Abstract
Objectives
Decision making, key to successful aging, has implications for financial success, physical health, and well-being. Decision making also has implications for age-related cognitive decline and dementia. While poor decision making has been linked with increased risk of mortality, less is known regarding its associations with chronic disease indicators. We investigated the associations of decision making with blood pressure values (i.e., systolic blood pressure, systolic BP, mean arterial pressure, MAP, and pulse pressure, PP, separately) in a community-based cohort study of aging.
Methods
Participants were 908 non-demented older adults (age ~81 years;75% women) from the Rush Memory and Aging Project. Decision making was measured using questions designed to simulate materials used in financial and healthcare settings in the real world and yielded a total score and domain-specific health and financial decision making scores. Two seated and one standing BP measurement were taken with all three contributing to average systolic BP, MAP i.e., [systolic BP + (2*diastolic BP)]/3, and PP, i.e., systolic - diastolic BP. Participants were queried about hypertension status and anti-hypertension medications were visually inspected and coded. Participants also underwent medical history and cognitive assessments.
Results
In separate multivariable linear regression models, total decision making scores were inversely associated with systolic BP, MAP, and PP after adjusting for age, sex, education, anti-hypertension medication use, diabetes, and cumulative cardiovascular disease burden (p-values=0.03). Decision making remained associated with these BP values after additional adjustment for global cognition.
Conclusions
Poorer decision making is associated with higher BP values in non-demented older adults.
Keywords: decision making, blood pressure, mean arterial pressure, pulse pressure, aging
Introduction
Decision making, particularly in health and financial realms, is key to successful aging. Advantageous decisions have far reaching implications including financial success, physical health, and well-being, while poor decision making skills can lead to financial losses, health risks, and long-terpm stressors [1–3]. We and others have reported that poor decision making abilities are associated with alterations in cognition and dementia [4–6]. Additionally, poor decision making skills have also been linked with incident adverse cognitive outcomes [7] and increased risk of mortality [8]. Less is known regarding the associations between decision making and chronic disease indicators, e.g., cardiovascular risk factor burden, that have been linked with other decision making related abilities [9] as well as cognitive decline, dementia [10], and death [11].
A main driver of cardiovascular risk factor burden is elevated blood pressure, as evidenced by the fact that in the United States alone, the prevalence of hypertension (as defined as systolic blood pressure greater than or equal to 140 mm/Hg, or diastolic blood pressure greater than or equal to 90 mm/Hg, or current medication use to lower high blood pressure) in adults 60 years of age and older is over 60% [12]. Furthermore, according to the World Health Organization (WHO), the risk of cardiovascular disease doubles for each increment of 20/10 mmHg of blood pressure, starting as low as 11/75 mm/Hg in some age groups [13]. Thus, this study examines the associations between decision making and objectively measured blood pressure values of systolic blood pressure (systolic BP), mean arterial pressure (MAP), and pulse pressure (PP) in a community-based cohort of older adults with and without hypertension. We included MAP and PP as additional blood pressure values given they considers both systolic and diastolic blood pressure, and have been related to other blood pressure indicators including cerebral blood flow [14, 15]. We hypothesize that poorer decision making is associated with higher levels of systolic BP and MAP after controlling for age and other relevant confounders. Furthermore, despite evidence that even subtle age-related changes in cognition have detrimental effects on decision making [4, 16], we hypothesize that the associations between decision making and blood pressure values will withstand additional adjustments for global cognitive functioning.
Methods
Participants
Individuals included in this research were participants from the Rush Memory and Aging Project (1997-present), an ongoing longitudinal clinical-pathologic cohort study of aging [17–19]. The Institutional Review Board of Rush University Medical Center approved the study and participants gave written informed consent in accordance with the Declaration of Helsinki. Participants were enrolled without known dementia and underwent annual clinical evaluations.
The Rush Memory and Aging Project started in 1997 and a decision making sub-study was introduced in 2010 [20]. Of the 1,939 participants enrolled, 1,146 participants were alive and active in the Rush Memory and Aging Project and of those, 1,056 had available decision making data. We excluded 62 participants who had dementia at the time of their decision making sub-study assessment. Dementia was diagnosed by experienced clinicians based on a uniform structured clinical evaluation [18] and per the criteria of the National Institute of Neurologic and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association [21]. Of the remaining 994 non-demented participants, 908 (91%) participants also had complete data on blood pressure values that coincided with their study visit and were included in these analyses.
