| 11. |
- Din, Lennox, et al.
(author)
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Genetic overlap between autoimmune diseases and non-Hodgkin lymphoma subtypes
- 2019
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In: Genetic Epidemiology. - : WILEY. - 0741-0395 .- 1098-2272. ; 43:7, s. 844-863
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Journal article (peer-reviewed)abstract
- Epidemiologic studies show an increased risk of non-Hodgkin lymphoma (NHL) in patients with autoimmune disease (AD), due to a combination of shared environmental factors and/or genetic factors, or a causative cascade: chronic inflammation/antigen-stimulation in one disease leads to another. Here we assess shared genetic risk in genome-wide-association-studies (GWAS). Secondary analysis of GWAS of NHL subtypes (chronic lymphocytic leukemia, diffuse large B-cell lymphoma, follicular lymphoma, and marginal zone lymphoma) and ADs (rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis). Shared genetic risk was assessed by (a) description of regional genetic of overlap, (b) polygenic risk score (PRS), (c)"diseasome", (d)meta-analysis. Descriptive analysis revealed few shared genetic factors between each AD and each NHL subtype. The PRS of ADs were not increased in NHL patients (nor vice versa). In the diseasome, NHLs shared more genetic etiology with ADs than solid cancers (p =.0041). A meta-analysis (combing AD with NHL) implicated genes of apoptosis and telomere length. This GWAS-based analysis four NHL subtypes and three ADs revealed few weakly-associated shared loci, explaining little total risk. This suggests common genetic variation, as assessed by GWAS in these sample sizes, may not be the primary explanation for the link between these ADs and NHLs.
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| 12. |
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| 13. |
- Grotz, Wolfgang, et al.
(author)
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Sleep apnea - Treatment improves hypertension
- 2006
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In: Medizinische Klinik. - : Springer Science and Business Media LLC. - 0723-5003 .- 1615-6722. ; 101:11, s. 880-885
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Research review (peer-reviewed)
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| 14. |
- Haugg, Amelie, et al.
(author)
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Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?
- 2020
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In: Human Brain Mapping. - : Wiley. - 1065-9471 .- 1097-0193. ; 41:14, s. 3839-3854
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Journal article (peer-reviewed)abstract
- Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.
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| 15. |
- Haugg, Amelie, et al.
(author)
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Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis.
- 2021
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In: NeuroImage. - : Elsevier. - 1053-8119 .- 1095-9572. ; 237
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Journal article (peer-reviewed)abstract
- Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.
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| 16. |
- Hermann, Silke, et al.
(author)
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Level of education and the risk of lymphoma in the European prospective investigation into cancer and nutrition
- 2010
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In: Journal of Cancer Research and Clinical Oncology. - : Springer Science and Business Media LLC. - 1432-1335 .- 0171-5216. ; 136:1, s. 71-77
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Journal article (peer-reviewed)abstract
- Lymphomas belong to the few cancer sites with increasing incidence over past decades, and only a few risk factors have been established. We explored the association between education and the incidence of lymphoma in the prospective EPIC study. Within 3,567,410 person-years of follow-up, 1,319 lymphoma cases [1,253 non-Hodgkin lymphomas (NHL) and 66 Hodgkin lymphomas (HL)] were identified. Cox proportional hazard regression was used to examine the association between highest educational level (primary school or less, technical/professional school, secondary school, university) and lymphoma risk. Overall, no consistent associations between educational level and lymphoma risk were observed; however, associations were found for sub-groups of the cohort. We observed a higher risk of B-NHL (HR = 1.31, 95% CI = 1.02-1.68; n = 583) in women with the highest education level (university) but not in men. Concerning sub-classes of B-NHL, a positive association between education and risk of B cell chronic lymphatic leukaemia (BCLL) was observed only in women. In both genders, the risk of diffuse large B cell lymphoma (DLBCL) was significantly lower for subjects with university degree (HR = 0.46, 95% CI = 0.27-0.79) versus lowest educational level. No association was found for HL. We could not confirm an overall consistent association of education and risk of HL or NHL in this large prospective study; although, education was positively related to the incidence of BCLL and B-NHL (in women) but inversely to incidence of DLBCL. Due to limited number of cases in sub-classes and the large number of comparisons, the possibility of chance findings can not be excluded.
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| 17. |
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| 18. |
- Machiela, Mitchell J., et al.
