ORCID Profile
0000-0002-0031-4152
Current Organisations
UiT The Arctic University of Norway
,
University Hospital of North Norway
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Publisher: Springer Nature Switzerland
Date: 2022
Publisher: American Association for Cancer Research (AACR)
Date: 16-06-2022
DOI: 10.1158/1055-9965.EPI-21-1176
Abstract: Current epidemiologic evidence indicates that smoking is associated with a lower endometrial cancer risk. However, it is unknown if this association is causal or confounded. To further elucidate the role of smoking in endometrial cancer risk, we conducted complementary observational and Mendelian randomization (MR) analyses. The observational analyses included 286,415 participants enrolled in the European Prospective Investigation into Cancer and Nutrition and 179,271 participants in the UK Biobank, and multivariable Cox proportional hazards models were used. In two-s le MR analyses, genetic variants robustly associated with lifetime amount of smoking (n = 126 variants) and ever having smoked regularly (n = 112 variants) were selected and their association with endometrial cancer risk (12,906 cancer/108,979 controls from the Endometrial Cancer Association Consortium) was examined. In the observational analysis, lifetime amount of smoking and ever having smoked regularly were associated with a lower endometrial cancer risk. In the MR analysis accounting for body mass index, a genetic predisposition to a higher lifetime amount of smoking was not associated with endometrial cancer risk (OR per 1-SD increment: 1.15 95% confidence interval: 0.91–1.44). Genetic predisposition to ever having smoked regularly was not associated with risk of endometrial cancer. Smoking was inversely associated with endometrial cancer in the observational analyses, although unsupported by the MR. Additional studies are required to better understand the possible confounders and mechanisms underlying the observed associations between smoking and endometrial cancer. The results from this analysis indicate that smoking is unlikely to be causally linked with endometrial cancer risk.
Publisher: American Association for Cancer Research (AACR)
Date: 15-07-2022
DOI: 10.1158/1055-9965.EPI-21-1033
Abstract: Tobacco exposure causes 8 of 10 lung cancers, and identifying additional risk factors is challenging due to confounding introduced by smoking in traditional observational studies. We used Mendelian randomization (MR) to screen 207 metabolites for their role in lung cancer predisposition using independent genome-wide association studies (GWAS) of blood metabolite levels (n = 7,824) and lung cancer risk (n = 29,266 cases/56,450 controls). A nested case–control study (656 cases and 1,296 matched controls) was subsequently performed using prediagnostic blood s les to validate MR association with lung cancer incidence data from population-based cohorts (EPIC and NSHDS). An MR-based scan of 207 circulating metabolites for lung cancer risk identified that blood isovalerylcarnitine (IVC) was associated with a decreased odds of lung cancer after accounting for multiple testing (log10-OR = 0.43 95% CI, 0.29–0.63). Molar measurement of IVC in prediagnostic blood found similar results (log10-OR = 0.39 95% CI, 0.21–0.72). Results were consistent across lung cancer subtypes. Independent lines of evidence support an inverse association of elevated circulating IVC with lung cancer risk through a novel methodologic approach that integrates genetic and traditional epidemiology to efficiently identify novel cancer biomarkers. Our results find compelling evidence in favor of a protective role for a circulating metabolite, IVC, in lung cancer etiology. From the treatment of a Mendelian disease, isovaleric acidemia, we know that circulating IVC is modifiable through a restricted protein diet or glycine and L-carnatine supplementation. IVC may represent a modifiable and inversely associated biomarker for lung cancer.
Publisher: JMIR Publications Inc.
