University of Groningen
The association of cognitive performance with vascular risk factors across adult life span
van Eersel, Maria Elisabeth Adriana
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2
The association of cognitive
performance with vascular risk
factors across adult life span
M.E.A. van Eersel
The association of cognitive performance with vascular risk factors across adult life span
Cover design and lay-out : Louise Uspessij - L.U. Vormgeving Cover image : RATOCA / Shutterstock.com
Printed by : Gildeprint - Enschede
ISBN/EAN : 978-97-034-0459-2 (printed book) 978-94-034-0485-5 (ebook) © 2018, M.E.A. van Eersel
All rights reserved. No part of this thesis may be reproduced, stored in retrieval systems, or transmitted in any form or by any means, mechanically, by photocopying, recording or otherwise, without the permission of the author.
4
The association of cognitive
performance with vascular risk
factors across adult life span
Proefschrift
ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen
op gezag van de
rector magnificus prof. dr. E. Sterken en volgens besluit van het College voor Promoties.
De openbare verdediging zal plaatsvinden op maandag 26 maart 2018 om 14.30 uur
door
Maria Elisabeth Adriana van Eersel
geboren op 3 november 1985 te ‘s Gravenmoer
Promotor Prof. dr. J.P.J. Slaets Copromotores Dr. G.J. Izaks Dr. J.M.H. Joosten Beoordelingscommissie Prof. dr. G.J. Blauw Prof. dr. Y. van der Graaf Prof. dr. P. Denig
6
Soms is loslaten veel krachtiger dan verdedigen of vasthouden Eckhart Tolle, auteur, 1948
CONTENTS
Chapter 1 General Introduction 9
General aim and outline of the thesis 16
Chapter 2 Cardiovascular Risk Profile and Cognitive Function in Young, 21 Middle-aged, and Elderly Subjects
Stroke. 2013 Jun; 44:1543-1549
Chapter 3 The Interaction of Age and Type 2 Diabetes on Executive 41 Function and Memory in Persons Aged 35 Years or Older
PLoS ONE. 2013 Dec; 8(12):e82991
Chapter 4 Longitudinal Study of Performance on the Ruff Figural 61 Fluency Test in Persons Aged 35 Years or Older
PLoS ONE. 2015 Mar; 10(3):e0121411
Chapter 5 Treatable Vascular Risk and Cognitive Performance in 83 Persons Aged 35 Years or Older: Longitudinal Study
Submitted
Chapter 6 Treatment of Vascular Risk Factors and Cognitive Performance 104 in Persons Aged 35 Years or Older: Longitudinal Study
Submitted
Chapter 7 Summary and general discussion 124
Chapter 8 Summary in Dutch - Nederlandse samenvatting - 136
Appendix Acknowledgments - Dankwoord - 143
Curriculum Vitae 148
List of publications - Publicaties - 151
1
General Introduction
General aim and outline of the thesis
10
GENERAL INTRODUCTION
Worldwide, since 1950 life expectancy has increased by 2.5 years per decade.1,2 This increase resulted from the interplay of advances in nutrition, education, sanitation and medicine.2 Unfortunately, in this longer life span additional years in good health are not guaranteed. The risk of (chronic) disease increases with age. In the older age groups, the prevalence of chronic diseases such as cancer, cardiovascular disease, diabetes mellitus, chronic kidney disease and dementia is highest. In addition, increasing age is often accompanied with multimorbidity.3 The burden of chronic diseases leads to dependence in daily life and disability.3,4 Worldwide, the leading cause of dependence and disability in the elderly is dementia.5,6
The global prevalence of dementia is expected to increase from 46.8 million persons in 2015 to 74.7 million persons in 2030.7 At the same time, the global economic costs of dementia will rise per year. It is estimated to increase from US $818 billion in 2015 to $2 trillion in 2030.7,8 The burden of dementia includes psychological distress and the impact on quality of life for both patient and caregiver. For the above-mentioned reasons the World Health Organization (WHO) concluded in 2012 that dementia should be regarded as a global public health priority. The WHO recommended to conduct more research on prevention strategies for dementia because up till now there is still no treatment to cure or to alter the progressive course of dementia.9 However, before prevention strategies for dementia can be examined, it is necessary to gain understanding about the course of cognitive impairment and factors associated with reduced cognitive performance.
Cognitive performance across life span
Cognitive performance develops and changes throughout life (Figure 1).10 The development of gray and white matter volume in the brain is dependent on the intrauterine environment, placental function and maternal nutrition during pregnancy and continues until the adolescence.10-13 A smaller brain volume is associated with reduced late-life cognitive performance.10 Not only the development of the brain volume has influence on cognitive performance, but also environmental factors in childhood. A higher socioeconomic status and a higher educational level in childhood are associated with better cognitive performance.10,14 Eventually, the development of cognitive performance achieves its peak around the third decade of life, after which it gradually declines.10
Recently, the observational Whitehall study found that cognitive impairment is already evident at the age of 45 years.15 Interestingly, it is even supposed that cognitive performance reduces from the third decade of life.10 Indeed, it is estimated that brain volume decreases by
Chapter 1
0.22% per year between the age of 20 and 80 years with accelerated decline with increasing age.16 Neuropathological changes in the brain like amyloid β and neurofibrillary tangles that are supposed to cause cognitive impairment may developed several decades before the clinical expression of dementia.17 Many factors may contribute to these neuropathological changes like increasing age, genetic factors and vascular risk factors. Cerebral vascular and neurodegenerative pathologies might arise with increasing age.18,19 Genetic factors as APOE ε4 carriership are associated with neurodegenerative changes due to amyloid β and neurofibrillary tangles, and with vascular changes due to atherosclerosis.20 Vascular risk factors (e.g. hypertension, diabetes mellitus, hypercholesterolemia and smoking) cause cerebrovascular lesions like lacunar infarcts or white matter lesions.21-23 In addition, intracranial atherosclerosis induce cerebral hypoperfusion and stimulate neurodegenerative changes like deposition of amyloid β.24 Thus, research showed various factors are related to cognitive impairment. However, the underlying pathogenesis of most factors are to be unraveled.
