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Subjective well-being in Swedish active seniors or seniors with cognitive complaints and its relation to commonly available biomarkers
Archives of Gerontology and Geriatrics 56 (2013) 303–308
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Subjective well-being in Swedish active seniors or seniors with cognitive complaints and its relation to commonly available biomarkers Lovisa A. Olsson a,d,*, Nils-Olof Hagnelius b,d, Henny Olsson c, Torbjo¨rn K. Nilsson a,d Department of Laboratory Medicine/Clinical Chemistry, O¨rebro University, Sweden Department of Geriatrics, O¨rebro University, Sweden c Centre for Health Care Sciences, O¨rebro University, Sweden d ¨ Orebro University Hospital, and School of Health and Medical Science, O¨rebro University, Sweden a
b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 9 May 2011 Received in revised form 25 July 2012 Accepted 27 July 2012 Available online 18 August 2012
Well-being (WB) is a complex variable in its relation to physical health and other personal and social characteristics. The aim was to study subjective well-being (SWB) and its possible associations with traditional biomarkers of cardiovascular risk or dementia, in Swedish seniors. SWB was estimated by the Psychological General Well-Being (PGWB) index in two study groups. The active seniors (AS) group consisted of community-dwelling elderly Swedes leading an active life (n = 389). The DGM cohort (n = 300) consisted of subjects referred to the Memory Unit at the Department of Geriatrics, the cognitive problems had to be subjective, mild or moderate (MMSE 10). There were differences in all six subdimensions of SWB or distress, and in the sum of PGWB scores, between the two study groups (p < 0.001 for all), and adjustment for differences in biomarkers of somatic health (age, sex, blood pressure, BMI, HDL cholesterol, ApoB/ApoA1 ratio, creatinine, and homocysteine) did not attenuate these differences. In addition, cognition as assessed by the Clock-Drawing Test (CDT) showed independent associations with four of the PGWB subdimensions and with the PGWB sum. Among the subjects in the DGM cohort, SWB was equally low among subjects with an MCI (minor cognitive impairment) diagnosis or without a dementia diagnosis as among subjects diagnosed with dementia disorder. We conclude that the nosological grouping variable (AS vs. DGM cohort) and a cognitive factor were the main independent predictors of SWB in this sample of elderly Swedes, whereas biomarkers of somatic health played a subordinated role. ß 2012 Elsevier Ireland Ltd. All rights reserved.
Keywords: Subjective well-being Dementia Biomarker Somatic health Cognitive complaints Elders
1. Introduction During the last decade there has been an increasing number of studies regarding relationship between SWB, physical health and active aging. WB is a complex variable in its relation to health and other personal and social characteristics (Ro¨ysamb, Tambs, Reichborn-Kjennerud, Neale, & Harris, 2003). It is reasonable to assume that humans have always been striving toward WB and tried to find means to achieve it, even in the face of grave problems. WB can be viewed from many different perspectives and has
Abbreviations: WB, well-being; SWB, subjective well-being; PGWB, Psychological General Well-Being index; AS, active seniors cohort (community dwelling seniors); MMSE, mini mental state examination; CDT, Clock Drawing Test; DGM, dementia, genetic and Mileau study (senior subjects referred to the memory Care Unit); BMI, body mass index; D, subgroup with a dementia diagnosis; ND, non-demented subgroup. * Corresponding author at: Department of Laboratory Medicine/Clinical Chemis¨ rebro University Hospital, and School of Health and Medical Science, O ¨ rebro try O University, Sweden. Tel.: +46 19 602 1571; fax: +46 19 602 3785. E-mail address: [email protected] (L.A. Olsson). 0167-4943/$ – see front matter ß 2012 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.archger.2012.07.009
become a multidisciplinary concern, including historical, economical and social perspectives. In this study, we take the revised WHO charter of health promotion from 1986 (WHO, 1986) as our starting point, and the definition of SWB ‘‘as an umbrella term for different valuations that people make regarding their lives, the events happening to them, their bodies and the circumstances in which they live’’ (Diener, 2006). SWB is the person’s own evaluation of his or her life. Such evaluations may be judgements about the person’s life as a whole or evaluations of specific dimensions of life. People evaluate conditions differently depending on their expectations, values, and previous experiences (Inui, 2003). SWB is thus not synonymous with mental health or psychological health (Diener & Suh, 1997). SWB has been regarded to consist of a cognitive variable (e.g. life satisfaction) and of two variables for emotion: a positive affect and a negative affect (Bradburn, 1969; Ryff et al., 2006). A person’s SWB is also influenced by the cultural value system of the surrounding society. Biomarkers are used for diagnostics purposes, or to monitor the course of a disease or a treatment (Sunderland, Gur, & Arnold, 2005), and have also been suggested to be useful as surrogate
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endpoints substituting for actual clinical outcomes (Lassere, 2008). More recently, biomarkers have been introduced in studies on WB and in quality of life research as markers of somatic health in order to extend our understanding of the complex interplay between somatic processes and SWB (Dockray & Steptoe, 2010; Friedman, Hayney, Love, Singer, & Ryff, 2007; Seplaki, Goldman, Weinstein, & Lin, 2004). For instance biomarkers of inflammation such as CRP and certain cytokines, for instance IL-6, have shown a relation to SWB (Friedman et al., 2007; Unde´n et al., 2007). There are also claims of a relation between biomarkers for metabolic syndrome and psychosocial factors (Bove et al., 2010). Among the traditional risk factors for cardiovascular disease, such as age, hypertension, inflammation, elevated plasma-homocystein, etc., age is the most inevitable risk factor and may even play a more dominant role than earlier believed (Sniderman & Furberg, 2008; Sniderman et al., 2007). These factors are also involved in cognitive decline over time, again with age as the single largest risk factor (Jorm & Jolley, 1998). Specifically, it has been shown that SWB is reduced in dementia (Steeman, de Casterle, Godderis, & Grypdonck, 2006). We hypothesized that part of the variance in SWB in elderly Swedes would be accounted for by somatic health status, as estimated by anthropometrical data, blood pressure, and selected biomarkers of metabolism, systemic inflammation, and renal function. We investigated SWB by the PGWB index, an index developed for the purpose of providing measures of six SWB or distress subdimensions: anxiety, depressed mood, positive wellbeing, self-control, general health, and vitality (Dupuy, 1984). The aim was to study possible associations of SWB with common clinically used biomarkers of somatic health and with measures of cognitive function, in samples of both active seniors and subjects with subjective or objective cognitive complaints.
over May 2003–August 2007. The cognitive problems had to be mild or moderate, defined as MMSE Score 10; according to current Swedish research ethics legislation it is unethical to include subjects with more severe dementia due to their reduced autonomy. Dementia diagnoses were based on DSM-IV criteria. To divide dementia patients into different diagnostic categories ICD10 criteria were used. Mixed dementia was diagnosed in cases of coexistence of AD and a history of vascular risk factors, e.g. transitory ischemic attack(s), atrial fibrillation, hypertension, diabetes mellitus, Hachinski Ischemic Score 6 and CT scan showing ischemic changes. Probable AD was diagnosed in accordance with the NINCDS-ADRDA criteria. Subjects were weighed clad in light indoor clothing but without shoes, and the weight was approximated to the nearest 0.1 kg. BMI (kg/m2) was calculated using height and weight. In the AS cohort, the resting systolic and diastolic blood pressure was measured using an automatic oscillometric method (Dinamap model XL Critikron, Inc., Tampa, FL). The equipment has been validated (Penny et al., 1999). The subjects were in a sitting, relaxed position, and registrations were taken every minute for 4 min with the aim of obtaining a set of systolic registrations not varying more than 5 mm Hg. A mean value of the last two registrations was used for both resting systolic and diastolic blood pressure, in mm Hg. The blood pressure was then registered after one minute of unsupported standing. In the DGM cohort, blood pressure was measured using a manometric cuff. Technical check of the blood pressure equipment against known compressor pressure was performed at ¨ rebro University Hospital, Department of Bioengineering, before O and after the study period. All subjects gave a specific and written informed consent to the present study including genotyping and biobanking of the donated ¨ rebro County samples. The Research Ethics Committees of O Council approved both the AS and the DGM study.
