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- Luukkonen, Panu K., et al.
(författare)
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Hydroxysteroid 17-β dehydrogenase 13 variant increases phospholipids and protects against fibrosis in nonalcoholic fatty liver disease
- 2020
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Ingår i: JCI Insight. - : American Society for Clinical Investigation. - 2379-3708. ; 5:5
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Tidskriftsartikel (refereegranskat)abstract
- Carriers of the hydroxysteroid 17-β dehydrogenase 13 (HSD17B13) gene variant (rs72613567:TA) have a reduced risk of NASH and cirrhosis but not steatosis. We determined its effect on liver histology, lipidome, and transcriptome using ultra performance liquid chromatography-mass spectrometry and RNA-seq. In carriers and noncarriers of the gene variant, we also measured pathways of hepatic fatty acids (de novo lipogenesis [DNL] and adipose tissue lipolysis [ATL] using 2H2O and 2H-glycerol) and insulin sensitivity using 3H-glucose and euglycemic-hyperinsulinemic clamp) and plasma cytokines. Carriers and noncarriers had similar age, sex and BMI. Fibrosis was significantly less frequent while phospholipids, but not other lipids, were enriched in the liver in carriers compared with noncarriers. Expression of 274 genes was altered in carriers compared with noncarriers, consisting predominantly of downregulated inflammation-related gene sets. Plasma IL-6 concentrations were lower, but DNL, ATL and hepatic insulin sensitivity were similar between the groups. In conclusion, carriers of the HSD17B13 variant have decreased fibrosis and expression of inflammation-related genes but increased phospholipids in the liver. These changes are not secondary to steatosis, DNL, ATL, or hepatic insulin sensitivity. The increase in phospholipids and decrease in fibrosis are opposite to features of choline-deficient models of liver disease and suggest HSD17B13 as an attractive therapeutic target.
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| 3. |
- Ruuth, Maija, et al.
(författare)
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Overfeeding Saturated Fat Increases LDL (Low-Density Lipoprotein) Aggregation Susceptibility While Overfeeding Unsaturated Fat Decreases Proteoglycan-Binding of Lipoproteins
- 2021
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Ingår i: Arteriosclerosis, Thrombosis and Vascular Biology. - : Lippincott Williams & Wilkins. - 1079-5642 .- 1524-4636. ; 41:11, s. 2823-2836
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: We recently showed that measurement of the susceptibility of LDL (low-density lipoprotein) to aggregation is an independent predictor of cardiovascular events. We now wished to compare effects of overfeeding different dietary macronutrients on LDL aggregation, proteoglycan-binding of plasma lipoproteins, and on the concentration of oxidized LDL in plasma, 3 in vitro parameters consistent with increased atherogenicity.Approach and Results: The participants (36 subjects; age, 48±10 years; body mass index, 30.9±6.2 kg/m2) were randomized to consume an extra 1000 kcal/day of either unsaturated fat, saturated fat, or simple sugars (CARB) for 3 weeks. We measured plasma proatherogenic properties (susceptibility of LDL to aggregation, proteoglycan-binding, oxidized LDL) and concentrations and composition of plasma lipoproteins using nuclear magnetic resonance spectroscopy, and in LDL using liquid chromatography mass spectrometry, before and after the overfeeding diets. LDL aggregation increased in the saturated fat but not the other groups. This change was associated with increased sphingolipid and saturated triacylglycerols in LDL and in plasma and reduction of clusterin on LDL particles. Proteoglycan binding of plasma lipoproteins decreased in the unsaturated fat group relative to the baseline diet. Lipoprotein properties remained unchanged in the CARB group.CONCLUSIONS: The type of fat during 3 weeks of overfeeding is an important determinant of the characteristics and functional properties of plasma lipoproteins in humans.REGISTRATION: URL: http://www.clinicaltrials.gov; Unique identifier NCT02133144.
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| 4. |
- Turkki, Riku, et al.
(författare)
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Breast cancer outcome prediction with tumour tissue images and machine learning
- 2019
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Ingår i: Breast Cancer Research and Treatment. - : SPRINGER. - 0167-6806 .- 1573-7217. ; 177:1, s. 41-52
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Recent advances in machine learning have enabled better understanding of large and complex visual data. Here, we aim to investigate patient outcome prediction with a machine learning method using only an image of tumour sample as an input.Methods: Utilising tissue microarray (TMA) samples obtained from the primary tumour of patients (N=1299) within a nationwide breast cancer series with long-term-follow-up, we train and validate a machine learning method for patient outcome prediction. The prediction is performed by classifying samples into low or high digital risk score (DRS) groups. The outcome classifier is trained using sample images of 868 patients and evaluated and compared with human expert classification in a test set of 431 patients.Results: In univariate survival analysis, the DRS classification resulted in a hazard ratio of 2.10 (95% CI 1.33-3.32, p=0.001) for breast cancer-specific survival. The DRS classification remained as an independent predictor of breast cancer-specific survival in a multivariate Cox model with a hazard ratio of 2.04 (95% CI 1.20-3.44, p=0.007). The accuracy (C-index) of the DRS grouping was 0.60 (95% CI 0.55-0.65), as compared to 0.58 (95% CI 0.53-0.63) for human expert predictions based on the same TMA samples.Conclusions: Our findings demonstrate the feasibility of learning prognostic signals in tumour tissue images without domain knowledge. Although further validation is needed, our study suggests that machine learning algorithms can extract prognostically relevant information from tumour histology complementing the currently used prognostic factors in breast cancer.
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