The advantages of physical exercise as a preventive strategy against NAFLD in postmenopausal women

Using broadly targeted plant metabolomics technology combined with network pharmacology to explore the mechanism of action of the Yishen Gushu formula in the treatment of postmenopausal osteoporosis in vivo

ETHNOPHARMACOLOGICAL RELEVANCE

Yishen Gushu Formula (YSGSF) is composed of Epimedium, prepared Rehmannia, Drynaria, Eucommia, Dodder, ginseng, Astragalus, Ligusticum wallichii, Aucklandia and Panax notoginseng. It can improve bone mineral density by regulating bone metabolism. However, the mechanism of YSGSF in the treatment of Postmenopausal osteoporosis (PMOP) remains unclear.

AIM OF THE STUDY

The compounds, targets, and molecular mechanisms of YSGSF in the treatment of PMOP were investigated using broad-spectrum target metabolomics from plants, combined with network pharmacology and animal studies, leading to a discussion on a novel approach to understanding YSGSF's action in PMOP treatment.

MATERIALS AND METHODS

Using ultra-performance liquid chromatography coupled with triple quadrupole-linear ion trap tandem mass spectrometry (UPLC-QTRAP-MS/MS) within a comprehensive targeted metabolomics framework, the active constituents of YSGSF were identified. This, alongside network pharmacology and molecular docking, facilitated the identification of critical signaling pathways and targets pertinent to YSGSF's therapeutic effect on PMOP. Subsequently, an animal model for PMOP was developed. Following intervention grouping, rats' weight changes were recorded; serum bone metabolic factors were assessed via ELISA; bone microstructure was examined using HE staining and Micro-CT; and key signaling pathway proteins and genes were analyzed through immunohistochemistry to validate YSGSF's potential mechanism in PMOP treatment.

RESULTS

A total of 84 main active components of YSGSF were identified. The key signaling pathways affected by YSGSF in the treatment of PMOP were the TNF and IL-7 signaling pathways, closely related to TNF-α, IL-1β, c-jun and other protein targets. The results of animal experiments showed that YSGSF could downregulate the expression of TNF-a, IL-1β and c-Jun proinflammatory factors by regulating the TNF and IL-7 signaling pathways and regulate the inflammatory response, osteocyte differentiation and apoptosis to control the development of PMOP.

CONCLUSION

YSGSF activates the TNF-α and IL-7 signaling pathways in PMOP rats, reducing TNF-α and IL-1β levels, the c-Jun inflammatory response, and osteocyte differentiation and apoptosis, thus playing a significant role in treating PMOP.

The emerging significance of mitochondrial targeted strategies in NAFLD treatment

Nonalcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease worldwide, ranging from liver steatosis to nonalcoholic steatohepatitis, which ultimately progresses to fibrosis, cirrhosis, and hepatocellular carcinoma. Individuals with NAFLD have a higher risk of developing cardiovascular and extrahepatic cancers. Despite the great progress being made in understanding the pathogenesis and the introduction of new pharmacological targets for NAFLD, no drug or intervention has been accepted for its management. Recent evidence suggests that NAFLD may be a mitochondrial disease, as mitochondrial dysfunction is involved in the pathological processes that lead to NAFLD. In this review, we describe the recent advances in our understanding of the mechanisms associated with mitochondrial dysfunction in NAFLD progression. Moreover, we discuss recent advances in the efficacy of mitochondria-targeted compounds (e.g., Mito-Q, MitoVit-E, MitoTEMPO, SS-31, mitochondrial uncouplers, and mitochondrial pyruvate carrier inhibitors) for treating NAFLD. Furthermore, we present some medications currently being tested in clinical trials for NAFLD treatment, such as exercise, mesenchymal stem cells, bile acids and their analogs, and antidiabetic drugs, with a focus on their efficacy in improving mitochondrial function. Based on this evidence, further investigations into the development of mitochondria-based agents may provide new and promising alternatives for NAFLD management.

A dynamic machine learning model for prediction of NAFLD in a health checkup population: A longitudinal study

Background

Non-alcoholic fatty liver disease (NAFLD) is one of the most common liver diseases worldwide. Currently, most NAFLD prediction models are diagnostic models based on cross-sectional data, which failed to provide early identification or clarify causal relationships. We aimed to use time-series deep learning models with longitudinal health checkup records to predict the onset of NAFLD in the future, and update the model stepwise by incorporating new checkup records to achieve dynamic prediction.