2. Methods 2.2. Assessment of subjective well-being 2.1. Subjects Active seniors (AS) were recruited by a multi-phase sampling procedure aimed at an elderly retired population, living in various communities in Central Sweden. The locations for the recruitment were selected to represent a broad range of socioeconomic levels and included rural as well as urban and suburban areas. The sample consisted of 389 senior citizens and was recruited from several retired persons’ organizations, which implicates that the subjects were independent and socially active. Being retired, living independently in their own homes in addition to participation in such organizations were the sole inclusion criteria, not preset health criteria or age. We have designated them as ‘Active Seniors’ in contrast to elderly persons that do not engage themselves in the mentioned social activity. All were Caucasians, most of them born in the 1920s and 1930s, mean age at sampling was 74 5 years for both sexes, and the sex ratio M/F was 127/262 (32.4/67.4%). A random subset of the AS cohort was assessed by the MMSE (n = 196) (Folstein, Folstein, & McHugh, 1975), and/or the CDT (n = 332) judged according to Shulman (2000). We used a combination of MMSE and CDT to assess whether the AS subjects were cognitively intact, defined as MMSE 28 and CDT 4 (Palmqvist et al., 2012). DGM study. The senior subjects with subjective or objective cognitive complaints (DGM) were recruited from an incident case ¨ rebro, Sweden. All diagnostic study at the University Hospital, O manuals and procedures were as described in our previous article (Hagnelius, Wahlund, & Nilsson, 2008). Briefly, this study population consisted of 300 consecutive patients (143 men and 157 women), who were referred to the Memory Unit at the Department of Geriatrics for diagnostic assessment and treatment of suspected cognitive problems. The inclusion period extended
The PGWB index, accessible from the MAPI Research institute, www.mapi-research.fr, was used to measure subjective wellbeing or distress (Dupuy, 1984). It consists of 22 items that reflect subjective well-being and distress during the past week. PGWB comprises six subdimensions: anxiety (5 items; score 5–30), depressed mood (3 items; score 3–18), positive well-being (4 items; score 4–24), self-control (3 items; score 3–18), general health (3 items; score 3–18), and vitality (2 items; score 2–12); these are also combined to a global overall score of SWB. A high value indicates a high level of well-being. In order to achieve this uniform interpretation of the score-points even for the subdimensions which reflect distress (depressed mood and anxiety), score-points for these items were assigned in the direction of ‘‘6’’ to ‘‘1’’ in contrast to the usual direction of ‘‘1’’ to ‘‘6’’. The overall sum of scores gives a maximum value of 132 (best SWB) and a minimum of 22 (poorest SWB). The Swedish version of the PGWB has been validated (Wiklund & Karlberg, 1991). Everyone in both study groups completed the PGWB questionnaire without assistance. 2.3. Blood sampling and biochemical measurements Blood samples were taken, with the subjects in the supine position, by venipuncture using vacuum tubes. Serum was obtained after clotting for 30–60 min at room temperature and centrifuging for 10 min at 2000 g. All samples were stored at 70 8C. The serum samples were analyzed on a Hitachi 911 multianalyser, Roche, Mannheim, FRG. Creatinine was analyzed using a enzymatic method, Crea plus, from Roche/Boehringer Mannheim, FRG high sensitivity CRP (hs-CRP) was analyzed using
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Table 1 Baseline clinical and biomarker characteristics of the studied 691 seniors, divided both in the subgroups AS and DGM. Mean (SD) are shown.
Age (years) BMI (kg/cm2) systBPsitting (mm Hg) diastBPsitting (mm Hg) Heart rate (s1) hsCRP (mg/L)z S-Creatinine (mmol/L)z S-HDL-cholesterol (mmol/L)z S-LDL-cholesterol (mmol/L)z Apo A1 (g/L) Apo B (g/L) ln Apo B/A1z MMSE sum CDT z
AS
n
DGM
n
p
74.0 (6.7) 26.0 (3.9) 147.8 (25.1) 76.7 (11.0) 70.8 (10.8) 2.36 (2.83) 91.7 (21.0) 1.63 (0.42) 3.54 (0.94) 1.61 (0.30) 0.90 (0.20) 0.58 (0.18) 28.8 (1.35) 4.35 (0.9)
389 387 387 386 388 382 386 387 388 382 382 382 196 332
72.9 (10.2) 26.0 (3.7) 149.8 (23.6) 83.0 (12.3) 69.4 (12.0) 4.40 (12.0) 87.8 (49.0) 1.52 (0.42) 3.58 (1.06) 1.44 (0.27) .93 (0.23) 0.67 (0.21) 23.0 (4.90) 3.22 (1.5)
300 300 300 300 300 278 300 295 295 295 295 295 300 297
NS NS NS <0.001 NS NS <0.001 <0.001 NS <0.001 NS <0.001 <0.001 <0.001
The biomarkers were ln transformed before one-way ANOVA were computed.
an latex enhanced immunoturbidimetry method CRP (Latex) HS, SAPOA1 and S-APO B were analyzed with a immunoturbidimetry method all from Roche/Boehringer Mannheim, FRG. LDL and HDL cholesterol were measured by direct, homogeneous assays based on detergent treatment of the serum, N-geneousTM HDL-c and NgeneousTM LDL reagents, respectively, from Genzyme Corporation, Cambridge, MA, USA. 2.4. Statistics Statistical analyses were performed using SPSS version 15.0 (Chicago, IL, USA). The biomarkers are presented as means SD and the PGWB subdimension are presented as mean and 95% confidence interval for the mean, together with the median. Continuous variables with a skewed distribution were transformed using natural logarithms before regression analyses were made. The Mann–Whitney rank sum test was used to compare the PGWB sum and PGWB subdimension between groups. In group comparisons with categorical variables, x2 tests were used. Associations between variables were assessed using Spearman rank correlations. A general linear model was used to assess independence of associations of biomarkers and cognitive variables with PGWB subdimensions. Statistical significance were considered with a probability value < 0.05. In the AS study group, 4 subjects failed to complete the PGWB questionnaire and responses from 12 subjects failed to answer one or two questions, randomly distributed among the 22 questions. The missing answers were imputed using the manual from MAPI research institute. Some biomarker data were missing in a few subjects; exact numbers are indicated in Table 1.
3. Results 3.1. Baseline values of biomarkers and subjective well being The basic clinical and laboratory characteristics of the two study groups are shown in Table 1. As seen the groups were well balanced for age. There were significant differences between the AS and DGM groups with respect to the physiological biomarker diastolic blood pressure, the renal function biomarker creatinine, and the lipid metabolic biomarkers HDL-cholesterol and apo A1, but not the inflammatory biomarker hsCRP, heart rate, body mass index, or LDL-cholesterol and apoB. Gender differences in the studied biomarkers are displayed in Table 2. In both AS and DGM subjects, females had higher heart rate, serum HDL-cholesterol and ApoA1 values, and lower creatinine values as expected. The women in the AS group also had a lower diastolic blood pressure, and ApoB/A1 ratio than the male participants, while the females in the DGM group had higher LDL cholesterol and ApoB values than the males. In all six studied subdimensions of PGWB as well as in the PGWB sum of scores, there were significant differences between the AS group and the DGM group, see Table 3. Previously published PGWB figures obtained in a Swedish representative population sample of comparable age are also given (Dimena¨s, Carlsson, Glise, Israelsson, & Wiklund, 1996). To assess the impact on SWB of a diagnosis of dementia vs. non-dementia within the DGM cohort, the DGM cohort was further subdivided according to the outcome of the diagnostic procedure in three subgroups, subjects with a dementia
Table 2 Baseline clinical and biomarker characteristics of the studied 691 seniors, divided both in the subgroups AS and DGM and according to gender. Mean (SD) are shown. Females
Age (years) BMI (kg/cm2) systBPsitting (mm Hg) diastBPsitting (mm Hg) Heart rate (s1) hsCRP (mg/L)a Creatinine (mmol/L)a HDL-cholesterol (mmol/L)a LDL-cholesterol (mmol/L)a Apo A1 Apo B Ratio Apo B/A1a MMSE CDT a
Males
AS
DGM
p
AS
DGM
p
73.8 (6.9) 26.0 (4.1) 148 (25.9) 75.6 (10.9) 72 (10.6) 2.06 (2.1) 87.0 (21.6) 1.75 (0.39) 3.6 (0.92) 1.70 (0.267) 0.91 (0.197) 0.55 (0.160) 29 (1.22) 4.4 (0.9)
73.3 (10.6) 25.8 (3.7) 150 (23.7) 82.3 (12.5) 71.3 (11.2) 3.3 (6.4) 78.9 (36.6) 1.62 (0.41) 3.8 (1.09) 1.52 (0.261) 0.97 (0.229) 0.66 (0.210) 22.7 (5.3) 3.3 (1.5)
NS NS NS <0.001 NS NS <0.001 <0.001 NS <0.001 0.003 <0.001 <0.001 <0.001
74.4 (6.4) 26.2 (3.5) 147 (23.4) 78.6 (10.9) 69 (10.8) 2.9 (3.7) 100.6 (16.4) 1.42 (0.41) 3.4 (0.97) 1.44 (0.293) 0.89 (0.205) 0.64 (0.189) 28.6 (1.5) 4.2 (4.5)
72.5 (9.5) 26.2 (3.7) 149 (23.6) 84.1 (12.1) 67.4 (12.6) 5.6 (16.2) 97.9 (58.9) 1.40 (0.39) 3.3 (0.96) 1.34 (0.230) 0.88 (0.212) 0.68 (0.213) 23.5 (4.5) 3.0 (3.5)
NS NS NS <0.001 NS NS 0.005 NS NS 0.002 NS NS <0.001 <0.001
The biomarkers were ln transformed before one-way ANOVA were computed.