Date: 08-11-2021
Abstract: ardiovascular diseases, cancers, chronic respiratory diseases, and diabetes are the 4 main noncommunicable diseases. These noncommunicable diseases share 4 modifiable risk factors (tobacco use, harmful use of alcohol, physical inactivity, and unhealthy diet). Short smartphone surveys have the potential to identify modifiable risk factors for in iduals to monitor trends. e aimed to pilot a smartphone-based information communication technology solution to collect nationally representative data, annually, on 4 modifiable risk factors. e developed an information communication technology solution with functionalities for capturing sensitive data from smartphones, receiving, and handling data in accordance with general data protection regulations. The main survey comprised 26 questions: 8 on socioeconomic factors, 17 on the 4 risk factors, and 1 about current or previous noncommunicable diseases. For answers to the continuous questions, a keyboard was displayed for entering numbers there were preset upper and lower limits for acceptable response values. For categorical questions, pull-down menus with response options were displayed. The second survey comprised 9 yes-or-no questions. For both surveys, we used SMS text messaging. For the main survey, we invited 11,000 in iduals, aged 16 to 69 years, selected randomly from the Norwegian National Population Registry (1000 from each of the 11 counties). For the second survey, we invited a random s le of 100 in iduals from each county who had not responded to the main survey. All data, except county of residence, were self-reported. We calculated the distribution for socioeconomic background, tobacco use, diet, physical activity, and health condition factors overall and by sex. he response rate was 21.9% (2303/11,000 women: 1397/2263 61.7%, men: 866/2263, 38.3% missing: 40/2303, 1.7%). The median age for men was 52 years (IQR 40-61) the median age for women was 48 years (IQR 35-58). The main reported reason for nonparticipation in the main survey was that the sender of the initial SMS was unknown. e successfully developed and piloted a smartphone-based information communication technology solution for collecting data on the 4 modifiable risk factors for the 4 main noncommunicable diseases. Approximately 1 in 5 invitees responded thus, these data may not be nationally representative. The smartphone-based information communication technology solution should be further developed with the long-term goal to reduce premature mortality from the 4 main noncommunicable diseases.
Publisher: MDPI AG
Date: 07-02-2023
DOI: 10.20944/PREPRINTS202302.0117.V1
Abstract: Machine Learning (ML) methods have become important to enhance the performance of decision-support predictive models. However, class imbalance is one of the main challenges for developing ML models, because it limits the generalization of these models, and biases the learning algorithms. In this paper, we consider overs ling methods for generating synthetic categorical clinical data aiming to improve the predictive performance in ML models, and the identification of risk factors for cardiovascular diseases (CVDs). We performed a comparative study of several categorical synthetic data generation methods, including Generative Adversarial Networks (GANs). Then, we assessed the impact of combining overs ling strategies and linear and nonlinear supervised ML methods. Lastly, we conducted a post-hoc model interpretability based on the importance of the risk factors. Experimental results show the potential of GAN-based models for generating high-quality categorical synthetic data, yielding probability mass functions that are highly close to real data, maintaining relevant insights, and contributing to increase the predictive performance. The GAN-based model and a linear classifier outperforms other overs ling techniques, improving 2\\% the area under the curve. These results demonstrate the capability of synthetic data to help both in determining risk factors and building models for CVD prediction.
Publisher: American Association for Cancer Research (AACR)
Date: 10-2020
DOI: 10.1158/1055-9965.EPI-20-0354
Abstract: Epithelial ovarian, fallopian tube, and primary peritoneal cancers have shared developmental pathways. Few studies have prospectively examined heterogeneity in risk factor associations across these three anatomic sites. We identified 3,738 ovarian, 337 peritoneal, and 176 fallopian tube incident cancer cases in 891,731 women from 15 prospective cohorts in the Ovarian Cancer Cohort Consortium. Associations between 18 putative risk factors and risk of ovarian, peritoneal, and fallopian tube cancer, overall and for serous and high-grade serous tumors, were evaluated using competing risks Cox proportional hazards regression. Heterogeneity was assessed by likelihood ratio tests. Most associations did not vary by tumor site (Phet ≥ 0.05). Associations between first pregnancy (Phet = 0.04), tubal ligation (Phet = 0.01), and early-adult (age 18–21 years) body mass index (BMI Phet = 0.02) and risk differed between ovarian and peritoneal cancers. The association between early-adult BMI and risk further differed between peritoneal and fallopian tube cancer (Phet = 0.03). First pregnancy and tubal ligation were inversely associated with ovarian, but not peritoneal, cancer. Higher early-adult BMI was associated with higher risk of peritoneal, but not ovarian or fallopian tube, cancer. Patterns were generally similar when restricted to serous and high-grade serous cases. Ovarian, fallopian tube, and primary peritoneal cancers appear to have both shared and distinct etiologic pathways, although most risk factors appear to have similar associations by anatomic site. Further studies on the mechanisms underlying the differences in risk profiles may provide insights regarding the developmental origins of tumors arising in the peritoneal cavity and inform prevention efforts.
