In vivo assessment of Lewy body and beta-amyloid copathologies in idiopathic normal pressure hydrocephalus: prevalence and associations with clinical features and surgery outcome.

BACKGROUND: Idiopathic normal pressure hydrocephalus (iNPH) is a clinico-radiological syndrome of elderly individuals likely sustained by different neurodegenerative changes as copathologies. Since iNPH is a potentially reversible condition, assessing neurodegenerative pathologies in vitam through CSF biomarkers and their influence on clinical features and surgical outcome represents crucial steps. METHODS: We measured α-synuclein seeding activity related to Lewy body (LB) pathology by the real-time quaking-induced conversion assay (RT-QuIC) and Alzheimer disease core biomarkers (proteins total-tau, phospho-tau, and amyloid-beta) by immunoassays in the cerebrospinal fluid (CSF) of 293 iNPH patients from two independent cohorts. To compare the prevalence of LB copathology between iNPH participants and a control group representative of the general population, we searched for α-synuclein seeding activity in 89 age-matched individuals who died of Creutzfeldt-Jakob disease (CJD). Finally, in one of the iNPH cohorts, we also measured the CSF levels of neurofilament light chain protein (NfL) and evaluated the association between all CSF biomarkers, baseline clinical features, and surgery outcome at 6 months. RESULTS: Sixty (20.5%) iNPH patients showed α-synuclein seeding activity with no significant difference between cohorts. In contrast, the prevalence observed in CJD was only 6.7% (p = 0.002). Overall, 24.0% of iNPH participants showed an amyloid-positive (A+) status, indicating a brain co-pathology related to Aß deposition. At baseline, in the Italian cohort, α-synuclein RT-QuIC positivity was associated with higher scores on axial and upper limb rigidity (p = 0.003 and p = 0.011, respectively) and lower MMSEc scores (p = 0.003). A+ patients showed lower scores on the MMSEc (p = 0.037) than A- patients. Higher NfL levels were also associated with lower scores on the MMSEc (rho = -0.213; p = 0.021). There were no significant associations between CSF biomarkers and surgical outcome at 6 months (i.e. responders defined by decrease of 1 point on the mRankin scale). CONCLUSIONS: Prevalent LB- and AD-related neurodegenerative pathologies affect a significant proportion of iNPH patients and contribute to cognitive decline (both) and motor impairment (only LB pathology) but do not significantly influence the surgical outcome at 6 months. Their effect on the clinical benefit after surgery over a more extended period remains to be determined.

Selection of memory clinic patients for CSF biomarker assessment can be restricted to a quarter of cases by using computerized decision support, without compromising diagnostic accuracy.

INTRODUCTION: An accurate and timely diagnosis for Alzheimer's disease (AD) is important, both for care and research. The current diagnostic criteria allow the use of CSF biomarkers to provide pathophysiological support for the diagnosis of AD. How these criteria should be operationalized by clinicians is unclear. Tools that guide in selecting patients in which CSF biomarkers have clinical utility are needed. We evaluated computerized decision support to select patients for CSF biomarker determination. METHODS: We included 535 subjects (139 controls, 286 Alzheimer's disease dementia, 82 frontotemporal dementia and 28 vascular dementia) from three clinical cohorts. Positive (AD like) and negative (normal) CSF biomarker profiles were simulated to estimate whether knowledge of CSF biomarkers would impact (confidence in) diagnosis. We applied these simulated CSF values and combined them with demographic, neuropsychology and MRI data to initiate CSF testing (computerized decision support approach). We compared proportion of CSF measurements and patients diagnosed with sufficient confidence (probability of correct class ≥0.80) based on an algorithm with scenarios without CSF (only neuropsychology, MRI and APOE), CSF according to the appropriate use criteria (AUC) and CSF for all patients. RESULTS: The computerized decision support approach recommended CSF testing in 140 (26%) patients, which yielded a diagnosis with sufficient confidence in 379 (71%) of all patients. This approach was more efficient than CSF in none (0% CSF, 308 (58%) diagnosed), CSF selected based on AUC (295 (55%) CSF, 350 (65%) diagnosed) or CSF in all (100% CSF, 348 (65%) diagnosed). CONCLUSIONS: We used a computerized decision support with simulated CSF results in controls and patients with different types of dementia. This approach can support clinicians in making a balanced decision in ordering additional biomarker testing. Computer-supported prediction restricts CSF testing to only 26% of cases, without compromising diagnostic accuracy.