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THE ASSOCIATION OF HEART/VASCULAR AGING WITH MILD COGNITIVE IMPAIRMENT IN A RURAL MULTIETHNIC COHORT: THE PROJECT FRONTIER STUDY

 

D. Appiah1, G. Ashworth2,3, A. Boles3, N. Nair4

 

1. Department of Public Health, Texas Tech University Health Sciences Center, Lubbock, TX, USA; 2. Department of Pharmacology and Neuroscience, Texas Tech University Health Sciences Center, Lubbock, TX, USA; 3. Garrison Institute on Aging. Texas Tech University Health Sciences Center, Lubbock, TX, USA; 4. School of Medicine. Texas Tech University Health Sciences Center. Lubbock, TX., USA

Corresponding Author: Duke Appiah, Department of Public Health, Texas Tech University Health Sciences Center, 3601 4th Street, STOP 9430 Lubbock, TX 79430. Phone: 806-743-9472. Email: duke.appiah@ttuhsc.edu

J Prev Alz Dis 2022;
Published online January 19, 2022, http://dx.doi.org/10.14283/jpad.2022.15

 


Abstract

BACKGROUND: Cardiovascular disease (CVD) and Alzheimer’s disease and related dementias (ADRD) disproportionately affect rural communities. Identifying strategies to effectively communicate CVD risk to prevent these conditions remains a high priority.
OBJECTIVE: We assessed the relation between predicted heart/vascular age (PHA), an easily communicated metric of CVD risk, and mild cognitive impairment (MCI), an early manifestation of ADRD.
DESIGN, SETTING, PARTICIPANTS: Data were from 967 rural West Texas residents aged ≥40 years without CVD at baseline (2009-2012) enrolled in Project FRONTIER, an ongoing, multi-ethnic cohort study on cognitive aging.
MEASUREMENTS: MCI was diagnosed using the standardized consensus review criteria. PHA was calculated using the Framingham CVD risk equation. High excess PHA (HEPHA) was defined as the difference between PHA and chronological age >5 years. Logistic regression models were used to calculate odds ratios (OR) and 95% confidence intervals (CI).
RESULTS: At baseline, the mean age of participants (70% women and 64% Hispanics) was 55 years. Almost 13% had MCI and 65% had HEPHA. After adjusting for socio-demographic and health factors, HEPHA was positively associated with MCI (OR=2.98; 95%CI: 1.72-5.15). Among participants without MCI at baseline who returned for follow-up exam after three years (n=238), a three-year negative change in PHA was seemingly associated with reduced odds for MCI (OR=0.98; 95%CI: 0.96-1.01).
CONCLUSIONS: In this study, PHA was positively associated with MCI, with improvement in CVD risk profile seemingly related to reduced odds for MCI. PHA may provide a low-cost means of communicating CVD risk in rural settings to prevent both CVD and ADRD.

Key words: Cognitive impairment, cognition, cardiovascular disease, epidemiology, rural health, prevention.


 

