Abstract. Introduction RESEARCH ARTICLE

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1 DIABETES/METABOLISM RESEARCH AND REVIEWS Diabetes Metab Res Rev 2010; 26: Published online in Wiley Online Library (wileyonlinelibrary.com).1144 RESEARCH ARTICLE Association of cognitive performance with the metabolic syndrome and with glycaemia in middle-aged and older European men: the European Male Ageing Study Jos Tournoy 1 *DavidM.Lee 2 Neil Pendleton 3 Terence W. O Neill 2 Daryl B. O Connor 4 Gyorgy Bartfai 5 Felipe F. Casanueva 6,7 Joseph D. Finn 8 Gianni Forti 9 Aleksander Giwercman 10 Thang S. Han 11 Ilpo T. Huhtaniemi 12 Krzysztof Kula 13 MichaelE.J.Lean 14 Carly M. Moseley 8 Margus Punab 15 Alan J. Silman 2 Dirk Vanderschueren 16 Frederick C. W. Wu 8 Steven Boonen 1 and the EMAS study group 1 Department of Experimental Medicine, Division of Gerontology and Geriatrics, Katholieke Universiteit Leuven, Leuven, Belgium; 2 School of Translational Medicine, Arthritis Research UK Epidemiology Unit, University of Manchester, Manchester, UK; 3 School of Community Based Medicine, Neurodegeneration and Mental Health Research Group, University of Manchester, Salford Royal NHS Trust, Salford, UK; 4 Institute of Psychological Sciences, University of Leeds, Leeds, UK; 5 Department of Obstetrics, Gynaecology and Andrology, Albert Szent-Gyorgy Medical University, Szeged, Hungary; 6 Department of Medicine, Santiago de Compostela University, Complejo Hospitalario Universitario de Santiago (CHUS), Spain; 7 CIBER de Fisiopatología Obesidad y Nutricion (CB06/03), Instituto Salud Carlos III; Santiago de Compostela, Spain; 8 Department of Endocrinology, Andrology Research Unit, Manchester Royal Infirmary, University of Manchester, Manchester, UK; 9 Andrology Unit, Department of Clinical Physiopathology, University of Florence, Florence, Italy; 10 Reproductive Medicine Centre, Malmö University Hospital, University of Lund, Malmö, Sweden; 11 Department of Endocrinology, Royal Free and University College Hospital Medical School, University College London, London, UK; 12 Department of Reproductive Biology, Imperial College London, Hammersmith Campus, London, UK; 13 Department of Andrology and Reproductive Endocrinology, Medical University of Lodz, Lodz, Poland; 14 Department of Human Nutrition, University of Glasgow, Glasgow, UK; 15 Andrology Unit, United Laboratories of Tartu University Clinics, Tartu, Estonia; 16 Department of Andrology and Endocrinology, Katholieke Universiteit Leuven, Leuven, Belgium *Correspondence to: Jos Tournoy, Department of Experimental Medicine, Division of Gerontology and Geriatrics, Katholieke Universiteit Leuven, Herestraat 49, 3000 Leuven, Belgium. jos.tournoy@uzleuven.be The EMAS Study Group: Florence (Gianni Forti, Luisa Petrone, Giovanni Corona); Leuven (Dirk Vanderschueren, Steven Boonen, Herman Borghs); Lodz (Krzysztof Kula, Jolanta Slowikowska-Hilczer, Renata Walczak- Jedrzejowska); London (Ilpo Huhtaniemi); Malmö (Aleksander Giwercman); Manchester (Frederick Wu, Alan Silman, Terence O Neill, Joseph Finn, Philip Steer, Abdelouahid Tajar, David Lee, Stephen Pye); Santiago (Felipe Casanueva, Marta Ocampo, Mary Lage); Szeged (Gyorgy Bartfai, Imre Földesi, Imre Fejes); Tartu (Margus Punab, Paul Korrovitz); Turku (Min Jiang). Received: 19 May 2010 Revised: 5 October 2010 Accepted: 6 October 2010 Abstract Background and aims Metabolic syndrome has been reported to have adverse effects on cognition although the results are conflicting. We investigated the association between metabolic syndrome and cognitive function in a population sample of middle-aged and older European men and whether any observed association could be explained by lifestyle or other confounding factors. Methods Atotalof3369men inthe40- to79-year age group were recruited from population registers in eight centres for participation in the European Male Ageing Study. The subjects completed a questionnaire instrument and several cognitive function tests including the test, the Camden Topographical test and the Digit Symbol Substitution Test. Metabolic syndrome data were assessed at an invited visit and metabolic syndrome was defined by the National Cholesterol Education Program s Adult Treatment Panel-III criteria. Associations between cognitive performance and metabolic syndrome were explored using linear regression. Results Complete