| 1. |
- Antila, Kari, et al.
(author)
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The PredictAD project : development of novel biomarkers and analysis software for early diagnosis of the Alzheimer's disease
- 2013
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In: Interface Focus. - : The Royal Society Publishing. - 2042-8898 .- 2042-8901. ; 3:2
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Journal article (peer-reviewed)abstract
- Alzheimer's disease (AD) is the most common cause of dementia affecting 36 million people worldwide. As the demographic transition in the developed countries progresses towards older population, the worsening ratio of workers per retirees and the growing number of patients with age-related illnesses such as AD will challenge the current healthcare systems and national economies. For these reasons AD has been identified as a health priority, and various methods for diagnosis and many candidates for therapies are under intense research. Even though there is currently no cure for AD, its effects can be managed. Today the significance of early and precise diagnosis of AD is emphasized in order to minimize its irreversible effects on the nervous system. When new drugs and therapies enter the market it is also vital to effectively identify the right candidates to benefit from these. The main objective of the PredictAD project was to find and integrate efficient biomarkers from heterogeneous patient data to make early diagnosis and to monitor the progress of AD in a more efficient, reliable and objective manner. The project focused on discovering biomarkers from biomolecular data, electrophysiological measurements of the brain and structural, functional and molecular brain images. We also designed and built a statistical model and a framework for exploiting these biomarkers with other available patient history and background data. We were able to discover several potential novel biomarker candidates and implement the framework in software. The results are currently used in several research projects, licensed to commercial use and being tested for clinical use in several trials.
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| 2. |
- Koikkalainen, Juha R., et al.
(author)
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Early familial dilated cardiomyopathy : identification with determination of disease state parameter from cine MR image data
- 2008
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In: Radiology. - : Radiological Society of North America, Inc. - 0033-8419 .- 1527-1315. ; 249:1, s. 88-96
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Journal article (peer-reviewed)abstract
- PURPOSE: To characterize early changes in cardiac anatomy and function for lamin A/C gene (LMNA) mutation carriers by using magnetic resonance (MR) imaging and to develop tools to analyze and visualize the findings.MATERIALS AND METHODS: The ethical review board of the institution approved the study, and informed written consent was obtained. The patient group consisted of 12 subjects, seven women (mean age, 36 years; age range, 18-54 years) and five men (mean age, 28 years; age range, 18-39 years) of Finnish origin, who were each heterozygotes with one LMNA mutation that may cause familial dilated cardiomyopathy (DCM). All the subjects were judged to be healthy with transthoracic echocardiography. The control group consisted of 14 healthy subjects, 11 women (mean age, 41 years; range, 23-54 years) and three men (mean age, 45 years; range, 34-57 years), of Finnish origin. Cine steady state free precession MR imaging was performed with a 1.5-T system. The volumes, wall thickness, and wall motion of both left ventricle (LV) and right ventricle were assessed. A method combining multiple MR image parameters was used to generate a global cardiac function index, the disease state parameter (DSP). A visual fingerprint was generated to assess the severity of familial DCM.RESULTS: The mean DSP of the patient group (0.69 +/- 0.15 [standard deviation]) was significantly higher than that of the control group (0.32 +/- 0.13) (P = .00002). One subject had an enlarged LV.CONCLUSION: Subclinical familial DCM was identified by determination of the DSP with MR imaging, and this method might be used to recognize familial DCM at an early stage.
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| 3. |
- Lindfors, Erno, et al.
(author)
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Heterogeneous biological network visualization system : case study in context of medical image data
- 2012
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In: Advances in Experimental Medicine and Biology. - New York, NY : Springer-Verlag New York. - 0065-2598 .- 2214-8019. ; 736, s. 95-118
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Journal article (peer-reviewed)abstract
- We have developed a system called megNet for integrating and visualizing heterogeneous biological data in order to enable modeling biological phenomena using a systems approach. Herein we describe megNet, including a recently developed user interface for visualizing biological networks in three dimensions and a web user interface for taking input parameters from the user, and an in-house text mining system that utilizes an existing knowledge base. We demonstrate the software with a case study in which we integrate lipidomics data acquired in-house with interaction data from external databases, and then find novel interactions that could possibly explain our previous associations between biological data and medical images. The flexibility of megNet assures that the tool can be applied in diverse applications, from target discovery in medical applications to metabolic engineering in industrial biotechnology.
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| 4. |
- Luikku, Antti J., et al.