General Introduction
1
Figure 1. Hypothesized model on development and changes of cognitive performance from
Muller et al., 2014.10
12 Interestingly, vascular risk factors are the only modifiable factors in contrast to age and genetic factors, and possibly useful as prevention target for cognitive impairment. Therefore, in line with the recommendation of the WHO, more research is needed to investigate the association of cognitive performance with vascular risk factors and its treatment.
Hypothesis in this thesis
As cognitive impairment is already evident at relatively young age and neuropathological changes associated with cognitive impairment may also be present at that moment, it is possible that starting treatment of vascular risk factors in as early stage as possible will be effective as prevention of cognitive impairment. Therefore, it needs to be explored whether vascular risk factors are associated with cognitive performance at a young age to confirm the hypothesis that vascular risk factors may contribute to cognitive impairment in its earliest stage. However, research in young persons may lead to methodological challenges. Methods have to be sensitive enough to measure the vascular burden and the first changes in cognitive performance in young persons. This should be further explicated before the research on the association of cognitive performance with vascular risk factors across the adult life span can start. If vascular risk factors are associated with cognitive performance at a young age, then the research of treatment of vascular risk factors as prevention target for cognitive impairment can be further explored.
The association of cognitive performance with vascular risk factors
As described above, vascular risk factors contribute to neurodegenerative changes in the brain. Hypertension, hypercholesterolemia, diabetes mellitus and smoking cause micro- and macrovascular changes resulting in brain atrophic lesions.21-23 Intracranial atherosclerosis is accompanied by thickening of capillary basement membrane and endothelial cell degeneration resulting in reduced cerebral blood flow.22,23 Chronic hyperglycaemia in diabetes mellitus affects brain tissue through direct toxic effect on neurons by oxidative stress and accumulation of advanced glycation end-products (AGEs) resulting in production of amyloid β neuritic plaques and neurofibrillary tangles.22 A high cholesterol level leads to increased deposition of cerebral amyloid β plaques.25 Thus, vascular risk factors contribute to neurodegenerative changes via various biological pathways and are thereby suspected to induce cognitive impairment.
In the recent decades, several observational studies examined the association of cognitive performance with vascular risk factors. The first studies observed whether a single vascular risk factor was associated with cognitive performance. In 1922, Miles and Root observed that persons with diabetes mellitus performed worse on measures of memory and
Chapter 1
information processing speed compared to persons without diabetes mellitus.26 Between 1970 and 1980, various studies observed a negative association of cognitive performance with midlife hypertension.27 Subsequently, several longitudinal studies followed showing the association of increased risk of dementia and cognitive impairment with hypertension, hypercholesterolemia, smoking and diabetes mellitus.23,28-31
After 2000, diverse observational studies showed that the risk of dementia increased if persons had two or more vascular risk factors suggesting that vascular risk factors interact or aggregate with each other and lead to a cumulative risk of dementia.32-35 Knowing that vascular risk factors often occur together, in the same period in vascular medicine several risk models were developed to predict an individuals’ risk of future cardiovascular disease or stroke based on combination of vascular risk factors.36-38 Consequently, given that elderly people have an increased vascular risk by the presence of multiple risk factors, various observational studies supported the idea that a high vascular risk is associated with poor cognitive performance.39
Notably, a limitation of these studies is that they investigated the association of cognitive performance with vascular risk factors in persons aged 50 years or older. However, vascular risk factors may contribute to the onset of neurodegenerative changes several decades prior to clinical expression of cognitive impairment.17 It is therefore likely that vascular risk factors are associated with cognitive performance from much younger age on. The question remains whether the association of cognitive performance with vascular risk factors is already present in early adulthood and whether this association is different for young persons compared to old persons.
Research in young persons
Little is known about the association of cognitive performance with vascular risk factors in young adulthood. It is suggested that cumulative burden of vascular risk factors from early adulthood is associated with worse cognitive performance in mid-life.40,41 However, the studies were limited by methods that were not sensitive enough to measure the vascular burden and changes in cognitive performance in young persons. As well, a longitudinal trial in young persons was difficult to perform because of high costs, ethical issues and research efforts. Thus, there are several challenges in exploring cognitive performance and vascular risk factors in young persons.
First, vascular burden in young persons can be underestimated because single vascular risk factors often are only marginally elevated. However, vascular risk factors often occur together and act via shared biological pathways that may result in vascular burden.42 This had led to the development of vascular risk scores as described above.36-38
General Introduction
14 By considering vascular risk factors together in young persons, it may result in a clearly increased vascular risk. An overall vascular risk estimation like in a vascular risk score is a good reflection of vascular burden in young persons.
