Journal of the Neurological Sciences 350 (2015) 69–74
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Hypercholesterolemia in elders is associated with slower cognitive decline: A prospective, population-based study (NEDICES) Julián Benito-León a,b,c,⁎, Saturio Vega-Quiroga d, Alberto Villarejo-Galende a,b,c, Félix Bermejo-Pareja a,b,c a
Department of Neurology, University Hospital “12 de Octubre”, Madrid, Spain Department of Medicine, Faculty of Medicine, Complutense University, Madrid, Spain Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid, Spain d Arévalo Health Center, Arévalo, Ávila, Spain b c
a r t i c l e
i n f o
Article history: Received 19 December 2014 Received in revised form 5 February 2015 Accepted 9 February 2015 Available online 16 February 2015 Keywords: Cognitive function Elderly Epidemiology Hypercholesterolemia Population-based study
a b s t r a c t Background: Studies investigating the association between hypercholesterolemia in the elderly and cognitive decline report discrepant outcomes. We determined in a prospective population-based cohort (NEDICES) in elders whether hypercholesterolemia was associated with slower cognitive decline. Methods: Participants were evaluated at baseline and 3 years later. Baseline demographic variables were recorded. Hypercholesterolemia was defined by total cholesterol of N200 mg/dl or current use of lipid-lowering drugs. At baseline and at follow-up, a 37-item version of the Mini-Mental State Examination (37-MMSE) was administered. Results: The final sample, 2015 participants (72.9 ± 6.1 years), comprised 1166 (57.9%) hypercholesterolemic and 849 (42.1%) non-hypercholesterolemic participants (reference category). The mean follow-up was 3.4 ± 0.5 years. During the three year follow-up period, the 37-MMSE declined by 0.7 ± 4.3 points (median = 0 point) in non-hypercholesterolemic participants vs. 0.3 ± 3.9 points in hypercholesterolemic participants (median = 0 points) (Mann–Whitney test, p = 0.007). In analyses adjusted for baseline age and other potential confounders, this difference remained robust. We also assessed the cognitive decline per unit time (i.e., the rate of cognitive decline). The rate of cognitive decline was 0.2 ± 1.3 (median = 0.0) points/year for nonhypercholesterolemic participants and 0.1 ± 1.2 (median = 0.0) points/year for hypercholesterolemic participants (Mann–Whitney test, p = 0.028). Conclusions: In this prospective population-based cohort study, cognitive test scores among hypercholesterolemic elders declined more slowly than observed in their non-hypercholesterolemic counterparts. Additional studies are needed to confirm these results. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Dementia and cognitive disorders are among the major public health challenges of aging societies today. Currently, no effective therapy can prevent deterioration. It is not surprising therefore, that scientific and clinical research in the area of cognitive disorders has shifted during the last decade to focus on the possible predictors of these disorders in an effort to prevent the consequences of dementia. Links between vascular disease and dementia in older age are increasingly recognized [1]. Specifically, understanding the link between these disorders and cholesterol may represent one important part of that effort [2]. Since total cholesterol serum levels are potentially modifiable, the relation between cholesterol and cognitive decline might well have practical implications for the primary prevention of these disorders [2]. ⁎ Corresponding author at: Av. de la Constitución 73, portal 3, piso 7° Izquierda. ES28821, Coslada, Madrid, Spain. Tel.: +34 916695467. E-mail address: [email protected] (J. Benito-León).
http://dx.doi.org/10.1016/j.jns.2015.02.016 0022-510X/© 2015 Elsevier B.V. All rights reserved.
Epidemiological studies indicate that high midlife plasma total cholesterol levels increases the risk of Alzheimer's disease and vascular dementia [3,4], but decreasing total plasma cholesterol levels after midlife may reflect ongoing disease processes and may represent a risk marker for late-life dementia [5–8]. A related issue that remains unclear in the elderly is whether hypercholesterolemia per se is associated or not with slower cognitive decline than non-hypercholesterolemic subjects. In other words, whether higher levels of cholesterol would be a marker of better health status in the elderly that could be associated with decreased risk of cognitive decline. Prospective population-based studies are preferential when investigating risk factors for cognitive decline. The few prospective populationbased studies investigating the association between hypercholesterolemia in the elderly and cognitive decline report discrepant outcomes [8–14]. In all these previous studies, very prevalent unmeasured confounders, which are well-known associated with cognitive impairment in elderly people, including medications that potentially affect cognitive function (e.g., anxiolytics, stimulants, antipsychotics, antidepressants,
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antihistamines, or antiepileptics drugs) or sleep problems [15,16] may have influenced the results. It is therefore not clear whether cognitive decline progresses in hypercholesterolemic subjects more slowly than in non-hypercholesterolemic subjects. We hypothesized that the cognitive deficits in hypercholesterolemic participants would worsen less than in non-hypercholesterolemic participants (i.e., controls). To address this question, we utilized data from the Neurological Disorders in Central Spain (NEDICES) study, in which participants were prospectively evaluated at two times points separated by three years. We aimed to adjust for confounders such as medications with central nervous system effects and usual sleep duration. 2. Material and methods 2.1. Study population Data for these analyses were derived from the NEDICES study, a longitudinal, population-based survey of the prevalence, incidence, and determinants of major age-associated conditions of the elderly, including Parkinson's disease (PD), essential tremor, stroke, and dementia [1,17–26]. Detailed accounts of the study population and sampling methods have been published [17–19]. The survey area consisted of three communities as follows: Margaritas (approximately 14,800 inhabitants), a working-class neighborhood in Getafe (Greater Madrid); Lista (approximately 150,000 inhabitants), a professional-class neighborhood in Salamanca district (Central Madrid); and Arévalo (approximately 9000 inhabitants), the agricultural zone of Arévalo County (125 km northwest of Madrid). Up-to-date lists of residents were generated from population registers. In each community, survey eligibility was restricted to residents aged 65 years or older who were present there on December 31, 1993, or during 6 or more months of 1993. Eligible persons who had moved away from the survey area were not traced. In Margaritas and Arévalo, every eligible subject was to be screened. However, in Lista, proportionate stratified random sampling was used to select subjects for screening because of the large number of elderly residents. All procedures were approved by the ethical standards committees on human experimentation at the University Hospitals “12 de Octubre” (Madrid) and “La Princesa” (Madrid). Written (signed) informed consent was obtained from all enrollees. 2.2. Study evaluation Briefly, at the time of their baseline assessment (1994–1995), 5278 elderly subjects were interviewed using a 500-item screening questionnaire that assessed demographic factors and medical conditions. The face-to-face interview included data collection on demographics, current medications (including drugs that affect the central nervous system), selfrated health [27], and medical conditions. Subjects were asked to bring all medications taken in the past one week to the clinic where the interviewer viewed and recorded the name and the dose of each one. A short form of the questionnaire was mailed to subjects who refused, or were unavailable for face-to-face or telephone screening. This form assessed demographic characteristics, several neurological disorders (essential tremor, stroke, dementia, and Parkinsonism), current medications, and the name of their family doctor. During the second (i.e., follow-up) evaluation (1997–1998), the same methods were used. A comorbidity index was calculated based on the presence of the following conditions, according to a recently published comorbidity score developed in ambulatory care settings [28]: atrial fibrillation, cancer, chronic obstructive pulmonary disease, depression, dementia, diabetes, epilepsy (treated), heart failure, myocardial infarction, psychiatric disorders, renal disease, and stroke. The score ranged from 0 to 28. To assess sleep duration, each participant was asked to indicate their “total hours of actual sleep in a 24-hour period” [15,16]. Participants indicated their typical total daily sleep duration as the sum of nighttime sleep and daytime napping [15,16,29]. In addition, blood samples for