Methods

10,493 participants with over 6 health checkup records from Beijing MJ Health Screening Center were included to conduct a retrospective cohort study, in which the constantly updated initial 5 checkup data were incorporated stepwise to predict the risk of NAFLD at and after their sixth health checkups. A total of 33 variables were considered, consisting of demographic characteristics, medical history, lifestyle, physical examinations, and laboratory tests. L1-penalized logistic regression (LR) was used for feature selection. The long short-term memory (LSTM) algorithm was introduced for model development, and five-fold cross-validation was conducted to tune and choose optimal hyperparameters. Both internal validation and external validation were conducted, using the 20% randomly divided holdout test dataset and previously unseen data from Shanghai MJ Health Screening Center, respectively, to evaluate model performance. The evaluation metrics included area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, Brier score, and decision curve. Bootstrap sampling was implemented to generate 95% confidence intervals of all the metrics. Finally, the Shapley additive explanations (SHAP) algorithm was applied in the holdout test dataset for model interpretability to obtain time-specific and sample-specific contributions of each feature.

Results

Among the 10,493 participants, 1662 (15.84%) were diagnosed with NAFLD at and after their sixth health checkups. The predictive performance of the deep learning model in the internal validation dataset improved over the incorporation of the checkups, with AUROC increasing from 0.729 (95% CI: 0.698,0.760) at baseline to 0.818 (95% CI: 0.798,0.844) when consecutive 5 checkups were included. The external validation dataset, containing 1728 participants, was used to verify the results, in which AUROC increased from 0.700 (95% CI: 0.657,0.740) with only the first checkups to 0.792 (95% CI: 0.758,0.825) with all five. The results of feature significance showed that body fat percentage, alanine transaminase (ALT), and uric acid owned the greatest impact on the outcome, time-specific, individual-specific and dynamic feature contributions were also produced for model interpretability.

Conclusion

A dynamic prediction model was successfully established in our study, and the prediction capability kept improving with the renewal of the latest checkup records. In addition, we identified key features associated with the onset of NAFLD, making it possible to optimize the prevention and control strategies of the disease in the general population.

The association between endocrine disrupting chemicals and MAFLD: Evidence from NHANES survey

Previous studies on the association of endocrine-disrupting chemicals (EDCs) with metabolic dysfunction-associated fatty liver disease (MAFLD) are very limited. This study analyzed the association of EDCs exposure with MAFLD among 5073 American adults from the 2017-2018 National Health and Nutrition Examination Survey. The results showed that increased exposure to 3 EDCs metabolites (namely As, DiNP and PFOA) were significantly associated with MAFLD, the odds ratio of which were 1.819 (95% CI: 1.224, 2.702), 1.959 (95% CI: 1.224, 3.136) and 2.148 (95% CI: 1.036, 4.456), respectively. Further, the bayesian kernel machine regression model also revealed that phthalates exposure was strongly connected with the MAFLD, particularly in females and the elderly over 65. Moderating effect analysis suggested that higher body mass index (BMI) and inflammatory diet habit (indicated by dietary inflammatory index) strengthened the association between EDCs and MAFLD, whereas population with higher level of insulin sensitivity showed lower risk. In conclusion, our results suggest that either single or combined exposure to EDCs metabolites is link to MAFLD. Our findings also encourage people to sustain a healthy diet, normal levels of insulin sensitivity and BMI, which may help to alleviate the association of MAFLD risk in exposure to EDCs. These results also help us to better understand the association of EDCs and MAFLD and provide effective evidences for preventing MAFLD from the EDCs exposure aspect.

Metabolic Disorders in Menopause

Menopause is an aging process and an important time equivalent to one-third of a woman's lifetime. Menopause significantly increases the risk of cardiometabolic diseases, such as obesity, type 2 diabetes, cardiovascular diseases, non-alcoholic liver disease (NAFLD)/metabolic associated fatty liver disease (MFFLD), and metabolic syndrome (MetS). Women experience a variety of symptoms in the perimenopausal period, and these symptoms are distressing for most women. Many factors worsen a woman's menopausal experience, and controlling these factors may be a strategy to improve postmenopausal women's health. This review aimed to confirm the association between menopause and metabolic diseases (especially MetS), including pathophysiology, definition, prevalence, diagnosis, management, and prevention.

Dose-Response Relationship of Resistance Training on Metabolic Phenotypes, Body Composition and Lipid Profile in Menopausal Women

The relationship between volume training of resistance training (RT), body composition and cardiometabolic profile in menopausal women is poorly understand. This study aimed to evaluate the dose−response relationship of RT on lipid profile, body composition and metabolic phenotypes in menopausal women. A total of 31 women were categorized according to different volume of RT. Body composition was evaluated by DEXA and the cardiometabolic risk by metabolic phenotypes and lipid profile. There was a higher frequency of metabolically unhealthy phenotype in women who practiced RT for less than two years and had a weekly frequency lower than three days a week (p > 0.05). Women with more than two years and a higher weekly frequency of RT had lower trunk fat mass than their counterparties (15.33 ± 7.56 versus 10.57 ± 4.87, p = 0.04; 16.31 ± 7.46 versus 10.98 ± 5.49, p = 0.03, respectively). There was an association between HDL-c and time of RT in years. A moderate correlation was identified between variables of body adiposity, time in years and weekly frequency of RT. The present study concludes that more time in years and weekly frequency of RT practice are associated with lower body adiposity in menopausal women, the first also being associated with HDL-c.