Plasma biomarkers of Alzheimer's disease and related dementias in American Indians: The Strong Heart Study

Astrid M Suchy‐Dicey; W T Longstreth, Jr; Kristoffer Rhoads; Jason Umans; Dedra Buchwald; Thomas Grabowski; Kaj Blennow; Eric Reiman; Henrik Zetterberg

doi:10.1002/alz.13664

. 2024 Jan 12;20(3):2072–2079. doi: 10.1002/alz.13664

Plasma biomarkers of Alzheimer's disease and related dementias in American Indians: The Strong Heart Study

Astrid M Suchy‐Dicey ^1,^2,^3,^4,^✉, W T Longstreth Jr ⁵, Kristoffer Rhoads ^4,⁵, Jason Umans ⁶, Dedra Buchwald ³, Thomas Grabowski ^4,⁵, Kaj Blennow ^7,⁸, Eric Reiman ⁹, Henrik Zetterberg ^7,⁸

PMCID: PMC10984473 PMID: 38215191

Abstract

INTRODUCTION

Identification of Alzheimer's disease (AD) needs inexpensive, noninvasive biomarkers, with validation in all populations.

METHODS

We collected plasma markers in older American Indian individuals: phosphorylated‐tau181 (pTau181); amyloid‐beta (Aβ) 40,42; glial fibrillary acidic protein (GFAP); and neurofilament light chain (NfL). Plasma markers were analyzed for discriminant properties with cognitive status and etiology using receiver operating characteristic (ROC) analysis.

RESULTS

PTau181, GFAP, NfL plasma values were significantly associated with cognition, but Aβ were not. Discriminant performance was moderate for individual markers, with pTau181, GFAP, NfL performing best, but an empirically selected panel of markers (age, sex, education, pTau181, GFAP, NfL, Aβ4240 ratio) had excellent discriminant performance (AUC > 0.8).

DISCUSSION

In American Indian individuals, pTau181 and Aβ values suggested more common pathology than in majority populations. Aβ was less informative than in other populations; however, all four markers were needed for a best‐performing dementia diagnostic model. These data validate utility of AD plasma markers, while suggesting population‐specific diagnostic characteristics.

Keywords: Alzheimer's disease, ATN biomarkers, cognition, imaging, memory, plasma markers

1. BACKGROUND

Early identification of Alzheimer's disease (AD) is critical to advancements in prevention and treatment efforts. The 2018 joint statement by National Institute on Aging and Alzheimer's Association recommends a research framework for defining AD using objective, quantifiable features reflecting presence and degree of amyloid deposition (A), pathologic tau (T), and neuronal injury (N). ¹ This “AT(N) framework” aims to distinguish AD pathology from the resulting clinical syndrome, as a more sensitive, more specific approach to case identification. In proof of concept, cerebrospinal fluid (CSF) AT(N) biomarkers have high sensitivity and specificity for AD pathology, even in advance of detectable changes on cranial imaging or pathology, and long before detectable changes in cognitive symptoms. ²

However, collection of CSF requires specific training, may be regarded as invasive by many patients, entails varying costs across healthcare systems, and is considered unacceptable in many communities. ³ The current alternative, positron emission tomography (PET) imaging, is highly expensive, involves exposure to radiation, and is unrealistic in settings with poor access to socioeconomic resources or specialty care. Even structural MRI, which reasonably reflects only (N) markers in the AT(N) framework, can be unattainably expensive, and still does not provide insight into A or T markers. Alternative AT(N) markers that are low or noninvasive, lower cost, and more widely acceptable are needed, especially for characterization of presymptomatic AD in populations heavily affected by health and socioeconomic disparities.

Circulating biomarkers for A, T, and N pathology may offer a viable low‐cost, widely acceptable, widely available measure, ⁴ providing an especially pragmatic innovation for the community clinic setting. Candidate circulating markers of interest in the AT(N) framework include amyloid beta 40 and 42 (Aβ₄₀, Aβ₄₂; A marker), phosphorylated tau 181 (pTau_181; T marker), glial fibrillary protein (GFAP; N marker), and neurofilament light chain (NfL; N marker). Each of these plasma markers may contribute insights into one of the A,T,N components in the AT(N) framework, and collectively have the potential to categorize presymptomatic individuals into those with or without (presymptomatic) AD pathology.

No prior study has measured or evaluated these plasma markers in American Indian individuals or other Indigenous populations, who are both heavily burdened with AD risk but also critically underrepresented in AD research. Therefore, this study aims to describe AT(N) plasma markers, as well as associations with clinical, MRI, and cognitive features of AD in a large cohort of community‐based American Indian individuals. This work has the potential to provide insights for researchers and clinicians on AD characteristics, diagnostics, and risk in American Indian elderly and their communities, with the ultimate goal to support future research and public health programs to improve detection, prevention, and treatment opportunities all peoples.

RESEARCH IN CONTEXT

Systematic review: AT(N) plasma marker distributions and their associations with brain imaging, cognitive, and memory features have yet to be established for many high‐risk but underserved populations, including American Indian individuals.
Interpretation: Our study reported both cognitive normal and impaired ranges for multiple plasma markers associated with Alzheimer's disease (AD) and related dementias in other populations, including phosphorylated tau181 (pTau181), amyloid‐beta (Aβ) 40,42, and 42/40, glial fibrillary protein (GFAP), and neurofilament light chain (NfL). Associations are reported for these plasma markers with conventional AD risk features such as apolipoprotein E (APOE) ε4, with brain imaging features of atrophy and vascular injury, with adjudicated cognitive status and etiology, and with categorization of memory status.
Future directions: Future research is needed to validate plasma markers with gold standard pathology, such as positron emission tomography (PET) imaging; to continue to establish cross‐study and cross‐population assay calibrations; and to collect these markers over time and in other populations.

2. METHODS

2.1. Setting

The Strong Heart Study (SHS) recruited American Indian adults from communities and tribes in the U.S. Northern Plains, Southern Plains, and Southwest starting in 1989–1991. Survivors from the initial SHS study cohort were recruited for examinations related to cognitive aging and AD in 2010–2013 (N = 818), and then invited back for a repeat cognitive and imaging visit in 2017–2019 (N = 403). Full recruitment and examination protocols, including CONSORT diagrams, ethical approvals, and registrations have been previously described. ⁵ , ⁶ All participating institutional, Indian Health Service, and tribal review boards approved study protocols. All participants provided written, informed consent.

