Willing, but unequally: Indigenous identity influences participation in Alzheimer's disease biomarker research in Chile

José M Aravena; Rodrigo Saguez; Moisés H Sandoval; Carolina Herrera; Cecilia Albala; Patricio Fuentes

doi:10.1002/dad2.70261

. 2026 Jan 28;18(1):e70261. doi: 10.1002/dad2.70261

Willing, but unequally: Indigenous identity influences participation in Alzheimer's disease biomarker research in Chile

José M Aravena ^1,^2,^✉, Rodrigo Saguez ², Moisés H Sandoval ², Carolina Herrera ³, Cecilia Albala ², Patricio Fuentes ⁴

PMCID: PMC12848899 PMID: 41613013

Abstract

INTRODUCTION

This study aimed to compare the willingness to use Alzheimer's disease (AD) predictive–diagnostic procedures between Mapuche (Indigenous) and non‐Indigenous older adults in Chile.

METHODS

We conducted a cross‐sectional study including Mapuche (n = 167) and non‐Indigenous (n = 248) older adults. Willingness to undergo five predictive–diagnostic procedures (AD risk test, neuropsychological assessment, blood test, brain imaging, and cerebrospinal fluid analysis) was evaluated. Logistic regression models were used to identify factors associated with willingness.

RESULTS

Overall willingness was high, except for cerebrospinal fluid testing (39.1%). In fully adjusted models, Mapuche participants were significantly less willing to undergo neuropsychological assessment (77.8% vs. 92.3%), blood testing (68.3% vs. 89.9%), and brain imaging (73.1% vs. 84.3%). Key determinants of willingness varied by ethnic group and included age, sex, AD‐related worry, social determinants, and number of dementia risk factors.

DISCUSSION

Despite high overall willingness, ethnic identity and psychosocial factors significantly influenced receptiveness to AD predictive–diagnostic procedures.

Highlights

Indigenous populations in high‐income countries face a higher risk of dementia, making Alzheimer's disease (AD) biomarker and diagnostic research a critical priority in these groups.
Despite this, Indigenous and Latin American populations remain among the most underrepresented in dementia research.
In a sample of Indigenous (Mapuche) and non‐Indigenous older adults in Chile, most participants reported willingness to use AD biomarkers and diagnostic procedures.
In fully adjusted models, Mapuche individuals were significantly less willing to undergo neuropsychological testing, blood tests, and brain imaging for AD risk prediction.
Willingness to use AD biomarkers varied by ethnic identity and was influenced by age, social determinants, and attitudes toward AD.

Keywords: dementia, ethnicity, health disparate minority and vulnerable populations, health inequities, patient acceptance of health care

1. BACKGROUND

More than 60% of the current and projected global burden of Alzheimer's disease (AD) and other dementias is concentrated in countries across Africa, Asia, and South America. ¹ Race and ethnic identity are social constructs that shape exposure to structural, contextual, and historical social determinants of health, which in turn can increase the risk of dementia. ² This underscores the urgent need to study brain health across diverse populations and cultural contexts.

Yet, most of the research on AD (89%) has been conducted in North America and Europe, with only 22% reporting race or ethnic identity of participants. ³ Latin American and Indigenous populations remain among the most underrepresented groups in this type of research (< 5% of studies). ³ This poses a challenge, as the vast majority of current knowledge has been generated predominantly from limited racial and ethnic groups, significantly narrowing our understanding of how ethnic identity may shape cognitive health. This gap becomes especially relevant considering that some underrepresented ethnic groups, particularly Indigenous peoples, have shown a higher risk of AD and other dementias in certain contexts.

The few existing studies on Indigenous populations have reported wide variation in the prevalence of cognitive impairment and dementia among older adults, depending on geographic location and population characteristics, ranging from 0.6% dementia prevalence among the Moseten people of Bolivia ⁴ to 21.0% among Aboriginal and Torres Strait Islander peoples in New South Wales, Australia. ⁵ However, consistent disparities emerge when comparing Indigenous and non‐Indigenous populations in countries with a high Human Development Index, where Indigenous groups show significantly higher prevalence and incidence of dementia, particularly at younger ages (< 70 years). ⁶ , ⁷ , ⁸ Furthermore, Indigenous groups in these contexts are highly exposed to the social determinants of dementia ⁹ and have experienced historical traumatic events (e.g., land dispossession, forced acculturation) and exclusion from health‐care systems, all of which may contribute to dementia risk. ¹⁰

Understanding patterns of AD biomarker use and diagnostic procedures among Indigenous groups compared to non‐Indigenous populations in diverse sociocultural contexts is therefore critical to uncovering mechanisms underlying dementia risk disparities.

Unfortunately, there exist a limited number of studies that have explored AD biomarkers and other diagnosis procedures on Indigenous people ¹¹ and Latin American populations. ¹² The use of biomarkers in Indigenous populations raises complex questions related to cultural meaning, trust in medical systems, historical trauma, and the potential misuse of predictive data. Therefore, understanding preferences and attitudes toward AD biomarkers is not only a clinical issue but also an ethical imperative. ¹³ To our knowledge, only one study has assessed the receptiveness of American Indian and Alaska Native people toward the use of biomarkers compared to other racial and ethnic groups. ¹⁴ Nevertheless, no studies have explored in depth the preferences and determinants related to the use of specific AD biomarkers and diagnostic procedures among Indigenous groups.

