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Objective The aim of the present study was to compare the levels of serum neurofilament light chain (sNfL) in frontotemporal lobar degeneration (FTLD) patients of different clinical subtypes (bvFTD, PPA, and FTLD‐MND) and with or without the C9orf72 repeat expansion, and to correlate sNfL levels to disease progression, assessed by the brain atrophy rate and survival time. Methods The sNfL levels were determined from 78 FTLD patients (C9orf72 repeat expansion carriers [n = 26] and non‐carriers [n = 52]) with Single Molecule Array (SIMOA). The progression of brain atrophy was evaluated using repeated T1‐weighted MRI scans and the survival time from medical records. Results In the total FTLD cohort, sNfL levels were significantly higher in C9orf72 repeat expansion carriers compared to non‐carriers. Considering clinical phenotypes, sNfL levels were higher in the C9orf72 repeat expansion carriers than in the non‐carriers in bvFTD and PPA groups. Furthermore, sNfL levels were the highest in the FTLD‐MND group (median 105 pg/mL) and the lowest in the bvFTD group (median 27 pg/mL). Higher sNfL levels significantly correlated with frontal cortical atrophy rate and subcortical grey matter atrophy rate. The higher sNfL levels also associated with shorter survival time. Interpretation Our results indicate that the C9orf72 repeat expansion carriers show elevated sNFL levels compared to non‐carriers and that the levels differ among different clinical phenotypes of FTLD. Higher sNfL levels correlated with a shorter survival time and cortical and subcortical atrophy rates. Thus, sNfL could prove as a potential prognostic biomarker in FTLD.

ATN (β-amyloid [Aβ], tau, neurodegeneration) system categorizes individuals based on their core Alzheimer disease (AD) biomarkers. An important potential future use for ATN is therapeutic decision-making in clinical practice once disease-modifying treatments (e.g., anti-amyloid), become widely available. In this cross-sectional study, we applied ATN and estimated potential eligibility for anti-amyloid treatment in a real-life memory clinic with biomarker assessments integrated into the routine diagnostic procedure and all specialized resources available for the implementation of novel treatments.We included all consecutive patients at the Karolinska University Hospital Memory clinic in Solna, Stockholm, Sweden, who had their first diagnostic visit in April 2018-February 2021, informed consent for the clinic research database, and available clinical and biomarker (CSF and imaging) data. ATN classification was based on CSF Aβ42 (or Aβ42/40; A), CSF phosphorylated tau (T), and medial temporal lobe atrophy (N). For CSF markers, we applied laboratory cutoffs and data-driven cutoffs for comparison (determined with Gaussian mixture modeling). Eligibility for anti-amyloid treatment was assessed following the published recommendations for aducanumab (AD dementia or mild cognitive impairment [MCI] with no evidence of non-AD etiology, appropriate level of cognition, and AD-consistent CSF profile).The study population consisted of 410 patients (52% subjective cognitive impairment, 23% MCI, and 25% any dementia; age 59 ± 7 years, 56% women). Regardless of biomarker cutoffs, most patients were A-T-N- (54%-57%). A+ prevalence was 17%-30% (higher with data-driven cutoffs). Up to 13% of all patients (27% of those with MCI and 28% of those with dementia) were potentially eligible for anti-amyloid treatment when AD-consistent CSF was defined as any A+ profile. When A+T+ profile was required, treatment was targeted more to the dementia than MCI stage (eligibility up to 14% in MCI and 22% in dementia). The opposite applied to earlier-stage intervention (A+T-N-; eligibility up to 12% in MCI and 2% in dementia).In a memory clinic setting with all necessary infrastructure and national guidelines in place for dementia diagnostic examination (best-case scenario), most of the patients did not meet the eligibility criteria for anti-amyloid treatment. Continuing the development of disease-modifying treatments with different mechanisms of action is a priority.

Decreased levels of serum high-density lipoprotein (HDL) cholesterol have previously been linked to systemic inflammation and neurodegenerative diseases, such as Alzheimer’s disease. Here, we aimed to analyze the lipoprotein profile and inflammatory indicators, the high-sensitivity C-reactive peptide (hs-CRP) and glycoprotein acetyls (GlycA), in sporadic and C9orf72 repeat expansion-associated frontotemporal lobar degeneration (FTLD) patients. The C9orf72 hexanucleotide repeat expansion is the most frequent genetic etiology underlying FTLD. The concentrations of different lipid measures in the sera of 67 FTLD patients (15 C9orf72 repeat expansion carriers), including GlycA, were analyzed by nuclear magnetic resonance spectroscopy. To verify the state of systemic inflammation, hs-CRP was also quantified from patient sera. We found that the total serum HDL concentration was decreased in C9orf72 repeat expansion carriers when compared to non-carriers. Moreover, decreased concentrations of HDL particles of different sizes and subclass were consistently observed. No differences were detected in the very low- and low-density lipoprotein subclasses between the C9orf72 repeat expansion carriers and non-carriers. Furthermore, hs-CRP and GlycA levels did not differ between the C9orf72 repeat expansion carriers and non-carriers. In conclusion, the HDL-related changes were linked with C9orf72 repeat expansion associated FTLD but were not seen to associate with systemic inflammation. The underlying reason for the HDL changes remains unclear.

