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Abstract Background Multimodal lifestyle interventions can benefit overall health, including cognition, in populations at-risk for dementia. However, little is known about the effect of lifestyle interventions in patients with prodromal Alzheimer’s disease (AD). Even less is known about dietary intake and adherence to dietary recommendations within this population making it difficult to design tailored interventions for them. Method A 6-month MIND-ADmini pilot randomized controlled trial (RCT) was conducted among 93 participants with prodromal AD in Sweden, Finland, Germany, and France. Three arms were included in the RCT: 1) multimodal lifestyle intervention (nutritional guidance, exercise, cognitive training, vascular/metabolic risk management, and social stimulation); 2) multimodal lifestyle intervention + medical food product; and 3) regular health advice (control group). Adherence to dietary advice was assessed with a brief food intake questionnaire by using the Healthy Diet Index (HDI) and Mediterranean Diet Adherence Screener (MEDAS). The intake of macro- and micronutrients were analyzed on a subsample using 3-day food records. Results The dietary quality in the intervention groups, pooled together, improved compared to that of the control group at the end of the study, as measured with by HDI (p = 0.026) and MEDAS (p = 0.008). The lifestyle-only group improved significantly more in MEDAS (p = 0.046) and almost significantly in HDI (p = 0.052) compared to the control group, while the lifestyle + medical food group improved in both HDI (p = 0.042) and MEDAS (p = 0.007) during the study. There were no changes in macro- or micronutrient intake for the intervention groups at follow-up; however, the intakes in the control group declined in several vitamins and minerals when adjusted for energy intake. Conclusion These results suggest that dietary intervention as part of multimodal lifestyle interventions is feasible and results in improved dietary quality in a population with prodromal AD. Nutrient intakes remained unchanged in the intervention groups while the control group showed a decreasing nutrient density. Trial registration ClinicalTrials.gov NCT03249688, 2017–07-08.

Abstract Background The Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) showed cognitive benefits from a multidomain lifestyle intervention in at-risk older people. The LipiDiDiet trial highlighted benefits of medical food in prodromal Alzheimer’s disease (AD). However, the feasibility and impact of multimodal interventions combining lifestyle with medical food in prodromal AD is unclear. Methods MIND-ADmini was a 6-month multinational (Sweden, Finland, Germany, France) proof-of-concept randomized controlled trial (RCT). Participants were 60–85 years old, had prodromal AD (International Working Group-1 criteria), and vascular/lifestyle risk factors. The parallel-group RCT had three arms: multimodal lifestyle intervention (nutritional guidance, exercise, cognitive training, vascular/metabolic risk management and social stimulation); multimodal lifestyle intervention + medical food (Fortasyn Connect); and regular health advice/care (control). Participants were randomized 1:1:1 (computer-generated allocation at each site). Outcome evaluators were blinded to randomization. Primary outcome was feasibility of the multimodal intervention, evaluated by recruitment rate during a 6-month recruitment phase, overall adherence in each intervention arm, and 6-month retention rate. Successful adherence was pre-specified as attending ≥ 40% of sessions/domain in ≥ 2/4 domains (lifestyle intervention), and consuming ≥ 60% of the medical food (lifestyle intervention + medical food). The secondary outcomes included adherence/participation to each intervention component and overall adherence to healthy lifestyle changes, measured using a composite score for healthy lifestyle. Cognitive assessments were included as exploratory outcomes, e.g. Clinical Dementia Rating scale. Results During September 2017-May 2019, 93 individuals were randomized (32 lifestyle intervention, 31 lifestyle + medical food, and 30 control group). Overall recruitment rate was 76.2% (64.8% during the first 6 months). Overall 6-month retention rate was 91.4% (lifestyle intervention 87.5%; lifestyle + medical food 90.3%; control 96.7%). Domain-specific adherence in the lifestyle intervention group was 71.9% to cognitive training, 78.1% exercise, 68.8% nutritional guidance, and 81.3% vascular risk management; and in the lifestyle + medical food group, 90.3% to cognitive training, 87.1% exercise, 80.7% nutritional guidance, 87.1% vascular risk management, and 87.1% medical food. Compared with control, both intervention arms showed healthy diet improvements (βLifestyle×Time = 1.11, P = 0.038; βLifestyle+medical food×Time = 1.43, P = 0.007); the lifestyle + medical food group also showed vascular risk reduction (P = 0.043) and less cognitive-functional decline (P

Introduction A better understanding of the earliest stages of Alzheimer’s disease (AD) could expedite the development or administration of treatments. Large population biobanks hold the promise to identify individuals at an elevated risk of AD and related dementias based on health registry information. Here, we establish the protocol for an observational clinical recall and biomarker study called TWINGEN with the aim to identify individuals at high risk of AD by assessing cognition, health and AD-related biomarkers. Suitable candidates were identified and invited to participate in the new study among THL Biobank donors according to TWINGEN study criteria.Methods and analysis A multi-centre study (n=800) to obtain blood-based biomarkers, telephone-administered and web-based memory and cognitive parameters, questionnaire information on lifestyle, health and psychological factors, and accelerometer data for measures of physical activity, sedentary behaviour and sleep. A subcohort is being asked to participate in an in-person neuropsychological assessment (n=200) and wear an Oura ring (n=50). All participants in the TWINGEN study have genome-wide genotyping data and up to 48 years of follow-up data from the population-based older Finnish Twin Cohort (FTC) study of the University of Helsinki. The data collected in TWINGEN will be returned to THL Biobank from where it can later be requested for other biobank studies such as FinnGen that supported TWINGEN.Ethics and dissemination This recall study consists of FTC/THL Biobank/FinnGen participants whose data were acquired in accordance with the Finnish Biobank Act. The recruitment protocols followed the biobank protocols approved by Finnish Medicines Agency. The TWINGEN study plan was approved by the Ethics Committee of Hospital District of Helsinki and Uusimaa (number 16831/2022). THL Biobank approved the research plan with the permission no: THLBB2022_83.

BackgroundPhysical activity is a modifiable lifestyle factor that has been previously associated with reduced vascular burden and reduced risk of dementia.ObjectivesThis study tested whether physical activity (i.e., being inactive vs. active) contributed to preservation of white matter microstructure in healthy aging controls and patients in prodromal to mild Alzheimer’s disease with low/high vascular burden.MaterialsA total of 213 participants were recruited from memory clinics. They were classified as being either physically active (n = 113) or inactive (n = 100) based on the Cardiovascular Risk Factors, Aging and Dementia (CAIDE) questionnaire. Diffusion-weighted images were acquired for all participants and pre-processed based on a standard protocol.MethodsA factorial design using voxel-wise tract-based spatial statistics (TBSS) was adopted, with 5,000 permutations and threshold-free cluster enhancement (TFCE), to identify significant clusters for fractional anisotropy (FA), axial diffusivity (AxD), mean diffusivity (MD), and radial diffusivity (RD).ResultsClusters of higher FA and lower AxD, MD, and RD values were found for physically active compared with inactive participants that were widespread covering mainly association and projection tracts but also some commissural tracts. A three-way Group × Physical Activity × Vascular Burden interaction effect was found for FA mostly in a variety of projection tracts with a right predominance, and some commissural and association tracts. Post hoc analyses revealed higher FA in patients with high vascular burden who were physically active compared with those patients with high vascular burden who were inactive mainly in projection and association/limbic tracts with a right predominance. Additionally, higher FA was observed in physically active patients with high vascular burden as compared with physically inactive controls with high vascular burden, mainly in bilateral projection fibers and cerebellar regions.ConclusionVoxel-wise TBSS analysis revealed better preservation of white matter microstructure that was prominent in the high-risk group such as the patients with high vascular burden, specifically those who were physically active. The beneficial effects of physical activity on white matter microstructure were not observed in the controls.

