (see, Welmer, Angleman, Rydwik, Fratiglioni, & Qiu, 2013, for details on assessment of CRFs and CVDs). Diagnosis of depression was based on ICD-10 criteria (Pantzar et al., 2014). Participation in physical activities was self-reported for the past 12 months and categorized according to level of intensity (light exercise such as short bike rides or usual paced walks or moderate to intense exercise such as brisk
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ACCEPTED MANUSCRIPT walks or jogging) and frequency (never, <2-3 times/month, 2-3 times/month, several times/week, and every day). Participants were considered physically inactive if they engaged less or equal to 2–3 times per month into light and/or moderate/intense exercise (Ferencz et al., 2014; Rydwik et al., 2013).
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The matching of participants was successful, as there were no differences in demographic, cardiovascular risk or drug-related variables between users and
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matched non-users of anticholinergic drugs (Table 2).
Cognitive Measures
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The cognitive test battery assessed six cognitive domains: episodic memory, semantic memory, perceptual speed, letter fluency, and category fluency (Laukka et al., 2013) and short-term memory (Pantzar et al., 2014; Papenberg et al., 2017). Below we briefly describe the tasks that were used to represent the cognitive constructs.
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Episodic memory. Free recall and recognition were assessed with a word list comprised of 16 unrelated concrete nouns. Words were presented both orally and visually, and the presentation rate was one new word every 5 s. Immediately after
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presentation, participants were given two minutes for oral free recall. Following free
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recall, participants were given a self-paced recognition task, where the 16 target words were presented randomly intermixed with 16 distracters. If participants responded “yes” on a recognition trial, their memory of the item was further probed by asking if (a) they remember hearing and/or seeing the presented word (recollection), (b) they recognize it, but have no clear recollection of the presented word (familiarity), or (c) neither of the two (guess) (Gardiner & Java, 1990; Gardiner et al., 1998). Recognition scores in this study were based solely on recollection
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ACCEPTED MANUSCRIPT responses (i.e., familiarity and guess responses were not included). Unit-weighted composite scores were computed based on the free recall and recognition scores. Semantic memory. Semantic memory was assessed with a vocabulary (SRB:1) and a general knowledge task. The SRB:1 is a 30-item vocabulary test (Dureman 1960;
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Nilsson et al. 1997), where each target word is presented together with 5 other words. Participants were instructed to underline the words representing the synonyms to the
target words (maximum time = 7 min). The general knowledge task (Dahl, Allwood,
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and Hagberg 2009) consisted of 10 questions (e.g., “What is the capital of
Uruguay?”). Participants were instructed to select the correct answer out of two
and general knowledge tasks.
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alternatives. Unit-weighted composite scores were computed based on the vocabulary
Short-term memory. In Digit Span forward, participants repeated lists of one digit numbers (starting with list length 3). In Digit Span backwards, participants repeated
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the lists of numbers in reversed order (starting with list length 2). If a participant failed to repeat two trials of a certain list length, the task was terminated. The outcome variables were number of correct repetitions for forward and backward digit
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span, which were combined into a unit-weighted composite score.
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Perceptual speed. The perceptual speed tasks comprised two paper-and-pencil tests, the digit cancellation (Zazzo 1974) and pattern comparison (Salthouse and Babcock 1991) tests. For digit cancellation, participants were instructed to sequentially go through 11 rows of random digits as quickly as possible and cross out every “4” they encountered during 30 sec. For pattern comparison, they were asked to sequentially go through line-segment patterns as rapidly as possible in two trials, each 30 seconds long, and mark whether the patterns were “same” or “different”. Unit-weighted
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ACCEPTED MANUSCRIPT composite scores were computed based on the digit cancellation and pattern comparison tests. Letter fluency. For the letter fluency tasks, participants were asked to orally generate as many words as possible within 60 sec, beginning with the letters F and A,
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respectively. They were instructed that proper names, numbers, or words with a
different suffix were not credited. Unit-weighted composite scores were computed based on the two letter-fluency measures.
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Category fluency. For category fluency, participants were asked to orally generate as many words as possible within 60 sec, belonging to the categories animals and
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professions. As before, unit-weighted composite scores were computed based on the two measures.