Introduction

Several lines of evidence confirm that Alzheimer’s disease-related dementias (ADRD share several pathophysiological pathways with cardiovascular disease (CVD), such as inflammation, increased oxidative stress, and changes to nitric oxide bioavailability (1). Thus, metabolic and vascular damage may play important roles in the etiology of cognitive impairment leading to dementia (1-4). Accordingly, up to a third of all ADRD cases are attributed to adverse CVD risk factors (1), suggesting that CVD prevention efforts may also be effective in slowing preclinical manifestations of ADRD such as mild cognitive impairment (MCI).
Rural populations are disproportionately affected by chronic diseases, such as CVD and ADRD (5, 6). Additionally, the prevalence of low socioeconomic status, cigarette smoking, hypertension, obesity, leisure-time physical inactivity, and low health literacy is higher among rural than urban populations (5, 7). In rural communities, most population-wide CVD educational prevention strategies targeted at improving heart health knowledge and habits do not consistently achieve success, partly due to the low education and health literacy in these communities, as well as limited access to health education/outreach programs (8-10). With more than 20% of rural populations in the U.S. already over the age of 65 years and bearing the greatest burden of CVD and ADRD risk factors (6) coupled with limited access to healthcare, there is an urgent need to identify and implement cost-effective evidence-based population-wide interventions that have the potential to advance cardiovascular health equity and in turn reduce ADRD, among rural communities.
The inability to understand the concept of risk has been identified as a major reason for the low rate of adoption of healthy behavioral habits among individuals (11, 12), especially those in rural and resource–limited settings (13, 14). An intuitive means of quantifying and communicating CVD risk is the concept of predicted heart/vascular age (PHA), which is the predicted age of a person’s cardiovascular system based on their CVD risk factor profile (15). The comparison of PHA to chronological age has been shown to provide a simple, meaningful, and easily understood measure of CVD risk (16-18), and it elicits more emotional impact to adopt healthy lifestyles especially among younger adults at higher levels of CVD risk than the 10-year absolute risk (12, 19).
Although the PHA is an intuitive and impactful metric of CVD risk in lower-resourced settings, no studies to date have investigated the relation of PHA with MCI in rural settings, especially among ethnic minority populations like Hispanics who bear a greater burden of ADRD among all ethnic groups in the U.S. (20). The first step in informing public health policy makers to adopting a low-cost measure to prevent MCI is to show that a low-cost tool such as predicted heart/vascular age (PHA) can predict MCI. This study aimed to investigate the association of PHA with MCI and evaluated the influence of sociodemographic and lifestyle-related factors on PHA in a multiethnic rural cohort of predominantly Hispanic middle-aged men and women.

 

Methods

Study population

Data for this study were from the Project FRONTIER (Facing Rural Obstacles to healthcare Now Through Intervention, Education & Research), an ongoing cohort study exploring the natural course of chronic disease development and its impact on cognitive, physical, social, and interpersonal functioning using a community-based participatory research approach in a multi-ethnic sample of adults living in rural communities of West Texas. The National Institute of Environmental Health Sciences recommends the use of community-based participatory research for rural research and underserved communities who may not respond well to classic research approaches such as recruitment by random digit dialing or obtaining information by mailed surveys (21). Details of the design and procedures for Project FRONTIER are provided elsewhere (22, 23). Briefly, before participant recruitment, partnerships with the local hospitals and clinics (who conduct the medical examinations and clinical labs as well as provide office space) and community organizations were created. Community recruiters and research personnel recruited potential participants through door-to-door solicitation, flyers, and at community events, churches, and food banks. To be eligible for the study, which begun in 2009, participants had to be 40 years or older and be currently living in four counties in West Texas namely Cochran, Bailey, Parmer, and Hockley. These counties encompass 2,482.07 square miles in total, and all of them have per-capita incomes that are below the national and Texas averages of per-capita income. Demographic characteristics of participants recruited for Project FRONTIER are similar to the demographic characteristics of the four counties from which participants were recruited (23). Two follow-up, exams 3 years apart, have occurred since baseline. All participants provided written informed consent with data collection protocols approved by the Institutional Review Board of Texas Tech University Health Sciences Center (IRB #: L06-028). After informed consent process was obtained, participants completed a detailed interview (in English or Spanish at the discretion of the participant) that included questions regarding demographic information, medical history, and neuropsychological assessments as well as assessment for depressive symptoms. The present analysis was restricted to 1093 participants aged 30–74 years at baseline (the age limits for the Framingham risk score equation). Of this number, the following exclusions were made: participants with prevalent CVD (n=85); participants with dementia (n=5), participants with insufficient data to diagnose MCI due to insufficient or incomplete information from cognitive tests (n=13), and participants with missing values for covariates used in calculating PHA (n=23). This resulted in an analytic sample of 967 men and women.

MCI

At every visit, cognitive function was assessed using a full cognitive testing battery. These included the Clinical Dementia Rating Scale; the Mini Mental State Exam (MMSE) to assess global cognitive function; the Trails Making Test (TMTA and TMTB) to measure attention, processing speed and mental flexibility; and detailed neuropsychological testing (e.g. Wechsler Memory Scale Logical Memory and FAS, Animal Naming, Clock Drawing, Boston Naming Test, Exit Interview (EXIT25) (24, 25) and Repeatable Battery for the Assessment of Neuropsychological Status (23)). Diagnosis of MCI was determined using standardized criteria by consensus review consisting of physicians and neuropsychologists who reviewed and analyzed participants’ performance across all tests (24, 26, 27). MCI is often considered as a transitional stage between normal cognition and dementia, during which a person is not demented but has measurable cognitive deficits in some form (28).