To address these gaps, we conducted a study among the Mapuche (“people of the land”), an Indigenous group originally from the rural southern region of Latin America (Chile and Argentina). The Mapuche have a strong sense of community and a worldview centered on connection and balance with nature and their ancestors. Similar to other Indigenous groups living in high‐income countries, Mapuche populations experience a significant burden of adverse social determinants of health ¹⁵ and have been exposed to historical traumas. ¹⁶ Regarding cognitive health, although their life expectancy is ≈ 8 years shorter than that of non‐Indigenous people, ¹⁷ Mapuche groups present higher odds of dementia compared to the non‐Indigenous population. ¹⁸ The Mapuche therefore represent an important Indigenous group in which to explore issues related to brain health.

In this study, we aimed to (1) compare the willingness to use AD biomarkers and diagnostic procedures between Mapuche and non‐Indigenous older adults and (2) identify factors associated with willingness to use these tools in both populations.

2. METHODS

We conducted a cross‐sectional survey between October 2023 and November 2024 among older adults from Indigenous (Mapuche) and non‐Indigenous backgrounds living in Chile.

2.1. Participants and sampling

Eligible participants were aged ≥ 60 years, lived in either urban or rural areas of the Metropolitan and Araucanía regions of Chile, and had no diagnosis of cognitive impairment or dementia. Participants were identified as Indigenous if they self‐identified as Mapuche and corroborated this identity through familial lineage or a Mapuche surname. Non‐Indigenous older adults residing in the same municipalities were also invited to participate, enabling geographic comparability between groups.

The selected regions represent the highest concentrations of Indigenous populations in Chile. Santiago (Metropolitan region) has the country's largest urban Mapuche population, while the Araucanía region hosts the largest number of rural Mapuche communities, many of which maintain traditional lifestyles.

RESEARCH IN CONTEXT

Systematic Review: The authors reviewed the literature using traditional databases (e.g., PubMed) and existing systematic reviews. Few studies have examined the use of Alzheimer's disease (AD) biomarkers or predictive procedures in Indigenous populations, and none have specifically explored willingness or preferences regarding different types of predictive–diagnostic procedures.
Interpretation: Our findings demonstrate that Mapuche identity, an Indigenous group from South America, significantly influenced willingness to use AD biomarkers and diagnostic procedures. Moreover, the determinants of willingness differed between Indigenous and non‐Indigenous older adults.
Future Directions: There is an urgent need for research aimed at developing and testing AD predictive–diagnostic procedures that are culturally sensitive and foster trust within Indigenous communities. Given their underrepresentation in dementia research, addressing group‐specific determinants of biomarker uptake will be essential to enhance participation and reduce disparities in dementia prevention.

2.2. Ethical considerations

Before initiating the study and data collection, we engaged with local Mapuche leaders and organizations to review the study objectives and ensure the cultural appropriateness of materials and procedures. The study was then approved by the Yale University Institutional Review Board (IRB), the National Service of Older Adults in Chile (SENAMA), and the Universidad Central de Chile IRB.

2.3. Recruitment procedures

Participants were recruited through local Indigenous affairs offices, social service centers, and health‐care facilities in Santiago and Araucanía. In addition, a subset of participants from the Aging, Demography, Ethnicity, and Health (EDES: Envejecimiento, Demografía, Etnicidad y Salud) survey was invited to participate. ¹⁹ The EDES survey was designed to study ethnic disparities among older adults in Chile. Launched in 2022, it used a probabilistic, geographically stratified, multistage sample with proportional representation of Mapuche and non‐Indigenous older adults in the Metropolitan and Araucanía regions.

2.4. Data collection

With the aim of exploring the most appropriate questions and terminology for assessing participation in AD biomarker research, we first conducted a qualitative study using 24 in‐depth interviews (14 Mapuche, 10 non‐Indigenous). ²⁰ This exploratory phase allowed us to refine the phrasing of questions, evaluate their cultural appropriateness, test the application of measurement scales, and determine the most suitable interview settings. The content and findings from this phase informed and refined the design, terminology, and survey items used in the larger study. All interviews were conducted in Spanish and carried out in person at locations preferred by participants (e.g., their homes or community centers) by trained Mapuche and non‐Indigenous interviewers with experience working with Mapuche communities. When necessary, interviewers were accompanied by a Mapudungun (Mapuche language) interpreter.

2.5. Willingness to use AD predictive–diagnosis procedures

Questions assessed participants’ willingness to participate in five different AD predictive–diagnostic procedures: an overall AD risk test, a neuropsychological assessment, a blood test, a brain imaging procedure, and a cerebrospinal fluid (CSF) test. Each question was introduced with a brief explanation stating that the scenarios were hypothetical and intended for research purposes only (Appendix A1 in supporting information). An example of these questions is: “If there were a test to determine your risk of developing Alzheimer's disease in the next 5 to 10 years, would you like to take that test?” Response options included yes, maybe, or no. In addition, participants were asked how interested they believed other people were in participating in this type of research (yes, not much, no) and were invited to explain their response in an open‐ended format.