total cholesterol determinations were drawn. Hypercholesterolemia and non-hypercholesterolemia were defined as total cholesterol N200 mg/dl and ≤200 mg/dl, respectively [30]. Participants were also considered as hypercholesterolemic if they were receiving lipidlowering drugs [31,32]. As described [17–19], a 37-item Mini-Mental State Examination (37MMSE) was administered in both baseline assessment (1994–1995) and follow-up evaluation (1997–1998) [33–37]. This was a Spanish adaptation of the standard MMSE [33–37]. It included all of the standard MMSE items as well as three additional items as follows: (1) an attention task, i.e., “say 1, 3, 5, 7, 9 backwards”, (2) a visual order, i.e., a man raising his arms, and (3) a simple construction task, i.e., copying two overlapping circles. Besides the total score (0–37 points), cognitive domain tasks were grouped into several subscores (orientation [0–10 points], memory [0–6 points], attention and calculation [0–10 points], language tasks [0–9 points], and construction/copying [0–2 points]) [33–37]. Diagnosis of dementia fulfilled the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition [38] and required evidence of cognitive impairment (based on a neuropsychological test battery and a clinical mental status examination) as well as impairment in social or occupational function. 2.3. Final selection of participants Of the 5278 participants evaluated at baseline, we excluded 1462 participants who were evaluated at baseline because they declined a follow-up assessment or had incomplete follow-up assessments, had died or were unreachable (Fig. 1). We further excluded 1025 participants with incomplete 37-MMSE examinations, and 776 without available data on cholesterol status, which left 2015 remaining participants who were included in our analyses (Fig. 1). The final sample of 2015 was similar to the base sample of 5278 participants in terms of sex (1141 [56.6%] vs. 3040 [57.6%] women, chi-square = 0.564, p = 0.45). However, they were more educated (160 [7.9%] vs. 711 [13.6%] were illiterate, chi-square = 69.87, p b 0.001) and, on average, 1.3 years younger (73.0 ± 6.0 vs. 74.3 ± 7.0 years, t = −8.15, p b 0.001). 2.4. Statistical analyses Analyses were performed in SPSS (version 21.0). All tests were two sided, and significance was accepted at the 5% level (α = 0.05). Age, years of education, comorbidity index, body mass index, and 37-MMSE total score were not normally distributed (Kolmogorov– Smirnov, p b 0.001), even after log-transformation. Therefore, although mean and median values were reported, differences were compared using nonparametric tests (Mann–Whitney and Kruskal–Wallis tests). The Χ2 test was used to analyze categorical variables. The change in 37-MMSE score = baseline score − follow-up score. The 37-MMSE scores (baseline, follow-up, and change in 37-MMSE) were not normally distributed, even after log transformation. Therefore, scores were compared using the same non-parametric approach (Mann–Whitney and Kruskal–Wallis tests). We divided change in 37MMSE into tertiles (lower tertile ≥2 point improvement in score, higher tertile ≥2 point decline in score). For the current analyses, we dichotomized this variable into higher vs. middle and lower tertiles. Logistic regression analyses were performed, thereby allowing us to assess, for a second time, potential confounders. In these models, the dependent (outcome) variable was “Decline” (higher tertile of 37-MMSE change) with “non-Decline” (middle and lower tertiles of 37-MMSE change) serving as the reference group. We began with an unadjusted model. Then, in adjusted models, we first considered baseline variables that in univariate analyses were associated at the p b 0.05 level with both the outcome (“Decline” vs “non-Decline” [the reference category]) and the exposure (hypercholesterolemia vs normocholesterolemia [the reference category]) (model 1 [more restrictive criteria for confounding]),
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Enrolled 5278
Excluded 3263 Declined / Incomplete follow-up 1462 Incomplete 37-Mini-Mental State Examination 1025 Participants with unavailable data on baseline cholesterol status 776
Final sample 2015
Hypercholesterolemic subjects 1,166 (57.9%)
Non-hypercholesterolemic subjects 849 (42.1%)
Fig. 1. Flow-chart of the study.
and then considered baseline variables that in univariate analyses were associated at the p b 0.05 level with either the outcome or the exposure (model 2 [less restrictive criteria for confounding]). Variables assessed at baseline that we considered included age in years, sex, years of education, comorbidity index, self-rated health (good/very good, fair, and bad/very bad), body mass index, current smoker, current drinker, usual sleep duration (≤ 5 h daily, 6 to 8 h daily, and ≥ 9 h daily), medications with central nervous system effects, and the 37-MMSE total score. 3. Results The final sample, 2015 participants (mean ± standard deviation age = 72.9 ± 6.0 years), comprised 1166 (57.9%) hypercholesterolemic and 849 (42.1%) non-hypercholesterolemic participants (reference category) (Fig. 1). The mean follow-up was 3.4 ± 0.5 years. Baseline characteristics of the participants in the two cholesterol status categories are shown (Table 1). At baseline, hypercholesterolemic participants were more frequently women and younger; a higher proportion was taking medications with central nervous system effects (Table 1). There were significant differences in baseline age, years of education, sleep duration, and the MMSE total score when participants within the higher tertile of 37-MMSE change and within the remaining tertiles were compared (Table 2). During the three year follow-up period, the 37-MMSE declined by 0.7 ± 4.3 points (median = 0 point) in non-hypercholesterolemic participants vs. 0.3 ± 3.9 points in hypercholesterolemic participants (median = 0 points) (Mann–Whitney test, p = 0.007) (Fig. 2).
Table 1 Baseline demographic and clinical characteristics of the study participants, according to their cholesterol status. Non-hypercholesterolemic Hypercholesterolemic p value (n = 849) (n = 1166) Age in years Sex (women) Years of education Comorbidity indexc Self-rated healthd Good/very good Fair Bad/very bad Body mass indexd Current smoker Current drinkerd Usual sleep durationd ≤5 h daily 6 to 8 h daily ≥9 h daily Medications with central nervous system effects 37-MMSE total score a
b0.001a b0.001b 0.505a 0.297a
73.6 ± 6.2 (72) 387 (45.6%) 7.7 ± 5.0 (8) 0.9 ± 1.3 (0)
72.5 ± 5.9 (71) 754 (64.7%) 7.8 ± 5.1 (8) 0.8 ± 1.3 (0)
530 (62.7%) 233 (27.6%) 82 (9.7%) 28.1 ± 5.6 (27.5) 110 (13.0%) 300 (35.3%)
745 (64.2%) 300 (25.8%) 116 (10.0%) 27.9 ± 6.3 (27.2) 133 (11.4%) 421 (36.1%)
74 (8.7%) 399 (47.1%) 374 (44.2%) 113 (13.3%)
102 (8.8%) 557 (48.0%) 501 (43.2%) 193 (16.6%)
0.909b
30.0 ± 5.2 (31)
30.1 ± 4.9 (31)
0.302a
0.685b
0.147a 0.291b 0.711b
0.045b
Mann–Whitney U test. Chi-square test. Comorbidity included 12 conditions: atrial fibrillation, cancer, chronic obstructive pulmonary disease, depression, dementia, diabetes, epilepsy (treated), heart failure, myocardial infarction, psychiatric disorders, renal disease, and stroke. d Data on some participants were missing. Mean ± standard deviation (median) and frequency (%) are reported. b c
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Table 2 Baseline demographic and clinical characteristics of participants within the higher tertile of 37-MMSE change vs those within the remaining tertiles.