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Table 3 Subjective well being in the two study groups assessed by the Psychological General Well-Being index. PGWB domain
AS Mean (CI)
Anxiety (range 5–30) Depressed mood (range 3–18) Pos. well-being (range 4–24) Self-control (range 3–18) General health (range 3–18) Vitality (range 2–12) PGWB sum
26.0 16.2 17.2 15.9 15.0 18.6
(25.7–26.4) (16.0–16.4) (16.9–17.5) (15.7–16.1) (14.7–15.3) (18.2–18.9)
108.9 (107.5–109.8)
p(Group)
p(CDT)
DGM
27 17 18 18 16 19
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001
0.004 0.004 0.026 NS 0.018 NS
23.7 14.9 15.1 14.2 13.9 17.0
112
<0.001
0.012
98.9 (97.1–100.8)
Md
Reference population
Mean (CI)
Md
(23.2–24.2) (14.6–15.3) (14.8–15.5) (13.9–14.5) (13.6–14.3) (16.6–17.5)
24 15 15 15 14 18 101
Mean (CI) 25.7 15.8 16.3 15.8 14.1 17.7
(25.1–26.4) (15.4–16.2) (15.8–16.9) (15.5–16.2) (13.7–14.6) (17.1–18.3)
105.6 (102.9–108.2)
Values for PGWB in the study groups AS and DGM. Mean and 95% confidence interval for the mean are shown. Md = Median. p(Group): Mann–Whitney two tailed test was used for comparing the study groups. p(CDT): significance level of Clock Drawing Test score in multivariate regression models; the other independent variables were age, gender, diastolic blood pressure, ln creatinine, ln HDL-cholesterol, lipoprotein A1, lipoprotein B, ratio ApoB/ApoA1. The population-based values from Dimena¨s et al. (1996) are shown as comparison (right column).
diagnosis (D), non-demented subjects (ND), and those diagnosed with mild cognitive impairment (MCI). This showed that there was no significant difference between the three groups in DGM for any of the PGWB subdimensions and all three groups differed to the same extent from the AS group (Table 4). There was also no significant difference in the AS subgroups defined as cognitively intact by the combined MMS and CDT scores, and the AS subjects in whom one of these two variables were missing, confirming that the AS group can be regarded as a homogeneous cohort of elderly subjects. 3.2. Relations between variables The relations between the PGWB subdimensions were assessed by Spearman correlations (Table 5). All were strongly correlated to each other, in both cohorts. To assess the impact of age on SWB, the AS cohort was divided in two age groups, younger and older than the median of 74.1 years. There were few significant differences in SWB between the two age groups: males above 74.1 years had a significantly lower score for the subdimension depressed mood, and women below 74.1 years had a significantly higher score for the subdimensions of general health, vitality and PGWB sum. In the DGM cohort there were no differences between the two age groups (data not shown). Relations between the PGWB subdimensions and selected clinical data and biomarkers of somatic health that showed any significant differences between the AS and DGM cohorts in either of the sexes (Table 2) were entered as independent variables,
together with age and gender, into seven multivariate regression analyses with each of the six PGWB subdimensions, and the PGWB sum of scores, as outcome variables. The grouping variable (AS vs. DGM) was used as a fixed factor in these models. The significant differences in PGWB subdimensions between the AS and the DGM as shown in Table 3 did not change appreciably after these adjustments (data not shown), indicating that the PGWB differences between these two groups were independent of these somatic biomarkers. Next, we wanted to assess the possible contribution of cognition to the SWB subdimensions, and therefore added separately either CDT or MMSE scores to the above seven multivariate regression analyses. As shown in Table 3, cognition as assessed by CDT showed an independent association with PGWB sum and the four subdimensions: anxiety, depressed mode, positive well-being, and general health. MMSE did not show any significant association in these models (not shown). To test whether self-control has a special status among the PGWB subdimensions, the two subdimensions general health and self-control were stratified into quartiles based on the combined cohorts. As seen in the three-dimensional histogram (Fig. 1), there is indeed a similar relation between the two dimensions in both the AS and the DGM cohorts: in Spearman correlations (Table 5) the Rho values were 0.421 and 0.511 in the AS and DGM subjects, respectively (p < 0.001 for both study groups). However, Rhovalues for the pairwise associations between General Health and the other five subdimensions of PGWB were all higher than these figures (Table 5).
Table 4 Subjective well being assessed by the Psychological General Well-Being index in the subgroups stratified according to diagnostic outcome. DGM
AS (MMSE 28 and CDT 4)
ND (n = 37) Anxiety Depressed mood Pos well being Self contr General health Vitality
PGWB sum
M (CI) Md M (CI) Md M (CI) Md M (CI) Md M (CI) Md M (CI) Md M (CI) Md
23.1 23 15.3 16.0 15.5 15.0 14.4 16.0 13.7 14.0 16.6 17.0
(21.6–24.5) (14.5–13.1) (14.4–16.7) (13.4–15.5) (12.7–14.7) (15.1–18.1)
98.7 (93.1–104.2) 100
D (n = 214) 23.7 24 14.8 15.0 15.1 15.0 14.1 15.0 14.0 15.0 17.2 18.0
(23.1–24.3) (14.4–15.7) (14.7–15.6) (13.8–14.5) (13.6–14.4) (16.7–17.7)
99.0 (96.9–101.1) 100.5
MCI (n = 48) 24.3 25 15.3 16.0 15.1 15.0 14.2 16.0 13.7 14.0 16.1 16.0
Yes (n = 154)
N.A. (n = 235)
(23.0–25.6)
26.2 (25.6–26.7)
25.9 (25.5–26.4)
(14.6–16.1)
16.1 (15.8–16.5)
16.2 (16.0–16.5)
(14.0–16.1)
17.1 (16.7–17.6)
17.3 (16.9–17.7)
(13.2–15.1)
15.9 (15.6–16.2)
15.9 (15.6–16.1)
(12.8–14.6)
15.1 (14.7–15.5)
14.9 (14.6–15.3)
(14.9–17.3)
18.6 (18.1–19.1)
18.6 (18.1–19.0)
98.7 (93.4–104.0) 104
109 (107.1–111)
108.7 (106.9–110.6)
Mean and 95% confidence interval of the mean are shown. The DGM study group was stratified according to the diagnostic outcome, and the AS study was stratified into two groups according to availability of the combined score of the MMSE test and CDT (definitions and reference: see Section 2.1); N.A.: not available. ND, non-demented; D, subjects diagnosed with any dementia; MCI, subjects diagnosed with mild cognitive impairment.
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Table 5 Correlations between the different PGWB subdimensions in the two study groups. Spearman Rho values are shown. Anxiety
Depressed mood
Pos well-being
Self-control
General health
Vitality
PGWB sum
AS Anxiety Depressed mood Pos well-being Self-control General health Vitality PGWB sum
1.000 0.584** 0.550** 0.585** 0.459** 0.526** 0.774**
1.000 0.683** 0.514** 0.447** 0.559** 0.774**
1.000 0.583** 0.456** 0.690** 0.860**
1.000 0.421** 0.563** 0.718**
1.000 0.602** 0.718**
1.000 0.841**
1.000
DGM Anxiety Depressed mood Pos well-being Self-control General health Vitality PGWB sum
1.000 0.724** 0.598** 0.607** 0.590** 0.525** 0.832**
1.000 0.619** 0.597** 0.603** 0.530** 0.825**
1.000 0.649** 0.508** 0.643** 0.821**
1.000 0.511** 0.509** 0.777**
1.000 0.576** 0.775**
1.000 0.792**
1.000
**
p < 0.001.
4. Discussion We have studied SWB, estimated by the PGWB index and its subdimensions in two different study groups of elderly Swedes. The groups were well matched for age but differed markedly in several of the studied biomarkers (Table 1). The main finding of the study was that there were significant differences in all six subdimensions of SWB between the two study groups (Table 3) and that this disparity persisted when we stratified the DGM group according to diagnoses (Table 4). Adjustment for the differences in biomarkers of somatic health also did not attenuate these SWB differences, demonstrating that the nosological grouping variable (AS vs. DGM) was a major predictor of SWB in this sample of elderly Swedes. In contrast, cognitive factors appear to play an independent role for several of the SWB subdimensions (Table 3). Another novel finding was that among the subjects with memory complaints (the DGM cohort), SWB was equally low among subjects without a final dementia diagnosis, or an MCI diagnosis, as among subjects which were found to have a dementia disorder (Table 4): we hypothesize that having been on the waiting-list for their assessment at a hospital memory unit may cause general stress, which might affect SWB negatively (Fleisher et al., 2007; Jorm et al., 2004; Petersen, 2005; Ramakers et al., 2009). To evaluate this possibility, prospective and longitudinal studies of relevant populations are needed. SWB is an important indicator of function in old age. There are a number of separable components of SWB, and to obtain a complete picture of a person’s evaluation of his or her life, more than one component must be measured. The PGWB instrument has six subdimensions: anxiety, depressed mood, positive well-being, selfcontrol, general health, and vitality. These subdimensions are thought to reflect the actual conditions in a person’s life. SWB includes diverse concepts ranging from momentary moods to life satisfaction, and from depression to euphoria. Scientists who study aging have shown particular interest in SWB, because of concern that SWB decline in old age could be accompanied by deteriorating happiness (Diener, Napa Scollon, Lucas, & Paul, 2003). On the other hand, a higher level of self-confidence comes with increasing age (Miner-Rubino, Winter, & Stewart, 2004), and older individuals may tend to accept a lower state of health (Brouwer et al., 2005; Butler & Ciarrochi, 2007). Our results support the position that with higher age there is an unchanged level of self-control and an acceptance of a lower state of health, but not less happiness (positive well being). Elderly may have a more stable sense of self-control than younger individuals, and self-reports of emotional experience may reflect this stable self-control, which is particularly important for old people (Rodin, 1986). It is generally thought that there is a connection between self-control, general health and vitality, and in
particular that self-control would be a more fundamental variable, largely determining the levels of the other PGWB dimensions. The present study, however, does not support any special status for self-control among the SWB variables (Fig. 1 and Table 5). [(Fig._1)TD$IG]
Fig. 1. Relation between self-control and general health in the two study groups: upper panel, AS cohort; lower panel, DGM cohort. The axes show the score points defining the quartiles of the respective PGWB subdimensions.