2.2. Plasma assays

Five AT(N) plasma biomarkers (pTau₁₈₁, Aβ₄₀, Aβ₄₂, GFAP, NfL) were measured in all available, stored samples from the second cognitive aging visit (2017–2019, N = 401). Standard sample handling for ethylene diamine tetraacetic acid (EDTA) plasma samples included: multiple inversion; centrifugation at 1000G at 4°C; immediate separation from buffy coat and aliquot into 2 mL cryovial tubes; and −80°C storage. Assays were done by the Clinical Neurochemistry Laboratory at the University of Gothenburg, according to standard protocols evaluated by the Standardization of Alzheimer's Blood Biomarkers workgroup of the Global Biomarker Standardization Consortium of the Alzheimer's Association. ⁷ Assays were Simoa Neurology 4‐Plex E kit (Quanterix) for Aβ_{42 & 40}, GFAP, and NfL, which has good test‐retest performance metrics. ⁸ Separately, pTau₁₈₁ was measured using the Simoa prototype two‐step assay, as previously described in detail. ⁹ The preparation protocol included thaw at room temperature, vortex, centrifuge 5 min at 10,000G (4‐plex kit) or centrifuge 10 min (two‐step assay). The resulting data concentrations are in pg/mL, except for Aβ_42/40 which is calculated as a ratio. All measurements were performed in one round of experiments, using one batch of reagents, by board‐certified laboratory technicians, who were blinded to clinical data. Intra‐assay coefficients of variation were below 10% for all of the markers.

2.3. Cognitive tests

Neuropyschological tests at both visits included the California Verbal Learning Test II Short Form (CVLT II‐SF; primary domains assessed: verbal learning, memory), ¹⁰ Modified Mini‐Mental Status Examination (3MSE; general cognition), ¹¹ Controlled Oral Word Association FAS (COWA‐FAS; phonemic fluency, executive function), ¹² WAIS digit symbol coding test (processing speed). Tests added at the second visit included the National Alzheimer's Coordinating Center Uniform Data Set (UDS) C2 forms, ¹³ which are comprised of Montreal Cognitive Assessment (MoCA; general function), ¹⁴ Number Span Test forward and backward (auditory attention, working memory), Benson Complex Figure copy and recall (visuospatial), ¹⁵ animal and vegetable naming tests (semantic fluency), Trail Making Test A and B (simple and divided attention, executive function), ¹⁶ Craft Story immediate and delayed recall (conextual verbal memory), ¹⁷ and Multilingual Naming Test (MINT; semantic naming). ¹⁸ Functional status was also assessed for instrumental activities of daily living using the Functional Activities Questionnaire.

2.4. Cognitive status

An expert panel adjudicated cognitive status and possible etiology by consensus from detailed case review of cognitive and functional data from both examinations. Cognitive case status was assigned as cognitive intact; mild cognitive impairment (MCI) for those with cognitive loss or significant impairment in > 1 test for a given cognitive domain but not significant loss in functional status or multi domain involvement; dementia for those with significant loss in functional ability in activities of daily living and/or significant, multi domain cognitive impairment; and impaired not MCI (InMCI) for those who are not intact but who do not fall into typical MCI and dementia patterns of impairment. Primary and secondary etiologies were assigned as one of several possible underlying causes of cognitive impairment, including AD, vascular brain injury (VBI), traumatic brain injury (TBI), or other. Etiologic assignments were based on patterns of cognitive domain loss and additionally informed by clinical and imaging data.

2.5. Other data

Field center staff collected self‐reported age (years), sex (male, female), years of formal education. Apolipoprotein E ε4 (APOE ε4) carrier status was measured by standard genotyping procedures ¹⁹ , ²⁰ using blood samples collected at the baseline SHS visit. Estimated glomerular filtration rate (eGFR) was calculated using serum creatinine via the Modification of Diet in Renal Disease (MDRD) equation. ²¹

2.6. Statistical analyses

We summarized participant characteristics for the study population overall and by APOE ε4 carrier status using mean and standard deviation (normal, continuous), median and interquartile range (skewed, continuous), or count and percent (dichotomous). Percent difference in median, mean, or count for biomarkers was calculated by comparing APOE ε4 carriers to noncarriers, in order to examine potential differences by endogenous or baseline risk. Graphical plots visually summarized distribution and range of plasma marker measures. Values of plasma marker measures and select participant characteristics were summarized by adjudicated cognitive status (cognitive intact, MCI, dementia, In‐MCI) or by probable underlying primary or secondary etiology (AD, VBI, TBI, AD). Receiver operating chracteristic (ROC) analysis was conducted to evaluate diagnostic performance, as estimated by area under the curve (AUC), for plasma markers for dementia (compared to cognitive intact) or AD etiology (compared to cognitive intact), with empirical estimation of optimal cutoff for each marker using Liu product maximization method. Lasso regression with bootstrap errors estimation was used to empirically identify the best performing discriminant panel of plasma markers, in combination with age, sex, and education. All statistics were conducted using Stata v17 (College Station, TX) and R v. 4 (R Foundation for Statistical Computing, Vienna Austria).

3. RESULTS

Our analysis included 401 (of 403, > 99%) participants from the 2017–2019 examination visit (Table 1). This study population was generally elderly (mean age 78, range 70–94), with 20.9% APOE ε4 allele carriers. Most AT(N) plasma markers had wide range of variance, with heavy right‐skew. Mean pTau₁₈₁ was 8.6 pg/mL (median 5.0), Aβ₄₀ 144.5 pg/mL (med 128), Aβ₄₂ 8.4 pg/mL (med 8.2), Aβ_42/40 ratio 0.06 (med 0.1), GFAP 178.2 pg/mL (med 150.0), and NfL 41.1 pg/mL (med 31.6). With respect to outliers, pTau₁₈₁ had 6 measured values substantially higher than the rest (108, 120, 131, 161, and 442 pg/mL); NfL also had 1 outlier (343 pg/mL). Bar and scatter plots of plasma marker measures (Figure S1) illustrate a heavy distribution skew for each of the plasma marker measures, with long right tails corresponding to high concentration of plasma proteins. Adjudicated cognitive status estimated that 54.3% of participants were impaired, with approximately 34% attributed to AD and 38% to VBI (not mutually exclusive).

TABLE 1.

Selected characteristics from American Indian participants of the Strong Heart Study (2017–2019).

