TWINGEN: protocol for an observational clinical biobank recall and biomarker cohort study to identify Finnish individuals with high risk of Alzheimer's disease.

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.

TWINGEN - protocol for an observational clinical biobank recall and biomarker study to identify individuals with high risk of Alzheimer's disease.

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 Finnish biobank donors according to TWINGEN study criteria. Methods and analysis: A multi-center 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 behavior and sleep. A sub-cohort are 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. TWINGEN data will be transferred to Finnish Institute of Health and Welfare (THL) biobank and we aim to further to transfer it to the FinnGen study where it will be combined with health registry data for prediction of AD. Ethics and dissemination: This recall study consists of FTC/THL/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.

A FinnGen pilot clinical recall study for Alzheimer's disease.

Successful development of novel therapies requires that clinical trials are conducted in patient cohorts with the highest benefit-to-risk ratio. Population-based biobanks with comprehensive health and genetic data from large numbers of individuals hold promise to facilitate identification of trial participants, particularly when interventions need to start while symptoms are still mild, such as for Alzheimer's disease (AD). This study describes a process for clinical recall studies from FinnGen. We demonstrate the feasibility to systematically ascertain customized clinical data from FinnGen participants with ICD10 diagnosis of AD or mild cognitive disorder (MCD) in a single-center cross-sectional study testing blood-based biomarkers and cognitive functioning in-person, computer-based and remote. As a result, 19% (27/140) of a pre-specified FinnGen subcohort were successfully recalled and completed the study. Hospital records largely validated registry entries. For 8/12 MCD patients, other reasons than AD were identified as underlying diagnosis. Cognitive measures correlated across platforms, with highest consistencies for dementia screening (r = 0.818) and semantic fluency (r = 0.764), respectively, for in-person versus telephone-administered tests. Glial fibrillary acidic protein (GFAP) (p < 0.002) and phosphorylated-tau 181 (pTau-181) (p < 0.020) most reliably differentiated AD from MCD participants. We conclude that informative, customized clinical recall studies from FinnGen are feasible.

A novel CT-based automated analysis method provides comparable results with MRI in measuring brain atrophy and white matter lesions.

PURPOSE: Automated analysis of neuroimaging data is commonly based on magnetic resonance imaging (MRI), but sometimes the availability is limited or a patient might have contradictions to MRI. Therefore, automated analyses of computed tomography (CT) images would be beneficial. METHODS: We developed an automated method to evaluate medial temporal lobe atrophy (MTA), global cortical atrophy (GCA), and the severity of white matter lesions (WMLs) from a CT scan and compared the results to those obtained from MRI in a cohort of 214 subjects gathered from Kuopio and Helsinki University Hospital registers from 2005 - 2016. RESULTS: The correlation coefficients of computational measures between CT and MRI were 0.9 (MTA), 0.82 (GCA), and 0.86 (Fazekas). CT-based measures were identical to MRI-based measures in 60% (MTA), 62% (GCA) and 60% (Fazekas) of cases when the measures were rounded to the nearest full grade variable. However, the difference in measures was 1 or less in 97-98% of cases. Similar results were obtained for cortical atrophy ratings, especially in the frontal and temporal lobes, when assessing the brain lobes separately. Bland-Altman plots and weighted kappa values demonstrated high agreement regarding measures based on CT and MRI. CONCLUSIONS: MTA, GCA, and Fazekas grades can also be assessed reliably from a CT scan with our method. Even though the measures obtained with the different imaging modalities were not identical in a relatively extensive cohort, the differences were minor. This expands the possibility of using this automated analysis method when MRI is inaccessible or contraindicated.

A Novel Genetic Marker for the C9orf72 Repeat Expansion in the Finnish Population.

