Alzheimer's blood‐based biomarkers, incident dementia, and interactions with age, APOE status, and hormone therapy

Michelle M Mielke; Sarah A Gaussoin; Ramon Casanova; Lauren A Latham; JoAnn E Manson; Charles P Mouton; Ted K S Ng; Stephen R Rapp; Susan M Resnick; Bonnie C Sachs; Nazmus Saquib; Aladdin H Shadyab; Linda K McEvoy; Andrea Z LaCroix; Robert B Wallace; Mark A Espeland; Jiu‐Chiuan Chen; Kathleen M Hayden

doi:10.1002/alz.71704

. 2026 Jul 27;22(7):e71704. doi: 10.1002/alz.71704

Alzheimer's blood‐based biomarkers, incident dementia, and interactions with age, APOE status, and hormone therapy

Michelle M Mielke ^1,^✉, Sarah A Gaussoin ², Ramon Casanova ², Lauren A Latham ³, JoAnn E Manson ^4,⁵, Charles P Mouton ⁶, Ted K S Ng ⁷, Stephen R Rapp ^8,⁹, Susan M Resnick ¹⁰, Bonnie C Sachs ¹¹, Nazmus Saquib ¹², Aladdin H Shadyab ^13,¹⁴, Linda K McEvoy ¹⁵, Andrea Z LaCroix ¹⁴, Robert B Wallace ¹⁶, Mark A Espeland ^2,¹¹, Jiu‐Chiuan Chen ¹⁷, Kathleen M Hayden ^9,¹⁸

^¹Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston‐Salem, North Carolina, USA

^²Department of Biostatistics and Data Sciences, Wake Forest University School of Medicine, Winston‐Salem, North Carolina, USA

^³Department of Internal Medicine, Section on Geriatrics and Gerontology, Wake Forest University School of Medicine, Winston‐Salem, North Carolina, USA

^⁴Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA

^⁵Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

^⁶Department of Family Medicine, University of Texas Medical Branch, Galveston, Texas, USA

^⁷Rush Institute for Healthy Aging, Rush University Medical Center, Chicago, Illinois, USA

^⁸Department of Psychiatry and Behavioral Medicine, Wake Forest University School of Medicine, Winston‐Salem, North Carolina, USA

^⁹Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, Winston‐Salem, North Carolina, USA

^¹⁰Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA

^¹¹Department of Internal Medicine, Section on Geriatrics and Gerontology, and Department of Neurology, Wake Forest University School of Medicine, Winston‐Salem, North Carolina, USA

^¹²Clinical Sciences Department, College of Medicine, Sulaiman Al Rajhi University, Al Bukayriyah, Al Qassim, Saudi Arabia

^¹³Division of Geriatrics, Gerontology, and Palliative Care, Department of Medicine, University of California San Diego, La Jolla, California, USA

^¹⁴Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, California, USA

^¹⁵Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA

^¹⁶Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, Iowa, USA

^¹⁷Departments of Population & Public Health Sciences and Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA

^¹⁸Trinity College Institute for Neuroscience, Trinity College Dublin, Dublin, Leinster, Ireland

Correspondence , Michelle M. Mielke, Wake Forest University School of Medicine, Department of Epidemiology and Prevention, 525 Vine Street, 5th floor, Winston‐Salem, NC 27101, USA. Email: michelle.mielke@wfusm.edu

^✉

Corresponding author.

PMCID: PMC13408014 PMID: 42509652

Abstract

INTRODUCTION

Cognitive impairment among older adults is often due to multiple pathologies and heterogenous risk factors. We assessed whether Alzheimer's blood‐based biomarkers (BBMs) were associated with incident mild cognitive impairment (MCI)/probable dementia, and whether associations were modified by age, apolipoprotein E (APOE), and hormone therapy (HT).

METHODS

Analyses included 2467 Women's Health Initiative Memory Study women (≥65 years of age) randomized between 1995 and 1998 to 3–5‐years of HT or placebo. Cox regression (mean 18‐year follow‐up) assessed associations between the z‐scored BBMs and MCI/dementia.

RESULTS

Lower baseline amyloid beta (Aβ)42/40 ratio and higher phosphorylated tau 181 (p‐tau181), glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) were associated with an increased risk of MCI and dementia; GFAP was most strongly associated. The p‐tau181 and NfL associations were stronger among APOE ε4 carriers; BBMs varied non‐linearly by age. The associations of BBMs with the cognitive outcomes also varied inconsistently between HT groups.

DISCUSSION

BBMs for AD and related dementias (ADRD) are associated with incident MCI/dementia in older women. Interactions between the BBMs and HT were inconsistent and require further investigation.

Keywords: Alzheimer's disease, blood‐based biomarkers, cognition, dementia, hormone therapy, mild cognitive impairment, women

Highlights

Lower amyloid beta (Aβ)42/40 ratio was associated with risk of mild cognitive impairment (MCI)/dementia
Higher phosphorylated tau 181 (p‐tau181), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) were associated with an increased risk of MCI/dementia
Plasma GFAP was most strongly associated
Associations were stronger among apolipoprotein E (APOE) ε4 carriers
Associations of blood‐based biomarkers (BBMs) and MCI/dementia varied inconsistently by hormone therapy groups

1. BACKGROUND

The underlying neuropathology of Alzheimer's disease (AD) and related dementias (ADRD) begins decades prior to clinical symptoms. ADRD blood‐based biomarkers (BBMs) have been shown to detect AD pathology (i.e., blood amyloid beta [Aβ]42/40 ratio and phosphorylated tau [p‐tau]), ¹ , ² , ³ , ⁴ , ⁵ neurodegeneration (i.e., blood neurofilament light chain [NfL]), ⁶ , ⁷ and neuroimmune alterations (i.e., blood glial fibrillary acidic protein [GFAP]). ⁸ Notably, cognitive impairment among older adults is often due to multiple pathologies and heterogenous risk factors. Some studies have suggested that ADRD BBMs may help to predict risk of mild cognitive impairment (MCI) and dementia. ⁹ , ¹⁰ However, additional research is needed to understand which BBMs best predict MCI and dementia among individuals with specific sociodemographic or other risk factors.

