Logo: to the web site of Uppsala University

Background and objectives: Idiopathic 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.

Methods: CSF 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.

Results: Eight 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.

Traditional statistical approaches have advanced our understanding of the genetics of complex diseases, yet are limited to linear additive models. Here we applied machine learning (ML) to genome-wide data from 41,686 individuals in the largest European consortium on Alzheimer's disease (AD) to investigate the effectiveness of various ML algorithms in replicating known findings, discovering novel loci, and predicting individuals at risk. We utilised Gradient Boosting Machines (GBMs), biological pathway-informed Neural Networks (NNs), and Model-based Multifactor Dimensionality Reduction (MB-MDR) models. ML approaches successfully captured all genome-wide significant genetic variants identified in the training set and 22% of associations from larger meta-analyses. They highlight 6 novel loci which replicate in an external dataset, including variants which map to ARHGAP25, LY6H, COG7, SOD1 and ZNF597. They further identify novel association in AP4E1, refining the genetic landscape of the known SPPL2A locus. Our results demonstrate that machine learning methods can achieve predictive performance comparable to classical approaches in genetic epidemiology and have the potential to uncover novel loci that remain undetected by traditional GWAS. These insights provide a complementary avenue for advancing the understanding of AD genetics. 

A polygenic score (PGS) for Alzheimer's disease (AD) was derived recently from data on genome-wide significant loci in European ancestry populations. We applied this PGS to populations in 17 European countries and observed a consistent association with the AD risk, age at onset and cerebrospinal fluid levels of AD biomarkers, independently of apolipoprotein E locus (APOE). This PGS was also associated with the AD risk in many other populations of diverse ancestries. A cross-ancestry polygenic risk score improved the association with the AD risk in most of the multiancestry populations tested when the APOE region was included. Finally, we found that the PGS/polygenic risk score captured AD-specific information because the association weakened as the diagnosis was broadened. In conclusion, a simple PGS captures the AD-specific genetic information that is common to populations of different ancestries, although studies of more diverse populations are still needed to better characterize the genetics of AD.

Background: Depression and dementia are known to be associated. The identification of characteristics distinguishing depression prodromal to dementia from other depressive symptoms would be of value for early identification of dementia. The study of risk factors for depressive symptoms prodromal to dementia could improve preventive care and provide clues to the causes of dementia.

Method: Dementia-free 82-year-old participants were stratified into groups that did (n = 126) and did not (n = 378) subsequently develop dementia. Examinations took place from 2003 to 2005 and follow-up ended 1 January 2015. Their baseline characteristics and depressive symptoms, measured using the 15-item Geriatric Depression Scale (GDS-15), were compared. Multivariate regression analyses were performed for the two groups separately, with the total GDS-15 score as the dependent variable.

Results: The groups did not differ significantly in answers to any of the GDS-15 questions, or mean +/- SD score, which was 2.4 +/- 2.5 among those who developed dementia and 2.1 +/- 2.3 among those who did not. (p = 0.33). Stroke before the age of 82 years and the inability to use stairs had significant impacts on the GDS-15 scores in both groups. For those who did not develop dementia, age, dependence in activities of daily living, and cancer also had significant impacts. Cancer had opposite associations with depressive symptoms in the two groups.

Conclusions: No difference was found in depressive symptoms preceding and not preceding dementia using the GDS-15. The results suggest that risk factors for depressive symptoms may differ depending on whether they precede dementia.

Bsckground and Purpose: Differentiating idiopathic normal pressure hydrocephalus (iNPH) from neurodegenerative disorders such as progressive supranuclear palsy (PSP), Multiple System Atrophy-parkinsonian type (MSA-P), and vascular dementia (VaD) is challenging due to overlapping clinical and neuroimaging findings. This study assesses if quantitative brain stem and cerebellum metrics can aid in this differentiation.

Methods: We retrospectively compared the sagittal midbrain area, midbrain to pons ratio, MR parkinsonism index (MRPI), and cerebellar atrophy in 30 PSP patients, 31 iNPH patients, 27 MSA-P patients, 32 VaD patients, and 25 healthy controls. Statistical analyses determined group differences, sensitivity, specificity, and the area under the receiver operating characteristic curves.

Results: There was an overlap in midbrain morphology between PSP and iNPH, as assessed with MRPI, midbrain to pons ratio, and midbrain area. A cutoff value of MRPI > 13 exhibited 84% specificity in distinguishing PSP from iNPH and 100% in discriminating PSP from all other conditions. A cutoff value of midbrain to pons ratio at <0.15 yielded 95% specificity for differentiating PSP from iNPH and 100% from all other conditions. A cutoff value of midbrain area at <87 mm² exhibited 97% specificity for differentiating PSP from iNPH and 100% from all other conditions. All measures showed low sensitivity. Cerebellar atrophy did not differ significantly among groups.

