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- Jacobsson, Josefin A., et al.
(författare)
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Detailed Analysis of Variants in FTO in Association with Body Composition in a Cohort of 70-Year-Olds Suggests a Weakened Effect among Elderly
- 2011
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 6:5, s. e20158-
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Tidskriftsartikel (refereegranskat)abstract
- Background: The rs9939609 single-nucleotide polymorphism (SNP) in the fat mass and obesity (FTO) gene has previously been associated with higher BMI levels in children and young adults. In contrast, this association was not found in elderly men. BMI is a measure of overweight in relation to the individuals' height, but offers no insight into the regional body fat composition or distribution. Objective: To examine whether the FTO gene is associated with overweight and body composition-related phenotypes rather than BMI, we measured waist circumference, total fat mass, trunk fat mass, leg fat mass, visceral and subcutaneous adipose tissue, and daily energy intake in 985 humans (493 women) at the age of 70 years. In total, 733 SNPs located in the FTO gene were genotyped in order to examine whether rs9939609 alone or the other SNPs, or their combinations, are linked to obesity-related measures in elderly humans. Design: Cross-sectional analysis of the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) cohort. Results: Neither a single SNP, such as rs9939609, nor a SNP combination was significantly linked to overweight, body composition-related measures, or daily energy intake in elderly humans. Of note, these observations hold both among men and women. Conclusions: Due to the diversity of measurements included in the study, our findings strengthen the view that the effect of FTO on body composition appears to be less profound in later life compared to younger ages and that this is seemingly independent of gender.
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| 2. |
- Dartora, Caroline, et al.
(författare)
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A deep learning model for brain age prediction using minimally preprocessed T1w images as input
- 2023
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Ingår i: Frontiers in Aging Neuroscience. - : Frontiers Media SA. - 1663-4365 .- 1663-4365. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: In the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w) using multivariate methods and machine learning. We developed and validated a convolutional neural network (CNN)-based biological brain age prediction model that uses one T1w MRI preprocessing step when applying the model to external datasets to simplify implementation and increase accessibility in research settings. Our model only requires rigid image registration to the MNI space, which is an advantage compared to previous methods that require more preprocessing steps, such as feature extraction. Methods: We used a multicohort dataset of cognitively healthy individuals (age range = 32.0–95.7 years) comprising 17,296 MRIs for training and evaluation. We compared our model using hold-out (CNN1) and cross-validation (CNN2–4) approaches. To verify generalisability, we used two external datasets with different populations and MRI scan characteristics to evaluate the model. To demonstrate its usability, we included the external dataset’s images in the cross-validation training (CNN3). To ensure that our model used only the brain signal on the image, we also predicted brain age using skull-stripped images (CNN4). Results: The trained models achieved a mean absolute error of 2.99, 2.67, 2.67, and 3.08 years for CNN1–4, respectively. The model’s performance in the external dataset was in the typical range of mean absolute error (MAE) found in the literature for testing sets. Adding the external dataset to the training set (CNN3), overall, MAE is unaffected, but individual cohort MAE improves (5.63–2.25 years). Salience maps of predictions reveal that periventricular, temporal, and insular regions are the most important for age prediction. Discussion: We provide indicators for using biological (predicted) brain age as a metric for age correction in neuroimaging studies as an alternative to the traditional chronological age. In conclusion, using different approaches, our CNN-based model showed good performance using one T1w brain MRI preprocessing step. The proposed CNN model is made publicly available for the research community to be easily implemented and used to study ageing and age-related disorders.
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| 3. |
- Pettersson, Erik, et al.
(författare)
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Allelotyping by massively parallel pyrosequencing of SNP-carrying trinucleotide threads
- 2008
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Ingår i: Human Mutation. - : Hindawi Limited. - 1059-7794 .- 1098-1004. ; 29:2, s. 323-329
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Tidskriftsartikel (refereegranskat)abstract
- Here we present an approach for allelotyping combining the multiplexing features of the trinucleotide threading (TnT) method with pooling of genomic DNA and massively parallel pyrosequencing, enabling reliable allele frequency estimation in large cohorts. The approach offers several benefits as compared to array-based methods and allows undertaking highly complex studies without compromising accuracy, while keeping the workload to a minimum. This proof-of-concept study involves formation of trinucleotide threads, targeting a total of 147 single-nucleotide polymorphisms (SNPs) related to obesity and cancer, for multiplex amplification and allele extraction from a pool of 462 genomes, followed by massively parallel pyrosequencing. Approximately 177k reads were approved, identified, and assigned to SNP-carrying threads rendering representative allele frequencies in the cohort.
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