Assessment of Decision Making
Described in detail elsewhere [20], our assessment of health and financial decision making consisted of a 12-item questionnaire [4, 8, 16, 22]. Participants are presented with a healthcare module that includes 6 questions of increasing complexity about HMO plans and a financial module that includes 6 questions of increasing complexity about mutual funds. This tool was specifically designed to simulate materials used in financial and healthcare settings in the real world. Participants were provided with preferences and questions measured decisions that reflect understanding of information presented. Complexity was increased by increasing the number of options provided. For example, one of the simple problems involves information on three mutual funds, including the gross annual return, account management fee, minimum investment, and years of activity, and asks respondents to select the fund with the smallest account management fee. A complex problem presents similar information but about seven mutual funds and asks participants to select the most appropriate fund given pre-specified preferences. The total score is the sum of items answered correctly (range = 0–12) with sub-scale scores for health and financial decision making ranging from 0 to 6. This measure has been shown to have adequate psychometric properties including high inter-rater reliability and short-term temporal stability [16].
Blood Pressure Assessment and Value Determinations
As previously described [23], blood pressure (BP) was measured in the right arm with a mercury sphygmomanometer by trained research assistants. Two seated BP readings were taken at 30 second intervals following a 5-minute rest period; 1 minute after being requested to stand, a third, standing BP was taken. Systolic and diastolic BP was calculated separately by averaging all three readings. In addition to systolic BP, our additional BP indices was mean arterial pressure and pulse pressure.
MAP is an index of the steady component or average of pressure in the arteries during a single cardiac cycle. While it can only be measured directly by invasive monitoring, it can be approximated using the following formula: [systolic BP + (2*diastolic BP)]/3. MAP was of particular interest because it is considered a critical hemodynamic factor that is a better marker of perfusion to vital organs than systolic BP [24]. For example, elevations in MAP above the normal range cut-point of 100–110 mmHg will, over time, contribute to increased oxygen demand and hence, arterial pressure, subsequent vascular injury and end organ damage including stroke [25]. Alternatively, MAP that falls below the normal range cut-point of 60–65 mmHg for a considerable time period has the potential to cause vital organ ischemia and shock.
PP is an index of the discrepancy between the maximum pressure applied during a heartbeat (i.e., systolic BP) and the pressure in the arteries between heartbeats (i.e., diastolic BP). Thus, PP is the difference between systolic and diastolic blood pressure, i.e., systolic BP minus diastolic BP. PP, thought to represent arterial stiffness, increases with increasing age [26], and has been associated with cognitive impairment [27] for meta-analytic review). Furthermore, older adults often experience PP above the normal range of 40–60 mm/Hg, and such age-related elevations have often been associated with increased risk of heart attack or stroke. In contrast, PP less than 40 mm/Hg may indicate decreased cardiac output and is often observed in individuals with heart failure.
Covariates
In addition to age, sex and years of education, we adjusted for the variables outlined below. Given that greater than 90% of participants are insured through Medicare, we did not adjust for insurance status in analyses.
Anti-hypertension medication use
Participants supplied all medications prescribed by a doctor, vitamins, supplements, and over-the-counter remedies/medicines taken in the 2 weeks prior to the study visit. Visual inspection of all containers allowed for medication documentation. Medications were coded using the Medi-Span Drug Data Base system [28] including prescription medications for hypertension. An individual was deemed positive for anti-hypertension medication use if they possessed prescription medication for hypertension. We chose to adjust for this factor given that adherence to anti-hypertensive medications and subsequent BP presumed to follow may dampen the associations between decision making and blood pressure values in individuals with hypertension.
Diabetes status
Participants were queried about diabetes status and supplied all medications prescribed by a doctor and taken in the 2 weeks prior to the study visit; they also provided a blood specimen using standard procedures that was analyzed by Quest Diagnostics. Using the criteria set forth by the America Diabetes Association [29], diabetes was defined by at least one of the following glycemic values: glucose of >126 mg/dL or hemoglobin A1C >6.5%. Diabetes was also defined based on participant’s self-report of taking medication for diabetes including prescription medications for diabetes (insulin and non-insulin related) that was verified by medication review and coding using the Medi-Span Drug Data Base system [28] at the study visit. Diabetes is often co-morbid with hypertension [30] and has been associated with cognitive impairment in MAP participants [31] as well as other studies ([32 ] for review). Thus, we adjusted for this factor in all analyses.
