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RESEARCH ARTICLE |
a Institute of Gerontology, University College of Health Sciences, Jönköping, Sweden
b Department of Psychology, Göteborg University, Sweden
c Department of Medical Epidemiology, Karolinska Institute, Stockholm, Sweden
d Department of Human Development and Family Studies, Pennsylvania State University, University Park
e Center for Developmental and Health Genetics, Pennsylvania State University, University Park
Linda B. Hassing, Department of Psychology, Göteborg University, Box 500, SE-405 30 Göteborg, Sweden E-mail: Linda.Hassing{at}psy.gu.se.
Decision Editor: Margie E. Lachman, PhD
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Abstract |
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There is evidence that poor cognitive performance among elderly individuals is associated with impending death (for reviews see Berg 1996
; Siegler 1975; Small and Backman 1999). However, several issues of the cognition–survival relationship remain to be clarified. For example, is the relationship specific or pervasive across groups differing in age and across cognitive tasks? Is there an accelerated cognitive decline or drop before death? Is the association between cognition and survival mediated by diseases and health-related changes? The present study addresses some of these questions in a longitudinal study of nondemented very old individuals.
It has been suggested that only crystallized knowledge (e.g., verbal ability, semantic memory) or abilities that are unaffected by aging are indicative of impending death (Bosworth and Schaie 1999; White and Cunningham 1988), whereas others hold that all cognitive abilities are affected (Ljungquist, Berg, and Steen 1996; Maier and Smith 1999). Mortality effects have been reported in age-maintained abilities including verbal abilities (Bosworth and Schaie 1999; Reimanis and Green 1971; Siegler, McCarty, and Logue 1982; White and Cunningham 1988) and short-term memory functioning (Johansson and Berg 1989), as well as in age-sensitive abilities such as psychomotor speed, verbal fluency, and episodic-memory functioning (Bosworth and Schaie 1999; Botwinick, West, and Storandt 1978; Hassing, Small, von Strauss, Fratiglioni, and Backman 2002; Maier and Smith 1999; Siegler et al. 1982; Small and Backman 1997). Thus, the evidence remains inconclusive as to whether the cognitive correlates of mortality are task specific or pervasive.
Another issue concerns the time frame of the predictive period. Although there is evidence that differences in level of cognitive functioning are associated with subsequent survival, it is not clear if there is a pronounced decline in function closer to time of death (i.e., terminal drop), or if there is a continuous decline for several years preceding death (Palmore and Cleveland 1976). To examine this we need longitudinal studies of individual trajectories that can be related to distance to death. Thus, it is important to distinguish between findings based on differences in level of performance at one point in time and subsequent mortality, and differences in change across two or more occasions.
Cross-sectional findings have shown that decedents perform at a lower level than survivors several years before dying. This has been shown for 2 to 3 years (Hassing et al. 2002; Small and Backman 1997; White and Cunningham 1988), 7 years (Maier and Smith 1999), and up to 14 years before death (Bosworth and Schaie 1999). Findings from longitudinal studies are somewhat mixed. Johansson and Berg 1989 found that decline in primary memory across three measurements over a period of 9 years was strongly associated with subsequent survival time. Similar findings were reported for episodic memory by Deeg, Hofman, and van Zonneveld 1990. However, in a study by Bosworth and Schaie 1999 based on two assessments over a 7-year period, no differences were found in rate of change in 11 of 13 cognitive measurements. In further analyses of the same data set it was concluded that distance to death was significantly predicted by level of cognitive performance but not by change in cognitive performance (Bosworth, Schaie, Willis, and Siegler 1999).
Although several studies have examined the relationship between cognition and survival, less effort has been given to the underlying mechanisms for the relationship. In a recent study by Smits, Deeg, Kriegsman, and Schmand 1999, several cognitive measures were related to mortality risk, but when self-rated health, medicine use, physical performance, and specific chronic diseases were controlled, only the measures of processing speed, problem solving, and episodic memory remained as significant predictors of mortality.