(author)
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Predicting Development of Alzheimer's Disease in Patients with Shunted Idiopathic Normal Pressure Hydrocephalus
- 2019
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In: Journal of Alzheimer's Disease. - 1387-2877 .- 1875-8908. ; 71:4, s. 1233-1243
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Journal article (peer-reviewed)abstract
- Background: Idiopathic normal pressure hydrocephalus (iNPH) patients often develop Alzheimer's disease (AD) related brain pathology. Disease State Index (DSI) is a method to combine data from various sources for differential diagnosis and progression of neurodegenerative disorders.Objective: To apply DSI to predict clinical AD in shunted iNPH-patients in a defined population.Methods: 335 shunted iNPH-patients (median 74 years) were followed until death (n = 185) or 6/2015 (n = 150). DSI model (including symptom profile, onset age of NPH symptoms, atrophy of medial temporal lobe in CT/MRI, cortical brain biopsy finding, and APOE genotype) was applied. Performance of DSI model was evaluated with receiver operating characteristic (ROC) curve analysis.Results: A total of 70 (21%) patients developed clinical AD during median follow-up of 5.3 years. DSI-model predicted clinical AD with moderate effectiveness (AUC= 0.75). Significant factors were cortical biopsy (0.69), clinical symptoms (0.66), and medial temporal lobe atrophy (0.66).Conclusion: We found increased occurrence of clinical AD in previously shunted iNPH patients as compared with general population. DSI supported the prediction of AD. Cortical biopsy during shunt insertion seems indicated for earlier diagnosis of comorbid AD.
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| 5. |
- Lundqvist, Roger, et al.
(author)
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Implementation and validation of an adaptive template registration method for 18F-flutemetamol imaging data.
- 2013
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In: Journal of Nuclear Medicine. - : Society of Nuclear Medicine. - 0161-5505 .- 1535-5667 .- 2159-662X. ; 54:8, s. 1472-8
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Journal article (peer-reviewed)abstract
- UNLABELLED: The spatial normalization of PET amyloid imaging data is challenging because different white and gray matter patterns of negative (Aβ-) and positive (Aβ+) uptake could lead to systematic bias if a standard method is used. In this study, we propose the use of an adaptive template registration method to overcome this problem.METHODS: Data from a phase II study (n = 72) were used to model amyloid deposition with the investigational PET imaging agent (18)F-flutemetamol. Linear regression of voxel intensities on the standardized uptake value ratio (SUVR) in a neocortical composite region for all scans gave an intercept image and a slope image. We devised a method where an adaptive template image spanning the uptake range (the most Aβ- to the most Aβ+ image) can be generated through a linear combination of these 2 images and where the optimal template is selected as part of the registration process. We applied the method to the (18)F-flutemetamol phase II data using a fixed volume of interest atlas to compute SUVRs. Validation was performed in several steps. The PET-only adaptive template registration method and the MR imaging-based method used in statistical parametric mapping were applied to spatially normalize PET and MR scans, respectively. Resulting transformations were applied to coregistered gray matter probability maps, and the quality of the registrations was assessed visually and quantitatively. For comparison of quantification results with an independent patient-space method, FreeSurfer was used to segment each subject's MR scan and the parcellations were applied to the coregistered PET scans. We then correlated SUVRs for a composite neocortical region obtained with both methods. Furthermore, to investigate whether the (18)F-flutemetamol model could be generalized to (11)C-Pittsburgh compound B ((11)C-PIB), we applied the method to Australian Imaging, Biomarkers and Lifestyle (AIBL) (11)C-PIB scans (n = 285) and compared the PET-only neocortical composite score with the corresponding score obtained with a semimanual method that made use of the subject's MR images for the positioning of regions.RESULTS: Spatial normalization was successful on all scans. Visual and quantitative comparison of the new PET-only method with the MR imaging-based method of statistical parametric mapping indicated that performance was similar in the cortical regions although the new PET-only method showed better registration in the cerebellum and pons reference region area. For the (18)F-flutemetamol quantification, there was a strong correlation between the PET-only and FreeSurfer SUVRs (Pearson r = 0.96). We obtained a similar correlation for the AIBL (11)C-PIB data (Pearson r = 0.94).CONCLUSION: The derived adaptive template registration method allows for robust, accurate, and fully automated quantification of uptake for (18)F-flutemetamol and (11)C-PIB scans without the use of MR imaging data.
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| 6. |
- Oresic, Matej, 1967-, et al.
