| 1. |
- Dickens, Alex Mountfort, et al.
(författare)
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Serum Metabolites Associated with Computed TomographyFindings after Traumatic Brain Injury
- 2018
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Ingår i: Journal of Neurotrauma. - : Mary Ann Liebert. - 0897-7151 .- 1557-9042. ; 35:22, s. 2673-2683
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Tidskriftsartikel (refereegranskat)abstract
- There is a need to rapidly detect patients with traumatic brain injury (TBI) who require head computed tomography (CT). Given the energy crisis in the brain following TBI, we hypothesized that serum metabolomics would be a useful tool for developing a set of biomarkers to determine the need for CT and to distinguish between different types of injuries observed. Logistic regression models using metabolite data from the discovery cohort (n=144, Turku, Finland) were used to distinguish between patients with traumatic intracranial findings and negative findings on head CT. The resultant models were then tested in the validation cohort (n=66, Cambridge, UK). The levels of glial fibrillary acidic protein and ubiquitin C-terminal hydrolase-L1 were also quantified in the serum from the same patients. Despite there being significant differences in the protein biomarkers in patients with TBI, the model that determined the need for a CT scan validated poorly (AUC=0.64: Cambridge patients). However, using a combination of six metabolites (two amino acids, three sugar derivatives and one ketoacid) it was possible to discriminate patients with intracranial abnormalities on CT and patients with a normal CT (AUC=0.77 in Turku patients and AUC=0.73 in Cambridge patients). Furthermore, a combination of three metabolites could distinguish between diffuse brain injuries and mass lesions (AUC=0.87 in Turku patients and AUC=0.68 in Cambridge patients). This study identifies a set of validated serum polar metabolites, which associate with the need for a CT scan. Additionally, serum metabolites can also predict the nature of the brain injury. These metabolite markers may prevent unnecessary CT scans, thus reducing the cost of diagnostics and radiation load.
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| 2. |
- Oresic, Matej, 1967-, et al.
(författare)
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Human Serum Metabolites Associate With Severity and Patient Outcomes in Traumatic Brain Injury
- 2016
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Ingår i: EBioMedicine. - : Elsevier. - 2352-3964. ; 12, s. 118-126
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Tidskriftsartikel (refereegranskat)abstract
- Traumatic brain injury (TBI) is a major cause of death and disability worldwide, especially in children and young adults. TBI is an example of a medical condition where there are still major lacks in diagnostics and outcome prediction. Here we apply comprehensive metabolic profiling of serum samples from TBI patients and controls in two independent cohorts. The discovery study included 144 TBI patients, with the samples taken at the time of hospitalization. The patients were diagnosed as severe (sTBI; n=22), moderate (moTBI; n=14) or mild TBI (mTBI; n=108) according to Glasgow Coma Scale. The control group (n=28) comprised of acute orthopedic non-brain injuries. The validation study included sTBI (n=23), moTBI (n=7), mTBI (n=37) patients and controls (n=27). We show that two medium-chain fatty acids (decanoic and octanoic acids) and sugar derivatives including 2,3-bisphosphoglyceric acid are strongly associated with severity of TBI, and most of them are also detected at high concentrations in brain microdialysates of TBI patients. Based on metabolite concentrations from TBI patients at the time of hospitalization, an algorithm was developed that accurately predicted the patient outcomes (AUC=0.84 in validation cohort). Addition of the metabolites to the established clinical model (CRASH), comprising clinical and computed tomography data, significantly improved prediction of patient outcomes. The identified 'TBI metabotype' in serum, that may be indicative of disrupted blood-brain barrier, of protective physiological response and altered metabolism due to head trauma, offers a new venue for the development of diagnostic and prognostic markers of broad spectrum of TBIs. (C) 2016 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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| 3. |
- Posti, Jussi P., et al.
(författare)
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SERUM METABOLITES ASSOCIATE WITH HEAD COMPUTED TOMOGRAPHY FINDINGS FOLLOWING TRAUMATIC BRAIN INJURY
- 2018
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Ingår i: Journal of Neurotrauma. - : Mary Ann Liebert. - 0897-7151 .- 1557-9042. ; 35:16, s. A67-A67
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- There is a need to rapidly detect patients with traumatic brain injury (TBI) who require head computed tomography (CT). Given the energy crisis in the brain following TBI, we hypothesized that serum metabolomics would be a useful tool for developing a set of bio-markers to determine the need for CT and to distinguish between different types of injuries observed. Logistic regression models using metabolite data from the discovery cohort (n=144, Turku, Finland) were used to distinguish between patients with traumatic intracranial findings and negative findings on head CT. The resultant models were then tested in the validation cohort (n=66, Cambridge, UK). The levels of glial fibrillary acidic protein and ubiquitin C-terminalhydrolase-L1 were also quantified in the serum from the same patients. Despite there being significant differences in the protein bio-markers in patients with TBI, the model that determined the need for a CT scan validated poorly (AUC=0.64: Cambridge patients). However, using a combination of six metabolites (two amino acids, thre esugar derivatives and one ketoacid) it was possible to discriminate patients with intracranial abnormalities on CT and patients with a normal CT (AUC=0.77 in Turku patients and AUC=0.73 in Cambridge patients). Furthermore, a combination of three metabolites could distinguish between diffuse brain injuries and mass lesions (AUC=0.87 in Turku patients and AUC=0.68 in Cambridge pa-tients). This study identifies a set of validated serum polar metabolites, which associate with the need for a CT scan. Additionally, serum metabolites can also predict the nature of the brain injury. These metabolite markers may prevent unnecessary CT scans, thus reducing the cost of diagnostics and radiation load.