A distinction made in the study of aging is that between normative or primary age-related change and change related to compromised health and disease. Dementia affects a substantial proportion of people in late life and represents the most devastating disease category for cognitive functioning. The dramatic effects of the dementing illnesses are well documented. Also, it is well established that dementia increases the risk of mortality (e.g., Johansson and Zarit 1997). Because dementia produces severe cognitive decline, it accounts for a substantial amount of the variance in the overall cognition–survival relationship. For this reason, many studies have deliberately excluded individuals with dementia (Bosworth and Schaie 1999; Bosworth, Schaie, and Willis 1999; Bosworth, Schaie, Willis, and Siegler 1999; Hassing et al. 2002; Small and Backman 1997) to investigate the robustness among nondemented individuals. A potential, but less investigated, source of cognitive decline and death in the elderly is that of cardiovascular disease (CVD). Although it is well established that CVD elevates the risk for stroke and that severe cerebrovascular disease is associated with lower levels of cognitive performance (Elias, Elias, and Elias 1990), the impact of these factors on the cognition–survival relationship has not been systematically studied.
The purpose of the present study was to examine the level of cognitive functioning and longitudinal changes in cognitive performance in relation to time to death in individuals that remain non-demented. More specifically, our aims were to (a) examine if types of cognitive abilities are differentially predictive of mortality, (b) study whether markers of cerebrovascular disease and CVD influence the cognition–survival association, and (c) examine if there is a pronounced decline in cognitive functioning associated with time to death.
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Methods |
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). The OCTO-Twin Study began with a sample of 702 individuals (351 twin pairs), aged 80 years and older, drawn from the Swedish Twin Registry (Cederlof and Lorich 1978). The sample was found to be similar to a population of Swedish singletons of the same age (Simmons et al. 1997). The participants were assessed three times at 2-year intervals. The first wave (T1) proceeded between 1991 and 1993, the second wave (T2) between 1993 and 1995, and the third wave (T3) between 1995 and 1997. All individuals were examined in their home by experienced registered nurses specifically trained for the study and continuously supervised. A complete testing session, including rest periods, took about 3.5 to 4.0 hr. Covariates of interest for the present study included demographic information (age, gender, and education), physical health (see Medical Information), as well as cognitive functioning (see Cognitive Measures).
The following individuals were excluded: People with dementia at the start of the study (n = 81) according to the Diagnostic and Statistical Manual of Mental Disorders (3rd ed., rev.; American Psychiatric Association 1987), those who became demented under the follow-up period (n = 68), persons who declined participation in the cognitive testing (n = 26), and persons who discontinued participation (dropout for reason other than death) in T2 and T3 (n = 49). The total sample that was included in this study comprised 466 persons.
The group that declined participation at follow-up (n = 49) did not differ from the total sample included in the study (N = 466) concerning age, gender, education, or performance on the cognitive tasks (ps > .05). However, stroke and heart failure were more frequent among the dropouts (ps < .05).
Mortality surveillance based on information from the Swedish Death Registry was gathered continuously throughout the study period (1991–1999). Four survival groups were defined on the basis of subsequent mortality (see Table 1 ). Sixty-three persons were deceased by the time of the second examination, another 67 persons had died before the third examination, and at the end of the study period (September, 1999) another 76 individuals were deceased. Thus, a total of 260 survivors and 206 decedents for whom the death dates were known had been tested during at least one of the three waves. The primary causes of death, gathered from death certificates, were vascular disease (62%), malignant tumors (19%), infectious diseases (10%), infirmities of old age (6%), and other causes (3%; e.g., suicide, accidents, unknown cause). Frequency of primary causes of death for those who declined participation at follow-up did not differ from those who continued participation.
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Stroke
A diagnosis of stroke is mainly based on medical exam information in medical records. Self-reports of stroke were, however, accepted as a valid diagnosis despite the lack of record confirmation in a few cases (about 10%).