PHA

PHA was calculated based on Framingham risk score equation for general CVD developed by D’Agostino et al (15). The algorithm included age, sex, systolic blood pressure, treatment for hypertension, current smoking status, diabetes status, HDL cholesterol and total cholesterol. PHA facilitates easier understanding than the concept of risk. In other words, PHA is a person’s CVD risk transformed into the units of age rather than risk. For example, if a 61-year-old woman with risk factors above normal levels has a PHA score of 73, she has a predicted heart age/vascular age equivalent to a 73-year-old woman with normal risk factors (15). Thus, the heart of the 61-year-old woman is predicted to be 12 years older than her chronological age. Excess PHA was calculated as the difference between PHA and chronological age, with a positive value indicating higher CVD risk and a greater burden of CVD risk factors. High excess PHA (HEPHA) was defined as the differences between PHA and chronological age > 5 years (19).

Measures

Age, sex, race and ethnicity, education, household income, health insurance, marital status, smoking status, general health, and intake of alcohol were all self-reported. BMI was calculated by dividing participants weight (kg) by height (m2). Blood pressure was measured three times with participants seated after 5 minutes of rest. The average of these values was used for the analysis. Participants were asked to fast before each exam. Fasting glucose, total cholesterol and high-density lipoprotein (HDL) cholesterol were all determined by enzymatic methods. Diabetes was defined by self-report, current treatment for diabetes, fasting blood glucose level ≥126 mg/dL or hemoglobin A1c ≥ 6.5. Other medical conditions/history were evaluated using portions of the CDC Behavioral Risk Factor Surveillance System (BRFSS) questionnaire capturing information on physician diagnosis of hypertension and cardiovascular disease (29).

Statistical analysis

Characteristics of the sample stratified by HEPHA were evaluated using chi-square and t-tests or Wilcoxon rank sum test for categorical and continuous variables, respectively. Logistic regression analyses were used to calculate odds ratios (OR) and 95% confidence intervals (CI) for the association of HEPHA with MCI at baseline (cross-sectional analysis) and three-year change in PHA and incident MCI (longitudinal analysis). For the longitudinal analyses, the sample was limited to participants without MCI at baseline who returned for follow-up exam after three years and had information for estimating PHA (n=238). All models were adjusted for ethnicity, education, income, marital status, health insurance status, alcohol use and general health. Interaction between ethnicity (Hispanic or non-Hispanic participants) and HEPHA (yes or no) on the odds of MCI was tested and found not to be statistically significant. A two-tailed probability value less than 0.05 was considered statistically significant. All analyses were performed using SAS software version 9.4 (SAS institute, Cary, NC) and R software version 4.0.5 (R Foundation for Statistical Computing, Vienna, Austria).

 

Results

At baseline, the mean age of the 967 participants was 55 (standard deviation: 9.6) years, with 70% of them being women. A greater proportion of participants reported Hispanic ethnicity (64%) compared to 34% reporting being non-Hispanic White and 2% being non-Hispanic Black. Overall, 12.6% of participants had MCI, and the prevalence of HEPHA was 64.8%. The prevalence of MCI was higher among participants who were Hispanic compared to non-Hispanic adults (15.0 vs. 8.5%), higher among participants with no college education compared to those with college education or higher (15.0 vs. 6.3%), higher among adults who were either separated/divorced/widowed (19.5%) or single (18.4%) compared to married participants (10.0%), and higher among participants with total household income less than $10,000 (21.6%) compared to those with household incomes of $10,000 to $40,000 (11.9%) or those with household income of more than $40,000 (6.5%). Characteristics of participants by HEPHA status are presented in Table 1. A greater proportion of HEPHA occurred among Hispanic or non-Hispanic Black participants, as well as participants with lower household income or poor general health. Table 2 shows the distribution of variables (and other CVD risk factors) included in the algorithm for estimating PHA. As expected, participants who had HEPHA were older, had higher systolic blood pressures and higher body mass index, with a greater proportion of them being current smokers, current users of anti-hypertensive medication and having diabetes. There was however, no difference in total cholesterol levels by HEPHA status. Although several differences were found in scores for the various cognitive tests between participants with HEPHA and those without HEPHA, there was no difference in family history of Alzheimer’s disease or the prevalence of physiologic depression by HEPHA status (Table 3). The prevalence of MCI was more than three times higher among participants with HEPHA than those with no HEPHA.

