ArticleFluids and barriers of the CNS2026
Targeted CSF metabolomics and conformal prediction improve diagnostic accuracy of normal pressure hydrocephalus.
Article in Fluids and barriers of the CNS, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
BACKGROUND AND
objectivesIdiopathic normal pressure hydrocephalus (iNPH) is a progressive but treatable neurological disorder. Yet, diagnosis is often confounded by overlapping symptoms and biomarker profiles with Alzheimer’s disease (AD), mild cognitive impairment (MCI), and frontotemporal dementia (FTD). We aimed to determine whether cerebrospinal fluid (CSF) metabolomic profiling, combined with uncertainty-aware machine learning using conformal prediction (CP), could improve diagnostic differentiation of iNPH.
methodsCSF samples were collected from 120 patients with iNPH, 44 healthy controls, and 152 individuals with AD, MCI, or FTD. Targeted metabolomics of 59 metabolites was performed using liquid chromatography–high-resolution mass spectrometry. Group differences were assessed using age- and sex-adjusted regression models. Multivariate classification with partial least squares discriminant analysis (PLS-DA) incorporated metabolites, demographics, and conventional biomarkers (amyloid-β42, tau, phosphorylated tau). CP was applied to address individual-level diagnostic uncertainty.
resultsEight metabolites (proline, threonine, histidine, tyrosine, tryptophan, isobutyrylcarnitine, citric acid, and dehydroascorbic acid) were consistently reduced in iNPH (q < 0.05), independent of ventricular volume and cortical tau or amyloid-β pathology. An integrated PLS-DA model combining metabolomic, demographic, and AD-biomarker data achieved excellent discrimination (AUC = 0.97). CP provided calibrated case-level confidence, identifying clear-cut and uncertain cases while maintaining high accuracy (94% for iNPH, 97% for not-iNPH). DISCUSSION: iNPH exhibits a distinct CSF metabolomic signature reflecting altered amino acid metabolism, mitochondrial function, and oxidative stress. Integrating metabolomic data with established biomarkers enhances diagnostic accuracy, while CP adds individualized uncertainty estimates to improve diagnostic confidence and guide treatment decisions.
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