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- Kuttner, C. S., et al.
(författare)
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Four-Week Omega-3 Supplementation in Carriers of the Prosteatotic PNPLA3 p.I148M Genetic Variant: An Open-Label Study
- 2019
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Ingår i: Lifestyle Genomics. - : S. Karger AG. - 2504-3161 .- 2504-3188. ; 12:1-6, s. 10-17
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Tidskriftsartikel (refereegranskat)abstract
- Background/Aims: The PNPLA3 loss-of-function variant p.I148M is a strong genetic determinant of nonalcoholic fatty liver disease. The PNPLA3 protein functions as an intracellular lipase in the liver, with a greater activity on unsaturated fatty acids. This study aimed to determine whether short-term supplementation with omega-3 fatty acids impacts hepatic steatosis differently in PNPLA3 p.148I wild-type individuals as compared to homozygous carriers of the PNPLA3 p.148M variant. Methods: Twenty subjects with hepatic steatosis (50% women, age 18–77 years) were included. Ten subjects homozygous for the PNPLA3 148M variant were matched to 10 wild-type individuals. The subjects received 4 g omega-3 fatty acids (1,840 mg eicosapentaenoic acid and 1,520 mg docosahexaenoic acid) a day for 4 weeks. Transient elastography with a controlled attenuation parameter (CAP) was used to quantify liver fat before and after the intervention. Body composition, fibrosis, liver function tests, serum free fatty acids (FFA) and glucose markers were compared. Results: Patients homozygous for the PNPLA3 p.148M variant (risk group) demonstrated no significant changes in CAP compared to baseline (284 ± 55 vs. 287 ± 65 dB/m) as did the control group (256 ± 56 vs. 262 ± 55 dB/m). While serum liver enzyme activities remained unchanged in both groups, the risk group displayed significantly (p = 0.02) lower baseline FFA concentrations (334.5 [range 281.0–431.0] vs. 564.5 [range 509.0–682.0] μmol/L), which markedly increased by 9.1% after the intervention. In contrast, FFA concentrations decreased significantly (p = 0.01) by 28.3% in the wild-type group. Conclusions: Short-term omega-3 fatty acid supplementation did not significantly alter hepatic steatosis. The nutrigenomic and metabolic effects of omega-3 fatty acids should be investigated further in carriers of the PNPLA3 148M risk variant.
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| 3. |
- Kuttner, Samuel, et al.
(författare)
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Machine learning derived input-function in a dynamic 18F-FDG PET study of mice
- 2020
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Ingår i: Biomedical Engineering & Physics Express. - : Institute of Physics Publishing (IOPP). - 2057-1976. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- Tracer kinetic modelling, based on dynamic 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is used to quantify glucose metabolism in humans and animals. Knowledge of the arterial input-function (AIF) is required for such measurements. Our aim was to explore two non-invasive machine learning-based models, for AIF prediction in a small-animal dynamic FDG PET study. 7 tissue regions were delineated in images from 68 FDG PET/computed tomography mouse scans. Two machine learning-based models were trained for AIF prediction, based on Gaussian processes (GP) and a long short-term memory (LSTM) recurrent neural network, respectively. Because blood data were unavailable, a reference AIF was formed by fitting an established AIF model to vena cava and left ventricle image data. The predicted and reference AIFs were compared by the area under curve (AUC) and root mean square error (RMSE). Net-influx rate constants, Ki , were calculated with a two-tissue compartment model, using both predicted and reference AIFs for three tissue regions in each mouse scan, and compared by means of error, ratio, correlation coefficient, P value and Bland-Altman analysis. The impact of different tissue regions on AIF prediction was evaluated by training a GP and an LSTM model on subsets of tissue regions, and calculating the RMSE between the reference and the predicted AIF curve. Both models generated AIFs with AUCs similar to reference. The LSTM models resulted in lower AIF RMSE, compared to GP. Ki from both models agreed well with reference values, with no significant differences. Myocardium was highlighted as important for AIF prediction, but AIFs with similar RMSE were obtained also without myocardium in the input data. Machine learning can be used for accurate and non-invasive prediction of an image-derived reference AIF in FDG studies of mice. We recommend the LSTM approach, as this model predicts AIFs with lower errors, compared to GP.
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