Statistical Analyses
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Behavioral and demographic data were analyzed using SPSS for Windows 22 (SPSS, Chicago, IL, USA). We conducted a repeated-measures analysis of covariance (ANCOVA) with treatment group (users, non-users) as a between-subjects factor and
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time (baseline, 6-year follow-up) as a within-subjects factor for the cognitive
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measures. Analyses were conducted both in the total sample and in the matched sample. The following potential confounders were included as covariates in all analyses: age, sex, education, CRFs, CVDs, physical inactivity, total number of drugs, and depression. For all analyses, the alpha level was set to p < .05. Effect sizes are indicated by partial eta squared. In addition, treatment effects are indicated by Cohen’s d. Results Mean scores on the cognitive tests for anticholinergic drug users and non-users are 10
ACCEPTED MANUSCRIPT presented in Table 3. In the total sample, we found a main effect of time for episodic memory, F(1,1437) = 142.2, p < .05, partial eta-squared = .090, indicating decline over 6 years (Fig. 1a). Moreover, the main effect of treatment group was reliable, F(1,1437) = 5.76, p < .05, partial eta-squared = .004, Cohen’s d= .42, reflecting worse
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overall memory performance for individuals taking anticholinergic drugs. Critically, the treatment group × time interaction was significant, F(1,1437) = 4.39, p <.05,
partial eta-squared = .003. Follow-up comparisons showed no differences in episodic-
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memory performance between treatment groups at baseline (p > .10). However, at follow-up, persons using anticholinergic drugs performed worse than non-users,
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F(1,1436) = 7.64, p <.05, partial eta-squared = .006, Cohen’s d = .59. By contrast, anticholinergic drug use was unrelated to perceptual speed, semantic memory, letter fluency, category fluency, short-term memory, and MMSE (ps >.10), suggesting a unique association between anticholinergics and decline in episodic memory. When
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including persons who took anticholinergic medication at only one time point (n = 55), the main effect of anticholinergic drug use and the treatment group × time interaction did not approach conventional significance (p < .10). Thus, the drug
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effects became diluted when including individuals that used anticholinergics at either
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baseline or follow-up.
Further, we repeated the analyses using a propensity-score matched sample to
achieve better control of selection bias and more precise estimation of anticholinergic effects on episodic memory. When considering the total sample of individuals taking anticholinergic drugs (n=55), the main effect of anticholinergic drugs was reliable, F(1,101) = 6.99, p <.05, partial eta-squared = .065 (Cohen’s d = 0.41). When focusing on continuous users only, as before both the main effect of anticholinergic drug use, F(1,48) = 6.13, p =.05, partial eta-squared = .111 (Cohen’s d = .60), and the treatment
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ACCEPTED MANUSCRIPT group × time interaction, F(1,48) = 3.56, p <.05, partial eta-squared = .068, were significant (Fig. 1b). Again, the effects were driven by episodic-memory differences at six years, F(1,48) = 9.21, p <.05, partial eta-squared = .158 (Cohen’s d = .66), as
Discussion
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group differences were not reliable at baseline (p >.10).
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We investigated the effects of anticholinergic drugs on cognitive decline over 6
years in a population-based sample of older adults without dementia. Our results show
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that older adults using anticholinergics both at baseline and follow-up declined more on episodic memory than non-users. These effects were replicated in a propensityscore matched control sample (n = 29), suggesting that the effects are independent of several potential confounders, including age, sex, education, depression status, as well as cardiovascular risk and diseases.
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Our findings are in accordance with evidence from different lines of research, implicating the cholinergic system in episodic memory. First, cholinergic activity and
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protein expression is highest in the hippocampus (Mesulam, Volicer, Marquis, Mufson, & Green, 1986), a brain region critical to successful episodic memory.