The importance of early interventions in Alzheimer’s disease (AD) emphasizes the need to accurately and efficiently identify at-risk individuals. Although many dementia prediction models have been developed, there are fewer studies focusing on detection of brain pathology. We developed a model for identification of amyloid-PET positivity using data on demographics, vascular factors, cognition, APOE genotype, and structural MRI, including regional brain volumes, cortical thickness and a visual medial temporal lobe atrophy (MTA) rating. We also analyzed the relative importance of different factors when added to the overall model. The model used baseline data from the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) exploratory PET sub-study. Participants were at risk for dementia, but without dementia or cognitive impairment. Their mean age was 71 years. Participants underwent a brain 3T MRI and PiB-PET imaging. PiB images were visually determined as positive or negative. Cognition was measured using a modified version of the Neuropsychological Test Battery. Body mass index (BMI) and hypertension were used as cardiovascular risk factors in the model. Demographic factors included age, gender and years of education. The model was built using the Disease State Index (DSI) machine learning algorithm. Of the 48 participants, 20 (42%) were rated as Aβ positive. Compared with the Aβ negative group, the Aβ positive group had a higher proportion of APOE ε4 carriers (53 vs. 14%), lower executive functioning, lower brain volumes, and higher visual MTA rating. AUC [95% CI] for the complete model was 0.78 [0.65–0.91]. MRI was the most effective factor, especially brain volumes and visual MTA rating but not cortical thickness. APOE was nearly as effective as MRI in improving detection of amyloid positivity. The model with the best performance (AUC 0.82 [0.71–0.93]) was achieved by combining APOE and MRI. Our findings suggest that combining demographic data, vascular risk factors, cognitive performance, APOE genotype, and brain MRI measures can help identify Aβ positivity. Detecting amyloid positivity could reduce invasive and costly assessments during the screening process in clinical trials.

Accurate differentiation between neurodegenerative diseases is developing quickly and has reached an effective level in disease recognition. However, there has been less focus on effectively distinguishing the prodromal state from later dementia stages due to a lack of suitable biomarkers. We utilized the Disease State Index (DSI) machine learning classifier to see how well quantified metabolomics data compares to clinically used cerebrospinal fluid (CSF) biomarkers of Alzheimer’s disease (AD). The metabolic profiles were quantified for 498 serum and CSF samples using proton nuclear magnetic resonance spectroscopy. The patient cohorts in this study were dementia (with a clinical AD diagnosis) (N = 359), mild cognitive impairment (MCI) (N = 96), and control patients with subjective memory complaints (N = 43). DSI classification was conducted for MCI (N = 51) and dementia (N = 214) patients with low CSF amyloid-β levels indicating AD pathology and controls without such amyloid pathology (N = 36). We saw that the conventional CSF markers of AD were better at classifying controls from both dementia and MCI patients. However, quantified metabolic subclasses were more effective in classifying MCI from dementia. Our results show the consistent effectiveness of traditional CSF biomarkers in AD diagnostics. However, these markers are relatively ineffective in differentiating between MCI and the dementia stage, where the quantified metabolomics data provided significant benefit.

Introduction: Web-based cognitive tests have potential for standardized screening in neurodegenerative disorders. We examined accuracy and consistency of cCOG, a computerized cognitive tool, in detecting mild cognitive impairment (MCI) and dementia.Methods: Clinical data of 306 cognitively normal, 120 mild cognitive impairment (MCI), and 69 dementia subjects from three European cohorts were analyzed. Global cognitive score was defined from standard neuropsychological tests and compared to the corresponding estimated score from the cCOG tool containing seven subtasks. The consistency of cCOG was assessed comparing measurements administered in clinical settings and in the home environment.Results: cCOG produced accuracies (receiver operating characteristic-area under the curve [ROC-AUC]) between 0.71 and 0.84 in detecting MCI and 0.86 and 0.94 in detecting dementia when administered at the clinic and at home. The accuracy was comparable to the results of standard neuropsychological tests (AUC 0.69-0.77 MCI/0.91-0.92 dementia).Discussion: cCOG provides a promising tool for detecting MCI and dementia with potential for a cost-effective approach including home-based cognitive assessments.