Table 1. The distribution of sociodemographic and lifestyle/behavior factors among adults aged 30-74 years enrolled in project FRONTIER at baseline

Table 2. The distribution of components of the Framingham risk equation among adults aged 30-74 years enrolled in project FRONTIER

* Values are mean (standard deviation) or median (interquartile range) for continuous variables and percentages for categorical variables. HEPHA: High excess predicted heart/vascular age (defined as the differences between PHA and chronological age >5 years)

Table 3. The distribution of cognitive factors among adults aged 30-74 years enrolled in project FRONTIER

CLOX: clock drawing task, MMSE: The Mini Mental State Exam, RBANS: Repeatable Battery for the Assessment of Neuropsychological Status. HEPHA: High excess predicted heart/vascular age (defined as the differences between PHA and chronological age >5 years)

 

Several sociodemographic and socioeconomic factors were associated with the odds of HEPHA (Figure 1). In multivariable adjusted models (Table 4), a one-year increment in excess PHA was associated with 3% elevated odds for MCI (OR=1.03, 95%CI: 1.01-1.05) while HEPHA was associated with almost threefold elevated risk for MCI (OR=2.98; 95%CI: 1.72-5.15). Although this association was higher among Hispanic participants than non-Hispanic participants, this difference was not statistically significant (OR= 3.29, 95%CI: 1.72- 6.27 vs. OR=2.26, 95% CI:0.82- 6.26, p=0.542). At the first follow-up exam (3 years from baseline), excess PHA was still significantly associated with MCI (OR=1.04, 95%CI: 1.01-1.08) but the elevated odds of MCI associated with HEPHA did not attain statistical significance (OR=1.73, 95%CI: 0.68-4.42). A three-year negative change, that is an improvement in cardiovascular health, was associated with reduced odds of MCI (or=0.98, 95%CI: 0.96-1.01), however, this did not attain statistical significance in the small sample of participants who returned for the follow-up visit.

Figure 1. Adjusted estimates for the association of sociodemographic and socioeconomic factors with high excess predicted heart/vascular age at baseline, Project FRONTIER

Table 4. The association between predicted heart age and mild cognitive impairment among adults aged 30-74 years enrolled in project FRONTIER

* All models were adjusted for ethnicity, education, income, marital status, health insurance, alcohol use and general health.

 

