The Effects of Physical Activity on Hepatic Lipid Metabolism During Weight-Loss

Non-alcoholic fatty liver disease (NAFLD) develops as a result of physical inactivity and overnutrition. Changing dietary behaviors and increasing physical activity are common strategies used for weight-loss; however, it remains unclear what additional benefits are provided by incorporating physical activity in a weight-loss program for the treatment of NAFLD. The purpose of this study was to determine how physical activity reduces hepatic steatosis and changes the expression of hepatic lipogenic genes during weight-loss. Male C57BL/6 mice were fed either a low-fat (LFD; 10% kcal fat) or high-fat (HFD; 60% kcal fat) diet for 10-weeks. Following 10-weeks, the HFD group was randomly assigned to either a LFD (Diet) or LFD with physical activity (Diet+PA) to induce weight-loss for 8-weeks. After 8-weeks of weight-loss, reductions in body and liver mass were observed in both Diet and Diet+PA groups (see Table 1.). Interestingly, the Diet+PA group lost significantly (P\u3c0.05) more body mass than the Diet group. Reductions in body mass and HOMA-IR in the Diet and Diet+PA groups were matched by reductions in hepatic triglyceride levels. In the Diet+PA group, liver triglyceride and cholesterol levels were significantly (P\u3c0.05) lower than all other groups. The greater reduction in hepatic triglyceride levels from physical activity was due to significant (P\u3c0.05) reductions in the expression of lipogenic FASN and SCD-1 mRNA. Interestingly, physical activity did not alter fatty acid uptake or fatty acid oxidation as observed with CD36 and CPT-1a mRNA levels, respectively. Based on these findings, the addition of physical activity to a diet-induced weight-loss intervention provides a more effective approach for the treatment of NAFLD than dieting alone. Table 1. Whole body and hepatic metabolic characteristics following weight-loss. Variables LFD (n=12) HFD (n=12) Diet (n=12) Diet+PA (n=12) Body mass (g) 30.2 ± 1.1 48.8 ± 0.5* 30.3 ± 0.7† 26.1 ± 0.3*,†,‡ Liver mass (g) 1.2 ± 0.1 2.9 ± 0.2* 1.2 ± 0.1† 1.2 ± 0.1† Triglyceride (mg/dL) 99.4 ± 8.7 96.7 ± 5.5 88.3 ± 6.1 88.4 ± 4.8 Cholesterol (mg/dL) 153.5 ± 10.1 246.0 ± 8.7* 148.2 ± 15.5† 127.6 ± 4.7*,† HOMA-IR 22.9 ± 1.2 187.3 ± 7.5* 19.4 ± 8.8† 25.3 ± 10.5† Liver Tg (mg/mg tissue) 1.18 ± 0.14 2.53 ± 0.05* 0.96 ± 0.15† 0.58 ± 0.07*,†,‡ Liver Chol (μg/mg tissue) 437.0 ± 43.0 585.2 ± 54.4* 527.0 ± 56.5 324.0 ± 27.3*,†,‡ FASN mRNA 1.00 ± 0.20 1.90 ± 0.34* 2.10 ± 0.54* 0.46 ± 0.11*,†,‡ CD36/FAT mRNA 1.00 ± 0.22 0.19 ± 0.20* 0.97 ± 0.10† 0.80 ± 0.04† SCD-1 mRNA 1.00 ± 0.28 1.94 ± 0.83* 0.76 ± 0.13† 0.44 ± 0.05*,†,‡ CPT-1a mRNA 1.00 ± 0.18 0.74 ± 0.04* 0.62 ± 0.08* 0.73 ± 0.05* Note. Data are presented as mean ± SEM.*Significantly (P\u3c0.05) different than LFD; †significantly (P\u3c0.05) different than HFD; ‡significantly (P\u3c0.05) different than Diet

Independent and Combined Effects of Menhaden Oil and High Fructose on Hepatic Lipid Metabolism

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The Effects of Physical Activity on Markers of Hepatic Inflammation During Weight-Loss