The rare A673T variant was the first variant found within the amyloid precursor protein (APP) gene conferring protection against Alzheimer's disease (AD). Thereafter, different studies have discovered that the carriers of the APP A673T variant show reduced levels of amyloid beta (Aβ) in the plasma and better cognitive performance at high age. Here, we analyzed cerebrospinal fluid (CSF) and plasma of APP A673T carriers and control individuals using a mass spectrometry-based proteomics approach to identify differentially regulated targets in an unbiased manner. Furthermore, the APP A673T variant was introduced into 2D and 3D neuronal cell culture models together with the pathogenic APP Swedish and London mutations. Consequently, we now report for the first time the protective effects of the APP A673T variant against AD-related alterations in the CSF, plasma, and brain biopsy samples from the frontal cortex. The CSF levels of soluble APPβ (sAPPβ) and Aβ42 were significantly decreased on average 9–26% among three APP A673T carriers as compared to three well-matched controls not carrying the protective variant. Consistent with these CSF findings, immunohistochemical assessment of cortical biopsy samples from the same APP A673T carriers did not reveal Aβ, phospho-tau, or p62 pathologies. We identified differentially regulated targets involved in protein phosphorylation, inflammation, and mitochondrial function in the CSF and plasma samples of APP A673T carriers. Some of the identified targets showed inverse levels in AD brain tissue with respect to increased AD-associated neurofibrillary pathology. In 2D and 3D neuronal cell culture models expressing APP with the Swedish and London mutations, the introduction of the APP A673T variant resulted in lower sAPPβ levels. Concomitantly, the levels of sAPPα were increased, while decreased levels of CTFβ and Aβ42 were detected in some of these models. Our findings emphasize the important role of APP-derived peptides in the pathogenesis of AD and demonstrate the effectiveness of the protective APP A673T variant to shift APP processing towards the non-amyloidogenic pathway in vitro even in the presence of two pathogenic mutations.

Objectives To recontact biobank participants and collect cognitive, behavioural and lifestyle information via a secure online platform.Design Biobank-based recontacting pilot study.Setting Three Finnish biobanks (Helsinki, Auria, Tampere) recruiting participants from February 2021 to July 2021.Participants All eligible invitees were enrolled in FinnGen by their biobanks (Helsinki, Auria, Tampere), had available genetic data and were >18 years old. Individuals with severe neuropsychiatric disease or cognitive or physical disabilities were excluded. Lastly, 5995 participants were selected based on their polygenic score for cognitive abilities and invited to the study. Among invitees, 1115 had successfully participated and completed the study questionnaire(s).Outcome measures The primary outcome was the participation rate among study invitees. Secondary outcomes included questionnaire completion rate, quality of data collected and comparison of participation rate boosting strategies.Results The overall participation rate was 18.6% among all invitees and 23.1% among individuals aged 18–69. A second reminder letter yielded an additional 9.7% participation rate in those who did not respond to the first invitation. Recontacting participants via an online healthcare portal yielded lower participation than recontacting via physical letter. The completion rate of the questionnaire and cognitive tests was high (92% and 85%, respectively), and measurements were overall reliable among participants. For example, the correlation (r) between self-reported body mass index and that collected by the biobanks was 0.92.Conclusion In summary, this pilot suggests that recontacting FinnGen participants with the goal to collect a wide range of cognitive, behavioural and lifestyle information without additional engagement results in a low participation rate, but with reliable data. We suggest that such information be collected at enrolment, if possible, rather than via post hoc recontacting.

Known genetic loci account for only a fraction of the genetic contribution to Alzheimer’s disease. Here, the authors have performed a large genome-wide meta-analysis comprising 409,435 individuals to discover 6 new loci and demonstrate the efficacy of an Alzheimer’s disease polygenic risk score.

Abstract Background 27-Hydroxycholesterol (27-OH), the main circulating oxysterol in humans and the potential missing link between peripheral hypercholesterolemia and Alzheimer’s disease (AD), has not been investigated previously in relation to cognition and neuroimaging markers in the context of preventive interventions. Methods The 2-year Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) included older individuals (60–77 years) at increased risk for dementia but without dementia or substantial cognitive impairment from the general population. Participants were randomized to a multidomain intervention (diet, exercise, cognitive training, and vascular risk management) or control group (general health advice) in a 1:1 ratio. Outcome assessors were masked to group allocation. This FINGER exploratory sub-study included 47 participants with measures of 27-OH, cognition, brain MRI, brain FDG-PET, and PiB-PET. Linear regression models were used to assess the cross-sectional and longitudinal associations between 27-OH, cognition, and neuroimaging markers, considering several potential confounders/intervention effect modifiers. Results 27-OH reduction during the intervention was associated with improvement in cognition (especially memory). This was not observed in the control group. The intervention reduced 27-OH particularly in individuals with the highest 27-OH levels and younger age. No associations were found between changes in 27-OH levels and neuroimaging markers. However, at baseline, a higher 27-OH was associated with lower total gray matter and hippocampal volume, and lower cognitive scores. These associations were unaffected by total cholesterol levels. While sex seemed to influence associations at baseline, it did not affect longitudinal associations. Conclusion 27-OH appears to be a marker not only for dementia/AD risk, but also for monitoring the effects of preventive interventions on cholesterol metabolism. Trial registration ClinicalTrials.gov , NCT01041989 . Registered on 4 January 2010

Abstract Background To explore the utility of the International Working Group (IWG)-1 criteria in recruitment for Alzheimer’s disease (AD) clinical trials, we applied the more recently proposed research diagnostic criteria to individuals enrolled in a randomized controlled prevention trial (RCT) and assessed their disease progression. Methods The multinational LipiDiDiet RCT targeted 311 individuals with IWG-1 defined prodromal AD. Based on centrally analyzed baseline biomarkers, participants were classified according to the IWG-2 and National Institute on Aging–Alzheimer’s Association (NIA-AA) 2011 and 2018 criteria. Linear mixed models were used to investigate the 2-year change in cognitive and functional performance (Neuropsychological Test Battery NTB Z scores, Clinical Dementia Rating-Sum of Boxes CDR-SB) (criteria × time interactions; baseline score, randomization group, sex, Mini-Mental State Examination (MMSE), and age also included in the models). Cox models adjusted for randomization group, MMSE, sex, age, and study site were used to investigate the risk of progression to dementia over 2 years. Results In total, 88%, 86%, and 69% of participants had abnormal cerebrospinal fluid (CSF) β-amyloid, total tau, and phosphorylated tau, respectively; 64% had an A+T+N+ profile (CSF available for N = 107). Cognitive-functional decline appeared to be more pronounced in the IWG-2 prodromal AD, NIA-AA 2011 high and intermediate AD likelihood, and NIA-AA 2018 AD groups, but few significant differences were observed between the groups within each set of criteria. Hazard ratio (95% CI) for dementia was 4.6 (1.6–13.7) for IWG-2 prodromal AD (reference group no prodromal AD), 7.4 (1.0–54.7) for NIA-AA 2011 high AD likelihood (reference group suspected non-AD pathology SNAP), and 9.4 (1.2–72.7) for NIA-AA 2018 AD (reference group non-Alzheimer’s pathologic change). Compared with the NIA-AA 2011 high AD likelihood group (abnormal β-amyloid and neuronal injury markers), disease progression was similar in the intermediate AD likelihood group (medial temporal lobe atrophy; no CSF available). Conclusions Despite being less restrictive than the other criteria, the IWG-1 criteria reliably identified individuals with AD pathology. More pragmatic and easily applicable selection criteria might be preferred due to feasibility in certain situations, e.g., in multidomain prevention trials that do not specifically target β-amyloid/tau pathologies. Trial registration Netherlands Trial Register, NL1620 . Registered on 9 March 2009