(author)
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Systems medicine and the integration of bioinformatic tools for the diagnosis of Alzheimer's disease
- 2010
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In: Genome Medicine. - : BioMed Central. - 1756-994X. ; 2:11
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Journal article (peer-reviewed)abstract
- Because of the changes in demographic structure, the prevalence of Alzheimer's disease is expected to rise dramatically over the next decades. The progression of this degenerative and terminal disease is gradual, with the subclinical stage of illness believed to span several decades. Despite this, no therapy to prevent or cure Alzheimer's disease is currently available. Early disease detection is still important for delaying the onset of the disease with pharmacological treatment and/or lifestyle changes, assessing the efficacy of potential therapeutic agents, or monitoring disease progression more closely using medical imaging. Sensitive cerebrospinal-fluid-derived marker candidates exist, but given the invasiveness of sample collection their use in routine diagnostics may be limited. The pathogenesis of Alzheimer's disease is complex and poorly understood. There is thus a strong case for integrating information across multiple physiological levels, from molecular profiling (metabolomics, lipidomics, proteomics and transcriptomics) and brain imaging to cognitive assessments. To facilitate the integration of heterogeneous data, such as molecular and image data, sophisticated statistical approaches are needed to segment the image data and study their dependencies on molecular changes in the same individuals. Molecular profiling, combined with biophysical modeling of molecular assemblies associated with the disease, offer an opportunity to link the molecular pathway changes with cell- and tissue-level physiology and structure. Given that data acquired at different levels can carry complementary information about early Alzheimer's disease pathology, it is expected that their integration will improve early detection as well as our understanding of the disease.
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| 7. |
- Sysi-Aho, Marko, et al.
(author)
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Serum lipidomics meets cardiac magnetic resonance imaging : profiling of subjects at risk of dilated cardiomyopathy
- 2011
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In: PLOS ONE. - : Public Library of Science. - 1932-6203. ; 6:1
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Journal article (peer-reviewed)abstract
- Dilated cardiomyopathy (DCM), characterized by left ventricular dilatation and systolic dysfunction, constitutes a significant cause for heart failure, sudden cardiac death or need for heart transplantation. Lamin A/C gene (LMNA) on chromosome 1p12 is the most significant disease gene causing DCM and has been reported to cause 7-9% of DCM leading to cardiac transplantation. We have previously performed cardiac magnetic resonance imaging (MRI) to LMNA carriers to describe the early phenotype. Clinically, early recognition of subjects at risk of developing DCM would be important but is often difficult. Thus we have earlier used the MRI findings of these LMNA carriers for creating a model by which LMNA carriers could be identified from the controls at an asymptomatic stage. Some LMNA mutations may cause lipodystrophy. To characterize possible effects of LMNA mutations on lipid profile, we set out to apply global serum lipidomics using Ultra Performance Liquid Chromatography coupled to mass spectrometry in the same LMNA carriers, DCM patients without LMNA mutation and controls. All DCM patients, with or without LMNA mutation, differed from controls in regard to distinct serum lipidomic profile dominated by diminished odd-chain triglycerides and lipid ratios related to desaturation. Furthermore, we introduce a novel approach to identify associations between the molecular lipids from serum and the MR images from the LMNA carriers. The association analysis using dependency network and regression approaches also helped us to obtain novel insights into how the affected lipids might relate to cardiac shape and volume changes. Our study provides a framework for linking serum derived molecular markers not only with clinical endpoints, but also with the more subtle intermediate phenotypes, as derived from medical imaging, of potential pathophysiological relevance.
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| 8. |
- Toppala, Sini, et al.
(author)
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Midlife Insulin Resistance as a Predictor for Late-Life Cognitive Function and Cerebrovascular Lesions
- 2019
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In: Journal of Alzheimer's Disease. - : IOS Press. - 1387-2877 .- 1875-8908. ; 72:1, s. 215-228
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Journal article (peer-reviewed)abstract
- Background: Type 2 diabetes (T2DM) increases the risk for Alzheimer’s disease (AD) but not for AD neuropathology. The association between T2DM and AD is assumed to be mediated through vascular mechanisms. However, insulin resistance (IR), the hallmark of T2DM, has been shown to associate with AD neuropathology and cognitive decline.Objective: To evaluate if midlife IR predicts late-life cognitive performance and cerebrovascular lesions (white matter hyperintensities and total vascular burden), and whether cerebrovascular lesions and brain amyloid load are associated with cognitive functioning.Methods: This exposure-to-control follow-up study examined 60 volunteers without dementia (mean age 70.9 years) with neurocognitive testing, brain 3T-MRI and amyloid-PET imaging. The volunteers were recruited from the Finnish Health 2000 survey (n = 6062) to attend follow-up examinations in 2014–2016 according to their insulin sensitivity in 2000 and their APOE genotype. The exposure group (n = 30) had IR in 2000 and the 30 controls had normal insulin sensitivity. There were 15 APOE ɛ4 carriers per group. Statistical analyses were performed with multivariable linear models.Results: At follow-up the IR+group performed worse on executive functions (p = 0.02) and processing speed (p = 0.007) than the IR- group. The groups did not differ in cerebrovascular lesions. No associations were found between cerebrovascular lesions and neurocognitive test scores. Brain amyloid deposition associated with slower processing speed.Conclusion: Midlife IR predicted poorer executive functions and slower processing speed, but not cerebrovascular lesions. Brain amyloid deposition was associated with slower processing speed. The association between midlife IR and late-life cognition might not be mediated through cerebrovascular lesions measured here.
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