1
Wen Deng,
Yaqin Zhang,
Weihua Cao,
Shuojie Wang,
Shiyu Wang,
Ziyu Zhang,
Xinxin Li,
Linmei Yao,
Zixuan Gao,
Xin Wei,
Tianyu Ma,
Dianya Qiu,
Hongxiao Hao,
Yao Xie,
Minghui Li
2026, 39(7): 799-808.
doi: 10.3967/bes2026.065
Objective Nonalcoholic fatty liver disease (NAFLD) is an increasing global health concern, with liver-related mortality increasing as fibrosis progresses. This study aimed to identify the key determinants and develop a noninvasive model to detect significant hepatic fibrosis. Methods A total of 466 patients with biopsy-confirmed NAFLD were retrospectively analyzed at Beijing Ditan Hospital between 2008 and 2018. The patients were classified into non-significant (S0–1) and significant fibrosis (S2–4) groups. Relevant features were selected using least absolute shrinkage and selection operator (LASSO) regression, followed by multivariate logistic regression to construct a model for the cross-sectional identification of significant fibrosis. Model performance was assessed using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and bootstrap validation. Results Of the 466 patients, 112 had significant fibrosis. LASSO regression identified 10 relevant features, and the model achieved an AUC of 0.919 (sensitivity, 83.9%; specificity, 85.3%) with a corrected AUC of 0.907 after bootstrap validation. It outperformed the APRI, FIB-4, and LSM (P < 0.001), and the DCA confirmed its clinical utility across probability thresholds. Conclusion The noninvasive model, incorporating demographic, laboratory, and imaging parameters, accurately identified significant hepatic fibrosis in NAFLD and outperformed existing noninvasive scores. This may facilitate interventions and guide personalized management.
2
Youjing Zhang,
Meiling Hu,
Ziyi Yang,
Jianxin Li,
Jie Cao,
Jichun Chen,
Fangchao Liu,
Keyong Huang,
Hongfan Li,
Chong Shen,
Dongsheng Hu,
Xiaoqing Liu,
Shujun Gu,
Ling Yu,
Jianfeng Huang,
Xiangfeng Lu,
Dongfeng Gu,
Shufeng Chen
2026, 39(6): 619-629.
doi: 10.3967/bes2026.034
Objective To examine the associations of sleep duration and physical activity (PA) with central obesity among Chinese adults. Methods Based on the Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR) project, 175,373 observations from 106,518 participants were included. Generalized estimating equations quantified the associations of sleep duration and PA with waist circumference (WC) and central obesity. Stratified and joint analyses were performed to evaluate combined effects, and an isotemporal substitution model was used to assess substitution effects. Results Suboptimal sleep duration (< 7 h/day or ≥ 9 h/day) and inadequate PA were associated with higher WC and an increased risk of central obesity. Compared with optimal sleep duration (7 – < 9 h/day), both longer (≥ 9 h/day) and shorter (< 7 h/day) sleep durations were associated with increased WC (0.27 cm [95% confidence interval (CI): 0.18, 0.35] and 0.15 cm [95% CI: 0.04, 0.27], respectively) and a higher risk of central obesity (odds ratio, 1.09 [95% CI: 1.07, 1.12] and 1.05 [95% CI: 1.02, 1.08], respectively). Joint analyses revealed that individuals with inadequate PA and short sleep duration had the highest WC and highest risk of central obesity. Among individuals sleeping > 8 h/day, substituting 30 min/day of sleep with moderate-to-vigorous PA significantly reduced the risk of central obesity. Conclusion Suboptimal sleep duration has a detrimental effect on central obesity, and adequate PA can mitigate this effect. The impact of reallocating sleep duration varies by sleep duration, highlighting the need to optimize both PA and sleep patterns in China.