Second, change in cognitive performance over time is small in young persons which may be underestimated by practice effects due to repeating the cognitive function test.43 Therefore, it needs to have a cognitive function test that is sensitive enough to the first changes in cognitive performance in young persons. The earliest changes in cognitive performance occur in the domain of executive function.44,45 Executive function encompass a variety of high-order cognitive processes, such as planning, inhibition, cognitive flexibility, decision-making and self-monitoring, and is commonly assessed by fluency tests.45,46 The mainly used fluency tests for evaluating cognitive performance in older persons in clinical care are Trail-Making-Test (TMT) and Stroop Color Word Test (SCWT). However, the differences between the test scores were too small in the young age groups to detect any changes in cognitive performance.47,48 A good possibly fluency test is the Ruff Figural Fluency Test (RFFT) that is sensitive to changes in cognitive performance in both young and old persons because of its wide score range.49,50 However, up till now, it is not clear whether the RFFT is hindered by a practice effect when it is repeated after years. Therefore, it needs to be explored how the performance on the RFFT after repeated measurements during years can be interpreted, especially in young persons.
Third, a longitudinal trial in young persons may be hindered by several methodological challenges. Such a study requires a large sample and long follow-up to detect the small changes in cognitive performance over time in young persons.51 This will be accompanied by high costs and research effort. Therefore, it is understandable to explore the association of cognitive performance with vascular risk factors cross-sectional first. Subsequently, if this association is found in a cross-sectional study, then it is possible to confirm this in a longitudinal study allowing a definitive conclusion on a causal relationship. The next desired step will be to explore the effect of treatment of vascular risk factors on cognitive performance in a randomized controlled trial (RCT). However, a RCT evaluating this research question is hindered by another methodological issue based on ethical principles. The importance of vascular risk management to prevent cardiovascular disease is undisputed and, therefore, withholding or withdrawing treatment in young control subjects for a long period would be unethical.51,52 Therefore, a good possibly design is a large observational longitudinal study to investigate the effect of treatment of vascular risk factors on cognitive performance, especially in young persons.
Chapter 1
Does treatment of vascular risk factors prevent cognitive impairment?
If it is shown that vascular risk factors are associated with cognitive performance in both young and old persons, then it could be hypothesized that treatment of vascular risk factors prevent cognitive impairment. In several countries a trend towards declining incidence of dementia is seen over the past decades. This temporal trend and parallel improvement in cardiovascular health over time might be attributed to the benefit of improvement vascular risk management.53,54 This suggest that earlier diagnosis and more effective treatment of stroke and heart disease might have contributed to a lower incidence of dementia.
However, up till now, various randomized-control trials (RCTs) have found inconsistent results about the effect of treatment of vascular risk factors on cognitive performance. From 1991, only the Syst-Eur trial suggested a protective effect of antihypertensive treatment on dementia in contrast to other trials.55,56 Similarly, from 2000, intensified single treatment of diabetes mellitus or cholesterol lowering treatment had no effect on cognitive performance in other RCTs such as the ADVANCE study and the PROSPER trial.56-58 A limitation of these studies is that the intervention was focused on only one single vascular risk factor and did not include treatment of other vascular risk factors. In addition, in these studies the treatment was started at the age of 60 years or older.55-58 However, it could be argued that starting treatment in old age may be too late for effective prevention of cognitive impairment, as vascular risk factors may contribute to the onset of neurodegenerative changes several decades prior to clinical expression of cognitive impairment. In summary, it is still unknown whether treatment of all vascular risk factors together is associated with cognitive performance, especially in young persons.
General Introduction
16
GENERAL AIM AND OUTLINE OF THE THESIS
The WHO recommends more research to prevention strategies for dementia. Vascular risk factors may contribute to the neurodegenerative changes in the brain associated with cognitive impairment. As cognitive impairment is already evident at relatively young age and neuropathological changes also be present at that moment, it is possible that starting treatment of vascular risk factors in as early stage as possible might be effective as prevention of cognitive impairment. Therefore, the general aim of this thesis is to explore the association of cognitive performance with vascular risk factors and treatment of vascular risk factors across the adult life span. We explore this association in the prospective observational Prevention of REnal and Vascular ENd-stage Disease (PREVEND) study. Details on this study have been published previously.59,60 In this study, we explore the association of cognitive performance with vascular risk factors cross-sectional and evaluate whether this association is different for young persons compared to old persons. Before we confirm this association in a longitudinal study, we evaluate the longitudinal performance on cognitive function test because of the methodological challenge of repeatedly measuring cognitive performance in young persons. If there is an association of cognitive performance with vascular risk factors, then we investigate whether cognitive performance is also associated with treatment of vascular risk factors.
In Chapter 2, we examine the association of cognitive performance with an overall vascular
risk and explore this association in various age groups including both young and old persons.
In Chapter 3, we study whether the association of cognitive performance with vascular risk
factors is different for young persons compared to old persons. We chose to investigate this with the single vascular risk factor type 2 diabetes mellitus.
In Chapter 4, we explore the longitudinal performance on the Ruff Figural Fluency Test
(RFFT) by measuring the cognitive test three times during a follow-up period of six years.
In Chapter 5, we investigate the association of the change in cognitive performance with a
treatable general vascular risk during a follow-up period of six years.
In Chapter 6, we examine the association of the change in cognitive performance with
treatment of vascular risk factors by comparing the cognitive performance of persons with and without treatment of vascular risk factors during a follow-up period of six years.
In Chapter 7, we describe a summary and general discussion of the key findings of this
thesis.
Chapter 1
General Introduction
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2
Cardiovascular Risk Profile and Cognitive
Function in Young, Middle-Aged,
and Elderly Subjects
Hanneke Joosten*
Marlise E.A. van Eersel*
Ron T. Gansevoort
Henk J.G. Bilo
Joris P.J. Slaets
Gerbrand J. Izaks
* these authors contributed equally to the manuscript
22
ABSTRACT
Background and Purpose: Cognitive decline occurs earlier than previously realized and is
already evident at the age of 45. Because cardiovascular risk factors are established risk factors for cognitive decline in old age, we investigated whether cardiovascular risk factors are also associated with cognitive decline in young and middle-aged groups.