OBJECTIVES We examined longitudinal associations between late-life personality traits and cognitive impairment, dementia, and mortality in the population-based Cardiovascular Risk Factors, Aging and Dementia (CAIDE) Study. METHODS Anger expression and trait anger (State-Trait Anger Expression Inventory), anxiety (State-Trait Anxiety Inventory), and sense of coherence (Sense of Coherence Scale) were assessed at the 1998 CAIDE visit (1266 cognitively normal individuals, mean age 71.0 years). Totally, 582 participants had complete re-examination in 2005-2008 (105 mild cognitive impairment, MCI; and 29 dementia). National registers data until 2008 were also used for both participants and nonparticipants to ascertain incident dementia (96 cases) and mortality (227 died). Analyses were adjusted for age, sex, education, follow-up time, cardiovascular and lifestyle factors, and depressive symptoms. RESULTS Higher anxiety was associated with higher risk of MCI/dementia (OR 1.68, 95% CI 1.07-2.63) and death (HR 1.46, 95% CI 1.08-1.98). High sense of coherence was associated with lower mortality (HR 0.65, 95% CI 0.45-0.93). These associations were attenuated after accounting for depressive symptoms (OR 1.57, 95% CI 0.96-2.58 for anxiety-MCI/dementia; HR 1.35, 95% CI 0.97-1.86 for anxiety-mortality; and HR 0.68, 95% CI 0.45-1.04 for sense of coherence-mortality). Trait anger was associated with higher dementia risk even after adjustments (HR 1.90, 95% CI 1.14-3.18). CONCLUSIONS Anxiety was linked to worse cognitive outcome and mortality and sense of coherence to lower mortality. Depressive symptoms attenuated the associations. As a novel finding, trait anger was connected to dementia risk. These findings emphasize the importance of personality-related risk factors for dementia and mortality. J Am Geriatr Soc 68:-, 2020.

Background and objective: This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study. Methods: The CAIDE study was based on previous population-based midlife surveys. CAIDE participants were re-examined twice in late-life, and the first late-life re-examination was used as baseline for the present study. The main study population included 709 cognitively normal subjects at first re-examination who returned to the second re-examination up to 10 years later (incident dementia n = 39). An extended population (n = 1009, incident dementia 151) included non-participants/non-survivors (national registers data). DSI was used to develop a dementia index based on first re-examination assessments. Performance in predicting dementia was assessed as area under the ROC curve (AUC).Results: AUCs for DSI were 0.79 and 0.75 for main and extended populations. Included predictors were cognition, vascular factors, age, subjective memory complaints, and APOE genotype.Conclusion: The supervised machine learning method performed well in identifying comprehensive profiles for predicting dementia development up to 10 years later. DSI could thus be useful for identifying individuals who are most at risk and may benefit from dementia prevention interventions.

Introduction: Depression in patients with mild cognitive impairment (MCI) and dementia of the Alzheimer’s type (AD) is associated with worse prognosis. Indeed, depressed MCI patients have worse cognitive performance and greater loss of gray-matter volume in several brain areas. To date, knowledge of the factors that can mitigate this detrimental effect is still limited. The aim of the present study was to understand in what way cognitive reserve/brain reserve and depression interact and are linked to regional atrophy in early stage AD.\ud \ud Methods: Depression was evaluated with the Patient Health Questionnaire-9 in 90 patients with early AD, and a cutoff of ≥ 5 was used to separate depressed (n = 44) from non-depressed (n = 46) patients. Each group was further stratified into high/low cognitive reserve/brain reserve. Cognitive reserve was calculated using years of education as proxy, while normalized parenchymal volumes were used to estimate brain reserve. Voxel-based morphometry was carried out to extract and analyze gray-matter maps. 2 × 2 ANCOVAs were run to test the effect of the reserve-by-depression interaction on gray matter. Age and hippocampal ratio were used as covariates. Composite indices of major cognitive domains were also analyzed with comparable models.\ud \ud Results: No reserve-by-depression interaction was found in the analytical models of gray matter. Depression was associated with less gray matter volume in the cerebellum and parahippocampal gyrus. The brain reserve-by-depression interaction was a significant predictor of executive functioning. Among those with high brain reserve, depressed patients had poorer executive skills. No significant results were found in association with cognitive reserve.\ud \ud Conclusion: These findings suggest that brain reserve may modulate the association between neurodegeneration and depression in patients with MCI and dementia of the AD type, influencing in particular executive functioning.

