From Biomarker Anchors to Disease-State Transitions: A Biologically Anchored Probabilistic Principal Component Analysis Framework for Alzheimer's Disease Progression Modeling.

INTRODUCTION: To develop and validate a biomarker-anchored probabilistic principal component analysis (PPCA) framework for identifying biologically interpretable latent dimensions of Alzheimer's disease (AD) and evaluating their utility for disease progression modeling.

METHODS: Data from 1058 participants that are amyloid-positive in the Alzheimer's Disease Neuroimaging Initiative (ADNI) were analyzed. Anchored PPCA used biomarker data only; cognitive, functional, diagnostic, and prognostic outcomes were withheld from latent-space construction and reserved for validation. Amyloid (A) and tau (T) factors were biologically anchored, neurodegeneration (N) was softly constrained, and a ventricular-vascular/residual (V/R) factor was empirically estimated. Robustness, reproducibility, held-out validity, clinical associations, prognostic performance, and clinical-state transitions were evaluated.

RESULTS: Anchored PPCA identified four biologically coherent dimensions: A, T, N, and V/R. Solutions were reproducible across repeated initializations and split-half analyses, and generalized to held-out participants, particularly for A, T, and N. The model explained approximately 60% of model-implied standardized biomarker variance, with highest explained variance for A and T and lowest for V/R. Latent factors explained variance in outcomes not used for model construction: 49.7% for ADAS-Cog13, 35.1% for CDR-SB, and 27.8% for FAQ; they also aligned with diagnostic classifications. Cox-model C-indices were 0.843 for latent factors alone and 0.933 for clinical-plus-latent factors, with better fit than the clinical benchmark. T showed the strongest prognostic association, followed by A, N, and V/R. After adjustment for baseline clinical severity, A, T, and N remained independently prognostic; V/R did not. Clinical-state transitions were predominantly monotonic and demonstrated marked sojourn-time dependence.

CONCLUSION: Biomarker-only anchored PPCA provides a biologically grounded representation of AD that validates against cognition, function, diagnosis, and clinical progression. A, T, and N may capture disease processes not fully reflected in cross-sectional clinical severity, whereas V/R appears more closely related to contemporaneous clinical status. Duration-dependent transitions support semi-Markov disease-progression and Shared Latent Disease Process models. External validation is warranted.