Methods: The cross-sectional study including 3,778 participants aged 35-82 years
(mean age, 54 years) and free of cardiovascular disease and stroke. Cognitive function was measured with the Ruff Figural Fluency test (RFFT; worst score, 0; best score, 175 points) and the Visual Association Test (VAT; worst score, 0; best score, 12 points). Overall cardiovascular risk was assessed with the Framingham Risk Score (FRS) for general cardiovascular disease (best score, -5; worst score, 33 points).
Results: Mean RFFT score (SD) was 70 (26) points, median VAT score (interquartile range)
was 10 (9-11) points, and mean FRS (SD) was 10 (6) points. Using linear regression analysis adjusting for educational level, RFFT was negatively associated with FRS. RFFT score decreased by 1.54 points (95%CI, -1.66 to -1.44; P <0.001) per point increase in FRS. This negative association was not only limited to older age groups, but also found in the young (35-44 years). The main influencing components of the FRS were age (P <0.001), diabetes (P = 0.001), and smoking (P <0.001). Similar results were found for VAT score as outcome measure.
Conclusions: In this large population-based cohort, a worse overall cardiovascular risk
profile was associated with poorer cognitive function. This association was already present in young adults aged 35 to 44 years.
Chapter 2
INTRODUCTION
It has become increasingly clear that the onset of cognitive decline is earlier than previously realized. Recently, it was found that cognitive decline is already evident at the age of 45 years.1 This has led to the belief that poor cognitive function in old age is the result of a long-term pathological process that spans at least two to three decades. These findings have important consequences because interventions designed to prevent or postpone cognitive decline may be most effective when started at young age. However, effective interventions can only be designed when more insight is gained in mechanisms that underlie early cognitive decline. Because midlife cardiovascular risk factors, such as hypercholesterolemia and hypertension, are associated with cognitive decline in older age,2 it is likely that cardiovascular risk factors are also associated with cognitive decline at younger age.
Despite the need for a better understanding of the determinants of early cognitive decline, data on the relationship of cardiovascular disease with cognitive function at young age are still limited. Some data point toward a negative effect of modifiable risk factors, such as obesity and smoking, on cognitive performance in young adults.3,4 However, it is unclear at what age the negative effects of cardiovascular risk factors on the brain begin. Elias et al. showed that young adults may be as vulnerable as older adults to the negative effect of hypertension on cognitive function.5 Thus, there is some evidence that an adverse impact of cardiovascular risk factors on cognitive performance is not limited to older adults.
Cardiovascular risk is often underestimated in young persons because at a young age, individual risk factors may not exceed threshold values. However, risk factors for cardiovascular disease, such as hypertension, dyslipidemia, and diabetes mellitus, often cluster within subjects, and it is generally assumed that they act via shared biological pathways.6 This has led to the development of multicomponent cardiovascular risk scores that can be used to predict an individual’s risk of a cardiovascular event within the next years.7-9 By accounting for the conjoint effects of risk factors, they can indicate an increase in cardiovascular risk even if separate risk factors are still subclinical.8 Thus, cardiovascular risk scores reliably reflect the overall cardiovascular risk profile in young as well as older persons.
Therefore, the aim of this study was to evaluate the association of overall cardiovascular risk profile with cognitive function and to explore this association in various age groups. The study included a large community-based cohort of 3,778 persons aged 35-82 years.
Cardiovascular Risk Profile and Cognitive Function in Young, Middle-Aged, and Elderly Subjects
24
METHODS
Study Design
The Prevention of REnal and Vascular ENd-stage Disease (PREVEND) study is a prospective cohort study investigating the natural course of microalbuminuria and its association with renal and cardiovascular disease. Details have been described elsewhere.10 In brief, at baseline 8,592 participants aged 28-75 years were selected from inhabitants of the city of Groningen (Netherlands) based on their urinary albumin excretion. These participants completed the baseline survey in 1997-1998 and were followed over time. During follow-up, 6,894 participants (80%) completed the second (2001-2003) and 5,862 (68%) the third survey (2003-2006). Surveys included assessment of demographic and cardiovascular risk factors, and measurements of haematological and biochemical parameters. All participants gave written informed consent. The PREVEND study was approved by the medical ethics committee (METc) of University Medical Center Groningen and conducted in accordance with the guidelines of the Helsinki declaration.
Cognitive Function
The Ruff Figural Fluency Test (RFFT) was the primary outcome measure for cognitive function. The RFFT was introduced at the third survey of the PREVEND study and requires the participants to draw as many designs as possible within a set time limit, whereas avoiding repetitions.11 The RFFT is generally seen as a measure of executive function but provides information about various cognitive abilities, such as initiation, planning, divergent reasoning and the ability to switch between different tasks. The RFFT is sensitive to changes in cognitive performance in young and old persons.11,12 The main outcome measure is the total number of unique designs which ranges from 0 to 175 points (worst and best score, respectively).11
The Visual Association Test (VAT) was used as a secondary outcome measure for cognitive function. The VAT is a brief learning task that is designed to detect anterograde amnesia. The test consists of six drawings of pairs of interacting objects of animals. The person is asked to name each object and, later, is presented with one object from the pair and asked to name the other. The lowest (worst) score is 0 points, and the highest (best) score is 12 points.13
Cardiovascular Risk
Overall, cardiovascular risk was measured by the Framingham Risk Score (FRS) for general cardiovascular disease,118 a composite measure designed to predict the risk of developing
Chapter 2
a cardiovascular, cerebrovascular, or peripheral vascular event within the next ten years. Calculation of the FRS is based on age, gender, diabetes mellitus, smoker status, systolic blood pressure, total cholesterol, HDL-cholesterol, and use of blood pressure-lowering agents. A higher FRS is associated with a higher risk of a new vascular event: the lowest score is -5 (risk <1%), and the highest score 33 (risk >30%).
