Evaluation of harmonization methods to mitigate assay and cohort effects in plasma p‐tau217
Year: 2026
Type: article
Abstract: INTRODUCTION: The growing number of assay platforms measuring blood-based biomarkers (BBMs) for Alzheimer's disease (AD) has introduced challenges in interpretability and comparability across assays. Differences across studies also limit comparability of data. To address these challenges, a systematic evaluation of harmonization methods is needed to support BBM data integration within or across studies. METHODS: = 111), were used to evaluate harmonization methods for mitigating assay and cohort effects in plasma phosphorylated tau 217 (p-tau217) measurements. Methods included various normalization, regression, and standardization approaches, including the recently developed CentiMarker. Assay effects were evaluated using repeated-measures data across assay platforms within each cohort, whereas cohort effects were assessed using pooled ADNI and HCP data. Harmonization performance was evaluated using distributional statistics and downstream modeling of p-tau217. RESULTS: Quantile normalization and quantile mapping methods were most effective for mitigating assay effects, whereas conditional quantile mapping performed best for pooled multi-cohort data. These methods also preserved biological variability. In contrast, simple means adjustment and reference-based Z-score standardization were least effective for mitigating assay effects, whereas simple means adjustment, Z-score standardization, and quantile normalization were least effective for mitigating cohort effects. CentiMarker had minimal impact on assay or cohort effects. DISCUSSION: Based on our evaluation, we recommend (conditional) quantile mapping for p-tau217 studies integrating data across multiple assays or cohorts. In contrast, we caution against using CentiMarker and Z-score-based methods, as they limit comparability and do not effectively mitigate technical variability.
DOI: 10.1002/dad2.70518
Institution: University of Pittsburgh
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,540
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OpenAlex ID: W7223375602