Conclusion: Our study questions MRPI's diagnostic performance in distinguishing PSP from iNPH. Simpler indices such as midbrain to pons ratio and midbrain area showed similar or better accuracy. However, all these indices displayed low sensitivity despite significant differences among PSP, MSA-P, and VaD.

This study aimed to evaluate the feasibility of implementing an electronic version of PURPOSE T, a risk assessment instrument for pressure ulcers, in a Swedish hospital ward. A mixed-method was used. Nursing staff received training in PURPOSE T and a record review was performed (n=30). PURPOSE T replaced the Modified Norton Scale, and after one month another record review was performed (n=30). Individual interviews with patients (n=15) and focus groups interviews with nursing staff (n=23) were performed after the implementation. The results of the record review and the focus groups interviews showed a good clinical feasibility of PURPOSE T. The record review showed that more patients were at risk of developing pressure ulcers and more nursing interventions were prescribed with PURPOSE T compared to the Modified Norton Scale. The focus group interviews showed that all nursing staff were satisfied with PURPOSE T. The instrument contributed to increased reflection and analysis as well as the opportunity for nursing staff to draw their own conclusions regarding patients´ risk status. The documentation encouraged the prescription of more preventive actions, and the nurses were more involved at bedside. However, almost all the patients experienced that they did not receive any information about pressure ulcers. 

Key Messages

· PURPOSE T demonstrates successful feasibility when implemented in clinical practice.

· The study provides evidence that an electronic version of PURPOSE T can replace pressure ulcer risk assessment instruments that are used today.

· Routines for sharing information with patients about pressure ulcers need to be prioritized in clinical practice.

Background: Neuroinflammatory processes have been suggested to play a role in the pathophysiology of neurodegenerative diseases and post-hemorrhagic hydrocephalus, but have rarely been investigated in patients with idiopathic normal pressure hydrocephalus (iNPH). The aim of this study was to investigate whether levels of inflammatory proteins in CSF are different in iNPH compared to healthy controls and patients with selected neurodegenerative disorders, and whether any of these markers can aid in the differential diagnosis of iNPH.

Methods: Lumbar CSF was collected from 172 patients from a single center and represented iNPH (n = 74), Alzheimer's disease (AD) (n = 21), mild cognitive impairment (MCI) due to AD (n = 21), stable MCI (n = 22), frontotemporal dementia (n = 13), and healthy controls (HC) (n = 21). Levels of 92 inflammatory proteins were analyzed using a proximity extension assay. As a first step, differences between iNPH and HC were investigated, and proteins that differed between iNPH and HC were then compared with those from the other groups. The linear regressions were adjusted for age, sex, and plate number.

Results: Three proteins showed higher (MCP-1, p = 0.0013; CCL4, p = 0.0008; CCL11, p = 0.0022) and one lower (PD-L1, p = 0.0051) levels in patients with iNPH compared to HC. MCP-1 was then found to be higher in iNPH than in all other groups. CCL4 was higher in iNPH than in all other groups, except in MCI due to AD. PD-L1 was lower in iNPH compared to all other groups, except in stable MCI. Levels of CCL11 did not differ between iNPH and the differential diagnoses. In a model based on the four proteins mentioned above, the mean area under the receiver operating characteristic curve used to discriminate between iNPH and the other disorders was 0.91.

Conclusions: The inflammatory cytokines MCP-1 and CCL4 are present at higher-and PD-L1 at lower-levels in iNPH than in the other investigated diagnoses. These three selected cytokines may have diagnostic potential in the work-up of patients with iNPH.

Background: Neuroinflammatory processes are common in neurodegenerative diseases such as Alzheimer's disease (AD) and frontotemporal dementia (FTD), but current knowledge is limited as to whether cerebrospinal fluid (CSF) levels of neuroinflammatory proteins are altered in these diseases.

Objective: To identify and characterize neuroinflammatory signatures in CSF from patients with AD, mild cognitive impairment (MCI), and FTD.

Methods: We used proximity extension assay and ANOVA to measure and compare levels of 92 inflammatory proteins in CSF from 42 patients with AD, 29 with MCI due to AD (MCI/AD), 22 with stable MCI, 42 with FTD, and 49 control subjects, correcting for age, gender, collection unit, and multiple testing.