Responses to these questions were dichotomized as willing (yes) versus uncertain/unwilling (maybe or no) to facilitate interpretation in logistic regression models.

2.6. Measures and covariates

2.6.1. Sociodemographic and social determinants

Collected covariates included sociodemographic variables (age, sex, ethnic identity, and place of residence) and social determinants of health (education level, difficulties meeting basic needs in the past year, number of health‐care visits per year, and difficulties accessing health‐care services).

2.6.2. Cognitive health risk and psychological factors

Worry about AD was assessed using a single item (“You worry about getting Alzheimer's someday”) rated on a 5‐point Likert scale. Participants who responded agree or strongly agree were classified as yes. ²¹

We determined the number of dementia risk factors based on 10 evidence‐based, potentially modifiable risk factors of dementia. ²² Physical activity level was measured using the Rapid Assessment of Physical Activity for older adults (RAPA), classifying individuals as sedentary if they scored 1 (rarely or never engage in any physical activity). ²³ Smoking was measured using the World Health Organization criteria for frequency and duration of smoking, classifying participants as current smokers or non‐smokers. ²⁴ Alcohol intake was measured using the Australian National University‐Alzheimer's Disease Risk Index (ANU‐ADRI), categorizing individuals as having excessive alcohol intake if they reported consuming ≥ 14 units per week. ²⁵ Depressive symptoms were measured with the five‐item Geriatric Depression Scale (GDS‐5), classifying individuals as having depression if they scored ≥ 2 points. ²⁶ In addition, self‐reported health conditions were recorded (yes/no): hypertension, diabetes, high cholesterol, hearing loss (or use of hearing aids), vision loss, and history of moderate or severe traumatic brain injury. Participants were classified into groups with 0 or 1, 2, 3, or ≥ 4 dementia risk factors.

2.7. Data analysis

Descriptive characteristics and responses assessing willingness to use AD predictive–diagnostic procedures were summarized for the total sample and compared between Mapuche and non‐Indigenous participants using X ² and analysis of variance.

Multivariate logistic regression models were used to estimate differences between Mapuche and non‐Indigenous older adults in their willingness to use AD predictive–diagnostic procedures (1 = yes; 0 = maybe or no), adjusting for all covariates. To identify factors associated with willingness to undergo an AD risk test, neuropsychological test, blood test, brain imaging, and CSF test within each group, subgroup analyses were performed separately for Mapuche and non‐Indigenous participants using fully adjusted multivariate logistic regression models. The Hosmer–Lemeshow test was applied to assess the goodness of fit for all models.

Finally, we described participants’ perceptions of other people's interest in participating in AD predictive–diagnostic procedure research, stratified by ethnic group. Open‐ended responses were independently coded by two blinded reviewers to identify common themes explaining reasons of others’ interest or lack of interest in participating in AD predictive–diagnostic research. Discrepancies were resolved through consensus, and codes were developed inductively.

In sensitivity analysis, Bonferroni correction was used to confirm the most significant variables related to willingness to use AD predictive–diagnosis procedures. In a second sensitivity analysis, we additionally adjusted by expressing lack of trust in medical research as major reason for not finding AD research relevant (yes/no).

Data management was performed with Python (version 3.9.6). Data analysis was conducted using STATA. All analysis followed a significance level of < 0.05 and 95% confidence interval (CI).

3. RESULTS

3.1. Sample characteristics

Table 1 presents the characteristics of the total sample and by ethnic group. Compared to non‐Indigenous participants, Mapuche older adults were younger, more likely to be men, lived more frequently in rural areas, reported greater difficulties meeting basic needs, and had a lower prevalence and count of potentially modifiable dementia risk factors.

TABLE 1.

Descriptive characteristics of the sample.

	Total sample (n: 415)	Mapuche (n: 167)	Non‐Indigenous (n: 248)	p value
Age Mean ± SD (IQR)	72.4 ± 7.64 (67–78)	69.3 ± 7.42 (63–74)	74.4 ± 7.11 (69–79)	<0.001
Sex % Yes (n)				0.006
Women	76.4% (317)	69.5% (116)	81.0% (201)
Men	23.6% (98)	30.5% (51)	19.0% (47)
Living in rural place % Yes (n)	23.1% (96)	48.5% (81)	6.0% (15)	<0.001
Complete high school (≥12 years) % Yes (n)	29.2% (121)	28.2% (47)	29.8% (74)	0.317
Difficulties in basic needs % Yes (n)	46.7% (194)	53.3% (89)	42.3% (105)	0.028
Health‐care services use % Yes (n)				0.134
Every 3 months or more	30.1% (125)	29.9% (50)	30.2% (75)
Every 4–6 months	30.4% (126)	25.7% (43)	33.5% (83)
Every 7–11 months	26.3% (109)	26.9% (45)	25.8% (64)
Every 1 year or less	13.3% (55)	17.4% (29)	10.5% (26)
Difficulties accessing health‐care services % Yes (n)	19.6% (81)	24.1% (40)	16.5% (41)	0.057
Worry to AD % Yes (n)	336 (81.0%)	130 (77.8%)	206 (83.1%)	0.184
Dementia risk factors
Hypertension	265 (63.9%)	88 (52.7%)	177 (71.4%)	<0.001
High cholesterol	181 (43.6%)	57 (34.1%)	124 (50.0%)	0.001
Diabetes	124 (29.9%)	37 (22.2%)	87 (35.1%)	0.005
Obesity	103 (24.8%)	31 (18.6%)	72 (29.0%)	0.015
Hearing loss or hearing aids	118 (28.4%)	38 (22.8%)	80 (32.3%)	0.035
Vision loss	114 (27.5%)	47 (28.1%)	67 (27.0%)	0.801
Smoking	44 (10.7%)	15 (9.1%)	29 (11.7%)	0.401
Excessive alcohol intake	16 (3.9%)	8 (4.8%)	8 (3.2%)	0.417
Sedentarism	156 (37.6%)	75 (44.9%)	81 (32.7%)	0.012
Depression	139 (33.5%)	26 (15.6%)	113 (45.6%)	<0.001
Moderate or severe TBI	160 (38.6%)	64 (38.3%)	96 (38.7%)	0.937
N of dementia risk factors ^a % Yes (n)
0–1	64 (15.4%)	38 (22.8%)	26 (10.5%)	<0.001
2	76 (18.3%)	37 (22.2%)	39 (15.7%)
3	79 (19.0%)	33 (19.8%)	46 (18.6%)
≥4	196 (47.2%)	59 (35.3%)	137 (55.2%)

