Comorbid Conditions Are Associated With Cognitive Impairment in Native Hawaiians and Pacific Islanders

Abstract

We examined the association between comorbid conditions and mild cognitive impairment (MCI) in Native Hawaiians and Pacific Islanders (NHPI) (n = 54). Cross-sectional, self-reported questionnaires were utilized to collect demographic, comorbid conditions, and MCI (via the AD8 index) data. Separate logistic regression models were conducted to investigate the relationship between comorbid conditions and MCI, adjusting for other covariates. We found significantly increased odds of MCI in those reporting high blood pressure (OR = 5.27; 95% CI: [1.36, 20.46]; p = 0.016), high cholesterol (OR = 7.30; 95% CI: [1.90, 28.14], p = 0.004), and prediabetes or borderline diabetes (OR = 4.53; 95% CI: [1.27, 16.16], p = 0.02) compared with those not reporting these respective conditions. These data show that hypertension, hypercholesterolemia, and prediabetes are associated with MCI in the NHPI community, suggesting that preventive strategies to reduce chronic conditions may also potentially slow cognitive decline in underrepresented/understudied NHPI.

Keywords

comorbid conditions hypertension cholesterol diabetes MCI cognitive impairment Native Hawaiian and Pacific Islander

Introduction

Neurocognitive aging (NA) produces changes in the brain, impacting memory, stress, decision-making, and other cognitive processes (Harada et al., 2013; Peters, 2006). The spectrum of cognitive decline in older adults ranges from normal aging to an intermediate mild cognitive impairment (MCI) stage to dementia, predominantly due to Alzheimer's disease (AD) (Davis et al., 2018). MCI prevalence impacts 6.7–25.2% of the population, increasing with age, while dementia affects nearly 50 million people worldwide (Jongsiriyanyong & Limpawattana, 2018; WHO, 2023). Brain-related aging and health have a profound impact not only on individual physical and mental functioning but also on families and society (Allen et al., 2017; Chiao et al., 2015; Deb et al., 2017; Fillit & Hill, 2005; Vandepitte et al., 2016). Therefore, examining factors related to NA is of critical public health importance.

As critically important, substantial disparities in NA, including depression, memory, stress, dementia and AD incidence, and outcomes across racial and ethnic groups, are urgent issues necessitating attention (Corriveau et al., 2017; Gianattasio et al., 2019; Mehta & Yeo, 2017). Numerous studies have demonstrated the disproportionate burden of disease, including obesity, type II diabetes, and cardiovascular disease in the Native Hawaiian and Pacific Islander community (NHPI), a rapidly growing population in the United States (Furubayashi & Look, 2005; Karter et al., 2013; Mau et al., 2009; McEligot et al., 2010, 2012). One recent retrospective medical record review study on 598 patients, including 224 Asians, 202 Whites, 87 NHPI, and 85 Other, showed the highest MCI in NHPI (Smith et al., 2021). However, very few studies have examined MCI (Ganbat & Wu, 2021; Hermosura et al., 2020; Smith et al., 2021), and very little is known about the risk factors associated with it, such as comorbid conditions (e.g., type II diabetes and hypertension) and their relationship with MCI in the NHPI community.

A meta-analysis of 17 longitudinal cohort studies involving ≈1.7 million people concluded that diabetes mellitus increased the risk of developing AD with a relative risk (RR) of ≈1.5 (Zhang et al., 2017). Studies show that type II diabetes induces vascular and cognitive decline (memory, mood, and behavior) (Lyu et al., 2020; Zilliox et al., 2016). Additionally, a history of prehypertension and hypertension in midlife or late in life has been shown to increase dementia risk and enhance AD neuropathology (Dickstein et al., 2010; Gottesman, Albert, et al., 2017). Although research shows a link between type II diabetes, hypertension, and cardiovascular disease with cognitive impairment, no studies have yet, to our knowledge, examined the relationship between comorbid conditions and MCI in NHPI.

Therefore, we conducted a community-based participatory research (CBPR) study and investigated the relationship between chronic health conditions, including blood pressure, cholesterol, prediabetes, diabetes, and stroke with MCI among NHPI older adults residing in Southern California. We hypothesized that having high blood pressure, high cholesterol, prediabetes, diabetes, and stroke would be associated with cognitive impairment in NHPI older adults.

