Changes in Alzheimer Disease Blood Biomarkers and Associations With Incident All-Cause Dementia

Yifei Lu; James Russell Pike; Jinyu Chen; Keenan A Walker; Kevin J Sullivan; Bharat Thyagarajan; Michelle M Mielke; Pamela L Lutsey; David Knopman; Rebecca F Gottesman; A Richey Sharrett; Josef Coresh; Thomas H Mosley; Priya Palta

doi:10.1001/jama.2024.6619

. 2024 Jul 28;332(15):1258–1269. doi: 10.1001/jama.2024.6619

Changes in Alzheimer Disease Blood Biomarkers and Associations With Incident All-Cause Dementia

Yifei Lu ¹, James Russell Pike ^2,³, Jinyu Chen ⁴, Keenan A Walker ⁵, Kevin J Sullivan ⁶, Bharat Thyagarajan ⁷, Michelle M Mielke ⁸, Pamela L Lutsey ⁹, David Knopman ¹⁰, Rebecca F Gottesman ¹¹, A Richey Sharrett ⁶, Josef Coresh ^2,³, Thomas H Mosley ⁶, Priya Palta ^12,^✉

¹Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill

²Division of Epidemiology, Department of Population Health, New York University Grossman School of Medicine, New York

³Optimal Aging Institute, New York University Grossman School of Medicine, New York

⁴Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland

⁵Laboratory of Behavioral Neuroscience, National Institute on Aging Intramural Research Program, Baltimore, Maryland

⁶Department of Medicine, MIND Center, University of Mississippi Medical Center, Jackson

⁷Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis

⁸Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina

⁹Division of Epidemiology and Community Health, University of Minnesota, Minneapolis

¹⁰Department of Neurology, Mayo Clinic, Rochester, Minnesota

¹¹National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, Maryland

¹²Department of Neurology, University of North Carolina at Chapel Hill

Accepted for Publication: March 29, 2024.

Published Online: July 28, 2024. doi:10.1001/jama.2024.6619

^✉

Corresponding Author: Priya Palta, PhD, MHS, Department of Neurology, UNC School of Medicine, 123 W Franklin St, Ste 410, Chapel Hill, NC 27516 (priya_palta@med.unc.edu).

Author Contributions: Drs Lu and Palta had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Lu, Sullivan, Sharrett, Coresh, Palta.

Acquisition, analysis, or interpretation of data: Lu, Pike, Chen, Walker, Sullivan, Thyagarajan, Mielke, Lutsey, Knopman, Gottesman, Coresh, Mosley, Palta.

Drafting of the manuscript: Lu, Pike, Chen, Palta.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Lu, Pike, Chen, Coresh, Palta.

Obtained funding: Coresh, Mosley, Palta.

Administrative, technical, or material support: Thyagarajan, Lutsey, Coresh, Palta.

Supervision: Coresh, Palta.

Other - Oversaw data collection: Lutsey.

Conflict of Interest Disclosures: Dr Mielke reported serving as a consultant to Biogen, Eisai, Merck, Eli Lilly, Novo Nordisk, and Roche outside the submitted work. Dr Knopman reported receiving grants from Biogen and Lilly and personal fees from Washington University St Louis outside the submitted work. No other disclosures were reported.

Funding/Support: The Atherosclerosis Risk in Communities (ARIC) study is carried out as a collaborative study supported by grants 75N92022D00001, 75N92022D00002, 75N92022D00003, 75N92022D00004, and 75N92022D00005 from the National Heart, Lung, and Blood Institute (NHLBI); the ARIC Neurocognitive Study is supported by grants U01HL096812, U01HL096814, U01HL096899, U01HL096902, and U01HL096917 from the NHLBI. The blood biomarker measurements were in part supported by grant R00AG052830 from the National Institute on Aging (NIA). Dr Lu was in part supported by grant R01AG066134 from the NIA. Dr Palta was in part supported by grants R00AG052830 and R01AG066134 from the NIA. Dr Gottesman was supported by the NINDS Intramural Research Program. Dr Walker was supported by the NIA Intramural Research Program.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2.

Additional Contributions: We thank the staff and participants of the ARIC study for their important contributions.

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Corresponding author.

PMCID: PMC11284635 PMID: 39068543

Key Points

Question

What is the association of plasma biomarker (amyloid-β 42 to amyloid-β 40 [Aβ42:Aβ40] ratio, phosphorylated tau at threonine [p-tau181], neurofilament light [NfL], glial fibrillary acid protein [GFAP]) changes from midlife to late life with all-cause dementia?

Findings

In this retrospective analysis of prospectively collected plasma biomarkers from 1525 adults from the Atherosclerosis Risk in Communities study, only Alzheimer disease (AD)–specific (Aβ42:Aβ40, p-Tau181) biomarkers in midlife demonstrated significant long-term associations with late-life dementia. In late life, each of the biomarkers and their change from midlife were significantly associated with incident all-cause dementia.

Meaning

AD-specific biomarkers’ association with dementia starts in midlife whereas late-life measures of AD, neuronal injury, and astrogliosis biomarkers are all associated with dementia.

Abstract

Importance

Plasma biomarkers show promise for identifying Alzheimer disease (AD) neuropathology and neurodegeneration, but additional examination among diverse populations and throughout the life course is needed.

Objective

To assess temporal plasma biomarker changes and their association with all-cause dementia, overall and among subgroups of community-dwelling adults.

