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Background: The metabolic score for visceral fat (METS-VF) quantifies the cumulative burden of visceral and intra-abdominal adipose tissues. However, the relationship between the METS-VF and carotid atherosclerosis (CAS) has not been extensively explored. Therefore, this study aimed to investigate the association between the METS-VF and CAS., Methods: This cross-sectional study enrolled 7089 Chinese adults who underwent physical examinations at the Zhenhai Lianhua Hospital, Zhejiang, China, in 2020. Multivariable logistic regression analysis was used to explore the linear relationship between METS-VF and CAS. Generalised additive models (GAM) were employed to evaluate potential nonlinear associations. The inflection points of METS-VF were determined using segmented logistic regression analysis optimised for maximum likelihood ratios and recursive algorithms., Results: Multivariable logistic regression analysis revealed a positive correlation between METS-VF and CAS (odds ratio [OR]: 1.824, 95% confidence interval [CI]: 1.753-1.899; P < 0.001). The GAM analysis confirmed a nonlinear association between them [effective degrees of freedom: 4.803, χ 2 : 876.7, P < 0.001], with an inflection point at a METS-VF of 8.09 (P < 0.001 for log-likelihood ratio test). Below this inflection point, METS-VF exhibited a significant positive association with CAS risk (OR: 1.874, 95% CI: 1.796-1.954; P < 0.001). Conversely, no significant association was observed when METS-VF ≥ 8.09 (OR: 0.998, 95% CI: 0.786-1.268; P = 0.989)., Conclusions: METS-VF and CAS demonstrated a positive non-linear correlation, with the curve indicating a saturation effect at METS-VF = 8.09., (© 2024. The Author(s).)

Background: Cardiovascular disease (CVD) has emerged as a global public health concern. Identifying and preventing subclinical atherosclerosis (SCAS), an early indicator of CVD, is critical for improving cardiovascular outcomes. This study aimed to construct interpretable machine learning models for predicting SCAS risk in type 2 diabetes mellitus (T2DM) patients., Methods: This study included 3084 T2DM individuals who received health care at Zhenhai Lianhua Hospital, Ningbo, China, from January 2018 to December 2022. The least absolute shrinkage and selection operator combined with random forest-recursive feature elimination were used to screen for characteristic variables. Linear discriminant analysis, logistic regression, Naive Bayes, random forest, support vector machine, and extreme gradient boosting were employed in constructing risk prediction models for SCAS in T2DM patients. The area under the receiver operating characteristic curve (AUC) was employed to assess the predictive capacity of the model through 10-fold cross-validation. Additionally, the SHapley Additive exPlanations were utilized to interpret the best-performing model., Results: The percentage of SCAS was 38.46% (n=1186) in the study population. Fourteen variables, including age, white blood cell count, and basophil count, were identified as independent risk factors for SCAS. Nine predictors, including age, albumin, and total protein, were screened for the construction of risk prediction models. After validation, the random forest model exhibited the best clinical predictive value in the training set with an AUC of 0.729 (95% CI: 0.709-0.749), and it also demonstrated good predictive value in the internal validation set [AUC: 0.715 (95% CI: 0.688-0.742)]. The model interpretation revealed that age, albumin, total protein, total cholesterol, and serum creatinine were the top five variables contributing to the prediction model., Conclusion: The construction of SCAS risk models based on the Chinese T2DM population contributes to its early prevention and intervention, which would reduce the incidence of adverse cardiovascular prognostic events., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Tusongtuoheti, Shu, Huang and Mao.)

Background: Nonalcoholic fatty liver disease (NAFLD) has emerged as a worldwide public health issue. Identifying and targeting populations at a heightened risk of developing NAFLD over a 5-year period can help reduce and delay adverse hepatic prognostic events., Objective: This study aimed to investigate the 5-year incidence of NAFLD in the Chinese population. It also aimed to establish and validate a machine learning model for predicting the 5-year NAFLD risk., Methods: The study population was derived from a 5-year prospective cohort study. A total of 6196 individuals without NAFLD who underwent health checkups in 2010 at Zhenhai Lianhua Hospital in Ningbo, China, were enrolled in this study. Extreme gradient boosting (XGBoost)-recursive feature elimination, combined with the least absolute shrinkage and selection operator (LASSO), was used to screen for characteristic predictors. A total of 6 machine learning models, namely logistic regression, decision tree, support vector machine, random forest, categorical boosting, and XGBoost, were utilized in the construction of a 5-year risk model for NAFLD. Hyperparameter optimization of the predictive model was performed in the training set, and a further evaluation of the model performance was carried out in the internal and external validation sets., Results: The 5-year incidence of NAFLD was 18.64% (n=1155) in the study population. We screened 11 predictors for risk prediction model construction. After the hyperparameter optimization, CatBoost demonstrated the best prediction performance in the training set, with an area under the receiver operating characteristic (AUROC) curve of 0.810 (95% CI 0.768-0.852). Logistic regression showed the best prediction performance in the internal and external validation sets, with AUROC curves of 0.778 (95% CI 0.759-0.794) and 0.806 (95% CI 0.788-0.821), respectively. The development of web-based calculators has enhanced the clinical feasibility of the risk prediction model., Conclusions: Developing and validating machine learning models can aid in predicting which populations are at the highest risk of developing NAFLD over a 5-year period, thereby helping delay and reduce the occurrence of adverse liver prognostic events., (©Guoqing Huang, Qiankai Jin, Yushan Mao. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 12.09.2023.)