BACKGROUND: C9orf72 repeat expansion (C9exp) is the most common genetic cause underlying frontotemporal lobar degeneration (FTLD) and amyotrophic lateral sclerosis (ALS). However, detection of the C9exp requires elaborative methods. OBJECTIVE: Identification of C9exp carriers from genotyped cohorts could be facilitated by using single nucleotide polymorphisms (SNPs) as markers for the C9exp. METHODS: We elucidated the potential of the previously described Finnish risk haplotype, defined by the SNP rs3849942, to identify potential C9exp carriers among 218,792 Finns using the FinnGen database. The haplotype approach was first tested in an idiopathic normal pressure hydrocephalus (iNPH) patient cohort (European Alzheimer's Disease DNA BioBank) containing C9exp carriers by comparing intermediate (15-30) and full-length (> 60 repeats) C9exp carriers (n = 41) to C9exp negative patients (< 15 repeats, n = 801). RESULTS: In this analysis, rs3849942 was associated with carriership of C9exp (OR 8.44, p < 2×10-15), while the strongest association was found with rs139185008 (OR 39.4, p < 5×10-18). Unbiased analysis of rs139185008 in FinnGen showed the strongest association with FTLD (OR 4.38, 3×10-15) and motor neuron disease ALS (OR 5.19, 3×10-21). rs139185008 was the top SNP in all diseases (iNPH, FTLD, ALS), and further showed a strong association with ALS in the UK Biobank (p = 9.0×10-8). CONCLUSION: Our findings suggest that rs139185008 is a useful marker to identify potential C9exp carriers in the genotyped cohorts and biobanks originating from Finland.

Cerebrospinal Fluid and MRI Biomarkers in Neurodegenerative Diseases: A Retrospective Memory Clinic-Based Study.

BACKGROUND: Cerebrospinal fluid (CSF) and magnetic resonance imaging (MRI) biomarkers of neurodegenerative diseases are relatively sensitive and specific in highly curated research cohorts, but proper validation for clinical use is mostly missing. OBJECTIVE: We studied these biomarkers in a novel memory clinic cohort with a variety of different neurodegenerative diseases. METHODS: This study consisted of 191 patients with subjective or objective cognitive impairment who underwent neurological, CSF biomarker (Aß42, p-tau, and tau) and T1-weighted MRI examinations at Kuopio University Hospital. We assessed CSF and imaging biomarkers, including structural MRI focused on volumetric and cortical thickness analyses, across groups stratified based on different clinical diagnoses, including Alzheimer's disease (AD), frontotemporal dementia, dementia with Lewy bodies, Parkinson's disease, vascular dementia, and mild cognitive impairment (MCI), and subjects with no evidence of neurodegenerative disease underlying the cognitive symptoms. Imaging biomarkers were also studied by profiling subjects according to the novel amyloid, tau, and, neurodegeneration (AT(N)) classification. RESULTS: Numerous imaging variables differed by clinical diagnosis, including hippocampal, amygdalar and inferior lateral ventricular volumes and entorhinal, lingual, inferior parietal and isthmus cingulate cortical thicknesses, at a false discovery rate (FDR)-corrected threshold for significance (analysis of covariance; p < 0.005). In volumetric comparisons by AT(N) profile, hippocampal volume significantly differed (p < 0.001) between patients with normal AD biomarkers and patients with amyloid pathology. CONCLUSION: Our analysis suggests that CSF and MRI biomarkers function well also in clinical practice across multiple clinical diagnostic groups in addition to AD, MCI, and cognitively normal groups.

Disease state fingerprint in frontotemporal degeneration with reference to Alzheimer's disease and mild cognitive impairment.

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.

Changes in vascular factors 28 years from midlife and late-life cortical thickness.

