Establishment of a machine learning predictive model for non-alcoholic fatty liver disease: A longitudinal cohort study.

BACKGROUND AND AIMS: Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver disease, which lacks effective drug treatments. This study aimed to construct an eXtreme Gradient Boosting (XGBoost) prediction model to identify or evaluate potential NAFLD patients. METHODS AND RESULTS: We conducted a longitudinal study of 22,140 individuals from the Beijing Health Management Cohort. Variable filtering was performed using the least absolute shrinkage and selection operator. Random Over Sampling Examples was used to address imbalanced data. Next, the XGBoost model and the other three machine learning (ML) models were built using balanced data. Finally, the variable importance of the XGBoost model was ranked. Among four ML algorithms, we got that the XGBoost model outperformed the other models with the following results: accuracy of 0.835, sensitivity of 0.835, specificity of 0.834, Youden index of 0.669, precision of 0.831, recall of 0.835, F-1 score of 0.833, and an area under the curve of 0.914. The top five variables with the greatest impact on the onset of NAFLD were aspartate aminotransferase, cardiometabolic index, body mass index, alanine aminotransferase, and triglyceride-glucose index. CONCLUSION: The predictive model based on the XGBoost algorithm enables early prediction of the onset of NAFLD. Additionally, assessing variable importance provides valuable insights into the prevention and treatment of NAFLD.

Association of sex-specific body mass index and waist circumference trajectories with non-alcoholic fatty liver disease incidence based on growth mixture modeling.

BACKGROUND AND AIMS: Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver disease. The relationship between the trajectories of obesity indicators and incident NAFLD is unknown. Therefore, this study aims to explore the sex-specific association between the trajectories of obesity indicators and the incidence of NAFLD. METHODS AND RESULTS: In total, 9067 participants were recruited for this longitudinal study. Obesity indicators use body mass index (BMI) and waist circumference (WC). The trajectory of obesity indicators was analyzed using the growth mixture modeling. The multivariate logistic regression model was used to analyze the association between obesity indicators' trajectories and incident NAFLD. Over a median follow-up of 1.82 years, 1013 (11.74%) participants developed NAFLD. We identified BMI and WC change trajectories as the stable group, increasing group, and decreasing group. After adjusting for baseline level and other confounders, multivariate logistic regression analysis showed that compared with stable group of BMI, the increasing group, and decreasing group odds ratio and 95% confidence interval of NAFLD were 2.10 (1.06-4.15), and 0.25 (0.09-0.67) in men, and 1.82 (1.08-3.04) and 0.32 (0.16-0.64) in women. Compared with stable group of WC, the increasing group was 2.57 (1.39-4.74) in men, the increasing group, and decreasing group were 2.29 (1.70-3.10) and 0.28 (0.12-0.64) in women. Sensitivity analysis showed that the results were stable. CONCLUSION: The BMI and WC changing trajectories are significantly associated with the incidence of NAFLD in men and women. Populations of real-world health examinations can be categorized based on obesity indicator changes to prevent NAFLD.

Temporal relationship between elevated serum uric acid levels and dyslipidemia: A 5-year cohort study using cross-lagged panel analysis.

BACKGROUND AND AIM: Previous studies have demonstrated an association between SUA and dyslipidemia. This study aims to explore the temporal relationship between SUA and dyslipidemia. METHODS AND RESULTS: Based on the Beijing Health Management Cohort conducted from 2013 to 2018, the data of a physical examination population was collected, including a total of 6630 study subjects. Cross-lagged panel analysis was employed to examine the temporal relationship between elevated SUA levels and dyslipidemia, indicated by either elevated TG or decreased HDL-C. The path coefficient and the 95 % CI from baseline TG to follow-up SUA were as follows: in the general population, men, women, and people with BMI ≥25 kg/m2were 0.027 (0.008-0.045), 0.024 (0.001-0.048), 0.032 (0.001-0.063) and 0.033 (0.006-0.059) (P < 0.05); however, the path coefficient from baseline SUA to follow-up TG and the 95 % CI were not statistically significant. Furthermore, the path coefficients and 95 % CIs between elevated SUA and decreased HDL-C were not statistically significant, both in the general population and in populations stratified by gender and BMI. CONCLUSIONS: We found a temporal relationship from elevated TG to elevated SUA in the general population and the populations stratified by gender and BMI (≥25 kg/m2). However, we did not observe a reverse relationship from elevated SUA to elevated TG. Additionally, we did not find a temporal relationship between decreased HDL-C and elevated SUA in both the general population and the stratified populations.