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The DGM group generally showed lower numerical PGWB scores (Table 3) than both the active seniors and a previously published Swedish population based reference group (Dimena¨s et al., 1996), although notably, there was no significant difference in the mean values for general health between the DGM group and the reference population (Table 3). Previous studies have been inconclusive regarding the SWB among subjects at risk of dementia (Degl’Innocenti et al., 2002; Gertz & Berwig, 2008; Netuveli & Blane, 2008; Smith, Fleeson, Geiselmann, Settersten, & Kunzmann, 1996). We found strong evidence for a major negative impact on SWB among subjects with subjective or objective memory complaints, and we demonstrated that the plasma levels of common biomarkers did not attenuate this finding. This suggests that there is a subtle but specific effect of both subjective and objective memory complaints on SWB rather than deterioration of SWB owing to a smoldering failing of somatic health. There are some limitations that should be considered in this study. For instance, the ability of mildly to moderately demented subjects to fill out questionnaires could be impaired. Studies that have addressed this possibility could not confirm this as a major problem (Trigg, Jones, & Skevington, 2007). Socioeconomic status and genetic predisposition both to subjective well- or ill-being and to cognitive impairment could also play a role. We conclude that somatic health, assessed by commonly used biomarkers, appears to play a subordinate role as a determinant of SWB in active elderly seniors as well as in patients with subjective or objective cognitive complaints attending a hospital memory unit, thus refuting our main hypothesis. Instead we found evidence of a small negative effect on SWB of the cognitive state of the individual subjects but clearly, factors besides cognition and somatic health affect SWB to a large extent. The clinical setting of the patient–doctor encounter and office-based diagnostic tools and instruments thus appear to provide sufficient means for an appropriate assessment of the patients’ SWB. Conflict of interest statement None of the authors report any conflict of interest. Acknowledgements ¨ rebro County Council and The Research Committee of O ¨ rebro are gratefully acknowledged for financial Nyckelfonden, O support. References Bove, M., Carnevali, L., Cicero, A. F. G., Grandi, E., Gaddoni, M., Noera, G., et al. (2010). Psychosocial factors and metabolic parameters: Is there any association in elderly people? The Massa Lombarda Project. Aging & Mental Health, 14, 801–806. Bradburn, N. M. (1969). The structure of psychological well-being. Chicago: Aldine Pub. Brouwer, W. B., van Exel, N. J., & Stolk, E. A. (2005). Acceptability of less than perfect health states. Soc Sci Med, 60, 237–246. Butler, J., & Ciarrochi, J. (2007). Psychological acceptance and quality of life in the elderly. Qual Life Res, 16, 607–615. Degl’Innocenti, A., Elmfeldt, D., Hansson, L., Breteler, M., James, O., Lithell, H., et al. (2002). Cognitive function and health-related quality of life in elderly patients with hypertension—Baseline data from the study on cognition and prognosis in the elderly (SCOPE). Blood Pressure, 11, 157–165. Diener, E. (2006). Guidelines for national indicators of subjective well-being and illbeing. Journal of Happiness Studies, 7, 397–404. Diener, E., Napa Scollon, C., Lucas, R. E., & Paul, C. (2003). The evolving concept of subjective well-being: The multifaceted nature of happiness. Advances in Cell Aging and Gerontology, Elsevier, 15, 187–219. Diener, E., & Suh, E. (1997). Measuring quality of life: Economic, social, and subjective indicators. Social Indicators Research, 40, 189–216. Dimena¨s, E., Carlsson, G., Glise, H., Israelsson, B., & Wiklund, I. (1996). Relevance of norm values as part of the documentation of quality of life instruments for use in upper gastrointestinal disease. Scandinavian Journal of Gastroenterology. Supplement, 221, 8–13. Dockray, S., & Steptoe, A. (2010). Positive affect and psychobiological processes. Neuroscience and Biobehavioral Reviews, 35, 69–75.
Dupuy, H. J. (1984). The Psychological General Well-Being (PGWB) index. In N. Wenger, M. Mattson, C. Furberg, & J. Elinson (Eds.), Assessment of Quality of life in clinical trials of cardiovascular therapies (pp. 170–183). New York: Le Jacq Pub. Fleisher, A. S., Sowell, B. B., Taylor, C., Gamst, A. C., Petersen, R. C., & Thal, L. J. (2007). Clinical predictors of progression to Alzheimer disease in amnestic mild cognitive impairment. Neurology, 68, 1588–1595. Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). ‘‘Mini-mental state’’. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198. Friedman, E. M., Hayney, M., Love, G. D., Singer, B. H., & Ryff, C. D. (2007). Plasma interleukin-6 and soluble IL-6 receptors are associated with psychological wellbeing in aging women. Health Psychology, 26, 305–313. Gertz, H. J., & Berwig, M. (2008). Critical observations on measuring quality of life of persons suffering from dementia. Nervenarzt, 79, 1023–1035. Hagnelius, N. O., Wahlund, L. O., & Nilsson, T. K. (2008). CSF/serum folate gradient: Physiology and determinants with special reference to dementia. Dementia and Geriatric Cognitive Disorders, 25, 516–523. Inui, T. S. (2003). The need for an integrated biopsychosocial approach to research on successful aging. Annals of Internal Medicine, 139, 391–394. Jorm, A. F., Butterworth, P., Anstey, K. J., Christensen, H., Easteal, S., Maller, J., et al. (2004). Memory complaints in a community sample aged 60–64 years: Associations with cognitive functioning, psychiatric symptoms, medical conditions, APOE genotype, hippocampus and amygdala volumes, and white-matter hyperintensities. Psychological Medicine, 34, 1495–1506. Jorm, A. F., & Jolley, D. (1998). The incidence of dementia: A meta-analysis. Neurology, 51, 728–733. Lassere, M. N. (2008). The Biomarker-Surrogacy Evaluation Schema: A review of the biomarker-surrogate literature and a proposal for a criterion-based, quantitative, multidimensional hierarchical levels of evidence schema for evaluating the status of biomarkers as surrogate endpoints. Statistical Methods in Medical Research, 17, 303–340. Miner-Rubino, K., Winter, D. G., & Stewart, A. J. (2004). Gender, social class, and the subjective experience of aging: Self-perceived personality change from early adulthood to late midlife. Personality & Social Psychology Bulletin, 30, 1599–1610. Netuveli, G., & Blane, D. (2008). Quality of life in older ages. British Medical Bulletin, 85, 113–126. Palmqvist, S., Hertze, J., Minthon, L., Wattmo, C., Zetterberg, H., Blennow, K., et al. (2012). Comparison of Brief Cognitive Tests and CSF biomarkers in predicting Alzheimer’s disease in mild cognitive impairment: Six-year follow-up study. PLoS One, 7, e38639. Penny, J. A., Shennan, A. H., Halligan, A. W., Taylor, D. J., de Swiet, M., & Anthony, J. (1999). The relative accuracy of sequential same-arm and simultaneous oppositearm measurements for the intra-arterial validation of blood pressure monitors. Blood Pressure Monitoring, 4, 91–95. Petersen, R. C. (2005). Mild cognitive impairment: Useful or not? Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 1, 5–10. Ramakers, I. H., Visser, P. J., Bittermann, A. J., Ponds, R. W., van Boxtel, M. P., & Verhey, F. R. (2009). Characteristics of help-seeking behaviour in subjects with subjective memory complaints at a memory clinic: A case–control study. International Journal of Geriatric Psychiatry, 24, 190–196. Rodin, J. (1986). Aging and health: Effects of the sense of control. Science, 233, 1271–1276. Ryff, C. D., Dienberg Love, G., Urry, H. L., Muller, D., Rosenkranz, M. A., Friedman, E. M., et al. (2006). Psychological well-being and ill-being: Do they have distinct or mirrored biological correlates? Psychotherapy and Psychosomatics, 75, 85–95. Ro¨ysamb, E., Tambs, K., Reichborn-Kjennerud, T., Neale, M. C., & Harris, J. R. (2003). Happiness and health: Environmental and genetic contributions to the relationship between subjective well-being, perceived health, and somatic illness. Journal of Personality and Social Psychology, 85, 1136–1146. Seplaki, C. L., Goldman, N., Weinstein, M., & Lin, Y. H. (2004). How are biomarkers related to physical and mental well-being? Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 59, 201–217. Shulman, K. I. (2000). Clock-drawing: Is it the ideal cognitive screening test? International Journal of Geriatric Psychiatry, 15, 548–561. Smith, J., Fleeson, W., Geiselmann, B., Settersten, R., & Kunzmann, U. (1996). Wohlbefinden im hohen Alter: Vorhersagen auf Grund objektiver Lebensbedingungen und subjektiver Bewertung. In K. U. Mayer & P. B. Baltes (Eds.), Die berliner alterstudie (pp. 497–523). Berlin: Akademie-Verlag. Sniderman, A. D., & Furberg, C. D. (2008). Age as a modifiable risk factor for cardiovascular disease. Lancet, 371, 1547–1549. Sniderman, A. D., Holme, I., Aastveit, A., Furberg, C., Walldius, G., & Jungner, I. (2007). Relation of age, the apolipoprotein B/apolipoprotein A-1 ratio, and the risk of fatal myocardial infarction and implications for the primary prevention of cardiovascular disease. The American Journal of Cardiology, 100, 217–221. Steeman, E., de Casterle, B. D., Godderis, J., & Grypdonck, M. (2006). Living with earlystage dementia: A review of qualitative studies. Journal of Advanced Nursing, 54, 722–738. Sunderland, T., Gur, R. E., & Arnold, S. E. (2005). The use of biomarkers in the elderly: Current and future challenges. Biological Psychiatry, 58, 272–276. Trigg, R., Jones, R. W., & Skevington, S. M. (2007). Can people with mild to moderate dementiaprovidereliableanswersabouttheirqualityoflife?AgeandAgeing,36,663–669. Unde´n, A. L., Andre´asson, A., Elofsson, S., Brismar, K., Mathsson, L., Ro¨nnelid, J., et al. (2007). Inflammatory cytokines, behaviour and age as determinants of self-rated health in women. Clinical Science (London), 112, 363–373. WHO. (1986). The Ottawa charter for health promotion. Wiklund, I., & Karlberg, J. (1991). Evaluation of quality of life in clinical trials. Selecting quality-of-life measures. Controlled Clinical Trials, 12(4 Suppl.), 204S–216S.