Cumulative vascular disease burden
Vascular disease burden, a known contributor to cognitive impairment (e.g., [33, 34]) was derived using self-report questions probing the presence or absence of claudication symptoms, stroke, heart conditions, or congestive heart failure. In addition to self-report, evaluation of the presence or absence of stroke was also based on neurological exam and interview from which the physician would render a diagnosis of stroke. As previously described [35], each item was given a value of 0 or 1 with the cumulative vascular burden score ranging from 0 to 4.
Global Cognitive Functioning
All participants underwent an annual cognitive evaluation detailed elsewhere [17–19]. Briefly, cognitive tests were administered in an identical fashion at annual evaluations. A global composite score was created by converting raw scores on the 19 cognitive tests to standard (z) scores using the mean and standard deviation from the baseline evaluation. A person’s standard scores across all tests were then averaged to yield a single composite score summarizing level of global cognitive function. Evidence suggests that even subtle age-related changes in cognition have detrimental effects on decision making [4, 16], thus, we added our metric of global cognition as an additional covariate in fully-adjusted models as outlined below.
Statistical Analyses
We tested bivariate associations between key participant characteristics and blood pressure values, i.e., systolic blood pressure, mean arterial pressure or MAP, and pulse pressure or PP, using correlations or independent sample t-tests as appropriate. We used linear regression models to examine the associations of decision making – separately for the total, health, and financial decision making scores – with blood pressure values (separately for systolic blood pressure, MAP, and PP). In these models, each blood pressure value was the continuous outcome. All models included terms for age, sex, and education (core model). Sequential models were then implemented first adding anti-hypertension medication use (i.e., medication use verified at study visit), diabetes status (presence/absence), and cumulative vascular disease burden (Model 1), then further adjusting for global cognitive functioning (fully-adjusted Model 2). All analyses were programed using SAS/STAT software, Version 9.4 of the SAS System for Linux (SAS Institute, Cary, NC) and significance was set using p<0.05.
Results
Participants were approximately 80 years of age with approximately 15 years of education; 75% of participants were female. The mean total decision making score was 7.8 out of 12. Average BP was 133/77 mmHg, average MAP and PP were 96 and 56 mmHg, respectively. Approximately 70% of our sample was taking medication for hypertension, and 20% met criteria for diabetes. Other covariates are described in Table 1.
Table 1.
Participant Characteristics, Blood Pressure Values, and Decision Making
| Values* | Minimum, Maximum | |
|---|---|---|
| Participant Characteristics | ||
| Age (years) | 81.1±7.4 | 58.8, 100.7 |
| Sex (female), n, % | 687, 75.6 | |
| Education (years) | 15.4±3.0 | 5, 28 |
| Diabetes Status (met criteria), n, % | 181, 19.9 | |
| Cumulative Vascular Disease Burden | 0.38±0.68 | 0, 4 |
| Global Cognitive Functioning | 0.23±0.51 | −1.56, 1.63 |
| Blood Pressure Values | ||
| Systolic Blood Pressure (mmHg) | 133.2±19.1 | 84.0, 215.5 |
| Diastolic Blood Pressure (mmHg) | 77.3±11.3 | 50.0, 122.5 |
| Mean Arterial Pressure (mmHg) | 95.9±12.8 | 62, 153.5 |
| Pulse Pressure (mmHg) | 55.9±13.7 | 23.5, 106.0 |
| Anti-Hypertensive Medication (use), n,% | 634, 69.8 | |
| Decision Making | ||
| Total Decision Making | 7.8±2.6 | 0, 12 |
| Health Decision Making | 4.1±1.5 | 0, 6 |
| Financial Decision Making | 3.6±1.4 | 0, 6 |
Data indicates mean±standard deviation unless otherwise noted.
Note: Total decision making is the average of health and financial decision making scores.