Women are more frequently diagnosed with ADRD compared to men and have a greater lifetime risk. ¹¹ Some studies suggest that ADRD BBM levels differ by sex, with higher levels of GFAP and lower levels of p‐tau for women compared to men. ² , ¹² , ¹³ However, studies have not specifically examined which ADRD BBMs have the best prognostic value for women and whether associations between these BBMs and risk of MCI or dementia differ by potential modifying factors including age, APOE ɛ4 carrier status, or use of hormone therapy (HT). The Women's Health Initiative Memory Study (WHIMS) is a large randomized clinical trial examining the effects of HT on MCI and dementia among postmenopausal women. In this study of 2467 WHIMS participants, we compared associations of multiple ADRD BBMs (Aβ42/40 ratio, p‐tau181, NfL, and GFAP) with risk of MCI/probable dementia over a mean 18.1 years of follow‐up. In additional analyses, we also examined whether these associations were modified by age, APOE e4 carrier status, or randomization to HT.

2. METHODS

2.1. WHIMS study design

RESEARCH IN CONTEXT

Systematic review: A comprehensive review of the literature was conducted using PubMed and Google Scholar. Studies have not specifically examined which Alzheimer's disease and related dementias (ADRD) blood‐based biomarkers (BBMs) have the best prognostic value for women and whether associations between these BBMs and risk of mild cognitive impairment (MCI) or probable dementia differ by potential modifying factors including age, apolipoprotein E (APOE) ɛ4 carrier status, or use of hormone therapy (HT).
Interpretation: All plasma biomarkers were associated with risk of incident MCI/probable dementia, but glial fibrillary acidic protein (GFAP) was most strongly associated with increased risk. Associations were consistently stronger among APOE ε4 carriers. Interactions between the BBMs and age or HT group were less consistent.
Future directions: Future studies should examine sex differences in the association between GFAP and incident MCI or dementia and assess potentially different pathways and mechanisms that lead to dementia and dementia type for women who use estrogen alone versus estrogen plus progestin.

WHIMS, an ancillary study to the Women's Health Initiative (WHI) Hormone Therapy trials, recruited and enrolled 7479 women between the ages of 65 and 79 years from 1995 to 1998. The institutional review board (IRB) at each clinic site approved the consent forms, and written informed consent was obtained from all participants. WHIMS was designed to study the effects of initiation or re‐initiation of post‐menopausal HT on the incidence of MCI/probable dementia in parallel clinical trials, as described. ¹⁴ , ¹⁵ , ¹⁶ , ¹⁷ The trials compared oral conjugated equine estrogens (CEEs; 0.625 mg/day) alone in women with a hysterectomy (CEE) versus placebo (CEE Placebo), or oral CEE combined with medroxyprogesterone acetate (progestin; 2.5 mg/day) in women with an intact uterus [CEE/MPA] versus placebo [CEE/MPA Placebo]. Participants were followed after the trials ended in 2002 (CEE/MPA) ¹⁶ and 2004 (CEE), ¹⁴ , ¹⁵ with annual in‐person cognitive assessments until 2007–2008 and subsequent telephone‐based cognitive assessments until 2019. The current analyses included 2467 women selected from those with available baseline blood. The sampling strategy for the BBM assays was based on WHIMS participation (≥ 1 cognitive assessment), APOE genotype, and cognitive status (i.e., a diagnosis of MCI or dementia) as of April 11, 2019. All 876 women with MCI/probable dementia and all women with an APOE ε4/ε4 (N = 54) or APOE ε4/ε3 (N = 624) genotype were included. Additional women were randomly selected for inclusion, balanced to the unimpaired APOE ε4/– with respect to age at enrollment and/or enrollment in the OPACH (Objective Physical Activity and Cardiovascular Health) WHI ancillary study.

2.2. Ascertainment of MCI and dementia

The Modified Mini‐Mental State Examination (3MSE) was administered to women annually in clinic up to 2007–2008. ¹⁸ Women scoring below predetermined cut points (based on age and education) were referred for a full clinical evaluation by a board certified physician and neuropsychological testing, a structured psychiatric interview (Primary Care Evaluation of Mental Disorders [PRIME‐MD]), ¹⁹ and the Geriatric Depression Scale short form. ²⁰ A knowledgeable informant completed the Acquired Cognitive and Behavior Changes form. ¹⁷ Starting in 2008, telephone‐based cognitive assessments and informant interviews were conducted. A central panel of dementia experts adjudicated cases, classifying participants as cognitively normal, MCI, or probable dementia, according to standardized criteria. ²¹