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Subjective well-being in Swedish active seniors or seniors with cognitive complaints and its relation to commonly available biomarkers Lovisa A. Olsson a,d,*, Nils-Olof Hagnelius b,d, Henny Olsson c, Torbjo¨rn K. Nilsson a,d Department of Laboratory Medicine/Clinical Chemistry, O¨rebro University, Sweden Department of Geriatrics, O¨rebro University, Sweden c Centre for Health Care Sciences, O¨rebro University, Sweden d ¨ Orebro University Hospital, and School of Health and Medical Science, O¨rebro University, Sweden a
b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 9 May 2011 Received in revised form 25 July 2012 Accepted 27 July 2012 Available online 18 August 2012
Well-being (WB) is a complex variable in its relation to physical health and other personal and social characteristics. The aim was to study subjective well-being (SWB) and its possible associations with traditional biomarkers of cardiovascular risk or dementia, in Swedish seniors. SWB was estimated by the Psychological General Well-Being (PGWB) index in two study groups. The active seniors (AS) group consisted of community-dwelling elderly Swedes leading an active life (n = 389). The DGM cohort (n = 300) consisted of subjects referred to the Memory Unit at the Department of Geriatrics, the cognitive problems had to be subjective, mild or moderate (MMSE 10). There were differences in all six subdimensions of SWB or distress, and in the sum of PGWB scores, between the two study groups (p < 0.001 for all), and adjustment for differences in biomarkers of somatic health (age, sex, blood pressure, BMI, HDL cholesterol, ApoB/ApoA1 ratio, creatinine, and homocysteine) did not attenuate these differences. In addition, cognition as assessed by the Clock-Drawing Test (CDT) showed independent associations with four of the PGWB subdimensions and with the PGWB sum. Among the subjects in the DGM cohort, SWB was equally low among subjects with an MCI (minor cognitive impairment) diagnosis or without a dementia diagnosis as among subjects diagnosed with dementia disorder. We conclude that the nosological grouping variable (AS vs. DGM cohort) and a cognitive factor were the main independent predictors of SWB in this sample of elderly Swedes, whereas biomarkers of somatic health played a subordinated role. ß 2012 Elsevier Ireland Ltd. All rights reserved.
Keywords: Subjective well-being Dementia Biomarker Somatic health Cognitive complaints Elders
1. Introduction During the last decade there has been an increasing number of studies regarding relationship between SWB, physical health and active aging. WB is a complex variable in its relation to health and other personal and social characteristics (Ro¨ysamb, Tambs, Reichborn-Kjennerud, Neale, & Harris, 2003). It is reasonable to assume that humans have always been striving toward WB and tried to find means to achieve it, even in the face of grave problems. WB can be viewed from many different perspectives and has
Abbreviations: WB, well-being; SWB, subjective well-being; PGWB, Psychological General Well-Being index; AS, active seniors cohort (community dwelling seniors); MMSE, mini mental state examination; CDT, Clock Drawing Test; DGM, dementia, genetic and Mileau study (senior subjects referred to the memory Care Unit); BMI, body mass index; D, subgroup with a dementia diagnosis; ND, non-demented subgroup. * Corresponding author at: Department of Laboratory Medicine/Clinical Chemis¨ rebro University Hospital, and School of Health and Medical Science, O ¨ rebro try O University, Sweden. Tel.: +46 19 602 1571; fax: +46 19 602 3785. E-mail address: [email protected] (L.A. Olsson). 0167-4943/$ – see front matter ß 2012 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.archger.2012.07.009
become a multidisciplinary concern, including historical, economical and social perspectives. In this study, we take the revised WHO charter of health promotion from 1986 (WHO, 1986) as our starting point, and the definition of SWB ‘‘as an umbrella term for different valuations that people make regarding their lives, the events happening to them, their bodies and the circumstances in which they live’’ (Diener, 2006). SWB is the person’s own evaluation of his or her life. Such evaluations may be judgements about the person’s life as a whole or evaluations of specific dimensions of life. People evaluate conditions differently depending on their expectations, values, and previous experiences (Inui, 2003). SWB is thus not synonymous with mental health or psychological health (Diener & Suh, 1997). SWB has been regarded to consist of a cognitive variable (e.g. life satisfaction) and of two variables for emotion: a positive affect and a negative affect (Bradburn, 1969; Ryff et al., 2006). A person’s SWB is also influenced by the cultural value system of the surrounding society. Biomarkers are used for diagnostics purposes, or to monitor the course of a disease or a treatment (Sunderland, Gur, & Arnold, 2005), and have also been suggested to be useful as surrogate
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endpoints substituting for actual clinical outcomes (Lassere, 2008). More recently, biomarkers have been introduced in studies on WB and in quality of life research as markers of somatic health in order to extend our understanding of the complex interplay between somatic processes and SWB (Dockray & Steptoe, 2010; Friedman, Hayney, Love, Singer, & Ryff, 2007; Seplaki, Goldman, Weinstein, & Lin, 2004). For instance biomarkers of inflammation such as CRP and certain cytokines, for instance IL-6, have shown a relation to SWB (Friedman et al., 2007; Unde´n et al., 2007). There are also claims of a relation between biomarkers for metabolic syndrome and psychosocial factors (Bove et al., 2010). Among the traditional risk factors for cardiovascular disease, such as age, hypertension, inflammation, elevated plasma-homocystein, etc., age is the most inevitable risk factor and may even play a more dominant role than earlier believed (Sniderman & Furberg, 2008; Sniderman et al., 2007). These factors are also involved in cognitive decline over time, again with age as the single largest risk factor (Jorm & Jolley, 1998). Specifically, it has been shown that SWB is reduced in dementia (Steeman, de Casterle, Godderis, & Grypdonck, 2006). We hypothesized that part of the variance in SWB in elderly Swedes would be accounted for by somatic health status, as estimated by anthropometrical data, blood pressure, and selected biomarkers of metabolism, systemic inflammation, and renal function. We investigated SWB by the PGWB index, an index developed for the purpose of providing measures of six SWB or distress subdimensions: anxiety, depressed mood, positive wellbeing, self-control, general health, and vitality (Dupuy, 1984). The aim was to study possible associations of SWB with common clinically used biomarkers of somatic health and with measures of cognitive function, in samples of both active seniors and subjects with subjective or objective cognitive complaints.
over May 2003–August 2007. The cognitive problems had to be mild or moderate, defined as MMSE Score 10; according to current Swedish research ethics legislation it is unethical to include subjects with more severe dementia due to their reduced autonomy. Dementia diagnoses were based on DSM-IV criteria. To divide dementia patients into different diagnostic categories ICD10 criteria were used. Mixed dementia was diagnosed in cases of coexistence of AD and a history of vascular risk factors, e.g. transitory ischemic attack(s), atrial fibrillation, hypertension, diabetes mellitus, Hachinski Ischemic Score 6 and CT scan showing ischemic changes. Probable AD was diagnosed in accordance with the NINCDS-ADRDA criteria. Subjects were weighed clad in light indoor clothing but without shoes, and the weight was approximated to the nearest 0.1 kg. BMI (kg/m2) was calculated using height and weight. In the AS cohort, the resting systolic and diastolic blood pressure was measured using an automatic oscillometric method (Dinamap model XL Critikron, Inc., Tampa, FL). The equipment has been validated (Penny et al., 1999). The subjects were in a sitting, relaxed position, and registrations were taken every minute for 4 min with the aim of obtaining a set of systolic registrations not varying more than 5 mm Hg. A mean value of the last two registrations was used for both resting systolic and diastolic blood pressure, in mm Hg. The blood pressure was then registered after one minute of unsupported standing. In the DGM cohort, blood pressure was measured using a manometric cuff. Technical check of the blood pressure equipment against known compressor pressure was performed at ¨ rebro University Hospital, Department of Bioengineering, before O and after the study period. All subjects gave a specific and written informed consent to the present study including genotyping and biobanking of the donated ¨ rebro County samples. The Research Ethics Committees of O Council approved both the AS and the DGM study.