Design, Setting, and Participants

In 1525 participants from the US-based Atherosclerosis Risk in Communities (ARIC) study, plasma biomarkers were measured using stored specimens collected in midlife (1993-1995, mean age 58.3 years) and late life (2011-2013, mean age 76.0 years; followed up to 2016-2019, mean age 80.7 years). Midlife risk factors (hypertension, diabetes, lipids, coronary heart disease, cigarette use, and physical activity) were assessed for their associations with change in plasma biomarkers over time. The associations of biomarkers with incident all-cause dementia were evaluated in a subpopulation (n = 1339) who were dementia-free in 2011-2013 and had biomarker measurements in 1993-1995 and 2011-2013.

Exposure

Plasma biomarkers of amyloid-β 42 to amyloid-β 40 (Aβ42:Aβ40) ratio, phosphorylated tau at threonine 181 (p-tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP) were measured using the Quanterix Simoa platform.

Main Outcomes and Measures

Incident all-cause dementia was ascertained from January 1, 2012, through December 31, 2019, from neuropsychological assessments, semiannual participant or informant contact, and medical record surveillance.

Results

Among 1525 participants (mean age, 58.3 [SD, 5.1] years), 914 participants (59.9%) were women, and 394 participants (25.8%) were Black. A total of 252 participants (16.5%) developed dementia. Decreasing Aβ42:Aβ40 ratio and increasing p-tau181, NfL, and GFAP were observed from midlife to late life, with more rapid biomarker changes among participants carrying the apolipoprotein E epsilon 4 (APOEε4) allele. Midlife hypertension was associated with a 0.15-SD faster NfL increase and a 0.08-SD faster GFAP increase per decade; estimates for midlife diabetes were a 0.11-SD faster for NfL and 0.15-SD faster for GFAP. Only AD-specific biomarkers in midlife demonstrated long-term associations with late-life dementia (hazard ratio per SD lower Aβ42:Aβ40 ratio, 1.11; 95% CI, 1.02-1.21; per SD higher p-tau181, 1.15; 95% CI, 1.06-1.25). All plasma biomarkers in late life had statistically significant associations with late-life dementia, with NfL demonstrating the largest association (1.92; 95% CI, 1.72-2.14).

Conclusions and Relevance

Plasma biomarkers of AD neuropathology, neuronal injury, and astrogliosis increase with age and are associated with known dementia risk factors. AD-specific biomarkers’ association with dementia starts in midlife whereas late-life measures of AD, neuronal injury, and astrogliosis biomarkers are all associated with dementia.

This cohort study characterizes temporal changes in plasma biomarkers, identifies factors associated with changes in plasma biomarkers over time, and evaluates the prospective associations of plasma biomarkers with late-life all-cause dementia.

Introduction

Alzheimer disease (AD) and related dementias feature a prolonged preclinical stage spanning decades,¹ with the transition from midlife to late life marking the critical period for the onset and accumulation of pathological brain changes. Plasma biomarkers have shown great promise in becoming a cost-effective and noninvasive screening tool for AD pathology and neurodegeneration in symptomatic persons, but their presymptomatic trajectories are not well understood.¹

Despite a surge in studies investigating plasma biomarkers,^{2,3,4,5,6,7,8,9} limitations persist, including primarily cross-sectional study designs, homogenous study populations,^3,4,10,11 and analyses restricted to older adults that do not account for potential attrition or selection biases.^2,5,6,7 Few studies have specifically examined plasma biomarker changes from midlife to late life,^4,9 particularly within diverse community-based cohorts. Furthermore, midlife cardiometabolic disorders, such as hypertension and diabetes, are well-established risk factors for dementia^{12,13,14,15,16}; however, their associations with intermediate stages of plasma biomarker changes, which could reveal potential mechanisms and actionable stages for preserving brain health, remain unclear.

Using the well-established community-based Atherosclerosis Risk in Communities study, we characterized temporal changes in plasma biomarkers, identified factors associated with changes in plasma biomarkers over time, and evaluated the prospective associations of plasma biomarkers with late-life all-cause dementia. Analyses were conducted overall and stratified by demographics (sex, race), apolipoprotein E epsilon 4 (APOE ε4) allele status, and cognitive diagnosis. We hypothesized that known dementia risk factors would be associated with changes in the biomarkers and that AD-specific biomarkers (the amyloid β 42 to amyloid β 40 [Aβ42/40] ratio and phosphorylated tau at threonine 181 [p-tau181]), and biomarkers of neurodegeneration (neurofilament light [NfL] and glial fibrillary acidic protein [GFAP]), would both be associated with all-cause dementia.

Methods

Study Design and Population

ARIC is a prospective cohort study (eFigure 1 in Supplement 1) that originally focused on the etiology of atherosclerosis in a middle-aged sample of largely Black and White participants (Table).¹⁷ Between 1987 and 1989, 15 792 participants were recruited from 4 US communities (Washington County, Maryland; Forsyth County, North Carolina; selected suburbs of Minneapolis, Minnesota; and Jackson, Mississippi). The visit 1 assessment was followed by visit 2 (1990-1992, n = 14 348), visit 3 (1993-1995, n = 12 887), visit 4 (1996-1998, n = 11 656), visit 5 (2011-2013, n = 6538), visit 6 (2016-2017, n = 4214), and visit 7 (2018-2019, n = 3589). ARIC participants or their proxies also completed annual (through 2011) and semiannual (starting in 2012) phone-based assessments and granted access to hospitalization records and death certificates. The protocol was approved by the institutional review boards at Johns Hopkins University, Wake Forest University, University of Mississippi Medical Center, the University of Minnesota, and the University of North Carolina at Chapel Hill. Written informed consent was obtained from each participant or their legal representative at each visit. This study conformed to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE).¹⁸

Table. Characteristics of Analytic Sample in Midlife and Late Life Stratified by Dementia Incidence in Late Life: The Atherosclerosis Risk in Communities Study, 1993-2019 (n = 1525)^a.