Abstract Background Missing data can complicate the interpretability of a clinical trial, especially if the proportion is substantial and if there are different, potentially outcome-dependent causes. Methods We aimed to obtain unbiased estimates, in the presence of a high level of missing data, for the intervention effects in a prodromal Alzheimer’s disease trial: the LipiDiDiet study. We used a competing risk joint model that can simultaneously model each patient’s longitudinal outcome trajectory in combination with the timing and type of missingness. Results Using the competing risk joint model, we were able to provide unbiased estimates of the intervention effects in the presence of the different types of missingness. For the LipiDiDiet study, the intervention effects remained statistically significant after this correction for the timing and type of missingness. Conclusion Missing data is a common problem in (Alzheimer) clinical trials. It is important to realize that statistical techniques make specific assumptions about the missing data mechanisms. When there are different missing data sources, a competing risk joint model is a powerful method because it can explicitly model the association between the longitudinal data and each type of missingness. Trial registration Dutch Trial Register, NTR1705 . Registered on 9 March 2009

Abstract Background People with neurodegenerative disorders show diverse clinical syndromes, genetic heterogeneity, and distinct brain pathological changes, but studies report overlap between these features. DNA methylation (DNAm) provides a way to explore this overlap and heterogeneity as it is determined by the combined effects of genetic variation and the environment. In this study, we aim to identify shared blood DNAm differences between controls and people with Alzheimer’s disease, amyotrophic lateral sclerosis, and Parkinson’s disease. Results We use a mixed-linear model method (MOMENT) that accounts for the effect of (un)known confounders, to test for the association of each DNAm site with each disorder. While only three probes are found to be genome-wide significant in each MOMENT association analysis of amyotrophic lateral sclerosis and Parkinson’s disease (and none with Alzheimer’s disease), a fixed-effects meta-analysis of the three disorders results in 12 genome-wide significant differentially methylated positions. Predicted immune cell-type proportions are disrupted across all neurodegenerative disorders. Protein inflammatory markers are correlated with profile sum-scores derived from disease-associated immune cell-type proportions in a healthy aging cohort. In contrast, they are not correlated with MOMENT DNAm-derived profile sum-scores, calculated using effect sizes of the 12 differentially methylated positions as weights. Conclusions We identify shared differentially methylated positions in whole blood between neurodegenerative disorders that point to shared pathogenic mechanisms. These shared differentially methylated positions may reflect causes or consequences of disease, but they are unlikely to reflect cell-type proportion differences.

Abstract Background Both serotonergic signalling disruption and systemic inflammation have been associated with the pathogenesis of Alzheimer’s disease (AD). The common denominator linking the two is the catabolism of the essential amino acid, tryptophan. Metabolism via tryptophan hydroxylase results in serotonin synthesis, whilst metabolism via indoleamine 2,3-dioxygenase (IDO) results in kynurenine and its downstream derivatives. IDO is reported to be activated in times of host systemic inflammation and therefore is thought to influence both pathways. To investigate metabolic alterations in AD, a large-scale metabolic phenotyping study was conducted on both urine and serum samples collected from a multi-centre clinical cohort, consisting of individuals clinically diagnosed with AD, mild cognitive impairment (MCI) and age-matched controls. Methods Metabolic phenotyping was applied to both urine (n = 560) and serum (n = 354) from the European-wide AddNeuroMed/Dementia Case Register (DCR) biobank repositories. Metabolite data were subsequently interrogated for inter-group differences; influence of gender and age; comparisons between two subgroups of MCI - versus those who remained cognitively stable at follow-up visits (sMCI); and those who underwent further cognitive decline (cMCI); and the impact of selective serotonin reuptake inhibitor (SSRI) medication on metabolite concentrations. Results Results revealed significantly lower metabolite concentrations of tryptophan pathway metabolites in the AD group: serotonin (urine, serum), 5-hydroxyindoleacetic acid (urine), kynurenine (serum), kynurenic acid (urine), tryptophan (urine, serum), xanthurenic acid (urine, serum), and kynurenine/tryptophan ratio (urine). For each listed metabolite, a decreasing trend in concentrations was observed in-line with clinical diagnosis: control > MCI > AD. There were no significant differences in the two MCI subgroups whilst SSRI medication status influenced observations in serum, but not urine. Conclusions Urine and serum serotonin concentrations were found to be significantly lower in AD compared with controls, suggesting the bioavailability of the neurotransmitter may be altered in the disease. A significant increase in the kynurenine/tryptophan ratio suggests that this may be a result of a shift to the kynurenine metabolic route due to increased IDO activity, potentially as a result of systemic inflammation. Modulation of the pathways could help improve serotonin bioavailability and signalling in AD patients.

Abstract Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer’s Disease. Few studies to date have examined urine, the most readily available biofluid. Here we report the largest study to date using comprehensive metabolic phenotyping platforms (NMR spectroscopy and UHPLC-MS) to probe the urinary metabolome in-depth in people with Alzheimer’s Disease and Mild Cognitive Impairment. Feature reduction was performed using metabolomic Quantitative Trait Loci, resulting in the list of metabolites associated with the genetic variants. This approach helps accuracy in identification of disease states and provides a route to a plausible mechanistic link to pathological processes. Using these mQTLs we built a Random Forests model, which not only correctly discriminates between people with Alzheimer’s Disease and age-matched controls, but also between individuals with Mild Cognitive Impairment who were later diagnosed with Alzheimer’s Disease and those who were not. Further annotation of top-ranking metabolic features nominated by the trained model revealed the involvement of cholesterol-derived metabolites and small-molecules that were linked to Alzheimer’s pathology in previous studies.

BackgroundDigital health interventions could help to prevent age-related diseases, but little is known about how older adults engage with such interventions, especially in the long term, or whether engagement is associated with changes in clinical, behavioral, or biological outcomes in this population. Disparities in engagement levels with digital health interventions may exist among older people and be associated with health inequalities. ObjectiveThis study aimed to describe older adults’ engagement with an eHealth intervention, identify factors associated with engagement, and examine associations between engagement and changes in cardiovascular and dementia risk factors (blood pressure, cholesterol, BMI, physical activity, diet, and cardiovascular and dementia risk scores). MethodsThis was a secondary analysis of the 18-month randomized controlled Healthy Ageing Through Internet Counselling in the Elderly trial of a tailored internet-based intervention encouraging behavior changes, with remote support from a lifestyle coach, to reduce cardiovascular and cognitive decline risk in 2724 individuals aged ≥65 years, recruited offline in the Netherlands, Finland, and France. Engagement was assessed via log-in frequency, number of lifestyle goals set, measurements entered and messages sent to coaches, and percentage of education materials read. Clinical and biological data were collected during in-person visits at baseline and 18 months. Lifestyle data were self-reported on a web-based platform. ResultsOf the 1389 intervention group participants, 1194 (85.96%) sent at least one message. They logged in a median of 29 times, and set a median of 1 goal. Higher engagement was associated with significantly greater improvement in biological and behavioral risk factors, with evidence of a dose-response effect. Compared with the control group, the adjusted mean difference (95% CI) in 18-month change in the primary outcome, a composite z-score comprising blood pressure, BMI, and cholesterol, was −0.08 (−0.12 to −0.03), −0.04 (−0.08 to 0.00), and 0.00 (−0.08 to 0.08) in the high, moderate, and low engagement groups, respectively. Low engagers showed no improvement in any outcome measures compared with the control group. Participants not using a computer regularly before the study engaged much less with the intervention than those using a computer up to 7 (adjusted odds ratio 5.39, 95% CI 2.66-10.95) or ≥7 hours per week (adjusted odds ratio 6.58, 95% CI 3.21-13.49). Those already working on or with short-term plans for lifestyle improvement at baseline, and with better cognition, engaged more. ConclusionsGreater engagement with an eHealth lifestyle intervention was associated with greater improvement in risk factors in older adults. However, those with limited computer experience, who tended to have a lower level of education, or who had poorer cognition engaged less. Additional support or forms of intervention delivery for such individuals could help minimize potential health inequalities associated with the use of digital health interventions in older people.

