Plasma Alzheimer's disease biomarker variability: Amyloid-independent and amyloid-dependent factors

Abstract

Introduction: We aimed to investigate which factors affect plasma biomarker levels via amyloid beta (Aβ)-independent or Aβ-dependent effects and improve the predictive performance of these biomarkers for Aβ positivity on positron emission tomography (PET).

Methods: A total of 2935 participants underwent blood sampling for measurements of plasma Aβ42/40 ratio, phosphorylated tau 217 (p-tau217; ALZpath), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) levels using single-molecule array and Aβ PET. Laboratory findings were collected using a routine blood test battery.

Results: Aβ-independent factors included hemoglobin and estimated glomerular filtration rate (eGFR) for p-tau217 and hemoglobin, eGFR, and triiodothyronine (T₃) for GFAP and NfL. Aβ-dependent factors included apolipoprotein E genotypes, body mass index status for Aβ42/40, p-tau217, GFAP, and NfL. However, these factors exhibited negligible or modest effects on Aβ positivity on PET.

Discussion: Our findings highlight the importance of accurately interpreting plasma biomarkers for predicting Aβ uptake in real-world settings.

Highlights: We investigated factor-Alzheimer's disease plasma biomarker associations in a large Korean cohort. Hemoglobin and estimated glomerular filtration rate affect the biomarkers independently of brain amyloid beta (Aβ). Apolipoprotein E genotypes and body mass index status affect the biomarkers dependent on brain Aβ. Addition of Aβ-independent factors shows negligible effect in predicting Aβ positivity. Adjusting for Aβ-dependent factors shows a modest effect in predicting Aβ positivity.

Keywords: Alzheimer's disease; amyloid beta–dependent variability; amyloid beta–independent variability; biomarker application; biomarker variability; comorbidity; plasma biomarkers; subcortical vascular cognitive impairment.

FIGURE 1

Forest plots for comorbidities and factors as predictors. This figure shows the result of linear regression for each plasma biomarker and Aβ uptake on PET using comorbidities and biological factors as predictors. APOE ε2 carriers were defined as individuals with the APOE ε2/ε2 or ε2/ε3 genotypes. APOE ε4 carriers were defined as individuals with the APOE ε2/ε4, ε3/ε4, or ε4/ε4 genotypes. Underweight was defined as BMI < 18.5 kg/m², normal weight as 18.5 kg/m² ≤ BMI < 25 kg/m², and obesity as BMI ≥ 25 kg/m². The laboratory findings that were measured by three different laboratories were z transformed within each laboratory, and the plasma biomarker levels were log‐transformed. *p < 0.05 after Bonferroni correction. Aβ, amyloid beta; ALT, alanine transaminase; AST, aspartate transaminase; BMI, body mass index; CAD, coronary artery disease; CKD, chronic kidney disease; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; ESR, erythrocyte sedimentation rate; GFAP, glial fibrillary acidic protein; Hb, hemoglobin; HDL‐C, high‐density lipoprotein cholesterol; HL, hyperlipidemia; hs‐CRP, high‐sensitivity C‐reactive protein; HTN, hypertension; LDL‐C, low‐density lipoprotein cholesterol; NfL, neurofilament light chain; O, obesity; PET, positron emission tomography; p‐tau217, phosphorylated tau 217; rdcCL, regional direct comparison Centiloid; T₃, triiodothyronine; T₄, thyroxine; TC, total cholesterol; TSH, thyroid stimulating hormone; UW, underweight

FIGURE 2

Effect of comorbidities and factors on plasma biomarkers with respect to the Aβ burden. This figure shows the results of mediation analyses for investigating whether each biological factor affected plasma biomarkers in relation to Aβ burden in the brain. The dashed lines indicate associations that were statistically insignificant. The β (standard error) value for each association is written on the line. APOE ε2 carriers were defined as individuals with the APOE ε2/ε2 or ε2/ε3 genotypes. APOE ε4 carriers were defined as individuals with the APOE ε2/ε4, ε3/ε4, or ε4/ε4 genotypes. Underweight was defined as BMI < 18.5 kg/m², normal weight as 18.5 kg/m² ≤ BMI < 25 kg/m², and obesity as BMI ≥ 25 kg/m². *p < 0.05; **p < 0.01; ***p < 0.001. Aβ, amyloid beta; ALT, alanine transaminase; APET rdcCL, amyloid positron emission tomography regional direct comparison Centiloid; BMI, body mass index; CAD, coronary artery disease; DM, diabetes mellitus; ε2, APOE ε2 carrier; ε3, APOE ε3/ε3 homozygote; ε4, APOE ε4 carrier; GFAP, glial fibrillary acidic protein; NfL, neurofilament light chain; NW, normal weight; O, obesity; p‐tau217, phosphorylated tau 217; TC, total cholesterol; UW, underweight

FIGURE 3

Improvement in performance of amyloid positivity prediction models by adding factors as covariates. The AUCs represent the performance of each plasma biomarker predicting Aβ (+) on PET. Model A included each plasma biomarker, age, and sex. Model B added factors related to Aβ‐independent variability to the covariates of model A (the presence of CKD, hemoglobin, and eGFR for p‐tau217; hemoglobin, plasma glucose, eGFR, and T₃ for GFAP; the presence of CKD, hemoglobin, eGFR, and T₃ for NfL). Model C added factors related to Aβ‐dependent variability to the covariates of model A (APOE ε4 carrier status and obesity for Aβ42/40 ratio; APOE genotypes, BMI status, and the presence of CAD for p‐tau217; APOE ε4 carrier status, BMI status, the presence of DM and CAD for GFAP; APOE ε4 carrier status, BMI status, and the presence of DM for NfL). Aβ, amyloid beta; AUC, area under the curve; BMI, body mass index; CAD, coronary artery disease; CKD, chronic kidney disease; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; GFAP, glial fibrillary acidic protein; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; NfL, neurofilament light chain; PET, positron emission tomography; p‐tau217, phosphorylated tau 217; T₃, triiodothyronine