ORCID Profile
0000-0002-5714-0288
Current Organisation
Norwegian University of Science and Technology
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Publisher: Public Library of Science (PLoS)
Date: 31-07-2013
Publisher: JMIR Publications Inc.
Date: 25-12-2022
Abstract: atients with substance use disorders (SUDs) are at increased risk for symptom deterioration following treatment, with up to 60% resuming substance use within the first year posttreatment. Substance use craving together with cognitive and mental health variables play important roles in the understanding of the trajectories from abstinence to substance use. his prospective observational feasibility study aims to improve our understanding of specific profiles of variables explaining SUD symptom deterioration, in particular, how in idual variability in mental health, cognitive functioning, and smartphone use is associated with craving and substance use in a young adult clinical population. n this pilot study, 26 patients with SUDs were included at about 2 weeks prior to discharge from inpatient SUD treatment from 3 different treatment facilities in Norway. Patients underwent baseline neuropsychological and mental health assessments they were equipped with smartwatches and they downloaded an app for mobile sensor data collection in their smartphones. Every 2 days for up to 8 weeks, the patients were administered mobile ecological momentary assessments (EMAs) to evaluate substance use, craving, mental health, cognition, and a mobile Go/NoGo performance task. Repeated EMAs as well as the smartphone’s battery use data were averaged across all days per in idual and used as candidate input variables together with the baseline measures in models of craving intensity and the occurrence of any substance use episodes. total of 455 momentary assessments were completed out of a potential maximum of 728 assessments. Using EMA and baseline data as candidate input variables and craving and substance use as responses, model selection identified mean craving intensity as the most important predictor of having one or more substance use episodes and with variabilities in self-reported impulsivity, mental health, and battery use as significant explanatory variables of craving intensity. his prospective observational feasibility study adds novelty by collecting high-intensity data for a considerable period of time, including mental health data, mobile cognitive assessments, and mobile sensor data. Our study also contributes to our knowledge about a clinical population with the most severe SUD presentations in a vulnerable period during and after discharge from inpatient treatment. We confirmed the importance of variability in cognitive function and mood in explaining variability in craving and that smartphone usage may possibly add to this understanding. Further, we found that craving intensity is an important explanatory variable in understanding substance use episodes. >
Publisher: JMIR Publications Inc.
Date: 23-06-2023
DOI: 10.2196/45254
Abstract: Patients with substance use disorders (SUDs) are at increased risk for symptom deterioration following treatment, with up to 60% resuming substance use within the first year posttreatment. Substance use craving together with cognitive and mental health variables play important roles in the understanding of the trajectories from abstinence to substance use. This prospective observational feasibility study aims to improve our understanding of specific profiles of variables explaining SUD symptom deterioration, in particular, how in idual variability in mental health, cognitive functioning, and smartphone use is associated with craving and substance use in a young adult clinical population. In this pilot study, 26 patients with SUDs were included at about 2 weeks prior to discharge from inpatient SUD treatment from 3 different treatment facilities in Norway. Patients underwent baseline neuropsychological and mental health assessments they were equipped with smartwatches and they downloaded an app for mobile sensor data collection in their smartphones. Every 2 days for up to 8 weeks, the patients were administered mobile ecological momentary assessments (EMAs) to evaluate substance use, craving, mental health, cognition, and a mobile Go/NoGo performance task. Repeated EMAs as well as the smartphone’s battery use data were averaged across all days per in idual and used as candidate input variables together with the baseline measures in models of craving intensity and the occurrence of any substance use episodes. A total of 455 momentary assessments were completed out of a potential maximum of 728 assessments. Using EMA and baseline data as candidate input variables and craving and substance use as responses, model selection identified mean craving intensity as the most important predictor of having one or more substance use episodes and with variabilities in self-reported impulsivity, mental health, and battery use as significant explanatory variables of craving intensity. This prospective observational feasibility study adds novelty by collecting high-intensity data for a considerable period of time, including mental health data, mobile cognitive assessments, and mobile sensor data. Our study also contributes to our knowledge about a clinical population with the most severe SUD presentations in a vulnerable period during and after discharge from inpatient treatment. We confirmed the importance of variability in cognitive function and mood in explaining variability in craving and that smartphone usage may possibly add to this understanding. Further, we found that craving intensity is an important explanatory variable in understanding substance use episodes.