2. Methods 2.2. Assessment of subjective well-being 2.1. Subjects Active seniors (AS) were recruited by a multi-phase sampling procedure aimed at an elderly retired population, living in various communities in Central Sweden. The locations for the recruitment were selected to represent a broad range of socioeconomic levels and included rural as well as urban and suburban areas. The sample consisted of 389 senior citizens and was recruited from several retired persons’ organizations, which implicates that the subjects were independent and socially active. Being retired, living independently in their own homes in addition to participation in such organizations were the sole inclusion criteria, not preset health criteria or age. We have designated them as ‘Active Seniors’ in contrast to elderly persons that do not engage themselves in the mentioned social activity. All were Caucasians, most of them born in the 1920s and 1930s, mean age at sampling was 74 5 years for both sexes, and the sex ratio M/F was 127/262 (32.4/67.4%). A random subset of the AS cohort was assessed by the MMSE (n = 196) (Folstein, Folstein, & McHugh, 1975), and/or the CDT (n = 332) judged according to Shulman (2000). We used a combination of MMSE and CDT to assess whether the AS subjects were cognitively intact, defined as MMSE 28 and CDT 4 (Palmqvist et al., 2012). DGM study. The senior subjects with subjective or objective cognitive complaints (DGM) were recruited from an incident case ¨ rebro, Sweden. All diagnostic study at the University Hospital, O manuals and procedures were as described in our previous article (Hagnelius, Wahlund, & Nilsson, 2008). Briefly, this study population consisted of 300 consecutive patients (143 men and 157 women), who were referred to the Memory Unit at the Department of Geriatrics for diagnostic assessment and treatment of suspected cognitive problems. The inclusion period extended
The PGWB index, accessible from the MAPI Research institute, www.mapi-research.fr, was used to measure subjective wellbeing or distress (Dupuy, 1984). It consists of 22 items that reflect subjective well-being and distress during the past week. PGWB comprises six subdimensions: anxiety (5 items; score 5–30), depressed mood (3 items; score 3–18), positive well-being (4 items; score 4–24), self-control (3 items; score 3–18), general health (3 items; score 3–18), and vitality (2 items; score 2–12); these are also combined to a global overall score of SWB. A high value indicates a high level of well-being. In order to achieve this uniform interpretation of the score-points even for the subdimensions which reflect distress (depressed mood and anxiety), score-points for these items were assigned in the direction of ‘‘6’’ to ‘‘1’’ in contrast to the usual direction of ‘‘1’’ to ‘‘6’’. The overall sum of scores gives a maximum value of 132 (best SWB) and a minimum of 22 (poorest SWB). The Swedish version of the PGWB has been validated (Wiklund & Karlberg, 1991). Everyone in both study groups completed the PGWB questionnaire without assistance. 2.3. Blood sampling and biochemical measurements Blood samples were taken, with the subjects in the supine position, by venipuncture using vacuum tubes. Serum was obtained after clotting for 30–60 min at room temperature and centrifuging for 10 min at 2000 g. All samples were stored at 70 8C. The serum samples were analyzed on a Hitachi 911 multianalyser, Roche, Mannheim, FRG. Creatinine was analyzed using a enzymatic method, Crea plus, from Roche/Boehringer Mannheim, FRG high sensitivity CRP (hs-CRP) was analyzed using
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Table 1 Baseline clinical and biomarker characteristics of the studied 691 seniors, divided both in the subgroups AS and DGM. Mean (SD) are shown.
Age (years) BMI (kg/cm2) systBPsitting (mm Hg) diastBPsitting (mm Hg) Heart rate (s1) hsCRP (mg/L)z S-Creatinine (mmol/L)z S-HDL-cholesterol (mmol/L)z S-LDL-cholesterol (mmol/L)z Apo A1 (g/L) Apo B (g/L) ln Apo B/A1z MMSE sum CDT z
AS
n
DGM
n
p
74.0 (6.7) 26.0 (3.9) 147.8 (25.1) 76.7 (11.0) 70.8 (10.8) 2.36 (2.83) 91.7 (21.0) 1.63 (0.42) 3.54 (0.94) 1.61 (0.30) 0.90 (0.20) 0.58 (0.18) 28.8 (1.35) 4.35 (0.9)
389 387 387 386 388 382 386 387 388 382 382 382 196 332
72.9 (10.2) 26.0 (3.7) 149.8 (23.6) 83.0 (12.3) 69.4 (12.0) 4.40 (12.0) 87.8 (49.0) 1.52 (0.42) 3.58 (1.06) 1.44 (0.27) .93 (0.23) 0.67 (0.21) 23.0 (4.90) 3.22 (1.5)
300 300 300 300 300 278 300 295 295 295 295 295 300 297
NS NS NS <0.001 NS NS <0.001 <0.001 NS <0.001 NS <0.001 <0.001 <0.001
The biomarkers were ln transformed before one-way ANOVA were computed.
an latex enhanced immunoturbidimetry method CRP (Latex) HS, SAPOA1 and S-APO B were analyzed with a immunoturbidimetry method all from Roche/Boehringer Mannheim, FRG. LDL and HDL cholesterol were measured by direct, homogeneous assays based on detergent treatment of the serum, N-geneousTM HDL-c and NgeneousTM LDL reagents, respectively, from Genzyme Corporation, Cambridge, MA, USA. 2.4. Statistics Statistical analyses were performed using SPSS version 15.0 (Chicago, IL, USA). The biomarkers are presented as means SD and the PGWB subdimension are presented as mean and 95% confidence interval for the mean, together with the median. Continuous variables with a skewed distribution were transformed using natural logarithms before regression analyses were made. The Mann–Whitney rank sum test was used to compare the PGWB sum and PGWB subdimension between groups. In group comparisons with categorical variables, x2 tests were used. Associations between variables were assessed using Spearman rank correlations. A general linear model was used to assess independence of associations of biomarkers and cognitive variables with PGWB subdimensions. Statistical significance were considered with a probability value < 0.05. In the AS study group, 4 subjects failed to complete the PGWB questionnaire and responses from 12 subjects failed to answer one or two questions, randomly distributed among the 22 questions. The missing answers were imputed using the manual from MAPI research institute. Some biomarker data were missing in a few subjects; exact numbers are indicated in Table 1.
3. Results 3.1. Baseline values of biomarkers and subjective well being The basic clinical and laboratory characteristics of the two study groups are shown in Table 1. As seen the groups were well balanced for age. There were significant differences between the AS and DGM groups with respect to the physiological biomarker diastolic blood pressure, the renal function biomarker creatinine, and the lipid metabolic biomarkers HDL-cholesterol and apo A1, but not the inflammatory biomarker hsCRP, heart rate, body mass index, or LDL-cholesterol and apoB. Gender differences in the studied biomarkers are displayed in Table 2. In both AS and DGM subjects, females had higher heart rate, serum HDL-cholesterol and ApoA1 values, and lower creatinine values as expected. The women in the AS group also had a lower diastolic blood pressure, and ApoB/A1 ratio than the male participants, while the females in the DGM group had higher LDL cholesterol and ApoB values than the males. In all six studied subdimensions of PGWB as well as in the PGWB sum of scores, there were significant differences between the AS group and the DGM group, see Table 3. Previously published PGWB figures obtained in a Swedish representative population sample of comparable age are also given (Dimena¨s, Carlsson, Glise, Israelsson, & Wiklund, 1996). To assess the impact on SWB of a diagnosis of dementia vs. non-dementia within the DGM cohort, the DGM cohort was further subdivided according to the outcome of the diagnostic procedure in three subgroups, subjects with a dementia
Table 2 Baseline clinical and biomarker characteristics of the studied 691 seniors, divided both in the subgroups AS and DGM and according to gender. Mean (SD) are shown. Females
Age (years) BMI (kg/cm2) systBPsitting (mm Hg) diastBPsitting (mm Hg) Heart rate (s1) hsCRP (mg/L)a Creatinine (mmol/L)a HDL-cholesterol (mmol/L)a LDL-cholesterol (mmol/L)a Apo A1 Apo B Ratio Apo B/A1a MMSE CDT a
Males
AS
DGM
p
AS
DGM
p
73.8 (6.9) 26.0 (4.1) 148 (25.9) 75.6 (10.9) 72 (10.6) 2.06 (2.1) 87.0 (21.6) 1.75 (0.39) 3.6 (0.92) 1.70 (0.267) 0.91 (0.197) 0.55 (0.160) 29 (1.22) 4.4 (0.9)
73.3 (10.6) 25.8 (3.7) 150 (23.7) 82.3 (12.5) 71.3 (11.2) 3.3 (6.4) 78.9 (36.6) 1.62 (0.41) 3.8 (1.09) 1.52 (0.261) 0.97 (0.229) 0.66 (0.210) 22.7 (5.3) 3.3 (1.5)
NS NS NS <0.001 NS NS <0.001 <0.001 NS <0.001 0.003 <0.001 <0.001 <0.001
74.4 (6.4) 26.2 (3.5) 147 (23.4) 78.6 (10.9) 69 (10.8) 2.9 (3.7) 100.6 (16.4) 1.42 (0.41) 3.4 (0.97) 1.44 (0.293) 0.89 (0.205) 0.64 (0.189) 28.6 (1.5) 4.2 (4.5)
72.5 (9.5) 26.2 (3.7) 149 (23.6) 84.1 (12.1) 67.4 (12.6) 5.6 (16.2) 97.9 (58.9) 1.40 (0.39) 3.3 (0.96) 1.34 (0.230) 0.88 (0.212) 0.68 (0.213) 23.5 (4.5) 3.0 (3.5)
NS NS NS <0.001 NS NS 0.005 NS NS 0.002 NS NS <0.001 <0.001
The biomarkers were ln transformed before one-way ANOVA were computed.