Plasma metabolomics reveals the intervention mechanism of different types of exercise on chronic unpredictable mild stress-induced depression rat model.

OBJECTIVE: To study the effects of different types of exercise on the plasma metabolomics of chronic unpredictable mild stress (CUMS)-induced depressed rats based on 1H-NMR metabolomics techniques, and to explore the potential mechanisms of exercise for the treatment of depression. Rats were randomly divided into blank control group (C), CUMS control group (D), pre-exercise with CUMS group (P), CUMS with aerobic exercise group, CUMS with resistance exercise group (R), and CUMS with aerobic + resistance exercise group (E). The corresponding protocol intervention was applied to each group of rats. Body weight, sucrose preference and open field tests were performed weekly during the experiment to evaluate the extent of depression in rats. Plasma samples from each group of rats were collected at the end of the experiment, and then the plasma was analyzed by 1H-NMR metabolomics combined with multivariate statistical analysis methods to identify differential metabolites and perform metabolic pathway analysis. (1) Compared with the group D, the body weight, sucrose preference rate, and the number of crossings and standings in the different types of exercise groups were significantly improved (p < 0.05 or p < 0.01). (2) Compared to group C, a total of 15 differential metabolites associated with depression were screened in the plasma of rats in group D, involving 6 metabolic pathways. Group P can regulate the levels of 6 metabolites: valine, lactate, inositol, glucose, phosphocreatine, acetoacetic acid. Group A can regulate the levels of 6 metabolites: N-acetylglycoprotein, leucine, lactate, low density lipoprotein, glucose and acetoacetic acid. Group R can regulate the levels of 6 metabolites: choline, lactate, inositol, glucose, phosphocreatine and acetoacetic acid. Group E can regulate the levels of 5 metabolites: choline, citric acid, glucose, acetone and acetoacetic acid. The different types of exercise groups can improve the depressive symptoms in CUMS rats, and there are common metabolites and metabolic pathways for their mechanism of effects. This study provides a powerful analytical tool to study the mechanism of the antidepressant effect of exercise, and provides an important method and basis for the early diagnosis, prevention and treatment of depression.

Bidirectional relationship between nonalcoholic fatty liver disease and serum creatinine-to-body weight ratio as a proxy for skeletal muscle mass index.

BACKGROUND AND AIM: Although an association between skeletal muscle mass index and nonalcoholic fatty liver disease (NAFLD) has previously been demonstrated, the causal direction remains unclear. Herein, we investigated the directional association between NAFLD and the serum creatinine-to-body weight ratio (sCr/bw), a surrogate marker of the muscle mass index, using longitudinal data. METHODS: We recruited 9662 participants in 2017 and performed follow-up over 4 years. We evaluated whether sCr/bw was related to NAFLD development (Analysis I) and whether NAFLD was associated with a low sCr/bw incidence (Analysis II) using logistic regression models. Furthermore, a random intercept cross-lagged panel model was applied to evaluate the bidirectional association between sCr/bw ratio and NAFLD (Analysis III). RESULTS: Analysis I demonstrated an association between sCr/bw and incident NAFLD (odds ratio [OR] = 0.160, 95% confidence interval [CI]:0.107-0.232). Analysis II indicated a relationship between NAFLD and subsequent low sCr/bw ratio (OR = 1.524, 95% CI: 1.258-1.846). Analysis III indicated that the standard regression coefficient from sCr/bw to subsequent hepatic steatosis (HS) was -0.053 for ßsCr/bw2017 â†’ HS2019 and -0.060 for ßsCr/bw2019 â†’ HS2021 and the coefficient from HS to subsequent sCr/bw was -0.093 for ßHS2017 â†’ sCr/bw2019 and -0.112 for ßHS2019 â†’ sCr/bw2021 (all P < 0.05). CONCLUSIONS: This study indicated mutual causality between sCr/bw and NAFLD. Considering that sCr/bw is a surrogate marker of muscle mass index, the findings emphasize that NAFLD and low muscle mass form a vicious cycle, which should be taken seriously in clinical practice.

