AbstractIntegrating cerebrospinal fluid-biomarkers into diagnostic workup of patients with sporadic cerebral amyloid angiopathy may support early and correct identification. We aimed to identify and validate clinical- and cerebrospinal fluid-biomarkers forin vivodiagnosis of cerebral amyloid angiopathy. This observational cohort study screened 2795 consecutive patients admitted for cognitive complaints to the academic departments of neurology and psychiatry over a 10-year period (2009–2018). We included 372 patients with available hemosiderin-sensitive MR imaging and cerebrospinal fluid-based neurochemical dementia diagnostics, i.e. Aβ40, Aβ42,t-tau,p-tau. We investigated the association of clinical- and cerebrospinal fluid-biomarkers with the MRI-based diagnosis of cerebral amyloid angiopathy, applying confounder-adjusted modelling, receiver operating characteristic and unsupervised cluster analyses. We identified 67 patients with cerebral amyloid angiopathy, 76 patients with Alzheimer’s disease, 75 patients with mild cognitive impairment due to Alzheimer’s disease, 76 patients with mild cognitive impairment with unlikely Alzheimer’s disease and 78 healthy controls. Patients with cerebral amyloid angiopathy showed a specific cerebrospinal fluid pattern: average concentration of Aß40 [13 792 pg/ml (10 081–18 063)] was decreased compared to all controls (P< 0.05); Aß42 [634 pg/ml (492–834)] was comparable to Alzheimer’s disease and mild cognitive impairment due to Alzheimer’s disease (P= 0.10,P= 0.93) but decreased compared to mild cognitive impairment and healthy controls (bothP< 0.001);p-tau [67.3 pg/ml (42.9–91.9)] andt-tau [468 pg/ml (275–698)] were decreased compared to Alzheimer’s disease (P< 0.001,P= 0.001) and mild cognitive impairment due to Alzheimer’s disease (P= 0.001,P= 0.07), but elevated compared to mild cognitive impairment and healthy controls (bothP< 0.001). Multivariate modelling validated independent clinical association of cerebral amyloid angiopathy with older age [odds-ratio: 1.06, 95% confidence interval (1.02–1.10),P< 0.01], prior lobar intracerebral haemorrhage [14.00 (2.64–74.19),P< 0.01], prior ischaemic stroke [3.36 (1.58–7.11),P< 0.01], transient focal neurologic episodes (TFNEs) [4.19 (1.06–16.64),P= 0.04] and gait disturbance [2.82 (1.11–7.15),P= 0.03]. For cerebrospinal fluid-biomarkers per 1 pg/ml, both lower Aß40 [0.9999 (0.9998–1.0000),P< 0.01] and lower Aß42 levels [0.9989 (0.9980–0.9998),P= 0.01] provided an independent association with cerebral amyloid angiopathy controlled for all aforementioned clinical confounders. Both amyloid biomarkers showed good discrimination for diagnosis of cerebral amyloid angiopathy among adjusted receiver operating characteristic analyses (area under the receiver operating characteristic curves, Aß40: 0.80 (0.73–0.86),P< 0.001; Aß42: 0.81 (0.75–0.88),P< 0.001). Unsupervised Euclidian clustering of all cerebrospinal fluid-biomarker-profiles resulted in distinct segregation of cerebral amyloid angiopathy patients from all controls. Together, we demonstrate that a distinctive set of cerebrospinal fluid-biomarkers effectively differentiate cerebral amyloid angiopathy patients from patients with Alzheimer’s disease, mild cognitive impairment with or without underlying Alzheimer’s disease, and healthy controls. Integrating our findings into a multiparametric approach may facilitate diagnosing cerebral amyloid angiopathy, and may aid clinical decision-making, but warrants future prospective validation.