Publisher: E.U. European Publishing
Date: 31-10-2022
DOI: 10.18332/TPC/155287
Publisher: MDPI AG
Date: 23-03-2023
DOI: 10.3390/APP13074119
Abstract: Machine Learning (ML) methods have become important for enhancing the performance of decision-support predictive models. However, class imbalance is one of the main challenges for developing ML models, because it may bias the learning process and the model generalization ability. In this paper, we consider overs ling methods for generating synthetic categorical clinical data aiming to improve the predictive performance in ML models, and the identification of risk factors for cardiovascular diseases (CVDs). We performed a comparative study of several categorical synthetic data generation methods, including Synthetic Minority Overs ling Technique Nominal (SMOTEN), Tabular Variational Autoencoder (TVAE) and Conditional Tabular Generative Adversarial Networks (CTGANs). Then, we assessed the impact of combining overs ling strategies and linear and nonlinear supervised ML methods. Lastly, we conducted a post-hoc model interpretability based on the importance of the risk factors. Experimental results show the potential of GAN-based models for generating high-quality categorical synthetic data, yielding probability mass functions that are very close to those provided by real data, maintaining relevant insights, and contributing to increasing the predictive performance. The GAN-based model and a linear classifier outperform other overs ling techniques, improving the area under the curve by 2%. These results demonstrate the capability of synthetic data to help with both determining risk factors and building models for CVD prediction.
Publisher: JMIR Publications Inc.
Date: 25-02-2022
DOI: 10.2196/33636
Abstract: Cardiovascular diseases, cancers, chronic respiratory diseases, and diabetes are the 4 main noncommunicable diseases. These noncommunicable diseases share 4 modifiable risk factors (tobacco use, harmful use of alcohol, physical inactivity, and unhealthy diet). Short smartphone surveys have the potential to identify modifiable risk factors for in iduals to monitor trends. We aimed to pilot a smartphone-based information communication technology solution to collect nationally representative data, annually, on 4 modifiable risk factors. We developed an information communication technology solution with functionalities for capturing sensitive data from smartphones, receiving, and handling data in accordance with general data protection regulations. The main survey comprised 26 questions: 8 on socioeconomic factors, 17 on the 4 risk factors, and 1 about current or previous noncommunicable diseases. For answers to the continuous questions, a keyboard was displayed for entering numbers there were preset upper and lower limits for acceptable response values. For categorical questions, pull-down menus with response options were displayed. The second survey comprised 9 yes-or-no questions. For both surveys, we used SMS text messaging. For the main survey, we invited 11,000 in iduals, aged 16 to 69 years, selected randomly from the Norwegian National Population Registry (1000 from each of the 11 counties). For the second survey, we invited a random s le of 100 in iduals from each county who had not responded to the main survey. All data, except county of residence, were self-reported. We calculated the distribution for socioeconomic background, tobacco use, diet, physical activity, and health condition factors overall and by sex. The response rate was 21.9% (2303/11,000 women: 1397/2263 61.7%, men: 866/2263, 38.3% missing: 40/2303, 1.7%). The median age for men was 52 years (IQR 40-61) the median age for women was 48 years (IQR 35-58). The main reported reason for nonparticipation in the main survey was that the sender of the initial SMS was unknown. We successfully developed and piloted a smartphone-based information communication technology solution for collecting data on the 4 modifiable risk factors for the 4 main noncommunicable diseases. Approximately 1 in 5 invitees responded thus, these data may not be nationally representative. The smartphone-based information communication technology solution should be further developed with the long-term goal to reduce premature mortality from the 4 main noncommunicable diseases.
No related grants have been discovered for Inger Torhild Gram.