Effect of Artificial Intelligence-based Health Education Accurately Linking System (AI-HEALS) for Type 2 diabetes self-management: protocol for a mixed-methods study.

BACKGROUND: Patients with type 2 diabetes (T2DM) have an increasing need for personalized and Precise management as medical technology advances. Artificial intelligence (AI) technologies on mobile devices are being developed gradually in a variety of healthcare fields. As an AI field, knowledge graph (KG) is being developed to extract and store structured knowledge from massive data sets. It has great prospects for T2DM medical information retrieval, clinical decision-making, and individual intelligent question and answering (QA), but has yet to be thoroughly researched in T2DM intervention. Therefore, we designed an artificial intelligence-based health education accurately linking system (AI-HEALS) to evaluate if the AI-HEALS-based intervention could help patients with T2DM improve their self-management abilities and blood glucose control in primary healthcare. METHODS: This is a nested mixed-method study that includes a community-based cluster-randomized control trial and personal in-depth interviews. Individuals with T2DM between the ages of 18 and 75 will be recruited from 40-45 community health centers in Beijing, China. Participants will either receive standard diabetes primary care (SDPC) (control, 3 months) or SDPC plus AI-HEALS online health education program (intervention, 3 months). The AI-HEALS runs in the WeChat service platform, which includes a KBQA, a system of physiological indicators and lifestyle recording and monitoring, medication and blood glucose monitoring reminders, and automated, personalized message sending. Data on sociodemography, medical examination, blood glucose, and self-management behavior will be collected at baseline, as well as 1,3,6,12, and 18 months later. The primary outcome is to reduce HbA1c levels. Secondary outcomes include changes in self-management behavior, social cognition, psychology, T2DM skills, and health literacy. Furthermore, the cost-effectiveness of the AI-HEALS-based intervention will be evaluated. DISCUSSION: KBQA system is an innovative and cost-effective technology for health education and promotion for T2DM patients, but it is not yet widely used in the T2DM interventions. This trial will provide evidence on the efficacy of AI and mHealth-based personalized interventions in primary care for improving T2DM outcomes and self-management behaviors. TRIAL REGISTRATION: Biomedical Ethics Committee of Peking University: IRB00001052-22,058, 2022/06/06; Clinical Trials: ChiCTR2300068952, 02/03/2023.

Encephalomyocarditis Virus Abrogates the Interferon Beta Signaling Pathway via Its Structural Protein VP2.

