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. 2023 Apr;19(4):1403-1414.
doi: 10.1002/alz.12787. Epub 2022 Sep 24.

Confounding factors of Alzheimer's disease plasma biomarkers and their impact on clinical performance

Alexa Pichet Binette  1 , Shorena Janelidze  1 , Nicholas Cullen  1 , Jeffrey L Dage  2 , Randall J Bateman  3 , Henrik Zetterberg  4   5   6   7   8 , Kaj Blennow  4   5 , Erik Stomrud  1   9 , Niklas Mattsson-Carlgren  1   10   11 , Oskar Hansson  1   9
Affiliations

Confounding factors of Alzheimer's disease plasma biomarkers and their impact on clinical performance

Alexa Pichet Binette et al. Alzheimers Dement. 2023 Apr.

Abstract

Introduction: Plasma biomarkers will likely revolutionize the diagnostic work-up of Alzheimer's disease (AD) globally. Before widespread use, we need to determine if confounding factors affect the levels of these biomarkers, and their clinical utility.

Methods: Participants with plasma and CSF biomarkers, creatinine, body mass index (BMI), and medical history data were included (BioFINDER-1: n = 748, BioFINDER-2: n = 421). We measured beta-amyloid (Aβ42, Aβ40), phosphorylated tau (p-tau217, p-tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP).

Results: In both cohorts, creatinine and BMI were the main factors associated with NfL, GFAP, and to a lesser extent with p-tau. However, adjustment for BMI and creatinine had only minor effects in models predicting either the corresponding levels in CSF or subsequent development of dementia.

Discussion: Creatinine and BMI are related to certain plasma biomarkers levels, but they do not have clinically relevant confounding effects for the vast majority of individuals.

Highlights: Creatinine and body mass index (BMI) are related to certain plasma biomarker levels. Adjusting for creatinine and BMI has minor influence on plasma-cerebrospinal fluid (CSF) associations. Adjusting for creatinine and BMI has minor influence on prediction of dementia using plasma biomarkers.

Keywords: amyloid; cerebrospinal fluid; dementia; glial fibrillary acidic protein; neurofilament light; p-tau.

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Conflict of interest statement

OH has acquired research support (for the institution) from ADx, AVID Radiopharmaceuticals, Biogen, Eli Lilly, Eisai, Fujirebio, GE Healthcare, Pfizer, and Roche. In the past 2 years, he has received consultancy/speaker fees from Amylyx, Alzpath, BioArctic, Biogen, Cerveau, Fujirebio, Genentech, Novartis, Roche, and Siemens.

JLD is an inventor on patents or patent applications of Eli Lilly and Company relating to the assays, methods, reagents and / or compositions of matter used in this work. JLD has served as a consultant for Genotix Biotechnologies Inc, Gates Ventures, Karuna Therapeutics, AlzPath Inc, and received research support from ADx Neurosciences, Roche Diagnostics and Eli Lilly and Company in the past two years. HZ has served at scientific advisory boards and/or as a consultant for Alector, Eisai, Denali, Roche Diagnostics, Wave, Samumed, Siemens Healthineers, Pinteon Therapeutics, Nervgen, AZTherapies, CogRx and Red Abbey Labs, has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure and Biogen. KB has served as a consultant, at advisory boards, or at data monitoring committees for Abcam, Axon, Biogen, JOMDD/Shimadzu. Julius Clinical, Lilly, MagQu, Novartis, Prothena, Roche Diagnostics, and Siemens Healthineers. HZ and KB are co-founders of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program. RJB cofounded C2N Diagnostics. Washington University and RJB have equity ownership interest in C2N Diagnostics and may receive income based on technology (stable isotope labeling kinetics and blood plasma assay) licensed by Washington University to C2N Diagnostics. RJB receives income from C2N Diagnostics for serving on the scientific advisory board. RJB has received honoraria as a speaker, consultant, or advisory board member from Amgen and Roche.

All other authors declare no conflict of interest.

Figures

Figure 1.
Figure 1.. Associations between creatinine, body mass index, comorbidities and medication use with plasma biomarkers
Standardized beta coefficients and 95% confidence interval from linear regression models adjusted for age and sex with plasma biomarkers in BioFINDER-1 (left) and BioFINDER-2 (right). The Aβ42/40 ratio was reversed so that the effect size moves in the same direction for each biomarker. As such, higher value on each biomarker is more abnormal (towards AD). Stars indicate that the association remained significant when further adjusting for flutemetamol global Aβ-PET SUVR. In BioFINDER-2, all associations also remained significant if adding temporal meta-ROI tau-PET SUVR from [18F]RO948 as a covariate. Abbreviations: Aβ, beta-amyloid; BMI, body mass index; CSF, cerebrospinal fluid, GFAP, glial fibrillary acidic protein; NfL, neurofilament light; p-tau217, phosphorylated tau 217
Figure 2.
Figure 2.. Associations between plasma and corresponding cerebrospinal fluid biomarkers
Scatter plots depict bivariate associations between plasma and CSF levels for Aβ42/40 ratio (A), p-tau217 (B), NfL (C) and GFAP (D) in BioFINDER-1 on the left-hand side and BioFINDER-2 on the right-hand side. To compare plasma coefficients between the base model and the one including creatinine and BMI as covariates, we generated 10 000 bootstrap samples of both models, shown in the histograms. Significant difference between models was based on the 95% confidence interval difference of the difference of plasma estimates between models. Abbreviations: Aβ, beta-amyloid; BMI, body mass index; Crt: creatinine, CSF, cerebrospinal fluid, GFAP, glial fibrillary acidic protein; NfL, neurofilament light; p-tau217, phosphorylated tau 217
Figure 3.
Figure 3.. Plasma biomarkers on assessing conversion to AD or all-cause dementia in non-demented participants
ROC curves showing accuracy to discriminate between BioFINDER-1 participants who remained cognitively normal vs those who progressed to AD dementia based on plasma Aβ42/Aβ40 ratio (A), p-tau217 (B) and GFAP (C), and to discriminate participants who remained cognitively normal vs those who progressed to all-cause dementia based on plasma NfL (D). Results from logistic regression including plasma levels, age and sex are sown in the blue curve and models including creatinine and BMI as additional covariates in the orange curve. Plasma estimates between both models were then compared using bootstrapping as shown in the histograms and as done previously.
See this image and copyright information in PMC

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