Measurements of Risk Score Components
As the FRS was validated in subjects without a cardiovascular history,8 participants with a history of cardiovascular events including peripheral vascular disease and stroke were excluded. Data on the FRS components were obtained as follows: fasting blood was drawn for the measurement of total cholesterol, HDL-cholesterol and glucose. Diabetes mellitus was defined as a fasting glucose ≥7.0 mmol/L or a non-fasting glucose ≥11.1 mmol/L or use of glucose lowering drugs. Participants were classified as current smokers based on reported smoking in a questionnaire. Systolic blood pressure was measured with an automatic device (Dinamap) on two separate occasions and calculated as the average of the last two measurements at each occasion. Subject-specific information on drug use was obtained from the InterAction DataBase, which comprises pharmacy-dispensing data from community pharmacies.
Covariate Assessment
Educational level was divided into four groups according to the International Standard Classification of Education (ISCED): primary school level corresponded to 0 to 8 years of education (ISCED 0-1); lower secondary level to 9 to 12 years (ISCED 2); higher secondary level to 13 to 15 years (ISCED 3-4); and university level to ≥16 years (ISCED 5). Because it was recently found that the effect of cardiovascular risk on cognitive function might be modified by APOE ε4 carriership,14 APOE ε4 genotype was also included as a covariate. Subjects were categorized as APOE ε4 carriers (allele ε2/ε4 or ε3/ε4 or ε4/ε4) or noncarriers (ε2/ε2 or ε2/ε3 or ε3/ε3).
Statistical Analysis
Normally distributed data are presented as means and standard deviation (SD), and skewed data are presented as medians and interquartile range. Differences were tested by t test or, if appropriate, Mann-Whitney U test. Trends were analyzed by ANOVA, and correlations between variables by Pearson correlation coefficient.
Cardiovascular Risk Profile and Cognitive Function in Young, Middle-Aged, and Elderly Subjects
26 The association of RFFT score with FRS was analyzed by four multiple linear regression models. In all models, RFFT score (points) was the dependent variable. In the first model, the association of RFFT score with FRS was investigated for the total study population and each separate age group (35-44, 45-54, 55-64, 65-74 and ≥75 years). The independent variables of this model were FRS (points) and educational level (categories). In the second model, it was investigated whether the association of RFFT score with FRS was dependent on age group. In this model, the independent variables were FRS (points), age group (categories), the product term FRS x age group, and educational level (categories). In the third model, the association of RFFT score with each separate component of the FRS was analyzed. The independent variables of this model were age (years), female gender (yes/no), diabetes mellitus (yes/no), current smoker (yes/no), systolic blood pressure (mmHg), use of blood pressure-lowering agents (yes/no), total cholesterol (mmol/L), HDL cholesterol (mmol/L), and educational level (categories). To investigate whether there was a dose-response effect of smoking, we also ran this model with smoking categorized into non-smoking, light smoking (1-15 cigarettes/day) and heavy smoking (≥16 cigarettes/day). Finally, in the fourth model, it was evaluated whether the association of RFFT score with FRS was dependent on APOE ε4 carriership. In this model, the independent variables were FRS (points), APOE ε4 carriership (yes/no), the product term FRS x APOE ε4 carriership, and educational level (categories).
Similar analyses were performed for VAT score as cognitive outcome measure. Because of its skewed distribution, VAT score was dichotomized at the median into low performance (≤10 points) and high performance (≥11 points). Accordingly, the association of VAT performance with FRS was evaluated by logistic regression analysis (adjusted for educational level).
Sensitivity Analyses
Various a priori-defined sensitivity analyses were performed. First, the analyses were repeated in the total study population, including persons with a cardiovascular disease history. Second, the PREVEND cohort is enriched for subjects with higher levels of albuminuria which may be negatively associated with cognitive function.15,16 Therefore, the analyses were repeated in a subsample of the cohort which is representative for the general population (prevalence of elevated albuminuria 7.5%).10 Third, the analyses were limited to persons aged 35-74 years, because the FRS was only validated for persons <75 years.8 Finally, to investigate the generalizability of our findings, analyses were repeated with other cardiovascular risk scores, like the Framingham risk score for coronary heart disease and the SCORE risk system which was developed in a European population.7,9
Chapter 2
Cardiovascular Risk Profile and Cognitive Function in Young, Middle-Aged, and Elderly Subjects
RESULTS
Study population
Overall, a total of 5,862 subjects completed the third survey, of which 1,271 participants (22%) refused to perform the RFFT and 433 (7%) had incomplete RFFT data.15 Of those with a complete RFFT score, subjects with a history of cardiovascular disease or stroke (n = 311; 5%) or with missing data on components of the FRS or educational level (n = 66; 1%) were excluded. Three participants aged <35 years were excluded because their number was too small to form a separate age group. Thus, the final study population included 3,778 persons (51% men) with an age range from 35 to 82 years with mean (SD) age 54 (11) years (Table 1). Mean RFFT score (SD) was 70 (26) points. RFFT score decreased with increasing age and increased with each higher level of education (Ptrend <0.001).12 FRS ranged from -3 to +32 points with a mean (SD) of 10 (6) points and increased with increasing age (Table 2).