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2024, 37(9): 949-992.
doi: 10.3967/bes2024.162
Since 1990, China has made considerable progress in resolving the problem of “treatment difficulty” of cardiovascular diseases (CVD). The prevalent unhealthy lifestyle among Chinese residents has exposed a massive proportion of the population to CVD risk factors, and this situation is further worsened due to the accelerated aging population in China. CVD remains one of the greatest threats to the health of Chinese residents. In terms of the proportions of disease mortality among urban and rural residents in China, CVD has persistently ranked first. In 2021, CVD accounted for 48.98% and 47.35% of deaths in rural and urban areas, respectively. Two out of every five deaths can be attributed to CVD. To implement a national policy “focusing on the primary health institute and emphasizing prevention” and truly achieve a shift of CVD prevention and treatment from hospitals to communities, the National Center for Cardiovascular Diseases has organized experts from relevant fields across China to compile the “Report on Cardiovascular Health and Diseases in China” annually since 2005. The 2024 report is established based on representative, published, and high-quality big-data research results from cross-sectional and cohort population epidemiological surveys, randomized controlled clinical trials, large sample registry studies, and typical community prevention and treatment cases, along with data from some projects undertaken by the National Center for Cardiovascular Diseases. These firsthand data not only enrich the content of the current report but also provide a more timely and comprehensive reflection of the status of CVD prevention and treatment in China.
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2026-6 Cover
(30 day view times: 41)
2026, 39(6).
5
2026, 39(7): 785-798.
doi: 10.3967/bes2026.064
Objective To examine the influence of metabolic dysfunction-associated steatotic liver disease (MASLD) on long-term outcomes of patients with chronic hepatitis B virus (HBV) infection. Methods A total of 3,269 participants with chronic HBV infection from the Kailuan Cohort (median follow-up: 13.7 years) were enrolled to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for outcomes associated with MASLD. In addition, 120,913 individuals without HBV infection were included to assess the independent and interactive associations between steatotic liver disease (SLD), cardiometabolic risk factors (CMRFs), and HBV infection. Results In individuals with chronic HBV infection, MASLD was not linked to primary liver cancer but was associated with an increased risk of extrahepatic cancers (HR = 1.50, 95% CI, 1.01–2.25) and cardiovascular diseases (HR = 1.98, 95% CI, 1.49–2.63), especially in participants with normal alanine aminotransferase, mild SLD, and persistent MASLD. The risk of cardiovascular disease remained elevated in the participants with reversed MASLD. Joint analysis indicated a significant synergistic interaction between HBV infection, CMRFs, and primary liver cancer. Conclusions In patients with chronic HBV infection, MASLD serves as a crucial indicator of significantly elevated systemic risk, underscoring the importance of addressing both virological control and metabolic health regardless of their current hepatic steatosis and liver enzyme status.
6
Huihuan Luo,
Yuanting Xie,
Xinyi Fang,
Bin Pan,
Yalan Xiao,
Jingyu Li,
Xiaoqing Hong,
Dongyang Han,
Wenyue Tu,
Haidong Kan,
Yanyi Xu,
Renjie Chen
Objective Prior epidemiological research demonstrated an association between short-term exposure to fine particulate matter (PM2.5) and acute diabetic events, specifically diabetic ketoacidosis (DKA). However, mechanistic investigations remain lacking to substantiate biological link. Methods Twenty 18-week-old male BKS db/db mice were randomly assigned to two groups (n = 10 per group). Ambient PM2.5 suspension (5 mg/kg in 50 μL) or an equal volume of phosphate-buffered saline was intratracheally instilled once daily for three consecutive days. Within 24 hours after the final instillation (Day 3), serum β-hydroxybutyrate was quantified, and liver tissues were collected for transcriptomic profiling (RNA-seq) to explore potential mechanisms linking PM2.5 to ketone body levels (i.e., β-hydroxybutyrate). Results The PM2.5 group exhibited higher 3-hydroxybutyric acid levels than controls. The liver transcriptome differed significantly between groups. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses indicated differentially expressed genes were primarily associated with lipid metabolism. Further, 43 genes exhibited moderate-to-strong correlations with 3-hydroxybutyric acid (16 positive, 27 negative; coefficients 0.56 – 0.76). These genes are involved in fatty acid oxidation, lipogenesis, lipid transport, glucose metabolism, and inflammation. Conclusion PM2.5 exposure may enhance ketogenesis through disruption of hepatic glucolipid metabolism, providing mechanistic insight into its potential role in acute diabetic metabolic decompensation.