Discussion

In this study of community-dwelling rural adults, more than two-thirds of participants were found to have adverse cardiovascular risk profiles as indicated by HEPHA. The prevalence of MCI was almost twice as high in Hispanic participants compared to non-Hispanic participants. HEPHA was associated with almost three-fold elevated odds for MCI, an association that was consistent across ethnicities. Improvement in cardiovascular risk profile over time among a subsample of the cohort appeared to be associated with reduced odds for MCI, however, this did not attain statistical significance.
The first step in informing public health policy makers in adopting this low-cost measure to prevent MCI is to show that PHA is related to MCI. To our knowledge this is the first study to evaluate the relation of PHA with MCI in rural settings, especially among ethnic minority populations like Hispanic adults who bear a greater burden of ADRD among all ethnic groups in the U.S. (20). The prevalence of CVD and ADRD is increasing in the US resulting in a decline in quality of life and considerable mortality. CVD remains the leading cause of mortality in the U.S. with 121.5 million American adults living with CVD (30). Currently, about 5.3 million cases of ADRD are known to occur in the U.S. and this number has been projected to increase to 8.4 million cases by 2030 (31). The increasing prevalence of CVD and ADRD are largely due to aging. By 2030, over 20% of the U.S. population is estimated to be at least 65 years old; the age at which the incidence of CVD and ADRD begins to increase rapidly (32). Rural populations are disproportionately affected by chronic diseases such as CVD and ADRD (5, 6). With more than 20% of rural populations in the U.S. already over the age of 65 years and bearing the greatest burden of CVD and ADRD risk factors (6) coupled with limited access to healthcare, there is a critical and an urgent need to identify and implement cost-effective evidence-based interventions that have the potential to advance cardiovascular health equity and in turn reduce ADRD among rural communities.
Several lines of evidence confirm that ADRD share several pathophysiological pathways with CVD, such as inflammation, increased oxidative stress, and changes to nitric oxide bioavailability (1). Thus, metabolic and vascular damage play important roles in the etiology of cognitive impairment leading to dementia (1-4). Accordingly, up to a third of all ADRD cases are attributed to adverse CVD risk factors (1), suggesting that CVD prevention efforts may also be effective in slowing early manifestations of ADRD, such as MCI. Disturbingly, in rural communities, most population-level CVD educational prevention strategies to improve knowledge, heart health habits, and related skills do not achieve consistent success, partly due to low education and health literacy (8-10). The inability to understand the concept of risk has been identified as a major reason for the low rate of adoption of healthy behavioral habits among individuals (11, 12), especially those in rural and resource–limited settings (13, 14).
A major factor that influences motivation to adapt healthy lifestyles to improve cardiovascular health is the individual’s perception of CVD risk (12, 19). Many CVD risk prediction algorithms which estimate absolute risk are not easily comprehended by individuals who are non-scientists resulting in underestimation of risk (19, 33, 34). Prior studies have demonstrated that PHA, a low-cost means of estimating and communicating CVD risk reduction, is superior in eliciting more emotional impact to adopt healthy lifestyles especially among adults at higher risk of CVD than the 10-year absolute risk (12, 19). Additionally, the comparison of PHA to chronological age has been shown to provide a simple, meaningful and easily understood measure of CVD risk compared to the commonly used 10-year absolute risk (16-18). In the current study, we observed that HEPHA was associated with substantially elevated odds for MCI, a finding that confirm that CVD and ADRD share similar pathways which offers opportunities for prevention of both conditions.