(author)
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Genetically predicted longer telomere length is associated with increased risk of B-cell lymphoma subtypes
- 2016
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In: Human Molecular Genetics. - : Oxford University Press (OUP). - 0964-6906 .- 1460-2083. ; 25:8, s. 1663-1676
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Journal article (peer-reviewed)abstract
- Evidence from a small number of studies suggests that longer telomere length measured in peripheral leukocytes is associated with an increased risk of non-Hodgkin lymphoma (NHL). However, these studies may be biased by reverse causation, confounded by unmeasured environmental exposures and might miss time points for which prospective telomere measurement would best reveal a relationship between telomere length and NHL risk. We performed an analysis of genetically inferred telomere length and NHL risk in a study of 10 102 NHL cases of the four most common B-cell histologic types and 9562 controls using a genetic risk score (GRS) comprising nine telomere length-associated single-nucleotide polymorphisms. This approach uses existing genotype data and estimates telomere length by weighing the number of telomere length-associated variant alleles an individual carries with the published change in kb of telomere length. The analysis of the telomere length GRS resulted in an association between longer telomere length and increased NHL risk [four B-cell histologic types combined; odds ratio (OR) = 1.49, 95% CI 1.22-1.82, P-value = 8.5 x 10(-5)]. Subtype-specific analyses indicated that chronic lymphocytic leukemia or small lymphocytic lymphoma (CLL/SLL) was the principal NHL subtype contributing to this association (OR = 2.60, 95% CI 1.93-3.51, P-value = 4.0 x 10(-10)). Significant interactions were observed across strata of sex for CLL/SLL and marginal zone lymphoma subtypes as well as age for the follicular lymphoma subtype. Our results indicate that a genetic background that favors longer telomere length may increase NHL risk, particularly risk of CLL/SLL, and are consistent with earlier studies relating longer telomere length with increased NHL risk.
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| 19. |
- Moore, Amy, et al.
(author)
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Genetically Determined Height and Risk of Non-hodgkin Lymphoma
- 2020
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In: Frontiers in Oncology. - : FRONTIERS MEDIA SA. - 2234-943X. ; 9
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Journal article (peer-reviewed)abstract
- Although the evidence is not consistent, epidemiologic studies have suggested that taller adult height may be associated with an increased risk of some non-Hodgkin lymphoma (NHL) subtypes. Height is largely determined by genetic factors, but how these genetic factors may contribute to NHL risk is unknown. We investigated the relationship between genetic determinants of height and NHL risk using data from eight genome-wide association studies (GWAS) comprising 10,629 NHL cases, including 3,857 diffuse large B-cell lymphoma (DLBCL), 2,847 follicular lymphoma (FL), 3,100 chronic lymphocytic leukemia (CLL), and 825 marginal zone lymphoma (MZL) cases, and 9,505 controls of European ancestry. We evaluated genetically predicted height by constructing polygenic risk scores using 833 height-associated SNPs. We used logistic regression to estimate odds ratios (OR) and 95% confidence intervals (CI) for association between genetically determined height and the risk of four NHL subtypes in each GWAS and then used fixed-effect meta-analysis to combine subtype results across studies. We found suggestive evidence between taller genetically determined height and increased CLL risk (OR = 1.08, 95% CI = 1.00-1.17, p = 0.049), which was slightly stronger among women (OR = 1.15, 95% CI: 1.01-1.31, p = 0.036). No significant associations were observed with DLBCL, FL, or MZL. Our findings suggest that there may be some shared genetic factors between CLL and height, but other endogenous or environmental factors may underlie reported epidemiologic height associations with other subtypes.
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| 20. |
- Nieters, Alexandra, et al.
(author)
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Smoking and lymphoma risk in the european prospective investigation into cancer and nutrition
- 2008
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In: American Journal of Epidemiology. - : Oxford University Press (OUP). - 0002-9262 .- 1476-6256. ; 167:9, s. 1081-1089
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Journal article (peer-reviewed)abstract
- Lymphomas are one of the few cancers that have been increasing in incidence over the past decades. So far, only a few established risk factors have been identified, including immunosuppression and viral infections. Recent evidence suggests etiologic heterogeneity of different lymphoma subtypes. Smoking may affect risk differently, depending on the lymphoma entity. The European Prospective Investigation into Cancer and Nutrition was used to study the role of smoking in the etiology of lymphomas and individual subtypes within a prospective study. Information on baseline and lifetime tobacco smoking by 478,590 participants was collected between 1992 and 2000. Cox proportional hazards regression was used to calculate multivariate-adjusted hazard ratios and 95% confidence intervals. During 3,567,410 person-years of follow-up, 1,371 lymphoma cases (1,304 non-Hodgkin's lymphomas and 67 Hodgkin's lymphomas) were identified. Relative risk for smokers at recruitment was more than twofold higher for Hodgkin's lymphoma (hazard ratio = 2.14, 95% confidence interval: 1.18, 3.87) but was not elevated for non-Hodgkin's lymphoma (hazard ratio = 1.06, 95% confidence interval: 0.94, 1.19) and individual B-cell non-Hodgkin's lymphoma subtypes. In this prospective study, smoking appeared to increase Hodgkin's lymphoma risk consistently in both genders, whereas B-cell non-Hodgkin's lymphoma risk was not associated. Future analysis should involve viral biomarkers and genetic susceptibility markers to elucidate potential mechanisms of smoking-induced carcinogenesis, particularly for Hodgkin's lymphoma.
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