We assessed midlife blood pressure (BP), body mass index, total cholesterol, and their changes over time in relation to cortical thickness on magnetic resonance imaging 28 years later in 63 elderly at risk of dementia. Participants in the population-based Cardiovascular Risk Factors, Aging, and Dementia study were first examined at midlife. A first follow-up was conducted after 21 years, and a second follow-up after an additional 7 years. Magnetic resonance images from the second follow-up were analyzed using algorithms developed at McGill University, Montreal, Canada. Midlife hypertension was related to thinner cortex in several brain areas, including insular, frontal, and temporal cortices. In elderly with thinner insular cortex, there was a continuous decline in systolic BP and an increase in pulse pressure after midlife, while in elderly with thicker insular cortex the decline in systolic BP started at older ages, paralleled by a decline in pulse pressure. No associations were found between body mass index, cholesterol, or apolipoprotein E ε4 allele and cortical thickness in this group of elderly at risk individuals.

Cortical thickness in frontotemporal dementia, mild cognitive impairment, and Alzheimer's disease.

Cortical thickness analysis has been proposed as a potential diagnostic measure in memory disorders. In this retrospective study, we compared the cortical thickness values of 24 patients with frontotemporal dementia (FTD) to those of 25 healthy controls, 45 symptomatic subjects with stable mild cognitive impairment (S-MCI), 15 subjects with progressive mild cognitive impairment (P-MCI), and 36 patients with Alzheimer's disease (AD). The patterns of regions of thinning in FTD when compared to controls and also S-MCI patients showed similar trends; thinning of the bilateral frontal poles and bilateral medial temporal lobe structures, especially the anterior part of the gingulum, the uncus, and parahippocampal gyri. Cortical thinning in FTD was also found on the boundary regions of parietal and occipital lobes. In the P-MCI group compared to FTD, the trend of thinning in small distinct areas of the parietal and occipital lobes was observed. The FTD and AD groups did not differ statistically, but we found trends toward thinning in FTD of the left cingulate gyrus, and the left occipitotemporal gyri, and in AD of the inferior parietal, occipitoparietal, and the pericalcarine regions, more in the right hemisphere. In FTD, increased slowness in the executive test (Trail-Making A) correlated with the thinner cortex, whereas the language tests showed the lower scores, the thinner cortex in the left hemisphere. Cortical thickness might be a tool for detecting subtle changes in brain atrophy in screening of dementia prior to the development of diffuse or lobar atrophies.

Education increases reserve against Alzheimer's disease--evidence from structural MRI analysis.

INTRODUCTION: The aim of this study was to determine whether years of schooling influences regional cortical thicknesses and volumes in Alzheimer's disease (AD), mild cognitive impairment (MCI), and healthy age-matched controls. METHODS: Using an automated image analysis pipeline, 33 regional cortical thickness and 15 regional volumes measures from MRI images were determined in 121 subjects with MCI, 121 patients with AD, and 113 controls from AddNeuroMed study. Correlations with years of schooling were determined and more highly and less highly educated subjects compared, controlling for intracranial volume, age, gender, country of origin, cognitive status, and multiple testing. RESULTS: After controlling for confounding factors and multiple testing, in the control group, subjects with more education had larger regional cortical thickness in transverse temporal cortex, insula, and isthmus of cingulate cortex than subjects with less education. However, in the AD group, the subjects with more education had smaller regional cortical thickness in temporal gyrus, inferior and superior parietal gyri, and lateral occipital cortex than the subjects with less education. No significant difference was found in the MCI group. CONCLUSION: Education may increase regional cortical thickness in healthy controls, leading to increased brain reserve, as well as helping AD patients to cope better with the effects of brain atrophy by increasing cognitive reserve.

Structural MRI in frontotemporal dementia: comparisons between hippocampal volumetry, tensor-based morphometry and voxel-based morphometry.