Methods

Study Overview and Sample. The present CBPR study was a cross-sectional study with a non-probability sample of NHPI adults ≥50 years of age residing in Southern California. Initially, a working group was convened, comprised of our key partners from the Pacific Islander Health Partnership, and the university research faculty, staff, and graduate students. The working group was integral to reviewing questionnaires, providing input, and assisting in recruitment. Participant recruitment was facilitated through PIHP outreach and specifically via trained Pacific Islander Navigators (PINs). PINs with PIHP leadership were trained regarding the study protocol, including questionnaire development, questions, and skip patterns. The eligibility criteria included ≥50 years of age, having some NHPI ancestry, and currently residing in Southern California. Community leaders and study staff/PINs obtained consent, and self-reported questionnaire data were collected via Qualtrics. A total of 54 recruited and consented NHPI individuals completed the demographic and other study questionnaires. A full study protocol review was conducted and approved by an Internal Review Board (IRB) (HSR-21-22-72).

Comorbid/Chronic Conditions Data. Data on comorbid conditions were collected via a self-reported questionnaire. The following question was used to collect data on chronic diseases: “Have you ever been told by a doctor, nurse, or other health professional that you have any of the following health conditions?” The participants were prompted to answer “yes” or “no” to 15 different health-related questions, including questions on comorbid conditions previously shown to be associated with cognitive decline (blood pressure, cholesterol, prediabetes or borderline diabetes, diabetes, and stroke), and investigated for the present study.

Cognitive Functioning. The AD8 dementia screening tool, a validated brief instrument, distinguishes between normal aging and mild dementia/cognitive impairment (Galvin et al., 2006) and has been validated against gold standard measurements, including the Clinical Dementia Rating (CDR) scale (Morris, 1993), neuropsychological testing (Galvin et al., 2006), and with imaging and AD cerebrospinal fluid biomarkers (Galvin et al., 2010). The AD8 is comprised of eight items and gauges domains on memory, orientation, judgment, and function, asking participants about cognitive changes over the past several years. The responses are summed, resulting in a possible range of 0–8. A score of 2 or greater indicates MCI, with higher scores indicating greater cognitive impairment.

Other Measures. Demographic, relationship, employment status, and educational attainment data were collected via a self-reported questionnaire. Demographic questions included questions on race/ethnicity, gender, and age. Race/ethnicity questions were asked on whether participants were Native Hawaiian (yes, no) and/or Pacific Islander (yes, no), as well as the option to indicate Pacific Islander subgroups, including Chamorro, Marshallese, Samoan, or Tongan. Employment status and education were based on responses ranging from divorced to widowed, employed to unable to work, and less than high school to postgraduate.

Statistical Analysis. Frequencies and descriptive statistics were performed on age (continuous), gender (male, female), ethnicity (Native Hawaiian or Pacific Islander with subgroups included in the Pacific Islander ethnic group), relationship status (single, married or partnered, widowed, divorced, and separated), employment status (employed, retired, unable to work, and out of work), and level of education (less than high school, some high school, high school/GED, associate degree, technical/vocational degree, college graduate [Bachelor's degree], and postgraduate degree). Frequencies were also conducted on responses (yes, no) for high blood pressure, high cholesterol, prediabetes, diabetes, and stroke.

We conducted a chi-square analysis to investigate the relationship between comorbid conditions and MCI. Also, due to collinearity between comorbid conditions, separate logistic regression analyses were conducted to measure the association between each of the following comorbid conditions (independent variable): high blood pressure, high cholesterol, prediabetes, diabetes, and stroke with MCI (the dependent variable). Cognitive functioning was dichotomized as follows: those with an AD8 summary score of 2 or higher as MCI and those indicating a score 0–1 as normal cognitive functioning. Comorbid conditions (independent variables) were included in separate models as dichotomized variables (yes, no [ref]). Other known covariates associated with cognitive impairment were also included as independent variables and are as follows: gender (female, male [ref]), education level (more than high school and less than high school [ref]), and age (continuous). Analyses were conducted using SPSS version 28.

Results

The mean age of the sample (n = 54) was 64 years (SD ± 9). All participants identified as Native Hawaiian (24.1%) or Pacific Islander (75.9%). Table 1 indicates that most participants were female (59.3%), married or partnered (66.7%), employed (46.3%), and had a high school/GED (48.1%). The participants responded having the following chronic conditions as follows: high blood pressure (59.3%), high cholesterol (50%), prediabetes (33.3%), diabetes (31.5%), and stroke (3.7%), while 38.9% reported MCI (data not shown).

Table 1.

Demographic and Other Sample (n = 54) Characteristics.

