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- Behzadi, Arvin, 1994-
(author)
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Biomarkers for diagnosis and prognosis in amyotrophic lateral sclerosis
- 2024
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Doctoral thesis (other academic/artistic)abstract
- Background: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder characterized by loss of upper and lower motor neurons, leading to paresis, muscle atrophy, and respiratory failure. ALS can be difficult to diagnose and prognosticate early.Aim: To investigate the diagnostic and prognostic characteristics of biomarkers in cerebrospinal fluid (CSF), plasma, and skeletal muscle tissue in patients with ALS.Paper I: Neurofilament light chain (NFL) and phosphorylated neurofilament heavy chain (pNFH) were analyzed in CSF using enzyme-linked immunosorbent assay (ELISA), and NFL in plasma was analyzed using single-molecule array (SIMOA). CSF NFL, CSF pNFH, and plasma NFL concentrations can differentiate ALS patients from ALS mimics, and were significantly negatively correlated with the disease duration in ALS patients.Paper II: Myosin heavy chain (MyHC) isoforms in extraocular muscles were investigated using immunofluorescence. Control donors had significantly higher proportion of myofibers containing MyHCIIa and significantly lower proportion of myofibers containing MyHCeom in the global layer compared to spinal-onset ALS and bulbar-onset ALS donors. Disease duration in the spinal-onset ALS donors was significantly correlated with the proportion of myofibers containing MyHCIIa in the global layer and MyHCeom in the orbital layer.Paper III: The study combined the neurofilament concentrations from Paper I, with cytokines previously analyzed in CSF and plasma using SIMOA, to investigate distinct molecular phenotypes in ALS. Patients with bulbar-onset ALS had significantly higher concentrations of CSF tumor necrosis factor α (TNF-α) compared to ALS mimics. TNF-α and NFL were significantly correlated with each other in both CSF and plasma in ALS patients. Combined analysis of NFL and IL-6 in plasma identified molecular prognostic subgroups in ALS patients.Paper IV: Creatine kinase (CK), high-sensitivity cardiac troponin T (hs-cTnT), hs-cTnI, and cystatin C (CysC) were analyzed in plasma in a fully accredited laboratory. CK and hs-cTnT concentrations were significantly elevated in limb-onset ALS compared to controls and bulbar-onset ALS. hs-cTnT concentrations were significantly elevated in truncal-onset ALS compared to controls and bulbar-onset ALS. Multivariable Cox proportional hazards models indicated elevated concentrations of CysC as a significant marker for worse prognosis in ALS.Conclusions: The papers report diagnostic and prognostic characteristics of biomarkers in CSF, plasma, and muscle tissue in ALS patients. The significant findings for biomarkers in plasma could be of value since plasma sampling does not involve a lumbar puncture.
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- Bergh, Johan, 1983-, et al.
(author)
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Structural and kinetic analysis of protein-aggregate strains in vivo using binary epitope mapping
- 2015
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In: Proceedings of the National Academy of Sciences of the United States of America. - : National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 112:14, s. 4489-4494
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Journal article (peer-reviewed)abstract
- Despite considerable progress in uncovering the molecular details of protein aggregation in vitro, the cause and mechanism of protein-aggregation disease remain poorly understood. One reason is that the amount of pathological aggregates in neural tissue is exceedingly low, precluding examination by conventional approaches. We present here a method for determination of the structure and quantity of aggregates in small tissue samples, circumventing the above problem. The method is based on binary epitope mapping using anti-peptide antibodies. We assessed the usefulness and versatility of the method in mice modeling the neurodegenerative disease amyotrophic lateral sclerosis, which accumulate intracellular aggregates of superoxide dismutase-1. Two strains of aggregates were identified with different structural architectures, molecular properties, and growth kinetics. Both were different from superoxide dismutase-1 aggregates generated in vitro under a variety of conditions. The strains, which seem kinetically under fragmentation control, are associated with different disease progressions, complying with and adding detail to the growing evidence that seeding, infectivity, and strain dependence are unifying principles of neurodegenerative disease.
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- Hamalainen, Markku D., et al.