Bivariate correlations between key participant characteristics and blood pressure values (Table 2) revealed that age correlated with positively with systolic BP and PP, and negatively with MAP (p-values≤0.03), but education only correlated with systolic BP and PP (negatively) (p≤0.01). Cumulative vascular disease burden correlated with PP only (p=0.007). Independent sample t-tests indicated that systolic BP and PP did not differ by sex, but women (96.5±13.1 mmHg) had higher MAP than men (93.9±11.9 mg/dL; p=0.008). All three decision making measures were negatively correlated with all three blood pressure values (p-values≤0.04). Global cognition negatively correlated with systolic BP and PP only (p=0.01).
Table 2.
Correlation of Key Participant Characteristics and Blood Pressure Values
| Systolic Blood Pressure (mmHg) | Mean Arterial Pressure (mmHg) | Pulse Pressure (mmHg) | |
|---|---|---|---|
| Age (years) | 0.07 (0.03) | −0.08 (0.01) | 0.27 (<0.0001) |
| Education (years) | −0.08 (0.01) | −0.04 (0.21) | −0.11 (0.0005) |
| Cumulative Vascular Disease Burden | −0.002 (0.93) | −0.05 (0.07) | 0.08 (0.007) |
| Global Cognitive Functioning | −0.10 (0.001) | −0.04 (0.22) | −0.15 (<0.0001) |
| Total Decision Making | −0.13 (<0.0001) | −0.08 (0.01) | −0.16 (<0.0001) |
| Health Decision Making | −0.12 (0.0002) | −0.06 (0.04) | −0.16 (<0.0001) |
| Financial Decision Making | −0.11 (0.0004) | −0.07 (0.02) | −0.14 (<0.0001) |
| Systolic Blood Pressure | -- | 0.91 (<0.0001) | 0.81 (<0.0001) |
| Mean Arterial Pressure | 0.50 (<0.0001) | ||
Note: Values are Pearson correlation coefficient (p-value). Bolded values met significance set at p<0.05.
Association of Decision Making with Blood Pressure Values
Total Score
We first examined the association of the total decision making score with systolic BP, MAP, and PP (separately). In the core regression model adjusted for age, sex, and education, there was an inverse association of total decision making with systolic BP (estimate = −0.76, SE = 0.26, p=0.004) as well as MAP (estimate = −0.48, SE = 0.17, p=0.006), separately. These association remained after additional adjustments for anti-hypertension medication use, diabetes status, cumulative vascular disease burden (Model 1: systolic BP estimate = −0.76, SE = 0.26, p=0.003; MAP estimate = −0.50, SE = 0.17, p=0.005), as well as global cognitive functioning (both p-values=0.03). Likewise, in the core regression model adjusted for age, sex, and education, there was an association of lower total decision making scores with higher PP (estimate = −0.41, SE = 0.18, p=0.02) that remained after additional adjustments for anti-hypertension medication use, diabetes status, cumulative vascular disease burden (Model 1: estimate = −0.39, SE = 0.18, p=0.03). It was modestly attenuated following additional adjustment for global cognitive functioning (p=0.09). Specific estimates for fully-adjusted Model 2 are outlined in Table 3.
Table 3.
Associations of Total Decision Making with Blood Pressure Values
| Systolic Blood Pressure (mmHg) |
Mean Arterial Pressure (mmHg) |
Pulse Pressure (mmHg) |
||||
|---|---|---|---|---|---|---|
| Adjusted for: | Core | Fully adjusted | Core | Fully adjusted | Core | Fully adjusted |
| Age | 0.09 (0.08, p=0.29) | 0.09 (0.09, p<0.00001) | −0.19 (0.06, p=0.001) | −0.18 (0.06, p=0.002) | 0.43 (0.06, p<0001) | 0.41 (0.06, p<0001) |
| Sex | −0.50 (1.50, p=0.73) | −0.75 (1.53, p=0.62) | −2.04 (1.01, p=0.04) | −2.12 (1.02, p=0.04) | 2.35 (1.04, p=0.02) | 2.05 (1.06, p=0.052) |
| Education | −0.24 (0.22, p=0.28) | −0.18 (0.23, p=0.43) | −0.01 (0.15, p=0.92) | 0.005 (0.15, p=0.97) | −0.34 (0.15, p=0.02) | −0.28 (0.16, p=0.08) |
| Total Decision Making | −0.76 (0.26, p=0.004) | −0.65 (0.30, p=0.03) | −0.48 (0.17, p=0.006) | −0.42 (0.20, p=0.03) | −0.41 (0.18, p=0.02) | −0.34 (0.21, p=0.09) |
| Anti-Hypertensive Medication Use | 1.26 (1.43, p=0.37) | 0.42 (0.95, p=0.66) | 1.27 (0.98, p=0.20) | |||
| Diabetes Status | 1.49 (1.61, p=0.35) | 0.32 (1.08, p=0.76) | 1.7 (1.11, p=0.11) | |||
| Cumulative Vascular Disease Burden | −0.99 (0.97, p=0.30) | −0.97 (0.65, p=0.13) | −0.03 (0.67, p=0.95) | |||
| Global Cognition | −1.17 (1.56, p=0.45) | 0.81 (1.04, p=0.43) | −0.53 (1.08, p=0.61) | |||
Note: Values are unstandardized coefficient (SE, p-value) from linear regression models with significance set at p<0.05.