Available sociodemographics, clinical data	N = 401
Age (years)	78.1 (4.7)
Male sex, n (%)	118 (29.4%)
Years education	13.0 (2.5)
APOEε4 status, n (%)	83 (20.9%)
Available plasma biomarkers data	*N = 401*
pTau₁₈₁ pg/mL, mean (SD); median; range	8.6 (25.6); 5.0; 1.5‐442.0
Aβ₄₀ pg/mL, mean (SD); median; range	144.5 (48.4); 128; 6‐518.0
Aβ₄₂ pg/mL, mean (SD); median; range	8.4 (2.8); 8.2; 2.2‐21.1
Aβ_42/40 ratio, mean (SD); median; range	0.06 (0.01); 0.1; 0.01‐0.18
GFAP pg/mL, mean (SD); median; range	178.7 (95.8); 150.0; 43.0‐651.0
NfL pg/mL, mean (SD); median; range	41.1 (30.5); 30.5; 9.7‐343.0
Available case review consensus data	*N = 396*
Adjudicated cognitive intact, n (%)	181 (45.7%)
Adjudicated MCI, n (%)	139 (35.1%)
Adjudicated dementia, n (%)	41 (10.4%)
Adjudicated Impaired not MCI, n (%)	35 (8.8%)
Available case review consensus data	*N = 396*
Any AD etiology, n (%) ^a	92 (33.7%)
Any VBI etiology, n (%) ^a	110 (37.8%)
Any TBI etiology, n (%) ^a	28 (13.4%)

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Note: Values provided as mean (SD) unless otherwise specified.

Abbreviations: APOE, apoprotein E; AD, Alzheimer's disease;Aβ, amyloid‐beta; GFAP, glial fibrillary protein; NfL, neurofilament light chain; pTau, phosphorylated tau;TBI, traumatic brain injury; VBI, vascular brain injury.

^{^a}

Adjudicated etiologies include primary and secondary assessments and thus not mutually exclusive; percentages calculated by comparison to no cognitive impairment (cognitive intact, n = 181).

Because of distribution skews, median values for plasma markers were used in comparisons between APOE ε4 allele carriers, a major risk factor for AD dementia (Table 2). Comparing APOE ε4 carriers to noncarriers, pTau₁₈₁ and GFAP were substantively higher (12% and 16% higher, respectively) and Aβ₄₂ and Aβ_42/40 ratio substantively lower (8.4%, 16.7% lower, respectively); however, only the difference for Aβ_42/40 was statistically significant after correction for multiple testing. There was no remarkable difference for Aβ₄₀ or for NfL.

TABLE 2.

Plasma marker measures, overall and by APOE ε4 status, in American Indian participants of the Strong Heart Study (2017–2019).

	Overall N = 401	No APOE ε4 allele n = 314	APOE ε4 carrier n = 83	Percent difference: APOE ε4 versus not	p‐Value	FDR Q‐value
pTau₁₈₁ pg/mL	5.0 (3.5, 7.4)	4.9 (3.4, 7.2)	5.5 (3.9, 8.3)	+ 12%	0.067	0.182
Aβ₄₀ pg/mL	128 (116, 175)	128 (116, 175)	127 (113, 177)	‐0.8%	0.780	0.780
Aβ₄₂ pg/mL	8.2 (6.7, 9.7)	8.3 (6.8, 9.8)	7.6 (6.2, 9.4)	‐8.4%	0.140	0.210
Aβ_42/40 ratio	0.06 (0.05, 0.07)	0.06 (0.05, 0.07)	0.05 (0.04, 0.06)	‐ 16.7%	0.001	0.006
GFAP pg/mL	150 (114, 218)	149 (112, 215)	173 (121, 240)	+ 16.1%	0.091	0.182
NfL pg/mL	31.6 (23.0, 48.9)	31.5 (22.9, 48.6)	31.8 (23.4, 49.6)	+ 3.2%	0.470	0.564

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Note: Values provided as med (IQR) unless otherwise indicated. p‐Values based on Wilcoxon rank‐sum test; FDR (false discovery rate). Q‐value based on Benjamini–Hochberg method and assessed for significance at Q < 0.1.

Abbreviations: APOE, apoprotein E; Aβ, amyloid‐beta; GFAP, glial fibrillary protein; NfL, neurofilament light chain; pTau, phosphorylated tau.

Quantitative comparison of plasma marker measures across adjudicated cognitive impairment categories (intact, MCI, dementia, In‐MCI) identified significant differences comparing participants who were cognitive‐intact versus impaired (Table 3). Participants with impairment had significantly higher pTau₁₈₁ than cognitive intact (p < 0.001); those with dementia had highest values and MCI, InMCI had intermediate values. Similarly, both GFAP and NfL were highest among dementia patients (p = 0.006, p < 0.001, respectively) and intermediate in MCI, InMCI groups. However, Aβ₄₀, Aβ₄₂, and Aβ_42/40 were not significantly or markedly different across cognitive categories. ROC analysis with empirical estimation of optimal cutoff for each plasma marker, comparing participants with dementia to those who were cognitive intact, identified GFAP and NfL with best performance (AUC ∼ 0.7), pTau_181, Aβ_42, Aβ₄₀ with moderate performance (AUC > 0.6), and Aβ₄₂₄₀ ratio with poor performance (AUC < 0.5). Furthermore, pTau₁₈₁, Aβ₄₂, and NfL were highly sensitive for discriminating dementia (sensitivity > 0.7), whereas GFAP was highly specific (> 0.7).

TABLE 3.

Plasma marker measures, comparing cognitive impairment categories, among American Indian participants of the Strong Heart Study (2017–2019).

					Comparing dementia, versus cognitive intact
	Cognitive intact N = 181 45.6%	MCI N = 140 35.3%	Dementia N = 41 10.3%	In‐MCI N = 35 8.8%	Empirical, optimal cut point	ROC (AUC, sensitivity, specificity) at cut point
pTau₁₈₁ pg/mL	7.6 (17.3)	6.8 (4.7)	10.2 (18.4)	6.6 (4.3)	>4.5	0.66 (0.78, 0.53)
Aβ₄₀ pg/mL	140.1 (43.6)	143.9 (45.3)	155.8 (41.7)	143.7 (49.0)	<135.5	0.63 (0.61, 0.65)
Aβ₄₂ pg/mL	8.2 (2.9)	8.5 (2.9)	8.8 (2.3)	8.3 (2.5)	<7.9	0.60 (0.71, 0.49)
Aβ_42/40 ratio	0.059 (0.014)	0.059 (0.014)	0.057 (0.009)	0.060 (0.013)	>0.06	0.46 (0.51, 0.42)
GFAP pg/mL	162.2 (80.9)	175.9 (88.0)	216.2 (88.6)	186.5 (125.3)	>199	0.73 (0.68, 0.78)
NfL pg/mL	34.9 (21.3)	42.4 (28.7)	53.7 (31.1)	43.7 (30.9)	>32.7	0.69 (0.73, 0.64)

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Note: Values provided as mean (SD) unless otherwise indicated. Cutpoints and ROC comparisons with major risk factors: age (> 75 years, AUC 0.61), sex (male, AUC 0.54), education (< 13, AUC 0.33), APOE (e4 carrier, AUC 0.59). Statistical tests by Kruskal–Wallis test across cognitive categories, with FDR (false discovery rate) Q‐value estimated by Benjamini–Hochberg method, assessed for significance at Q < 0.1: pTau181 Q < 0.001; Ab40 Q = 0.192; Ab42 Q = 0.420; Ab4240 Q = 0.420; GFAP Q = 0.006, NFL Q < 0.001.