Variable
Age (M ± SD)	64 ± 9 years
Gender, n (%)
Female	32 (59.3%)
Male	22 (40.7%)
Race, n (%)
Native Hawaiian	13 (24.1%)
Pacific Islander	41 (75.9)
Relationship status, n (%)
Divorced	3 (5.6%)
Married or partnered	36 (66.7%)
Separated	1 (1.9%)
Single	4 (7.4%)
Widowed	10 (18.5%)
Employment status, n (%)
Employed	25 (46.3%)
Out of work	1 (1.9%)
Retired	23 (42.6%)
Unable to work	5 (9.3%)
Level of education, n (%)
Less than high school	3 (5.6%)
Some high school	4 (7.4%)
High school/GED	26 (48.1%)
Associate degree	6 (11.1%)
Technical/vocational degree	8 (14.8%)
College graduate (bachelor's degree)	4 (7.4%)
Postgraduate degree	3 (5.6%)

Table 2 compares comorbid conditions (high blood pressure, high cholesterol, prediabetes, diabetes, and stroke) between normal cognition and MCI cognitive decline. High blood pressure, high cholesterol, and prediabetes were significantly related to MCI, with p = .010, .002, and .018, respectively, and the direction of the correlation indicated that the diagnoses of these respective conditions were associated with an increased risk of MCI. Specifically, the proportion of those with MCI compared with those with normal cognition were more likely to report high blood pressure (53.1% vs. 46.9%, p = 0.01), high cholesterol (59.3% vs. 40.7%, p = 0.002), and prediabetes (61.1% vs. 38.9%, p = 0.18).

Table 2.

Proportion of Comorbid Conditions Between Normal Cognition and MCI.

Variable	Normal cognition	MCI	p
High blood pressure
No	18 (81.8%)	4 (18.2%)	.010
Yes	15 (46.9%)	17 (53.1%)
High cholesterol
No	22 (81.5%)	5 (18.5%)	.002
Yes	11 (40.7%)	16 (59.3%)
Prediabetes
No	26 (72.2%)	10 (27.8%)	.018
Yes	7 (38.9%)	11 (61.1%)
Diabetes
No	24 (64.9%)	13 (35.1%)	.404
Yes	9 (52.9%)	8 (47.1%)
Stroke
No	32 (61.5%)	20 (38.5%)	.743
Yes	1 (50.0%)	1 (50%)

The multivariate association between each respective comorbid condition and MCI is shown in Table 3. For each separate model, after adjusting for covariates, comorbid conditions were associated with MCI; specifically, those reporting high blood pressure (OR = 5.27; 95% CI: [1.36, 20.46]; p = 0.016), high cholesterol levels (OR = 7.30; 95% CI: [1.90, 28.14], p = 0.004), and prediabetes or borderline diabetes (OR = 4.53; 95% CI: [1.27, 16.16], p = 0.02) were significantly associated with MCI. In all models, age was also significantly associated with MCI (p < 0.05).

Table 3.

The Association (Multivariable Odds Ratio, OR [95% CI]) Between Comorbid Conditions With MCI.

	Model 1^a	Model 2^b	Model 3^c	Model 4^d	Model 5^e
Age, yrs.	1.08 (1.01, 1.16)*	1.09 (1.01, 1.18)*	1.08 (1.00, 1.17)*	1.08 (1.00, 1.16)*	1.08 (1.01, 1.16)*
Gender	1.40 (0.38, 5.07)	1.30 (0.35, 4.80)	1.35 (0.37, 4.83)	1.25 (0.37, 4.16)	1.23 (0.37, 4.13)
Education level	1.36 (0.38, 4.91)	1.12 (0.30, 4.25)	1.76 (0.50, 6.24)	1.46 (0.44, 4.85)	1.56 (0.46, 5.24)
High blood pressure	5.27 (1.36, 20.46)*	–	–	–	–
High cholesterol	–	7.30 (1.90, 28.14)*	–	–	–
Prediabetes	–	–	4.53 (1.27, 16.16)*	–	–
Diabetes	–	–	–	1.67 (0.49, 5.65)	–
Stroke	–	–	–	–	2.38 (0.12, 46.01)

^aModel 1 refers to the model with high blood pressure.

^bModel 2 refers to the model with high cholesterol.

^cModel 3 refers to the model with prediabetes or borderline diabetes.

^dModel 4 refers to the model with diabetes.

^eModel 5 refers to the model with stroke.

*p < 0.05.