	No./total (%) of patients		Absolute difference (95% CI)	P value^b
	No dementia (n = 1273)	Incident dementia in late life (n = 252)	Absolute difference (95% CI)	P value^b
Demographics
Visit 3 age, mean (SD), y [No.]	57.7 (4.9) [1273]	61.4 (5.0) [252]	3.7 (3.2 to 4.2)	<.001
Female	755/1273 (59.3)	159/252 (63.1)	3.8 (−1.2 to 9.0)	.26
Male	518/1273 (40.7)	93/252 (36.9)	−3.8 (−9.0 to 1.2)	.26
Black	332/1273 (26.1)	62/252 (24.6)	−1.5 (−6.1 to 2.9)	.62
White	941/1273 (73.9)	190/252 (75.4)	1.5 (−2.9 to 6.1)	.62
Race by center
Forsyth County, North Carolina
White	309/1273 (24.3)	50/252 (19.8)	−4.5 (−8.5 to 0.1)	.03
Black	27/1273 (2.1)	0/252 (0.0)	−2.1 (−2.7 to −1.5)
Black, Jackson, Mississippi	305/1273 (24.0)	62/252 (24.6)	0.6 (−3.9 to 5.0)
White, Minneapolis, Minnesota	304/1273 (23.9)	63/252 (25.0)	1.1 (−3.4 to 5.2)
White, Washington County, Maryland	328/1273 (25.8)	77/252 (30.6)	4.8 (−0.2 to 9.9)
Education
<High school	142/1272 (11.2)	45/252 (17.9)	6.7 (2.6 to 10.4)	<.001
High school, GED, or vocational school	514/1272 (40.4)	118/252 (46.8)	6.4 (1.5 to 12.0)
Some college, graduate, or professional school	616/1272 (48.4)	89/252 (35.3)	−13.1 (−17.8 to −8.2)
Lifestyle and clinical characteristics
≥1 Apolipoprotein ε4 alleles	334/1232 (27.1)	84/242 (34.7)	7.6 (2.6 to 12.7)	.02
BMI, mean (SD) [No.]	27.9 (5.0) [1172]	28.0 (5.3) [252]	0.1 (−0.5 to 0.6)	.91
eGFR, mean (SD), mL/min/1.73 m²[No.]	92.4 (14.1) [1125]	89.0 (13.3) [241]	−3.4 (−4.9 to −2.0)	<.001
Hypertension	347/1167 (29.7)	97/250 (38.8)	9.1 (3.7 to 14.2)	<.001
Diabetes	88/1171 (7.5)	19/252 (7.5)	0.0 (−2.7 to 2.7)	.99
Total cholesterol, mean (SD), mg/dL [No.]	206.2 (35.1) [1172]	211.2 (37.6) [252]	4.9 (1.1 to 9.1)	.05
HDL cholesterol, mean (SD), mg/dL [No.]	55.4 (18.6) [1172]	55.1 (19.3) [252]	−0.3 (−2.4 to 1.5)	.77
Coronary heart disease	12/1172 (1.0)	2/252 (0.8)	−0.2 (−1.2 to 0.6)
Cigarette use
Current	140/1171 (12.0)	24/252 (9.5)	−2.5 (−5.8 to 0.7)	.21
Former	473/1171 (40.4)	99/252 (39.3)	−1.1 (−6.0 to 3.7)
Never	558/1171 (47.7)	129/252 (51.2)	3.5 (−1.2 to 8.6)
Moderate to vigorous physical activity, mean (SD), min/wk [No.]	143.9 (152.3) [1170]	149.4 (154.5) [252]	5.5 (−10.7 to 21.5)	.60
Plasma biomarkers, mean (SD) [No.]
Amyloid-β 40, pg/mL
1993-1995	59.0 (28.2) [1172]	62.3 (28.1) [252]	3.3 (0.3 to 6.2)	.09
2011-2013	107.6 (24.8) [1273]	114.5 (26.4) [251]	6.9 (4.2 to 9.7)	<.001
Amyloid-β 42, pg/mL
1993-1995	3.8 (1.8) [1172]	3.8 (1.6) [252]	0.0 (−0.2 to 0.2)	.93
2011-2013	6.4 (1.9) [1273]	6.3 (1.8) [251]	−0.1 (−0.4 to 0.0)	.16
Amyloid-β 42 to amyloid-β 40 ratio
1993-1995	0.068 (0.019) [1143]	0.065 (0.018) [248]	−0.003 (−0.005 to −0.001)	.04
2011-2013	0.060 (0.013) [1271]	0.055 (0.011) [251]	−0.005 (−0.007 to −0.004)	<.001
Phosphorylated tau-181, pg/mL
1993-1995	1.5 (0.9) [1171]	1.8 (1.3) [252]	0.3 (0.1 to 0.4)	<.001
2011-2013	2.9 (1.6) [1271]	4.0 (2.1) [252]	1.1 (0.9 to 1.3)	<.001
Neurofilament light chain, pg/mL
1993-1995	12.4 (7.6) [1172]	14.1 (6.5) [252]	1.7 (1.0 to 2.3)	<.001
2011-2013	23.0 (12.5) [1273]	32.2 (14.9) [251]	9.2 (7.7 to 10.7)	<.001
Glial fibrillary acidic protein, pg/mL
1993-1995	102.0 (59.7) [1172]	121.6 (60.1) [252]	18.6 (12.2 to 24.7)	<.001
2011-2013	178.5 (88.3) [1273]	252.4 (134.7) [251]	73.9 (59.5 to 87.4)	<.001
Cognitive and death status
Global cognition factor score, mean (SD) [No.]
1990-1992	0.890 (0.753) [1168]	0.681 (0.848) [252]	−0.209 (−0.293 to −0.113)	<.001
2011-2013	0.135 (0.792) [1272]	−0.543 (0.757) [252]	−0.678 (−0.754 to −0.595)	<.001
Visit 5 cognitive diagnosis
Normal	896/1272 (70.4)	98/251 (39.0)	−31.4 (−36.9 to −26.4)	<.001
Mild cognitive impairment	376/1272 (29.6)	153/251 (61.0)	31.4 (26.4 to 36.9)	<.001
Death by 2019	142/1273 (11.2)	116/252 (46.0)	−34.8 (−39.9 to −30.1)	<.001