Similar quantitative comparisons of plasma marker diagnostic performance, comparing probable primary or secondary etiologies underlying cognitive impairment (AD, VBI, TBI) with no cognitive impairment, suggested moderate performance for pTau_181, GFAP in discriminating AD from intact (AUC > 0.6; Table 4). Similarly, GFAP and NfL had moderate performance discriminating VBI from intact (AUC > 0.6), and pTau₁₈₁ in discriminating TBI from intact (AUC > 0.6). Furthermore, pTau₁₈₁ and GFAP were highly specific in discriminating TBI (specificity > 0.7), but none of the markers was highly sensitive or specific for AD or VBI.

TABLE 4.

Plasma marker measures, comparing cognitive impairment etiologies, among American Indian participants of the Strong Heart Study (2017–2019).

	Plasma marker values among AD etiology n = 92 (33.7%)	AUC (sensitivity, specificity) comparing AD versus intact	Plasma marker values among VBI etiology n = 110 (37.8%)	AUC (sensitivity, specificity) comparing VBI versus intact	Plasma marker values among TBI etiology n = 28 (13.4%)	AUC (sensitivity, specificity) comparing TBI versus intact
pTau₁₈₁ pg/mL	7.9 (12.6)	0.61 (0.61, 0.62)	8.4 (12.4)	0.59 (0.58, 0.60)	6.5 (3.8)	0.63 (0.54, 0.72)
Aβ₄₀ pg/mL	143.8 (41.8)	0.55 (0.53, 0.57)	151.2 (47.6)	0.58 (0.68, 0.47)	144.7 (62.3)	0.52 (0.43, 0.61)
Aβ₄₂ pg/mL	8.4 (2.6)	0.53 (0.44, 0.61)	8.9 (3.0)	0.56 (0.65, 0.48)	8.0 (2.9)	0.50 (0.54, 0.46)
Aβ_42/40 ratio	0.06 (0.02)	0.48 (0.43, 0.53)	0.06 (0.01)	0.51 (0.54, 0.47)	0.06 (0.01)	0.50 (0.64, 0.36)
GFAP pg/mL	193.5 (101.2)	0.60 (0.53, 0.68)	187.0 (90.4)	0.61 (0.54, 0.68)	150.9 (74.2)	0.56 (0.36, 0.77)
NfL pg/mL	41.2 (27.9)	0.57 (0.57, 0.58)	51.4 (33.7)	0.65 (0.64, 0.66)	41.5 (32.3)	0.58 (0.61, 0.56)

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Note: Values provided as mean (SD) unless otherwise indicated.Etiologies assigned as primary or secondary and thus not mutually exclusive. Comparators for AD, VBI, TBI are no cognitive impairment. ROC analysis assessed at empirically defined cutpoint (Liu product maximization method).

Finally, an empirically selected panel of plasma markers, in combination with age, sex, and education, identified pTau_181, Aβ₄₂₄₀ ratio, GFAP, and NfL to comprise the best‐performing model for discriminating dementia from cognitive intact participants (AUC 0.83, 95% 0.77, 0.89).

Examination of plasma markers comparing memory impairment categories demonstrated similar patterns (Supplement Table): pTau and NfL were associated with memory impairment, but GFAP and Aβ markers were not. Associations for pTau and NfL were highest among those with encoding type memory impairment, and NfL values were intermediate among those with retrieval type impairment.

Qualitative, visual examination of plasma marker distributions suggests differences by adjudicated etiologies underlying cognitive case status, as well (Figure S2). In general, distribution and mean of pTau was higher among those assessed with possible AD, both as primary or as mixed etiology, as well as those with TBI etiology, but not among those with vascular etiology. In contrast, Aβ40 and 42 were lower for those with TBI etiology, but not different among those with AD or vascular etiologies. Aβ42/40 ratio was not different across any groups. GFAP was slightly higher among those with vascular injury, both primary and mixed, and also somewhat higher (bimodal) among those with possible underlying TBI etiology. For NfL, all distributions had long tails, but means were slightly higher among those with vascular and TBI etiologies.

4. DISCUSSION

Overall, this is the first report on plasma markers related to AD and other brain diseases—including pTau₁₈₁, Aβ₄₀, Aβ₄₂, Aβ_42/40 ratio, GFAP, and NfL—among older American Indian individuals, describing associations with clinical, imaging, and cognitive findings. Our findings include association of APOE ε4 with Aβ_42/40 ratio only; associations of imaged brain volumes with Aβ₄₀, Aβ₄₂, GFAP, and NfL but imaged brain infarcts with pTau₁₈₁; and associations of memory impairments with pTau₁₈₁, NfL, age, and sex. We also identified differences in distribution of pTau₁₈₁ closely associated with possible AD etiology, as well as other markers (GFAP, NfL) with vascular and traumatic injury—all of which are important underlying features for cognitive status in this population.

In this study of American Indian individuals, our marker of pTau₁₈₁ (mean 8.6 pg/mL; range 1.5–442.0), had a lower overall mean but a larger variance, compared with prior studies in non‐Hispanic White (mean 16, range 10‐23), African American (mean 14, range 9.4–22.9), and Hispanic/Latino (mean 18.0, range 11.3–25.0) individuals. Values > 40‐60 are often considerd consistent with AD. ²² , ²³ Thus, our population of American Indian individuals did not appear to have evidence of mean differences in pathologic tau (T), but did have some outliers with much higher values than in prior studies.

In contrast, two of our markers of amyloid (A) pathology—Aβ₄₂ and Aβ_42/40—were much lower than in non‐Hispanic White or other populations, also consistent with much greater degree of pathology (3X), especially for the larger, more insoluble, more AD‐specific Aβ₄₂. ²⁴ In cognitive intact non‐Hispanic White individuals, Aβ₄₀ is estimated to have mean 95 pg/mL, Aβ₄₂ 22 pg/mL, and Aβ_42/40 ratio 0.23; for cognitive impaired, these numbers are 98 pg/mL, 20 pg/mL, 0.2 ratio (respectively). ²⁵ In our study, mean Aβ₄₀ was 125 pg/mL, Aβ₄₂ 8.0 pg/mL, and Aβ_42/40 ratio 0.06 among cognitive‐intact participants and Aβ₄₀ 143 pg/mL, Aβ₄₂ 8.3 pg/mL, and Aβ_42/40 ratio 0.06 in dementia participants. Thus, compared with prior studies of non‐Hispanic White individuals, this study of American Indian individuals had 1.3X (intact) to 1.5X (impaired) Aβ₄₀, that is, less abnormal; 0.3X (intact), 0.4X (impaired) Aβ₄₂, that is, more abnormal; and 0.26X (intact), 0.3X (impaired) Aβ_42/40 ratio, that is, more abnormal. Together, these findings suggest that American Indian individuals may have earlier, faster, or more common accumulation of AD pathology than non‐Hispanic White individuals, ²⁴ possibly to the point of obscuring within‐group comparisons. Furthermore, these data suggest that prior epidemiologic reports of comparable risk between American Indian and non‐Hispanic White individuals may be underestimates.