(author)
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Breathalyser-Based eHealth Data Suggest That Self-Reporting of Abstinence Is a Poor Outcome Measure for Alcohol Use Disorder Clinical Trials
- 2020
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In: Alcohol and Alcoholism. - : Oxford University Press (OUP). - 0735-0414 .- 1464-3502. ; 55:3, s. 237-245
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Research review (peer-reviewed)abstract
- Aims: To evaluate the efficacy and monitoring capabilities of a breathalyser-based eHealth system for patients with alcohol use disorder (AUD) and to investigate the quality and validity of timeline follow-back (TLFB) as outcome measure in clinical trials and treatment.Methods: Patients (n = 115) were recruited to clinical trials from a 12-step aftercare programme (12S-ABS) and from hospital care with abstinence (HC-ABS) or controlled drinking (HC-CDR) as goal and randomly divided into an eHealth and a control group. The effect of the eHealth system was analysed with TLFB-derived primary outcomes-change in number of abstinent days (AbsDay) and heavy drinking days (HDDs) compared to baseline-and phosphatidyl ethanol (PEth) measurements. Validity and quality of TLFB were evaluated by comparison with breath alcohol content (BrAC) and eHealth digital biomarkers (DBs): Addiction Monitoring Index (AMI) and Maximum Time Between Tests (MTBT). TLFB reports were compared to eHealth data regarding reported abstinence.Results: The primary outcome (TLFB) showed no significant difference between eHealth and control groups, but PEth did show a significant difference especially at months 2 and 3. Self-reported daily abstinence suffered from severe quality issues: of the 28-day TLFB reports showing full abstinence eHealth data falsified 34% (BrAC measurements), 39% (MTBT), 54% (AMI) and 68% (BrAC/MTBT/AMI). 12S-ABS and HC-ABS patients showed severe under-reporting.Conclusions: No effect of the eHealth system was measured with TLFB, but a small positive effect was measured with PEth. The eHealth system revealed severe quality problems with TLFB, especially regarding abstinence-should measurement-based eHealth data replace TLFB as outcome measure for AUD?
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- Park, Julien H., et al.
(author)
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The motor system is exceptionally vulnerable to absence of the ubiquitously expressed superoxide dismutase-1
- 2023
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In: Brain Communications. - : Oxford University Press. - 2632-1297. ; 5:1
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Journal article (peer-reviewed)abstract
- Superoxide dismutase-1 is a ubiquitously expressed antioxidant enzyme. Mutations in SOD1 can cause amyotrophic lateral sclerosis, probably via a toxic gain-of-function involving protein aggregation and prion-like mechanisms. Recently, homozygosity for loss-of-function mutations in SOD1 has been reported in patients presenting with infantile-onset motor neuron disease. We explored the bodily effects of superoxide dismutase-1 enzymatic deficiency in eight children homozygous for the p.C112Wfs∗11 truncating mutation. In addition to physical and imaging examinations, we collected blood, urine and skin fibroblast samples. We used a comprehensive panel of clinically established analyses to assess organ function and analysed oxidative stress markers, antioxidant compounds, and the characteristics of the mutant Superoxide dismutase-1. From around 8 months of age, all patients exhibited progressive signs of both upper and lower motor neuron dysfunction, cerebellar, brain stem, and frontal lobe atrophy and elevated plasma neurofilament concentration indicating ongoing axonal damage. The disease progression seemed to slow down over the following years. The p.C112Wfs∗11 gene product is unstable, rapidly degraded and no aggregates were found in fibroblast. Most laboratory tests indicated normal organ integrity and only a few modest deviations were found. The patients displayed anaemia with shortened survival of erythrocytes containing decreased levels of reduced glutathione. A variety of other antioxidants and oxidant damage markers were within normal range. In conclusion, non-neuronal organs in humans show a remarkable tolerance to absence of Superoxide dismutase-1 enzymatic activity. The study highlights the enigmatic specific vulnerability of the motor system to both gain-of-function mutations in SOD1 and loss of the enzyme as in the here depicted infantile superoxide dismutase-1 deficiency syndrome.
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- Wallden, Mats, et al.
(author)
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Evaluation of 6 years of eHealth data in the alcohol use disorder field indicates improved efficacy of care
- 2024
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In: Frontiers in Digital Health. - : Frontiers Media S.A.. - 2673-253X. ; 5
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Journal article (peer-reviewed)abstract
- BackgroundPredictive eHealth tools will change the field of medicine, however long-term data is scarce. Here, we report findings on data collected over 6 years with an AI-based eHealth system for supporting the treatment of alcohol use disorder.MethodsSince the deployment of Previct Alcohol, structured data has been archived in a data warehouse, currently comprising 505,641 patient days. The frequencies of relapse and caregiver-patient messaging over time was studied. The effects of both introducing an AI-driven relapse prediction tool and the COVID-19 pandemic were analyzed.ResultsThe relapse frequency per patient day among Previct Alcohol users was 0.28 in 2016, 0.22 in 2020 and 0.25 in 2022 with no drastic change during COVID-19. When a relapse was predicted, the actual occurrence of relapse in the days immediately after was found to be above average. Additionally, there was a noticeable increase in caregiver interactions following these predictions. When caregivers were not informed of these predictions, the risk of relapse was found to be higher compared to when the prediction tool was actively being used. The prediction tool decreased the relapse risk by 9% for relapses that were of short duration and by 18% for relapses that lasted more than 3 days.ConclusionsThe eHealth system Previct Alcohol allows for high resolution measurements, enabling precise identifications of relapse patterns and follow up on individual and population-based alcohol use disorder treatment. eHealth relapse prediction aids the caregiver to act timely, which reduces, delays, and shortens relapses.