TIA
TIA refers to a condition with duration shorter than 24 hr. A diagnosis of TIA is based on medical exam as well as on self-reports.
Myocardial infarction
The diagnostic information is mainly drawn from the records in which a diagnosis requires laboratory evidence of marker enzymes in conjunction with the typical clinical manifestations and/or an ECG pattern with specific QRS aberrations. Self-reports about myocardial infarction were accepted in a few cases in which there was a lack of medical-record information (about 10%). These self-reports include histories with a hospital observation period for suspect infarction but with an unresolved diagnosis or other instances in which the diagnostic criteria are not fully met.
Congestive heart failure
The diagnosis is mainly based on medical records presenting the diagnosis or explicit symptoms of insufficient cardiac function characterized by edema and dyspnoe, including treatments with diuretics, digitalis, and/or angiotensin converting enzyme blockers, providing evidence for the diagnosis. Self-reports without medical-record confirmation were included for about 25%.
Angina pectoris
A diagnosis is based on explicit information in medical records supplemented with information about the use of prescribed nitrates for suspected angina pectoris. A myocardial infarction diagnosis is registered in cases in which angina symptoms were observed in temporal connection with a myocardial infarct.
Arterial hypertension
A diagnosis of hypertension is based on medical-record information of specific treatments for hypertension and a record/in-person examination systolic blood pressure value higher than 160 mm/Hg and/or a diastolic value above 95 mm/Hg. The recommendation from the Joint National Committee on Detection, Evaluation, and Treatment of High Blood Pressure, suggesting limit values of 140/90, was considered too inclusive for the present sample of very old individuals.
Cognitive measures
Eleven tests were used to measure cognitive functioning. The tests represent crystallized knowledge and five abilities associated with fluid abilities; that is, inductive reasoning, visuospatial ability, short-term memory, episodic memory, and speed. We constructed composite scores for crystallized knowledge, short-term memory, episodic memory, and speed for each participant by computing a weighted mean based on the observed test scores in each ability domain.
Crystallized knowledge
To measure crystallized knowledge we used two tasks from the Wechsler Adult Intelligence Scale (WAIS; Wechsler 1991): the Swedish version of the Information Task (Jonsson and Molander 1964), including questions of general knowledge, and the Verbal Meaning test, which required finding a synonym to match a target word.
Inductive reasoning
The Figure Logic task (Dureman and Salde 1959) requires the person to identify one figure out of five in a row that is different in concept from the rest.
Visuospatial ability
In the Koh's Block Design Test (Dureman and Salde 1959) the participant is presented with red and white blocks and several patterns written on cards. The task is to reproduce the pattern shown on the cards by assembling the proper blocks and arranging them to form the design shown on the card.
Short-term memory
In short-term memory, incoming information is registered and retained in accessible form for a very short period of time after the input (a few seconds). The capacity of short-term memory is roughly equivalent to the number of items that a person can reproduce accurately immediately after hearing them. To measure the passive part of short-term memory, the Digit Span Subtest (WAIS; Wechsler 1991) was used. The task is to repeat a sequence of digits in the same order as originally presented (Digit Span Forward). To measure the active part of short-term memory (i.e., working memory) we used the Digit Span Backward, in which the task is to repeat a sequence of digits in the reverse order.
Episodic memory
Episodic memory deals with everyday experiences encoded in a particular time and place. It enables conscious recollection of personal events and episodes from one's personal past (Tulving 1983). The tasks reflecting episodic memory included a Swedish Prose Recall task similar to logic memory in WAIS, Thurstone's Picture Memory (Thurstone and Thurstone 1949; recognition of drawn pictures), and Memory-in-Reality Test (Johansson 1988/ 1989; free recall of 10 objects).