Type I interferon (IFN)-mediated antiviral responses are critical for modulating host-virus responses, and indeed, viruses have evolved strategies to antagonize this pathway. Encephalomyocarditis virus (EMCV) is an important zoonotic pathogen, which causes myocarditis, encephalitis, neurological disease, reproductive disorders, and diabetes in pigs. This study aims to understand how EMCV interacts with the IFN pathway. EMCV circumvents the type I IFN response by expressing proteins that antagonize cellular innate immunity. Here, we show that EMCV VP2 is a negative regulator of the IFN-ß pathway. This occurs via the degradation of the MDA5-mediated cytoplasmic double-stranded RNA (dsRNA) antiviral sensing RIG-I-like receptor (RLR) pathway. We show that structural protein VP2 of EMCV interacts with MDA5, MAVS, and TBK1 through its C terminus. In addition, we found that EMCV VP2 could significantly degrade RLRs by the proteasomal and lysosomal pathways. For the first time, EMCV VP2 was shown to play an important role in EMCV evasion of the type I IFN signaling pathway. This study expands our understanding that EMCV utilizes its capsid protein VP2 to evade the host antiviral response.IMPORTANCE Encephalomyocarditis virus is an important pathogen that can cause encephalitis, myocarditis, neurological diseases, and reproductive disorders. It also causes huge economic losses for the swine industry worldwide. Innate immunity plays an important role in defending the host from pathogen infection. Understanding pathogen microorganisms evading the host immune system is of great importance. Currently, whether EMCV evades cytosolic RNA sensing and signaling is still poorly understood. In the present study, we found that viral protein VP2 antagonized the RLR signaling pathway by degrading MDA5, MAVS, and TBK1 protein expression to facilitate viral replication in HEK293 cells. The findings in this study identify a new mechanism for EMCV evading the host's innate immune response, which provide new insights into the virus-host interaction and help develop new antiviral approaches against EMCV.

Establishment of dynamic nomogram and risk score models for T2DM: a retrospective cohort study in Beijing.

BACKGROUND: Health interventions can delay or prevent the occurrence and development of diabetes. Dynamic nomogram and risk score (RS) models were developed to predict the probability of developing type 2 diabetes mellitus (T2DM) and identify high-risk groups. METHODS: Participants (n = 44,852) from the Beijing Physical Examination Center were followed up for 11 years (2006-2017); the mean follow-up time was 4.06 ± 2.09 years. Multivariable Cox regression was conducted in the training cohort to identify risk factors associated with T2DM and develop dynamic nomogram and RS models using weighted estimators corresponding to each covariate derived from the fitted Cox regression coefficients and variance estimates, and then undergone internal validation and sensitivity analysis. The concordance index (C-index) was used to assess the accuracy and reliability of the model. RESULTS: Of the 44,852 individuals at baseline, 2,912 were diagnosed with T2DM during the follow-up period, and the incidence density rate per 1,000 person-years was 16.00. Multivariate analysis indicated that male sex (P < 0.001), older age (P < 0.001), high body mass index (BMI, P < 0.05), high fasting plasma glucose (FPG, P < 0.001), hypertension (P = 0.015), dyslipidaemia (P < 0.001), and low serum creatinine (sCr, P < 0.05) at presentation were risk factors for T2DM. The dynamic nomogram achieved a high C-index of 0.909 in the training set and 0.905 in the validation set. A tenfold cross-validation estimated the area under the curve of the nomogram at 0.909 (95% confidence interval 0.897-0.920). Moreover, the dynamic nomogram and RS model exhibited acceptable discrimination and clinical usefulness in subgroup and sensitivity analyses. CONCLUSIONS: The T2DM dynamic nomogram and RS models offer clinicians and others who conduct physical examinations, respectively, simple-to-use tools to assess the risk of developing T2DM in the urban Chinese current or retired employees.

First report of Colletotrichum fioriniae causing leaf spot on Schima superba in the world.