Background: Idiopathic normal pressure hydrocephalus (iNPH) patients often develop Alzheimer’s disease (AD) related brain pathology. Disease State Index (DSI) is a method to combine data from various sources for differential diagnosis and progression of neurodegenerative disorders. Objective: To apply DSI to predict clinical AD in shunted iNPH-patients in a defined population. Methods: 335 shunted iNPH-patients (median 74 years) were followed until death (n = 185) or 6/2015 (n = 150). DSI model (including symptom profile, onset age of NPH symptoms, atrophy of medial temporal lobe in CT/MRI, cortical brain biopsy finding, and APOE genotype) was applied. Performance of DSI model was evaluated with receiver operating characteristic (ROC) curve analysis. Results: A total of 70 (21%) patients developed clinical AD during median follow-up of 5.3 years. DSI-model predicted clinical AD with moderate effectiveness (AUC = 0.75). Significant factors were cortical biopsy (0.69), clinical symptoms (0.66), and medial temporal lobe atrophy (0.66). Conclusion: We found increased occurrence of clinical AD in previously shunted iNPH patients as compared with general population. DSI supported the prediction of AD. Cortical biopsy during shunt insertion seems indicated for earlier diagnosis of comorbid AD.

Background We developed multifactorial models for predicting incident dementia and brain pathology in the oldest old using the Vantaa 85+ cohort. Methods We included participants without dementia at baseline and at least 2 years of follow-up (N = 245) for dementia prediction or with autopsy data (N = 163) for pathology. A supervised machine learning method was used for model development, considering sociodemographic, cognitive, clinical, vascular, and lifestyle factors, as well as APOE genotype. Neuropathological assessments included β-amyloid, neurofibrillary tangles and neuritic plaques, cerebral amyloid angiopathy (CAA), macro- and microscopic infarcts, α-synuclein pathology, hippocampal sclerosis, and TDP-43. Results Prediction model performance was evaluated using AUC for 10 × 10-fold cross-validation. Overall AUCs were 0.73 for dementia, 0.64–0.68 for Alzheimer’s disease (AD)- or amyloid-related pathologies, 0.72 for macroinfarcts, and 0.61 for microinfarcts. Predictors for dementia were different from those in previous reports of younger populations; for example, age, sex, and vascular and lifestyle factors were not predictive. Predictors for dementia versus pathology were also different, because cognition and education predicted dementia but not AD- or amyloid-related pathologies. APOE genotype was most consistently present across all models. APOE alleles had a different impact: ε4 did not predict dementia, but it did predict all AD- or amyloid-related pathologies; ε2 predicted dementia, but it was protective against amyloid and neuropathological AD; and ε3ε3 was protective against dementia, neurofibrillary tangles, and CAA. Very few other factors were predictive of pathology. Conclusions Differences between predictors for dementia in younger old versus oldest old populations, as well as for dementia versus pathology, should be considered more carefully in future studies. Electronic supplementary material The online version of this article (10.1186/s13195-018-0450-3) contains supplementary material, which is available to authorized users.

Aims: We assessed the value of automated MRI quantification methods in the differential diagnosis of behavioral-variant frontotemporal dementia (bvFTD) from Alzheimer disease (AD), Lewy body dementia (LBD), and subjective memory complaints (SMC). We also examined the role of the C9ORF72-related genetic status in the differentiation sensitivity. Methods: The MRI scans of 50 patients with bvFTD (17 C9ORF72 expansion carriers) were analyzed using 6 quantification methods as follows: voxel-based morphometry (VBM), tensor-based morphometry, volumetry (VOL), manifold learning, grading, and white-matter hyperintensities. Each patient was then individually compared to an independent reference group in order to attain diagnostic suggestions. Results: Only VBM and VOL showed utility in correctly identifying bvFTD from our set of data. The overall classification sensitivity of bvFTD with VOL + VBM achieved a total sensitivity of 60%. Using VOL + VBM, 32% were misclassified as having LBD. There was a trend of higher values for classification sensitivity of the C9ORF72 expansion carriers than noncarriers. Conclusion: VOL, VBM, and their combination are effective in differential diagnostics between bvFTD and AD or SMC. However, MRI atrophy profiles for bvFTD and LBD are too similar for a reliable differentiation with the quantification methods tested in this study.