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2026, 39(7): 743-744.
doi: 10.3967/bes2026.060
9
2026, 39(7): 839-846.
doi: 10.3967/bes2026.067
11
2026, 39(7): 833-838.
doi: 10.3967/bes2026.066
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2026, 39(7): 769-784.
doi: 10.3967/bes2026.063
Objective Intensive-care-unit–acquired weakness (ICU-AW), including critical illness polyneuropathy (CIP), critical illness myopathy (CIM), and critical illness neuromyopathy, is a common neuromuscular complication of sepsis. An interpretable machine-learning model for the early prediction of ICU-AW in patients with sepsis was developed and validated using the Medical Information Market for Intensive Care (MIMIC)-IV v3.1 database and local hospital data. Methods A total of 3,842 adult patients who met the Sepsis-3 criteria were enrolled to create the MIMIC-IV database. ICU-AW was defined as per International Classification of Diseases codes in the MIMIC cohort and with a Medical Research Council score of ≤ 48 in the external cohort. Baseline demographics, vital signs, severity scores, and laboratory data within the first 48 h of intensive care unit (ICU) admission were recorded. Features were selected using least absolute shrinkage and selection operator (LASSO) regression and the Boruta algorithm. The dataset was split into training and validation sets in a 7:3 ratio. Seven machine-learning models were constructed: LightGBM, XGBoost, logistic regression, Naïve Bayes, random forest, CatBoost, and a support vector machine. Model performance was assessed in terms of the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, calibration curves, and decision curve analysis. SHapley Additive explanations (SHAP) analysis was used to interpret the optimal model. Results Among 3,842 patients, 203 (5.28%) were diagnosed with CIM/CIP. Seven key features were selected using the LASSO and Boruta methods. The random forest model performed the best, with an AUC of 0.772 in the validation set and 0.753 in the external cohort. It exhibited good calibration and the highest net benefit. The SHAP analysis revealed that early antibiotic use, early mechanical ventilation, sequential organ failure assessment scores, and age were the main predictors of ICU-AW. Conclusion A random forest model using early ICU data could effectively predict the risk of ICU-AW in patients with sepsis and offer interpretation via SHAP. Thus, it may serve as a clinical decision-making tool for early risk identification and optimized prevention.
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2022, 35(11): 1025-1037.
doi: 10.3967/bes2022.131
Objective This study was designed to provide the evidences on the toxicokinetics of microplastics (MPs) and nanoplastics (NPs) in the bodies of mammals. Methods 100 nm, 3 μm, and 10 μm fluorescent polystyrene (PS) beads were administered to mice once by gavage at a dose of 200 mg/kg body weight. The levels and change of fluorescence intensity in samples of blood, subcutaneous fat, perirenal fat, peritesticular fat, cerebrum, cerebellum, testis, and epididymis were measured at 0.5, 1, 2, and 4 h after administration using an IVIS Spectrum small-animal imaging system. Histological examination, confocal laser scanning, and transmission electron microscope were performed to corroborate the findings. Results After confirming fluorescent dye leaching and impact of pH value, increased levels of fluorescence intensity in blood, all adipose tissues examined, cerebrum, cerebellum, and testis were measured in the 100 nm group, but not in the 3 and 10 μm groups except in the cerebellum and testis at 4 h for the 3 μm PS beads. The presence of PS beads was further corroborated. Conclusion After a single oral exposure, NPs are absorbed rapidly in the blood, accumulate in adipose tissues, and penetrate the blood-brain/testis barriers. As expected, the toxicokinetics of MPs is significantly size-dependent in mammals.
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Queyun Sun,
Cheng Cui,
Weiting Cai,
Lin Jiang,
Jingjing Xu,
Yi Yao,
Na Xu,
Xiaozeng Wang,
Zhenyu Liu,
Zheng Zhang,
Yongzhen Zhang,
Xiaogang Guo,
Zhifang Wang,
Yingqing Feng,
Qingsheng Wang,
Jianxin Li,
Xueyan Zhao,
Jue Chen,
Runlin Gao,
Lei Song,
Yaling Han,
Jinqing Yuan,
Ying Song
2026, 39(6): 630-640.