GFAP and NfL, were similar in this study as in other populations, especially in consideration of age‐comparable standards. ²³ , ²⁵ , ²⁶ , ²⁷ , ²⁸ , ²⁹ However, given that plasma biomarker data from these studies have all reported research‐grade assays that are not calibrated to a set standard, and thus may not be directly comparable, any comparisons for differences in absolute concentrations should be interpreted with caution.

Prior studies of plasma marker diagnostics similarly detected excellent discriminant capacity in pathological as well as neuropsychological studies; however, in contrast to our data, with best performance from GFAP, NfL, and pTau₁₈₁, prior studies found Aβ_42/40 ratio and pTau₁₈₁ performed best. ³⁰ In addition, prior studies detected very good performance, with pTau₁₈₁ AUC > 0.9 (cutoff > 2.7 pg/mL), ³¹ whereas our findings were more moderate, with pTau₁₈₁ AUC = 0.6 (cutoff > 7.9 pg/mL). However, similar to studies identifying that a panel of markers performs better than individual markers, ³² we detected a combination of core markers (age, sex, education, pTau_181, Aβ_42/40 ratio, GFAP, NfL) had excellent performance, despite poor to moderate performance of any marker individually.

4.1. Prior null findings for APOE ε4

In the context of prior reports from our research group that APOE ε4 had no detectable association with imaging, cognitive, or memory features in this population, ³³ these current findings are consistent with lack of generalizability of conventional markers to this population, perhaps because of risk saturation throughout the population. However, selective survival as well as latent resilience factors can also contribute to observed null associations. Future research should continue to examine these methodological issues, and to establish population strata for whom conventional AD biomarkers may be differentially accurate.

4.2. Strengths and limitations

These analyses include data from comprehensive, standardized collection protocols in a well‐characterized cohort of an understudied population. The novel biomarker assays have potential to inform better cognitive impairment and dementia case definitions, as well as guide future research for the purposes of evaluating diagnostic, therapeutic, and prevention efforts. Furthermore, these analyses are theory‐driven, and not empirical, with the strong potential to provide novel information both about this population as well as about the underlying neurology. As mentioned, because this population represents a survival cohort, differential selection may influence our findings, if likelihood of participation is associated with the outcome. Previous reports in this cohort have found little evidence of selective survival using indirect analysis. ³⁴ However, future research should focus on younger population strata in order to include preclinical groups in the study sampling frame.

In this study, we adjusted for eGFR because renal filtration losses have been reported to increase plasma marker concentrations. However, the association of plasma markers with renal dysfunction may not be mediated by filtration function, as measured by eGFR. Proteins are not cleared by the glomerular basement membrane, where defects result in proteinuria or lower plasma protein concentrations. Therefore, alternative mechanisms accounting for observed renal associations, such as proximal tubular secretion (dys)function or hormone (dys)production, may be needed in future research on plasma biomarkers of brain injury. Furthermore, we did not evaluate associations for endocrine features, which may further serve as mediating or modifying clinical features in these associations, and represent an important avenue for future investigation.

5. CONCLUSION

In summary, this report contains seminal evaluation of blood biomarkers for brain injury, especially AD, in American Indian elders, using sociodemographic, clinical features, imaging, and cognitive evaluations. Future research to validate these measures using PET or other gold standards, to examine these measures in younger groups, to examine standardized data for comparison to existing studies, and to examine the predictive and diagnostic utility of these markers are still needed. However, our findings establish these measures, and their coinciding features, as potential markers in determinance of brain injury, including AD, with implications for researchers and clinicians developing understanding of this complex and devastating condition in this unique population.

CONFLICT OF INTEREST STATEMENT

A.S.D. receives support from several NIH‐funded projects. A.S.D. has no COI to report. Longstreth: W.T.L. receives support from several NIH‐funded projects. W.T.L. has no COI to report. Rhoads: K.R. receives support from several NIH‐funded projects. K.R. provides 2–3 expert witness consultations per year.Umans: J.U. receives support from several NIH‐funded projects. J.U. has no COI to report. Buchwald: D.B. receives support from several NIH‐funded projects. D.B. has no COI to report. T.G. receives support from several NIH‐funded projects. T.G. has no COI to report. K.B. is supported by the Swedish Research Council (#2017‐00915 and #2022‐00732), the Swedish Alzheimer Foundation (#AF‐930351, #AF‐939721 and #AF‐968270), Hjärnfonden, Sweden (#FO2017‐0243 and #ALZ2022‐0006), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF‐agreement (#ALFGBG‐715986 and #ALFGBG‐965240), the European Union Joint Program for Neurodegenerative Disorders (JPND2019‐466‐236), the Alzheimer's Association 2021 Zenith Award (ZEN‐21‐848495), and the Alzheimer's Association 2022‐2025 Grant (SG‐23‐1038904 QC). KB has served as a consultant, at advisory boards, or at data monitoring committees for Abcam, Axon, BioArctic, Biogen, JOMDD/Shimadzu. Julius Clinical, Lilly, MagQu, Novartis, Ono Pharma, Pharmatrophix, Prothena, Roche Diagnostics, and Siemens Healthineers, and is a co‐founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program, outside the work presented in this paper. E.R. receives support from several NIH‐funded projects. E.R. is co‐founder and advisor for ALZPath. H.Z. is a Wallenberg Scholar supported by grants from the Swedish Research Council (#2018‐02532), the European Union's Horizon Europe research and innovation programme under grant agreement No 101053962, Swedish State Support for Clinical Research (#ALFGBG‐71320), the Alzheimer Drug Discovery Foundation (ADDF), USA (#201809‐2016862), the AD Strategic Fund and the Alzheimer's Association (#ADSF‐21‐831376‐C, #ADSF‐21‐831381‐C, and #ADSF‐21‐831377‐C), the Bluefield Project, the Olav Thon Foundation, the Erling‐Persson Family Foundation, Stiftelsen för Gamla Tjänarinnor, Hjärnfonden, Sweden (#FO2022‐0270), the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska‐Curie grant agreement No 860197 (MIRIADE), the European Union Joint Programme – Neurodegenerative Disease Research (JPND2021‐00694), and the UK Dementia Research Institute at UCL (UKDRI‐1003). H.Z. has served at scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZPath, Annexon, Apellis, Artery Therapeutics, AZTherapies, CogRx, Denali, Eisai, Nervgen, Novo Nordisk, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen, and Roche, and is a co‐founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). Author disclosures are available in the supporting information.