We evaluated the performance of the Disease State Index (DSI) method when predicting progression to Alzheimer's disease (AD) in patients with subjective cognitive impairment (SCI), amnestic or non-amnestic mild cognitive impairment (aMCI, naMCI). The DSI model measures patients' similarity to diagnosed cases based on available data, such as cognitive tests, the APOE genotype, CSF biomarkers and MRI. We applied the DSI model to data from the DE-SCRIPA cohort, where non-demented patients (N=775) with different subtypes of cognitive impairment were followed for 1 to 5 years. Classification accuracies for the subgroups were calculated with the DSI using leave-one-out cross-validation. The DSI's classification accuracy in predicting progression to AD was 0.75 (AUC=0.83) in the total population, 0.70 (AUC=0.77) for aMCI and 0.71 (AUC=0.76) for naMCI. For a subset of approximately half of the patients with high or low DSI values, accuracy reached 0.86 (all), 0.78 (aMCI), and 0.85 (naMCI). For patients with MRI or CSF biomarker data available, they were 0.78 (all), 0.76 (aMCI) and 0.76 (naMCI), while for clear cases the accuracies rose to 0.90 (all), 0.83 (aMCI) and 0.91 (naMCI). The results show that the DSI model can distinguish between clear and ambiguous cases, assess the severity of the disease and also provide information on the effectiveness of different biomarkers. While a specific test or biomarker may confound analysis for an individual patient, combining several different types of tests and biomarkers could be able to reveal the trajectory of the disease and improve the prediction of AD progression.

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While behavioral symptoms are both early and prevalent features of behavioral variant frontotemporal dementia (bvFTD), they can be present in other types of dementia as well, including Alzheimer’s disease (AD) and even mild cognitive impairment (MCI). The Frontal Behavioral Inventory (FBI) was specifically developed to capture the behavioral and personality changes in bvFTD; it has also been modified into a self-administered caregiver questionnaire (FBI-mod). We examined the utility of the FBI-mod in differentiating bvFTD (n= 26), primary progressive aphasia (PPA) (n= 7), AD(n=53),and MCI (n= 50) patients, and investigated how the FBI-mod may be associated with neuropsychological measures.The bvFTD patients scored significantly higher as compared to all other patient groups on the FBI-mod Total (p< 0.005),Negative (p< 0.005), and Positive (p< 0.01) scores. The cut-off point for the FBI-mod Total score that best discriminated thebvFTD and AD patients in our sample was 16, thus substantially lower than reported for the original FBI. For the bvFTD group, only mild correlations emerged between the FBI-mod and the cognitive measures. However, significant correlations between the FBI-mod and depressive symptoms as measured by the BDI-II were found for bvFTD. This suggests thatwhile behavioral symptoms appear independent from cognitive deficits in bvFTD, they may nevertheless be interrelated with depressive symptoms. We conclude that the FBI-mod is an easily administered behavioral scale that can aid in differential diagnosis of bvFTD and should be used in clinical practice. The FBI-mod may further be considered as an outcome measure in clinical trials., final draft, peerReviewed

BACKGROUND: Lifestyle factors have been associated with the risk of dementia, but the association with Alzheimer's disease (AD) remains unclear. OBJECTIVE: To examine the association between later life lifestyle factors and AD biomarkers (i.e., amyloid-β 1-42 (Aβ42) and tau in cerebrospinal fluid (CSF), and hippocampal volume) in individuals with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). In addition, to examine the effect of later life lifestyle factors on developing AD-type dementia in individuals with MCI. METHODS: We selected individuals with SCD (n = 111) and MCI (n = 353) from the DESCRIPA and Kuopio Longitudinal MCI studies. CSF Aβ42 and tau concentrations were assessed with ELISA assay and hippocampal volume with multi-atlas segmentation. Lifestyle was assessed by clinical interview at baseline for: social activity, physical activity, cognitive activity, smoking, alcohol consumption, and sleep. We performed logistic and Cox regression analyses adjusted for study site, age, gender, education, and diagnosis. Prediction for AD-type dementia was performed in individuals with MCI only. RESULTS: Later life lifestyle factors were not associated with AD biomarkers or with conversion to AD-type dementia. AD biomarkers were strongly associated with conversion to AD-type dementia, but these relations were not modulated by lifestyle factors. Apolipoprotein E (APOE) genotype did not influence the results. CONCLUSIONS: Later life lifestyle factors had no impact on key AD biomarkers in individuals with SCD and MCI or on conversion to AD-type dementia in MCI., Funding Agency:European Commission as part of the 5th Framework Program QLK-6-CT-2002-02455