Although correlated with late-life CVD risk factor burden, several reports show that early life CVD risk factors are associated with greater late-life cognitive decline (20, 35, 36). Unlike the current study that looked holistically at the burden of CVD risk, most prior studies looked at the influence of individual CVD risk factors on cognitive function. While a negative change in PHA has been observed to result in favorable changes in cardiovascular health (12), evidence for change in PHA or changes in individual CVD risk burden influencing future cognitive function is limited. A recent report from the Prevention of Renal and Vascular End-Stage Disease cohort evaluated the relation of treatable vascular risk which was defined based on components of the Framingham Risk Score for cardiovascular disease that are amenable to treatment (diabetes mellitus, current smoker status, systolic blood pressure, total cholesterol, HDL cholesterol and use of blood pressure lowering drugs) with changes in cognitive performance over 5.5 years (37). In that large community-based cohort of 3,572 participants, aged 35–82 years, van Eersel et al (37) observed that poor cognitive performance was associated with high treatable vascular risk scores.
In the current study of predominantly midlife adults, favorable changes in CVD burden over three years was associated with reduced odds for MCI. Although this finding did not meet statistical significance in part due to limited statistical power as a result of the small sample with the necessary follow-up data, they do not understate the importance of CVD risk factor burden on future cognitive function. Accordingly, due to the ability of PHA to communicate CVD risk to influences adaption of healthy lifestyles and to improve cardiovascular health especially among young adults, some medical organizations such as the Canadian Cardiovascular Society (38) and the Joint British Societies recommend the use of PHA to communicate lifetime risk and in some cases, inform pharmacological therapy decision making (39). Taken together, the findings from the current study and those of other studies suggest that young adulthood and midlife offer a window of opportunity to provide public health education regarding the importance of cardiovascular health and brain health as well as a time to implement interventions to reduce CVD risk factor burden, and eventually ADRD (35).
This study has notable strengths that include the use of a multi-ethnic cohort of rural community-dwelling adults with extensive assessment of CVD risk factors and cognitive function. The primary limitation of this study is that the sample were obtained from a geographically limited area and therefore the findings of this study may or may not be generalizable to other rural communities in the U.S. Although the sample was multi-ethnic, a greater proportion of them were non-Hispanic White and Hispanic participants. Finally, due to the high attrition of participants during follow-up, the presence of selection bias cannot be ruled out. However, in cross-sectional analyses at the follow-up exam, a positive association was observed between PHA and MCI, similar to those observed at baseline. Therefore, the potential for selection bias influencing the results of the current study, if present, may be minimal.
In conclusion, findings of this study show that PHA is positively associated with MCI, with improvement in CVD risk profile seemingly related to reduced odds for MCI. This study adds valuable information to the expanding evidence of midlife CVD risk factor burden influencing cognitive dysfunction. Therefore, the use of PHA in understanding MCI holds promise of finding a cost-effective and health literacy-appropriate means of communicating risk to prevent CVD and ADRD in rural communities and resource-limited settings. Future short-term, small-scale prevention trials with individuals randomized to receive regular app-based information of their PHA compared to conventional CVD risk communication on cognitive function among rural community-dwelling adults are warranted. Because the burden of CVD, ADRD and their risk factors are common in rural communities where 20% of the U.S. population (60 million persons) are reported to live (5), the potential public health impact of reducing these risk factors will be significant on the health of Americans.