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Abbreviations: BMI, body mass index, calculated as weight in kilograms divided by height in meters squared; eGFR, estimated glomerular filtration rate; GED, General Educational Development credential; HDL, high-density lipoprotein.

SI conversion factor: To convert HDL and total cholesterol from mg/dL to mmol/L, multiply by 0.0259.

^{^a}

The analytic sample was restricted to predementia plasma biomarkers. Dementia diagnoses were determined from adjudicated review of in-person cognitive examinations, telephone interviews, informant interviews, hospitalization records, and death certificates. Absolute differences with 95% CIs between participants who remained dementia free and participants diagnosed with dementia in late life were estimated via percentile bootstrapping with 1000 samples.

^{^b}

P values were calculated using χ² tests, t tests, and Cochran-Armitage trend tests.

The design and selection for this study were based on participants within the ARIC study who had previously enrolled in a neuroimaging substudy at ARIC visit 5 (eFigure 1 in Supplement 1). A subsample of participants from visit 5 were screened for eligibility for neuroimaging.¹⁹ Participants were eligible if they had no contraindications and previously participated in an ARIC brain magnetic resonance imaging (MRI) study,²⁰ had evidence of cognitive impairment, or were part of a random sample of participants without cognitive impairment. Among 1977 participants who had completed a valid MRI scan at visit 5, a subset of 1539 had stored plasma samples from 2 or more different visits (3, 5, and 6 or 7). Fourteen participants who were neither Black nor White were excluded due to small sample sizes. Temporal changes in plasma biomarkers from midlife (visit 3) were examined in a sample of 1525 participants. Any plasma measurement available after incident all-cause dementia was excluded from the analysis, thereby focusing analysis on biomarker trajectories preceding incident dementia. The association of visit 3 risk and protective factors with changes in plasma biomarkers were estimated in a subsample (n = 1424) that excluded participants without plasma biomarkers at visit 3. The association of plasma biomarkers with incident dementia was evaluated in a subsample (n = 1339) that excluded participants with prevalent dementia at visit 5 or those who lacked further follow-up for dementia after visit 5 due to death or study dropout.

Measures

Plasma Biomarkers

Plasma samples were analyzed by the Neurology 4-Plex E assay Simoa platform (Quanterix) with an HD-X instrument (eMethods in Supplement 1). The multiplex assay included measures of Aβ40, Aβ42, NfL, and GFAP. A singleplex assay measured p-tau181 (version 1.0). Reproducibility (eTables 1 and 2 in Supplement 1) was examined in a subsample at visit 3 (n = 38) and visit 5 (n = 90). The intraclass correlation coefficient was acceptable at visit 3 (range, 0.75-0.93) and excellent at visit 5 (range, 0.84-0.95). The ratio of Aβ42 to Aβ40 was calculated, and measures of p-tau181, NfL, and GFAP were base 2 log transformed. Lower levels of Aβ42:Aβ40 and higher levels of log₂ p-tau181, NfL, and GFAP indicate greater pathology.^21,22

Incident All-Cause Dementia

Dementia was diagnosed using a protocol modeled on the National Institute on Aging–Alzheimer Association criteria^23,24 and the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition).²⁵ A panel of clinicians and neuropsychologists adjudicated a diagnosis based on a comprehensive cognitive examination.²⁶ When a cognitive examination could not be performed, dementia was ascertained from informant interviews, phone-based assessments, medical records, and death certificates (eMethods in Supplement 1). Administrative censoring occurred on December 31, 2019.