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Abbreviations: AD, Alzheimer's disease; ANU‐ADRI, Australian National University‐Alzheimer's Disease Risk Index; IQR, interquartile range; n, number; RAPA, Rapid Assessment of Physical Activity; SD, standard deviation; TBI, traumatic brain injury.

^{^a}

N of dementia risk factors: hypertension, diabetes, high cholesterol, obesity, sedentarism (RAPA scale), hearing loss or hearing aids, vision loss, TBI, depression (Geriatric Depression Scale), excessive alcohol intake (ANU‐ADRI scale), and smoking.

3.2. Ethnic differences in willingness to use AD predictive–diagnosis procedures

Figure 1 illustrates willingness to use five AD predictive–diagnostic procedures by group. Overall, 87.9% of participants were willing to undergo an AD risk test (Mapuche: 81.4%, non‐Indigenous: 92.3%, P = 0.001); 87.7% were willing to take a neuropsychological test (Mapuche: 77.8%, non‐Indigenous: 94.4%, P < 0.001); 81.2% a blood test (Mapuche: 68.3%, non‐Indigenous: 89.9%, P < 0.001); 79.8% a brain imaging test (Mapuche: 73.1%, non‐Indigenous: 84.3%, P = 0.005); and 37.4% a CSF test (Mapuche: 34.7%, non‐Indigenous: 39.1%, P = 0.365).

Willingness to use Alzheimer's disease biomarkers and diagnostic procedures in the total sample and by ethnic group. **P < 0.001. AD, Alzheimer's disease; CSF, cerebrospinal fluid.

In fully adjusted models, Mapuche identity was significantly associated with lower willingness to undergo neuropsychological testing (odds ratio [OR]: 0.21, 95% CI: 0.09–0.48, P < 0.001), blood testing (OR: 0.25, 95% CI: 0.13–0.49, P < 0.001), and brain imaging (OR: 0.52, 95% CI: 0.28–0.97, P = 0.039) compared to non‐Indigenous participants. No significant ethnic differences were found in willingness to undergo AD risk tests (OR: 0.59, 95% CI: 0.27–1.30, P = 0.188) or CSF tests (OR: 0.90, 95% CI: 0.54–1.50, P = 0.685; Table SA.1 in supporting information).

3.3. Determinants of willingness to use AD predictive–diagnosis procedures by ethnic group

Table 2 and Figure 2 describe determinants related to willingness to use AD biomarker tests by ethnic group. Among Mapuche participants, older age was associated with reduced willingness to undergo AD risk (OR: 0.92, 95% CI: 0.86–0.98, P = 0.015), neuropsychological (OR: 0.93, 95% CI: 0.87–0.98, P = 0.009), blood (OR: 0.95, 95% CI: 0.90–0.99, P = 0.034), and brain imaging tests (OR: 0.95, 95% CI: 0.90–0.99, P = 0.046; Table 2, Figure 2). In contrast, worry about AD was strongly associated with greater willingness to undergo AD risk (OR: 9.34, 95% CI: 3.21–27.23, P < 0.001), neuropsychological (OR: 2.73, 95% CI: 1.07–6.97, P = 0.036), and CSF tests (OR: 2.82, 95% CI: 1.12–7.12, P = 0.028; Table 2, Figure 2). Having completed formal education was associated with higher willingness to undergo blood tests (OR: 6.03, 95% CI: 1.93–18.83, P = 0.002), and having four or more dementia risk factors increased willingness to undergo CSF testing (OR: 5.23, 95% CI: 1.67–16.35, P = 0.004; Table 2, Figure 2).

TABLE 2.

Characteristics related to willingness to use AD biomarker tests by ethnic group.