Discussion

The current study examined the association between comorbid conditions and MCI in the NHPI population residing in Southern California. We showed that approximately half of our sample reported high blood pressure and high cholesterol, while a third reported prediabetes and diabetes. Our data indicate that high blood pressure, high cholesterol, and prediabetes are associated with declining cognitive impairment in the NHPI community. Specifically, those with high blood pressure and prediabetes had 5 times the odds of MCI compared with those not reporting high blood pressure or prediabetes, and similarly those with high cholesterol (compared with those without high cholesterol) had 7 times the odds of MCI. Our risk estimates (OR and CI) should be interpreted with caution due to our small sample size; nonetheless, the direction of association shows increased odds of MCI for those with comorbid conditions.

Our findings of the association between high blood pressure and cognitive decline in NHPI have been reported in other ethnic/racial groups (Abell et al., 2018; Gottesman, Albert et al., 2017; Skoog et al., 1996). The Whitehall II cohort study, in predominantly UK non-Hispanic Whites, showed that participants with longer exposure to hypertension (SBP ≥ 130 mmHg), during midlife, had an increased risk of dementia compared to those with shorter exposure to hypertension (HR 1.29, 95% CI 1.00, 1.66) (Abell et al., 2018). A recent meta-analysis of over 2 million individuals from 135 prospective cohort studies further supported this association, indicating a significant relationship between midlife history of hypertension and the risk of dementia. Importantly, a meta-analysis of 12 trials with 92,135 participants showed that blood pressure-lowering hypertensive medication was associated with lower dementia or cognitive impairment development compared with controls (7.0% vs 7.5%, respectively, p < 0.05) (Hughes et al., 2020). Although research shows a link between hypertension and cognitive impairment, awareness and management of these comorbid conditions are still lacking in the NHPI community, which is a vulnerable and at-risk group.

Studies on the relationship between elevated cholesterol and cognitive decline are equivocal. Sáiz-Vazquez and colleagues (2020) conducted a meta-meta-analysis on 100 primary studies and five meta-analyses showing an association between only low-density lipoprotein (LDL) with AD, and no effects of high-density lipoproteins, total cholesterol, or triglycerides on AD. The biological plausibility of the influence of cholesterol on cognitive decline shows that increased LDL has vascular and neurotoxic effects (Nagga et al., 2018; Versmissen et al., 2011; Whitmer et al., 2005). We demonstrated an association between high cholesterol and MCI in NHPI; however, we only queried on total cholesterol, and potentially, cholesterol subgroup analyses may produce null results. Nonetheless, studies have consistently shown hypercholesterolemia in NHPI (Chiem et al., 2006; Mau et al., 2009; Moy et al., 2010; Sundaram et al., 2005), and therefore, further research needs to be conducted on the association between high cholesterol and MCI in NHPI.

Research also suggests that prediabetes (independent of diabetes) has been associated with adverse cognitive test results and cognitive decline (Dybjer et al., 2018; Marseglia et al., 2019). A study utilizing brain magnetic resonance imaging markers, including total brain tissue, white matter, and gray matter, showed that prediabetes was cross-sectionally associated with a smaller total brain tissue volume (p < .01), particularly smaller white matter volume (Marseglia et al., 2019). Dybjer et al. (2018) examined the relationship between prediabetes and cognitive function utilizing two cognitive tests, the Mini Mental State Examination (MMSE), measuring global cognitive function, and A Quick Test of Cognitive Speed (AQT), measuring processing speed and executive functioning. The results showed that those with prediabetes had worse cognitive function results than those without prediabetes. Similarly, our findings in NHPI showed a strong relationship between prediabetes and MCI. The sustained and prolonged exposure to insulin in prediabetics subsequently results in insulin resistance, whereby increasing oxidative stress and brain insulin signaling impairment, which may also subsequently lead to a neurodegeneration cascade in late AD (Bitra et al., 2015); therefore, early preventive efforts during prediabetes and/or borderline diabetes may potentially reduce AD risks in the NHPI community.

Studies have shown an association between diabetes and cardiovascular disease/stroke with cognitive decline (Biessels, & Despa, 2018; Kalaria et al., 2016); however, we did not show a relationship between these variables, which may be predominantly due to the small sample reporting of these respective conditions. Other limitations include the generalizability of our findings as our sample was recruited from Southern California. Further, our cross-sectional design makes it difficult to draw causal conclusions. However, via the utilization of a validated cognitive tool, as well as the CBPR approach, we were able to demonstrate important links between comorbid conditions and MCI. Additionally, due to cultural and linguistic hurdles, as well as historical suspicion/mistrust of research, recruiting participants from the NHPI populations can be difficult; therefore, we showed that with strategic CBPR collaborations, successful recruitment is feasible to investigate cognitive decline in NHPI older adults.