Abstract Background The pathophysiological basis of idiopathic normal pressure hydrocephalus (iNPH) is still unclear. Previous studies have shown a familial aggregation and a potential heritability when it comes to iNPH. Our aim was to conduct a novel case-controlled comparison between familial iNPH (fNPH) patients and their elderly relatives, involving multiple different families. Methods Questionnaires and phone interviews were used for collecting the data and categorising the iNPH patients into the familial (fNPH) and the sporadic groups. Identical questionnaires were sent to the relatives of the potential fNPH patients. Venous blood samples were collected for genetic studies. The disease histories of the probable fNPH patients (n = 60) were compared with their ≥ 60-year-old relatives with no iNPH (n = 49). A modified Charlson Comorbidity Index (CCI) was used to measure the overall disease burden. Fisher’s exact test (two-tailed), the Mann–Whitney U test (two-tailed) and a multivariate binary logistic regression analysis were used to perform the statistical analyses. Results Diabetes (32% vs. 14%, p = 0.043), arterial hypertension (65.0% vs. 43%, p = 0.033), cardiac insufficiency (16% vs. 2%, p = 0.020) and depressive symptoms (32% vs. 8%, p = 0.004) were overrepresented among the probable fNPH patients compared to their non-iNPH relatives. In the age-adjusted multivariate logistic regression analysis, diabetes remained independently associated with fNPH (OR = 3.8, 95% CI 1.1–12.9, p = 0.030). Conclusions Diabetes is associated with fNPH and a possible risk factor for fNPH. Diabetes could contribute to the pathogenesis of iNPH/fNPH, which motivates to further prospective and gene-environmental studies to decipher the disease modelling of iNPH/fNPH.

The genetic architecture underlying brainstem regions and how this links to common brain disorders is not well understood. Here, the authors use MRI and GWAS data from 27,034 individuals to identify genetic and morphological brainstem features that influence common brain disorders.

Abstract Background The oldest old is the fastest growing age group worldwide and the most prone to severe disability, especially in relation to loss of cognitive function. Improving our understanding of the predictors of cognitive, physical and psychosocial wellbeing among the oldest old can result in substantial benefits for the individuals and for the society as a whole. The Cardiovascular Risk Factors, Aging and Dementia (CAIDE) study investigated risk factors and determinants of cognitive impairment in a population-based longitudinal cohort, which was first examined between 1972 and 1992, when individuals were in their midlife, and re-assessed in 1998 and 2005–2009. Most of the study participants are currently aged 85 years or older. We aim to re-examine the cohort’s survivors and gain further insights on the mechanisms underlying both cognitive and overall healthy ageing at old age. Methods CAIDE85+ is the third follow-up of the CAIDE study participants. All individuals still alive and living in the Kuopio and Joensuu areas of Eastern Finland, from the original CAIDE cohort (two random samples, N = 2000 + ~ 900), will be invited to a re-examination. The assessment includes self-reported data related to basic demographics and lifestyle, as well as psychosocial and physical health status. Cognitive and physical evaluations are also conducted. Blood biomarkers relevant for dementia and ageing are assessed. Primary outcomes are the measurements related to cognition and daily life functioning (CERAD, Trail Making Test-A, Letter-Digit Substitution Test, Clinical Dementia Rating and Activities of Daily Living). Secondary endpoints of the study are outcomes related to physical health status, psychosocial wellbeing, as well as age-related health indicators. Discussion Through a follow-up of more than 40 years, CAIDE85+ will provide invaluable information on the risk and protective factors that contribute to cognitive and physical health, as well as ageing and longevity. Study registration The present study protocol has been registered at https://clinicaltrials.gov/ (registration nr NCT03938727 , date 03.05.2019).

Abstract Background A better insight into older adults’ understanding of and attitude towards cognitive disorders and their prevention, as well as expectations and reasons for participation in prevention trials, would help design, conduct, and implement effective preventive interventions. This qualitative study aimed at exploring the knowledge and perceptions of cognitive disorders and their prevention among participants in a prevention trial. Methods Semi-structured interviews were conducted among the participants of a multinational randomised controlled trial testing the efficacy of a lifestyle-based eHealth intervention in preventing cardiovascular disease or cognitive decline in community dwellers aged 65+. Participants were probed on their reasons for participation in the trial and their views on general health, cardiovascular disease, ageing, and prevention. The subset of data focusing on cognitive disorders (15 interviewees; all in Finland) was considered for this study. Data were analysed using content analysis. Results Participants’ knowledge of the cause and risk factors of cognitive disorders and prevention was limited and superficial, and a need for up-to-date, reliable, and practical information and advice was expressed. Cognitive disorders evoked fear and concern, and feelings of hopelessness and misery were frequently expressed, indicating a stigma. Strong heredity of cognitive disorders was a commonly held belief, and opinions on the possibility of prevention were doubtful, particularly in relation to primary prevention. Family history and/or indirect experiences of cognitive disorders was a recurrent theme and it showed to be linked to both the knowledge of and feelings associated with cognitive disorders, as well as attitude towards prevention. Indirect experiences were linked to increased awareness and knowledge, but also uncertainty about risk factors and possibility of prevention. Distinct fear and concerns, particularly over one’s own cognition/risk, and high motivation towards engaging in prevention and participating in a prevention trial were also identified in connection to this theme. Conclusions Family history and/or indirect experiences of cognitive disorders were linked to sensitivity and receptiveness to brain health and prevention potential. Our findings may be helpful in addressing older adults’ expectations in future prevention trials to improve recruitment, maximise adherence, and facilitate the successful implementation of interventions.

Alzheimer's disease (AD) is the most common form of dementia, which is neuropathologically characterized by extracellular senile plaques containing amyloid-β and intracellular neurofibrillary tangles composed of hyperphosphorylated tau protein. Previous studies have suggested a role for septin (SEPTIN) protein family members in AD-associated cellular processes. Here, we elucidated the potential role of presynaptic SEPTIN5 protein and its post-translational modifications in the molecular pathogenesis of AD. RNA and protein levels of SEPTIN5 showed a significant decrease in human temporal cortex in relation to the increasing degree of AD-related neurofibrillary pathology. Conversely, an increase in the phosphorylation of the functionally relevant SEPTIN5 phosphorylation site S327 was observed already in the early phases of AD-related neurofibrillary pathology, but not in the cerebrospinal fluid of individuals fulfilling the criteria for mild cognitive impairment due to AD. According to the mechanistic assessments, a link between SEPTIN5 S327 phosphorylation status and the effects of SEPTIN5 on amyloid precursor protein processing and markers of autophagy was discovered in mouse primary cortical neurons transduced with lentiviral constructs encoding wild type SEPTIN5 or SEPTIN5 phosphomutants (S327A and S327D). C57BL/6 J mice intrahippocampally injected with lentiviral wild type SEPTIN5 or phosphomutant constructs did not show changes in cognitive performance after five to six weeks from the start of injections. However, SEPTIN5 S327 phosphorylation status was linked to changes in short-term synaptic plasticity ex vivo at the CA3-CA1 synapse. Collectively, these data suggest that SEPTIN5 and its S327 phosphorylation status play a pivotal role in several cellular processes relevant for AD.