CONSENT STATEMENT

All participating institutional, Indian Health Service, and tribal review boards approved study protocols. All participants provided written, informed consent.

Supporting information

Supporting Information

ALZ-20-2072-s001.docx^{(3.1MB, docx)}

Supporting Information

ALZ-20-2072-s002.pdf^{(613.7KB, pdf)}

ACKNOWLEDGMENTS

The authors with to acknowledge the continued participation and efforts of all SHS field centers, staff, communities, tribes, and participants in the conduct of this research. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Indian Health Service. The Strong Heart Study and its ancillary programs have been funded in whole or in part with federal funds from the National Institutes of Health, including R01HL093086, P50AG005136, K01AG057821; and with philanthropic funds from the University of Washington Alzheimer's Disease Research Center. The Strong Heart Study has been funded in whole or in part with federal funds from the National Heart, Lung, and Blood Institute, National Institute of Health, Department of Health and Human Services, under contract numbers 75N92019D00027, 75N92019D00028, 75N92019D00029, & 75N92019D00030. The study was previously supported by research grants: R01HL109315, R01HL109301, R01HL109284, R01HL109282, and R01HL109319 and by cooperative agreements: U01HL41642, U01HL41652, U01HL41654, U01HL65520, and U01HL65521.

Suchy‐Dicey AM, Longstreth WT Jr, Rhoads K, et al. Plasma biomarkers of Alzheimer's disease and related dementias in American Indians: The Strong Heart Study. Alzheimer's Dement. 2024;20:2072–2079. 10.1002/alz.13664

REFERENCES

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10. Delis DC, Kramer JH, Kaplan E, Ober BA. California Verbal Learning Test (CVLT‐II). 2nd ed. Adult Version. The Psychological Corporation; 2000. [Google Scholar]
11. Teng EL, Chang Chui H. The Modified Mini‐Mental (3MS) Examination. Journal Clinical Psychiatry. 1987;48(8):314‐318. [PubMed] [Google Scholar]
12. Benton AL, Hansher K. Multilingual Aphasia Examination. 2nd ed. AJA Associates; 1976. [Google Scholar]
13. Morris JC, Weintraub S, Chui HC, et al. The Uniform Data Set (UDS): clinical and cognitive variables and descriptive data from Alzheimer Disease Centers. Alzheimer Dis Assoc Disord. 2006;20(4):210‐216. doi: 10.1097/01.wad.0000213865.09806.92 [DOI] [PubMed] [Google Scholar]
14. Nasreddine ZS, Phillips NA, Bedirian V, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695‐699. doi: 10.1111/j.1532-5415.2005.53221.x [DOI] [PubMed] [Google Scholar]
15. Possin KL, Laluz VR, Alcantar OZ, Miller BL, Kramer JH. Distinct neuroanatomical substrates and cognitive mechanisms of figure copy performance in Alzheimer's disease and behavioral variant frontotemporal dementia. Neuropsychologia. 2011;49(1):43‐48. doi: 10.1016/j.neuropsychologia.2010.10.026 [DOI] [PMC free article] [PubMed] [Google Scholar]
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17. Liew TM. Developing a brief neuropsychological battery for early diagnosis of cognitive impairment. J Am Med Dir Assoc. 2019;20(8):1054 e11‐1054 e20. doi: 10.1016/j.jamda.2019.02.028 [DOI] [PMC free article] [PubMed] [Google Scholar]
18. Ivanova I, Salmon DP, Gollan TH. The multilingual naming test in Alzheimer's disease: clues to the origin of naming impairments. J Int Neuropsychol Soc. 2013;19(3):272‐283. doi: 10.1017/S1355617712001282 [DOI] [PMC free article] [PubMed] [Google Scholar]
19. North KE, Goring HH, Cole SA, et al. Linkage analysis of LDL cholesterol in American Indian populations: the Strong Heart Family Study. J Lipid Res. 2006;47(1):59‐66. doi: 10.1194/jlr.M500395-JLR200 [DOI] [PubMed] [Google Scholar]
20. Kataoka S, Robbins DC, Cowan LD, et al. Apolipoprotein E polymorphism in American Indians and its relation to plasma lipoproteins and diabetes. The Strong Heart Study. Arterioscler Thromb Vasc Biol. 1996;16(8):918‐925. [DOI] [PubMed] [Google Scholar]
21. Rothberg MB, Kehoe ED, Courtemanche AL, et al. Recognition and management of chronic kidney disease in an elderly ambulatory population. J Gen Intern Med. 2008;23(8):1125‐1130. doi: 10.1007/s11606-008-0607-z [DOI] [PMC free article] [PubMed] [Google Scholar]
22. Tsiknia AA, Edland SD, Sundermann EE, et al. Sex differences in plasma p‐Tau181 associations with Alzheimer's disease biomarkers, cognitive decline, and clinical progression. Mol Psychiatry. 2022;27(10):4314‐4322. doi: 10.1038/s41380-022-01675-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
23. Windon C, Iaccarino L, Mundada N, et al. Comparison of plasma and CSF biomarkers across ethnoracial groups in the ADNI. Alzheimers Dement. 2022;14(1):e12315. doi: 10.1002/dad2.12315 [DOI] [PMC free article] [PubMed] [Google Scholar]
24. Gu L, Guo Z. Alzheimer's Abeta42 and Abeta40 peptides form interlaced amyloid fibrils. J Neurochem. 2013;126(3):305‐311. doi: 10.1111/jnc.12202 [DOI] [PMC free article] [PubMed] [Google Scholar]
25. Chatterjee P, Pedrini S, Stoops E, et al. Plasma glial fibrillary acidic protein is elevated in cognitively normal older adults at risk of Alzheimer's disease. Transl Psychiatry. 2021;11(1):27. doi: 10.1038/s41398-020-01137-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
26. Pereira JB, Janelidze S, Smith R, et al. Plasma GFAP is an early marker of amyloid‐beta but not tau pathology in Alzheimer's disease. Brain. 2021;144(11):3505‐3516. doi: 10.1093/brain/awab223 [DOI] [PMC free article] [PubMed] [Google Scholar]
27. Mielke MM, Syrjanen JA, Blennow K, et al. Plasma and CSF neurofilament light: relation to longitudinal neuroimaging and cognitive measures. Neurology. 2019;93(3):e252‐e260. doi: 10.1212/WNL.0000000000007767 [DOI] [PMC free article] [PubMed] [Google Scholar]
28. Simren J, Andreasson U, Gobom J, et al. Establishment of reference values for plasma neurofilament light based on healthy individuals aged 5–90 years. Brain Commun. 2022;4(4):fcac174. doi: 10.1093/braincomms/fcac174 [DOI] [PMC free article] [PubMed] [Google Scholar]
29. Harp C, Thanei GA, Jia X, et al. Development of an age‐adjusted model for blood neurofilament light chain. Ann Clin Transl Neurol. 2022;9(4):444‐453. doi: 10.1002/acn3.51524 [DOI] [PMC free article] [PubMed] [Google Scholar]
30. Benedet AL, Brum WS, Hansson O, et al. The accuracy and robustness of plasma biomarker models for amyloid PET positivity. Alzheimers Res Ther. 2022;14(1):26. doi: 10.1186/s13195-021-00942-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
31. Coomans EM, Verberk IMW, Ossenkoppele R, et al. A head‐to‐head comparison between plasma pTau181 and tau PET Along the Alzheimer's disease continuum. J Nucl Med. 2023;64(3):437‐443. doi: 10.2967/jnumed.122.264279 [DOI] [PMC free article] [PubMed] [Google Scholar]
32. Benussi A, Cantoni V, Rivolta J, et al. Classification accuracy of blood‐based and neurophysiological markers in the differential diagnosis of Alzheimer's disease and frontotemporal lobar degeneration. Alzheimers Res Ther. 2022;14(1):155. doi: 10.1186/s13195-022-01094-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
33. Suchy‐Dicey A, Howard B, Longstreth WT Jr, Reiman EM, Buchwald D. APOE genotype, hippocampus, and cognitive markers of Alzheimer's disease in American Indians: data from the Strong Heart Study. Alzheimers Dement. 2022;18(12):2518‐2526. doi: 10.1002/alz.12573 [DOI] [PMC free article] [PubMed] [Google Scholar]
34. Suchy‐Dicey AM, Muller CJ, Madhyastha TM, et al. Telomere length and magnetic resonance imaging findings of vascular brain injury and Central Brain Atrophy: the Strong Heart Study. Am J Epidemiol. 2018;187(6):1231‐1239. doi: 10.1093/aje/kwx368 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting Information