cognitive and metabolic syndrome data from 3152 subjects were included in the analysis, of whom 1007 (32%) fulfilled criteria for metabolic syndrome. After adjustment for putative health and lifestyle confounders, no significant associations were found between any of the cognitive function scores and metabolic syndrome or between cognitive performance and high-sensitivity C-reactive protein. Analysis of the individual metabolic syndrome factors, however, revealed an inverse association between the level of glucose and cognitive performance. Conclusions Metabolic syndrome was not associated with cognitive impairment in this population. Of the individual components of the syndrome, diabetes was associated with poorer performances in memory, executive functions and processing speed, associations that warrant further investigation. Copyright 2010 John Wiley & Sons, Ltd. Keywords Introduction metabolic syndrome X; cognition; diabetes mellitus; men Ageing is associated with a loss of cognitive performance and increased risk of dementia. The aetiology is likely to be multi-factorial and precise mechanisms are unknown, although cardiovascular risk factors have been implicated. Metabolic syndrome refers to a cluster of cardiovascular risk factors which include abdominal obesity, hypertriglyceridaemia, low high-density lipoprotein-cholesterol (HDL-c) levels, high blood pressure and elevated blood glucose levels [1]. The presence of the syndrome has been linked with the occurrence of cardiovascular events [2] and also other Copyright 2010 John Wiley & Sons, Ltd.

2 Association of Cognitive Performance With the Metabolic Syndrome 669 adverse effects including fatty liver disease and polycystic ovarian syndrome. Cognitive impairment has been reported to be associated with the syndrome; however, data is limited and sometimes conflicting. Thus, some studies have demonstrated an inverse association between the metabolic syndrome and cognition [3] or cognitive decline [4 8], while other studies have not been able to confirm these findings [9,10]. Both age [10,11] and gender [5,11] may also play a role; it has been suggested that older women with metabolic syndrome may be less vulnerable to develop cognitive decline than younger women [12] and men [5,11]. Higher high-sensitivity C- reactive protein (hs-crp) levels have been associated with metabolic syndrome and also an increased risk of cardiovascular events [13]. In one study, inflammation was found to be negatively associated with cognition in the presence of metabolic syndrome [7]. Further data are, however, needed to better define the relationship between the metabolic syndrome, cognition and the inflammatory response. Our objectives in this analysis were to investigate the association between cognitive function and the metabolic syndrome in a representative sample of middle-aged and older European men [14] and explore whether any of the associations were influenced by putative confounding factors. A secondary objective was to study the influence of the individual components of the metabolic syndrome on cognitive performance. Thirdly, we explored if there was any association between hs-crp and cognition and the influence of the presence or absence of metabolic syndrome on any putative association. Methods Study participants Our analyses are based on the cross-sectional baseline data from the European Male Ageing Study, a non-interventional cohort study of male ageing in Europe. Details regarding recruitment, response rates and assessments have been described [14]. In brief, 8416 community-dwelling men in the 40- to 79-year age group were invited to attend the study from municipal or population registers in eight centres: Florence, Italy; Leuven, Belgium; Lodz, Poland; Malmö, Sweden; Manchester, UK; Santiago de Compostela, Spain; Szeged, Hungary and Tartu, Estonia. Of these, 3369 agreed to participate in the full study. Stratified random sampling was used for the baseline survey to obtain an equal distribution in four age bands (40 49, 50 59, and years). The letter of invitation sent to the subjects had questions about smoking, frequency of alcohol consumption, comorbidity including diabetes and hypertension, and age leaving education. Those who agreed to participate were seen at an assessment facility where they completed an interviewerassisted questionnaire and a number