Population-based reference equations and Z-scores for blood biomarkers of neurodegenerative diseases

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Abstract Background Blood biomarkers are increasingly used to support the diagnosis and monitoring of neurodegenerative diseases. However, their interpretation is complicated by physiological determinants, including age, sex, body-mass index, and renal function, and by differences in absolute concentrations between analytical methods. We aimed to develop population-based reference equations allowing individualized interpretation of the main blood biomarkers used in neurology. Methods In this cross-sectional study, we analysed plasma samples from cognitively unimpaired participants selected from the French CONSTANCES and Three-City population-based cohorts. Generalized additive models for location, scale, and shape were used to model neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), phosphorylated tau 181 (p-tau181), amyloid-β40, amyloid-β42, and their ratios according to age, sex, body-mass index, and renal function. The resulting equations provided individualized expected concentrations, percentiles, and Z-scores. Previously established disease-specific concentrations were converted into Z-score. Cross-calibration equations were developed for NfL measurements across analytical methods and sample matrices. Findings The final reference populations comprised 5123 participants for amyloid biomarkers and p-tau181 and 5122 for NfL and GFAP; median age was 52.3 years and half were women. Between ages 40 and 80 years, expected NfL and GFAP concentrations increased by an average of 2.6% and 2.2% per year, respectively. Renal function, body-mass index, and sex had additional biomarker-specific effects. Application of the equations to clinical cohorts preserved distinct disease-associated profiles: NfL Z-scores were increased across disorders characterised by neuroaxonal injury, whereas p-tau181 and GFAP showed its greatest increase in Alzheimer disease. NfL cross-calibration equations showed excellent agreement between methods and matrices, with intraclass correlation coefficients greater than 0.90. Interpretation This population-based multibiomarker framework enables blood biomarker concentrations to be interpreted relative to individuals with similar physiological characteristics. Publicly available equations, reference curves, and standardized Z-scores could improve individualized interpretation and comparability across biomarkers, laboratories, and clinical populations.

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