Previous clinical studies highlighted nonalcoholic fatty liver disease (NAFLD) as a hepatic facet of metabolic syndrome, which progresses toward Type 2 diabetes along with an elevation of HbA1c in the blood. Longitudinal observations were performed in a cohort of 2811 participants with no liver disease at inception. The rate of the conversion into NAFLD was 15.7% (440/2811), with a steady increase in prevalence observed in sub-cohorts with increasing HbA1c levels. Moreover, regression analysis indicated that HbA1c levels serve as the risk factors for NAFLD after multiple adjustments (odds ratio: 1.58, P-value < 0.004). When HbA1c-related molecular networks were investigated using natural language programming algorithms, multiple genetic/small molecular (SM) pathways were highlighted as connectors between the HbA1c levels and the development of NAFLD, including ones for nitric oxide, hypoxia and receptor for advanced glycation end products (RAGE). Our results suggest that increased levels of HbA1c may contribute to the progression of NAFLD either directly, by stimulating RAGE or indirectly, through the promotion of hypoxia and suppression of the release of NO. Further studies are needed to test the impact of HbA1c on the development of the chronic liver disease., (© 2020 The Author(s).)

Background: The treatment status of type 2 diabetes mellitus (T2DM) in Ningbo has not been reported in the past. To evaluate the current status of T2DM in Ningbo and provide evidence to formulate more policies, a multicenter investigation was needed., Methods: The Ningbo Clinical Research Group of Diabetes constituted nine hospitals. Participants included 3015 patients who visited the nine hospitals from June to December 2016. General characteristics, the medication situation, the laboratory indexes in nearly 3 months consisting of glycosylated hemoglobin level (HbA1c), low-density lipoprotein cholesterol (LDL-C), and fasting blood glucose (FBG), and the results of ophthalmologic examination were investigated. The evaluation criteria were defined based on 2013 China guideline for T2DM., Results: The 3015 subjects included 1685 men and 1330 women. The average age was 63.3 ± 13.0 years. The prevalence of hypertension and dyslipidemia was 58.7% and 56.7%, respectively. In the examinees, nephropathy appeared in 11.6% and retinopathy in 14.5%. More than half (50.9%) of the subjects were overweight. The achievement rate of blood pressure (BP) was 39.6% (<140/80 mm Hg), FBG was 46.0% (4.4-7.0 mmol/L), HbA1c was 41.7% (<7.0%), and LDL-C was 51.7% (<1.8 mmol/L; and if accompanied by CHD, <2.6)., Conclusion: Ningbo City T2DM status is not optimistic, and there is a big gap with the indicators., (© 2018 Wiley Periodicals, Inc.)

Background: Gastric Helicobacter pylori (H. pylori) is linked with chronic gastritis, peptic ulcer disease, and gastric malignancy. This study aims to investigate the association of gastric H. pylori with colorectal adenomatous polyps (CAP) in the Chinese population., Methods: One thousand three hundred seventy five workers of China Petroleum and Chemical Corporation Sinopec Zhenhai Refining & Chemical Branch were recruited. Carbon-13 urea breathes test, and colorectal biopsies were utilized to detect H. pylori and CAP. The correlation between the number and distribution of CAP with H. pylori infection (HPI) was determined. Logistic regression models were applied to calculate the effect of H. pylori on the risk of CAP and pathway studio was used to attribute the cellular processes linking HPI and adenomatous polyps., Results: One hundred Eighty participants were diagnosed as CAP, and 1195 participants were classified as healthy control. The prevalence of HPI in the CAP group was significantly higher than that in the healthy control group (57.8% verse 40.1%) (p<0.001). It was the number not the distribution of CAP corrected with H. pylori status. An increased risk of CAP was found to be associated with H. pylori (OR = 3.237; 95.0% CI 2.184-4.798, p = 0.00) even after multiple parameters adjustment. Pathway studio analysis demonstrated that HPI connected with CAP at multi-level., Conclusions: HPI is associated with an increased risk of CAP in the Chinese population.