of cognitive function tests. A fasting blood sample was also taken. The study was funded by the European Union and ethical approval was obtained in agreement with local institutional requirements. Interviewer-assisted questionnaire The interviewer-assisted questionnaire included questions about physical activity and depressive symptoms. Physical activity was assessed using the Physical Activity Scale for the Elderly [15] and depressive symptoms using the Beck Depression Inventory [16]. Current prescription and non-prescription medication use was also documented. Assessment of cardiovascular risk factors Seated pulse and blood pressure [Omron 500I, Omron Healthcare (UK) Ltd, Milton Keynes, UK] was recorded after a 5-min rest period. Waist circumference was measured using anthropometric tape, and the median of three measurements was used as the recorded value. Fasting blood samples were taken by morning phlebotomy before 10 AM either when subjects attended the clinic or alternatively at their homes if they had difficulty attending. Glucose and lipid measurements were undertaken in each centre and assessed at the local health care facility. The presence of the metabolic syndrome was defined according to the National Cholesterol Education Program Adult Treatment Panel-III definition [17]. Three or more of the following five criteria had to be met: waist circumference 102 cm, fasting triglyceride 1.7 mmol/l, fasting HDL-c <1.03 mmol/l, blood pressure 130/85 mmhg or currently using anti-hypertensive medication, fasting glucose 5.6 mmol/l or using antidiabetic medication. Assessment of cognitive function Several cognitive domains were assessed in this study: visuo-constructional ability, visual memory, executive function, attention and processing speed. The test battery included the Rey Osterrieth (ROCF) to test visuo-constructional ability and memory, the Camden Topographical (CTRM) to test topographical memory and the Substitution Test (DSST) to assess attention and processing speed. This battery was specifically selected to minimize theinfluence of language and culture, facilitating standardization across different centres. Further details of the tests are outlined below. The copying and delayed reproduction of the ROCF is a measure of visual perception, memory and executive function [18]. Subjects were instructed to complete the copy and, without being pre-informed, asked to reproduce the figure after 30 min. The applied ROCF scoring criteria were based on the original test procedure, which defines 18 units of the drawing and assigns point values of 0 2

3 670 J. Tournoy et al. to each unit dependent upon the degree to which the units are correctly drawn and placed. Both ROCF tests had a maximum score of 36. The CTRM test measures the recognition component of visual memory retrieval, tapping into the cortical component of visual memory [19]. The CTRM test involves the presentation of 30 coloured photographs of outdoor topographical scenes, each shown for 3 s, followed by a three-way forced recognition component. The CTRM had a maximum score of 30. The DSST is a subtest adopted from the Wechsler Adult Intelligence Scales and provides a reliable measure of psychomotor speed and visual scanning [20]. Within a 1-min time frame, participants were asked to make as many correct symbol-for-digit substitutions as possible. Higher scores for each test indicate better cognitive performance. Measurement of hs-crp hs-crp levels were determined using a solid-phase, chemiluminescent immunometric assay (Immulite 2000 hs-crp assay; Diagnostics Products Corporation, Siemens, Deerfield, IL, USA) with a sensitivity of 0.01 mg/dl. The mean replicate coefficient of variation was less than 3%. All measurements were made at a central laboratory facility in Santiago. Analysis Cognitive scores, individual components of the metabolic syndrome, age, age leaving education, the Physical Activity Scale for the Elderly and Beck Depression Inventory score and hs-crp were treated as continuous variables, while the metabolic syndrome (absent versus present), smoking (non-smoking versus currently smoking) and alcohol consumption (<1 day/week versus 1day/week) as categorical variables. Linear regression was used to determine the association of the cognitive test scores (dependent variables) with the metabolic syndrome and its components (independent