Publisher: Oxford University Press (OUP)
Date: 12-2009
Publisher: American Physiological Society
Date: 03-2008
DOI: 10.1152/PHYSIOLGENOMICS.00269.2007
Abstract: Aerobic capacity is a strong predictor of cardiovascular mortality. To determine the relationship between inborn aerobic capacity and cardiac gene expression we examined genome-wide gene expression in hearts of rats artificially selected for high and low running capacity (HCR and LCR, respectively) over 16 generations. The artificial selection of LCR caused accumulation of risk factors of cardiovascular disease similar to the metabolic syndrome seen in human, whereas HCR had markedly better cardiac function. We also studied alterations in gene expression in response to exercise training in these animals. Left ventricle gene expression of both sedentary and exercise-trained HCR and LCR was characterized by microarray and gene ontology analysis. Out of 28,000 screened genes, 1,540 were differentially expressed between sedentary HCR and LCR. Only one gene was found differentially expressed by exercise training, but this gene had unknown name and function. Sedentary HCR expressed higher amounts of genes involved in lipid metabolism, whereas sedentary LCR expressed higher amounts of the genes involved in glucose metabolism. This suggests a switch in cardiac energy substrate utilization from normal mitochondrial fatty acid β-oxidation in HCR to carbohydrate metabolism in LCR, an event that often occurs in diseased hearts. LCR were also associated with pathological growth signaling and cellular stress. Hypoxic conditions seemed to be a common source for several of these observations, triggering hypoxia-induced alterations of transcription. In conclusion, inborn high vs. low aerobic capacity was associated with differences in cardiac energy substrate, growth signaling, and cellular stress.
Publisher: American Physiological Society
Date: 11-2008
DOI: 10.1152/PHYSIOLGENOMICS.90282.2008
Abstract: The relationship between inborn maximal oxygen uptake (VO 2max ) and skeletal muscle gene expression is unknown. Since low VO 2max is a strong predictor of cardiovascular mortality, genes related to low VO 2max might also be involved in cardiovascular disease. To establish the relationship between inborn VO 2max and gene expression, we performed microarray analysis of the soleus muscle of rats artificially selected for high- and low running capacity (HCR and LCR, respectively). In LCR, a low VO 2max was accompanied by aggregation of cardiovascular risk factors similar to the metabolic syndrome. Although sedentary HCR were able to maintain a 120% higher running speed at VO 2max than sedentary LCR, only three transcripts were differentially expressed (FDR ≤ 0.05) between the groups. Sedentary LCR expressed high levels of a transcript with strong homology to human leucyl-transfer RNA synthetase, of whose overexpression has been associated with a mutation linked to mitochondrial dysfunction. Moreover, we studied exercise-induced alterations in soleus gene expression, since accumulating evidence indicates that long-term endurance training has beneficial effects on the metabolic syndrome. In terms of gene expression, the response to exercise training was more pronounced in HCR than LCR. HCR upregulated several genes associated with lipid metabolism and fatty acid elongation, whereas LCR upregulated only one transcript after exercise training. The results indicate only minor differences in soleus muscle gene expression between sedentary HCR and LCR. However, the inborn level of fitness seems to influence the transcriptional adaption to exercise, as more genes were upregulated after exercise training in HCR than LCR.
Publisher: Public Library of Science (PLoS)
Date: 14-03-2023
DOI: 10.1371/JOURNAL.PCBI.1010963
Abstract: Estimating feature importance, which is the contribution of a prediction or several predictions due to a feature, is an essential aspect of explaining data-based models. Besides explaining the model itself, an equally relevant question is which features are important in the underlying data generating process. We present a Shapley-value-based framework for inferring the importance of in idual features, including uncertainty in the estimator. We build upon the recently published model-agnostic feature importance score of SAGE (Shapley additive global importance) and introduce Sub-SAGE. For tree-based models, it has the advantage that it can be estimated without computationally expensive res ling. We argue that for all model types the uncertainties in our Sub-SAGE estimator can be estimated using bootstrapping and demonstrate the approach for tree ensemble methods. The framework is exemplified on synthetic data as well as large genotype data for predicting feature importance with respect to obesity.
Publisher: Cold Spring Harbor Laboratory
Date: 16-05-2020
DOI: 10.1101/2020.05.13.20100149
Abstract: The identification of gene-gene and gene-environment interactions in genome-wide association studies is challenging due to the unknown nature of the interactions and the overwhelmingly large number of possible combinations. Classical logistic regression models are suitable to look for pre-defined interactions while more complex models, such as tree ensemble models, with the ability to detect any interactions have previously been difficult to interpret. However, with the development of methods for model explainability, it is now possible to interpret tree ensemble models with a strong theoretical ground and efficiently. We propose a tree ensemble- and SHAP-based method for identifying as well as interpreting both gene-gene and gene-environment interactions on large-scale biobank data. A set of independent cross-validation runs are used to implicitly investigate the whole genome. We apply and evaluate the method using data from the UK Biobank with obesity as the phenotype. The results are in line with previous research on obesity as we identify top SNPs previously associated with obesity. We further demonstrate how to interpret and visualize interactions. The analysis suggests that the new method finds interactions between features that logistic regression models have difficulties in detecting. The new method robustly detects interesting interactions, and can be applied to large-scale biobanks with high-dimensional data.
No related grants have been discovered for Mette Langaas.