Research priorities for aging adults are becoming increasingly important as life expectancies increase and our population ages. Our findings demonstrate an association between high blood pressure, high cholesterol, and prediabetes with MCI in at-risk NHPI older adults. Research focusing on identifying risk factors for age-related disease, establishing effective preventative and treatment strategies in curbing comorbid conditions (which are all modifiable risk factors), and improving health outcomes is crucial for promoting healthful aging and meeting health needs for the NHPI community. Additionally, NHPI populations are disproportionately affected by these health conditions and are largely underrepresented in health research. Our findings emphasize the importance of continuing to address NHPI health disparities, as well as the need for culturally sensitive and targeted interventions to improve NHPI health outcomes.

Footnotes

Acknowledgements

Research reported in this publication was supported by the National Institute On Aging of the National Institutes of Health under Award Number R25AG069711. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We also acknowledge Ua’alani Hopi, MPH, Charlene Kazner, Monica Avila, MPH and Irma Rodriguez Hernandez, MPH for their assistance in data collection. We also acknowledge the contribution and collaboration of Dr. Ka'imi Sinclair, PhD, for sharing of study questionnaires and other protocols.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute on Aging of the National Institutes of Health (grant number R25AG069711).

ORCID iD

Archana J. McEligot

Author Biographies

Archana J. McEligot, PhD, is a professor in Department of Public Health and director of the Neurocognitive Aging and Analytics Research Education (NAARE) program at California State University, Fullerton. She received her PhD from the joint doctoral program at the University of California, San Diego (UCSD) and San Diego State University. She received her MS in Biology from UCSD. Her research interest focuses on the intersection between modifiable risk factors and chronic disease outcomes, including neurocognitive aging in efforts to reduce health disparities and reach health equity.

Ka'ala Pang, BSN, is the founder and previous program manager of Pacific Islander Health Partnership. She was a nursing specialist/case manager for 39 years with Los Angeles County and Rancho Los Amigos National Rehab Center. She received her BSN from the University of Hawai'i. Her interests focus on Native Hawaiian and Pacific Islander community health and well-being.

Sabrina Moran-Gomez, MPH, is a recent graduate from California State University, Fullerton, receiving her Master in Public Health. Her research interests include infectious disease epidemiology, health disparities, and environmental health.

Sinjini Mitra, PhD, is a professor in the Department of Information Systems and Decision Sciences (ISDS) at the College of Business and Economics at California State University, Fullerton. She received her PhD in Statistics from Carnegie Mellon University. Her research interests include statistical applications in education, security, healthcare, and business.

Mariella Santos is an undergraduate student and Neurocognitive Aging and Analytics Research Education (NAARE) Scholar in the Department of Public Health at California State University, Fullerton (CSUF). She is currently pursuing her Bachelor's degree in Public Health at CSUF. Her research interests include health disparities and health promotion and disease prevention.

Zahra Tahmasebi, MA, is a lab manager and research assistant in the Department of Psychology at the University of California, Los Angeles, USA. She received her MA in Experimental Psychology from California State University, Fullerton. She is interested in cognitive psychology and decision-making, and her research focuses on moral judgment.

Sanam Kazemi, DrPH, MPH, is a part-time faculty in the Department of Public Health at California State University, Fullerton. She earned her doctorate and master's degree in public health from Loma Linda University, California. Her research focuses on physical activity, nutrition and social determinants of health and impacts on the health outcomes among individuals.

References

Abell

J. G.

Kivimäki

Dugravot

Tabak

A. G.

Fayosse

Shipley

Sabia

Singh-Manoux

(2018). Association between systolic blood pressure and dementia in the Whitehall II cohort study: Role of age, duration, and threshold used to define hypertension. European Heart Journal, 39(33), 3119–3125. https://doi.org/10.1093/eurheartj/ehy288

Allen

A. P.

Curran

E. A.

Duggan

Cryan

J. F.

Chorcoráin

A. N.

Dinan

T. G.

Molloy

D. W.

Kearney

P. M.

Clarke

(2017). A systematic review of the psychobiological burden of informal caregiving for patients with dementia: Focus on cognitive and biological markers of chronic stress. Neuroscience & Biobehavioral Reviews, 73, 123–164. https://doi.org/10.1016/j.neubiorev.2016.12.006