Non‐Indigenous (N: 248)
	AD risk		Neuropsychological test		Blood test		Brain image		CSF
Variables	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P
Women	3.50 (1.02–12.07)	0.047	1.70 (0.35–8.27)	0.511	2.11 (0.72–6.13)	0.171	2.61 (1.07–6.36)	0.034	1.41 (0.68–2.91)	0.352
Age	0.99 (0.92–1.07)	0.879	0.95 (0.87–1.04)	0.258	0.96 (0.90–1.02)	0.199	0.98 (0.93–1.04)	0.470	0.95 (0.91–0.99)	0.011
Rural	0.07 (0.01–0.32)	<0.001	0.27 (0.04–1.79)	0.173	0.52 (0.10–2.66)	0.431	0.37 (0.10–1.36)	0.134	0.71 (0.22–2.27)	0.564
Complete formal education (≥12 years)	2.42 (0.62–9.38)	2.201	7.79 (0.89–67.77)	0.063	1.61 (0.55–4.67)	0.382	2.02 (0.82–5.02)	0.128	0.55 (0.30–1.03)	0.062
N visits to health‐care services per year (ref: every 3 months or more)
Every 4–6 months	0.46 (0.94–2.29)	0.345	0.20 (0.04–1.04)	0.056	0.43 (0.11–1.62)	0.210	0.47 (0.15–1.44)	0.184	0.70 (0.27–1.82)	0.463
Every 7–11 months	0.68 (0.18–2.50)	0.557	4.22 (0.42–42.06)	0.219	0.90 (0.28–2.87)	0.853	1.69 (0.60–4.74)	0.323	0.62 (0.30–1.31)	0.211
Every 1 year or less	2.49 (0.54–11.43)	0.239	0.69 (0.15–3.10)	0.624	1.74 (0.50–6.07)	0.386	1.65 (0.62–4.38)	0.316	0.102 (0.52–1.98)	0.956
Difficulties accessing health‐care services	0.63 (0.18–2.52)	0.482	0.85 (0.19–3.79)	0.836	1.02 (0.32–3.28)	0.967	1.39 (0.47–4.10)	0.556	0.79 (0.37–1.68)	0.542
Difficulties in basic needs	0.59 (0.18–1.88)	0.370	0.26 (0.07–1.01)	0.052	0.58 (0.21–1.55)	0.276	0.74 (0.32–1.72)	0.480	1.26 (0.70–2.26)	0.446
Fear of AD	2.89 (0.79–10.67)	0.110	1.82 (0.36–9.34)	0.469	2.57 (0.92–7.42)	0.070	2.12 (0.86–5.26)	0.103	0.85 (0.40–1.77)	0.660
N of dementia risk factors (ref: 0–1)
2	0.31 (0.02–3.84)	0.360	1.49 (0.15–14.84)	0.736	0.69 (0.16–2.96)	0.618	0.90 (0.26–3.09)	0.870	0.73 (0.23–2.28)	0.588
3	0.22 (0.02–2.60)	0.230	0.84 (0.11–6.66)	0.871	1.38 (0.31–6.22)	0.673	2.05 (0.59–7.18)	0.262	1.49 (0.51–3.43)	0.467
≥4	0.22 (0.02–2.26)	0.371	2.13 (0.28–16.13)	0.466	2.06 (0.51–8.29)	0.310	3.87 (1.21–12.37)	0.022	1.23 (0.47–3.23)	0.681
Hosmer–Lemeshow goodness‐of‐fit test (χ ²) ^b	2.88	0.942	8.27	0.407	6.46	0.595	14.17	0.078	8.11	0.423

Mapuche (N: 167)
	AD risk		Neuropsychological test		Blood test		Brain image		CSF
Variables	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P
Women	0.64 (0.21–1.93)	0.427	0.63 (0.24–1.69)	0.363	0.95 (0.40–2.25)	0.912	0.68 (0.29–1.64)	0.394	0.82 (0.38–1.77)	0.605
Age	0.92 (0.86–0.98)	0.015	0.93 (0.87–0.98)	0.009	0.95 (0.90–0.99)	0.034	0.95 (0.90–0.99)	0.046	1.03 (0.98–1.09)	0.205
Rural	0.47 (0.15–1.48)	0.194	0.44 (0.16–1.19)	0.105	0.76 (0.32–1.80)	0.533	1.29 (0.55–3.05)	0.560	1.05 (0.46–2.39)	0.910
Complete formal education (≥12 years)	2.63 (0.67–10.39)	0.194	3.149 (0.90–11.27	0.071	6.03 (1.93–18.82)	0.002	1.78 (0.69–4.64)	0.235	1.89 (0.79–4.51)	0.150
N visits to health‐care services per year (ref: every 3 months or more)
Every 4–6 months	1.84 (0.31–11.08)	0.504	0.59 (0.14–2.58)	0.476	3.27 (0.85–12.51)	0.084	1.56 (0.45–5.45)	0.487	2.30 (0.72–7.38)	0.162
Every 7–11 months	0.36 (0.10–1.29)	0.116	0.41 (0.13–1.26)	0.119	1.14 (0.42–3.10)	0.800	1.18 (0.43–3.22)	0.752	0.94 (0.34–2.58)	0.899
Every 1 year or less	1.76 (0.41–7.50)	0.443	2.36 (0.66–8.47)	0.186	2.45 (0.91–6.62)	0.078	1.55 (0.57–4.22)	0.394	1.16 (0.44–3.03)	0.764
Difficulties accessing health‐care services	0.41 (0.12–1.33)	0.135	0.94 (0.33–2.70)	0.914	1.30 (0.51–3.33)	0.586	0.67 (0.26–1.72)	0.410	1.98 (0.83–4.72)	0.124
Difficulties in basic needs	1.15 (0.40–3.31)	0.793	0.86 (0.35–2.11)	0.739	1.15 (0.53–2.53)	0.720	1.35 (0.61–2.97)	0.458	1.21 (0.56–2.58)	0.629
Fear of AD	9.34 (3.21–27.23)	<0.001	2.73 (1.07–6.97)	0.036	1.37 (0.56–3.36)	0.496	1.20 (0.50–2.88)	0.680	2.82 (1.12–7.12)	0.028
N of dementia risk factors ^a (ref: 0–1)
2	0.47 (0.10–2.14)	0.328	0.99 (0.23–4.28)	0.989	0.89 (0.28–2.81)	0.836	1.01 (0.33–3.08)	0.986	3.07 (0.95–9.94)	0.062
3	0.77 (0.17–3.59)	0.740	0.44 (0.11–1.75)	0.244	1.71 (0.54–5.45)	0.361	0.91 (0.29–2.74)	0.862	3.17 (0.90–11.11)	0.071
≥4	1.61 (0.37–7.06)	0.529	0.57 (0.16–2.07)	0.397	2.64 (0.87–7.98)	0.086	2.40 (0.79–7.26)	0.122	5.23 (1.67–16.35)	0.004
Hosmer–Lemeshow goodness‐of‐fit test (χ ²) ^b	4.75	0.784	7.21	0.515	9.59	0.295	3.67	0.886	8.13	0.421