Speed
We used two tasks to measure speed—a modified version of the Symbol–Digit Substitution Test (WAIS; Wechsler 1991), which required a verbal rather than a written response that the digit matched or did not match the symbol, and the Perceptual Speed Task (Dureman and Salde 1959). In the Perceptual Speed Task there is one item at the far left in a row, and the task is to detect the identical item among five alternatives on the right as fast as possible.
Data Analyses
The statistical package used for all analyses was the Statistical Package for Social Sciences (Version 10.0; SPSS 2000) for PCs. In the first set of analyses (cross-sectional), level of cognitive performance at baseline was compared across survival groups using univariate analysis of variance (ANOVA). Next, cognitive performance at baseline was examined in relation to survival. For this purpose Cox proportional hazard regression analyses (Cox 1972) were performed. Cox analyses are well suited to analyze data based on individuals that are in the study for different lengths of time because they model the hazard rate rather than mortality status as the outcome variable. As mortality status assessment was discontinued at the end of the study, right censoring was applied (censored cases are those for which the event has not yet occurred).
Two nested Cox regression models were evaluated. In both models mortality was used as the outcome variable, with varying number of covariates and cognitive domains as the predictor variables. The two models were repeated for each cognitive domain separately. Model 1 included age, gender, and education as covariates. Model 2 was extended by adding two variables based on physician evaluation: one that indicated a history of stroke (0 = no history of stroke; 1 = history of stroke) and one that indicated markers of CVD (0 = no history of CVD; 1 = history of CVD).
Parameter estimates obtained from the Cox regression models were used to assess the associations between cognitive functioning and survival. Odds ratios (OR; obtained by exponentiating the parameter estimate) are reported with a 95% confidence interval (CI). An OR of 1.00 means that there is no increased mortality risk, whereas an OR of, for example, 1.30 indicates a 30% increased mortality risk. In the Cox regression models, all measures were standardized (equal means and standard deviations across tasks) prior to analysis to facilitate comparisons of odds ratios across tasks. Also, all cognitive scales were reversed such that high scores indicated low performance.
In the second set of analyses (longitudinal), cognitive data from T1 through T3 were analyzed to examine if the three survival groups showed the same pattern of change across time. For this purpose, mixed ANOVAs were conducted for each cognitive task using a repeated measurements design. The group composed of individuals who died between T1 and T2 and were assessed at only one occasion was excluded from the longitudinal analyses. In the first set of longitudinal analyses, data from T1 and T2 are compared across the three survival groups. In the second set of analyses, data from T1 through T3 are compared across the two groups that survived all three waves.
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Results |
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2 = .040. A Tukey's post hoc test showed that the group with the longest survival time was significantly younger than the two groups with the shorter survival time (ps < .05). There was a difference in gender distribution, 
2(3, N = 466) = 10.98, p < .05, reflecting more women among the survivors as compared with the group who died between T3 and the end of the study. The groups were similar with respect to education. Concerning the diseases, fewer survivors than decedents experienced a stroke, 2(3, N = 466) = 43.39, p < .01, but there were no differences in TIA. Fewer survivors experienced myocardial infarction than decedents had, 2(3, N = 466) = 38.95, p < .001. Congestive heart failure was less frequent among the survivors as compared with the groups that died between T2 and T3 and between T3 and the end of the study, respectively, 2(3, N = 466) = 29.27, p < .001. Angina pectoris was more frequent among the group who died between T3 and the end of the study as compared with the survivors, 2(3, N = 466) = 8.62, p < .05. No differences were seen in arterial hypertension across groups.
Cognitive Performance at Baseline
Baseline performance across groups is portrayed in Fig. 1 as transformed scores (T scores), to facilitate comparisons across domains. Univariate ANOVAs on the cognitive data showed significant differences across survival groups in all cognitive domains (for statistics see Fig. 1 caption). Survivors showed superior performance as compared with the group with the shortest survival time.
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Model 2.