2.3. Blood‐based biomarkers for ADRD

Blood was collected at WHI baseline. Plasma samples were stored at −80 C° using standardized protocols, as described (www.whi.org). Ethylenediaminetetraacetic acid (EDTA) plasma samples were sent to Quanterix to run the ADRD BBM assays in singlet using their single molecular array (Simoa) HD‐X platform (Quanterix Corporation, Billerica, MA, USA). Concentrations of Aβ42, Aβ40, GFAP, and NfL were measured using the Simoa Human Neurology 4‐Plex E (N4PE). The p‐tau181 concentration was measured using the Simoa pTau‐181 V2 Advantage kit. Mean coefficients of variation were 16.3% for Aβ40, 14.3% for Aβ42, 13.1% for p‐tau181, 7.3% for NfL, and 7.1% for GFAP. In a random sample of 172 blind duplicates, the correlation was 0.72 for Aβ40, 0.80 for Aβ42, 0.92 for p‐tau181, 0.98 for NfL, and 0.98 for GFAP. Increasing values of GFAP, NfL and p‐tau181 correspond to greater ADRD pathology; a decreasing Aβ42/40 ratio reflects greater amyloid pathology.

2.4. Covariates

Participants reported their age, race, ethnicity, education level, and alcohol use. Body mass index (BMI) was calculated from baseline height and weight. Blood was drawn for APOE genotyping and determination of creatinine levels. APOE ε4 carrier status was determined from blood samples using the single nucleotide polymorphisms (SNPs) rs429358 and rs7412. ²² Creatinine levels were used to compute estimated glomerular filtration rate (eGFR) using the 2021 CKD‐EPI calculation. Self‐report of hypertension, diabetes, cardiovascular disease, and stroke were obtained. Alcohol use was self‐reported as number of drinks per week.

2.5. Statistical analysis

Baseline demographic characteristics were compared between participants with and without available baseline BBM data using general linear models and chi‐square tests. Time‐to‐event analyses for MCI, probable dementia, and a combined endpoint of MCI/probable dementia were performed using Cox proportional hazards regression models, adjusting for age, education, APOE genotype, race/ethnicity, diabetes, hypertension, alcohol use, body mass index (BMI), eGFR, and HT randomization group. Because chronic kidney disease (CKD) increases ADRD BBM levels, but is not a confounder of the association between ADRD BBMs and MCI or dementia, ² , ²³ sensitivity analyses were conducted excluding women with baseline eGFR ≤60 mL/min/1.73 m². Interactions between biomarker z‐scores and age, APOE genotype, and HT group (CEE vs CEE Placebo; CEE/MPA vs Cee/MPA Placebo) were examined. To account for the non‐random selection of participants into the BBM subsample, inverse probability weighting (IPW) was applied to all multivariable analyses. The IPW model included age, education, race/ethnicity, diabetes, hypertension, alcohol use, and BMI. Death as a competing risk was examined using Fine and Gray models in sensitivity analyses.

All statistical analyses were conducted using SAS software, version 9.4 (SAS Institute Inc., Cary, NC). Hypothesis tests were two‐sided. Given the number of models, p‐values < 0.01 were considered statistically significant.

3. RESULTS

3.1. Baseline participant characteristics

Of the 7479 women enrolled in WHIMS, a total of 2467 (33.0%) had available baseline ADRD BBMs. Compared to women without ADRD BBMs, those with data on BBMs were more frequently White, APOE ε4 allele carriers, and had more years of education, and were less likely to have a history of diabetes or hypertension (Table 1). Baseline BBM levels did not differ by HT randomization group.

TABLE 1.

Comparison of baseline characteristics for those with and without blood‐based biomarkers of Alzheimer's disease and related dementias.

	Missing AD baseline biomarker data (N = 5012)	AD baseline biomarker data present (N = 2467)	Overall (N = 7479)	p‐value
Age, years
Mean (SD)	70.9 (3.9)	71.1 (3.8)	71.0 (3.8)	0.0649
Race/ethnicity (missing = 16), no. (%)				<0.0001
American Indian/Alaska Native	24 (0.5)	2 (0.1)	26 (0.3)
Asian/Pacific Islander	115 (2.3)	12 (0.5)	127 (1.7)
Black/African American	355 (7.1)	180 (7.3)	535 (7.2)
Hispanic/Latino	143 (2.9)	36 (1.5)	179 (2.4)
White	4274 (85.5)	2219 (90.1)	6493 (87.0)
Other	89 (1.8)	14 (0.6)	103 (1.4)
Education (missing = 21), no. (%)				<0.0001
<13 years	1542 (30.9)	681 (27.6)	2223 (29.8)
13–16 years	2435 (48.8)	1180 (47.8)	3615 (48.5)
>16 years	1014 (20.3)	606 (24.6)	1620 (21.7)
APOE (missing = 1173), no. (%)				<0.0001
ε4+	818 (20.8)	699 (31.3)	1517 (24.1)
ε4–	3256 (79.9)	1533 (68.7)	4789 (75.9)
Alcoholic drinks per week (missing = 19), mean (SD)	2.35 (5.14)	2.41 (5.36)	2.37 (5.21)	0.6497
Body mass index (missing = 46), mean (SD), kg/m²	28.6 (5.8)	28.3 (5.5)	28.5 (5.7)	0.0606
Diabetes (missing = 11), no. (%)	454 (9.1)	172 (7.0)	626 (8.4)	0.0022
eGFR, (missing = 951), mean (SD)	83.5 (13.5)	83.7 (13.3)	83.6 (13.4)	0.4436
Hypertension (missing = 2), no. (%)	2551 (50.9)	1177 (47.7)	3728 (49.9)	0.0091
Cardiovascular disease, no. (%)	345 (6.9)	137 (5.6)	482 (6.4)	0.0276
Randomization arm, no. (%)				0.0700
CEE Placebo	1013 (20.2)	470 (19.1)	1483 (19.8)
CEE	989 (19.7)	475 (19.3)	1464 (19.6)
CEE/MPA	1445 (28.8)	784 (31.8)	2229 (29.8)
CEE/MPA Placebo	1565 (31.2)	738 (29.9)	2303 (30.8)
Blood‐based biomarker, mean (SD)
Aβ42 (missing = 89)		5.84 (1.66)
Aβ40 (missing = 87)		98.41 (28.58)
Aβ42/40 ratio (missing = 90)		0.06 (0.02)
p‐tau181 (missing = 39)		2.19 (1.45)
GFAP (missing = 86)		144.92 (66.55)
NfL (missing = 87)		15.97 (8.56)

