Biology subjects

Pedersen, N.

Publications and source records attributed to Pedersen, N..

3 recordsLinked to original sources

bioRxiv2018

The frailty index is a predictor of cause-specific mortality independent of familial effects from midlife onwards

BackgroundFrailty index (FI) is a well-established predictor of all-cause mortality, but less is known for cause-specific mortality and whether familial effects influence the associations. Furthermore, the population mortality impact of frailty remains understudied.\n\nObjectivesTo estimate the predictive value of frailty for all-cause and cause-specific mortality, and to test whether the associations are time-dependent. We also assessed the proportion of deaths that are attributable to increased levels of frailty.\n\nMethodsWe analyzed 42,953 participants from the Screening Across the Lifespan Twin Study (aged 41-95 years at baseline) with up to 20-years mortality follow-up. The FI was constructed using 44 health-related items. Deaths due to cardiovascular disease (CVD), respiratory-related causes and cancer were considered in the cause-specific analysis. Generalized survival models were used in the analysis.\n\nResultsIncreased FI was associated with higher risks of all-cause, CVD, and respiratory-related mortality. No significant associations were observed for cancer mortality. No attenuation of the mortality associations was found in unrelated individuals when adjusting for familial effects in twin pairs. The associations were time-dependent with relatively greater effects observed in younger ages. The proportion of deaths attributable to FI levels >0.10 were 13.0% of all-cause deaths, 14.7% of CVD deaths and 12.5% of respiratory-related deaths in men, and 12.2% of all-cause deaths, 9.9% of CVD deaths and 21.9% of respiratory-related deaths in women.\n\nConclusionsIncreased FI predicts higher risks of all-cause, CVD, and respiratory-related mortality independent of familial effects. Increased FI levels have a significant population mortality impact in both men and women.

epidemiology↗

bioRxiv2018

Single Trial Decoding of Scalp EEG Under Naturalistic Stimuli

There is significant current interest in decoding mental states from electro-encephalography (EEG) recordings. EEG signals are subject-specific, sensitive to disturbances, and have a low signal-to-noise ratio, which has been mitigated by the use of laboratory-grade EEG acquisition equipment under highly controlled conditions. In the present study, we investigate single-trial decoding of natural, complex stimuli based on scalp EEG acquired with a portable, 32 dry-electrode sensor system in a typical office setting. We probe generalizability by a leave-one-subject-out cross-validation approach. We demonstrate that Support Vector Machine (SVM) classifiers trained on a relatively small set of de-noised (averaged) pseudo-trials perform on par with classifiers trained on a large set of noisy single-trial samples. For visualization of EEG signatures exploited by SVM classifiers, we propose a novel method for computing sensitivity maps of EEG-based SVM classifiers. Moreover, we apply the NPAIRS resampling framework for estimation of map uncertainty and show that effect sizes of sensitivity maps for classifiers trained on small samples of de-noised data and large samples of noisy data are similar. Finally, we demonstrate that the average pseudo-trial classifier can successfully predict the class of single trials from withheld subjects, which allows for fast classifier training, parameter optimization and unbiased performance evaluation in machine learning approaches for brain decoding.

neuroscience↗

bioRxiv2018

Peripheral blood DNA methylation differences in twin pairs discordant for Alzheimer’s disease

Alzheimers disease (AD) results from a neurodegenerative process that starts well before the diagnosis can be made. New prognostic or diagnostic markers enabling early intervention into the disease process would be highly valuable. As life style factors largely modulate the disease risk, we hypothesised that the disease associated DNA methylation signatures are detectable in the peripheral blood of discordant twin pairs. Reduced Representation Bisulfite Sequencing, single cell RNA-sequencing and gene array data were utilised to examine DNA methylation signatures and associated gene expression changes in blood and hippocampus, and targeted bisulfite sequencing in cross cohort validation. Our results reveal that discordant twin pairs have disease associated differences in their peripheral blood epigenomes. A subset of affected genes, e.g. ADARB2 contain differentially methylated sites also in anterior hippocampus. The DNA methylation differences seem to influence gene expression in brain rather than in blood cells. The affected genes are associated with neuronal functions and pathologies. These DNA methylation signatures are valuable disease marker candidates and may provide insights into the molecular mechanisms of pathogenesis.

genomics↗