Age in years Sex (women) Years of education Comorbidity indexb Self-rated healthc Good/very good Fair Bad/very bad Body mass indexc Current smoker Current drinkerc Usual sleep durationc ≤5 h daily 6 to 8 h daily ≥9 h daily Medications with central nervous system effects 37-MMSE total score
Lower and middle tertiles of 37-MMSE change (n = 1330)
Higher tertile of 37-MMSE change (n = 685)
p value
72.5 ± 5.7 (71) 746 (56.1%) 8.0 ± 5.0 (8) 0.9 ± 1.3 (0)
73.9 ± 6.5 (73) 395 (57.7%) 7.4 ± 5.1 (8) 0.9 ± 1.3 (0)
b0.001a 0.499a 0.005a 0.556a
855 (64.5%) 349 (26.3%) 121 (9.1%) 28.0 ± 6.3 (27.4) 165 (12.4%) 491 (36.9%)
420 (61.7%) 184 (27.0%) 77 (11.3%) 28.0 ± 5.6 (27.3) 78 (11.4%) 230 (33.6%)
108 (8.2%) 662 (50.0%) 554 (41.8%) 199 (15.0%) 29.7 ± 5.1 (31)
68 (10.0%) 294 (43.0%) 321 (47.0%) 107 (15.6%) 30.5 ± 5.1 (32)
0.245d
0.565a 0.506d 0.135d 0.011d
0.697d b0.001a
a
Mann–Whitney U test. Comorbidity included 12 conditions: atrial fibrillation, cancer, chronic obstructive pulmonary disease, depression, dementia, diabetes, epilepsy (treated), heart failure, myocardial infarction, psychiatric disorders, renal disease, and stroke. c Data on some participants were missing. Mean ± standard deviation (median) and frequency (%) are reported. d Chi-square test. b
We also assessed the cognitive decline per unit time (i.e., the rate of cognitive decline). The rate of cognitive decline was 0.2 ± 1.3 (median = 0.0) points/year for non-hypercholesterolemic participants and 0.1 ± 1.2 (median = 0.0) points/year for hypercholesterolemic participants (Mann–Whitney test, p = 0.028). In a logistic regression model, hypercholesterolemic participants were approximately 0.8 times more likely than the reference group (non-hypercholesterolemic participants) to have a “decline” in 37-MMSE (OR = 0.77, 95% CI = 0.67–0.93, p = 0.007) (Table 3). In a logistic regression model that adjusted for baseline age (i.e., variable that was associated with both cholesterol status and 37-MMSE change tertiles), the odds of cognitive decline remained decreased in hypercholesterolemic participants (model 1 in Table 3). The results did not change in a logistic regression model that adjusted for variables that were
associated with either cholesterol status or 37-MMSE change tertile (i.e., baseline age, sex, years of education, usual sleep duration, medications with central nervous system effects, and the 37-MMSE total score) (model 2 in Table 3). Furthermore, in a model that adjusted for baseline age, sex, educational level, comorbidity index, self-rated health, body mass index, current smoker, current drinker, usual sleep duration, or medications with central nervous system effects, and the 37-MMSE total score (i.e., all potential confounders independent of their statistical significance) (model 3 in Table 3), the results remained unchanged. In another analysis, we excluded all participants with prevalent dementia (N = 30). In these analyses, participants with hypercholesterolemia were 0.7 times more likely to decline than the reference group (unadjusted OR = 0.76, 95%, CI = 0.63–0.92, p = 0.004; OR model 1 = 0.79, 95%, CI = 0.65–0.96, p = 0.015; OR model 2 = 0.75, 95%, CI = 0.62–0.92, p = 0.005; and OR model 3 = 0.77, 95%, CI = 0.63–0.93, p = 0.009). Finally, we excluded all participants who were taking statins (N = 215) and the results were similar (unadjusted OR = 0.80, 95%, CI = 0.65–0.97, p = 0.022; OR model 1 = 0.82, 95%, CI = 0.67–0.99, p = 0.048; OR model 2 = 0.78, 95%, CI = 0.64–0.96, p = 0.020; and OR model 3 = 0.80, 95%, CI = 0.65–0.99, p = 0.038). The 37-MMSE was divided into sub-scores, and changes in 37-MMSE subscores were compared in non-hypercholesterolemic and hypercholesterolemic participants (Table 4). The greatest significant difference was in the area of orientation, where decline was significantly higher in nonhypercholesterolemic participants (Table 4). 4. Discussion
Fig. 2. MMSE decline from baseline to follow up at the NEDICES cohort.
In the current prospective study of community-dwelling elders, we further demonstrated that baseline cognitive test scores declined at a slower rate in participants with hypercholesterolemia than the reference group. Hypercholesterolemic subjects on average experienced a 0.3 point reduction in the 37-MMSE over 3 years compared with their counterparts without hypercholesterolemia. Although this reduction was significantly greater than that seen in controls, in absolute terms, it was a modest change. Slower cognitive decline in subjects with hypercholesterolemia has been observed in only a handful of previous prospective community or population-based studies [8–11]. In a sample of 377 non-demented community dwellers aged 75 years and over recruited from the Sydney Older Persons Study, participants with reported hypercholesterolemia showed a significantly smaller decline in MMSE score over a 6-year
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Table 3 Odds of cognitive decline in participants with hypercholesterolemia vs. those without hypercholesterolemia. Unadjusted
Hypercholesterolemic (N = 1166) Non-hypercholesterolemic (N = 849) (reference category)
Model 1
Model 2
Model 3
Odds ratio
95% CI
p value
Odds ratio
95% CI
p value
Odds ratio
95% CI
p value
Odds ratio
95% CI
p value
0.77
0.67–0.93
0.007
0.80
0.67–0.97
0.023
0.77
0.63–0.93
0.008
0.79
0.65–0.96
0.017
1.00
–
1.00
–
1.00
–
1.00
–
Model 1: Adjusted for baseline age. Model 2: Adjusted for baseline age, sex, years of education, usual sleep duration, medications with central nervous system effects, and the 37-MMSE total score. Model 3: Adjusted for baseline age, sex, years of education, comorbidity index, self-rated health, body mass index, current smoker, current drinker, usual sleep duration, medications with central nervous system effects, and the 37-MMSE total score.
period compared to those who did not have a high level of cholesterol (0.38 compared to 2.15 points, respectively) [8]. In the Longitudinal Aging Study Amsterdam, which recruited 1181 community dwellers aged 65 years and over, lower cholesterol at baseline was negatively associated with both general cognition and information processing speed [9]. In ApoE-ε4 carriers, lower cholesterol was related to a higher rate of decline on information processing speed and a higher ratio of 27-hydroxycholesterol to cholesterol was related to a lower level of general performance and memory functioning [9]. In a populationbased sample of 101 women aged 60–70 years at baseline with low baseline levels of high-density lipoprotein (HDL) cholesterol were more likely to have poor memory at 12-year follow-up than those with higher HDL levels [10]. In a sample of 1382 non-demented participants from the Cardiovascular risk factors, aging and dementia (CAIDE) study, a more pronounced decrease of total cholesterol was related to poorer late-life episodic memory and psychomotor speed, but not if subjects used statins [11]. In contrast, in a prospective communitybased cohort study involving 1147 elderly individuals without dementia or cognitive impairment at baseline followed for 7 years elderly subjects residing in Northern Manhattan (NY), plasma lipid levels or lipid-lowering treatment were not associated with changes in cognitive function [12]. In line with this, in a population-based cohort design involving 2312 subjects aged 50–75 years enrolled in the University of Edinburgh Aspirin for Asymptomatic Atherosclerosis trial, total cholesterol level was not associated with cognitive function or estimated decline [13]. Finally, in a population-based cohort recruited from three French cities involving 6855 participants, a hypercholesterolemic pattern in late-life was associated with a 25 to 50% increased risk of decline over seven years in psychomotor speed, executive abilities, and verbal fluency [14]. In contrast, in women, a 30% higher rate of decline was found in psychomotor speed with high HDL levels and in executive abilities with low levels of LDL and triglycerides [14]. Although the current findings suggest that hypercholesterolemia may be associated with a slower cognitive decline in the elderly, the mechanisms underlying this association remain unknown. One possibility is that decreased levels of serum total cholesterol in the elderly may reflect ongoing pathological brain changes, which may prone to a higher risk of mild cognitive impairment or dementia. Second, lower total
cholesterol levels in the elderly have been associated with higher levels of the serum interleukin-6, which in turn has been associated with dementia [39]. Third, lower total cholesterol in the elderly could be an effect rather than a cause of cognitive decline, because of decreased nutrition [40]. Currently, however, we know of no plausible physiologic explanation for such a cause-and-effect relationship. This study had several limitations. First, the 37-MMSE is a relatively abbreviated screening tool for dementia. The use of more detailed neuropsychological test batteries would enable future investigators to study these changes in greater detail. Nevertheless, even with this relatively simple, abbreviated tool, we were able to establish clear case–control differences. Second, the 37-MMSE was administered at two time points; use of additional time points would allow one to assess the extent to which the case–control difference continued beyond the three-year time window. Third, we did not control for apolipoprotein-E alleles/ genotypes. This study also had several strengths. First, hypercholesterolemic subjects were compared to a large sample size of several hundred non-hypercholesterolemic participants. Second, the assessments were conducted prospectively in a standardized manner. Finally, we were able to adjust for the potential confounding effects of a number of important factors. In closing, using a prospective, population-based design, we further demonstrated that cognitive test scores in hypercholesterolemic subjects declined more slowly compared to those without hypercholesterolemia. Disclosures All authors report no disclosures. Author roles Dr. Benito-León ([email protected]) collaborated in 1) the conception, organization and execution of the research project; 2) the statistical analysis design, and; 3) the writing of the manuscript first draft and the review and critique of the manuscript. Dr. Vega-Quiroga ([email protected]) collaborated in 1) the conception, organization and execution of the research project;
Table 4 Decline in 37-MMSE sub-scores during follow-up. Cholesterol status
37-MMSE sub-scores Orientation (10 points) Immediate recall (3 points) Attention and calculation (10 points) Delayed recall (3 points) Language (9 points) Copying (2 points)
p value
Non-hypercholesterolemic
Hypercholesterolemic
0.4 (0.0) ± 1.5 0.0 (0.0) ± 0.4 0.0 (0.0) ± 2.6 0.1 (0.0) ± 1.2 0.1 (0.0) ± 1.4 0.0 (0.0) ± 0.7
0.3 (0.0) ± 1.3 0.0 (0.0) ± 0.3 −0.1 (0.0) ± 2.5 0.0 (0.0) ± 1.2 0.0 (0.0) ± 1.2 0.1 (0.0) ± 0.7
Mean (median) ± SD are reported. All the positive values indicate a decline in the MMSE score. Mann–Whitney U test was used for comparisons of data.