Covariates

Race, sex, date of birth, and educational attainment (<high school; high school, General Educational Development test, or vocational school; some college, graduate, or professional school) were self-reported at visit 1. Race was self-identified by participants from 4 fixed categories (American Indian or Alaska Native, Asian, Black, or White). Race was examined given documented differences in dementia rates.²⁷ A 5-group covariate of race-field center (Minnesota, Maryland, and North Carolina White individuals and North Carolina and Mississippi Black individuals) was specified due to confounding of the race-field center because only sufficient numbers of Black participants were recruited at the Forsyth County and Jackson field centers. The presence of APOE ε4 alleles was ascertained by the TaqMan assay (Applied Biosystems).²⁸

Height and weight were measured, and body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) was calculated. Creatinine-cystatin C estimated glomerular filtration rates (eGFR) were generated.²⁹ Systolic and diastolic blood pressure (BP) were measured by the Omron HEM-907 XL oscillometric automated sphygmomanometer (Omron Healthcare). Hypertension was defined as systolic BP of 140 mm Hg or higher, diastolic BP of 90 mm Hg or higher, or self-report of antihypertensive medication use. Diabetes was defined as fasting glucose of 126 mg/dL or higher, nonfasting glucose of 200 mg/dL or higher, use of glucose-lowering medication, or self-reported physician diagnosis. Total cholesterol was determined using enzymatic methods.³⁰ A Beckman Olympus AU400 Series (Olympus) chemistry analyzer calculated total cholesterol and high density lipoprotein (HDL) cholesterol. Medical record review identified cases of coronary heart disease.¹⁷ Cigarette use was ascertained by self-report. Physical activity was measured by the Baecke questionnaire³¹ and classified as meeting or not meeting the US Physical Activity Guidelines.³² Additional details about study measures are provided in the eMethods section in Supplement 1.

Analysis

Descriptive Statistics

Analyses were conducted using SAS version 9.4 (SAS Institute Inc) and Stata version 14.0 (StataCorp). Statistical significance for all estimates was defined as a 2-sided P value <.05. Characteristics of 1525 participants in the analytic sample were stratified by a subsequent diagnosis of dementia. Absolute differences between subgroups were calculated using percentile bootstrapping. P values were calculated using χ² tests, t tests, and Cochran-Armitage trend tests. To evaluate potential selection bias, descriptive statistics were generated of ARIC participants who completed visit 5 stratified by inclusion in the analytic sample (eTable 3 in Supplement 1).

Change in Plasma Biomarkers From Midlife to Late Life

Plasma biomarkers were standardized to visit 3 to facilitate comparisons of the rate of change in SDs over time (analysis 1 involving 1525 participants, eFigure 1 in Supplement 1). Change per decade was estimated using 2-level, linear mixed-effects models that calculated subject-specific point estimates and 95% CIs. Linear mixed-effects models specified years from age 60 as the timescale, included a random intercept and time slope, and employed an unstructured variance-covariance matrix. Covariates included birth decade, sex, race center, education, and APOE ε4 allele status and their interaction with time. Time-varying measures of eGFR and BMI were included to reduce biased estimates.^33,34 Midlife (visit 3) risk factors (hypertension, diabetes, total cholesterol, HDL cholesterol levels, coronary heart disease, and cigarette use) and protective factors (physical activity) associated with cognitive decline and dementia were also included.³⁵ Multiple imputation was used to impute missing covariates in all linear mixed-effects models. Sampling weights were used to account for selection bias, and inverse probability weights were used to mitigate attrition bias (eMethods in Supplement 1). Sensitivity analyses were performed to examine change in SDs over time with different covariate adjustments and without weights.

Factors Associated With Change in Plasma Biomarkers From Midlife to Late Life

The association of midlife (visit 3) risk or protective factors with the rate of change per decade in standardized plasma biomarkers was estimated using linear mixed-effects models that adjusted for age, sex, race center, education, APOE ε4, and time-varying measures of eGFR and BMI. Time from visit 3 was specified as the timescale. Interactions of each visit 3 risk and protective factors with time were included to estimate their associations with differences in biomarker rates of change (analysis 2 involving 1424 participants, eFigure 1 in Supplement 1).

Associations of Plasma Biomarkers With Incident All-Cause Dementia

To facilitate comparisons between biomarkers, each biomarker was standardized to their respective visit or to the change between visits (analysis 3 involving 1339 participants, eFigure 1 in Supplement 1). Age, sex, and race center adjusted incidence rates of dementia were estimated using Poisson regression models with robust error variance. Dementia risks associated with a 1-SD difference in each biomarker were estimated using Cox regression models that used the Efron method to handle tied onset times. The assumption of linearity was evaluated by examining Martingale residuals, and the proportional hazards assumption was assessed by inspecting Schoenfeld residuals. Neither modeling assumption was violated.

Cox models adjusted for visit 5 measures of all covariates included in linear mixed-effects models. Each model included sampling weights to account for selection bias and used multiple imputation to impute missing covariates. Sensitivity analyses were performed to examine HRs with different covariate adjustments, without weights, and when accounting for the competing risk of death using Fine-Gray regression models. In exploratory analyses, fully-adjusted, weighted Cox models tested for multiplicative interactions, additive interactions, and effect modification by demographics (race, sex), APOE ε4 allele status, and cognitive diagnosis.

The accuracy with which plasma biomarkers discriminated incident all-cause dementia was evaluated using time-dependent receiver operator characteristic (ROC) curves generated from Cox models with censoring weights³⁶ and sampling weights (eMethods in Supplement 1). Because the area under the curve (AUC) was stable over time, ROC curves are shown at only the median follow-up of 7.4 years. Sensitivity and specificity were determined for each biomarker separately, collectively, and with or without APOE ε4 allele or demographics.