ALZ-20-2072-s001.docx^{(3.1MB, docx)}

Supporting Information

ALZ-20-2072-s002.pdf^{(613.7KB, pdf)}

[alz13664-bib-0001] 1. Jack CR Jr, Bennett DA, Blennow K, et al. NIA‐AA Research framework: toward a biological definition of Alzheimer's disease. Alzheimers Dement. 2018;14(4):535‐562. doi: 10.1016/j.jalz.2018.02.018 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0002] 2. Counts SE, Ikonomovic MD, Mercado N, Vega IE, Mufson EJ. Biomarkers for the early detection and progression of Alzheimer's disease. Neurotherapeutics. 2017;14(1):35‐53. doi: 10.1007/s13311-016-0481-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0003] 3. Tsvetkova DZ, Bergquist SH, Parker MW, et al. Fear and uncertainty do not influence reported willingness to undergo lumbar punctures in a U.S. Multi‐cultural cohort. Front Aging Neurosci. 2017;9:22. doi: 10.3389/fnagi.2017.00022 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0004] 4. Olsson B, Lautner R, Andreasson U, et al. CSF and blood biomarkers for the diagnosis of Alzheimer's disease: a systematic review and meta‐analysis. Lancet Neurol. 2016;15(7):673‐684. doi: 10.1016/S1474-4422(16)00070-3 [DOI] [PubMed] [Google Scholar]

[alz13664-bib-0005] 5. Suchy‐Dicey AM, Shibata D, Best LG, et al. Cranial magnetic resonance imaging in elderly American Indians: design, methods, and implementation of the cerebrovascular disease and its consequences in American Indians study. Neuroepidemiology. 2016;47(2):67‐75. doi: 10.1159/000443277 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0006] 6. Suchy‐Dicey AM, Oziel K, Sawyer C, et al. Educational and clinical associations with longitudinal cognitive function and brain imaging in American Indians: the strong heart study. Neurology. 2022;99(24):e2637‐e2647. doi: 10.1212/WNL.0000000000201261 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0007] 7. Verberk IMW, Misdorp EO, Koelewijn J, et al. Characterization of pre‐analytical sample handling effects on a panel of Alzheimer's disease‐related blood‐based biomarkers: results from the Standardization of Alzheimer's Blood Biomarkers (SABB) working group. Alzheimers Dement. 2022;18(8):1484‐1497. doi: 10.1002/alz.12510 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0008] 8. Cullen NC, Janelidze S, Mattsson‐Carlgren N, et al. Test‐retest variability of plasma biomarkers in Alzheimer's disease and its effects on clinical prediction models. Alzheimers Dement. 2022. Epub ahead of print. PMID: 35699240; PMCID: PMC9747985. doi: 10.1002/alz.12706 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0009] 9. Karikari TK, Pascoal TA, Ashton NJ, et al. Blood phosphorylated tau 181 as a biomarker for Alzheimer's disease: a diagnostic performance and prediction modelling study using data from four prospective cohorts. Lancet Neurol. 2020;19(5):422‐433. doi: 10.1016/S1474-4422(20)30071-5 [DOI] [PubMed] [Google Scholar]

[alz13664-bib-0010] 10. Delis DC, Kramer JH, Kaplan E, Ober BA. California Verbal Learning Test (CVLT‐II). 2nd ed. Adult Version. The Psychological Corporation; 2000. [Google Scholar]

[alz13664-bib-0011] 11. Teng EL, Chang Chui H. The Modified Mini‐Mental (3MS) Examination. Journal Clinical Psychiatry. 1987;48(8):314‐318. [PubMed] [Google Scholar]

[alz13664-bib-0012] 12. Benton AL, Hansher K. Multilingual Aphasia Examination. 2nd ed. AJA Associates; 1976. [Google Scholar]

[alz13664-bib-0013] 13. Morris JC, Weintraub S, Chui HC, et al. The Uniform Data Set (UDS): clinical and cognitive variables and descriptive data from Alzheimer Disease Centers. Alzheimer Dis Assoc Disord. 2006;20(4):210‐216. doi: 10.1097/01.wad.0000213865.09806.92 [DOI] [PubMed] [Google Scholar]

[alz13664-bib-0014] 14. Nasreddine ZS, Phillips NA, Bedirian V, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695‐699. doi: 10.1111/j.1532-5415.2005.53221.x [DOI] [PubMed] [Google Scholar]