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Table 3 Subjective well being in the two study groups assessed by the Psychological General Well-Being index. PGWB domain
AS Mean (CI)
Anxiety (range 5–30) Depressed mood (range 3–18) Pos. well-being (range 4–24) Self-control (range 3–18) General health (range 3–18) Vitality (range 2–12) PGWB sum
26.0 16.2 17.2 15.9 15.0 18.6
(25.7–26.4) (16.0–16.4) (16.9–17.5) (15.7–16.1) (14.7–15.3) (18.2–18.9)
108.9 (107.5–109.8)
p(Group)
p(CDT)
DGM
27 17 18 18 16 19
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001
0.004 0.004 0.026 NS 0.018 NS
23.7 14.9 15.1 14.2 13.9 17.0
112
<0.001
0.012
98.9 (97.1–100.8)
Md
Reference population
Mean (CI)
Md
(23.2–24.2) (14.6–15.3) (14.8–15.5) (13.9–14.5) (13.6–14.3) (16.6–17.5)
24 15 15 15 14 18 101
Mean (CI) 25.7 15.8 16.3 15.8 14.1 17.7
(25.1–26.4) (15.4–16.2) (15.8–16.9) (15.5–16.2) (13.7–14.6) (17.1–18.3)
105.6 (102.9–108.2)
Values for PGWB in the study groups AS and DGM. Mean and 95% confidence interval for the mean are shown. Md = Median. p(Group): Mann–Whitney two tailed test was used for comparing the study groups. p(CDT): significance level of Clock Drawing Test score in multivariate regression models; the other independent variables were age, gender, diastolic blood pressure, ln creatinine, ln HDL-cholesterol, lipoprotein A1, lipoprotein B, ratio ApoB/ApoA1. The population-based values from Dimena¨s et al. (1996) are shown as comparison (right column).
diagnosis (D), non-demented subjects (ND), and those diagnosed with mild cognitive impairment (MCI). This showed that there was no significant difference between the three groups in DGM for any of the PGWB subdimensions and all three groups differed to the same extent from the AS group (Table 4). There was also no significant difference in the AS subgroups defined as cognitively intact by the combined MMS and CDT scores, and the AS subjects in whom one of these two variables were missing, confirming that the AS group can be regarded as a homogeneous cohort of elderly subjects. 3.2. Relations between variables The relations between the PGWB subdimensions were assessed by Spearman correlations (Table 5). All were strongly correlated to each other, in both cohorts. To assess the impact of age on SWB, the AS cohort was divided in two age groups, younger and older than the median of 74.1 years. There were few significant differences in SWB between the two age groups: males above 74.1 years had a significantly lower score for the subdimension depressed mood, and women below 74.1 years had a significantly higher score for the subdimensions of general health, vitality and PGWB sum. In the DGM cohort there were no differences between the two age groups (data not shown). Relations between the PGWB subdimensions and selected clinical data and biomarkers of somatic health that showed any significant differences between the AS and DGM cohorts in either of the sexes (Table 2) were entered as independent variables,
together with age and gender, into seven multivariate regression analyses with each of the six PGWB subdimensions, and the PGWB sum of scores, as outcome variables. The grouping variable (AS vs. DGM) was used as a fixed factor in these models. The significant differences in PGWB subdimensions between the AS and the DGM as shown in Table 3 did not change appreciably after these adjustments (data not shown), indicating that the PGWB differences between these two groups were independent of these somatic biomarkers. Next, we wanted to assess the possible contribution of cognition to the SWB subdimensions, and therefore added separately either CDT or MMSE scores to the above seven multivariate regression analyses. As shown in Table 3, cognition as assessed by CDT showed an independent association with PGWB sum and the four subdimensions: anxiety, depressed mode, positive well-being, and general health. MMSE did not show any significant association in these models (not shown). To test whether self-control has a special status among the PGWB subdimensions, the two subdimensions general health and self-control were stratified into quartiles based on the combined cohorts. As seen in the three-dimensional histogram (Fig. 1), there is indeed a similar relation between the two dimensions in both the AS and the DGM cohorts: in Spearman correlations (Table 5) the Rho values were 0.421 and 0.511 in the AS and DGM subjects, respectively (p < 0.001 for both study groups). However, Rhovalues for the pairwise associations between General Health and the other five subdimensions of PGWB were all higher than these figures (Table 5).
Table 4 Subjective well being assessed by the Psychological General Well-Being index in the subgroups stratified according to diagnostic outcome. DGM
AS (MMSE 28 and CDT 4)
ND (n = 37) Anxiety Depressed mood Pos well being Self contr General health Vitality
PGWB sum
M (CI) Md M (CI) Md M (CI) Md M (CI) Md M (CI) Md M (CI) Md M (CI) Md
23.1 23 15.3 16.0 15.5 15.0 14.4 16.0 13.7 14.0 16.6 17.0
(21.6–24.5) (14.5–13.1) (14.4–16.7) (13.4–15.5) (12.7–14.7) (15.1–18.1)
98.7 (93.1–104.2) 100
D (n = 214) 23.7 24 14.8 15.0 15.1 15.0 14.1 15.0 14.0 15.0 17.2 18.0
(23.1–24.3) (14.4–15.7) (14.7–15.6) (13.8–14.5) (13.6–14.4) (16.7–17.7)
99.0 (96.9–101.1) 100.5
MCI (n = 48) 24.3 25 15.3 16.0 15.1 15.0 14.2 16.0 13.7 14.0 16.1 16.0
Yes (n = 154)
N.A. (n = 235)
(23.0–25.6)
26.2 (25.6–26.7)
25.9 (25.5–26.4)
(14.6–16.1)
16.1 (15.8–16.5)
16.2 (16.0–16.5)
(14.0–16.1)
17.1 (16.7–17.6)
17.3 (16.9–17.7)
(13.2–15.1)
15.9 (15.6–16.2)
15.9 (15.6–16.1)
(12.8–14.6)
15.1 (14.7–15.5)
14.9 (14.6–15.3)
(14.9–17.3)
18.6 (18.1–19.1)
18.6 (18.1–19.0)
98.7 (93.4–104.0) 104
109 (107.1–111)
108.7 (106.9–110.6)
Mean and 95% confidence interval of the mean are shown. The DGM study group was stratified according to the diagnostic outcome, and the AS study was stratified into two groups according to availability of the combined score of the MMSE test and CDT (definitions and reference: see Section 2.1); N.A.: not available. ND, non-demented; D, subjects diagnosed with any dementia; MCI, subjects diagnosed with mild cognitive impairment.
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307
Table 5 Correlations between the different PGWB subdimensions in the two study groups. Spearman Rho values are shown. Anxiety
Depressed mood
Pos well-being
Self-control
General health
Vitality
PGWB sum
AS Anxiety Depressed mood Pos well-being Self-control General health Vitality PGWB sum
1.000 0.584** 0.550** 0.585** 0.459** 0.526** 0.774**
1.000 0.683** 0.514** 0.447** 0.559** 0.774**
1.000 0.583** 0.456** 0.690** 0.860**
1.000 0.421** 0.563** 0.718**
1.000 0.602** 0.718**
1.000 0.841**
1.000
DGM Anxiety Depressed mood Pos well-being Self-control General health Vitality PGWB sum
1.000 0.724** 0.598** 0.607** 0.590** 0.525** 0.832**
1.000 0.619** 0.597** 0.603** 0.530** 0.825**
1.000 0.649** 0.508** 0.643** 0.821**
1.000 0.511** 0.509** 0.777**
1.000 0.576** 0.775**
1.000 0.792**
1.000
**
p < 0.001.
4. Discussion We have studied SWB, estimated by the PGWB index and its subdimensions in two different study groups of elderly Swedes. The groups were well matched for age but differed markedly in several of the studied biomarkers (Table 1). The main finding of the study was that there were significant differences in all six subdimensions of SWB between the two study groups (Table 3) and that this disparity persisted when we stratified the DGM group according to diagnoses (Table 4). Adjustment for the differences in biomarkers of somatic health also did not attenuate these SWB differences, demonstrating that the nosological grouping variable (AS vs. DGM) was a major predictor of SWB in this sample of elderly Swedes. In contrast, cognitive factors appear to play an independent role for several of the SWB subdimensions (Table 3). Another novel finding was that among the subjects with memory complaints (the DGM cohort), SWB was equally low among subjects without a final dementia diagnosis, or an MCI diagnosis, as among subjects which were found to have a dementia disorder (Table 4): we hypothesize that having been on the waiting-list for their assessment at a hospital memory unit may cause general stress, which might affect SWB negatively (Fleisher et al., 2007; Jorm et al., 2004; Petersen, 2005; Ramakers et al., 2009). To evaluate this possibility, prospective and longitudinal studies of relevant populations are needed. SWB is an important indicator of function in old age. There are a number of separable components of SWB, and to obtain a complete picture of a person’s evaluation of his or her life, more than one component must be measured. The PGWB instrument has six subdimensions: anxiety, depressed mood, positive well-being, selfcontrol, general health, and vitality. These subdimensions are thought to reflect the actual conditions in a person’s life. SWB includes diverse concepts ranging from momentary moods to life satisfaction, and from depression to euphoria. Scientists who study aging have shown particular interest in SWB, because of concern that SWB decline in old age could be accompanied by deteriorating happiness (Diener, Napa Scollon, Lucas, & Paul, 2003). On the other hand, a higher level of self-confidence comes with increasing age (Miner-Rubino, Winter, & Stewart, 2004), and older individuals may tend to accept a lower state of health (Brouwer et al., 2005; Butler & Ciarrochi, 2007). Our results support the position that with higher age there is an unchanged level of self-control and an acceptance of a lower state of health, but not less happiness (positive well being). Elderly may have a more stable sense of self-control than younger individuals, and self-reports of emotional experience may reflect this stable self-control, which is particularly important for old people (Rodin, 1986). It is generally thought that there is a connection between self-control, general health and vitality, and in
particular that self-control would be a more fundamental variable, largely determining the levels of the other PGWB dimensions. The present study, however, does not support any special status for self-control among the SWB variables (Fig. 1 and Table 5). [(Fig._1)TD$IG]
Fig. 1. Relation between self-control and general health in the two study groups: upper panel, AS cohort; lower panel, DGM cohort. The axes show the score points defining the quartiles of the respective PGWB subdimensions.
