Breathalyser-Based eHealth Data Suggest That Self-Reporting of Abstinence Is a Poor Outcome Measure for Alcohol Use Disorder Clinical Trials.

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?

Maximum Time Between Tests: A Digital Biomarker to Detect Therapy Compliance and Assess Schedule Quality in Measurement-Based eHealth Systems for Alcohol Use Disorder.

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.

Real-time Monitoring using a breathalyzer-based eHealth system can identify lapse/relapse patterns in alcohol use disorder Patients.

AIM: We introduce a new remote real-time breathalyzer-based method for monitoring and early identification of lapse/relapse patterns for alcohol use disorder (AUD) patients using a composite measure of sobriety, the Addiction Monitoring Index (AMI). METHODS: We constructed AMI from (a) obtained test results and (b) the pattern of ignored tests using data from the first 30 patients starting in the treatment arms of two on-going clinical trials. The patients performed 2-4 scheduled breath alcohol content (BrAC)-tests per day presented as blood alcohol content (BAC) data. In total, 10,973 tests were scheduled, 7743 were performed and 3230 were ignored during 3982 patient days. RESULTS: AMI-time profiles could be used to monitor the daily trends of alcohol consumption and detect early signs of lapse and relapses. The pattern of ignored tests correlates with the onset of drinking. AMI correlated with phosphatidyl ethanol (n = 61, F-ratio = 34.6, P < 0.0001, R = -0.61). The recognition of secret drinking could further be improved using a low alcohol detection threshold (BrAC = 0.025 mg/l, BACSwe = 0.05‰ or US = 0.0053g/dl), in addition to the legal Swedish traffic limit (BrAC = 0.1 mg/l, BACSwe = 0.2‰ or US = 0.021 g/dl). Nine out of 10 patients who dropped out from the study showed early risk signs as reflected in the level and pattern in AMI before the actual dropout. CONCLUSIONS: AMI-time profiles from an eHealth system are useful for monitoring the recovery process and for early identification of lapse/relapse patterns. High-resolution monitoring of sobriety enables new measurement-based treatment methods for proactive personalized long-term relapse prevention and treatment of AUD patients. CLINICAL TRIAL REGISTRATION: The data used for construction of AMI was from two clinical trials approved by the Regional Ethics Committee of Uppsala, Sweden and performed in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participating subjects. The study was registered at ClinicalTrials.gov (NCT03195894).

Uncovering effects of ex vivo protease activity during proteomics and peptidomics sample extraction in rat brain tissue by oxygen-18 labeling.

In biological samples, proteins and peptides are altered by proteolytic activity. The actual ex vivo form of the peptidome or proteome analyzed, therefore, does not always reflect the natural in vivo state. Sample stabilization and sample treatment are thereby decisive for how far these two states diverge. To assess ex vivo formation of peptides, we used enzymatic incorporation of oxygen-18 water during proteolysis (PALeO approach) to label ex-vivo-formed peptides in rodent brain tissue. Rates of ex-vivo-formed peptides were determined in 25 samples that were stabilized and treated by six different protocols, whereby samples were subjected to different conditions such as temperature, urea concentration, and duration of treatment. Samples were measured by nano LC-Orbitrap-MS, and incorporation of oxygen-18 was determined by MS/MS database search and analysis of the precursor isotope pattern. Extent of ex vivo degradations was affected relevantly by the sample treatment protocol applied and stopped almost completely by heat stabilization. Determination of the formation state by oxygen-18 incorporation by MS/MS database search correlated well to more elaborate analysis of the MS isotope pattern. Overall, oxygen-18 labeling in combination with shotgun data-acquisition and MS/MS database search offers an adjuvant and easily applicable tool to monitor sample quality and fidelity in peptide and neuropeptide sample preparations.

Impact of temperature dependent sampling procedures in proteomics and peptidomics--a characterization of the liver and pancreas post mortem degradome.

Little is known about the nature of post mortem degradation of proteins and peptides on a global level, the so-called degradome. This is especially true for nonneural tissues. Degradome properties in relation to sampling procedures on different tissues are of great importance for the studies of, for instance, post translational modifications and/or the establishment of clinical biobanks. Here, snap freezing of fresh (<2 min post mortem time) mouse liver and pancreas tissue is compared with rapid heat stabilization with regard to effects on the proteome (using two-dimensional differential in-gel electrophoresis) and peptidome (using label free liquid chromatography). We report several proteins and peptides that exhibit heightened degradation sensitivity, for instance superoxide dismutase in liver, and peptidyl-prolyl cis-trans isomerase and insulin C-peptides in pancreas. Tissue sampling based on snap freezing produces a greater amount of degradation products and lower levels of endogenous peptides than rapid heat stabilization. We also demonstrate that solely snap freezing related degradation can be attenuated by subsequent heat stabilization. We conclude that tissue sampling involving a rapid heat stabilization step is preferable to freezing with regard to proteomic and peptidomic sample quality.

The Clinical Course of Alcohol Use Disorder Depicted by Digital Biomarkers.

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.

Post-sampling release of free fatty acids - effects of heat stabilization and methods of euthanasia.

INTRODUCTION: The field of lipid research has made progress and it is now possible to study the lipidome of cells and organelles. A basic requirement of a successful lipid study is adequate pre-analytical sample handling, as some lipids can be unstable and postmortem changes can cause substantial accumulation of free fatty acids (FFAs). METHODS: The aim of the present study was to investigate the effects of conductive heat stabilization and euthanasia methods on FFA levels in the rat brain and liver using liquid chromatography tandem mass spectrometry. RESULTS: The analysis of brain homogenates clearly demonstrated phospholipase activity and time-dependent post-sampling changes in the lipid pool of snap frozen non-stabilized tissue. There was a significant increase in FFAs already at 2min, which continued over time. Heat stabilization was shown to be an efficient method to reduce phospholipase activity and ex vivo lipolysis. Post-sampling effects due to tissue thawing and sample preparation induced a massive release of FFAs (up to 3700%) from non-stabilized liver and brain tissues compared to heat stabilized tissue. Furthermore, the choice of euthanasia method significantly influenced the levels of FFAs in the brain. The FFAs were decreased by 15-44% in the group of animals euthanized by pentobarbital injection compared with CO2 inhalation or decapitation. DISCUSSION: Our results highlight the importance of considering euthanasia methods and pre-analytical treatment in lipid analysis, factors which may otherwise interfere with the outcome of the experiments.