0.049 0.454 0.279 0.064 0.486 0.139
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2) the statistical analysis design, and; 3) the review and critique of the manuscript. Dr. Villarejo-Galende ([email protected]) collaborated in 1) the conception, organization and execution of the research project, and; 2) the review and critique of the manuscript. Dr. Bermejo-Pareja ([email protected]) collaborated in 1) the conception, organization and execution of the research project, and; 2) the review and critique of the manuscript. Acknowledgments and funding Additional information about the collaborators and detailed funding of the NEDICES study can be found in the web (http://www.ciberned.es/ estudio-nedices). Dr. Benito-León is supported by the National Institutes of Health, Bethesda, MD, USA (NINDS #R01 NS039422), the Commission of the European Union (grant ICT-2011-287739, NeuroTREMOR), and the Spanish Health Research Agency (grant FIS PI12/01602). Dr. VegaQuiroga is supported by the National Institutes of Health, Bethesda, MD, USA (NINDS #R01 NS039422). Dr. Bermejo-Pareja is supported by the National Institutes of Health, Bethesda, MD, USA (NINDS #R01 NS039422) and the Commission of the European Union (grant ICT-2011-287739, NeuroTREMOR). References [1] Bermejo-Pareja F, Benito-León J, Vega S, Medrano MJ, Román GC. Incidence and subtypes of dementia in three elderly populations of central Spain. J Neurol Sci 2008;264(1–2):63–72. [2] Sánchez-Ferro A, Benito-León J, Mitchell AJ, Bermejo-Pareja F. A review of the potential therapeutic role of statins in the treatment of Alzheimer's disease: current research and opinion. Neuropsychiatr Dis Treat 2013;9:55–63. [3] Kivipelto M, Helkala EL, Laakso MP, Hanninen T, Hallikainen M, Alhainen K, et al. Midlife vascular risk factors and Alzheimer's disease in later life: longitudinal, population based study. BMJ 2001;322(7300):1447–51. [4] Solomon A, Kivipelto M, Wolozin B, Zhou J, Whitmer RA. Midlife serum cholesterol and increased risk of Alzheimer's and vascular dementia three decades later. Dement Geriatr Cogn Disord 2009;28(1):75–80. [5] Romas SN, Tang MX, Berglund L, Mayeux R. APOE genotype, plasma lipids, lipoproteins, and AD in community elderly. Neurology 1999;53(3):517–21. [6] Mielke MM, Zandi PP, Sjogren M, Gustafson D, Ostling S, Steen B, et al. High total cholesterol levels in late life associated with a reduced risk of dementia. Neurology 2005;64(10):1689–95. [7] Stewart R, White LR, Xue QL, Launer LJ. Twenty-six-year change in total cholesterol levels and incident dementia: the Honolulu-Asia Aging Study. Arch Neurol 2007; 64(1):103–7. [8] Piguet O, Grayson DA, Creasey H, Bennett HP, Brooks WS, Waite LM, et al. Vascular risk factors, cognition and dementia incidence over 6 years in the Sydney Older Persons Study. Neuroepidemiology 2003;22(3):165–71. [9] van den Kommer TN, Dik MG, Comijs HC, Fassbender K, Lutjohann D, Jonker C. Total cholesterol and oxysterols: early markers for cognitive decline in elderly? Neurobiol Aging 2009;30(4):534–45. [10] Komulainen P, Lakka TA, Kivipelto M, Hassinen M, Helkala EL, Haapala I, et al. Metabolic syndrome and cognitive function: a population-based follow-up study in elderly women. Dement Geriatr Cogn Disord 2007;23(1):29–34. [11] Solomon A, Kareholt I, Ngandu T, Wolozin B, Macdonald SW, Winblad B, et al. Serum total cholesterol, statins and cognition in non-demented elderly. Neurobiol Aging 2009;30(6):1006–9. [12] Reitz C, Luchsinger J, Tang MX, Manly J, Mayeux R. Impact of plasma lipids and time on memory performance in healthy elderly without dementia. Neurology 2005; 64(8):1378–83. [13] Okusaga O, Stewart MC, Butcher I, Deary I, Fowkes FG, Price JF. Smoking, hypercholesterolaemia and hypertension as risk factors for cognitive impairment in older adults. Age Ageing 2013;42(3):306–11. [14] Ancelin ML, Ripoche E, Dupuy AM, Samieri C, Rouaud O, Berr C, et al. Genderspecific associations between lipids and cognitive decline in the elderly. Eur Neuropsychopharmacol 2014;24(7):1056–66. [15] Benito-León J, Louis ED, Bermejo-Pareja F. Cognitive decline in short and long sleepers: a prospective population-based study (NEDICES). J Psychiatr Res 2013; 47(12):1998–2003.