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| 4. |
- Antila, Kari, et al.
(författare)
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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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Ingår i: Interface Focus. - : The Royal Society Publishing. - 2042-8898 .- 2042-8901. ; 3:2
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Tidskriftsartikel (refereegranskat)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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| 5. |
- Hall, Anette, et al.
(författare)
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Predicting Progression from Cognitive Impairment to Alzheimer's Disease with the Disease State Index
- 2015
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Ingår i: Current Alzheimer Research. - : Bentham Science Publishers Ltd.. - 1875-5828 .- 1567-2050. ; 12:1, s. 69-79
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Tidskriftsartikel (refereegranskat)abstract
- We evaluated the performance of the Disease State Index (DSI) method when predicting progression to Alzheimer's disease (AD) in patients with subjective cognitive impairment (SCI), amnestic or non-amnestic mild cognitive impairment (aMCI, naMCI). The DSI model measures patients' similarity to diagnosed cases based on available data, such as cognitive tests, the APOE genotype, CSF biomarkers and MRI. We applied the DSI model to data from the DE-SCRIPA cohort, where non-demented patients (N=775) with different subtypes of cognitive impairment were followed for 1 to 5 years. Classification accuracies for the subgroups were calculated with the DSI using leave-one-out cross-validation. The DSI's classification accuracy in predicting progression to AD was 0.75 (AUC=0.83) in the total population, 0.70 (AUC=0.77) for aMCI and 0.71 (AUC=0.76) for naMCI. For a subset of approximately half of the patients with high or low DSI values, accuracy reached 0.86 (all), 0.78 (aMCI), and 0.85 (naMCI). For patients with MRI or CSF biomarker data available, they were 0.78 (all), 0.76 (aMCI) and 0.76 (naMCI), while for clear cases the accuracies rose to 0.90 (all), 0.83 (aMCI) and 0.91 (naMCI). The results show that the DSI model can distinguish between clear and ambiguous cases, assess the severity of the disease and also provide information on the effectiveness of different biomarkers. While a specific test or biomarker may confound analysis for an individual patient, combining several different types of tests and biomarkers could be able to reveal the trajectory of the disease and improve the prediction of AD progression.
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| 6. |
- Hernberg, Micaela, et al.
(författare)
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The prognostic role of blood lymphocyte subset distribution in patients with resected high-risk primary or regionally metastatic melanoma.
- 2007
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Ingår i: Journal of immunotherapy (Hagerstown, Md. : 1997). - 1524-9557. ; 30:7, s. 773-9
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this study was to investigate whether the profile of peripheral blood lymphocyte subsets of patients with high-risk malignant melanoma is associated with prognosis. Blood samples were systematically obtained from 31 patients with high-risk melanoma eligible for the Nordic Melanoma Cooperative Group adjuvant interferon study. The frequencies of peripheral blood lymphocyte subsets were monitored by flow cytometry using CD3, CD4, CD8, CD56, and CD69 monoclonal antibodies. Patients with low proportions of CD3+CD4+CD69+ cells and of CD3+CD56+ cells before treatment had an improved disease-free survival compared to those with high proportions [77.7 vs. 16.8 mo, hazard ratio (HR) 0.25, confidence interval (CI) 0.09-0.71, P=0.005 and 77.2 vs. 16.0 mo, HR: 0.25, CI 0.086-0.73, P=0.001, respectively]. Low pretreatment levels of these cell populations also correlated with a better overall survival (79.2 vs. 22.6 mo, HR: 0.17, CI 0.05-0.52, P=0.0005 and 78.2 vs. 21.4 mo, HR: 0.2, CI 0.07-0.59, P=0.001, respectively). In the multivariate analysis both the pretreatment proportion of CD3+CD4+CD69+ cells (P=0.01, HR: 0.21, CI 0.07-0.67) and CD3+CD56+ cells (P=0.01, HR: 0.22, CI 0.062-0.65) were independent prognostic factors for overall survival. Our data show that both the proportions of CD3+CD4+CD69+ cells and of CD3+CD56+ cells seem to have a prognostic potential in the natural course of melanoma. These results need to be confirmed in larger studies.