RFFT and Framingham Risk Score
The RFFT score was dependent on the FRS (Figure 1). The mean RFFT score (SD) decreased from 93 (20) points in persons with the lowest FRS to 44 (19) points in persons with the highest FRS (Ptrend <0.001). The negative association of RFFT score with FRS persisted after adjustment for educational level: the RFFT score decreased 1.54 points (95%CI, -1.66 to -1.44; P <0.001) with each point increase in FRS.
RFFT and Framingham Risk Score per Age Group
The negative association of FRS with RFFT score was not only found in the overall study population but also in all age groups, including the youngest (35-44 years). Figure 2 shows that the strength of the association was similar in all age groups. Indeed, there was no interaction between age group and FRS in their association with RFFT (P ≥0.43). The correlation coefficients (95%CI) between RFFT score and FRS were comparable between the age groups and ranged from -0.20 (-0.25 to 0.15) to -0.13 (-0.19 to -0.07). Adjustment for educational level did not essentially change results.
28 Abbreviations: BMI, body mass index; HDL, high-density lipoprotein; FRS, Framingham Risk Score; RFFT, Ruff Figural Fluency Test; VAT, Visual Association Test; SD, standard deviation; IQR, interquartile range.
a Including ε2/ε4, ε3/ε4 and ε4/ε4.
b FRS for general cardiovascular disease and includes age, gender, diabetes mellitus, current smoker status,
systolic blood pressure, use of blood pressure-lowering agents, total cholesterol and HDL-cholesterol. A higher FRS is associated with a higher risk of cardiovascular, cerebrovascular and peripheral vascular events within the
next ten years.8
Chapter 2
n (%)
Age (years), mean (SD) Gender, male (%) Age, n (%) 35-44 years 45-54 years 55-64 years 65-74 years ≥75 years
Cardiovascular risk factors Hypertension, n (%) Diabetes, n (%) Smoker, n (%)
BMI (kg/m2), mean (SD)
Systolic blood pressure (mmHg), mean (SD) Total cholesterol (mmol/L), mean (SD) HDL-cholesterol (mmol/L), mean (SD) Non-HDL cholesterol (mmol/L), mean (SD) Elevated albuminuria, n (%) APOE ε4 genotype, n (%) Carriera, n (%) Noncarrier, n (%) Unknown, n (%) Current medication, n (%)
Blood pressure-lowering agents Lipid-lowering agents
FRS (points)b, mean (SD)
RFFT score (points), mean (SD) VAT score (points), median (IQR)
Low performance (≤10 points), n (%) High performance (≥11 points), n (%) Unknown, n (%) All 3778 (100) 54 (11) 1927 (51) 900 (24) 1221 (32) 904 (24) 564 (15) 189 (5) 1173 (31) 204 (5) 893 (24) 27 (4) 125 (17) 5.42 (1.04) 1.42 (0.38) 4.00 (1.02) 493 (13) 1060 (28) 2472 (65) 246 (7) 721 (19) 380 (10) 10 (6) 70 (26) 10 (9-11) 2176 (58) 1530 (40) 72 (2)
Table 1. Characteristics of the study population.
Cardiovascular Risk Profile and Cognitive Function in Young, Middle-Aged, and Elderly Subjects
Table 2. RFFT Score and Framingham Risk Score (FRS) in Age Group Strata.
Age (years) 35-44 45-54 55-64 65-74 ≥75 67 (34) 59 (25) 49 (18) 40 (15) 39 (15) 5 (4) 11 (4) 13 (4) 17 (4) 19 (4) 73 (23) 64 (21) 57 (20) 49 (17) 43 (16) 5 (4) 9 (4) 13 (4) 16 (4) 19 (3) 82 (25) 74 (23) 66 (20) 54 (16) 46 (17) 4 (4) 9 (4) 12 (4) 16 (4) 19 (3) 93 (21) 87 (21) 75 (22) 61 (20) 55 (25) 3 (3) 7 (4) 12 (4) 16 (4) 19 (3) 85 (24) 76 (24) 64 (22) 50 (18) 45 (18) 4 (4) 9 (4) 12 (4) 16 (4) 19 (3) Ptrend <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 Educational level Primary school RFFT (points) FRS (points)
Lower secondary education RFFT (points)
FRS (points)
Higher secondary education RFFT (points) FRS (points) University RFFT (points) FRS (points) All RFFT (points) FRS (points)
All values are listed as mean (SD). Abbreviations: FRS, Framingham Risk Score for general cardiovascular disease and includes age, gender, diabetes mellitus, current smoker status, systolic blood pressure, use of blood pressure-lowering agents, total cholesterol, and HDL-cholesterol; RFFT, Ruff Figural Fluency Test.
30
RFFT and Separate Risk Factors
In univariate analyses, RFFT score was not only associated with the overall FRS, but also with each separate risk factor component of the FRS, except for gender (data not shown). However, in multiple linear regression analysis (with adjustment for educational level) only age, DM, HDL-cholesterol and smoking were statistically significantly associated with RFFT score (Table 3). Compared with non-smoking, smoking 1 to 15 cigarettes/day was associated with a decrease of 2.41 points in RFFT score (95%CI, -4.40 to -0.53; P = 0.02), and smoking ≥16 cigarettes/day was associated with a decrease of 3.43 points in RFFT score (95%CI, -5.90 to -0.96; P = 0.007).
Chapter 2
Figure 1. Mean Ruff Figural Fluency Test (RFFT) score dependent on overall cardiovascular risk
as measured by the FRS in the overall study population.