 

Funding: Project FRONTIER is supported by the Garrison Institute of Aging at Texas Tech University Health Science Center, Lubbock, TX, USA. Dr. Appiah was supported by the Collaborative Seed Grant Program in Aging from the Garrison Institute of Aging. The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript

Conflict of Interest: None.

Ethical Standards: All participants provided written informed consent with data collection protocols approved by the Institutional Review Board of Texas Tech University Health Sciences Center (IRB #: L06-028)

 

References

1. Kulshreshtha A, Saini J, German T, Alonso A. Association of Cardiovascular Health and Cognition. Current Epidemiology Reports. 2019;6(3):347-63. doi: 10.1007/s40471-019-00210-8.
2. Samieri C, Perier MC, Gaye B, Proust-Lima C, Helmer C, Dartigues JF, et al. Association of Cardiovascular Health Level in Older Age With Cognitive Decline and Incident Dementia. JAMA : the journal of the American Medical Association. 2018;320(7):657-64. doi: 10.1001/jama.2018.11499.
3. Stampfer MJ. Cardiovascular disease and Alzheimer’s disease: common links. Journal of internal medicine. 2006;260(3):211-23. doi: 10.1111/j.1365-2796.2006.01687.x.
4. Leritz EC, McGlinchey RE, Kellison I, Rudolph JL, Milberg WP. Cardiovascular Disease Risk Factors and Cognition in the Elderly. Curr Cardiovasc Risk Rep. 2011;5(5):407-12. doi: 10.1007/s12170-011-0189-x.
5. Garcia MC, Rossen LM, Bastian B, Faul M, Dowling NF, Thomas CC, et al. Potentially Excess Deaths from the Five Leading Causes of Death in Metropolitan and Nonmetropolitan Counties – United States, 2010-2017. Morbidity and mortality weekly report Surveillance summaries. 2019;68(10):1-11. doi: 10.15585/mmwr.ss6810a1.
6. Abner EL, Jicha GA, Christian WJ, Schreurs BG. Rural-Urban Differences in Alzheimer’s Disease and Related Disorders Diagnostic Prevalence in Kentucky and West Virginia. J Rural Health. 2016;32(3):314-20. doi: 10.1111/jrh.12155.
7. Zahnd WE, Scaife SL, Francis ML. Health literacy skills in rural and urban populations. Am J Health Behav. 2009;33(5):550-7. doi: 10.5993/ajhb.33.5.8.
8. Abbott LS, Slate EH. Improving Cardiovascular Disease Knowledge among Rural Participants: The Results of a Cluster Randomized Trial. Healthcare (Basel). 2018;6(3). doi: 10.3390/healthcare6030071.
9. Ruiz-Perez I, Bastos A, Serrano-Ripoll MJ, Ricci-Cabello I. Effectiveness of interventions to improve cardiovascular healthcare in rural areas: a systematic literature review of clinical trials. Prev Med. 2019;119:132-44. doi: 10.1016/j.ypmed.2018.12.012.
10. Record NB, Onion DK, Prior RE, Dixon DC, Record SS, Fowler FL, et al. Community-wide cardiovascular disease prevention programs and health outcomes in a rural county, 1970-2010. JAMA : the journal of the American Medical Association. 2015;313(2):147-55. doi: 10.1001/jama.2014.16969.
11. Kreuter MW, Strecher VJ. Changing inaccurate perceptions of health risk: results from a randomized trial. Health Psychol. 1995;14(1):56-63.
12. Lopez-Gonzalez AA, Aguilo A, Frontera M, Bennasar-Veny M, Campos I, Vicente-Herrero T, et al. Effectiveness of the Heart Age tool for improving modifiable cardiovascular risk factors in a Southern European population: a randomized trial. European journal of preventive cardiology. 2015;22(3):389-96. doi: 10.1177/2047487313518479.
13. Homko CJ, Santamore WP, Zamora L, Shirk G, Gaughan J, Cross R, et al. Cardiovascular disease knowledge and risk perception among underserved individuals at increased risk of cardiovascular disease. J Cardiovasc Nurs. 2008;23(4):332-7. doi: 10.1097/01.JCN.0000317432.44586.aa.
14. Wiese LK, Williams CL, Tappen RM, Newman D. An updated measure for investigating basic knowledge of Alzheimer’s disease in underserved rural settings. Aging Ment Health. 2019:1-8. doi: 10.1080/13607863.2019.1584880.
15. D’Agostino RB, Sr., Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743-53. doi: 10.1161/CIRCULATIONAHA.107.699579.
16. Yang Q, Zhong Y, Ritchey M, Cobain M, Gillespie C, Merritt R, et al. Vital Signs: Predicted Heart Age and Racial Disparities in Heart Age Among U.S. Adults at the State Level. MMWR Morbidity and mortality weekly report. 2015;64(34):950-8. doi: 10.15585/mmwr.mm6434a6.
17. Wells S, Kerr A, Eadie S, Wiltshire C, Jackson R. ‘Your Heart Forecast’: a new approach for describing and communicating cardiovascular risk? Heart. 2010;96(9):708-13. doi: 10.1136/hrt.2009.191320.