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Note: Values in bold indicate a significance of p < 0.05. Each p‐value is shown next to its corresponding OR.

Abbreviations: AD, Alzheimer's disease; ANU‐ADRI, Australian National University‐Alzheimer's Disease Risk Index; CI, confidence interval; CSF, cerebrospinal fluid; n, number; OR, odds ratio; P, P value; RAPA, Rapid Assessment of Physical Activity; ref, reference value; TBI, traumatic brain injury; χ ², chi‐squared test.

^{^a}

^{^b}

Hosmer–Lemeshow goodness‐of‐fit test (χ2): All models satisfied the goodness‐of‐fit criterion (P > 0.05).

Significant determinants of willingness to use AD biomarkers and predictive–diagnosis procedures by ethnic group. Figure created with Biorender.com. AD, Alzheimer's disease; RF, risk factors.

Among non‐Indigenous participants, living in a rural area was associated with reduced willingness to undergo AD risk tests (OR: 0.07, 95% CI: 0.01–0.32, P < 0.001), and older age was associated with lower willingness to undergo CSF testing (OR: 0.95, 95% CI: 0.91–0.98, P = 0.011; Table 2, Figure 2). In contrast, being female was associated with greater willingness to undergo AD risk (OR: 3.50, 95% CI: 1.02–12.07, P < 0.001) and brain imaging tests (OR: 2.61, 95% CI: 1.07–6.36, P = 0.034; Table 2, Figure 2). Additionally, having four or more dementia risk factors was associated with increased willingness to undergo brain imaging (OR: 3.87, 95% CI: 1.21–12.37, P = 0.022; Table 2, Figure 2).

3.4. Perceived interest in participating in AD biomarker research

Figure 3 presents the main reasons underlying participants’ perceptions of others’ interest in participating in AD biomarker research. Overall, 38.3% of participants reported that they believed other people were interested in participating in this type of research. Fewer Mapuche participants perceived others as interested compared to non‐Indigenous participants (Mapuche: 31.7%; non‐Indigenous: 42.7%; P = 0.024). In both groups, the most frequently cited reasons for participation in AD biomarker research were AD prevention (Mapuche: 43.4%; non‐Indigenous: 34.4%; P = 0.199) and health maintenance (Mapuche: 30.2%; non‐Indigenous: 20.8%; P = 0.144). Among non‐Indigenous participants, common reasons for non‐participation included lack of interest (Mapuche: 25.9%; non‐Indigenous: 22.5%; P = 0.421) and limited knowledge (Mapuche: 6.5%; non‐Indigenous: 25.2%; P < 0.001), whereas among Mapuche participants, the most frequently cited reason was distrust in medical research (Mapuche: 38.7%; non‐Indigenous: 4.9%; P < 0.001).

Main reasons underlying participants’ perceptions of others’ interest in AD biomarker research, by ethnic group. *P < 0.05; **P < 0.01. AD, Alzheimer's disease.

3.5. Sensitivity analysis

Bonferroni‐corrected sensitivity analyses confirmed that Mapuche identity remained significantly associated with lower willingness to undergo neuropsychological assessment (P = 0.003) and blood testing (P < 0.001). Among Mapuche participants, worry about AD (P < 0.001), formal education (P = 0.026), and having four or more dementia risk factors (P = 0.050) were associated with greater willingness to undergo AD risk, blood, and CSF tests, respectively. Among non‐Indigenous participants, rural residence remained associated with lower willingness to undergo the AD risk test (P = 0.006). In a second sensitivity analysis adjusting for expressed lack of trust in medical research, the results remained unchanged (Table SA.2 in supporting information).