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Prevalence of stroke and transient ischemic attack in three elderly populations of central Spain. Neuroepidemiology 2008;30(4):247–53. [25] Martínez-Salio A, Benito-León J, Díaz-Guzmán J, Bermejo-Pareja F. Cerebrovascular disease incidence in central Spain (NEDICES): a population-based prospective study. J Neurol Sci 2010;298(1–2):85–90. [26] Bermejo-Pareja F, Benito-León J, Vega S, Olazarán J, de Toledo M, Díaz-Guzmán J, et al. Consistency of clinical diagnosis of dementia in NEDICES: a population-based longitudinal study in Spain. J Geriatr Psychiatry Neurol 2009;22(4):246–55. [27] Fernández-Ruiz M, Guerra-Vales JM, Trincado R, Fernández R, Medrano MJ, Villarejo A, et al. The ability of self-rated health to predict mortality among communitydwelling elderly individuals differs according to the specific cause of death: data from the NEDICES cohort. Gerontology 2013;59(4):368–77. [28] Carey IM, Shah SM, Harris T, DeWilde S, Cook DG. A new simple primary care morbidity score predicted mortality and better explains between practice variations than the Charlson index. J Clin Epidemiol 2013;66(4):436–44. [29] Benito-León J, Louis ED, Villarejo-Galende A, Romero JP, Bermejo-Pareja F. Long sleep duration in elders without dementia increases risk of dementia mortality (NEDICES). Neurology 2014;83(17):1530–7. [30] Costa J, Borges M, Oliveira E, Gouveia M, Carneiro AV. Incidence and prevalence of hypercholesterolemia in Portugal: a systematic review. Part III. Rev Port Cardiol 2003;22(6):829–36. [31] Plans P, Pardell H, Salleras L. Epidemiology of cardiovascular disease risk factors in Catalonia (Spain). Eur J Epidemiol 1993;9(4):381–9. [32] Guallar-Castillon P, Gil-Montero M, León-Muñoz LM, Graciani A, Bayan-Bravo A, Taboada JM, et al. Magnitude and management of hypercholesterolemia in the adult population of Spain, 2008–2010: the ENRICA Study. Rev Esp Cardiol (Engl Ed) 2012;65(6):551–8. [33] Benito-León J, Louis ED, Bermejo-Pareja F. Population-based case–control study of cognitive function in essential tremor. Neurology 2006;66(1):69–74. [34] Benito-León J, Louis ED, Vega S, Bermejo-Pareja F. Statins and cognitive functioning in the elderly: a population-based study. J Alzheimers Dis 2010;21(1):95–102. [35] Benito-León J, Mitchell AJ, Vega S, Bermejo-Pareja F. A population-based study of cognitive function in older people with subjective memory complaints. J Alzheimers Dis 2010;22(1):159–70. [36] Benito-León J, Louis ED, Posada IJ, Sánchez-Ferro A, Trincado R, Villarejo A, et al. Population-based case–control study of cognitive function in early Parkinson's disease (NEDICES). J Neurol Sci 2011;310(1–2):176–82. [37] Benito-León J, Romero JP, Louis ED, Bermejo-Pareja F. Faster cognitive decline in elders without dementia and decreased risk of cancer mortality: NEDICES study. Neurology 2014;82(16):1441–8. [38] American Psychiatric Association, Task Force on DSM-IV. Diagnostic and statistical manual of mental disorders: DSM-IV. 4th ed. Washington, DC: American Psychiatric Association; 1994. [39] Solfrizzi V, D'Introno A, Colacicco AM, Capurso C, Todarello O, Pellicani V, et al. Circulating biomarkers of cognitive decline and dementia. Clin Chim Acta 2006; 364(1–2):91–112. [40] van Vliet P, Westendorp RG, van Heemst D, de Craen AJ, Oleksik AM. Cognitive decline precedes late-life longitudinal changes in vascular risk factors. J Neurol Neurosurg Psychiatry 2010;81(9):1028–32.
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Hypercholesterolemia in elders is associated with slower cognitive decline: A prospective, population-based study (NEDICES) Julián Benito-León a,b,c,⁎, Saturio Vega-Quiroga d, Alberto Villarejo-Galende a,b,c, Félix Bermejo-Pareja a,b,c a
Department of Neurology, University Hospital “12 de Octubre”, Madrid, Spain Department of Medicine, Faculty of Medicine, Complutense University, Madrid, Spain Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid, Spain d Arévalo Health Center, Arévalo, Ávila, Spain b c
a r t i c l e
i n f o
Article history: Received 19 December 2014 Received in revised form 5 February 2015 Accepted 9 February 2015 Available online 16 February 2015 Keywords: Cognitive function Elderly Epidemiology Hypercholesterolemia Population-based study
a b s t r a c t Background: Studies investigating the association between hypercholesterolemia in the elderly and cognitive decline report discrepant outcomes. We determined in a prospective population-based cohort (NEDICES) in elders whether hypercholesterolemia was associated with slower cognitive decline. Methods: Participants were evaluated at baseline and 3 years later. Baseline demographic variables were recorded. Hypercholesterolemia was defined by total cholesterol of N200 mg/dl or current use of lipid-lowering drugs. At baseline and at follow-up, a 37-item version of the Mini-Mental State Examination (37-MMSE) was administered. Results: The final sample, 2015 participants (72.9 ± 6.1 years), comprised 1166 (57.9%) hypercholesterolemic and 849 (42.1%) non-hypercholesterolemic participants (reference category). The mean follow-up was 3.4 ± 0.5 years. During the three year follow-up period, the 37-MMSE declined by 0.7 ± 4.3 points (median = 0 point) in non-hypercholesterolemic participants vs. 0.3 ± 3.9 points in hypercholesterolemic participants (median = 0 points) (Mann–Whitney test, p = 0.007). In analyses adjusted for baseline age and other potential confounders, this difference remained robust. We also assessed the cognitive decline per unit time (i.e., the rate of cognitive decline). The rate of cognitive decline was 0.2 ± 1.3 (median = 0.0) points/year for nonhypercholesterolemic participants and 0.1 ± 1.2 (median = 0.0) points/year for hypercholesterolemic participants (Mann–Whitney test, p = 0.028). Conclusions: In this prospective population-based cohort study, cognitive test scores among hypercholesterolemic elders declined more slowly than observed in their non-hypercholesterolemic counterparts. Additional studies are needed to confirm these results. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Dementia and cognitive disorders are among the major public health challenges of aging societies today. Currently, no effective therapy can prevent deterioration. It is not surprising therefore, that scientific and clinical research in the area of cognitive disorders has shifted during the last decade to focus on the possible predictors of these disorders in an effort to prevent the consequences of dementia. Links between vascular disease and dementia in older age are increasingly recognized [1]. Specifically, understanding the link between these disorders and cholesterol may represent one important part of that effort [2]. Since total cholesterol serum levels are potentially modifiable, the relation between cholesterol and cognitive decline might well have practical implications for the primary prevention of these disorders [2]. ⁎ Corresponding author at: Av. de la Constitución 73, portal 3, piso 7° Izquierda. ES28821, Coslada, Madrid, Spain. Tel.: +34 916695467. E-mail address: [email protected] (J. Benito-León).
http://dx.doi.org/10.1016/j.jns.2015.02.016 0022-510X/© 2015 Elsevier B.V. All rights reserved.
Epidemiological studies indicate that high midlife plasma total cholesterol levels increases the risk of Alzheimer's disease and vascular dementia [3,4], but decreasing total plasma cholesterol levels after midlife may reflect ongoing disease processes and may represent a risk marker for late-life dementia [5–8]. A related issue that remains unclear in the elderly is whether hypercholesterolemia per se is associated or not with slower cognitive decline than non-hypercholesterolemic subjects. In other words, whether higher levels of cholesterol would be a marker of better health status in the elderly that could be associated with decreased risk of cognitive decline. Prospective population-based studies are preferential when investigating risk factors for cognitive decline. The few prospective populationbased studies investigating the association between hypercholesterolemia in the elderly and cognitive decline report discrepant outcomes [8–14]. In all these previous studies, very prevalent unmeasured confounders, which are well-known associated with cognitive impairment in elderly people, including medications that potentially affect cognitive function (e.g., anxiolytics, stimulants, antipsychotics, antidepressants,
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antihistamines, or antiepileptics drugs) or sleep problems [15,16] may have influenced the results. It is therefore not clear whether cognitive decline progresses in hypercholesterolemic subjects more slowly than in non-hypercholesterolemic subjects. We hypothesized that the cognitive deficits in hypercholesterolemic participants would worsen less than in non-hypercholesterolemic participants (i.e., controls). To address this question, we utilized data from the Neurological Disorders in Central Spain (NEDICES) study, in which participants were prospectively evaluated at two times points separated by three years. We aimed to adjust for confounders such as medications with central nervous system effects and usual sleep duration. 