Aims: There are very few detailed post-mortem studies on idiopathic normal-pressure hydrocephalus (iNPH) and there is a lack of proper neuropathological criteria for iNPH. This study aims to update the knowledge on the neuropathology of iNPH and to develop the neuropathological diagnostic criteria of iNPH. Methods: We evaluated the clinical lifelines and post-mortem findings of 29 patients with possible NPH. Pre-mortem cortical brain biopsies were taken from all patients during an intracranial pressure measurement or a cerebrospinal fluid (CSF) shunt surgery. Results: The mean age at the time of the biopsy was 70±8 SD years and 74±7 SD years at the time of death. At the time of death, 11/29 patients (38%) displayed normal cognition or mild cognitive impairment (MCI), 9/29 (31%) moderate dementia and 9/29 (31%) severe dementia. Two of the demented patients had only scarce neuropathological findings indicating a probable hydrocephalic origin for the dementia. Amyloid-β (Aβ) and hyperphosphorylated τ (HPτ) in the biopsies predicted the neurodegenerative diseases so that there were 4 Aβ positive/low Alzheimer’s disease neuropathological change (ADNC) cases, 4 Aβ positive/intermediate ADNC cases, 1 Aβ positive case with both low ADNC and progressive supranuclear palsy (PSP), 1 HPτ/PSP and primary age-related tauopathy (PART) case, 1 Aβ/HPτ and low ADNC/synucleinopathy case and 1 case with Aβ/HPτ and high ADNC. The most common cause of death was due to cardiovascular diseases (10/29, 34%), followed by cerebrovascular diseases or subdural hematoma (SDH) (8/29, 28%). Three patients died of a postoperative intracerebral hematoma (ICH). Vascular lesions were common (19/29, 65%). Conclusions: We update the suggested neuropathological diagnostic criteria of iNPH, which emphasize the rigorous exclusion of all other known possible neuropathological causes of dementia. Despite the first 2 probable cases reported here, the issue of “hydrocephalic dementia” as an independent entity still requires further confirmation. Extensive sampling (with fresh frozen tissue including meninges) with age-matched neurologically healthy controls is highly encouraged.

Background Obstructive sleep apnoea (OSA) is associated with higher body mass index (BMI), diabetes, older age and male gender, which are all risk factors for severe COVID-19.We aimed to study if OSA is an independent risk factor for COVID-19 infection or for severe COVID-19.Methods OSA diagnosis and COVID-19 infection were extracted from the hospital discharge, causes of death and infectious diseases registries in individuals who participated in the FinnGen study (n=260 405). Severe COVID-19 was defined as COVID-19 requiring hospitalisation. Multivariate logistic regression model was used to examine association. Comorbidities for either COVID-19 or OSA were selected as covariates. We performed a meta-analysis with previous studies.Results We identified 445 individuals with COVID-19, and 38 (8.5%) of them with OSA of whom 19 out of 91 (20.9%) were hospitalised. OSA associated with COVID-19 hospitalisation independent from age, sex, BMI and comorbidities (p-unadjusted=5.13×10−5, OR-adjusted=2.93 (95% CI 1.02 to 8.39), p-adjusted=0.045). OSA was not associated with the risk of contracting COVID-19 (p=0.25). A meta-analysis of OSA and severe COVID-19 showed association across 15 835 COVID-19 positive controls, and n=1294 patients with OSA with severe COVID-19 (OR=2.37 (95% 1.14 to 4.95), p=0.021).Conclusion Risk for contracting COVID-19 was the same for patients with OSA and those without OSA. In contrast, among COVID-19 positive patients, OSA was associated with higher risk for hospitalisation. Our findings are in line with earlier works and suggest OSA as an independent risk factor for severe COVID-19.

Abstract Background Many prodromal Alzheimer’s disease trials collect two types of data: the time until clinical diagnosis of dementia and longitudinal patient information. These data are often analysed separately, although they are strongly associated. By combining the longitudinal and survival data into a single statistical model, joint models can account for the dependencies between the two types of data. Methods We illustrate the major steps in a joint modelling approach, motivated by data from a prodromal Alzheimer’s disease study: the LipiDiDiet trial. Results By using joint models we are able to disentangle baseline confounding from the intervention effect and moreover, to investigate the association between longitudinal patient information and the time until clinical dementia diagnosis. Conclusions Joint models provide a valuable tool in the statistical analysis of clinical studies with longitudinal and survival data, such as in prodromal Alzheimer’s disease trials, and have several added values compared to separate analyses.

Abstract Background The Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) was a multicenter randomized controlled trial that reported beneficial effects on cognition for a 2-year multimodal intervention (diet, exercise, cognitive training, vascular risk monitoring) versus control (general health advice). This study reports exploratory analyses of brain MRI measures. Methods FINGER targeted 1260 older individuals from the general Finnish population. Participants were 60–77 years old, at increased risk for dementia but without dementia/substantial cognitive impairment. Brain MRI scans were available for 132 participants (68 intervention, 64 control) at baseline and 112 participants (59 intervention, 53 control) at 2 years. MRI measures included regional brain volumes, cortical thickness, and white matter lesion (WML) volume. Cognition was assessed at baseline and 1- and 2-year visits using a comprehensive neuropsychological test battery. We investigated the (1) differences between the intervention and control groups in change in MRI outcomes (FreeSurfer 5.3) and (2) post hoc sub-group analyses of intervention effects on cognition in participants with more versus less pronounced structural brain changes at baseline (mixed-effects regression models, Stata 12). Results No significant differences between the intervention and control groups were found on the changes in MRI measures. Beneficial intervention effects on processing speed were more pronounced in individuals with higher baseline cortical thickness in Alzheimer’s disease signature areas (composite measure of entorhinal, inferior and middle temporal, and fusiform regions). The randomization group × time × cortical thickness interaction coefficient was 0.198 (p = 0.021). A similar trend was observed for higher hippocampal volume (group × time × hippocampus volume interaction coefficient 0.1149, p = 0.085). Conclusions The FINGER MRI exploratory sub-study did not show significant differences between the intervention and control groups on changes in regional brain volumes, regional cortical thicknesses, or WML volume after 2 years in at-risk elderly without substantial impairment. The cognitive benefits on processing speed of the FINGER intervention may be more pronounced in individuals with fewer structural brain changes on MRI at baseline. This suggests that preventive strategies may be more effective if started early, before the occurrence of more pronounced structural brain changes. Trial registration ClinicalTrials.gov, NCT01041989. Registered January 5, 2010.

No single-omic approach completely elucidates the multitude of alterations taking place in Alzheimer's disease (AD). Here, we coupled transcriptomic and phosphoproteomic approaches to determine the temporal sequence of changes in mRNA, protein, and phosphopeptide expression levels from human temporal cortical samples, with varying degree of AD-related pathology. This approach highlighted fluctuation in synaptic and mitochondrial function as the earliest pathological events in brain samples with AD-related pathology. Subsequently, increased expression of inflammation and extracellular matrix-associated gene products was observed. Interaction network assembly for the associated gene products, emphasized the complex interplay between these processes and the role of addressing post-translational modifications in the identification of key regulators. Additionally, we evaluate the use of decision trees and random forests in identifying potential biomarkers differentiating individuals with different degree of AD-related pathology. This multiomic and temporal sequence-based approach provides a better understanding of the sequence of events leading to AD.

Abstract Background In clinical practice, it is often difficult to predict which patients with cognitive complaints or impairment will progress or remain stable. We assessed the impact of using a clinical decision support system, the PredictND tool, to predict progression in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in memory clinics. Methods In this prospective multicenter study, we included 429 patients with SCD (n = 230) and MCI (n = 199) (female 54%, age 67 ± 9, MMSE 28 ± 2) and followed them for at least 12 months. Based on all available patient baseline data (demographics, cognitive tests, cerebrospinal fluid biomarkers, and MRI), the PredictND tool provides a comprehensive overview of the data and a classification defining the likelihood of progression. At baseline, a clinician defined an expected follow-up diagnosis and estimated the level of confidence in their prediction using a visual analogue scale (VAS, 0–100%), first without and subsequently with the PredictND tool. As outcome measure, we defined clinical progression as progression from SCD to MCI or dementia, and from MCI to dementia. Correspondence between the expected and the actual clinical progression at follow-up defined the prognostic accuracy. Results After a mean follow-up time of 1.7 ± 0.4 years, 21 (9%) SCD and 63 (32%) MCI had progressed. When using the PredictND tool, the overall prognostic accuracy was unaffected (0.4%, 95%CI − 3.0%; + 3.9%; p = 0.79). However, restricting the analysis to patients with more certain classifications (n = 203), we found an increase of 3% in the accuracy (95%CI − 0.6%; + 6.5%; p = 0.11). Furthermore, for this subgroup, the tool alone showed a statistically significant increase in the prognostic accuracy compared to the evaluation without tool (6.4%, 95%CI 2.1%; 10.7%; p = 0.004). Specifically, the negative predictive value was high. Moreover, confidence in the prediction increased significantly (∆VAS = 4%, p

Abstract 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.