For all analyses, mortality risk was increased by stroke (OR between 2.09 and 2.57) and by markers of CVD (OR between 2.31 and 2.72). However, after adjusting for these covariates, we found that the association between mortality and cognitive performance was reduced to a nonsignificant level for crystallized knowledge and inductive reasoning (see Table 2 ).
Longitudinal Comparisons
The outcome of the longitudinal analyses, conducted to examine whether there is an accelerated decline prior to death, is presented in Table 3 , and the patterns are graphically shown in Fig. 2. The first set of longitudinal analyses compared cognitive data from T1 and T2 across the three survival groups. A significant decline between T1 and T2 was found in crystallized knowledge, visuospatial ability, episodic memory, and speed. There were no significant interaction effects between survival group and time in inductive reasoning, visuospatial ability, short-term memory, and speed. This indicates that those who deceased did not decline more rapidly across a 2-year period than those who survived. There was an exception to this in crystallized knowledge and episodic memory. In these two domains, individuals with a shorter survival (deceased between T2 and T3) showed a steeper decline compared with those who survived longer.
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Discussion |
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Previous findings from studies on the relationship between cognitive performance and survival show that level of cognitive performance at one point in time is related to distance to death. Our findings confirm this and indicate that all cognitive abilities are affected, with the exception of short-term memory. Thus, we did not find support for the hypothesis of White and Cunningham 1988, that mortality-related effects are only present in abilities that are typically unaffected by aging (crystallized knowledge). Moreover, we found that the cognitive abilities that were the strongest predictors of mortality (in terms of regression coefficients) were the fluid abilities (i.e., visuospatial ability, episodic memory, and speed). The same pattern was found in the study of Maier and Smith 1999, who concluded that the mortality-related effects associated with intellectual functioning were pervasive rather than ability specific.
The next question of interest concerned the biobehavioral mechanisms in the relationship between cognitive functioning and survival. As has already been noted, a reasonable explanation is systematic decline due to changes associated with compromised physical health. Cerebrovascular disease is a prime candidate because it is highly prevalent in old age and also related to cognitive decline (e.g., Carmelli, Swan, LaRue, and Eslinger 1997; Elias et al. 1990) and mortality. Furthermore, as CVD is the leading cause of death (American Heart Association 1998), and is known to elevate the risk for stroke (a marker of cerebrovascular disease), it is reasonable to consider the impact of both of these conditions. In the present study, stroke and two markers of CVD (myocardial infarction and congestive heart failure) were the conditions that most clearly differed between the survivors and the deceased. Thus, it was of special interest to examine the effect of these diseases. After adjusting for history of stroke and markers of CVD, we found that crystallized knowledge and inductive reasoning were no longer associated with mortality. These findings suggest that CVD and cerebrovascular disease significantly affect the cognition–survival relationship. To our knowledge, there are no studies that have systematically examined the effect of CVD or cerebrovascular disease on the cognition–survival association. Thus, at this stage it can only be speculated how these factors are related. The neurobiological mechanisms between CVD and cognitive functioning are most likely related to increased risk for stroke, as well as diminished cardiac output and decreased cerebral perfusion, with the cerebral consequences being compromised oxygen support.
The finding that stroke and markers of CVD are significant factors in the cognition–survival relationship for selected cognitive tasks remains to be further analyzed. Interestingly, a cognitive domain that largely remains unaffected in normal aging (e.g., crystallized knowledge) was also affected by vascular diseases. Performance decline in tasks reflecting crystallized knowledge may therefore be more indicative of secondary aging processes, like compromised cerebrovascular supply. Further, certain cognitive abilities (i.e., visuospatial ability, episodic memory, and speed) are affected by other factors as well. One suggestion, out of a number of potential mediating factors, relates to incipient dementia disorders. It is interesting to note that episodic memory functioning is the most salient predictor of incident dementia (e.g., Howieson et al. 1997; Jacobs et al. 1995); measures of speeded psychomotor performance have also been shown to predict incident dementia (Masur, Sliwinski, Lipton, Blau, and Crystal 1994). Although our sample comprised only nondemented individuals, it is not unlikely that individuals in a preclinical phase of Alzheimer's disease were included, given the long preclinical phase of this disease (e.g., Linn et al. 1995; Small, Herlitz, Fratiglioni, Almkvist, and Backman 1997).