2. Material and methods 2.1. Study population Data for these analyses were derived from the NEDICES study, a longitudinal, population-based survey of the prevalence, incidence, and determinants of major age-associated conditions of the elderly, including Parkinson's disease (PD), essential tremor, stroke, and dementia [1,17–26]. Detailed accounts of the study population and sampling methods have been published [17–19]. The survey area consisted of three communities as follows: Margaritas (approximately 14,800 inhabitants), a working-class neighborhood in Getafe (Greater Madrid); Lista (approximately 150,000 inhabitants), a professional-class neighborhood in Salamanca district (Central Madrid); and Arévalo (approximately 9000 inhabitants), the agricultural zone of Arévalo County (125 km northwest of Madrid). Up-to-date lists of residents were generated from population registers. In each community, survey eligibility was restricted to residents aged 65 years or older who were present there on December 31, 1993, or during 6 or more months of 1993. Eligible persons who had moved away from the survey area were not traced. In Margaritas and Arévalo, every eligible subject was to be screened. However, in Lista, proportionate stratified random sampling was used to select subjects for screening because of the large number of elderly residents. All procedures were approved by the ethical standards committees on human experimentation at the University Hospitals “12 de Octubre” (Madrid) and “La Princesa” (Madrid). Written (signed) informed consent was obtained from all enrollees. 2.2. Study evaluation Briefly, at the time of their baseline assessment (1994–1995), 5278 elderly subjects were interviewed using a 500-item screening questionnaire that assessed demographic factors and medical conditions. The face-to-face interview included data collection on demographics, current medications (including drugs that affect the central nervous system), selfrated health [27], and medical conditions. Subjects were asked to bring all medications taken in the past one week to the clinic where the interviewer viewed and recorded the name and the dose of each one. A short form of the questionnaire was mailed to subjects who refused, or were unavailable for face-to-face or telephone screening. This form assessed demographic characteristics, several neurological disorders (essential tremor, stroke, dementia, and Parkinsonism), current medications, and the name of their family doctor. During the second (i.e., follow-up) evaluation (1997–1998), the same methods were used. A comorbidity index was calculated based on the presence of the following conditions, according to a recently published comorbidity score developed in ambulatory care settings [28]: atrial fibrillation, cancer, chronic obstructive pulmonary disease, depression, dementia, diabetes, epilepsy (treated), heart failure, myocardial infarction, psychiatric disorders, renal disease, and stroke. The score ranged from 0 to 28. To assess sleep duration, each participant was asked to indicate their “total hours of actual sleep in a 24-hour period” [15,16]. Participants indicated their typical total daily sleep duration as the sum of nighttime sleep and daytime napping [15,16,29]. In addition, blood samples for
total cholesterol determinations were drawn. Hypercholesterolemia and non-hypercholesterolemia were defined as total cholesterol N200 mg/dl and ≤200 mg/dl, respectively [30]. Participants were also considered as hypercholesterolemic if they were receiving lipidlowering drugs [31,32]. As described [17–19], a 37-item Mini-Mental State Examination (37MMSE) was administered in both baseline assessment (1994–1995) and follow-up evaluation (1997–1998) [33–37]. This was a Spanish adaptation of the standard MMSE [33–37]. It included all of the standard MMSE items as well as three additional items as follows: (1) an attention task, i.e., “say 1, 3, 5, 7, 9 backwards”, (2) a visual order, i.e., a man raising his arms, and (3) a simple construction task, i.e., copying two overlapping circles. Besides the total score (0–37 points), cognitive domain tasks were grouped into several subscores (orientation [0–10 points], memory [0–6 points], attention and calculation [0–10 points], language tasks [0–9 points], and construction/copying [0–2 points]) [33–37]. Diagnosis of dementia fulfilled the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition [38] and required evidence of cognitive impairment (based on a neuropsychological test battery and a clinical mental status examination) as well as impairment in social or occupational function. 2.3. Final selection of participants Of the 5278 participants evaluated at baseline, we excluded 1462 participants who were evaluated at baseline because they declined a follow-up assessment or had incomplete follow-up assessments, had died or were unreachable (Fig. 1). We further excluded 1025 participants with incomplete 37-MMSE examinations, and 776 without available data on cholesterol status, which left 2015 remaining participants who were included in our analyses (Fig. 1). The final sample of 2015 was similar to the base sample of 5278 participants in terms of sex (1141 [56.6%] vs. 3040 [57.6%] women, chi-square = 0.564, p = 0.45). However, they were more educated (160 [7.9%] vs. 711 [13.6%] were illiterate, chi-square = 69.87, p b 0.001) and, on average, 1.3 years younger (73.0 ± 6.0 vs. 74.3 ± 7.0 years, t = −8.15, p b 0.001). 2.4. Statistical analyses Analyses were performed in SPSS (version 21.0). All tests were two sided, and significance was accepted at the 5% level (α = 0.05). Age, years of education, comorbidity index, body mass index, and 37-MMSE total score were not normally distributed (Kolmogorov– Smirnov, p b 0.001), even after log-transformation. Therefore, although mean and median values were reported, differences were compared using nonparametric tests (Mann–Whitney and Kruskal–Wallis tests). The Χ2 test was used to analyze categorical variables. The change in 37-MMSE score = baseline score − follow-up score. The 37-MMSE scores (baseline, follow-up, and change in 37-MMSE) were not normally distributed, even after log transformation. Therefore, scores were compared using the same non-parametric approach (Mann–Whitney and Kruskal–Wallis tests). We divided change in 37MMSE into tertiles (lower tertile ≥2 point improvement in score, higher tertile ≥2 point decline in score). For the current analyses, we dichotomized this variable into higher vs. middle and lower tertiles. Logistic regression analyses were performed, thereby allowing us to assess, for a second time, potential confounders. In these models, the dependent (outcome) variable was “Decline” (higher tertile of 37-MMSE change) with “non-Decline” (middle and lower tertiles of 37-MMSE change) serving as the reference group. We began with an unadjusted model. Then, in adjusted models, we first considered baseline variables that in univariate analyses were associated at the p b 0.05 level with both the outcome (“Decline” vs “non-Decline” [the reference category]) and the exposure (hypercholesterolemia vs normocholesterolemia [the reference category]) (model 1 [more restrictive criteria for confounding]),
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71
Enrolled 5278
Excluded 3263 Declined / Incomplete follow-up 1462 Incomplete 37-Mini-Mental State Examination 1025 Participants with unavailable data on baseline cholesterol status 776
Final sample 2015
Hypercholesterolemic subjects 1,166 (57.9%)
Non-hypercholesterolemic subjects 849 (42.1%)
Fig. 1. Flow-chart of the study.
and then considered baseline variables that in univariate analyses were associated at the p b 0.05 level with either the outcome or the exposure (model 2 [less restrictive criteria for confounding]). Variables assessed at baseline that we considered included age in years, sex, years of education, comorbidity index, self-rated health (good/very good, fair, and bad/very bad), body mass index, current smoker, current drinker, usual sleep duration (≤ 5 h daily, 6 to 8 h daily, and ≥ 9 h daily), medications with central nervous system effects, and the 37-MMSE total score. 3. Results The final sample, 2015 participants (mean ± standard deviation age = 72.9 ± 6.0 years), comprised 1166 (57.9%) hypercholesterolemic and 849 (42.1%) non-hypercholesterolemic participants (reference category) (Fig. 1). The mean follow-up was 3.4 ± 0.5 years. Baseline characteristics of the participants in the two cholesterol status categories are shown (Table 1). At baseline, hypercholesterolemic participants were more frequently women and younger; a higher proportion was taking medications with central nervous system effects (Table 1). There were significant differences in baseline age, years of education, sleep duration, and the MMSE total score when participants within the higher tertile of 37-MMSE change and within the remaining tertiles were compared (Table 2). During the three year follow-up period, the 37-MMSE declined by 0.7 ± 4.3 points (median = 0 point) in non-hypercholesterolemic participants vs. 0.3 ± 3.9 points in hypercholesterolemic participants (median = 0 points) (Mann–Whitney test, p = 0.007) (Fig. 2).
Table 1 Baseline demographic and clinical characteristics of the study participants, according to their cholesterol status. Non-hypercholesterolemic Hypercholesterolemic p value (n = 849) (n = 1166) Age in years Sex (women) Years of education Comorbidity indexc Self-rated healthd Good/very good Fair Bad/very bad Body mass indexd Current smoker Current drinkerd Usual sleep durationd ≤5 h daily 6 to 8 h daily ≥9 h daily Medications with central nervous system effects 37-MMSE total score a
b0.001a b0.001b 0.505a 0.297a
73.6 ± 6.2 (72) 387 (45.6%) 7.7 ± 5.0 (8) 0.9 ± 1.3 (0)
72.5 ± 5.9 (71) 754 (64.7%) 7.8 ± 5.1 (8) 0.8 ± 1.3 (0)
530 (62.7%) 233 (27.6%) 82 (9.7%) 28.1 ± 5.6 (27.5) 110 (13.0%) 300 (35.3%)
745 (64.2%) 300 (25.8%) 116 (10.0%) 27.9 ± 6.3 (27.2) 133 (11.4%) 421 (36.1%)
74 (8.7%) 399 (47.1%) 374 (44.2%) 113 (13.3%)
102 (8.8%) 557 (48.0%) 501 (43.2%) 193 (16.6%)
0.909b
30.0 ± 5.2 (31)
30.1 ± 4.9 (31)
0.302a
0.685b
0.147a 0.291b 0.711b
0.045b
Mann–Whitney U test. Chi-square test. Comorbidity included 12 conditions: atrial fibrillation, cancer, chronic obstructive pulmonary disease, depression, dementia, diabetes, epilepsy (treated), heart failure, myocardial infarction, psychiatric disorders, renal disease, and stroke. d Data on some participants were missing. Mean ± standard deviation (median) and frequency (%) are reported. b c
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Table 2 Baseline demographic and clinical characteristics of participants within the higher tertile of 37-MMSE change vs those within the remaining tertiles.