BackgroundVery few studies have explored the patterns of cardiovascular health (CVH) metrics in midlife and late life in relation to risk of dementia. We examined the associations of composite CVH metrics from midlife to late life with risk of incident dementia.Methods and findingsThis cohort study included 1,449 participants from the Finnish Cardiovascular Risk Factors, Aging, and Dementia (CAIDE) study, who were followed from midlife (baseline from1972 to 1987; mean age 50.4 years; 62.1% female) to late life (1998), and then 744 dementia-free survivors were followed further into late life (2005 to 2008). We defined and scored global CVH metrics based on 6 of the 7 components (i.e., smoking, physical activity, and body mass index [BMI] as behavioral CVH metrics; fasting plasma glucose, total cholesterol, and blood pressure as biological CVH metrics) following the modified American Heart Association (AHA)'s recommendations. Then, the composite global, behavioral, and biological CVH metrics were categorized into poor, intermediate, and ideal levels. Dementia was diagnosed following the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria. Data were analyzed with Cox proportional hazards and the Fine and Gray competing risk regression models. During the follow-up examinations, dementia was diagnosed in 61 persons in 1998 and additional 47 persons in 2005 to 2008. The fully adjusted hazard ratio (HR) of dementia was 0.71 (95% confidence interval [CI]: 0.43, 1.16; p = 0.174) and 0.52 (0.29, 0.93; p = 0.027) for midlife intermediate and ideal levels (versus poor level) of global CVH metrics, respectively; the corresponding figures for late-life global CVH metrics were 0.60 (0.22, 1.69; p = 0.338) and 0.91 (0.34, 2.41; p = 0.850). Compared with poor global CVH metrics in both midlife and late life, the fully adjusted HR of dementia was 0.25 (95% CI: 0.08, 0.86; p = 0.028) for people with intermediate global CVH metrics in both midlife and late life and 0.14 (0.02, 0.76; p = 0.024) for those with midlife ideal and late-life intermediate global CVH metrics. Having an intermediate or ideal level of behavioral CVH in both midlife and late life (versus poor level in both midlife and late life) was significantly associated with a lower dementia risk (HR range: 0.03 to 0.26; p < 0.05), whereas people with midlife intermediate and late-life ideal biological CVH metrics had a significantly increased risk of dementia (p = 0.031). Major limitations of this study include the lack of data on diet and midlife plasma glucose, high rate of attrition, as well as the limited power for certain subgroup analyses.ConclusionsIn this study, we observed that having the ideal CVH metrics, and ideal behavioral CVH metrics in particular, from midlife onwards is associated with a reduced risk of dementia as compared with people having poor CVH metrics. Maintaining life-long health behaviors may be crucial to reduce late-life risk of dementia.

Objectives Prevention of cardiovascular disease (CVD) and dementia is a key health priority among older adults. Understanding individuals’ attitudes to, the prevention of these conditions, particularly when delivered through novel eHealth tools, could help in designing effective prevention programmes. The aim of the study was to explore the attitudes of older adults at increased risk of CVD and dementia regarding engagement in eHealth self-management prevention programmes, and to describe the facilitators and barriers.Design A qualitative research approach was used. Data were collected through eight focus groups in Finland, France and the Netherlands. Data were analysed following the principles of grounded theory.Setting and participants Forty-four community-dwellers aged 65+ at risk of CVD were recruited from a previous trial cohort in Finland, and through general practices in France and the Netherlands.Results The study identified three categories: access to reliable information, trust in the healthcare providers and burden and stigma of dementia. A core category was also identified: the interactive process of the three categories influencing engagement in self-management prevention programme. The categories were interconnected through an interactive process and influenced by the local healthcare culture and context which shaped them differently, becoming either facilitators or barriers to engage in eHealth self-management prevention programmes.Conclusions The study emphasises the importance of considering the interactions between the identified categories in this study, grounded in the local healthcare culture and context in further developments of eHealth self-management interventions that aim to prevent CVD and dementia.Trial registration number ISRCTN48151589

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.

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.

BACKGROUND:Pooling individual participant data to enable pooled analyses is often complicated by diversity in variables across available datasets. Therefore, recoding original variables is often necessary to build a pooled dataset. We aimed to quantify how much information is lost in this process and to what extent this jeopardizes validity of analyses results. METHODS:Data were derived from a platform that was developed to pool data from three randomized controlled trials on the effect of treatment of cardiovascular risk factors on cognitive decline or dementia. We quantified loss of information using the R-squared of linear regression models with pooled variables as a function of their original variable(s). In case the R-squared was below 0.8, we additionally explored the potential impact of loss of information for future analyses. We did this second step by comparing whether the Beta coefficient of the predictor differed more than 10% when adding original or recoded variables as a confounder in a linear regression model. In a simulation we randomly sampled numbers, recoded those < = 1000 to 0 and those >1000 to 1 and varied the range of the continuous variable, the ratio of recoded zeroes to recoded ones, or both, and again extracted the R-squared from linear models to quantify information loss. RESULTS:The R-squared was below 0.8 for 8 out of 91 recoded variables. In 4 cases this had a substantial impact on the regression models, particularly when a continuous variable was recoded into a discrete variable. Our simulation showed that the least information is lost when the ratio of recoded zeroes to ones is 1:1. CONCLUSIONS:Large, pooled datasets provide great opportunities, justifying the efforts for data harmonization. Still, caution is warranted when using recoded variables which variance is explained limitedly by their original variables as this may jeopardize the validity of study results.

Abstract 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.

Summary: Background: Although web-based interventions have been promoted for cardiovascular risk management over the past decade, there is limited evidence for effectiveness of these interventions in people older than 65 years. The healthy ageing through internet counselling in the elderly (HATICE) trial aimed to determine whether a coach-supported internet intervention for self-management can reduce cardiovascular risk in community-dwelling older people. Methods: This prospective open-label, blinded endpoint clinical trial among people age 65 years or over at increased risk of cardiovascular disease randomly assigned participants in the Netherlands, Finland, and France to an interactive internet intervention stimulating coach-supported self-management or a control platform. Primary outcome was the difference from baseline to 18 months on a standardised composite score (Z score) of systolic blood pressure, LDL cholesterol, and body-mass index (BMI). Secondary outcomes included individual risk factors and cardiovascular endpoints. This trial is registered with the ISRCTN registry, 48151589, and is closed to accrual. Findings: Among 2724 participants, complete primary outcome data were available for 2398 (88%). After 18 months, the primary outcome improved in the intervention group versus the control group (0·09 vs 0·04, respectively; mean difference −0·05, 95% CI −0·08 to −0·01; p=0·008). For individual components of the primary outcome, mean differences (intervention vs control) were systolic blood pressure −1·79 mm Hg versus −0·67 mm Hg (−1·12, −2·51 to 0·27); BMI −0·23 kg/m2 versus −0·08 kg/m2 (−0·15, −0·28 to −0·01); and LDL −0·12 mmol/L versus −0·07 mmol/L (−0·05, −0·11 to 0·01). Cardiovascular disease occurred in 30 (2·2%) of 1382 patients in the intervention versus 32 (2·4%) of 1333 patients in the control group (hazard ratio 0·86, 95% CI 0·52 to 1·43). Interpretation: Coach-supported self-management of cardiovascular risk factors using an interactive internet intervention is feasible in an older population, and leads to a modest improvement of cardiovascular risk profile. When implemented on a large scale this could potentially reduce the burden of cardiovascular disease. Funding: European Commission Seventh Framework Programme.