Schima superba Gardn. et Champ. is a subtropical evergreen tree species naturally distributed mainly in China, Japan, and Vietnam. It is primarily planted for its timber and urban landscaping in China (Ni, 1996). In September 2018, leaves necrotic spots were observed on S. superba in Jiangxi Forest Breeding Center (28°57'19.52" N, 115°39'21.32" E), Jiangxi Province, China. The disease incidence was about 30%. Initially, spots were circular to semicircular, grayish-brown in the center with dark brown margin, then expanded and eventually collapsed into sunken necrotic lesions. To identify the agent, diseased leaves were collected randomly. Pieces (5 × 5 mm) from the lesion borders were surfaced sterilized in 70% ethanol (30 s), 3% NaOCl (60 s), and rinsed 3 times in sterile water. These pieces were put on potato dextrose agar (PDA) and cultured at 25 °C. Pure cultures were obtained by monosporic isolation, and 3 isolates (MH-1, MH-2, MH-3) were used for morphological studies and phylogenetic analyses. On PDA, colonies were initially white, cottony, then became pinkish to deep-pink at the center and pink on the reverse. Conidia were fusiform with acute ends, smooth-walled, hyaline, 13.7-18.5 × 4.6-6.1 µm (16.4 ± 1.3× 5.3 ± 0.6 µm, n = 100). Conidiophores were colorless to pale brown, smooth, septate. Conidiogenous cells were colorless to pale brown, smooth, cylindrical to ampulliform. The morphological characteristics fit the descriptions of Colletotrichum acutatum J. H. Simmonds sensu lato (Damm et al., 2012). For accurate identification, genomic DNA of 3 isolates was extracted, and the internal transcribed spacer (ITS), actin (ACT), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), beta-tubulin 2 (TUB2), and chitin synthase (CHS-1) were amplified and sequenced using the corresponding primers (Weir et al., 2012). The sequences were deposited in GenBank (ITS: MZ325946, MZ325947, MW584318; ACT: MZ399375, MZ419566, MW661171; CHS-1: MZ399376, MZ419567, MW661172; MZ399377, GAPDH: MZ419568, MW661173; TUB2: MZ399378, MZ419569, MW661174). Five loci were concatenated, and the aligned sequences (1528bp) were 99.89% homologous to ex-type C. fioriniae (Marcelino & Gouli) R. G. Shivas & Y. P. Tan CBS128517. Phylogenetic analysis using the maximum likelihood showed that 3 isolates were clustered in C. fioriniae clade with 100% bootstrap support. Based on the multi-locus phylogeny and morphology, 3 isolates were identified as C. fioriniae. Pathogenicity tests were performed on 36 seedlings of S. superba (2-year-old). The leaves were wounded slightly and inoculated with a drop of spore suspension (106 conidia/mL). The sterile water was used as controls. All the tested leaves were covered with black plastic bags to keep them moist for 2 days. All seedlings were placed in the greenhouse (25 °C, 12 h light/dark) for 10 days, and all inoculated leaves had typical symptoms. The controls were asymptomatic. The same fungus was reisolated from the lesions, fulfilling Koch's postulates. Colletotrichum fioriniae was described as a new species from the C. acutatum s. l. (Shivas et al., 2009), and it was an important plant pathogen, such as Pyrus spp. (Pavlovic et al., 2019), Morus alba L. (Xue et al., 2019), and so on. This is the first report of the newly emerging disease of S. superba caused by C. fioriniae in the world, and its potential threat should be evaluated in the future. This study provided crucial information for epidemiologic studies and appropriate control strategies.

Stable Isotope-Resolved Metabolomics Reveals the Abnormal Brain Glucose Catabolism in Depression Based on Chronic Unpredictable Mild Stress Rats.

The severe harm of depression to human life has attracted great attention to neurologists, but its pathogenesis is extremely complicated and has not yet been fully elaborated. Here, we provided a new strategy for revealing the specific pathways of abnormal brain glucose catabolism in depression, based on the supply of energy substrates and the evaluation of the mitochondrial structure and function. By using stable isotope-resolved metabolomics, we discovered that the tricarboxylic acid cycle (TCA cycle) is blocked and gluconeogenesis is abnormally activated in chronic unpredictable mild stress (CUMS) rats. In addition, our results showed an interesting phenomenon that the brain attempted to activate all possible metabolic enzymes in energy-producing pathways, but CUMS rats still exhibited a low TCA cycle activity due to impaired mitochondria. Depression caused the mitochondrial structure and function to be impaired and then led to abnormal brain glucose catabolism. The combination of the stable isotope-resolved metabolomics and mitochondrial structure and function analysis can accurately clarify the mechanism of depression. The mitochondrial pyruvate carrier and acetyl-CoA may be the key targets for depression treatment. The strategy provides a unique insight for exploring the mechanism of depression, the discovery of new targets, and the development of ideal novel antidepressants. Data are available via ProteomeXchange with identifier PXD025548.