Age in years Sex (women) Years of education Comorbidity indexb Self-rated healthc Good/very good Fair Bad/very bad Body mass indexc Current smoker Current drinkerc Usual sleep durationc ≤5 h daily 6 to 8 h daily ≥9 h daily Medications with central nervous system effects 37-MMSE total score
Lower and middle tertiles of 37-MMSE change (n = 1330)
Higher tertile of 37-MMSE change (n = 685)
p value
72.5 ± 5.7 (71) 746 (56.1%) 8.0 ± 5.0 (8) 0.9 ± 1.3 (0)
73.9 ± 6.5 (73) 395 (57.7%) 7.4 ± 5.1 (8) 0.9 ± 1.3 (0)
b0.001a 0.499a 0.005a 0.556a
855 (64.5%) 349 (26.3%) 121 (9.1%) 28.0 ± 6.3 (27.4) 165 (12.4%) 491 (36.9%)
420 (61.7%) 184 (27.0%) 77 (11.3%) 28.0 ± 5.6 (27.3) 78 (11.4%) 230 (33.6%)
108 (8.2%) 662 (50.0%) 554 (41.8%) 199 (15.0%) 29.7 ± 5.1 (31)
68 (10.0%) 294 (43.0%) 321 (47.0%) 107 (15.6%) 30.5 ± 5.1 (32)
0.245d
0.565a 0.506d 0.135d 0.011d
0.697d b0.001a
a
Mann–Whitney U test. Comorbidity included 12 conditions: atrial fibrillation, cancer, chronic obstructive pulmonary disease, depression, dementia, diabetes, epilepsy (treated), heart failure, myocardial infarction, psychiatric disorders, renal disease, and stroke. c Data on some participants were missing. Mean ± standard deviation (median) and frequency (%) are reported. d Chi-square test. b
We also assessed the cognitive decline per unit time (i.e., the rate of cognitive decline). The rate of cognitive decline was 0.2 ± 1.3 (median = 0.0) points/year for non-hypercholesterolemic participants and 0.1 ± 1.2 (median = 0.0) points/year for hypercholesterolemic participants (Mann–Whitney test, p = 0.028). In a logistic regression model, hypercholesterolemic participants were approximately 0.8 times more likely than the reference group (non-hypercholesterolemic participants) to have a “decline” in 37-MMSE (OR = 0.77, 95% CI = 0.67–0.93, p = 0.007) (Table 3). In a logistic regression model that adjusted for baseline age (i.e., variable that was associated with both cholesterol status and 37-MMSE change tertiles), the odds of cognitive decline remained decreased in hypercholesterolemic participants (model 1 in Table 3). The results did not change in a logistic regression model that adjusted for variables that were
associated with either cholesterol status or 37-MMSE change tertile (i.e., baseline age, sex, years of education, usual sleep duration, medications with central nervous system effects, and the 37-MMSE total score) (model 2 in Table 3). Furthermore, in a model that adjusted for baseline age, sex, educational level, comorbidity index, self-rated health, body mass index, current smoker, current drinker, usual sleep duration, or medications with central nervous system effects, and the 37-MMSE total score (i.e., all potential confounders independent of their statistical significance) (model 3 in Table 3), the results remained unchanged. In another analysis, we excluded all participants with prevalent dementia (N = 30). In these analyses, participants with hypercholesterolemia were 0.7 times more likely to decline than the reference group (unadjusted OR = 0.76, 95%, CI = 0.63–0.92, p = 0.004; OR model 1 = 0.79, 95%, CI = 0.65–0.96, p = 0.015; OR model 2 = 0.75, 95%, CI = 0.62–0.92, p = 0.005; and OR model 3 = 0.77, 95%, CI = 0.63–0.93, p = 0.009). Finally, we excluded all participants who were taking statins (N = 215) and the results were similar (unadjusted OR = 0.80, 95%, CI = 0.65–0.97, p = 0.022; OR model 1 = 0.82, 95%, CI = 0.67–0.99, p = 0.048; OR model 2 = 0.78, 95%, CI = 0.64–0.96, p = 0.020; and OR model 3 = 0.80, 95%, CI = 0.65–0.99, p = 0.038). The 37-MMSE was divided into sub-scores, and changes in 37-MMSE subscores were compared in non-hypercholesterolemic and hypercholesterolemic participants (Table 4). The greatest significant difference was in the area of orientation, where decline was significantly higher in nonhypercholesterolemic participants (Table 4). 4. Discussion
Fig. 2. MMSE decline from baseline to follow up at the NEDICES cohort.
In the current prospective study of community-dwelling elders, we further demonstrated that baseline cognitive test scores declined at a slower rate in participants with hypercholesterolemia than the reference group. Hypercholesterolemic subjects on average experienced a 0.3 point reduction in the 37-MMSE over 3 years compared with their counterparts without hypercholesterolemia. Although this reduction was significantly greater than that seen in controls, in absolute terms, it was a modest change. Slower cognitive decline in subjects with hypercholesterolemia has been observed in only a handful of previous prospective community or population-based studies [8–11]. In a sample of 377 non-demented community dwellers aged 75 years and over recruited from the Sydney Older Persons Study, participants with reported hypercholesterolemia showed a significantly smaller decline in MMSE score over a 6-year
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Table 3 Odds of cognitive decline in participants with hypercholesterolemia vs. those without hypercholesterolemia. Unadjusted
Hypercholesterolemic (N = 1166) Non-hypercholesterolemic (N = 849) (reference category)
Model 1
Model 2
Model 3
Odds ratio
95% CI
p value
Odds ratio
95% CI
p value
Odds ratio
95% CI
p value
Odds ratio
95% CI
p value
0.77
0.67–0.93
0.007
0.80
0.67–0.97
0.023
0.77
0.63–0.93
0.008
0.79
0.65–0.96
0.017
1.00
–
1.00
–
1.00
–
1.00
–
Model 1: Adjusted for baseline age. Model 2: Adjusted for baseline age, sex, years of education, usual sleep duration, medications with central nervous system effects, and the 37-MMSE total score. Model 3: Adjusted for baseline age, sex, years of education, comorbidity index, self-rated health, body mass index, current smoker, current drinker, usual sleep duration, medications with central nervous system effects, and the 37-MMSE total score.