Results

Participant Characteristics

Among the 1525 participants in the analytic sample, the mean (SD) age at visit 3 was 58.3 (5.1) years, 914 (59.9%) were women, 394 (25.8%) were Black individuals, and 252 (16.5%) developed dementia (Table). In late life, 994 participants (65.3%) of 1523 were adjudicated as having normal cognition and 529 (34.7%) as having mild cognitive impairment. Participants diagnosed with dementia by 2019 were more likely to be older and have lower levels of Aβ42:Aβ40 and higher levels of p-tau181, NfL, and GFAP.

Change in Plasma Biomarkers From Midlife to Late Life

Aβ42:Aβ40 ratio decreased with age whereas p-tau181, NfL, and GFAP increased (Figure 1 and eFigure 2 and eTable 4 in Supplement 1). Participants with at least 1 APOE ε4 allele compared with those with no APOE ε4 alleles had more rapid changes in biomarkers. Participants adjudicated as having cognitive impairment compared with those with normal cognition had more rapid changes in biomarkers. Variations in rates of biomarker change by race and sex were mixed, with no clear pattern of one group consistently showing faster rates of change than the other (eFigures 3 and 4 in Supplement 1).

Factors Associated With Change in Plasma Biomarkers From Midlife to Late Life

The rate of change from midlife to late life (Figure 2; eTable 5 in Supplement 1) were greater among participants with vs without hypertension (differences in the rate of change: p-tau181, 0.13 SD per decade; NfL, 0.15 SD per decade; and GFAP, 0.08 SD per decade). Participants with diabetes had a slower rate of change in Aβ42:Aβ40 (0.16 SD per decade) and faster rate of change in NfL (0.11 SD per decade) and GFAP (0.15 SD per decade) than did those without diabetes. The associations of midlife cholesterol levels, coronary heart disease, cigarette use, and physical activity with biomarker trajectories were not statistically significant in most models.

Associations of Plasma Biomarkers With Incident All-Cause Dementia

The median follow-up among 1339 participants to incident dementia was 7.4 years (IQR, 6.5-7.9 years). Age-, sex-, race center–adjusted incidence rates (eFigure 5 in Supplement 1) ranged from 17.33 to 18.64 per 1000 person-years at the mean level of plasma biomarkers measured in midlife and late life. Late-life measurement levels showed a greater difference in incidence than did midlife measurement levels. A 1-SD–higher level of Aβ42:Aβ40 and 1-SD–lower level of p-tau181, NfL, and GFAP in late life corresponded to an incidence that ranged from 10.20 to 12.38 per 1000 person-years, whereas a 1-SD lower Aβ42:Aβ40 and 1-SD higher p-tau181, NfL, and GFAP corresponded to an incidence that ranged from 25.20 to 29.45 per 1000 person-years.

In midlife, lower Aβ42:Aβ40 (hazard ratio [HR] per 1-SD increase, 1.11; 95% CI, 1.02-1.21) and higher p-tau181 (HR, 1.15 per 1-SD increase; 95% CI, 1.06-1.25) exhibited statistically significant associations with incident all-cause dementia whereas higher NfL and higher GFAP did not (Figure 3). All plasma biomarkers had statistically significant associations with dementia in late life, with NfL demonstrating the largest association (HR per 1-SD increase, 1.92; 95% CI, 1.72-2.14). Change in biomarkers from midlife to late life also had statistically significant associations with incident dementia. Estimates were consistent in sensitivity analyses (eTables 6 to 8 in Supplement 1).

Figure 3. — A dementia diagnosis was determined from adjudicated review of in-person cognitive examinations, telephone interviews, informant interviews, hospitalization records, and death certificates. Hazard ratios (HRs) and 95% CIs for incident dementia were calculated from cause-specific Cox proportional hazards regression models. All models were adjusted for visit 5 measures of age, sex, race center, education, the presence of apolipoprotein ε4 alleles, estimated glomerular filtration rate, body mass index, hypertension, diabetes, total cholesterol, high-density lipoprotein cholesterol, coronary heart disease, cigarette use, and physical activity as time-invariant covariates. Multiple imputation by chained equations was employed to impute missing covariates. Inverse probability weighting was used to account for selection bias.

^aBiomarkers were standardized to visit 3.

^bThe Aβ42:Aβ40 (amyloid-β 42 to amyloid-40) ratio was inverted so that higher values indicate greater risk of incident dementia.

^cBiomarkers were standardized to visit 5.

^dStandardized change in biomarkers measured at visit 3 and visit 5.

p-tau181 indicates phosphorylated tau-181 at threonine 181.

Plasma Aβ42:Aβ40 and p-tau181 measured in midlife exhibited greater associations with incident dementia in APOE ε4 allele carriers than in noncarriers (eTable 9 in Supplement 1) as did p-tau181 measured in late life. Consistently larger associations with incident dementia were observed for NfL and GFAP measured in late life than in midlife; however, the associations among APOE ε4 allele carriers and noncarriers were comparable.

Among all biomarkers measured at different times, NfL measured in late life showed the highest discrimination for incident dementia (AUC = 0.69, eFigure 6 in Supplement 1). The 4 plasma biomarkers together (Figure 4) measured in late life outperformed any single biomarker (AUC = 0.74) but did not outperform demographics (AUC = 0.76). The highest discrimination for all-cause dementia was achieved by combining late-life biomarkers and demographics (AUC = 0.81); the addition of the APOE ε4 allele did not lead to further improvement (AUC = 0.81).