BACKGROUND Several risk loci for Alzheimer's disease (AD) have been identified during recent years in large-scale genome-wide association studies. However, little is known about the mechanisms by which these loci influence AD pathogenesis. OBJECTIVE To investigate the individual and combined risk effects of the newly identified AD loci. METHODS Association of 12 AD risk loci with AD and AD-related cerebrospinal fluid (CSF) biomarkers was assessed. Furthermore, a polygenic risk score combining the effect sizes of the top 22 risk loci in AD was calculated for each individual among the clinical and neuropathological cohorts. Effects of individual risk loci and polygenic risk scores were assessed in relation to CSF biomarker levels as well as neurofibrillary pathology and different biochemical measures related to AD pathogenesis obtained from the temporal cortex. RESULTS Polygenic risk scores associated with CSF amyloid-β42 (Aβ42) levels in the clinical cohort, and with soluble Aβ42 levels and γ-secretase activity in the neuropathological cohort. The γ-secretase effect was independent of APOE. APOE-e4 associated with CSF Aβ42 (p < 0.001) levels. For the other risk loci, no significant associations with AD risk or CSF biomarkers were detected after multiple testing correction. CONCLUSIONS AD risk loci polygenically contribute to Aβ pathology in the CSF and temporal cortex, and this effect is potentially associated with increased γ-secretase activity.

Background: Poly-T repeat lengths of rs10524523 in TOMM40 together with APOE polymorphism have been reported to affect the risk of late-onset Alzheimer's disease (LOAD) and the age of onset (AOO). Objective: To explore whether the AOO and cerebrospinal fluid biomarkers Aβ42, total tau and phosphorylated tau are associated with different repeat lengths. Methods: We conducted both the fragment and sequencing analysis of rs10524523 in 336 LOAD patients with a known APOE genotype. Results: AOO and Aβ42 levels associated significantly with certain poly-T repeat lengths of rs10524523 in LOAD patients encompassing APOE 34/44 genotype. Conclusion: We conclude that the poly-T repeat associations of rs10524523 in TOMM40 reflect the APOE ε4-dependent association in LOAD.

Objectives: Optimal selection of idiopathic normal pressure hydrocephalus (iNPH) patients for shunt surgery is challenging. Disease State Index (DSI) is a statistical method that merges multimodal data to assist clinical decision-making. It has previously been shown to be useful in predicting progression in mild cognitive impairment and differentiating Alzheimer's disease (AD) and frontotemporal dementia. In this study, we use the DSI method to predict shunt surgery response for patients with iNPH. Methods: In this retrospective cohort study, a total of 284 patients (230 shunt responders and 54 non-responders) from the Kuopio NPH registry were analyzed with the DSI. Analysis included data from patients' memory disorder assessments, age, clinical symptoms, comorbidities, medications, frontal cortical biopsy, CT/MRI imaging (visual scoring of disproportion between Sylvian and suprasylvian subarachnoid spaces, atrophy of medial temporal lobe, superior medial subarachnoid spaces), APOE genotyping, CSF AD biomarkers, and intracranial pressure. Results: Our analysis showed that shunt responders cannot be differentiated from non-responders reliably even with the large dataset available (AUC?=?0.58). Conclusions: Prediction of the treatment response in iNPH is challenging even with our extensive dataset and refined analysis. Further research of biomarkers and indicators predicting shunt responsiveness is still needed.

Background: Disease State Index (DSI) and its visualization, Disease State Fingerprint (DSF), form a computer-assisted clinical decision making tool that combines patient data and compares them with cases with known outcomes. Aims: To investigate the ability of the DSI to diagnose frontotemporal dementia (FTD) and Alzheimer's disease (AD). Methods: The study cohort consisted of 38 patients with FTD, 57 with AD and 22 controls. Autopsy verification of FTD with TDP-43 positive pathology was available for 14 and AD pathology for 12 cases. We utilized data from neuropsychological tests, volumetric magnetic resonance imaging, single-photon emission tomography, cerebrospinal fluid biomarkers and the APOE genotype. The DSI classification results were calculated with a combination of leave-one-out cross-validation and bootstrapping. A DSF visualization of a FTD patient is presented as an example. Results: The DSI distinguishes controls from FTD (area under the receiver-operator curve, AUC = 0.99) and AD (AUC = 1.00) very well and achieves a good differential diagnosis between AD and FTD (AUC = 0.89). In subsamples of autopsy-confirmed cases (AUC = 0.97) and clinically diagnosed cases (AUC = 0.94), differential diagnosis of AD and FTD performs very well. Conclusions: DSI is a promising computer-assisted biomarker approach for aiding in the diagnostic process of dementing diseases. Here, DSI separates controls from dementia and differentiates between AD and FTD.