4. DISCUSSION

In this study of Mapuche and non‐Indigenous older adults in Chile, most participants were willing to undergo AD predictive–diagnostic procedures. The most accepted procedure was a test to estimate AD risk, and the least accepted was CSF testing. After adjusting for covariates, ethnic identity emerged as a significant factor influencing willingness: Mapuche individuals were less willing than their non‐Indigenous counterparts to undergo neuropsychological assessments, blood tests, and brain imaging. They also perceived this type of research as less relevant. Factors such as age, sex, social determinants of health, worry about AD, and the number of dementia risk factors were associated with willingness in both populations. These findings underscore the importance of integrating ethnic identity and its determinants into both AD biomarker research and clinical practice.

Regardless of ethnic background, the AD risk prediction test was the most widely accepted among all procedures, with the primary reason being an interest in preventing dementia. This is encouraging, especially in a population at increased risk of dementia, as it indicates that individuals are both aware of and motivated to take preventive action. Recent advances in blood‐ and image‐based biomarkers could make predictive risk testing more feasible in clinical practice. ²⁷ However, Indigenous identity significantly determined willingness to use AD biomarkers for risk prediction such as blood and brain imaging tests. Willingness to engage in this research is lower among those who may benefit most—underrepresented groups such as the Mapuche. ²⁸ As biomarker use expands clinically, if no appropriate guidelines or strategies are developed to increase receptiveness among underrepresented groups, such disparities could further widen existing racial and ethnic inequities in dementia risk and care. ²⁹

One potential explanation for these ethnic disparities in willingness to use AD diagnostic procedures may relate to culturally informed understandings of aging and disease. Dementia continues to be widely perceived as a normal part of aging by the general public—up to 80% of people globally hold this belief—and even by many health‐care providers (65%). ³⁰ While education about AD could help improve participation, previous research among Black and Hispanic individuals in the United States shows that knowledge about AD may not fully explain differences in willingness between racial groups. ²⁸ This previous research aligns with our findings, showing that sociodemographic and social determinant factors such as age, sex, education level, and worry about AD are among the most important predictors of willingness to use AD biomarkers and other diagnostic procedures across diverse racial and ethnic groups. ²⁸ , ³¹ Addressing structural barriers and building culturally responsive frameworks that acknowledge ethnic identity and social context is therefore essential for fostering trust and inclusivity in AD biomarker research.

Beyond structural factors, cultural beliefs may also shape receptivity to specific diagnostic procedures. In our study, older age was the strongest determinant of lower willingness to use all AD biomarkers among Mapuche participants. This finding aligns with previous studies showing that Indigenous older adults are more likely than younger individuals to endorse culturally rooted views of health. ³² Among Mapuche and other Indigenous groups, older adults play a central role in preserving cultural traditions, including health beliefs and practices aligned with their philosophical worldview. ³³ As such, testing “Western” methods such as extracting a blood sample, scanning the brain, or assessing “smartness,” can be interpreted as invasive, disrespectful, or misaligned with Mapuche cultural views of health, which emphasize harmony with nature, ancestry, and collective well‐being. ³⁴ In addition, for individuals more strongly connected to traditional religious and cultural practices, perceiving dementia as a natural transition to ancestral spiritual planes may reduce receptiveness to diagnostic methods, as dementia may be viewed as part of life. ²⁰ Positively, previous research has shown when medical and research procedures are developed in collaboration with Indigenous people and their communities, incorporating their sociocultural preferences and needs, it is possible to increase receptivity to use biomarkers on these groups. ³⁵ Therefore, we urge researchers working with AD biomarkers and diagnosis procedures to connect with Indigenous leaders and communities to develop culturally pertinent research procedures.

Another important cultural difference was that expressing worry about AD was the most significant determinant of greater willingness to use AD predictive–diagnostic procedures, but only among Mapuche individuals. This may reflect cultural or linguistic barriers to understanding the biomedical concept of AD. Many Mapuche people live in rural areas and speak Mapudungun, which includes culturally specific terms for cognitive decline that do not correspond directly to the biomedical category of AD. ¹¹ Those who express greater worry about AD may therefore be more acculturated and consequently more aware of the condition, its prognosis, and the potential for biomedical prevention through biomarkers. Among non‐Indigenous participants, who are more frequently exposed to the term and concept of AD, ³⁶ worry may not be as strongly linked to willingness. Future research should explore whether reframing biomarker communication using culturally grounded language can improve engagement in diverse populations.

One of the most important potential explanations behind Mapuche people's willingness to use AD predictive–diagnosis procedures is related to trust in research and medical institutions. In our study, Mapuche participants were more likely to cite lack of trust in medical research as the main reason for non‐participation in AD biomarker research. This echoes prior studies on Black communities in the United States, in which medical mistrust has been shown to be a significant barrier to biomarker participation. ²⁸ , ³⁷ When we included “distrust in medical research” in our models, this variable was not significantly associated with willingness and did not explain the ethnic differences observed. However, this measure captured general perceptions of AD research rather than individual decisions to undergo testing. Thus, the lack of statistical association likely reflects distinct attitudinal versus behavioral dimensions of trust. Consistent with this interpretation, no ethnic differences were found in health‐care use, suggesting pragmatic engagement with health systems despite underlying mistrust; a pattern described in other groups in which individuals may use biomedical services viewed as beneficial while retaining skepticism toward research institutions. ³⁸ As historical trauma, systemic discrimination, and exclusion from health‐care systems continue to undermine trust among Indigenous peoples and other historically marginalized groups, ³⁴ , ³⁹ strengthening both institutional trust and procedural willingness will therefore be vital.