variables). Adjustments were made for age, age leaving education, smoking, alcohol consumption, physical activity, depression and hs-crp. To allow for the likelihood that observations are independent across centres, but not necessarily within centres, robust standard errors were requested using Stata s cluster subcommand with centre as the clustering variable. Results are expressed as beta coefficients (β) and 95% confidence intervals. Statistical analyses were undertaken using Intercooled STATA version 9.2 (StataCorp, College Station, TX, USA). Results Subject characteristics In total, 3369 men participated in the full study, corresponding to a response rate of 41%. A total of 217 subjects with incomplete cognitive and/or metabolic syndrome data were excluded and 3152 subjects were included in the analysis. Of the latter, baseline characteristics are shown in Table 1. Metabolic syndrome was present in 1007 (32%) of the subjects. Among those with metabolic syndrome, 981 (97%) had high blood pressure, 775 (77%) had abdominal obesity, 725 (72%) had high fasting glucose or were taking anti-diabetic medication, 663 (66%) had hypertriglyceridaemia and 303 (30%) had low HDL-c. Subjects with metabolic syndrome were slightly older (61.0 ± 10.4 versus 59.3 ± 11.2), had higher body mass indexes (30.7 ± 4.1 versus 26.3 ± 3.3) and had higher scores for depressive symptoms as measured by the Beck Depression Inventory (7.8 ± 6.9 versus 6.4 ± 6.1). Furthermore, participants fulfilling the metabolic syndrome criteria were less physically active as measured by the Physical Activity Scale for the Elderly (188 ± 96 versus 200 ± 89) and had higher hs-crp levels (5.5 ± 8.3 versus 3.9 ± 7.9). In the group without metabolic syndrome, significantly fewer subjects suffered a heart condition (15 versus 20%). There were no significant differences regarding age leaving education, alcohol consumption and current smoking. Cognitive performance and metabolic syndrome Men without the metabolic syndrome performed significantly better on all cognitive tests compared with men having the syndrome (Table 1). After adjustment for age, the metabolic syndrome was associated with lower scores on the ROCF copy (β = 0.608, p < 0.001) and DSST (β = 0.813, p < 0.01). No significant differences were observed for ROCF recall (β = 0.381, p > 0.05) and CTRM (β = 0.228, p > 0.05) scores (Table 2). The association with ROCF copy and DSST disappeared after adjustment for various lifestyle factors and depressive symptoms (all p > 0.05). Table 3 summarizes the results from the regression models of the five individual components accounting for the metabolic syndrome and cognitive test scores. After adjusting for age, education, smoking, alcohol consumption, physical activity, depressive symptoms and centre, glucose levels were negatively associated with all cognitive test scores: ROCF copy (β = 0.261, p < 0.05), ROCF recall (β = 0.175, p < 0.05), CTRM (β = 0.254, p < 0.05) and DSST (β = 0.552, p < 0.01). Diastolic blood pressure was positively associated with ROCF recall score (β = 0.025, p < 0.05) and HDL-c with ROCF copy (β = 0.581, p < 0.05) and recall (β = 0.922, p < 0.05) scores. After additional analysis adjusting for hs-crp, the associations between glucose levels and ROCF recall (β = 0.156, p > 0.05) and CTRM scores (β = 0.246, p > 0.05) and between diastolic blood pressure and ROCF recall (β = 0.026, p > 0.05) were no longer significant (Table 4). Additional analyses of the number of added individual components of the metabolic syndrome and cognitive scores in subjects with metabolic

4 Association of Cognitive Performance With the Metabolic Syndrome 671 Table 1. Baseline characteristics Metabolic syndrome Absent (n = 2145) Present (n = 1007) Mean (SD) p-value a Age (years) 59.3 (11.2) 61.0 (10.4) <0.001 Age leaving education 20.7 (7.4) 21.1 (8.1) 0.15 Beck Depression Inventory (BDI) 6.4 (6.1) 7.8 (6.9) <0.001 Body mass index (kg/m 2 ) 26.3 (3.3) 30.7 (4.1) <0.001 Physical activity (Physical Activity Scale for the Elderly) 200 (89) 188 (96) <0.001 High-sensitivity C-reactive protein (mg/l) b 3.9 (7.9) 5.5 (8.3) <0.001 Metabolic syndrome criteria Waist circumference (cm) 94.4 (8.9) 107 (9.8) <0.001 Systolic blood pressure (mmhg) 143 (20) 152 (20) <0.001 Diastolic blood pressure (mmhg) 86 (12) 90 (12) <0.001 High-density lipoprotein-cholesterol (mmol/l) 