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.

Abstract Introduction We studied, using a data‐driven approach, how different combinations of diagnostic tests contribute to the differential diagnosis of dementia. Methods In this multicenter study, we included 356 patients with Alzheimer's disease, 87 frontotemporal dementia, 61 dementia with Lewy bodies, 38 vascular dementia, and 302 controls. We used a classifier to assess accuracy for individual performance and combinations of cognitive tests, cerebrospinal fluid biomarkers, and automated magnetic resonance imaging features for pairwise differentiation between dementia types. Results Cognitive tests had good performance in separating any type of dementia from controls. Cerebrospinal fluid optimally contributed to identifying Alzheimer's disease, whereas magnetic resonance imaging features aided in separating vascular dementia, dementia with Lewy bodies, and frontotemporal dementia. Combining diagnostic tests increased the accuracy, with balanced accuracies ranging from 78% to 97%. Discussion Different diagnostic tests have their distinct roles in differential diagnostics of dementias. Our results indicate that combining different diagnostic tests may increase the accuracy further.

Abstract Background Cerebrovascular disease (CVD) and amyloid-β (Aβ) often coexist, but their influence on neurodegeneration and cognition in predementia stages remains unclear. We investigated the association between CVD and Aβ on neurodegenerative markers and cognition in patients without dementia. Methods We included 271 memory clinic patients with subjective or objective cognitive deficits but without dementia from the BioBank Alzheimer Center Limburg cohort (n = 99) and the LeARN (n = 50) and DESCRIPA (n = 122) multicenter studies. CSF Aβ1–42 and white matter hyperintensities (WMH) on magnetic resonance imaging (MRI) scans were used as measures of Aβ and CVD, respectively. Individuals were classified into four groups based on the presence (+) or absence (−) of Aβ and WMH. We investigated differences in phosphorylated tau, total tau (t-tau), and medial temporal lobe atrophy (MTA) between groups using general linear models. We examined cognitive decline and progression to dementia using linear mixed models and Cox proportional hazards models. All analyses were adjusted for study and demographics. Results MTA and t-tau were elevated in the Aβ − WMH+, Aβ + WMH−, and Aβ + WMH+ groups. MTA was most severe in the Aβ + WMH+ group compared with the groups with a single pathology. Both WMH and Aβ were associated with cognitive decline, but having both pathologies simultaneously was not associated with faster decline. Conclusions In the present study, we found an additive association of Aβ and CVD pathology with baseline MTA but not with cognitive decline. Because our findings may have implications for diagnosis and prognosis of memory clinic patients and for future scientific research, they should be validated in a larger sample with longer follow-up.

Abstract Background DHCR24, involved in the de novo synthesis of cholesterol and protection of neuronal cells against different stress conditions, has been shown to be selectively downregulated in neurons of the affected brain areas in Alzheimer’s disease. Methods Here, we investigated whether the overexpression of DHCR24 protects neurons against inflammation-induced neuronal death using co-cultures of mouse embryonic primary cortical neurons and BV2 microglial cells upon acute neuroinflammation. Moreover, the effects of DHCR24 overexpression on dendritic spine density and morphology in cultured mature mouse hippocampal neurons and on the outcome measures of ischemia-induced brain damage in vivo in mice were assessed. Results Overexpression of DHCR24 reduced the loss of neurons under inflammation elicited by LPS and IFN-γ treatment in co-cultures of mouse neurons and BV2 microglial cells but did not affect the production of neuroinflammatory mediators, total cellular cholesterol levels, or the activity of proteins linked with neuroprotective signaling. Conversely, the levels of post-synaptic cell adhesion protein neuroligin-1 were significantly increased upon the overexpression of DHCR24 in basal growth conditions. Augmentation of DHCR24 also increased the total number of dendritic spines and the proportion of mushroom spines in mature mouse hippocampal neurons. In vivo, overexpression of DHCR24 in striatum reduced the lesion size measured by MRI in a mouse model of transient focal ischemia. Conclusions These results suggest that the augmentation of DHCR24 levels provides neuroprotection in acute stress conditions, which lead to neuronal loss in vitro and in vivo.

Abstract Background Insulin resistance (IR) has previously been associated with an increased risk of developing Alzheimer’s disease (AD), although the relationship between IR and AD is not yet clear. Here, we examined the influence of IR on AD using plasma and cerebrospinal fluid (CSF) biomarkers related to IR and AD in cognitively healthy men. We also aimed to characterise the shared protein signatures between IR and AD. Methods Fifty-eight cognitively healthy men, 28 IR and 30 non-IR (age and APOE ε4 matched), were drawn from the Metabolic Syndrome in Men study in Kuopio, Finland. CSF AD biomarkers (amyloid β-peptide (Aβ), total tau and tau phosphorylated at the Thr181 epitope) were examined with respect to IR. Targeted proteomics using ELISA and Luminex xMAP assays were performed to assess the influence of IR on previously identified CSF and plasma protein biomarker candidates of AD pathology. Furthermore, CSF and plasma SOMAscan was performed to discover proteins that associate with IR and CSF AD biomarkers. Results CSF AD biomarkers did not differ between IR and non-IR groups, although plasma insulin correlated with CSF Aβ/tau across the whole cohort. In total, 200 CSF and 487 plasma proteins were differentially expressed between IR and non-IR subjects, and significantly enriched pathways, many of which have been previously implicated in AD, were identified. CSF and plasma proteins significantly associated with CSF AD biomarkers were also discovered, and those sensitive to both IR and AD were identified. Conclusions These data indicate that IR is not directly related to the level of CSF AD pathology in cognitively healthy men. Proteins that associated with both AD and IR are potential markers indicative of shared pathology.

The hippocampus in mammalian brain varies in size across individuals. Here, Hibar and colleagues perform a genome-wide association meta-analysis to find six genetic loci with significant association to hippocampus volume.

Differentiating between different types of neurodegenerative diseases is not only crucial in clinical practice when treatment decisions have to be made, but also has a significant potential for the enrichment of clinical trials. The purpose of this study is to develop a classification framework for distinguishing the four most common neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobe degeneration, Dementia with Lewy bodies and vascular dementia, as well as patients with subjective memory complaints. Different biomarkers including features from images (volume features, region-wise grading features) and non-imaging features (CSF measures) were extracted for each subject. In clinical practice, the prevalence of different dementia types is imbalanced, posing challenges for learning an effective classification model. Therefore, we propose the use of the RUSBoost algorithm in order to train classifiers and to handle the class imbalance training problem. Furthermore, a multi-class feature selection method based on sparsity is integrated into the proposed framework to improve the classification performance. It also provides a way for investigating the importance of different features and regions. Using a dataset of 500 subjects, the proposed framework achieved a high accuracy of 75.2% with a balanced accuracy of 69.3% for the five-class classification using ten-fold cross validation, which is significantly better than the results using support vector machine or random forest, demonstrating the feasibility of the proposed framework to support clinical decision making. Keywords: Neurodegenerative diseases, Differential diagnosis, MRI, Dementia, Imbalance learning, Multi-class feature selection

Upon publication of this article [1], it was noticed that there were some inconsistencies in Tables 1, 2 and 3. Some of the superscript letters were incorrectly assigned. Please see below the correct tables:

Our aim was to investigate the association between behavioral symptoms of agitation, disinhibition, irritability, elation, and aberrant motor behavior to frontal brain volumes in a cohort with various neurodegenerative diseases. A total of 121 patients with mild cognitive impairment (MCI, n = 58), Alzheimer's disease (AD, n = 45) and behavioral variant frontotemporal dementia (bvFTD, n = 18) were evaluated with a Neuropsychiatric Inventory (NPI). A T1-weighted MRI scan was acquired for each participant and quantified with a multi-atlas segmentation method. The volumetric MRI measures of the frontal lobes were associated with neuropsychiatric symptom scores with a linear model. In the regression model, we included CDR score and TMT B time as covariates to account for cognitive and executive functions. The brain volumes were corrected for age, gender and head size. The total behavioral symptom score of the five symptoms of interest was negatively associated with the volume of the subcallosal area (β = −0.32, p = 0.002). High disinhibition scores were associated with reduced volume in the gyrus rectus (β = −0.30, p = 0.002), medial frontal cortex (β = −0.30, p = 0.002), superior frontal gyrus (β = −0.28, p = 0.003), inferior frontal gyrus (β = −0.28, p = 0.005) and subcallosal area (β = −0.28, p = 0.005). Elation scores were associated with reduced volumes of the medial orbital gyrus (β = −0.30, p = 0.002) and inferior frontal gyrus (β = −0.28, p = 0.004). Aberrant motor behavior was associated with atrophy of frontal pole (β = −0.29, p = 0.005) and the subcallosal area (β = −0.39, p < 0.001). No significant associations with frontal brain volumes were found for agitation and irritability. We conclude that the subcallosal area may be common neuroanatomical area for behavioral symptoms in neurodegenerative diseases, and it appears to be independent of disease etiology.

The possibilities of computer-based cognitive training (CCT) in postponing the onset of dementia are currently unclear, but promising. Our aim is to investigate older adults´ adherence to a long-term CCT program, and which participant characteristics are associated with adherence to the CCT. This study was part of the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER). Participants were 60-77-year-old individuals with increased dementia risk, recruited from previous population-based studies. The participants included in this study (n = 631) had been randomized to receive a multi-domain lifestyle intervention, including CCT. The measure of adherence was the number of completed CCT sessions (max = 144) as continuous measure. Due to a substantial proportion of participants with 0 sessions, the zero inflated negative binomial regression analyses were used to enable assessment of both predictors of starting the training and predictors of completing a higher number of training sessions. Several cognitive, demographic, lifestyle, and health-related variables were examined as potential predictors of adherence to CCT. Altogether, 63% of the participants participated in the CCT at least once, 20% completed at least half of the training, and 12% completed all sessions. Previous experience with computers, being married or cohabiting, better memory performance, and positive expectations toward the study predicted greater odds for starting CCT. Previous computer use was the only factor associated with a greater number of training sessions completed. Our study shows that there is a large variation in adherence to a long-lasting CCT among older adults with an increased risk of dementia. The results indicate that encouraging computer use, and taking into account the level of cognitive functioning, may help boost adherence to CCT.

Background/Aims: We aimed to assess the association between in volumetric measures of hippocampal sub-regions – in healthy older controls (HC), subjects with mild cognitive impairment (MCI) and AD- with circulating levels of IL-4.Methods: From AddNeuroMed Project 113 HC, 101 stable MCI (sMCI), 22 converter MCI (cMCI) and 119 AD were included. Hippocampal subfield volumes were analyzed using Freesurfer 6.0.0 on high-resolution sagittal 3D-T1W MP-RAGE acquisitions. Plasmatic IL-4 was measured using ELISA assay.Results: IL-4 was found to be (a) positively associate with left subiculum volume (β = 0.226, p = 0.037) in sMCI and (b) negatively associate with left subiculum volume (β = -0.253, p = 0.011) and left presubiculum volume (β = -0.257, p = 0.011) in AD.Conclusion: Our results indicate a potential neuroprotective effect of IL-4 on the areas of the hippocampus more vulnerable to aging and neurodegeneration.

Background: Diagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal dementia subtypes from other diagnostic groups, and from one another. Methods: In this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two memory clinic cohorts: 116 frontotemporal dementia, 341 Alzheimer's disease, 66 Dementia with Lewy bodies, 40 vascular dementia, 104 other dementias, 229 mild cognitive impairment, and 317 subjective cognitive decline. Three MRI atrophy biomarkers were derived from the normalized volumes of automatically segmented cortical regions: 1) the anterior vs. posterior index, 2) the asymmetry index, and 3) the temporal pole left index. We used the following performance metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To account for the low prevalence of frontotemporal dementia we pursued a high specificity of 95%. Cross-validation was used in assessing the performance. The generalizability was assessed in an independent cohort (n = 200). Results: The anterior vs. posterior index performed with an AUC of 83% for differentiation of frontotemporal dementia from all other diagnostic groups (Sensitivity = 59%, Specificity = 95%, positive likelihood ratio = 11.8, negative likelihood ratio = 0.4). The asymmetry index showed highest performance for separation of primary progressive aphasia and behavioral variant frontotemporal dementia (AUC = 85%, Sensitivity = 79%, Specificity = 92%, positive likelihood ratio = 9.9, negative likelihood ratio = 0.2), whereas the temporal pole left index was specific for detection of semantic variant primary progressive aphasia (AUC = 85%, Sensitivity = 82%, Specificity = 80%, positive likelihood ratio = 4.1, negative likelihood ratio = 0.2). The validation cohort provided corresponding results for the anterior vs. posterior index and temporal pole left index. Conclusion: This study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia. Keywords: Dementia, Frontotemporal lobar degeneration, Differential diagnosis, Behavioral variant frontotemporal dementia, Primary progressive aphasia, MRI

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.

Accumulation of β-amyloid (Aβ) and phosphorylated tau in the brain are central events underlying Alzheimer's disease (AD) pathogenesis. Aβ is generated from amyloid precursor protein (APP) by β-site APP-cleaving enzyme 1 (BACE1) and γ-secretase-mediated cleavages. Ubiquilin-1, a ubiquitin-like protein, genetically associates with AD and affects APP trafficking, processing and degradation. Here, we have investigated ubiquilin-1 expression in human brain in relation to AD-related neurofibrillary pathology and the effects of ubiquilin-1 overexpression on BACE1, tau, neuroinflammation, and neuronal viability in vitro in co-cultures of mouse embryonic primary cortical neurons and microglial cells under acute neuroinflammation as well as neuronal cell lines, and in vivo in the brain of APdE9 transgenic mice at the early phase of the development of Aβ pathology. Ubiquilin-1 expression was decreased in human temporal cortex in relation to the early stages of AD-related neurofibrillary pathology (Braak stages 0–II vs. III–IV). There was a trend towards a positive correlation between ubiquilin-1 and BACE1 protein levels. Consistent with this, ubiquilin-1 overexpression in the neuron-microglia co-cultures with or without the induction of neuroinflammation resulted in a significant increase in endogenously expressed BACE1 levels. Sustained ubiquilin-1 overexpression in the brain of APdE9 mice resulted in a moderate, but insignificant increase in endogenous BACE1 levels and activity, coinciding with increased levels of soluble Aβ40 and Aβ42. BACE1 levels were also significantly increased in neuronal cells co-overexpressing ubiquilin-1 and BACE1. Ubiquilin-1 overexpression led to the stabilization of BACE1 protein levels, potentially through a mechanism involving decreased degradation in the lysosomal compartment. Ubiquilin-1 overexpression did not significantly affect the neuroinflammation response, but decreased neuronal viability in the neuron-microglia co-cultures under neuroinflammation. Taken together, these results suggest that ubiquilin-1 may mechanistically participate in AD molecular pathogenesis by affecting BACE1 and thereby APP processing and Aβ accumulation.

Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making.