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Second, formation of episodic memories relies on the cholinergic system, which enhances long-term potentiation and facilitates encoding without interference from previously stored information by suppressing excitatory connections (Hasselmo, 1999, 2006). Moreover, a receptor imaging study showed that individual differences in the integrity of the cholinergic system are associated with episodic memory functioning in older adults (Richter et al., 2014). Our data extend these results and show that continuous use of anticholinergic drugs is associated with episodic memory decline in older adults. Importantly, the
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ACCEPTED MANUSCRIPT detrimental effects of anticholinergic drug use were confined to episodic memory; there were no negative effects for speed, verbal fluency, semantic, and short-term memory. This pattern is in line with a review implicating the cholinergic system particularly in episodic memory (Bentley et al., 2011).
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Previously reported effects of anticholinergic medication on processing speed might rather be associated with the different comorbidities of individuals taking
anticholinergic drugs (Ancelin et al., 2006), such as Parkinson patients (Karayanidis,
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1989) and depressed persons (Brewster, Peterson, Roker, Ellis, & Edwards, 2016; Pantzar, Atti, Bäckman, & Laukka, 2015). Similarly, past research has reported
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associations between anticholinergic drugs and verbal fluency (Uusvaara, Pitkala, Kautiainen, Tilvis, & Strandberg, 2013). This association may also be related to other confounders, as the analyses were only adjusted for age, sex, and education in that particular study. Similarly, another cross-sectional study reported an association
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between anticholinergic load and impairments in verbal fluency among older women, in addition to impairments in episodic memory (Koyama et al., 2004). However, this study did not adjust for Parkinson’s disease, which also impairs verbal fluency (Henry
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& Crawford, 2004). Our data strongly suggest that anticholinergics may accelerate
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cognitive decline in older adults without dementia. In terms of effect sizes, the observed differences are of medium size and, as such, should be clinically relevant. An interesting avenue for future research is to examine whether the observed effects on episodic memory are reversible or permanent. Some limitations of the present investigation should be acknowledged. Notably, only about 2% of the participants used anticholinergic drugs over 6 years, which is a low number compared to the point prevalence of anticholinergic medication use in previous reports (e.g., Haasum et al., 2012; Ness et al., 2006). This low number is
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ACCEPTED MANUSCRIPT likely due to the fact that we focused on participants that took the drug at both time points only and excluded participants with different pathologies (e.g., dementia, Parkinson’s disease). Given the association between dementia and depression (e.g., Hermida, McDonald, & Steenland, 2012), the fact that we excluded participants with
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dementia likely resulted in an exclusion of participants taking antidepressants. However, we should also acknowledge that our sample is in general slightly
positively selected (Laukka et al., 2013), which may also contribute to the relatively
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small number of drug users. Whereas we controlled for the most important
confounders, we cannot rule out the possibility that other individual factors associated
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with long-term anticholinergic drug use may underlie the faster episodic memory decline in users. Another limitation is that self-reported data on drug use can be subject to recall bias. However, compared with refill register data, our data were based on medications that were actually used and not only dispensed (Sköldunger,
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Fastbom, Wimo, Fratiglioni, & Johnell, 2016).
Taken together, our results support evidence regarding the influence of anticholinergic drugs on episodic memory: Older adults taking anticholinergic drugs
Acknowledgments
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declined more on episodic memory over six years than non-users.
We thank all the participants and staff who were involved in the collection and
management of the data for their contribution to the study. The Swedish National study on Aging and Care, SNAC, (www.snac.org) is financially supported by the Ministry of Health and Social Affairs, Sweden, the participating County Councils and Municipalities, and the Swedish Research Council. In addition, grants were obtained from the Stiftelsen för Gamla Tjänarinnor and the Swedish Research Council. LB was supported by grants from the Swedish Research Council, the Swedish Research
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ACCEPTED MANUSCRIPT Council for Health, Working Life, and Welfare, an Alexander von Humboldt
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Research Award, and a donation from the af Jochnick Foundation.
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ACCEPTED MANUSCRIPT Table 1 Frequency of Different Anticholinergic Drugs. Continuous and Discontinuous Users n=55
1 2 23 2 1 9
n=29 1
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2
10
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Synthetic anticholinergics, quarternary ammonium compounds A04AD Other antiemetics C01BA Antiarrhythmics, class Ia G04BD Drugs for urinary frequency and incontinence N05AA Antipsychotics N05AB04 Prochlorperazine N05BB01 Hydroxyzine N06AA Non-selective monoamine reuptake inhibitors (tricyclic antidepressants) R06AA02 Diphenhydramine R06AB Substituted alkylamines R06AD Phenothiazine derivatives Note. ATC= Anatomical Therapeutic Chemical code.