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Abbreviations: Aβ40, amyloid beta 40; Aβ42, amyloid beta 42; APOE ε4, apolipoprotein E gene ε4 carrier status; CEE, conjugated equine estrogen; CEE Placebo, Placebo group to the CEE group; CEE/MPA, Conjugated equine estrogen + medroxyprogesterone acetate; CEE/MPA Placebo, Placebo group to the CEE/MPA group; eGFR, estimated glomerular filtration rate; GFAP, glial fibrillary acidic protein; NfL, neurofilament light chain; p‐tau181, phosphorylated tau 181; SD, standard deviation.

3.2. Baseline BBMs and long‐term incident MCI and probable dementia over a mean 18.1 years

Associations between baseline ADRD BBM z‐scores and risk of MCI, probable dementia, or combined MCI/probable dementia are shown in Table 2. Higher p‐tau181, GFAP, and NfL z‐scores were associated with an increased risk of all outcomes. Higher levels of p‐tau181 and GFAP were associated with a slightly greater risk of probable dementia compared to MCI, but overall there was little difference in the hazard ratios (HRs) and confidence intervals (CIs) for a specific BBM in relation to MCI, probable dementia, or combined MCI/probable dementia. A higher Aβ42/40 ratio was associated with a reduced risk of probable dementia or the combined MCI/probable dementia endpoint, but not for MCI alone. Higher GFAP was the strongest predictor for all outcomes. In sensitivity analyses, excluding the 131 women at baseline with eGFR ≤60, did not change the results. Results were similar after considering death as a competing risk.

TABLE 2.

Associations of baseline blood‐based biomarker z‐scores and risk of incident mild cognitive impairment or probable dementia, or a combined endpoint, among a sample of post‐menopausal women enrolled in the WHIMS study over a mean follow‐up of 18.1 years.

Biomarker			Model 1		Model 2		Model 3
z‐scores	Events	person‐years	HR (95% CI)	p‐value	HR (95% CI)	p‐value	HR (95% CI)	p‐value
Aβ42/40
MCI	633	27,067.33	0.94 (0.90, 0.99)	0.017	0.95 (0.91, 1.00)	0.041	0.95 (0.91, 1.00)	0.036
Dementia	605	28,712.52	0.93 (0.89, 0.98)	0.004	0.93 (0.88, 0.97)	0.002	0.92 (0.88, 0.97)	0.001
MCI/dementia	1011	28,329.59	0.94 (0.90, 0.97)	0.004	0.93 (0.90, 0.96)	<0.001	0.93 (0.90, 0.96)	<0.0001
p‐tau181
MCI	649	27,737.98	1.07 (1.05, 1.10)	<0.0001	1.08 (1.05, 1.10)	<0.0001	1.08 (0.05, 1.11)	<0.0001
Dementia	621	29,404.50	1.11 (1.09, 1.13)	<0.0001	1.11 (1.09, 1.13)	<0.0001	1.11 (1.09, 1.13)	<0.0001
MCI/dementia	1036	29,909.55	1.08 (1.06, 1.10)	<0.0001	1.08 (1.06, 1.10)	<0.0001	1.08 (1.06, 1.10)	<0.0001
GFAP
MCI	634	27,214.58	1.19 (1.15, 1.24)	<0.0001	1.23 (1.18, 1.28)	<0.0001	1.23 (1.18, 1.28)	<0.0001
Dementia	605	28,769.77	1.32 (1.28, 1.36)	<0.0001	1.34 (1.20, 1.39)	<0.0001	1.34 (1.30, 1.38)	<0.0001
MCI/dementia	1012	28,386.84	1.24 (1.21, 1.28)	<0.0001	1.28 (1.24, 1.31)	<0.0001	1.28 (1.24, 1.31)	<0.0001
NfL
MCI	634	27,106.22	1.09 (1.07, 1.12)	<0.0001	1.10 (1.08, 1.13)	<0.0001	1.10 (1.08, 1.13)	<0.0001
Dementia	605	28,751.42	1.10 (1.08, 1.13)	<0.0001	1.10 (1.08, 1.13)	<0.0001	1.10 (1.08, 1.13)	<0.0001
MCI/dementia	1012	28,368.49	1.10 (1.09, 1.12)	<0.0001	1.11 (1.09, 1.13)	<0.0001	1.11 (1.09, 1.13)	<0.0001

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Note: Model 1 adjusts for age, education, and APOE. Model 2 adjusts for variables in Model 1 and race/ethnicity, diabetes, hypertension, alcohol use, body mass index, and estimated glomerular filtration rate. Model 3 adjusts for variables in Model 2 and hormone therapy group.