[alz13664-bib-0015] 15. Possin KL, Laluz VR, Alcantar OZ, Miller BL, Kramer JH. Distinct neuroanatomical substrates and cognitive mechanisms of figure copy performance in Alzheimer's disease and behavioral variant frontotemporal dementia. Neuropsychologia. 2011;49(1):43‐48. doi: 10.1016/j.neuropsychologia.2010.10.026 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0016] 16. Llinas‐Regla J, Vilalta‐Franch J, Lopez‐Pousa S, Calvo‐Perxas L, Torrents Rodas D, Garre‐Olmo J. The trail making test. Assessment. 2017;24(2):183‐196. doi: 10.1177/1073191115602552 [DOI] [PubMed] [Google Scholar]

[alz13664-bib-0017] 17. Liew TM. Developing a brief neuropsychological battery for early diagnosis of cognitive impairment. J Am Med Dir Assoc. 2019;20(8):1054 e11‐1054 e20. doi: 10.1016/j.jamda.2019.02.028 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0018] 18. Ivanova I, Salmon DP, Gollan TH. The multilingual naming test in Alzheimer's disease: clues to the origin of naming impairments. J Int Neuropsychol Soc. 2013;19(3):272‐283. doi: 10.1017/S1355617712001282 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0019] 19. North KE, Goring HH, Cole SA, et al. Linkage analysis of LDL cholesterol in American Indian populations: the Strong Heart Family Study. J Lipid Res. 2006;47(1):59‐66. doi: 10.1194/jlr.M500395-JLR200 [DOI] [PubMed] [Google Scholar]

[alz13664-bib-0020] 20. Kataoka S, Robbins DC, Cowan LD, et al. Apolipoprotein E polymorphism in American Indians and its relation to plasma lipoproteins and diabetes. The Strong Heart Study. Arterioscler Thromb Vasc Biol. 1996;16(8):918‐925. [DOI] [PubMed] [Google Scholar]

[alz13664-bib-0021] 21. Rothberg MB, Kehoe ED, Courtemanche AL, et al. Recognition and management of chronic kidney disease in an elderly ambulatory population. J Gen Intern Med. 2008;23(8):1125‐1130. doi: 10.1007/s11606-008-0607-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0022] 22. Tsiknia AA, Edland SD, Sundermann EE, et al. Sex differences in plasma p‐Tau181 associations with Alzheimer's disease biomarkers, cognitive decline, and clinical progression. Mol Psychiatry. 2022;27(10):4314‐4322. doi: 10.1038/s41380-022-01675-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0023] 23. Windon C, Iaccarino L, Mundada N, et al. Comparison of plasma and CSF biomarkers across ethnoracial groups in the ADNI. Alzheimers Dement. 2022;14(1):e12315. doi: 10.1002/dad2.12315 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0024] 24. Gu L, Guo Z. Alzheimer's Abeta42 and Abeta40 peptides form interlaced amyloid fibrils. J Neurochem. 2013;126(3):305‐311. doi: 10.1111/jnc.12202 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0025] 25. Chatterjee P, Pedrini S, Stoops E, et al. Plasma glial fibrillary acidic protein is elevated in cognitively normal older adults at risk of Alzheimer's disease. Transl Psychiatry. 2021;11(1):27. doi: 10.1038/s41398-020-01137-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0026] 26. Pereira JB, Janelidze S, Smith R, et al. Plasma GFAP is an early marker of amyloid‐beta but not tau pathology in Alzheimer's disease. Brain. 2021;144(11):3505‐3516. doi: 10.1093/brain/awab223 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0027] 27. Mielke MM, Syrjanen JA, Blennow K, et al. Plasma and CSF neurofilament light: relation to longitudinal neuroimaging and cognitive measures. Neurology. 2019;93(3):e252‐e260. doi: 10.1212/WNL.0000000000007767 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0028] 28. Simren J, Andreasson U, Gobom J, et al. Establishment of reference values for plasma neurofilament light based on healthy individuals aged 5–90 years. Brain Commun. 2022;4(4):fcac174. doi: 10.1093/braincomms/fcac174 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0029] 29. Harp C, Thanei GA, Jia X, et al. Development of an age‐adjusted model for blood neurofilament light chain. Ann Clin Transl Neurol. 2022;9(4):444‐453. doi: 10.1002/acn3.51524 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0030] 30. Benedet AL, Brum WS, Hansson O, et al. The accuracy and robustness of plasma biomarker models for amyloid PET positivity. Alzheimers Res Ther. 2022;14(1):26. doi: 10.1186/s13195-021-00942-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0031] 31. Coomans EM, Verberk IMW, Ossenkoppele R, et al. A head‐to‐head comparison between plasma pTau181 and tau PET Along the Alzheimer's disease continuum. J Nucl Med. 2023;64(3):437‐443. doi: 10.2967/jnumed.122.264279 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0032] 32. Benussi A, Cantoni V, Rivolta J, et al. Classification accuracy of blood‐based and neurophysiological markers in the differential diagnosis of Alzheimer's disease and frontotemporal lobar degeneration. Alzheimers Res Ther. 2022;14(1):155. doi: 10.1186/s13195-022-01094-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0033] 33. Suchy‐Dicey A, Howard B, Longstreth WT Jr, Reiman EM, Buchwald D. APOE genotype, hippocampus, and cognitive markers of Alzheimer's disease in American Indians: data from the Strong Heart Study. Alzheimers Dement. 2022;18(12):2518‐2526. doi: 10.1002/alz.12573 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz13664-bib-0034] 34. Suchy‐Dicey AM, Muller CJ, Madhyastha TM, et al. Telomere length and magnetic resonance imaging findings of vascular brain injury and Central Brain Atrophy: the Strong Heart Study. Am J Epidemiol. 2018;187(6):1231‐1239. doi: 10.1093/aje/kwx368 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Plasma biomarkers of Alzheimer's disease and related dementias in American Indians: The Strong Heart Study

Astrid M Suchy‐Dicey

W T Longstreth Jr

Kristoffer Rhoads

Jason Umans

Dedra Buchwald

Thomas Grabowski

Kaj Blennow

Eric Reiman

Henrik Zetterberg

Abstract

INTRODUCTION

METHODS

RESULTS

DISCUSSION

1. BACKGROUND

RESEARCH IN CONTEXT

2. METHODS

2.1. Setting

2.2. Plasma assays

2.3. Cognitive tests

2.4. Cognitive status

2.5. Other data

2.6. Statistical analyses

3. RESULTS

TABLE 1.

TABLE 2.

TABLE 3.

TABLE 4.

4. DISCUSSION

4.1. Prior null findings for APOE ε4

4.2. Strengths and limitations

5. CONCLUSION

CONFLICT OF INTEREST STATEMENT

CONSENT STATEMENT

Supporting information

ACKNOWLEDGMENTS

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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