For clarity, data are presented as mean and 95% confidence intervals (bars) per 10-year age group. FRS indicates Framingham Risk Score for general cardiovascular disease and includes age, gender, diabetes mellitus, current smoker status, sys-tolic blood pressure, use of blood pressure-lowering agents, total cho-lesterol, and HDL-cholesterol.
Cardiovascular Risk Profile and Cognitive Function in Young, Middle-Aged, and Elderly Subjects
Figure 2. Mean Ruff Figural Fluency Test (RFFT) score dependent on overall cardiovascular risk
as measured by the FRS per age group.
For clarity, data are presented as mean and 95% confidence intervals (bars) per 10-year age group. FRS indicates Framingham Risk Score for general cardiovascular disease and includes age, gender, diabetes mellitus, current smoker status, systolic blood pressure, use of blood pressure-lowering agents, total cholesterol, and HDL-cholesterol.
Effect of APOE ε4 carriership
The study population included 1,060 APOE ε4 carriers and 2,472 noncarriers (Table 1). The association of RFFT score with FRS was not dependent on APOE ε4 carriership (B-coefficient, 2.49; 95%CI, -0.77 to 5.74; P = 0.13), and there was no statistically significant interaction between APOE ε4 carriership and FRS (P = 0.84). Similar results were found if all APOE ε2 carriers were excluded.
32
Chapter 2
VAT and Framingham Risk Score
Analysis of the association of VAT score with FRS yielded similar findings. VAT scores were obtained in 3,706 subjects (98%). Overall, 58% (n = 2,176) had a VAT score of ≤10 (Table 1). The percentage with low performance gradually increased from 33% in the group with the lowest FRS to 78% in the group with the highest FRS (P <0.001) (Figure 3). A similar increase was found in all age groups except one (Figure 4). The odds ratio for low performance on the VAT increased by factor 1.08 (95%CI, 1.07-1.10; P <0.001) per point increase in FRS (adjusted for educational level).
Age (years) Gender Men Women Diabetes mellitus No Yes Current smoker No Yes
Systolic blood pressure (mmHg) Total cholesterol (mmol/l) HDL cholesterol (mmol/l)
Use of blood pressure lowering agents No Yes B-coefficient -0.88 1.00 -0.98 1.00 -6.44 1.00 -2.75 -0.03 -0.17 2.43 1.00 -1.48 95%CI -0.95 to -0.81 reference -2.47 to 0.52 reference -9.55 to -3.33 reference -4.35 to -1.15 -0.07 to 0.02 -0.84 to 0.50 0.45 to 4.41 reference -3.37 to 0.42 standardized β -0.38 -0.02 -0.06 -0.05 -0.02 -0.01 0.04 -0.02 P <0.001 0.20 <0.001 0.001 0.20 0.62 0.02 0.13
Table 3. Multiple Linear Regression Analysis of RFFT Score with all Separate Components
of the Framingham Risk Score (FRS)a
Abbreviations: CI, confidence interval; FRS, Framingham Risk Score for general cardiovascular disease and in-cludes age, gender, diabetes mellitus, current smoker status, systolic blood pressure, use of blood pressure-lowe-ring agents, total cholesterol, and HDL-cholesterol; HDL, high-density lipoprotein; RFFT, Ruff Figural Fluency Test.
a All components of the Framingham risk score were entered into the regression model. The model also included
educational level (data not shown). Adjusted R2 of the full model, 0.36; residual standard deviation, 21.
Cardiovascular Risk Profile and Cognitive Function in Young, Middle-Aged, and Elderly Subjects
FRS indicates Framingham Risk Score for general cardiovascular disease and includes age, gender, diabetes mellitus, current smoker status, systolic blood pressure, use of blood pressure-lowering agents, total cholesterol, and HDL-cholesterol. Figure 3. Percentage of subjects with low vs. high performance on the Visual Association Test
(VAT) dependent on overall cardiovascular risk as measured by Framingham risk score in the total study population.
Figure 4. Percentage of subjects with low vs. high performance on the Visual Association Test (VAT)
dependent on overall cardiovascular risk as measured by Framingham risk score per age group.
FRS indicates Framingham Risk Score for general cardiovascular disease and includes age, gender, diabetes mellitus, current smoker status, systolic blood pressure, use of blood pressure-lowering agents, total cholesterol, and HDL- cholesterol.
34
Chapter 2
Sensitivity Analyses
Various sensitivity analyses gave essentially similar results. First, if persons with a history of cardiovascular events and stroke were not excluded from the analysis, RFFT score decreased by 1.54 points (95%CI, -1.66 to -1.43; P <0.001) per point increase in FRS. If the analysis was limited to the subsample that was comparable to the general population with regard to microalbuminuria, the RFFT decreased by 1.45 points (95%CI, -1.65 to -1.24;
P <0.001) per point increase in FRS. Also, results did not change in case the analysis was
limited to persons aged <75 years (n = 3,589) (B-coefficient, -1.44; 95%CI, -1.57 to -1.30;
P <.001). Finally, if the analyses were repeated with the FRS for coronary heart disease
or the SCORE risk system as independent variable, RFFT score decreased by 1.51 point (95%CI, -1.66 to -1.36; P <0.001), or 1.86 point (95%CI, -2.13 to -1.59; P <0.001) per point increase in risk score, respectively. In all sensitivity analyses, the negative association of RFFT score with FRS (or alternative risk scores) persisted in all age groups (data not shown).
DISCUSSION
In this large population-based study, a worse general cardiovascular risk profile was associated with poorer cognitive function. Importantly, this negative association was not only found in older persons, but also already present in young and middle-aged subgroups. Cardiovascular risk profile was based on eight individual risk factors. Within this composite risk score, the factors age, diabetes mellitus, smoking, and HDL-cholesterol proved to be the strongest determinants of cognitive function.