Abbreviations: Aβ, amyloid beta; CI, confidence interval; GFAP, glial fibrillary acidic protein; HR, hazard ratio; MCI, mild cognitive impairment; NfL, neurofilament light chain; p‐tau181, phosphorylated tau 181; WHIMS, Women's Health Initiative Memory Study.

3.3. Examination of potential interactions between the BBMs and age, APOE status, or HT randomization in relationship to each cognitive outcome

Except for the Aβ42/40 ratio, associations between the BBMs and MCI/probable dementia varied non‐linearly with age (all interactions p < 0.01; Figure 1). Higher p‐tau181 and GFAP levels were most strongly associated with risk of MCI/probable dementia for women with a baseline age of 70–74, whereas higher NfL levels were associated with less risk for age 70–74 compared to younger (64–69 years) or older (75+ years) women.

Impact of age on associations between each blood‐based biomarker and risk of mild cognitive impairment/probable dementia over a mean follow‐up of 18.1 years. Abbreviations: 3MSE, Modified Mini‐Mental State Examination; Aβ, Amyloid beta; CI, confidence interval; GFAP, glial fibrillary acidic protein; HR, Hazard Ratio; NfL, neurofilament light chain; Ptau181, phosphorylated tau 181. The sample size of those aged 64‐69 was 1098, of those aged 70‐74 was 922, and of those aged 75+ was 447.

We observed interactions between APOE ε4 carriership and NfL (p = 0.008) for risk of MCI/probable dementia (Figure 2). The association between higher NfL and risk of MCI/probable dementia was stronger among ε4 carriers compared to non‐carriers.

Impact of apolipoprotein E (*APOE*) ε4 carrier status on associations between each blood‐based biomarker and risk of mild cognitive impairment/probable dementia over a mean follow‐up of 18.1 years. Abbreviations: Aβ, Amyloid beta; *APOE* ε4, Apolipoprotein E gene ε4 carrier status; CI, confidence interval; GFAP, glial fibrillary acidic protein; HR, Hazard Ratio; NfL, neurofilament light chain; Ptau181, phosphorylated tau 181. The sample size of APOE e4 carriers (e4 +) was 699 and the sample size of APOE e4 non‐carriers (e4 ‐) was 1533.

Finally, we examined interactions between the ADRD BBMs and HT group (CEE vs CEE Placebo, Figure 3; CEE/MPA vs CEE/MPA Placebo, Figure 4) for risk of MCI/probable dementia. When comparing CEE to CEE Placebo for women without a uterus, higher levels of the Aβ42/40 ratio (interaction p < 0.001) and p‐tau181 (interaction p = 0.005) were associated with an increased risk of MCI/probable dementia for women assigned to CEE. When comparing CEE/MPA to CEE/MPA Placebo for women with a uterus, higher NfL levels (interaction p < 0.001) were associated with increased risk of MCI/probable dementia for women assigned to CEE/MPA.

Impact of hormone therapy randomization group (CEE vs CEE Placebo) on associations between each blood‐based biomarker and risk of mild cognitive impairment/probable dementia over a mean follow‐up of 18.1 years. Abbreviations: Aβ, Amyloid beta; CEE, conjugated equine estrogens; CI, confidence interval; GFAP, glial fibrillary acidic protein; HR, Hazard Ratio; NfL, neurofilament light chain; Ptau181, phosphorylated tau 181. The sample size of the CEE group was 475; the sample size of the CEE/MPA Placebo group was 470.

Impact of hormone therapy randomization group on associations between each blood‐based biomarker and risk of mild cognitive impairment/probable dementia over a mean follow‐up of 18.1 years. Abbreviations: Aβ, Amyloid beta; CEE/MPA, conjugated equine estrogens and medroxyprogesterone acetate; CI, confidence interval; GFAP, glial fibrillary acidic protein; HR, Hazard Ratio; NfL, neurofilament light chain; Ptau181, phosphorylated tau 181. The sample size of the CEE/MPA group was 784; the sample size of the CEE/MPA Placebo group was 738.

4. DISCUSSION

Utilizing WHIMS data, we examined associations of ADRD BBMs with risk of MCI/probable dementia over a mean 18.1 years of follow‐up. Higher levels of p‐tau181, GFAP and NfL were associated with increased risk of MCI/probable dementia, whereas a higher Aβ42/40 ratio was associated with a lower risk. Notably, of the BBMs, GFAP was most strongly associated with risk of MCI, probable dementia, or a combined MCI/probable dementia outcome. In additional analyses, we also assessed whether associations between the ADRD BBMs and incident MCI/probable dementia were modified by age, APOE, or HT assignment. Although associations were consistently stronger among APOE ε4 carriers, interactions between the BBMs and age or HT group were less consistent and require further investigation.