Health and Financial Sub-scores
Next, we examined the associations of the health and financial decision making sub-scores, separately, with blood pressure values to determine if there was a particular domain-specific aspect of decision making that accounted for the results outlined above. In the core regression model adjusted for age, sex, and education, there was an inverse association of health decision making with systolic BP (estimate = −1.14, SE = 0.44, p=0.009) and MAP (estimate = −0.71, SE = 0.30, p=0.01), separately. The association of lower health decision making scores and higher systolic BP remained significant after adjustments for anti-hypertension medication use, diabetes status, and cumulative vascular disease burden (Model 1: estimate = −1.15, SE = 0.44, p=0.009). The same was true for the association of lower decision making scores and higher MAP (Model 1: estimate = −0.74, SE = 0.30, p=0.01). These associations were modestly attenuated following additional adjustment for global cognitive functioning (fully-adjusted Model 2: systolic BP estimate = −0.94, SE = 0.50, p=0.055; MAP estimate = −0.60, SE = 0.33, p=0.07). There was an inverse association of lower health decision making scores with higher PP in the core model adjusted for age, sex, and education, estimate = −0.64, SE = 0.30, p=0.03, that remained significant after adjustments for anti-hypertension medication use, diabetes status, and cumulative vascular disease burden (Model 1: estimate = −0.62, SE = 0.30, p=0.04). The association between health decision making and PP did not reach significance in fully adjusted Model 2 (estimate = −0.52, SE = 0.34, p=0.12).
There was an association of lower financial decision making scores with higher systolic BP after adjusting for age, sex, and education (estimate = −1.22, SE = 0.50, p=0.01) that remained after Model 1 adjustments for anti-hypertension medication use, diabetes status, and cumulative vascular disease burden (estimate = −1.22, SE = 0.50, p=0.01); it was modestly attenuated following additional adjustment for global cognitive functioning (estimate = −0.97, SE = 0.54, p=0.07). The association of financial decision making with MAP followed a similar pattern of significance in initial age, sex, and education adjusted (estimate = −0.80, SE = 0.33, p=0.01) and additionally adjusted Model 1 (estimate = −0.82, SE = 0.33, p=0.01); results were modestly attenuated following additional adjustment for global cognitive functioning in Model 2 (estimate = −0.65, SE = 0.36, p=0.07). In contrast, financial decision making was not associated with PP, regardless of adjustments (core Model: estimate = −0.62, SE 0.34, p=0.07; Model 1: estimate = −0.67, SE = 0.34, p=0.07; Model 2: estimate = −0.47, SE 0.37, p=0.20).
Interaction Between Decision Making and Hypertension on Blood Pressure Values
Given that our sample consisted of participants with and without hypertension, we investigated whether hypertension status modified our aforementioned results. Thus, we added the interaction term of decision making and hypertension to fully-adjusted models outlined above. There were no significant interactions of decision making (total, health, or financial, separately) and hypertension with either systolic BP (p-values≥0.77), MAP (p-values≥0.55), or PP (p-values≥0.45).