Biessels

G. J.

Despa

(2018). Cognitive decline and dementia in diabetes mellitus: Mechanisms and clinical implications. Nature Reviews Endocrinology, 14(10), 591–604. https://doi.org/10.1038/s41574-018-0048-7

Bitra

V. R.

Rapaka

Akula

(2015). Prediabetes and Alzheimer's disease. Indian Journal of Pharmaceutical Sciences, 77(5), 511–514. https://doi.org/10.4103/0250-474x.169026

Chiao

C. Y.

H. S.

Hsiao

C. Y.

(2015). Caregiver burden for informal caregivers of patients with dementia: A systematic review. International Nursing Review, 62(3), 340–350. https://doi.org/10.1111/inr.12194

Chiem

Nguyen

P. L.

C. M.

Cruz

L. A.

Sadler

G. R.

(2006). Cardiovascular risk factors among Chamorros. BMC Public Health, 6(1), 298. https://doi.org/10.1186/1471-2458-6-298

Corriveau

R. A.

Koroshetz

W. J.

Gladman

J. T.

Jeon

Babcock

Bennett

D. A.

Carmichael

S. T.

Dickinson

S. L.

Dickson

D. W.

Emr

Fillit

Greenberg

S. M.

Hutton

M. L.

Knopman

D. S.

Manly

J. J.

Marder

K. S.

Moy

C. S.

Phelps

C. H.

Scott

P. A.

, … Holtzman

D. M.

(2017). Alzheimer's disease-related dementias summit 2016: National research priorities. Neurology, 89(23), 2381–2391. https://doi.org/10.1212/WNL.0000000000004717

Davis

O'Connell

Johnson

Cline

Merikle

Martenyi

Simpson

(2018). Estimating Alzheimer's disease progression rates from normal cognition through mild cognitive impairment and stages of dementia. Current Alzheimer Research, 15(8), 777–788. https://doi.org/10.2174/1567205015666180119092427

Deb

Thornton

J. D.

Sambamoorthi

Innes

(2017). Direct and indirect cost of managing Alzheimer’s disease and related dementias in the United States. Expert Review of Pharmacoeconomics & Outcomes Research, 17(2), 189–202. https://doi.org/10.1080/14737167.2017.1313118

10.

Dickstein

D. L.

Walsh

Brautigam

Stockton

S. D.

Jr Gandy

Hof

P. R.

(2010). Role of vascular risk factors and vascular dysfunction in Alzheimer's disease. Mount Sinai Journal of Medicine: A Journal of Translational and Personalized Medicine, 77(1), 82–102. https://doi.org/10.1002/msj.20155

11.

Dybjer

Nilsson

P. M.

Engström

Helmer

Nägga

(2018). Pre-diabetes and diabetes are independently associated with adverse cognitive test results: A cross-sectional, population-based study. BMC Endocrine Disorders, 18(1), 91. https://doi.org/10.1186/s12902-018-0318-3

12.

Fillit

Hill

(2005). Economics of dementia and pharmacoeconomics of dementia therapy. The American Journal of Geriatric Pharmacotherapy, 3(1), 39–49. https://doi.org/10.1016/j.amjopharm.2005.03.005

13.

Furubayashi

J. K.

Look

M. A.

(2005). Type 2 diabetes in Native Hawaiians and Pacific Islanders in Hawaii. Pacific Health Dialog, 12(2), 103–110. https://pubmed.ncbi.nlm.nih.gov/18181501/

14.

Galvin

J. E.

Fagan

A. M.

Holtzman

D. M.

Mintun

M. A.

Morris

J. C.

(2010). Relationship of dementia screening tests with biomarkers of Alzheimer's disease. Brain: A Journal of Neurology, 133(11), 3290–3300. https://doi.org/10.1093/brain/awq204

15.

Galvin

J. E.

Roe

C. M.

Xiong

Morris

J. C.

(2006). Validity and reliability of the AD8 informant interview in dementia. Neurology, 67(11), 1942–1948. https://doi.org/10.1212/01.wnl.0000247042.15547.eb

16.

Ganbat

Y. Y.

(2021). Disparities of subjective cognitive decline among Native Hawaiians/other Pacific Islanders, Asian Americans, and White Americans in Hawai'i: Behavioral Risk Factor Surveillance System 2015 and 2017. Asia-Pacific Journal of Public Health, 33(5), 587–594. https://doi.org/10.1177/10105395211020902

17.