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| 8. |
- Zetterström, Andreas, et al.
(author)
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Maximum Time Between Tests : A Digital Biomarker to Detect Therapy Compliance and Assess Schedule Quality in Measurement-Based eHealth Systems for Alcohol Use Disorder
- 2019
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In: Alcohol and Alcoholism. - : OXFORD UNIV PRESS. - 0735-0414 .- 1464-3502. ; 54:1, s. 70-72
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Journal article (peer-reviewed)abstract
- Aim: To evaluate, in a breathalyzer-based eHealth system, whether the time-based digital biomarker maximum time between tests' (MTBT) brings valuable information on alcohol consumption patterns as confirmed by correlation with blood phosphatidyl ethanol (PEth), serum carbohydrate deficient transferrin (CDT) and timeline follow-back data.Method: Data on 54 patients in follow-up for treatment of alcohol use disorder were analysed.Results: The model of weekly averages of 24-log transformed MTBT adequately described timeline follow-back data (P < 0.0001, R = 0.27-0.38, n = 650). Significant correlations were noted between MTBT and PEth (P < 0.0001, R = 0.41, n = 148) and between MTBT and CDT (P < 0.0079, R = 0.22, n = 120).Conclusions: The time-based digital biomarker maximum time between tests' described here has the potential to become a generally useful metric for all scheduled measurement-based eHealth systems to monitor test behaviour and compliance, factors important for dosing' of eHealth systems and for early prediction and interventions of lapse/relapse.
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| 9. |
- Zetterström, Andreas, et al.
(author)
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Processing incomplete questionnaire data into continuous digital biomarkers for addiction monitoring
- 2022
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In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 17:7
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Journal article (peer-reviewed)abstract
- Purpose: eHealth systems allow efficient daily smartphone-based collection of self-reported data on mood, wellbeing, routines, and motivation; however, missing data is frequent. Within addictive disorders, missing data may reflect lack of motivation to stay sober. We hypothesize that qualitative questionnaire data contains valuable information, which after proper handling of missing data becomes more useful for practitioners.Methods: Anonymized data from daily questionnaires containing 11 questions was collected with an eHealth system for 751 patients with alcohol use disorder (AUD). Two digital continuous biomarkers were composed from 9 wellbeing questions (WeBe-i) and from two questions representing motivation/self-confidence to remain sober (MotSC-i). To investigate possible loss of information in the process of composing the digital biomarkers, performance of neural networks to predict exacerbation events (relapse) in alcohol use disorder was compared.Results: Long short-term memory (LSTM) neural networks predicted a coming exacerbation event 1-3 days (AUC 0.68-0.70) and 5-7 days (AUC 0.65-0.68) in advance on unseen patients. The predictive capability of digital biomarkers and raw questionnaire data was equal, indicating no loss of information. The transformation into digital biomarkers enable a continuous graphical display of each patient's clinical course and a combined interpretation of qualitative and quantitative aspects of recovery on a time scale.Conclusion: By transforming questionnaire data with large proportion of missing data into continuous digital biomarkers, the information captured by questionnaires can be more easily used in clinical practice. Information, assessed by the capability to predict exacerbation events of AUD, is preserved when processing raw questionnaire data into digital biomarkers.
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| 10. |
- Zetterström, Andreas, et al.
(author)
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The Clinical Course of Alcohol Use Disorder Depicted by Digital Biomarkers
- 2021
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In: Frontiers in Digital Health. - : Frontiers Media S.A.. - 2673-253X. ; 3
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Journal article (peer-reviewed)abstract
- Aims: This study introduces new digital biomarkers to be used as precise, objective tools to measure and describe the clinical course of patients with alcohol use disorder (AUD).Methods: An algorithm is outlined for the calculation of a new digital biomarker, the recovery and exacerbation index (REI), which describes the current trend in a patient's clinical course of AUD. A threshold applied to the REI identifies the starting point and the length of an exacerbation event (EE). The disease patterns and periodicity are described by the number, length, and distance between EEs. The algorithms were tested on data from patients from previous clinical trials (n = 51) and clinical practice (n = 1,717).Results: Our study indicates that the digital biomarker-based description of the clinical course of AUD might be superior to the traditional self-reported relapse/remission concept and conventional biomarkers due to higher data quality (alcohol measured) and time resolution. We found that EEs and the REI introduce distinct tools to identify qualitative and quantitative differences in drinking patterns (drinks per drinking day, phosphatidyl ethanol levels, weekday and holiday patterns) and effect of treatment time.Conclusions: This study indicates that the disease state-level, trend and periodicity-can be mathematically described and visualized with digital biomarkers, thereby improving knowledge about the clinical course of AUD and enabling clinical decision-making and adaptive care. The algorithms provide a basis for machine-learning-driven research that might also be applied for other disorders where daily data are available from digital health systems.
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