1.5 (0.3) 1.2 (0.3) <0.001 Triglycerides (mmol/l) 1.3 (0.7) 2.2 (1.4) <0.001 Glucose (mmol/l) 5.3 (0.9) 6.4 (1.8) <0.001 scores copy 33.6 (4.2) 32.8 (5.0) <0.001 recall 17.2 (6.7) 16.4 (6.5) Camden Topographical 22.9 (4.7) 22.4 (4.7) Substitution Test 28.1 (8.6) 26.6 (9.2) <0.001 Number (%) Adult Treatment Panel-III metabolic syndrome 2145 (68) 1007 (32) Waist circumference >102 cm 334 (16) 775 (77) <0.001 Blood pressure >= 130/85 mm Hg and/or using anti-hypertensive drugs 1701 (79) 981 (97) <0.001 High-density lipoprotein-cholesterol <1.03 mmol/l 98 (5) 303 (30) <0.001 Triglycerides 1.7 mmol/l 298 (14) 663 (66) <0.001 Measured blood glucose 5.6 mmol/l and/or using anti-diabetic drugs 412 (19) 725 (72) <0.001 Depressive symptoms None (BDI 10) 1691 (79) 748 (75) <0.001 Mild-borderline (BDI 11 20) 375 (18) 189 (19) Moderate-extreme (BDI 21) 63 (3) 59 (6) Self-reported diabetes and/or using anti-diabetic drugs 72 (3) 169 (17) <0.001 Obese (body mass index 30) 238 (11) 540 (54) <0.001 Current smoker 459 (21) 196 (19) 0.21 Alcohol ( 1 day/week) 1232 (58) 539 (54) 0.04 Heart condition 317 (15) 201 (20) <0.001 Stroke 70 (3) 43 (4) 0.16 a T-test or rank sum test for continuous variables and χ 2 test for categorical variables: between metabolic syndrome groups. b Distribution very positively skewed, median (interquartile range): absent = 0.20 (0.29); present = 0.33 (0.43). syndrome revealed no significant association (p > 0.05, data not shown). The association between glucose levels and cognition was further explored according to diagnostic categories (Table 5). Therefore, diabetic men had significantly lower scores on all cognitive tests except for CTRM (β = 1.111, p > 0.05). The latter was negatively associated with impaired fasting glucose (β = 0.778, p < 0.01). We found no significant association between anti-diabetic or anti-hypertensive drug use and cognition. Cognition, hs-crp and metabolic syndrome status We assessed for a possible association between hs- CRP levels as a marker of inflammation and cognitive performance (Table 6). In the adjusted model for age alone, a significant inverse association was observed between hs-crp levels and DSST scores. This association persisted after additional adjustment for education, smoking, alcohol consumption, physical activity, centre and depression (data not shown). However, when this model was analysed with adjustment for metabolic syndrome, no significant association was found. There was no evidence that the association of hs-crp with any of the cognitive outcomes was modified by metabolic syndrome status (metabolic syndrome hs-crp)] (all p interaction > 0.05, data not shown). Discussion In this cross-sectional study of middle-aged and older European men, the prevalence of the metabolic syndrome was 32%. The prevalence of the metabolic syndrome reported in previous studies varied between 5 and 53% in men, depending on the population, age and applied criteria [ [2] and references herein], making direct comparisons difficult. Our prevalence rate of metabolic syndrome is in agreement with a median

5 672 J. Tournoy et al. Table 2. Multi-variable linear regression of cognitive test scores on presence of metabolic syndrome (absent = reference group): covariates in the model Complex Figure copy Complex Figure recall Camden Topographical Substitution Test Covariates in the model β-coefficient (95% confidence interval) Age a ( 0.929, 0.288) ( 0.842, 0.080) ( 0.558, 0.103) ( 1.379, 0.248) Age, lifestyle, ( 1.411, 0.065) ( 1.233, 0.348) ( 0.817, 0.437) ( 1.347, 0.123) depression b p < a Adjusted for age alone. b Adjusted for age, education, smoking (non-smoking versus currently smoking), alcohol consumption (<1 day/week versus 1 day/week), physical activity (Physical Activity Scale for the Elderly), centre and depressive symptoms (Beck Depression Inventory score). Table 3. Multi-variable linear regression of cognitive test scores on components of the metabolic syndrome copy recall Camden Topographical Substitution Test Components of the metabolic syndrome β-coefficient (95% confidence interval) a Waist circumference ( 0.039, 0.004) ( 0.040, 0.019) ( 0.039, 0.023) ( 0.058, 0.005) (cm) Systolic blood ( 0.014, 0.011) ( 0.006, 0.019) ( 0.019, 0.011) ( 0.039, 0.010) pressure (mmhg) Diastolic blood ( 0.005, 0.030) (0.001, 0.049) ( 0.019, 0.027) ( 0.068, 0.025) pressure (mmhg) High-density (0.047, 1.114) (0.054, 1.789) ( 0.637, 1.184) ( 1.462, 2.172) lipoproteincholesterol (mmol/l) Triglycerides (mmol/l) ( 0.269, 0.061) ( 0.244, 0.282) ( 0.240, 0.085) ( 0.310, 0.187) Glucose (mmol/l) ( 0.458, 0.064) ( 0.343, 0.007) ( 0.484, 0.022) ( 0.855, 0.248) Although the distribution of triglycerides was positively skewed, using log transformed triglycerides in the above regressions did not substantively change the associations (all p > 0.05) from those using the untransformed variable. p < 0.05, p < a Adjusted for age, education, smoking (non-smoking versus currently smoking), alcohol consumption (<1 day/week versus 1 day/week), physical activity (Physical Activity Scale for the Elderly), depressive symptoms (Beck Depression Inventory score) and centre. prevalence of 31% in elderly populations [21]. Among those with the metabolic syndrome, the prevalence of individual metabolic syndrome criteria was consistent with findings in similar studies, with hypertension and waist circumference being the most frequent components [7,22]. However, large variations between different studies have been reported [21] due to differences in age, population and lifestyle factors. We found a significantly higher percentage of participants with either hyperglycaemia or taking anti-diabetic medication compared with other studies [21], most likely because we used the proposed lowered criterion of normal fasting plasma glucose of 5.6 mmol/l, while a limit of 6.1 mmol/l was used in other studies [21]. In general, no association was observed in our cohort between the presence of metabolic syndrome and cognition after adjustment for confounders. Our data agree with previous cross-sectional data reports of a lack of association between metabolic syndrome and baseline cognitive performance [6,7] or dementia [9]. In contrast, several other studies did observe an association with cognitive impairment [3,23] or dementia [4,5]. Available evidence had several reasons that could explain some of the inconsistencies. Differences in study population may have in part explained the discrepant findings. Second, relationships between metabolic syndrome and cognitive decline or the development of dementia over time have been primarily reported in longitudinal studies [6,8,24,25]. Some of these studies showed no significant association at baseline but accelerated cognitive decline in subjects with metabolic syndrome [6]. Lack of evidence of an association between metabolic syndrome and cognitive impairment at baseline, as in our current analysis, does not exclude the potential for such a relationship during follow-up. Third, our cross-sectional results are based on static data, while factors that define the metabolic

6 Association of Cognitive Performance With the Metabolic Syndrome 673 Table 4. Multi-variable linear regression of cognitive test scores on components of the metabolic syndrome copy recall Camden Topographical Substitution Test Components of the metabolic syndrome β-coefficient (95% confidence interval) a Waist circumference ( 0.046, 0.004) ( 0.042, 0.023) ( 0.048, 0.017) ( 0.068, 0.010) (cm) Systolic blood ( 0.012, 0.015) ( 0.006, 0.023) ( 0.019, 0.016) ( 0.044, 0.018) pressure (mmhg) Diastolic blood ( 0.002, 0.034) ( 0.002, 0.054) ( 0.025, 0.035) ( 0.078, 0.036) pressure (mmhg) High-density (0.130, 1.342) (0.212, 1.801) ( 0.734, 1.416) ( 1.964, 2.475) lipoproteincholesterol (mmol/l) Triglycerides (mmol/l) ( 0.290, 0.105) ( 0.257, 0.337) ( 0.193, 0.127) ( 0.387, 0.204) Glucose (mmol/l) ( 0.448, 0.036) ( 0.316, 0.004) ( 0.496, 0.004) ( 0.858, 0.203) p < 0.05, p < a Adjusted for the same variables as the model in Table 3, plus high-sensitivity C-reactive protein. syndrome are dynamic and their modulating effect on the risk of developing cognitive impairment might change over time. In a recent longitudinal study by Akbaraly et al. [26], no differences in cognitive function were observed in participants with non-persistent metabolic syndrome, but the opposite was found if metabolic syndrome persisted over time. Our current baseline analysis did not allow assessment of the duration of risk factors that could modify the risk of cognitive impairment. Fourth, because of our study design, allowing standardization across centres independent of culture and language, only certain cognitive domains were assessed, including visuoconstructional