Gianattasio

K. Z.

Prather

Glymour

M. M.

Ciarleglio

Power

M. C.

(2019). Racial disparities and temporal trends in dementia misdiagnosis risk in the United States. Alzheimer's & Dementia: Translational Research & Clinical Interventions, 5(1), 891–898. https://doi.org/10.1016/j.trci.2019.11.008

18.

Gottesman

R. F.

Albert

M. S.

Alonso

Coker

L. H.

Coresh

Davis

S. M.

Deal

J. A.

McKhann

G. M.

Mosley

T. H.

Sharrett

A. R.

Schneider

A. L. C.

Windham

B. G.

Wruck

L. M.

Knopman

D. S.

(2017). Associations between midlife vascular risk factors and 25-year incident dementia in the atherosclerosis risk in communities (ARIC) Cohort. JAMA Neurology, 74(10), 1246–1254. https://doi.org/10.1001/jamaneurol.2017.1658

19.

Harada

C. N.

Natelson Love

M. C.

Triebel

K. L.

(2013). Normal cognitive aging. Clinics in Geriatric Medicine, 29(4), 737–752. https://doi.org/10.1016/j.cger.2013.07.002

20.

Hermosura

A. H.

Noonan

C. J.

Fyfe-Johnson

A. L.

Seto

T. B.

Kaholokula

J. K.

MacLehose

R. F.

(2020). Hospital disparities between Native Hawaiian and other Pacific Islanders and non-Hispanic Whites with Alzheimer's disease and related dementias. Journal of Aging and Health, 32(10), 1579–1590. https://doi.org/10.1177/0898264320945177

21.

Hughes

Judge

Murphy

Loughlin

Costello

Whiteley

Bosch

O'Donnell

M. J.

Canavan

(2020). Association of blood pressure lowering with incident dementia or cognitive impairment: A systematic review and meta-analysis. JAMA, 323(19), 1934–1944. https://doi.org/10.1001/jama.2020.4249

22.

Jongsiriyanyong

Limpawattana

(2018). Mild cognitive impairment in clinical practice: A review article. American Journal of Alzheimer's Disease & Other Dementias, 33(8), 500–507. https://doi.org/10.1177/1533317518791401

23.

Kalaria

R. N.

Akinyemi

Ihara

(2016). Stroke injury, cognitive impairment and vascular dementia. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, 1862(5), 915–925. https://doi.org/10.1016/j.bbadis.2016.01.015

24.

Karter

A. J.

Schillinger

Adams

A. S.

Moffet

H. H.

Liu

Adler

N. E.

Kanaya

A. M.

(2013). Elevated rates of diabetes in Pacific Islanders and Asian subgroups: The diabetes study of Northern California (DISTANCE). Diabetes Care, 36(3), 574–579. https://doi.org/10.2337/dc12-0722

25.

Lyu

Wei

(2020). Vascular cognitive impairment and dementia in type 2 diabetes mellitus: An overview. Life Sciences, 254, 117771. https://doi.org/10.1016/j.lfs.2020.117771

26.

Marseglia

Fratiglioni

Kalpouzos

Wang

Bäckman

(2019). Prediabetes and diabetes accelerate cognitive decline and predict microvascular lesions: A population-based cohort study. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 15(1), 25–33. https://doi.org/10.1016/j.jalz.2018.06.3060

27.

Mau

M. K.

Sinclair

Saito

E. P.

Baumhofer

K. N.

Kaholokula

J. K.

(2009). Cardiometabolic health disparities in Native Hawaiians and other Pacific Islanders. Epidemiologic Reviews, 31(1), 113–129. https://doi.org/10.1093/ajerev/mxp004

28.

McEligot

A. J.

McMullin

Pang

Bone

Winston

Ngewa

Tanjasiri

S. P.

(2010). Diet, psychosocial factors related to diet and exercise, and cardiometabolic conditions in Southern Californian Native Hawaiians. Hawaii Medical Journal, 69(5 Suppl 2), 16–20. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3158438/

29.

McEligot

A. J.

McMullin

Pang

Bone

Winston

Ngewa

Tanjasiri

S. P.

(2012). Dietary intakes, obesity and health behaviors in Native Hawaiians residing in Southern California. Hawai'i Journal of Medicine & Public Health: A Journal of Asia Pacific Medicine & Public Health, 71(5), 124–128. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3360080/

30.