Non-alcoholic fatty liver disease (NAFLD) represents a continuum that begins with accumulation of lipid in hepatic cells progressing to hepatic steatosis with inflammation (steatohepatitis), fibrosis, and cirrhosis. Weight-loss using dietary modification and physical activity are common strategies used for the treatment of NAFLD; however, it remains to be determined the effects of physical activity on hepatic inflammation during weight-loss. The purpose of this study was to determine the therapeutic role of physical activity on plasma and hepatic inflammatory markers during weight-loss. Male C57BL/6 mice were fed either a low-fat (LFD; 10% kcal fat) or high-fat (HFD; 60% kcal fat) diet for 10-weeks. Following 10-weeks, the HFD group was randomly assigned to either a LFD (Diet) or LFD with physical activity (Diet+PA) to induce weight loss for 8-weeks. After 8-weeks, reductions in body mass were observed in both Diet and Diet+PA groups (see Table 1.). Interestingly, the Diet+PA group lost significantly (P\u3c0.05) more body mass than the Diet group. Despite significant (P\u3c0.05) reductions in body mass and HOMA-IR, plasma TNF-α remained elevated in the Diet and Diet+PA groups. Moreover, Diet+PA plasma TNF-α was significantly (P\u3c0.05) greater than the HFD obese controls. Elevated plasma TNF-α in the Diet+PA was matched by a greater hepatic expression of IL-1β and IL-6 mRNA when compared to all groups. Interestingly, the expression of TGF-β1 mRNA was significantly (P\u3c0.05) reduced in the Diet+PA when compared to all groups. The elevated plasma TNF-α and expression of IL-1β and IL-6 mRNA are likely due to physical activity. It remains unclear as to the pro-inflammatory effects of physical activity during weight-loss; however, this may be part of a protective adaption to regular exercise. Furthermore, the reduced hepatic TGF-β1 mRNA levels suggest a protective strategy against fibrogenesis in the spectrum of liver disease. Table 1. Whole body and hepatic metabolic characteristics following weight-loss. Variables LFD (n=12) HFD (n=12) Diet (n=12) Diet+PA (n=12) Body mass (g) 30.2 ± 1.1 48.8 ± 0.5* 30.3 ± 0.7† 26.1 ± 0.3*,†,‡ HOMA-IR 22.9 ± 1.2 187.3 ± 7.5* 19.4 ± 8.8† 25.3 ± 10.5† IL-6 (pg/mL) 6.4 ± 0.7 6.2 ± 1.0 5.9 ± 0.9 6.4 ± 0.9 TNF-α (pg/mL) 30.8 ± 6.7 60.6 ± 5.3* 74.0 ± 8.1* 82.5 ± 7.7*,† IL-1β mRNA 1.00 ± 0.51 0.97 ± 0.34 1.20 ± 0.59 2.83 ± 0.62*,†,‡ IL-6 mRNA 1.00 ± 0.45 1.53 ± 0.50 1.16 ± 0.72 2.36 ± 0.55*,†,‡ TNF-α mRNA 1.00 ± 0.09 0.89 ± 0.08 0.94 ± 0.14 0.83 ± 0.06 TGF-β1 mRNA 1.00 ± 0.06 1.02 ± 0.06 1.02 ± 0.10 0.84 ± 0.05† Note. Data are presented as mean ± SEM. *Significantly (P\u3c0.05) different than LFD; †significantly (P\u3c0.05) different than HFD; ‡significantly (P\u3c0.05) different than Diet

The Lipogenic Effect of High-Fructose Consumption on NAFLD During Weight Loss

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Investigation into cases of hepatitis of unknown aetiology among young children, Scotland, 1 January 2022 to 12 April 2022

On 31 March 2022, Public Health Scotland was alerted to five children aged 3–5 years admitted to hospital with severe hepatitis of unknown aetiology. Retrospective investigation identified eight additional cases aged 10 years and younger since 1 January 2022. Two pairs of cases have epidemiological links. Common viral hepatitis causes were excluded in those with available results. Five children were adenovirus PCR-positive. Other childhood viruses, including SARS-CoV-2, have been isolated. Investigations are ongoing, with new cases still presenting

Predicting pulmonary function from the analysis of voice: A machine learning approach

Introduction: To self-monitor asthma symptoms, existing methods (e.g. peak flow metre, smart spirometer) require special equipment and are not always used by the patients. Voice recording has the potential to generate surrogate measures of lung function and this study aims to apply machine learning approaches to predict lung function and severity of abnormal lung function from recorded voice for asthma patients.Methods: A threshold-based mechanism was designed to separate speech and breathing from 323 recordings. Features extracted from these were combined with biological factors to predict lung function. Three predictive models were developed using Random Forest (RF), Support Vector Machine (SVM), and linear regression algorithms: (a) regression models to predict lung function, (b) multi-class classification models to predict severity of lung function abnormality, and (c) binary classification models to predict lung function abnormality. Training and test samples were separated (70%:30%, using balanced portioning), features were normalised, 10-fold cross-validation was used and model performances were evaluated on the test samples.Results: The RF-based regression model performed better with the lowest root mean square error of 10·86. To predict severity of lung function impairment, the SVM-based model performed best in multi-class classification (accuracy = 73.20%), whereas the RF-based model performed best in binary classification models for predicting abnormal lung function (accuracy = 85%).Conclusion: Our machine learning approaches can predict lung function, from recorded voice files, better than published approaches. This technique could be used to develop future telehealth solutions including smartphone-based applications which have potential to aid decision making and self-monitoring in asthma.</p