period compared to those who did not have a high level of cholesterol (0.38 compared to 2.15 points, respectively) [8]. In the Longitudinal Aging Study Amsterdam, which recruited 1181 community dwellers aged 65 years and over, lower cholesterol at baseline was negatively associated with both general cognition and information processing speed [9]. In ApoE-ε4 carriers, lower cholesterol was related to a higher rate of decline on information processing speed and a higher ratio of 27-hydroxycholesterol to cholesterol was related to a lower level of general performance and memory functioning [9]. In a populationbased sample of 101 women aged 60–70 years at baseline with low baseline levels of high-density lipoprotein (HDL) cholesterol were more likely to have poor memory at 12-year follow-up than those with higher HDL levels [10]. In a sample of 1382 non-demented participants from the Cardiovascular risk factors, aging and dementia (CAIDE) study, a more pronounced decrease of total cholesterol was related to poorer late-life episodic memory and psychomotor speed, but not if subjects used statins [11]. In contrast, in a prospective communitybased cohort study involving 1147 elderly individuals without dementia or cognitive impairment at baseline followed for 7 years elderly subjects residing in Northern Manhattan (NY), plasma lipid levels or lipid-lowering treatment were not associated with changes in cognitive function [12]. In line with this, in a population-based cohort design involving 2312 subjects aged 50–75 years enrolled in the University of Edinburgh Aspirin for Asymptomatic Atherosclerosis trial, total cholesterol level was not associated with cognitive function or estimated decline [13]. Finally, in a population-based cohort recruited from three French cities involving 6855 participants, a hypercholesterolemic pattern in late-life was associated with a 25 to 50% increased risk of decline over seven years in psychomotor speed, executive abilities, and verbal fluency [14]. In contrast, in women, a 30% higher rate of decline was found in psychomotor speed with high HDL levels and in executive abilities with low levels of LDL and triglycerides [14]. Although the current findings suggest that hypercholesterolemia may be associated with a slower cognitive decline in the elderly, the mechanisms underlying this association remain unknown. One possibility is that decreased levels of serum total cholesterol in the elderly may reflect ongoing pathological brain changes, which may prone to a higher risk of mild cognitive impairment or dementia. Second, lower total
cholesterol levels in the elderly have been associated with higher levels of the serum interleukin-6, which in turn has been associated with dementia [39]. Third, lower total cholesterol in the elderly could be an effect rather than a cause of cognitive decline, because of decreased nutrition [40]. Currently, however, we know of no plausible physiologic explanation for such a cause-and-effect relationship. This study had several limitations. First, the 37-MMSE is a relatively abbreviated screening tool for dementia. The use of more detailed neuropsychological test batteries would enable future investigators to study these changes in greater detail. Nevertheless, even with this relatively simple, abbreviated tool, we were able to establish clear case–control differences. Second, the 37-MMSE was administered at two time points; use of additional time points would allow one to assess the extent to which the case–control difference continued beyond the three-year time window. Third, we did not control for apolipoprotein-E alleles/ genotypes. This study also had several strengths. First, hypercholesterolemic subjects were compared to a large sample size of several hundred non-hypercholesterolemic participants. Second, the assessments were conducted prospectively in a standardized manner. Finally, we were able to adjust for the potential confounding effects of a number of important factors. In closing, using a prospective, population-based design, we further demonstrated that cognitive test scores in hypercholesterolemic subjects declined more slowly compared to those without hypercholesterolemia. Disclosures All authors report no disclosures. Author roles Dr. Benito-León ([email protected]) collaborated in 1) the conception, organization and execution of the research project; 2) the statistical analysis design, and; 3) the writing of the manuscript first draft and the review and critique of the manuscript. Dr. Vega-Quiroga ([email protected]) collaborated in 1) the conception, organization and execution of the research project;
Table 4 Decline in 37-MMSE sub-scores during follow-up. Cholesterol status
37-MMSE sub-scores Orientation (10 points) Immediate recall (3 points) Attention and calculation (10 points) Delayed recall (3 points) Language (9 points) Copying (2 points)
p value
Non-hypercholesterolemic
Hypercholesterolemic
0.4 (0.0) ± 1.5 0.0 (0.0) ± 0.4 0.0 (0.0) ± 2.6 0.1 (0.0) ± 1.2 0.1 (0.0) ± 1.4 0.0 (0.0) ± 0.7
0.3 (0.0) ± 1.3 0.0 (0.0) ± 0.3 −0.1 (0.0) ± 2.5 0.0 (0.0) ± 1.2 0.0 (0.0) ± 1.2 0.1 (0.0) ± 0.7
Mean (median) ± SD are reported. All the positive values indicate a decline in the MMSE score. Mann–Whitney U test was used for comparisons of data.
0.049 0.454 0.279 0.064 0.486 0.139
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2) the statistical analysis design, and; 3) the review and critique of the manuscript. Dr. Villarejo-Galende ([email protected]) collaborated in 1) the conception, organization and execution of the research project, and; 2) the review and critique of the manuscript. Dr. Bermejo-Pareja ([email protected]) collaborated in 1) the conception, organization and execution of the research project, and; 2) the review and critique of the manuscript. Acknowledgments and funding Additional information about the collaborators and detailed funding of the NEDICES study can be found in the web (http://www.ciberned.es/ estudio-nedices). Dr. Benito-León is supported by the National Institutes of Health, Bethesda, MD, USA (NINDS #R01 NS039422), the Commission of the European Union (grant ICT-2011-287739, NeuroTREMOR), and the Spanish Health Research Agency (grant FIS PI12/01602). Dr. VegaQuiroga is supported by the National Institutes of Health, Bethesda, MD, USA (NINDS #R01 NS039422). Dr. Bermejo-Pareja is supported by the National Institutes of Health, Bethesda, MD, USA (NINDS #R01 NS039422) and the Commission of the European Union (grant ICT-2011-287739, NeuroTREMOR). References [1] Bermejo-Pareja F, Benito-León J, Vega S, Medrano MJ, Román GC. Incidence and subtypes of dementia in three elderly populations of central Spain. J Neurol Sci 2008;264(1–2):63–72. [2] Sánchez-Ferro A, Benito-León J, Mitchell AJ, Bermejo-Pareja F. A review of the potential therapeutic role of statins in the treatment of Alzheimer's disease: current research and opinion. Neuropsychiatr Dis Treat 2013;9:55–63. [3] Kivipelto M, Helkala EL, Laakso MP, Hanninen T, Hallikainen M, Alhainen K, et al. Midlife vascular risk factors and Alzheimer's disease in later life: longitudinal, population based study. BMJ 2001;322(7300):1447–51. [4] Solomon A, Kivipelto M, Wolozin B, Zhou J, Whitmer RA. Midlife serum cholesterol and increased risk of Alzheimer's and vascular dementia three decades later. Dement Geriatr Cogn Disord 2009;28(1):75–80. [5] Romas SN, Tang MX, Berglund L, Mayeux R. APOE genotype, plasma lipids, lipoproteins, and AD in community elderly. Neurology 1999;53(3):517–21. [6] Mielke MM, Zandi PP, Sjogren M, Gustafson D, Ostling S, Steen B, et al. High total cholesterol levels in late life associated with a reduced risk of dementia. Neurology 2005;64(10):1689–95. [7] Stewart R, White LR, Xue QL, Launer LJ. Twenty-six-year change in total cholesterol levels and incident dementia: the Honolulu-Asia Aging Study. Arch Neurol 2007; 64(1):103–7. [8] Piguet O, Grayson DA, Creasey H, Bennett HP, Brooks WS, Waite LM, et al. Vascular risk factors, cognition and dementia incidence over 6 years in the Sydney Older Persons Study. Neuroepidemiology 2003;22(3):165–71. [9] van den Kommer TN, Dik MG, Comijs HC, Fassbender K, Lutjohann D, Jonker C. Total cholesterol and oxysterols: early markers for cognitive decline in elderly? Neurobiol Aging 2009;30(4):534–45. [10] Komulainen P, Lakka TA, Kivipelto M, Hassinen M, Helkala EL, Haapala I, et al. Metabolic syndrome and cognitive function: a population-based follow-up study in elderly women. Dement Geriatr Cogn Disord 2007;23(1):29–34. [11] Solomon A, Kareholt I, Ngandu T, Wolozin B, Macdonald SW, Winblad B, et al. Serum total cholesterol, statins and cognition in non-demented elderly. Neurobiol Aging 2009;30(6):1006–9. [12] Reitz C, Luchsinger J, Tang MX, Manly J, Mayeux R. Impact of plasma lipids and time on memory performance in healthy elderly without dementia. Neurology 2005; 64(8):1378–83. [13] Okusaga O, Stewart MC, Butcher I, Deary I, Fowkes FG, Price JF. Smoking, hypercholesterolaemia and hypertension as risk factors for cognitive impairment in older adults. Age Ageing 2013;42(3):306–11. [14] Ancelin ML, Ripoche E, Dupuy AM, Samieri C, Rouaud O, Berr C, et al. Genderspecific associations between lipids and cognitive decline in the elderly. Eur Neuropsychopharmacol 2014;24(7):1056–66. [15] Benito-León J, Louis ED, Bermejo-Pareja F. Cognitive decline in short and long sleepers: a prospective population-based study (NEDICES). J Psychiatr Res 2013; 47(12):1998–2003.
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