Mehta

K. M.

Yeo

G. W.

(2017). Systematic review of dementia prevalence and incidence in United States race/ethnic populations. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 13(1), 72–83. https://doi.org/10.1016/j.jalz.2016.06.2360

31.

Morris

J. C.

(1993). The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology, 43(11), 2412–2414. https://doi.org/10.1212/wnl.43.11.2412-a

32.

Moy

K. L.

Sallis

J. F.

David

K. J.

(2010). Health indicators of Native Hawaiian and Pacific Islanders in the United States. Journal of Community Health, 35(1), 81–92. https://doi.org/10.1007/s10900-009-9194-0

33.

Nägga

Gustavsson

A. M.

Stomrud

Lindqvist

van Westen

Blennow

Zetterberg

Melander

Hansson

(2018). Increased midlife triglycerides predict brain β-amyloid and tau pathology 20 years later. Neurology, 90(1), e73–e81. https://doi.org/10.1212/WNL.0000000000004749

34.

Peters

(2006). Ageing and the brain. Postgraduate Medical Journal, 82(964), 84–88. https://doi.org/10.1136/pgmj.2005.036665

35.

Sáiz-Vazquez

Puente-Martínez

Ubillos-Landa

Pacheco-Bonrostro

Santabárbara

(2020). Cholesterol and Alzheimer's disease risk: A meta-meta-analysis. Brain Sciences, 10(6), 386. https://doi.org/10.3390/brainsci10060386

36.

Skoog

Lernfelt

Landahl

Palmertz

Andreasson

L. A.

Nilsson

Persson

Odén

Svanborg

(1996). 15-year Longitudinal study of blood pressure and dementia. Lancet (London, England), 347(9009), 1141–1145. https://doi.org/10.1016/s0140-6736(96)90608-x

37.

Smith

Van

Roberts

Hosaka

K. R. J.

Choi

S. Y.

Viereck

Carrazana

Borman

Chen

J. J.

Liow

K. K.

(2021). Disparities in Alzheimer disease and mild cognitive impairment among Native Hawaiians and Pacific Islanders. Cognitive and Behavioral Neurology: Official Journal of the Society for Behavioral and Cognitive Neurology, 34(3), 200–206. https://doi.org/10.1097/WNN.0000000000000279

38.

Sundaram

A. A.

Ayala

Greenlund

K. J.

Keenan

N. L.

(2005). Differences in the prevalence of self-reported risk factors for coronary heart disease among American women by race/ethnicity and age: Behavioral Risk Factor Surveillance System, 2001. American Journal of Preventive Medicine, 29(5 Suppl 1), 25–30. https://doi.org/10.1016/j.amepre.2005.07.027

39.

Vandepitte

Van Den Noortgate

Putman

Verhaeghe

Faes

Annemans

(2016). Effectiveness of supporting informal caregivers of people with dementia: A systematic review of randomized and non-randomized controlled trials. Journal of Alzheimer's Disease, 52(3), 929–965. https://doi.org/10.3233/JAD-151011

40.

Versmissen

Oosterveer

D. M.

Hoekstra

Out

Berbée

J. F.

Blommesteijn-Touw

A. C.

van Vark-van der Zee

Vongpromek

Vanmierlo

Defesche

J. C.

Mulder

Kastelein

J. J.

Sijbrands

E. J.

(2011). Apolipoprotein isoform E4 does not increase coronary heart disease risk in carriers of low-density lipoprotein receptor mutations. Circulation: Cardiovascular Genetics, 4(6), 655–660. https://doi.org/10.1161/CIRCGENETICS.111.959858

41.

Whitmer

R. A.

Sidney

Selby

Johnston

S. C.

Yaffe

(2005). Midlife cardiovascular risk factors and risk of dementia in late life. Neurology, 64(2), 277–281. https://doi.org/10.1212/01.WNL.0000149519.47454.F2

42.

World Health Organization. (2023, March 15). Dementia fact sheet . https://www.who.int/news-room/fact-sheets/detail/dementia

43.

Zhang

Chen

Hua

Liao

Wang

Xiong

Cao

(2017). An updated meta-analysis of cohort studies: Diabetes and risk of Alzheimer's disease. Diabetes Research and Clinical Practice, 124, 41–47. https://doi.org/10.1016/j.diabres.2016.10.024

44.

Zilliox

L. A.

Chadrasekaran

Kwan

J. Y.

Russell

J. W.

(2016). Diabetes and cognitive impairment. Current Diabetes Reports, 16(9), 87. https://doi.org/10.1007/s11892-016-0775-x