BACKGROUND: MRI is an important clinical tool for diagnosing dementia-like diseases such as Frontemporal Dementia (FTD). However there is a need to develop more accurate and standardized MRI analysis methods. OBJECTIVE: To compare FTD with Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) with three automatic MRI analysis methods - Hippocampal Volumetry (HV), Tensor-based Morphometry (TBM) and Voxel-based Morphometry (VBM), in specific regions of interest in order to determine the highest classification accuracy. METHODS: Thirty-seven patients with FTD, 46 patients with AD, 26 control subjects, 16 patients with progressive MCI (PMCI) and 48 patients with stable MCI (SMCI) were examined with HV, TBM for shape change, and VBM for gray matter density. We calculated the Correct Classification Rate (CCR), sensitivity (SS) and specificity (SP) between the study groups. RESULTS: We found unequivocal results differentiating controls from FTD with HV (hippocampus left side) (CCR = 0.83; SS = 0.84; SP = 0.80), with TBM (hippocampus and amygdala (CCR = 0.80/SS = 0.71/SP = 0.94), and with VBM (all the regions studied, especially in lateral ventricle frontal horn, central part and occipital horn) (CCR = 0.87/SS = 0.81/SP = 0.96). VBM achieved the highest accuracy in differentiating AD and FTD (CCR = 0.72/SS = 0.67/SP = 0.76), particularly in lateral ventricle (frontal horn, central part and occipital horn) (CCR = 0.73), whereas TBM in superior frontal gyrus also achieved a high accuracy (CCR = 0.71/SS = 0.68/SP = 0.73). TBM resulted in low accuracy (CCR = 0.62) in the differentiation of AD from FTD using all regions of interest, with similar results for HV (CCR = 0.55). CONCLUSION: Hippocampal atrophy is present not only in AD but also in FTD. Of the methods used, VBM achieved the highest accuracy in its ability to differentiate between FTD and AD.

Evolution of global and local grey matter atrophy on serial MRI scans during the progression from MCI to AD.

Mild cognitive impairment (MCI) often represents a prodromal form of dementia, conferring a significantly higher risk of converting to probable Alzheimer's disease (AD). The aim of this study is to characterise the differences of grey matter (GM) distribution and dynamics between progressive and stable MCI subjects during a 2 year period preceding the conversion to AD. We included 48 stable MCI and 12 progressive MCI cases based on the availability of 3 serial scans acquired with approximately 1 year scan interval. For the progressive MCI group, the third scan was acquired at the time of the clinical diagnosis of AD, while the first two scans were acquired approximately 2 and 1 years earlier. For the stable MCI group, the three scans were acquired at approximately 1 year intervals during a period free from significant cognitive decline. We used longitudinal voxel-based morphometry (VBM) for mapping the progression of GM loss over time. For the progressive MCI group, the cross-sectional analysis revealed areas of lower GM volumes in the parahippocampal gyrus, precuneus and posterior cingulate 12 months before the AD diagnosis. For the longitudinal VBM analysis the progressive MCI group revealed increased GM loss in cortical regions belonging to the temporal neocortex, parahippocampal cortex, and cingulate gyrus. The frontal lobe, insula and the cerebellum were also affected. This accelerated atrophy may offer new insights into the understanding of neurodegenerative pathology and the clinical relevance of these changes remains to be verified by subsequent studies.

Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease.

The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning. Baseline MRIs acquired from all 834 subjects (231 healthy controls (HC), 238 stable mild cognitive impairment (S-MCI), 167 MCI to AD progressors (P-MCI), 198 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used for evaluation. We compared the classification accuracy achieved with linear discriminant analysis (LDA) and support vector machines (SVM). The best results achieved with individual features are 90% sensitivity and 84% specificity (HC/AD classification), 64%/66% (S-MCI/P-MCI) and 82%/76% (HC/P-MCI) with the LDA classifier. The combination of all features improved these results to 93% sensitivity and 85% specificity (HC/AD), 67%/69% (S-MCI/P-MCI) and 86%/82% (HC/P-MCI). Compared with previously published results in the ADNI database using individual MR-based features, the presented results show that a comprehensive analysis of MRI images combining multiple features improves classification accuracy and predictive power in detecting early AD. The most stable and reliable classification was achieved when combining all available features.