18. Groenewegen KA, den Ruijter HM, Pasterkamp G, Polak JF, Bots ML, Peters SA. Vascular age to determine cardiovascular disease risk: A systematic review of its concepts, definitions, and clinical applications. European journal of preventive cardiology. 2016;23(3):264-74. doi: 10.1177/2047487314566999.
19. Appiah D, Capistrant BD. Cardiovascular Disease Risk Assessment in the United States and Low- and Middle-Income Countries Using Predicted Heart/Vascular Age. Sci Rep. 2017;7(1):16673. doi: 10.1038/s41598-017-16901-5.
20. Lamar M, Durazo-Arvizu RA, Sachdeva S, Pirzada A, Perreira KM, Rundek T, et al. Cardiovascular disease risk factor burden and cognition: Implications of ethnic diversity within the Hispanic Community Health Study/Study of Latinos. PloS one. 2019;14(4):e0215378. doi: 10.1371/journal.pone.0215378.
21. O’Fallon LR, Dearry A. Commitment of the National Institute of Environmental Health Sciences to community-based participatory research for rural health. Environ Health Perspect. 2001;109 Suppl 3:469-73. doi: 10.1289/ehp.109-1240567.
22. O’Bryant S, Zhang Y, Owen D. The Cochran County aging study: methodology and descriptive statistics. Texas Public Health Journal. 2009;61:5-7.
23. O’Bryant SE, Edwards M, Menon CV, Gong G, Barber R. Long-term low-level arsenic exposure is associated with poorer neuropsychological functioning: a Project FRONTIER study. Int J Environ Res Public Health. 2011;8(3):861-74. doi: 10.3390/ijerph8030861.
24. O’Bryant SE, Johnson L, Reisch J, Edwards M, Hall J, Barber R, et al. Risk factors for mild cognitive impairment among Mexican Americans. Alzheimers Dement. 2013;9(6):622-31 e1. doi: 10.1016/j.jalz.2012.12.007.
25. Johnson LA, Mauer C, Jahn D, Song M, Wyshywaniuk L, Hall JR, et al. Cognitive differences among depressed and non-depressed MCI participants: a project FRONTIER study. Int J Geriatr Psychiatry. 2013;28(4):377-82. doi: 10.1002/gps.3835.
26. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology. 1984;34(7):939-44. doi: 10.1212/wnl.34.7.939.
27. Mild cognitive impairment: Aging to Alzheimer’s disease. Mild cognitive impairment: Aging to Alzheimer’s disease. New York, NY, US: Oxford University Press; 2003.
28. Huang C, Mattis P, Perrine K, Brown N, Dhawan V, Eidelberg D. Metabolic abnormalities associated with mild cognitive impairment in Parkinson disease. Neurology. 2008;70(16 Pt 2):1470-7. doi: 10.1212/01.wnl.0000304050.05332.9c.
29. Menon C, Westervelt HJ, Jahn DR, Dressel JA, O’Bryant SE. Normative performance on the Brief Smell Identification Test (BSIT) in a multi-ethnic bilingual cohort: a Project FRONTIER study. Clin Neuropsychol. 2013;27(6):946-61. doi: 10.1080/13854046.2013.796406.
30. Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, et al. Heart Disease and Stroke Statistics-2019 Update: A Report From the American Heart Association. Circulation. 2019;139(10):e56-e528. doi: 10.1161/CIR.0000000000000659.
31. Alzheimer’s A. 2015 Alzheimer’s disease facts and figures. Alzheimers Dement. 2015;11(3):332-84. doi: 10.1016/j.jalz.2015.02.003.
32. Ortman JM, Velkoff VA, Hogan H. An Aging Nation: The Older Population in the United States, Current Population Reports, P25-1140. Washington, DC: U.S. Census Bureau; 2014.
33. Robertson RM. Women and cardiovascular disease: the risks of misperception and the need for action. Circulation. 2001;103(19):2318-20.
34. Webster R, Heeley E. Perceptions of risk: understanding cardiovascular disease. Risk Manag Healthc Policy. 2010;3:49-60. doi: 10.2147/RMHP.S8288.
35. Yaffe K, Vittinghoff E, Hoang T, Matthews K, Golden SH, Zeki Al Hazzouri A. Cardiovascular Risk Factors Across the Life Course and Cognitive Decline: A Pooled Cohort Study. Neurology. 2021;96(17):e2212-e9. doi: 10.1212/WNL.0000000000011747.
36. Vintimilla R, Balasubramanian K, Hall J, Johnson L, O’Bryant S. Cardiovascular Risk Factors, Cognitive Dysfunction, and Mild Cognitive Impairment. Dement Geriatr Cogn Dis Extra. 2020;10(3):154-62. doi: 10.1159/000511103.
37. van Eersel MEA, Joosten H, Gansevoort RT, Slaets JPJ, Izaks GJ. Treatable Vascular Risk and Cognitive Performance in Persons Aged 35 Years or Older: Longitudinal Study of Six Years. J Prev Alzheimers Dis. 2019;6(1):42-9. doi: 10.14283/jpad.2018.47.
38. Anderson TJ, Gregoire J, Pearson GJ, Barry AR, Couture P, Dawes M, et al. 2016 Canadian Cardiovascular Society Guidelines for the Management of Dyslipidemia for the Prevention of Cardiovascular Disease in the Adult. Can J Cardiol. 2016;32(11):1263-82. doi: 10.1016/j.cjca.2016.07.510.
39. Board JBS. Joint British Societies’ consensus recommendations for the prevention of cardiovascular disease (JBS3). Heart. 2014;100 Suppl 2:ii1-ii67. doi: 10.1136/heartjnl-2014-305693.