Studies suggest that ADRD BBMs (i.e., Aβ42/40 ratio, p‐tau181, p‐tau217, GFAP, and NfL) are associated with an increased risk of MCI or dementia. ⁹ , ¹⁰ , ²⁴ These studies utilized different assays and lengths of follow‐up, which may have resulted in different ADRD BBMs being the most robust risk factor across studies. The current WHIMS results similarly demonstrate associations between these BBMs and risk of MCI/probable dementia over a mean follow‐up of 18.1 years. Notably, GFAP was the strongest predictor for all cognitive outcomes. We hypothesize that these results could be due to multiple factors. First, plasma p‐tau217 is a better diagnostic marker for elevated brain amyloid than p‐tau181, ² and it is possible p‐tau217 would be more strongly associated with MCI/probable dementia than GFAP, had it been assayed. Indeed, a recent published study with BBMs measured at a different laboratory and applied to WHIMS data demonstrated that p‐tau217 was associated with increased risk of MCI/probable dementia. However, the study only reported on p‐tau217 and did not compare to GFAP or to other ADRD BBMs. ²⁵ Notably, another recent article examined all ADRD BBMs in a cohort of dementia‐free individuals in Stockholm followed for 16 years and found that the top quartile of GFAP was associated with a higher HR than the top quartile of p‐tau217 for both all‐cause dementia and AD dementia. ⁹ These results suggest that high levels of GFAP may be an important prognostic marker of dementia years later in the community. Second, the plasma Aβ42 and 40 assays used in this study are less accurate than mass spectrometry‐based assays, ²⁶ and the correlation of Aβ42 and 40 in blind duplicates was the lowest of all BBMs. Third, the etiology of MCI or probable dementia was not determined. Given that p‐tau181 is an AD‐specific biomarker, the association between the biomarker and incident MCI/dementia would be reduced in a sample of mixed etiological heterogeneity, which is more common than pure AD among older adults. Fourth, previous studies did not examine sex differences in the association between GFAP and risk of MCI/dementia. GFAP levels are higher among women than men and increase at greater rates with age. ²⁷ , ²⁸ , ²⁹ Sex steroids have been shown to alter brain and plasma levels of GFAP, an astrocyte‐specific intermediate filament protein. ³⁰ , ³¹ Thus, it is plausible that GFAP may be a more robust predictor of cognitive impairment for women than men, and future studies are needed to examine potential sex differences.

The associations between the ADRD BBMs and cognition were significantly modified by age and APOE. With regard to APOE, increases in NfL or p‐tau181 were associated with a greater risk of cognitive impairment for ε4 carriers compared to non‐carriers across all outcomes. These findings are consistent with previous studies. ¹⁰ , ³² , ³³ The interactions between BBMs and age were less clear and tended to be non‐linear. It is possible that the smaller number of incident events contributed to more variation.

HT randomization assignment modified associations between the ADRD BBMs and risk of MCI/probable dementia, but results differed based on assigned HT regimen for women with, versus without, a uterus. When comparing CEE versus CEE/MPA Placebo for women with a uterus, higher NfL levels, a non‐specific marker of large caliber axonal degeneration, were associated with an increased risk of MCI/probable dementia for women assigned to CEE/MPA. These results may suggest that women with baseline lower white matter integrity, indicated by higher plasma NfL, could have been more vulnerable to MCI/probable dementia with CEE/MPA. In contrast, HT interactions with AD‐specific biomarkers (Aβ42/40 ratio, p‐tau181) were stronger among women assigned to CEE versus CEE Placebo. However, the finding of an interaction between CEE and high Aβ42/40 ratio, indicating lower risk of AD, and high p‐tau181, indicating higher risk, is unexpected. As mentioned, the plasma Aβ42 and 40 assays used in this study are less accurate than mass spectrometry–based assays and had high variability, ²⁶ which may have contributed to the unexpected results. Of note, the recent WHIMs article with p‐tau217 showed elevations in risk of dementia associated with higher p‐tau217 levels among women assigned to either CEE alone or placebo; however, the interaction was not significant. A nominally significant interaction of p‐tau217 levels and CEE/MPA versus placebo was observed, although dementia HRs for p‐tau217 in both the CEE/MPA and placebo groups were elevated. ²⁵

The different BBMs associated with risk of MCI/probable dementia for CEE versus CEE/MPA suggest that there are different pathways and mechanisms that lead to dementia and dementia type for women who use estrogen alone versus estrogen plus progestin. Some studies of rat models have shown that progesterone can be antagonistic to estrogen's effects on the brain. ³⁴ However, the underlying mechanisms and whether they are generalizable to all types of progestogens and estrogens are not understood. A woman's history of hysterectomy, with or without ovarian conservation, has been associated with an increased risk of cardiovascular and metabolic morbidity. ³⁵ In the current study, we could not separate the long‐term effects of a hysterectomy, and conditions related to hysterectomy versus progestin use on cognition and ADRD BBMs because the hysterectomy CEE only groups are demographically different from women without hysterectomy randomized to either CEE/MPA or Placebo.

4.1. Strengths and limitations

There are multiple strengths to the study including the large sample size, long follow‐up, rigorously characterized participants, use of IPW to account for the non‐random selection of participants into the BBM subsample, and consideration of death as a competing risk. Furthermore, we examined a cohort of women who were randomized to HT or placebo, providing the unique opportunity to assess whether associations of AD BBMs with cognition and MCI/dementia differed by HT regimen, without the usual challenges invoked by self‐selection and self‐report of HT use. Limitations also warrant consideration. First, the WHI study only examined CEE and CEE/MPA, the most used HT at the time and prescribed at a higher dose than current regimens, so results may not be generalizable to current forms or doses of HT. Second, the women were 65 years of age and older and appropriate candidates for HT prescription. During the years WHI was conducted, it was common for older women of all ages to be prescribed HT so the current results may not be as generalizable. The sample was skewed toward White, better‐educated women, as these are the predominant characteristics of the WHIMS sample. In addition, although we adjusted for several known covariates, there may be other age‐related chronic diseases that influence plasma levels of biomarkers for which we did not account.