Background: The Disease State Index (DSI) is a method which interprets data originating from multiple different sources, assisting the clinician in the diagnosis and follow-up of dementia diseases. Objective: We compared the differences in accuracy in differentiating stable mild cognitive impairment (S-MCI) and progressive MCI (P-MCI) obtained from different data combinations using the DSI. Methods: We investigated 212 cases with S-MCI and 165 cases with P-MCI from the Alzheimer's Disease Neuroimaging Initiative cohort. Data from neuropsychological tests, cerebrospinal fluid, apolipoprotein E (APOE) genotype, magnetic resonance imaging (MRI) and positron emission tomography (PET) were included. Results: The combination of all parameters gave the highest accuracy (accuracy 0.70, sensitivity 0.71, specificity 0.68). In the different categories, neuropsychological tests (0.65, 0.65, 0.65) and hippocampal volumetry (0.66, 0.66, 0.66) achieved the highest accuracy. Conclusion: In addition to neuropsychological testing, MRI is recommended to be included for differentiating S-MCI from P-MCI. APOE genotype, CSF and PET may provide some additional information.

Even in small amounts, glycolipids are an integral part of lipid rafts and their cellular functions. Here we employ atomistic molecular dynamics simulations to consider galactosylceramide (GalCer), one of the common glycosphingolipids, and investigate its interactions with other raft components (cholesterol, POPC, sphingomyelin) as well as the role and the effects of GalCer on the physical properties of raft-like membranes. Our results for 5 mol % GalCer indicate that whereas the thickness of raft membranes is clearly increased by the addition of GalCer, the average area per lipid and lipid conformational order remain virtually unchanged. Notable changes are observed in lateral diffusion of the raft lipids. This is found to be associated with the interdigitation of GalCer. With cholesterol, GalCer is observed to interact specifically by shielding it from the water phase.

Background: The Disease State Index (DSI) prediction model measures the similarity of patient data to diagnosed stable and progressive mild cognitive impairment (MCI) cases to identify patients who are progressing to Alzheimer's disease. Objectives: We evaluated how well the DSI generalizes across four different cohorts: DESCRIPA, ADNI, AddNeuroMed, and the Kuopio MCI study. Methods: The accuracy of the DSI in predicting progression was examined for each cohort separately using 10 x 10-fold cross-validation and for inter-cohort validation using each cohort as a test set for the model built from the other independent cohorts using bootstrapping with 10 repetitions. Altogether 875 subjects were included in the analysis. The analyzed data included a comprehensive set of age and gender corrected magnetic resonance imaging (MRI) features from hippocampal volumetry, multi-template tensor-based morphometry, and voxel-based morphometry as well as Mini-Mental State Examination (MMSE), APOE genotype, and additional cohort specific data from neuropsychological tests and cerebrospinal fluid measurements (CSF). Results: The DSI model was used to classify the patients into stable and progressive MCI cases. AddNeuroMed had the highest classification results of the cohorts, while ADNI and Kuopio MCI exhibited the lowest values. The MRI features alone achieved a good classification performance for all cohorts. For ADNI and DESCRIPA, adding MMSE, APOE genotype, CSF, and neuropsychological data improved the results. Conclusions: The results reveal that the prediction performance of the combined cohort is close to the average of the individual cohorts. It is feasible to use different cohorts as training sets for the DSI, if they are sufficiently similar.

1⁄40.001) and body/occipital horn ventriculomegaly (left p corrected1⁄40.003, right p corrected1⁄40.002). Trend significant effects of MS4A6Awere found for the temporal horns (right p corrected1⁄40.053, left p corrected1⁄40.07) and BIN1 for the right temporal (p corrected1⁄40.06) and left frontal (p corrected 1⁄40.09) horns. Conclusions:MCI subjects predestined to develop dementia showed significantly larger ventricles relative to both stable MCI and NC at baseline. The differences between stable MCI and those who developed dementia at follow-up localized to the frontal and body/occipital horn. The groups had comparable temporal horns’ radial distance suggesting a similar degree of neurodegenerative changes (hippocampal and temporal lobe atrophy) between the two groups.

Poly-T repeat lengths of rs10524523 in TOMM40 together with APOE polymorphism have been reported to affect the risk of late-onset Alzheimer's disease (LOAD) and the age of onset (AOO).To explore whether the AOO and cerebrospinal fluid biomarkers Aβ42, total tau and phosphorylated tau are associated with different repeat lengths.We conducted both the fragment and sequencing analysis of rs10524523 in 336 LOAD patients with a known APOE genotype.AOO and Aβ42 levels associated significantly with certain poly-T repeat lengths of rs10524523 in LOAD patients encompassing APOE 34/44 genotype.We conclude that the poly-T repeat associations of rs10524523 in TOMM40 reflect the APOE ε4-dependent association in LOAD.