Fast and robust extraction of hippocampus from MR images for diagnostics of Alzheimer's disease.

Assessment of temporal lobe atrophy from magnetic resonance images is a part of clinical guidelines for the diagnosis of prodromal Alzheimer's disease. As hippocampus is known to be among the first areas affected by the disease, fast and robust definition of hippocampus volume would be of great importance in the clinical decision making. We propose a method for computing automatically the volume of hippocampus using a modified multi-atlas segmentation framework, including an improved initialization of the framework and the correction of partial volume effect. The method produced a high similarity index, 0.87, and correlation coefficient, 0.94, with semi-automatically generated segmentations. When comparing hippocampus volumes extracted from 1.5T and 3T images, the absolute value of the difference was low: 3.2% of the volume. The correct classification rate for Alzheimer's disease and cognitively normal cases was about 80% while the accuracy 65% was obtained for classifying stable and progressive mild cognitive impairment cases. The method was evaluated in three cohorts consisting altogether about 1000 cases, the main emphasis being in the analysis of the ADNI cohort. The computation time of the method is about 2 minutes on a standard laptop computer. The results show a clear potential for applying the method in clinical practice.

Differences in cortical thickness in healthy controls, subjects with mild cognitive impairment, and Alzheimer's disease patients: a longitudinal study.

In this study, we analyzed differences in cortical thickness (CTH) between healthy controls (HC), subjects with stable mild cognitive impairment (S-MCI), progressive MCI (P-MCI), and Alzheimer's disease (AD), and assessed correlations between CHT and clinical disease severity, education, and apolipoprotein E4 (APOE) genotype. Automated CTH analysis was applied to baseline high-resolution structural MR images of 145 subjects with a maximum followup time of 7.4 years pooled from population-based study databases held in the University of Kuopio. Statistical differences in CTH between study groups and significant correlations between CTH and clinical and demographic factors were assessed and displayed on a cortical surface model. Compared to HC group (n = 26), the AD (n = 21) group displayed significantly reduced CTH in several areas of frontal and temporal cortices of the right hemisphere. Higher education and lower MMSE scores were correlated with reduced CTH in the AD group, whereas no significant correlation was found between CDR-SB scores or APOE genotype and CTH. The P-MCI group demonstrated significantly reduced CTH compared to S-MCI in frontal, temporal and parietal cortices even after statistically adjusting for all confounding variables. Ultimately, analysis of CTH can be used to detect cortical thinning in subjects with progressive MCI several years before conversion and clinical diagnosis of AD dementia, irrespective of their cognitive performance, education level, or APOE genotype.

Cortical thickness analysis to detect progressive mild cognitive impairment: a reference to Alzheimer's disease.

BACKGROUND/AIMS: Mild cognitive impairment (MCI) is associated with an increased risk of Alzheimer's disease (AD). It would be advantageous to be able to distinguish the characteristics of those MCI patients with a high probability to progress to AD if one wishes to monitor the disease development and treatment. METHODS: We assessed the baseline MRI and maximum of 7 years clinical follow-up data of 60 MCI subjects in order to examine differences in cortical thickness (CTH) between the progressive MCI (P-MCI) and stable MCI (S-MCI) subjects. CTH was measured using an automatic computational surface-based method. During the follow-up, 15 MCI subjects converted to AD on average 1.9 +/- 1.3 years after the baseline examination, while 45 MCI subjects remained stable. RESULTS: The P-MCI group displayed significantly reduced CTH bilaterally in the superior and middle frontal, superior, middle and inferior temporal, fusiform and parahippocampal regions as well as the cingulate and retrosplenial cortices and also in the right precuneal and paracentral regions compared to S-MCI subjects. CONCLUSIONS: Analysis of CTH could be used in conjunction with neuropsychological testing to identify those subjects with imminent conversion from MCI to AD several years before dementia diagnosis.