Biological Changes in Early Adulthood
It is generally assumed that the presence of cardiovascular risk factors at young age has important consequences later in life. Numerous studies showed that early presence of cardiovascular risk factors leads to the acceleration of atherosclerosis in young people and increases the long-term risks of cardiovascular disease.17,18 Autopsy studies showed that hyperlipidemia, hypertension, smoking and hyperglycemia are associated with the prevalence and severity of atherosclerotic lesions in young people.19 We showed that increased cardiovascular risk profile also associates with cognitive function at a young age. To our knowledge only two previous studies reported on the relationship between overall cardiovascular risk profile and cognitive function with disparate findings.20,21 Beason
et al. saw little effect of FRS on cognitive function in 97 cognitively normal middle-aged
Cardiovascular Risk Profile and Cognitive Function in Young, Middle-Aged, and Elderly Subjects
and elderly subjects,20 whereas Kaffashian et al. suggested that an adverse cardiovascular risk profile may be related to poorer cognitive function in a large population of middle-aged civil servants.21 Besides, the results cannot be extended towards subjects <50 years of age, because both studies populations did not include younger adults.20,21 Our study shows a clear association between overall cardiovascular risk and cognitive function. Most importantly, this association was independent of age and was found also in young adults. Interestingly, the association of cognitive function with cardiovascular risk in young adults matches the association of subclinical biological changes with cardiovascular risk in this age group. The most important biological changes indicating early cardiovascular disease include increased intimal-media thickness, carotid coronary artery calcification, pulse pressure, and arterial pulse wave velocity.22 Several studies showed that an adverse cardiovascular risk factor profile predicts increased intimal-media thickness, pulse wave velocity, and coronary artery calcification in young adults.23,24 It seems plausible that the presence of these subclinical biological changes is associated with adverse outcome with respect to cognitive function later in life. Indeed, three previous large population-based studies showed that premature atherosclerotic changes predict clinically relevant cognitive decline,25-27 although in one other study the results were equivocal.28
Implications
Many risk factors for premature atherosclerosis are modifiable. This strengthens the idea that early intervention at a young age may contribute to better cognitive function later in life. In this study, we found two risk factors, smoking and diabetes mellitus that were strong determinants of cognitive function and can be changed or controlled by effective interventions.
Our data suggested a dose-response relationship between smoking and cognitive function because heavy smokers had lower performance on the cognitive test than light smokers and non-smokers. However, nicotine dependence is still highly prevalent in young adults and there has been no decline in smoking among young adults since 2003.29 Nevertheless, it is likely that smoking cessation has a beneficial effect on cognitive function.30 Therefore, our study underlines the need for effective smoking cessation treatments - not only for the prevention of cancer, cardiovascular events and stroke but also for the prevention of cognitive decline.
Diabetes mellitus was also negatively associated with cognitive function in our study. It is generally assumed that the effect of diabetes mellitus is at least partially modifiable because improved glucose regulation ameliorates important negative outcomes.31
36
Chapter 2
Until now, however, it was not clear whether improved glucose regulation also ameliorates cognitive decline. In two intervention studies, intensive glucose-lowering treatment was not associated with better cognitive outcomes in middle-aged and elderly persons with type 2 diabetes mellitus.32,33 However, both studies were of relatively short duration and, possibly, a longer time frame is necessary to show that stricter glucose regulation is beneficial for cognitive function.
Strengths and Limitations
Some limitations of this study must be acknowledged. First, the primary outcome measure was based on a single cognitive test. However, the RFFT is a composite measure of very different cognitive abilities, such as initiation, planning, divergent reasoning and the ability to switch between different tasks. In addition, because of its wide score range, the RFFT is not limited by a ceiling or floor effect and, thereby, sensitive to subtle changes in cognitive performance in young and old persons.12 Also, the main findings were confirmed in the analyses with the VAT as cognitive outcome measure. Second, the PREVEND cohort is enriched for elevated albuminuria, which could induce selection bias, because albuminuria is a risk factor for cardiovascular disease.15 However, a sensitivity analysis in a subsample, representative for the general population, did not change results. Finally, the cross-sectional design of this study does not formally allow a firm conclusion on a causal relationship. For example, it is possible that persons with low cardiovascular risk and poor cognitive performance were underrepresented in our study. However, there were no clear signs of selection bias. Moreover, the association of cardiovascular risk profile with cognitive function that we found in this study seems plausible on biological grounds and is supported by findings of other studies. Nevertheless, the causality of this relationship should be confirmed in longitudinal studies.
Despite these limitations, our study also has several strengths. We included a large community-based population with a wide age range, whereas others used selected populations, such as the elderly or subjects with diabetes mellitus. The generalizability of our data is, therefore, well preserved. In contrast to many previous studies, we explored the synergistic effects of cardiovascular risk factors instead of focusing on single risk factors that probably have complex interactions.17,18,34 Risk score composites have the advantage to weigh multiple variables to generate optimal overall risk estimation. Additionally, they generate a single variable for overall cardiovascular burden, which limits the number of variables in small studies or extensive multivariate analyses. Using risk scores composites may, therefore, have advantages in both clinical practice and cardiovascular research.
Cardiovascular Risk Profile and Cognitive Function in Young, Middle-Aged, and Elderly Subjects
Conclusions
In this large population-based cohort, a worse cardiovascular risk profile was associated with poorer cognitive function. This association was already present in young adults. In our opinion, there is need for further investigation of cognitive function as a new clinical
38
Chapter 2
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