4.2. Conclusions

Women are more frequently diagnosed with ADRD compared to men and have a greater lifetime risk. ¹¹ It is important to understand which BBMs best predict MCI and dementia among women with specific sociodemographic or other risk factors. Utilizing long‐term follow‐up of women enrolled in WHIMS, higher levels of p‐tau181, GFAP, and NfL were associated with greater risk of incident MCI/probable dementia over 18.1 year follow‐up; GFAP was the stronger risk factor. Associations were consistently stronger among APOE ε4 carriers, but interactions between the BBMs and age or HT group were less consistent and require further investigation.

CONFLICT OF INTEREST STATEMENT

M.M.M. has served on scientific advisory boards and/or has consulted for Acadia, Althira, Beckman Coulter, Biogen, Cognito Therapeutics, Eisai, Lilly, Merck, Novo Nordisk, Neurogen Biomarking, Roche, and Seimens Healthineers, and receives grant support from the National Institutes of Health, Department of Defense, Alzheimer's Association, and Davos Alzheimer's Collaborative. S.M.R. reported being a retired employee from the National Institute on Aging during the conduct of the study. M.A.E. receives research funding from the Alzheimer's Association and compensation from committee work from Neste, Annovis Bio, and Acumen Pharmaceuticals. All other authors have no conflicts of interest to report. Author disclosures are available in the Supporting Information.

CONSENT STATEMENT

The institutional review board at each clinic site approved the consent forms, and written informed consent was obtained from all participants.

Supporting information

Supporting Information

ALZ-22-e71704-s001.pdf^{(770.3KB, pdf)}

ACKNOWLEDGMENTS

We thank the following Women's Health Initiative (WHI) investigators:

Program Office: (National Heart, Lung, and Blood Institute, Bethesda, Maryland) Jacques Rossouw, Jared Reis, and Candice Price

Clinical Coordinating Center: (Fred Hutchinson Cancer Center, Seattle, WA) Garnet Anderson, Ross Prentice, Andrea LaCroix, and Charles Kooperberg

Steering Committee and Academic Centers: (University of Alabama at Birmingham) Gretchen Wells; (Albert Einstein College of Medicine) Yasmin Mossavar‐Rahmani; (University at Buffalo) Amy Millen; (University at Buffalo) Jean Wactawski‐Wende; (Fred Hutchinson Cancer Center) Marian Neuhouser; (Fred Hutchinson Cancer Center) Holly Harris; (University of Massachusetts) Brian Silver; (University of North Carolina) Nora Franceschini; (Stanford Prevention Research Center) Marcia L. Stefanick; (The Ohio State University) Electra Paskett; (Wake Forest University) Mara Vitolins. These analyses were supported from the following National Institute on Aging grants: RF1 AG054068, R01 AG074345, R01 AG079397, R01 AG079149, and U24 AG082930. The Women's Health Initiative (WHI) program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through 75N92021D00001, 75N92021D00002, 75N92021D00003, 75N92021D00004, and 75N92021D00005.

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Associated Data

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

Supplementary Materials

Supporting Information

ALZ-22-e71704-s001.pdf^{(770.3KB, pdf)}

[alz71704-bib-0001] 1. Janelidze S, Mattsson N, Palmqvist S, et al. Plasma P‐tau181 in Alzheimer's disease: relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer's dementia. Nat Med. 2020;26(3):379‐386. doi: 10.1038/s41591-020-0755-1 [DOI] [PubMed] [Google Scholar]

[alz71704-bib-0002] 2. Mielke MM, Dage JL, Frank RD, et al. Performance of plasma phosphorylated tau 181 and 217 in the community. Nat Med. 2022;28(7):1398‐1405. doi: 10.1038/s41591-022-01822-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz71704-bib-0003] 3. Mielke MM, Hagen CE, Xu J, et al. Plasma phospho‐tau181 increases with Alzheimer's disease clinical severity and is associated with tau‐ and amyloid‐positron emission tomography. Alzheimers Dement. 2018;14(8):989‐997. doi: 10.1016/j.jalz.2018.02.013 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz71704-bib-0004] 4. Nakamura A, Kaneko N, Villemagne VL, et al. High performance plasma amyloid‐beta biomarkers for Alzheimer's disease. Nature. 2018;554(7691):249‐254. doi: 10.1038/nature25456 [DOI] [PubMed] [Google Scholar]

[alz71704-bib-0005] 5. Ovod V, Ramsey KN, Mawuenyega KG, et al. Amyloid beta concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis. Alzheimers Dement. 2017;13(8):841‐849. doi: 10.1016/j.jalz.2017.06.2266 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz71704-bib-0006] 6. Mattsson N, Andreasson U, Zetterberg H, Blennow K. Alzheimer's Disease Neuroimaging I. Association of plasma neurofilament light With neurodegeneration in patients With Alzheimer disease. JAMA Neurol. 2017;74(5):557‐566. doi: 10.1001/jamaneurol.2016.6117 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz71704-bib-0007] 7. 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]