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ATC code A03AB
Continuous Users
1 1 5
-
15 1 1 4 5 1 3
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Table 2 Demographic, Drug-related, and Vascular Risk Variables for Anticholinergic Medication Users and Non-Users. Users
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Age, M ± SD Women, % Years of education, M ± SD Depression, % Number of drugs, M ± SD Cardiovascular risk factor burden, M ± SD Cardiovascular disease burden, M ± SD Physical inactivity, %
Anticholinergic Medication Non-Users Matched Non-Users
n = 29
n = 1418
n = 29
71.1 (7.2) 65.5 12.4 (3.4) 6.8 8.1 (4.5) 1.2 (.86)
68.9 (8.4) a 61.0 b 13.02 (4.0) a 1.6 b 3.05 (2.8) c .95 (.85) a
70.8 (8.5) a 62.1 b 13.1 (2.9) a 6.8 b 7.7 (4.0) a 1.2 (.76) a
.52 (.68)
.26 (.39) c
.41 (.73) a
27.6
18.8 b
24.1 b
a
ANOVAs = n.s. Chi-square test = n.s. c ANOVAs: Number of drugs, F(1,1438) = 87.5, p < .05, partial eta-squared = .057; Cardiovascular disease burden, F(1,1438) = 5.5, p < .05, partial eta-squared = .019 b
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Table 3 Cognitive Measures (M ± SD) for Anticholinergic Medication Users and Non-Users. Anticholinergic Medication Non-Users Matched Non-Users n = 1418 n = 29 7.4 (2.8) 8.2 (3.1) 6.5 (2.9) 7.2 (3.4) 15.6 (2.5) 15.9 (2.3) 15.2 (2.6) 15.9 (2.1) 6.7 (1.8) 6.7 (1.8) 6.7 (1.9) 6.5 (1.9) 20.2 (5.0) 20.2 (5.4) 19.7 (5.2) 18.9 (4.3) 14.8 (4.6) 15.1 (4.6) 14.6 (4.8) 15.0 (5.0) 16.9 (3.2) 16.3 (2.9) 16.2 (3.5) 15.8 (3.3) 29.3 (.9) 29.3 (.9) 28.4 (2.0) 28.5 (1.3)
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n = 29 Episodic Memory Baseline 7.0 (3.1) Episodic Memory Follow-Up 4.9 (2.5) Semantic Memory Baseline 15.9 (2.7) Semantic Memory Follow-Up 15.7 (2.1) Short-Term Memory Baselinea 6.5 (1.5) b Short-Term Memory Follow-Up 6.2 (1.7) Category Fluency Baseline 19.0 (4.4) Category Fluency Follow-Up 17.7 (4.5) Letter Fluency Baseline 15.9 (5.2) Letter Fluency Follow-Up 14.9 (4.4) Processing Speed Baseline 16.0 (2.2) Processing Speed Follow-Up 14.7 (2.4) MMSE Baseline 29.2 (.9) MMSE Follow-Up 28.0 (1.4) Note. MMSE= Mini-Mental-State Examination. a Sample size for users: n=28; Sample size for non-users: 1384. b Sample size for users: n=29; Sample size for non-users: 1409.
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Users
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Figure 1. (a). Episodic memory composite score at baseline and 6-year follow-up for non-users (n=1418) and users (n=29) of anticholinergic drugs in the total sample. (b). Episodic memory performance at baseline and 6-year follow-up for users of anticholinergic drugs (n=29) and propensity-score matched controls (n=29). Error bars represent standard errors around the means.
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ACCEPTED MANUSCRIPT Highlights • • •
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•
Cholinergic system plays an important role in episodic memory Anticholinergic drug use is common in older adults Drug users declined more on episodic memory over 6 years compared to nonusers Drug use was unrelated to speed, fluency, MMSE, short-term, and semantic memory