ability, visual memory, executive function, attention and processing speed. Several studies have reported that the metabolic syndrome, or its individual components, may be associated with specific areas of cognition that we did not assess, such as verbal learning and semantic memory [27,28]. Analyzing the potential effect of individual components of the metabolic syndrome on cognition revealed that blood glucose levels were inversely associated with all cognitive scores. To further explore this association, we discriminated subjects with normal fasting glucose, impaired fasting glucose and subjects with diabetes. Here, we found inverse associations between the presence of diabetes and ROCF copy and recall and DSST scores. These data are largely consistent with published evidence for a role of (pre)diabetes in cognitive impairment [29,30] and the risk of developing dementia [30,31]. Also, in studies where metabolic syndrome was associated with cognitive decline, hyperglycaemia was the main contributor [3,25]. Specific cognitive functions that were associated with diabetes included verbal and non-verbal memory, executive functioning and processing speed [32]. These cognitive domains may be impaired as a consequence of hypo- and hyperglycaemia, vascular disease and insulin resistance [33]. Also, diabetes may interfere with Aβ and tau metabolism, the main components of the pathological hallmarks of Alzheimer s disease, plaques and tangles, respectively [34]. Our finding of a relation between high serum HDL-c levels and cognitive performance is in agreement with previous observations, where higher HDL-c levels were associated with reduction of cognitive decline among older participants [11,35] and lower HDL-c levels with impaired memory [8,36]. Several plausible explanations might account for this association. First, HDL is the main carrier of cholesterol in the brain and enhances synaptic growth and regeneration [37]. Second, low HDL-c is a known risk factor for cardiovascular disease, which in turn can lead to dementia. Third, HDL plays an important role in Aβ metabolism by preventing its aggregation [38]. However, not all data support the association between HDL-c and cognitive function [39], and the absence of any association has specifically been reported in older women [40,41]. Although subjects with metabolic syndrome had higher levels of inflammation, we did not demonstrate any association between hs-crp and cognition either in the presence or absence of metabolic syndrome. This is in contrast to other findings, where metabolic syndrome was negatively associated with cognition especially in subjects with high inflammation [3,6,7]. Again, study population differences might account for these differences or associative effects could depend on followup observations. To our knowledge, we report the first study to specifically analyse the association between cognitive function and metabolic syndrome in a large sample of middle-aged and older European men and demonstrated no specific association. The key strengths of our study are that it is based on a representative populationbased framework and that it used uniform methods to evaluate biophysical and laboratory parameters and a

7 674 J. Tournoy et al. Table 5. Multi-variable linear regression of cognitive test scores on fasting glucose and on use of anti-diabetic and anti-hypertensive medications copy recall Camden Topographical Substitution Test Diagnostic categories β-coefficient (95% confidence interval) a Glycaemia group Normal fasting glucose Reference Reference Reference Reference (glucose <5.6 mmol/l and not using anti-diabetic medication) Impaired fasting glucose ( 1.694, 0.475) ( 1.635, 0.526) ( 1.277, 0.278) ( 2.076, 0.447) (glucose 5.6 mmol/l and <7.0 mmol/l) Diabetic ( 1.993, 0.563) ( 1.498, 0.548) ( 2.423, 0.200) ( 4.608, 1.012) (glucose 7.0 mmol/l or using anti-diabetic medication) Anti-diabetic/anti-hypertensive drugs No usage Reference Reference Reference Reference Any usage ( 1.037, 0.645) ( 0.718, 0.707) ( 0.671, 0.573) ( 0.476, 0.763) p < a Adjusted for age, education, smoking (non-smoking versus currently smoking), alcohol consumption (<1 day/week versus 1 day/week), physical activity (Physical Activity Scale for the Elderly), depressive symptoms (Beck Depression Inventory score), high-sensitivity C-reactive protein and centre. Table 