Results: Levels of matrix metalloproteinase-10 (MMP-10) were increased in AD, MCI/AD, and FTD compared with controls (AD: fold change [FC] = 1.32, 95% confidence interval [CI] 1.14-1.53, q = 0.018; MCI/AD: FC = 1.53, 95% CI 1.20-1.94, q = 0.045; and FTD: FC = 1.42, 95% CI 1.10-1.83, q = 0.020). MMP-10 and eleven additional proteins were increased in MCI/AD, compared with MCI (q < 0.05). In FTD, 36 proteins were decreased, while none was decreased in AD or MCI/AD, compared with controls (q < 0.05).

Conclusion: In this cross-sectional multi-center study, we found distinct patterns of CSF inflammatory marker levels in FTD and in both early and established AD, suggesting differing neuroinflammatory processes in the two disorders.

Background: Vascular dementia (VaD) and atypical parkinsonism often present with symptoms that can resemble idiopathic normal pressure hydrocephalus (iNPH) and enlarged cerebral ventricles, and can be challenging differential diagnoses. The aim was to investigate frequencies of imaging features usually associated with iNPH and their radiological diagnostic accuracy in a sample containing the relevant differential diagnoses VaD, progressive supranuclear palsy (PSP), multiple system atrophy parkinsonian type (MSA-P), and healthy controls.

Methods: Nine morphological imaging features usually associated with iNPH were retrospectively investigated in MR images of 55 patients with shunt-responsive iNPH, 32 patients with VaD, 30 patients with PSP, 27 patients with MSA-P, and 39 age-matched healthy controls. Logistic regression and receiver operating characteristic curves were used to assess diagnostic accuracy, sensitivity, and specificity for each imaging finding.

Results: In a logistic regression model using iNPH diagnosis as a dependent variable, the following imaging features contributed significantly to the model: callosal angle (OR = 0.95 (0.92-0.99), p = 0.012), Evans' index * 100 (OR = 1.51 (1.23-1.86), p < 0.001), enlarged Sylvian fissures (OR = 6.01 (1.42-25.40), p = 0.015), and focally enlarged sulci (OR = 10.18 (1.89-55.02), p = 0.007). Imaging features with 95% specificity for iNPH were: callosal angle <= 71 degrees, temporal horns >= 7 mm, Evans' index >= 0.37, iNPH Radscale >= 9, and presence of DESH, bilateral ventricular roof bulgings or focally enlarged sulci. A simplified version of the iNPH Radscale with only four features resulted in equally high diagnostic accuracy as the original iNPH Radscale.

Conclusions: There is a notable overlap between some of the commonly used imaging markers regarding iNPH, VaD and atypical parkinsonism, such as PSP. However, this study shows that the specificity of imaging markers usually associated with iNPH was high even when comparing with these challenging differential diagnoses. The callosal angle was the single imaging feature with highest diagnostic accuracy to discriminate iNPH from its mimics. A simplified rating scale using only a few selected features could be used with retained specificity.

Objective: Decreased amyloid beta (Aβ) 42 together with increased tau and phospho-tau in cerebrospinal fluid (CSF) is indicative of Alzheimer’s disease (AD). However, the molecular pathophysiology underlying the slowly progressive cognitive decline observed in AD is not fully understood and it is not known what other CSF biomarkers may be altered in early disease stages.

Methods: We utilized an antibody-based suspension bead array to analyze levels of 216 proteins in CSF from AD patients, patients with mild cognitive impairment (MCI), and controls from two independent cohorts collected within the AETIONOMY consortium. Two additional cohorts from Sweden were used for biological verification.

Results: Six proteins, amphiphysin (AMPH), aquaporin 4 (AQP4), cAMP-regulated phosphoprotein 21 (ARPP21), growth-associated protein 43 (GAP43), neurofilament medium polypeptide (NEFM), and synuclein beta (SNCB) were found at increased levels in CSF from AD patients compared with controls. Next, we used CSF levels of Aβ42 and tau for the stratification of the MCI patients and observed increased levels of AMPH, AQP4, ARPP21, GAP43, and SNCB in the MCI subgroups with abnormal tau levels compared with controls. Further characterization revealed strong to moderate correlations between these five proteins and tau concentrations.

Interpretations: In conclusion, we report six extensively replicated candidate biomarkers with the potential to reflect disease development. Continued evaluation of these proteins will determine to what extent they can aid in the discrimination of MCI patients with and without an underlying AD etiology, and if they have the potential to contribute to a better understanding of the AD continuum.