After having found that level of cognitive performance at baseline was related to impending death, our next question of interest was to explore whether there was an accelerated decline among decedents as compared with the survivors. Results based on two measurement occasions across a 2-year period and three measurement points across a 4-year period showed decline in all domains with the exception of inductive reasoning and short-term memory. Concerning the question of differential rate in decline between survival groups, an accelerated decline in the group with the shortest survival time was observed only across a 2-year interval in crystallized knowledge and in episodic memory. This was, however, not seen in the other ability domains and across the 4-year interval. In a study based on change over a 7-year period, Bosworth, Schaie, Willis, and Siegler 1999 reported no accelerated decline. However, two studies have reported steeper decline among decedents (e.g., Deeg et al. 1990; Johansson and Berg 1989). Thus, the question still remains open as to whether a greater decline is to be expected closer to death.
Bringing together the results from the cross-sectional and the longitudinal analyses, our findings suggest that cognitive performance in late life is associated with proximity to death and that this relationship can be partially accounted for by compromised cardiovascular and cerebrovascular functioning. Thus, we conclude that terminal decline is influenced by disease-related changes that affect cognitive functioning. The longitudinal analyses showed interaction effects for survival group and decline in only two cognitive abilities. Thus, we find only weak support for a differential decline in relation to impending death. Also, the disease-related effects on the cognition–survival association make it unlikely that all individuals will experience a marked decline before death. Closer inspection of cause of death and illnesses in late life is required to establish a more solid basis for this cognitive phenomenon.
There are several limitations in our study that need to be addressed. In terms of statistical power, there appears to be no problem in the cross-sectional analyses in which we found differences at baseline given the moderate effect sizes and adequate sample size. Also, in the longitudinal analyses, we have sufficient power to detect differences across time. However, the power to detect interaction effects is typically much lower, such as those between survival group and time. It is often stated that with greater sample size, effects could have been detected. However, when effect size is very small, as was the case in the interaction analyses, sample size is not the only critical factor. A design with more measurement occasions and more individuals would of course be beneficial for examining potential interactions between time and survival groups. However, the fact that we did not find more marked decline and differences in decline rates between survivors and decedents (see Fig. 2) may also be related to the fact that our sample excluded individuals with dementia. This exclusion was, however, deliberate so we could examine other common health-related factors in the cognition–survival relationship.
To summarize, the present study indicates that there are differences in cognitive performance between those who die earlier and those who survive longer. Moreover, at least some of these differences can be ascribed to CVD and cerebrovascular disease. Our findings of few interaction effects between performance change and subsequent survival provide weak support for an accelerated decline closer to death. Future studies should address the underlying mechanisms in the cognition–mortality relationship by using longitudinal designs with closer measurement occasions and extensive health-related measurements.
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Acknowledgments |
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The OCTO Twin Study is an ongoing longitudinal study conducted at the Institute of Gerontology, University College of Health Sciences in Jönköping, Sweden, in collaboration with the Center for Developmental and Health Genetics at the Pennsylvania State University, Philadelphia, Pennsylvania, and the Department of Medical Epidemiology at the Karolinska Institute in Stockholm, Sweden.
We thank registered nurses Lene Ahlbäck (1991–present), Agneta Carlholt (1993–present), Gunilla Hjalmarsson (1991–1993), Eva Georgsson (1993–1995), and Anna-Lena Wetterholm (1993–1994), who traveled throughout the country and examined the participants.
Received for publication June 27, 2000. Accepted for publication July 9, 2001.
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