Discussion
We investigated the association of decision making with blood pressure values in a community-based study of more than 900 non-demented older adults with and without hypertension. Poorer decision making was associated with elevations in systolic BP, MAP, and PP, respectively. Furthermore, these relationships were not modified by hypertension status. Thus, results suggest an important link between decision making and blood pressure values in old age, even among individuals who are not being monitored and/or treated for hypertension-related issues.
These results build upon the literature on decision making and adverse health outcomes in several ways. First, while poorer decision making has been linked to increased risk of mortality [8], to our knowledge, this is the first study that suggests a possible chronic health-related driver of this association, i.e., elevations in blood pressure values. Second, although abilities that are critical for decision making including health literacy [2] have been associated with elevations in blood pressure [36], our results extends this work to examine decision making directly. Taken together, our results suggest that poorer decision making not only has negative implications for end of life [8], it is also associated with other adverse health outcomes of a more chronic nature.
In addition to the total score on our measure of decision making being associated with blood pressure values, domain-specific health and financial decision making were significantly associated with systolic BP, MAP, and PP. Domain specific health decision making scores were associated with all three blood pressure values with systolic BP and MAP results modestly attenuated following final adjustment for global cognition; PP results were no longer significant in the fully adjusted model. Like results using the health decision making score, systolic BP and MAP results for domain-specific financial decision making were modestly attenuated following final adjustment for global cognition; financial decision making was not associated with PP, regardless of adjustment. While for the health and financial decision making analyses involving systolic BP and MAP, the fully-adjusted models were short of significance, the results were not materially different suggesting that the restricted range of scores on the domain-specific indices limited our power to detect a robust signal in their associations with these two blood pressure values.
While the basis of the association between decision making and blood pressure values in older adults is unknown, the cross-sectional nature of our data demands that we consider both directions of our resulting associations. For example, poorer decision making may lead to negative health behaviors across the lifespan which may lead to more adverse health status in older age. Thus, older persons who historically made poor decisions may have engaged in behaviors associated with elevations in blood pressure values including smoking, sedentary behavior, and/or poorer eating habits, chosen not to seek medical attention for signs of elevated blood pressure, and/or decided not to fill or adhere to anti-hypertension prescriptions from their doctor. Alternatively, it is also equally feasible that chronic elevations in blood pressure values including systolic BP, MAP, and PP may have promoted alterations to end organs including brain [25] in the form of white matter hyperintensities [37] and/or micro-infarcts [38] that contributed to impairments in complex thinking and behavioral processes that underlie decision making. Thus, blood pressure values could affect decision making as an earlier marker of cognitive decline. Future longitudinal studies of decision making and these blood pressure values as well as incident hypertension are needed to examine temporal associations and the underlying mechanisms linking them.
This study has a number of strengths including a well-validated measure of health and financial decision making. Likewise, by incorporating systolic BP, MAP, and PP, all of which have been associated with numerous other cardiovascular-related indices [14, 39], our results suggest that decision making is related to multiple blood pressure values in older adults. Additionally, we adjusted for multiple potential confounders including other chronic cardiovascular diseases including diabetes, and anti-hypertension medication use. Limitations of this study included the cross-sectional nature of the analyses. Additionally, 70% of participants were found to have hypertension as diagnosed using the latest American Heart Association criteria. While this is consistent with the fact that older adults have the greatest prevalence of hypertension [12, 40], the high prevalence in our sample meant we were unable to perform sensitivity analyses with and without participants with hypertension. It should be noted, however, that controlling for anti-hypertension medication – one criteria by which to diagnose hypertension – did not affect our results. Our participants were primarily non-Latino white and highly educated, thus, our results may lack generalizability; however, we are actively collecting similar decision making data in non-Latino blacks. Even with these limitations, our findings associating decision making with systolic BP, MAP, and PP, separately, provide support for promoting decision making capacity in older adults across the spectrum of health and disease and suggest that better decision making may help facilitate regulation of blood pressure values.
Acknowledgements
The authors thank the participants in the Rush Memory and Aging Project and the staff of the Rush Alzheimer’s Disease Center. More information regarding obtaining data from the Rush Memory and Aging Project for research use can be found at the RADC Research Resource Sharing Hub (www.radc.rush.edu).
The study was supported by National Institute on Aging (grant number R01AG17917, R01AG34374 and R01AG33678).
Footnotes
Competing Interest: None declared.
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