[alz71704-bib-0008] 8. Bandara EMS, Asih PR, Pedrini S, Hone E, Fernando W, Martins RN. The role of glial fibrillary acidic protein in the neuropathology of Alzheimer's Disease and its potential as a blood biomarker for early diagnosis and progression. Mol Neurobiol. 2025;62(12):15576‐15608. doi: 10.1007/s12035-025-05219-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz71704-bib-0009] 9. Grande G, Valletta M, Rizzuto D, et al. Blood‐based biomarkers of Alzheimer's disease and incident dementia in the community. Nat Med. 2025;31(6):2027‐2035. doi: 10.1038/s41591-025-03605-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz71704-bib-0010] 10. Lu Y, Pike JR, Chen J, et al. Changes in Alzheimer disease blood biomarkers and associations With incident all‐cause dementia. JAMA. 2024;332(15):1258‐1269. doi: 10.1001/jama.2024.6619 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz71704-bib-0011] 11. Mielke MM, Aggarwal NT, Vila‐Castelar C, et al. Consideration of sex and gender in Alzheimer's disease and related disorders from a global perspective. Alzheimers Dement. 2022;18(12):2707‐2724. doi: 10.1002/alz.12662 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz71704-bib-0012] 12. Ghanbarian E, Qian T, Khorsand B, et al. Prognostic value of plasma glial fibrillary acidic protein in cognitively unimpaired older adults: results from the A4 study. Alzheimers Dement. 2025;21(11):e70948. doi: 10.1002/alz.70948 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz71704-bib-0013] 13. Wu Z, Mielke MM, Murray AM, et al. Plasma biomarkers of Alzheimer's disease and related dementias are associated with cognitive change in community‐dwelling older individuals in Australia and the US. Geroscience. 2025;48(2):2001‐2012. doi: 10.1007/s11357-025-01985-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz71704-bib-0014] 14. Anderson GL, Limacher M, Assaf AR, et al. Effects of conjugated equine estrogen in postmenopausal women with hysterectomy: the Women's Health Initiative randomized controlled trial. JAMA. 2004;291(14):1701‐1712. doi: 10.1001/jama.291.14.1701 [DOI] [PubMed] [Google Scholar]

[alz71704-bib-0015] 15. Shumaker SA, Legault C, Kuller L, et al. Conjugated equine estrogens and incidence of probable dementia and mild cognitive impairment in postmenopausal women: Women's Health Initiative Memory Study. JAMA. 2004;291(24):2947‐2958. doi: 10.1001/jama.291.24.2947 [DOI] [PubMed] [Google Scholar]

[alz71704-bib-0016] 16. Shumaker SA, Legault C, Rapp SR, et al. Estrogen plus progestin and the incidence of dementia and mild cognitive impairment in postmenopausal women: the Women's Health Initiative Memory Study: a randomized controlled trial. JAMA. 2003;289(20):2651‐2662. doi: 10.1001/jama.289.20.2651 [DOI] [PubMed] [Google Scholar]

[alz71704-bib-0017] 17. Shumaker SA, Reboussin BA, Espeland MA, et al. The Women's Health Initiative Memory Study (WHIMS): a trial of the effect of estrogen therapy in preventing and slowing the progression of dementia. Control Clin Trials. 1998;19(6):604‐621. doi: 10.1016/s0197-2456(98)00038-5 [DOI] [PubMed] [Google Scholar]

[alz71704-bib-0018] 18. Teng EL, Chui HC. The Modified Mini‐Mental State (3MS) examination. J Clin Psychiatry. 1987;48(8):314‐318. [PubMed] [Google Scholar]

[alz71704-bib-0019] 19. Spitzer RL, Williams JB, Kroenke K, et al. Utility of a new procedure for diagnosing mental disorders in primary care. The PRIME‐MD 1000 study. JAMA. 1994;272(22):1749‐1756. [PubMed] [Google Scholar]

[alz71704-bib-0020] 20. Yesavage JA, Brink TL, Rose TL, et al. Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res. 1983;17:37‐49. [DOI] [PubMed] [Google Scholar]

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[alz71704-bib-0026] 26. Janelidze S, Teunissen CE, Zetterberg H, et al. Head‐to‐head comparison of 8 plasma amyloid‐beta 42/40 assays in Alzheimer disease. JAMA Neurol. 2021;78(11):1375‐1382. doi: 10.1001/jamaneurol.2021.3180 [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Alzheimer's blood‐based biomarkers, incident dementia, and interactions with age, APOE status, and hormone therapy

Michelle M Mielke

Sarah A Gaussoin

Ramon Casanova

Lauren A Latham

JoAnn E Manson

Charles P Mouton

Ted K S Ng

Stephen R Rapp

Susan M Resnick

Bonnie C Sachs

Nazmus Saquib

Aladdin H Shadyab

Linda K McEvoy

Andrea Z LaCroix

Robert B Wallace

Mark A Espeland

Jiu‐Chiuan Chen

Kathleen M Hayden

Abstract

INTRODUCTION

METHODS

RESULTS

DISCUSSION

Highlights

1. BACKGROUND

2. METHODS

2.1. WHIMS study design

RESEARCH IN CONTEXT

2.2. Ascertainment of MCI and dementia

2.3. Blood‐based biomarkers for ADRD

2.4. Covariates

2.5. Statistical analysis

3. RESULTS

3.1. Baseline participant characteristics

TABLE 1.

3.2. Baseline BBMs and long‐term incident MCI and probable dementia over a mean 18.1 years

TABLE 2.

3.3. Examination of potential interactions between the BBMs and age, APOE status, or HT randomization in relationship to each cognitive outcome

FIGURE 1.

FIGURE 2.

FIGURE 3.

FIGURE 4.

4. DISCUSSION

4.1. Strengths and limitations

4.2. Conclusions

CONFLICT OF INTEREST STATEMENT

CONSENT STATEMENT

Supporting information

ACKNOWLEDGMENTS

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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