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The DGM group generally showed lower numerical PGWB scores (Table 3) than both the active seniors and a previously published Swedish population based reference group (Dimena¨s et al., 1996), although notably, there was no significant difference in the mean values for general health between the DGM group and the reference population (Table 3). Previous studies have been inconclusive regarding the SWB among subjects at risk of dementia (Degl’Innocenti et al., 2002; Gertz & Berwig, 2008; Netuveli & Blane, 2008; Smith, Fleeson, Geiselmann, Settersten, & Kunzmann, 1996). We found strong evidence for a major negative impact on SWB among subjects with subjective or objective memory complaints, and we demonstrated that the plasma levels of common biomarkers did not attenuate this finding. This suggests that there is a subtle but specific effect of both subjective and objective memory complaints on SWB rather than deterioration of SWB owing to a smoldering failing of somatic health. There are some limitations that should be considered in this study. For instance, the ability of mildly to moderately demented subjects to fill out questionnaires could be impaired. Studies that have addressed this possibility could not confirm this as a major problem (Trigg, Jones, & Skevington, 2007). Socioeconomic status and genetic predisposition both to subjective well- or ill-being and to cognitive impairment could also play a role. We conclude that somatic health, assessed by commonly used biomarkers, appears to play a subordinate role as a determinant of SWB in active elderly seniors as well as in patients with subjective or objective cognitive complaints attending a hospital memory unit, thus refuting our main hypothesis. Instead we found evidence of a small negative effect on SWB of the cognitive state of the individual subjects but clearly, factors besides cognition and somatic health affect SWB to a large extent. The clinical setting of the patient–doctor encounter and office-based diagnostic tools and instruments thus appear to provide sufficient means for an appropriate assessment of the patients’ SWB. Conflict of interest statement None of the authors report any conflict of interest. Acknowledgements ¨ rebro County Council and The Research Committee of O ¨ rebro are gratefully acknowledged for financial Nyckelfonden, O support. References Bove, M., Carnevali, L., Cicero, A. F. G., Grandi, E., Gaddoni, M., Noera, G., et al. (2010). Psychosocial factors and metabolic parameters: Is there any association in elderly people? The Massa Lombarda Project. Aging & Mental Health, 14, 801–806. Bradburn, N. M. (1969). The structure of psychological well-being. Chicago: Aldine Pub. Brouwer, W. B., van Exel, N. J., & Stolk, E. A. (2005). Acceptability of less than perfect health states. Soc Sci Med, 60, 237–246. Butler, J., & Ciarrochi, J. (2007). Psychological acceptance and quality of life in the elderly. Qual Life Res, 16, 607–615. Degl’Innocenti, A., Elmfeldt, D., Hansson, L., Breteler, M., James, O., Lithell, H., et al. (2002). Cognitive function and health-related quality of life in elderly patients with hypertension—Baseline data from the study on cognition and prognosis in the elderly (SCOPE). Blood Pressure, 11, 157–165. Diener, E. (2006). Guidelines for national indicators of subjective well-being and illbeing. Journal of Happiness Studies, 7, 397–404. Diener, E., Napa Scollon, C., Lucas, R. E., & Paul, C. (2003). The evolving concept of subjective well-being: The multifaceted nature of happiness. Advances in Cell Aging and Gerontology, Elsevier, 15, 187–219. Diener, E., & Suh, E. (1997). Measuring quality of life: Economic, social, and subjective indicators. Social Indicators Research, 40, 189–216. Dimena¨s, E., Carlsson, G., Glise, H., Israelsson, B., & Wiklund, I. (1996). Relevance of norm values as part of the documentation of quality of life instruments for use in upper gastrointestinal disease. Scandinavian Journal of Gastroenterology. Supplement, 221, 8–13. Dockray, S., & Steptoe, A. (2010). Positive affect and psychobiological processes. Neuroscience and Biobehavioral Reviews, 35, 69–75.
Dupuy, H. J. (1984). The Psychological General Well-Being (PGWB) index. In N. Wenger, M. Mattson, C. Furberg, & J. Elinson (Eds.), Assessment of Quality of life in clinical trials of cardiovascular therapies (pp. 170–183). New York: Le Jacq Pub. Fleisher, A. S., Sowell, B. B., Taylor, C., Gamst, A. C., Petersen, R. C., & Thal, L. J. (2007). Clinical predictors of progression to Alzheimer disease in amnestic mild cognitive impairment. Neurology, 68, 1588–1595. Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). ‘‘Mini-mental state’’. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198. Friedman, E. M., Hayney, M., Love, G. D., Singer, B. H., & Ryff, C. D. (2007). Plasma interleukin-6 and soluble IL-6 receptors are associated with psychological wellbeing in aging women. Health Psychology, 26, 305–313. Gertz, H. J., & Berwig, M. (2008). Critical observations on measuring quality of life of persons suffering from dementia. Nervenarzt, 79, 1023–1035. Hagnelius, N. O., Wahlund, L. O., & Nilsson, T. K. (2008). CSF/serum folate gradient: Physiology and determinants with special reference to dementia. Dementia and Geriatric Cognitive Disorders, 25, 516–523. Inui, T. S. (2003). The need for an integrated biopsychosocial approach to research on successful aging. Annals of Internal Medicine, 139, 391–394. Jorm, A. F., Butterworth, P., Anstey, K. J., Christensen, H., Easteal, S., Maller, J., et al. (2004). Memory complaints in a community sample aged 60–64 years: Associations with cognitive functioning, psychiatric symptoms, medical conditions, APOE genotype, hippocampus and amygdala volumes, and white-matter hyperintensities. Psychological Medicine, 34, 1495–1506. Jorm, A. F., & Jolley, D. (1998). The incidence of dementia: A meta-analysis. Neurology, 51, 728–733. Lassere, M. N. (2008). The Biomarker-Surrogacy Evaluation Schema: A review of the biomarker-surrogate literature and a proposal for a criterion-based, quantitative, multidimensional hierarchical levels of evidence schema for evaluating the status of biomarkers as surrogate endpoints. Statistical Methods in Medical Research, 17, 303–340. Miner-Rubino, K., Winter, D. G., & Stewart, A. J. (2004). Gender, social class, and the subjective experience of aging: Self-perceived personality change from early adulthood to late midlife. Personality & Social Psychology Bulletin, 30, 1599–1610. Netuveli, G., & Blane, D. (2008). Quality of life in older ages. British Medical Bulletin, 85, 113–126. Palmqvist, S., Hertze, J., Minthon, L., Wattmo, C., Zetterberg, H., Blennow, K., et al. (2012). Comparison of Brief Cognitive Tests and CSF biomarkers in predicting Alzheimer’s disease in mild cognitive impairment: Six-year follow-up study. PLoS One, 7, e38639. Penny, J. A., Shennan, A. H., Halligan, A. W., Taylor, D. J., de Swiet, M., & Anthony, J. (1999). The relative accuracy of sequential same-arm and simultaneous oppositearm measurements for the intra-arterial validation of blood pressure monitors. Blood Pressure Monitoring, 4, 91–95. Petersen, R. C. (2005). Mild cognitive impairment: Useful or not? Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 1, 5–10. Ramakers, I. H., Visser, P. J., Bittermann, A. J., Ponds, R. W., van Boxtel, M. P., & Verhey, F. R. (2009). Characteristics of help-seeking behaviour in subjects with subjective memory complaints at a memory clinic: A case–control study. International Journal of Geriatric Psychiatry, 24, 190–196. Rodin, J. (1986). Aging and health: Effects of the sense of control. Science, 233, 1271–1276. Ryff, C. D., Dienberg Love, G., Urry, H. L., Muller, D., Rosenkranz, M. A., Friedman, E. M., et al. (2006). Psychological well-being and ill-being: Do they have distinct or mirrored biological correlates? Psychotherapy and Psychosomatics, 75, 85–95. Ro¨ysamb, E., Tambs, K., Reichborn-Kjennerud, T., Neale, M. C., & Harris, J. R. (2003). Happiness and health: Environmental and genetic contributions to the relationship between subjective well-being, perceived health, and somatic illness. Journal of Personality and Social Psychology, 85, 1136–1146. Seplaki, C. L., Goldman, N., Weinstein, M., & Lin, Y. H. (2004). How are biomarkers related to physical and mental well-being? Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 59, 201–217. Shulman, K. I. (2000). Clock-drawing: Is it the ideal cognitive screening test? International Journal of Geriatric Psychiatry, 15, 548–561. Smith, J., Fleeson, W., Geiselmann, B., Settersten, R., & Kunzmann, U. (1996). Wohlbefinden im hohen Alter: Vorhersagen auf Grund objektiver Lebensbedingungen und subjektiver Bewertung. In K. U. Mayer & P. B. Baltes (Eds.), Die berliner alterstudie (pp. 497–523). Berlin: Akademie-Verlag. Sniderman, A. D., & Furberg, C. D. (2008). Age as a modifiable risk factor for cardiovascular disease. Lancet, 371, 1547–1549. Sniderman, A. D., Holme, I., Aastveit, A., Furberg, C., Walldius, G., & Jungner, I. (2007). Relation of age, the apolipoprotein B/apolipoprotein A-1 ratio, and the risk of fatal myocardial infarction and implications for the primary prevention of cardiovascular disease. The American Journal of Cardiology, 100, 217–221. Steeman, E., de Casterle, B. D., Godderis, J., & Grypdonck, M. (2006). Living with earlystage dementia: A review of qualitative studies. Journal of Advanced Nursing, 54, 722–738. Sunderland, T., Gur, R. E., & Arnold, S. E. (2005). The use of biomarkers in the elderly: Current and future challenges. Biological Psychiatry, 58, 272–276. Trigg, R., Jones, R. W., & Skevington, S. M. (2007). Can people with mild to moderate dementiaprovidereliableanswersabouttheirqualityoflife?AgeandAgeing,36,663–669. Unde´n, A. L., Andre´asson, A., Elofsson, S., Brismar, K., Mathsson, L., Ro¨nnelid, J., et al. (2007). Inflammatory cytokines, behaviour and age as determinants of self-rated health in women. Clinical Science (London), 112, 363–373. WHO. (1986). The Ottawa charter for health promotion. Wiklund, I., & Karlberg, J. (1991). Evaluation of quality of life in clinical trials. Selecting quality-of-life measures. Controlled Clinical Trials, 12(4 Suppl.), 204S–216S.