Figure 4. — Biomarkers included the amyloid-β 42 to amyloid-40 (Aβ42:Aβ40) ratio, log₂ phosphorylated tau at threonine 181, log₂ neurofilament light, and log₂ glial fibrillary acidic protein. Receiver operating characteristic curves depict the extent to which plasma biomarkers collectively discriminate incident dementia with or without the apolipoprotein ε4 (*APOE ε4*) allele or demographics (age, sex, and race by center). A higher area under the curve indicates greater discriminatory accuracy. Differences between panels represent changes caused by using plasma biomarkers from midlife, late life, or change between midlife and late life. Receiver operating characteristic curves were generated from cause-specific, Cox proportional hazards regression models that estimated discriminatory accuracy at the median follow-up time (7.4 years) after visit 5 (2011-2013). Multiple imputation by chained equations was employed to impute missing covariates. Inverse probability weighting was used to account for selection bias.

Discussion

In this community-based cohort, changes in plasma biomarkers were observed from midlife to late life, including decreasing Aβ42:Aβ40 and increasing p-tau181, NfL, and GFAP. APOE ε4 allele carriers had greater changes in biomarker levels than did noncarriers. Midlife hypertension and diabetes were associated with a more rapid rise in both NfL and GFAP. AD-specific biomarkers (ie, Aβ42:Aβ40, p-tau181) measured in midlife were associated with late-life all-cause dementia, but biomarkers not specific to AD (ie, NfL, GFAP) measured in midlife were not. All plasma biomarkers in late life had large and statistically significant associations with late-life dementia, with NfL demonstrating the greatest association. The highest discrimination for all-cause dementia was attained by combining late-life biomarkers and demographics.

To enhance the use of AD biomarkers in research and clinical settings, it is crucial to describe biomarker trajectories and factors associated with variations in their rates of change. In line with prior research,² these findings demonstrated a decline in Aβ42:Aβ40 levels and an increase in p-tau181, NfL, and GFAP levels over age. APOE ε4 allele carriers consistently demonstrated faster and more unfavorable changes in all biomarkers over time, supporting the earlier dementia onset in this genetically at-risk group.^7,9 Although there is prior research into factors associated with plasma biomarker concentrations,^1,11,37 the data on factors associated with longitudinal biomarker changes remain limited.⁴ Midlife cardiovascular risk factors, including hypertension and diabetes, have been reported to be associated with dementia and cognitive decline.^{12,13,14,15,16} This study further underscores the role of these conditions on faster longitudinal changes in NfL and GFAP that reflect the accumulation of brain neurodegeneration. Should the findings be confirmed, monitoring NfL and GFAP trajectories could yield insights for assessing the efficacy of interventions targeting midlife vascular risk factors to prevent brain damage.

Sequential changes in individual plasma biomarkers over the disease course and their sensitivity to biological variation (ie, influence of renal function, blood volume, extracerebral expression) may affect their associations with dementia risk. Aβ42:Aβ40 and p-tau181 demonstrate specificity to AD pathology, showing alterations decades before amyloid positivity is detected by positron emission tomographic brain scans.² In contrast, plasma NfL and GFAP levels appear to increase closer to the time of brain amyloid accumulation and correlate with declines in cognitive function.^1,2 These biomarkers signify neuronal inflammation, neuroaxonal injury, or degeneration, irrespective of their root causes.³⁸ The results of this study align with these findings because biomarker measures in midlife showed significant associations only for the Aβ42:Aβ40 ratio and the p-tau181 biomarker with incident all-cause dementia. In addition, the Aβ42/40 ratio, which demonstrates insensitivity to biological variation (Aβ42/40 ratio dissects away the impact of factors not associated with neuropathology on absolute levels of individual biomarkers) may make it more informative in preclinical settings.^11,37,39

Among all biomarkers measured at different times, NfL measured in late life showed the highest discrimination for incident dementia. Despite evidence suggesting plasma p-tau181’s potential for classifying amyloid burden and monitoring disease progression,^5,38 it did not outperform other biomarkers in the current study. Plasma p-tau181 is specific to AD-related pathology, and it predicts conversion from normal status or mild cognitive impairment to AD dementia.^40,41,42 The outcome of the current study, however, is all-cause dementia; if etiology had been documented, greater associations of the AD biomarkers with AD-related dementia would be expected. In addition, a model with all 4 biomarkers in late life outperformed any individual biomarker in discriminating all-cause dementia. This finding aligns with previous studies that showed that test-retest variability limited the predictive performance of individual biomarkers for dementia, whereas a combined model with all biomarkers could reduce the effect of assay variability on their performance.^40,43 Interestingly, despite the association of the APOE ε4 allele with early amyloid deposition,⁷ it appeared not to improve discriminatory accuracy for incident all-cause dementia in models including biomarkers because its impact on dementia risk may not be independent of plasma biomarkers and its association with dementia are reported to be greatest with AD subtypes.