Background: Disease State Index and Disease State Fingerprint represent a novel tool which collates data information from different sources, helping the clinician in the diagnosis and follow-up of dementia diseases. It has been demonstrated that it is applicable in the diagnosis of Alzheimer’s disease (AD). Objective: We applied this novel tool to classify frontotemporal dementia (FTD) cases in comparison with controls, AD, and mild cognitive impairment (MCI) subjects. Methods: Thirty seven patients with FTD, 35 patients with AD, 26 control subjects, and 64 subjects with MCI were included in the study. The Disease State Index encompassed data from cognitive performance assessed by Mini-Mental State Examination, cerebrospinal fluid biomarkers, MRI volumetric and morphometric parameters as well as APOE genotype. Results: We applied the Disease State Index for comparisons at the group level. The data showed that FTD patients could be differentiated with a high accuracy, sensitivity, and specificity from controls (0.84, 0.84, 0.83) and from MCI (0.79, 0.78, 0.80). However, the correct accuracy was lower in the FTD versus AD comparison (0.69, 0.70, 0.71). In addition, we demonstrated the use of Disease State Fingerprint by comparing one particular FTD case with control, AD, and MCI population data. Conclusion: The results suggest that the Disease State Fingerprint and the underlying Disease State Index are particularly useful in differentiating between normal status and disease in patients with dementia, but it may also help to distinguish between the two dementia diseases, FTD and AD.

Objectives To understand the relation between risk genes for Alzheimer’s disease (AD) and their influence on biomarkers for AD, we examined the association of AD in the Finnish cohort with single nucleotide polymorphisms (SNPs) from top AlzGene loci, genome-wide association studies (GWAS), and candidate gene studies; and tested the correlation between these SNPs and AD markers Aβ1–42, total tau (t-tau), and phosphorylated tau (p-tau) in cerebrospinal fluid (CSF). Methods We tested 25 SNPs for genetic association with clinical AD in our cohort comprised of 890 AD patients and 701-age matched healthy controls using logistic regression. For the correlational study with biomarkers, we tested 36 SNPs in a subset of 222 AD patients with available CSF using mixed models. Statistical analyses were adjusted for age, gender and APOE status. False discovery rate for multiple testing was applied. All participants were from academic hospital and research institutions in Finland. Results APOE-ε4, CLU rs11136000, and MS4A4A rs2304933 correlated with significantly decreased Aβ1–42 (corrected p0.05). Conclusions We provide evidence that APOE-ε4, CLU and MS4A4A, which have been identified in GWAS to be associated with AD, also significantly reduced CSF Aβ1–42 in AD. None of the other AlzGene and GWAS loci showed significant effects on CSF tau. The effects of other SNPs on CSF biomarkers and clinical AD diagnosis did not reach statistical significance. Our findings suggest that APOE-ε4, CLU and MS4A4A influence both AD risk and CSF Aβ1–42. Public Library of Science open access

We use atom-scale molecular dynamics simulations to clarify the role of glycosphingolipids in the dynamics of cholesterol-rich lipid rafts. To this end, we consider lipid membranes that contain varying. amounts of galactosylceramide (GalCer), sphingomyelin, cholesterol, and phosphatidylcholine. The results indicate that increasing the portion of GalCer molecules greatly slows down the lateral diffusion, Only 5-10 mol % of GalCer causes a decrease of almost an order of magnitude compared to corresponding membranes without GalCer. The slowing down is not related to interdigitation, which becomes weaker with increasing GalCer concentration. Instead, the decrease in diffusion is found to correlate with the increasing number of hydrogen bonds formed between GalCer and the phospholipid molecules, which is also observed to have other effects, such as to increase the friction between the membrane leaflets.

Triglycerides are a major component of many important biological entities such as lipoproteins and lipid droplets. This work focuses on two common triglycerides, tripalmitin and triolein, which have been simulated through atomistic molecular dynamics at temperatures of 310 and 350 K for 300-700 ns. In these systems, both structural and dynamical properties have been characterized, paying particular attention to understanding the packing of triglyceride molecules and their molecular conformations. Additionally, we study the liquid-to-crystalline phase transition of tripalmitin through a temperature quench from the high-temperature isotropic liquid phase to 310 K, corresponding to a polymorphic, crystalline-like phase. The transition is characterized in detail through density, average molecular shape, and, in particular, the relevant order parameter describing the transition.