6. Multi-variable linear regression of cognitive test scores on high-sensitivity C-reactive protein copy recall Camden Topographical Substitution Test Covariates in the model β-coefficient (95% confidence interval) Age a ( 0.289, 0.117) ( 0.475, 0.101) ( 0.368, 0.047) ( 1.006, 0.293) Age, lifestyle, depression and metabolic syndrome b ( 0.147, 0.188) ( 0.498, 0.336) ( 0.383, 0.252) ( 0.724, 0.005) p < a Adjusted for age alone. b Adjusted for age, education, smoking (non-smoking versus currently smoking), alcohol consumption (<1 day/week versus 1 day/week), physical activity (Physical Activity Scale for the Elderly), centre, depressive symptoms (Beck Depression Inventory score) and metabolic syndrome. battery of cognitive tests easily applicable across different cultures and languages. In addition, we assessed a large variety of potential confounders, such as education, depressive symptoms, smoking and physical activity. Nevertheless,thefindingshavetobeinterpretedinthe context of the study design which was not without limitations, including some general methodological limitations [14]. First, the overall response rate for the study (41%) could have created a selection bias. Although the general characteristics of responders versus nonresponders were reported to be largely similar [14], some observed differences, including smoking prevalence or socioeconomic status, could have created a response bias, where those who responded may have differed regarding the association between metabolic syndrome status and overall cognitive ability compared with those who did not respond. Second, survival bias or reverse causality, the latter in which lower cognitive skills could lead to a higher risk of diabetes, may bias the observed association, but the magnitude of these effects is unknown. Third, specific cognitive domains like verbal memory or semantic functions were not assessed for their association with the metabolic syndrome in this study. Fourth, we did not evaluate subsyndromal cognitive defects and impairment, so the current data cannot be extrapolated to specific diseases or conditions. Fifth, the National Cholesterol Education Program criteria are not the only standardized definition for metabolic syndrome. Using other definitions might well influence the observed effects. However, the National Cholesterol Education Program definition was chosen because it better predicts outcome in elderly subjects than other metabolic syndrome criteria [42].

8 Association of Cognitive Performance With the Metabolic Syndrome 675 Sixth, conditions contributing to the metabolic syndrome may change over time. Since the duration of specific conditions was not assessed at baseline, the potential influence of their persistence in time on our results could not be analysed. However, longitudinal European Male Ageing Study data will become available and will allow more in-depth cause-and-effect analyses. In conclusion, prevalent metabolic syndrome was not associated with cognitive impairment in a large community-dwelling sample of middle-aged and older European men. When analyzing individual components of the syndrome, those with diabetes were found to have poorer performance with respect to memory, executive functions and processing speed. The nature of this association remains unclear and requires further investigation. Acknowledgements The European Male Aging Study is funded by the Commission of the European Communities Fifth Framework Programme Quality of Life and Management of Living Resources Grant QLK6-CT Additional support was also provided by Arthritis Research UK. The authors thank the men who participated in the eight countries, the research/nursing staff in the eight centres: C. Pott (Manchester), E. Wouters (Leuven), M. Nilsson (Malmö), M. del Mar Fernandez (Santiago de Compostela), M. Jedrzejowska (ŁódY), H.-M. Tabo (Tartu), A. Heredi (Szeged) for their data collection and C. Moseley (Manchester) for data entry and project co-ordination. Dr S. Boonen is senior clinical investigator of the Fund for Scientific Research, Flanders, Belgium (F.W.O. Vlaanderen) and holder of the Novartis Leuven University Chair in Gerontology and Geriatrics. Conflict of interest The authors have no financial arrangements or conflict of interest to disclose concerning this manuscript. References 1. 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