Limitations

This study has several limitations. First, plasma biomarkers were measured at 3 time points at most, potentially limiting the precision of trajectory estimates. Second, only modest associations between p-tau181 and incident dementia were observed. Previous studies highlight the likely superior performance of other p-tau assays, such as p-tau217 and p-tau231.^3,44 Third, plasma biomarkers are probably less related than cerebral spinal fluid biomarkers to dementia pathology due to extracerebral production and levels,^8,38,45 underscoring the importance of identifying potential factors affecting the plasma biomarker levels. Fourth, Aβ and p-tau are most specific to AD; however, only assessments of all-cause dementia were obtained. Due to limited information available on dementia diagnoses ascertained through methods other than expert adjudication (ie, telephone cognitive interviews, informant interviews, hospitalization records, and death certificates), the etiology of dementia was not available for this study. Although dementia subtypes were not differentiated—it is anticipated because AD alone or in concert with other brain disease is the most common etiology for late-life dementia—it likely explains the observed associations with AD biomarkers in this study. Fifth, although plasma biomarkers provide easy accessibility and thus provide an essential tool for use in large population-wide studies to assess and predict the disease burden,⁴⁶ future research is needed to quantify how the biomarkers and their changes are related to best practice measures of dementia pathology (ie, direct tau and Aβ imaging) and whether informative plasma biomarker thresholds may be identified to discriminate high-risk groups that may benefit from earlier therapeutic or lifestyle interventions. Sixth, this is an analysis of a select subset of participants (eFigure 1 in Supplement 1) who survived to late life. Although the impact of selection bias was mitigated by using sampling weights (Supplement 1), participants at the highest risk of dementia were less likely to have been included, which may underestimate associations between plasma biomarkers and incident dementia. Despite these limitations, this study offers insights into a large community-based sample with more than 20 years of follow-up documenting biomarker trajectories across the critical midlife to late-life transition period when changes in brain pathology are expected.

Conclusions

Plasma biomarkers of AD neuropathology, neuronal injury, and astrogliosis rise with age and are associated with known dementia risk factors. The association of AD-specific biomarkers with dementia starts in midlife whereas late-life measures of AD, neuronal injury, and astrogliosis biomarkers are all associated with dementia.

Supplement 1.

eMethods

eTable 1. Test-Retest Reliability Estimates and Minimal Detectable Change of Unstandardized Plasma Biomarkers in Midlife Measured Using the Quanterix Simoa Platform: The Atherosclerosis Risk in Communities (ARIC) Study, Visit 3 (1993-1995, N=38)

eTable 2. Test-Retest Reliability Estimates and Minimal Detectable Change of Unstandardized Plasma Biomarkers in Late-Life Measured Using the Quanterix Simoa Platform: The Atherosclerosis Risk in Communities (ARIC) Study, Visit 5 (2011-2013, N=90)

eTable 3. Characteristics of Atherosclerosis Risk in Communities (ARIC) Cohort Stratified by Inclusion in Analytic Sample, 1993-2019 (N=6538)

eTable 4. Biomarker Rate of Change per Decade from Midlife to Late-Life: The Atherosclerosis Risk in Communities (ARIC) Study, 1993-2019 (N=1525)

eTable 5. Differences in Biomarker Rate of Change per Decade in Nanograms Per Milliliter From Midlife to Late-Life Associated With Midlife Risk and Protective Factors: The Atherosclerosis Risk in Communities (ARIC) Study, 1993-2019 (N=1424)

eTable 6. Weighted Association of Plasma Biomarkers with Incident Dementia in Late-Life: The Atherosclerosis Risk in Communities (ARIC) Study, 2011-2019 (N=1339)

eTable 7. Unweighted Association of Plasma Biomarkers with Incident Dementia in Late-Life: The Atherosclerosis Risk in Communities (ARIC) Study, 2011-2019 (N=1339)

eTable 8. Competing Risk Associations of Plasma Biomarkers with Incident Dementia in Late-Life: The Atherosclerosis Risk in Communities (ARIC) Study, 2011-2019 (N=1339)

eTable 9. Weighted Association of Plasma Biomarkers with Incident Dementia in Late-Life by Subgroups: The Atherosclerosis Risk in Communities (ARIC) Study, 2011-2019

eFigure 1. Study Design, Timing of Assessments, and Participants Selected for Analysis: The Atherosclerosis Risk in Communities (ARIC) Study, 1987-2019

eFigure 2. Unstandardized Biomarker Rate of Change per Decade from Midlife to Late-Life Stratified by Presence of APOE Alleles and Cognitive Diagnosis: The Atherosclerosis Risk in Communities (ARIC) Study, 1993-2019

eFigure 3. Biomarker Rate of Change per Decade from Midlife to Late-Life Stratified by Race and Sex: The Atherosclerosis Risk in Communities (ARIC) Study, 1993-2019

eFigure 4. Unstandardized Biomarker Rate of Change per Decade from Midlife to Late-Life Stratified by Race and Sex: The Atherosclerosis Risk in Communities (ARIC) Study, 1993-2019

eFigure 5. Association of Plasma Biomarkers with Incidence Rates of Dementia in Late-Life: The Atherosclerosis Risk in Communities (ARIC) Study, 2011-2019 (N=1339)

eFigure 6. Discriminatory Accuracy of Individual Plasma Biomarkers for Incident Dementia in Late-Life: The Atherosclerosis Risk in Communities (ARIC) Study, 2011-2019 (N=1339)

eReferences

jama-e246619-s001.pdf^{(884.6KB, pdf)}

Supplement 2.

Data Sharing Statement

jama-e246619-s002.pdf^{(15.7KB, pdf)}

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