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| 7. |
- Lappalainen, Raimo, et al.
(författare)
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The effectiveness and applicability of different lifestyle interventions for enhancing wellbeing : the study design for a randomized controlled trial for persons with metabolic syndrome risk factors and psychological distress.
- 2014
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Ingår i: BMC Public Health. - : BioMed Central. - 1471-2458. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Obesity and stress are among the most common lifestyle-related health problems. Most of the current disease prevention and management models are not satisfactorily cost-effective and hardly reach those who need them the most. Therefore, novel evidence-based controlled interventions are necessary to evaluate models for prevention and treatment based on self-management. This randomized controlled trial examines the effectiveness, applicability, and acceptability of different lifestyle interventions with individuals having symptoms of metabolic syndrome and psychological distress. The offered interventions are based on cognitive behavioral approaches, and are designed for enhancing general well-being and supporting personalized lifestyle changes.METHODS/DESIGN: 339 obese individuals reporting stress symptoms were recruited and randomized to either (1) a minimal contact web-guided Cognitive Behavioral Therapy-based (CBT) intervention including an approach of health assessment and coaching methods, (2) a mobile-guided intervention comprising of mindfulness, acceptance and value-based exercises, (3) a face-to-face group intervention using mindfulness, acceptance and value-based approach, or (4) a control group. The participants were measured three times during the study (pre = week 0, post = week 10, and follow-up = week 36). Psychological well-being, lifestyles and habits, eating behaviors, and user experiences were measured using online surveys. Laboratory measurements for physical well-being and general health were performed including e.g. liver function, thyroid glands, kidney function, blood lipids and glucose levels and body composition analysis. In addition, a 3-day ambulatory heart rate and 7-day movement data were collected for analyzing stress, recovery, physical activity, and sleep patterns. Food intake data were collected with a 48 -hour diet recall interview via telephone. Differences in the effects of the interventions would be examined using multiple-group modeling techniques, and effect-size calculations.DISCUSSION: This study will provide additional knowledge about the effects of three low intensity interventions for improving general well-being among individuals with obesity and stress symptoms. The study will show effects of two technology guided self-help interventions as well as effect of an acceptance and value-based brief group intervention. Those who might benefit from the aforesaid interventions will increase knowledge base to better understand what mechanisms facilitate effects of the interventions.TRIAL REGISTRATION: Current Clinical Trials NCT01738256, Registered 17 August, 2012.
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| 8. |
- Lindfors, Erno, et al.
(författare)
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Heterogeneous biological network visualization system : case study in context of medical image data
- 2012
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Ingår i: 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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Tidskriftsartikel (refereegranskat)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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| 9. |
- Luikku, Antti J., et al.
(författare)
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Multimodal analysis to predict shunt surgery outcome of 284 patients with suspected idiopathic normal pressure hydrocephalus
- 2016
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Ingår i: Acta Neurochirurgica. - : Springer Science and Business Media LLC. - 0001-6268 .- 0942-0940. ; 158:12, s. 2311-2319
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Tidskriftsartikel (refereegranskat)abstract
- Optimal selection of idiopathic normal pressure hydrocephalus (iNPH) patients for shunt surgery is challenging. Disease State Index (DSI) is a statistical method that merges multimodal data to assist clinical decision-making. It has previously been shown to be useful in predicting progression in mild cognitive impairment and differentiating Alzheimer's disease (AD) and frontotemporal dementia. In this study, we use the DSI method to predict shunt surgery response for patients with iNPH. In this retrospective cohort study, a total of 284 patients (230 shunt responders and 54 non-responders) from the Kuopio NPH registry were analyzed with the DSI. Analysis included data from patients' memory disorder assessments, age, clinical symptoms, comorbidities, medications, frontal cortical biopsy, CT/MRI imaging (visual scoring of disproportion between Sylvian and suprasylvian subarachnoid spaces, atrophy of medial temporal lobe, superior medial subarachnoid spaces), APOE genotyping, CSF AD biomarkers, and intracranial pressure. Our analysis showed that shunt responders cannot be differentiated from non-responders reliably even with the large dataset available (AUC = 0.58). Prediction of the treatment response in iNPH is challenging even with our extensive dataset and refined analysis. Further research of biomarkers and indicators predicting shunt responsiveness is still needed.
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| 10. |
- Luikku, Antti J., et al.
(författare)
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Predicting Development of Alzheimer's Disease in Patients with Shunted Idiopathic Normal Pressure Hydrocephalus
- 2019
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Ingår i: Journal of Alzheimer's Disease. - 1387-2877 .- 1875-8908. ; 71:4, s. 1233-1243
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Tidskriftsartikel (refereegranskat)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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