Interestingly, the one previous study examining willingness to use dementia biomarkers among Indigenous people found that American Indian and Alaska Native individuals were more receptive to biomarker utility than non‐Hispanic White participants, and no differences were found between groups in attitudes toward research. ¹⁴ However, that study included younger participants (aged ≥ 25 years) and did not account for rural versus urban residence. In our study, we found that both variables (age and place of residence) can be significant indicators of AD biomarker uptake and may serve as markers of Indigenous people's acculturation and receptiveness to biomedical research. ⁴⁰ We encourage AD biomarker researchers to expand their efforts to rebuild trust through ethical engagement, partnership with communities, and culturally sensitive study protocols.

This study has limitations and strengths that are important to acknowledge. Its cross‐sectional design limits causal inference, and the specific setting in which the study was conducted may restrict generalizability to other populations. Despite these limitations, the study has several strengths that add robustness to the findings, including being the largest sample of Indigenous older adults to date to report preferences regarding the use of AD biomarkers and diagnostic procedures, the inclusion of participants from both rural and urban settings, and being the first study to examine willingness to use AD predictive–diagnostic procedures in Latin America.

In conclusion, while overall willingness to use AD biomarkers and diagnostic procedures is high in Chile, this study demonstrates that Indigenous identity and its associated social and cultural determinants significantly influence participation. These factors must be considered in research design, recruitment, and implementation strategies. Indigenous groups such as the Mapuche—who face higher dementia risk but remain underrepresented in research—should be prioritized for inclusion. Ethical and equitable implementation of AD biomarkers will require co‐designed protocols that rebuild trust and align with cultural values. Bridging these gaps is not only a matter of scientific accuracy but also of social justice.

CONFLICT OF INTEREST STATEMENT

All the authors declare no conflicts of interest.

CONSENT STATEMENT

All human subjects provided informed consent. The study was approved by the Yale University IRB, the National Service of Older Adults in Chile (SENAMA), and the Universidad Central de Chile IRB.

Supporting information

Supporting Information

DAD2-18-e70261-s002.docx^{(26.2KB, docx)}

Supporting Information

DAD2-18-e70261-s001.pdf^{(409.9KB, pdf)}

ACKNOWLEDGMENTS

The author thanks to all the participants and Mapuche communities for their contribution and collaboration. We thank to Habana Muñoz, Oscar Revellin, Orieta Huichal, Waldo Torres, Maria Aravena, Paula Vivar, Mary Huaiquin, Almendra Camaño, Angela Jaque, Daniela Inostroza, Paula Chacon, Damaris Imio, and Matias Rios for their work interviewing and interpreting participants. Jose Aravena was supported by a Fulbright and National Research and Development Agency of Chile (ANID) fellowship, the Yale University Council on Latin American and Iberian Studies, the Yale MacMillan Center for International and Area Studies, the Yale Social and Behavioral Sciences Research Fund, and the Yale Center for the Study of Race, Indigeneity, and Transnational Migration (RITM).

Aravena JM, Saguez R, Sandoval MH, Herrera C, Albala C, Fuentes P. Willing, but unequally: Indigenous identity influences participation in Alzheimer's disease biomarker research in Chile. Alzheimer's Dement. 2026;18:e70261. 10.1002/dad2.70261

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Associated Data

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Supplementary Materials

Supporting Information

DAD2-18-e70261-s002.docx^{(26.2KB, docx)}

Supporting Information

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PERMALINK

Willing, but unequally: Indigenous identity influences participation in Alzheimer's disease biomarker research in Chile

José M Aravena

Rodrigo Saguez

Moisés H Sandoval

Carolina Herrera

Cecilia Albala

Patricio Fuentes

Abstract

INTRODUCTION

METHODS

RESULTS

DISCUSSION

Highlights

1. BACKGROUND

2. METHODS

2.1. Participants and sampling

RESEARCH IN CONTEXT

2.2. Ethical considerations

2.3. Recruitment procedures

2.4. Data collection

2.5. Willingness to use AD predictive–diagnosis procedures

2.6. Measures and covariates

2.6.1. Sociodemographic and social determinants

2.6.2. Cognitive health risk and psychological factors

2.7. Data analysis

3. RESULTS

3.1. Sample characteristics

TABLE 1.

3.2. Ethnic differences in willingness to use AD predictive–diagnosis procedures

FIGURE 1.

3.3. Determinants of willingness to use AD predictive–diagnosis procedures by ethnic group

TABLE 2.

FIGURE 2.

3.4. Perceived interest in participating in AD biomarker research

FIGURE 3.

3.5. Sensitivity analysis

4. DISCUSSION

CONFLICT OF INTEREST STATEMENT

CONSENT STATEMENT

Supporting information

ACKNOWLEDGMENTS

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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