doi: 10.3967/bes2026.015
Objective To investigate the joint effect of free fatty acid (FFA) and the triglyceride-glucose (TyG) index on the prognosis of overweight and obese coronary artery disease (CAD) patients. Methods A total of 5,887 patients were enrolled in this study. Restricted cubic spline analyses were used to assess the dose-response relationship of FFA and TyG with major adverse cardiovascular and cerebrovascular events (MACCE). Mediation analysis was used to examine whether TyG mediated the association between FFA and MACCE. Kaplan-Meier survival curves were used to compare the cumulative incidence of events. Multivariable Cox models were used to explore the independent association between Low-/High-FFA and Low-/High-TyG on outcomes. Results FFA and TyG were independent predictors of MACCE. TyG mediated 10.7% of the association between FFA and MACCE. Patients with high FFA and TyG levels exhibited a markedly higher MACCE risk (adjusted hazard ratio: 1.951, 95% confidence interval: 1.533–2.484; P < 0.001), with a significant interaction between FFA and TyG. Among patients with elevated FFA levels, MACCE increased progressively across higher TyG tertiles (P for trend = 0.001). Conclusions FFA and the TyG index independently predict adverse outcomes in overweight or obese CAD patients, with the TyG index mediating the relationship between FFA and MACCE. Their combined assessment enhances the risk stratification in this population.
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2026, 39(6): 677-689.
doi: 10.3967/bes2026.049
Objective This study aimed to investigate the association between exposure to mixtures of environmental endocrine-disrupting chemicals (EDCs) and metabolic dysfunction-associated steatotic liver disease (MASLD) and to assess the potential mediating role of iron metabolism. Methods A total of 6,989 adults from the China Health and Nutrition Survey (2015 cycle) were included. The serum concentrations of 22 EDCs were measured. Logistic regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) models were used to evaluate the association between EDC exposure and risk of MASLD. Mediation analyses were performed to assess the mediating role of serum ferritin (SF). Results Eight EDCs were positively associated with MASLD. The WQS regression model identified six major contributors, including β-hexachlorocyclohexane, p,p’-DDT, monoethyl phthalate, acenaphthene, perfluorooctanoic acid, and perfluoro-n-pentanoic acid, in mixture effects. The BKMR model demonstrated that higher levels of EDC mixture were associated with an increased risk of MASLD. Subgroup analyses suggested stronger correlations in males and in individuals aged < 65 years. SF was estimated to mediate 11.2%–32.1% of the association between key EDCs and MASLD. Conclusion Exposure to EDC mixtures was associated with an increased risk of MASLD, with iron metabolism playing a notable mediating role. Reducing the exposure to key EDCs may help alleviate the burden of MASLD.
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Bingqing Kou,
Yifan Zang,
Bisen Liu,
Yijin Pei,
Chong Shen,
Jianxin Li,
Fangchao Liu,
Jie Cao,
Shufeng Chen,
Jianfeng Huang,
Dongfeng Gu,
Tong Wang,
Keyong Huang,
Xiangfeng Lu
2026, 39(6): 641-651.
doi: 10.3967/bes2026.044
Objective Dyslipidemia has been linked to increased arterial stiffness. However, few studies have comprehensively assessed the cumulative effects of lipid profiles on arterial stiffness. Methods Based on the initial recruitment of 7,134 participants from the China-PAR cohort, we finally included 6,717 participants with up to four repeated lipid measurements between baseline (1998–2008) and the most recent follow-up (2018–2020). Cumulative exposure to total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), non-HDL-C, and remnant cholesterol (RC) was estimated using the area under the curve method. Arterial stiffness was measured in 2018–2020 using the arterial pressure-volume index (API) and the arterial velocity-pulse index (AVI), which reflect the stiffness of peripheral and central arteries, respectively. Results Participants (mean age: 51.4 ± 10.3 years) included 2,598 men (38.68%), with a mean cumulative lipid exposure duration of 14.02 years. Cumulative TG, HDL-C, and RC were significantly associated with API levels, with adjusted βs (95% confidence intervals [CIs]) of 2.31 (1.53, 3.08), −1.14 (−2.24, −0.04), and 2.39 (1.52, 3.25), respectively, for the highest quartile compared with the lowest quartile. Restricted cubic splines showed nonlinear associations of cumulative TG and RC with API and a linear association for HDL-C (all P < 0.05). For AVI, only cumulative HDL-C showed a significant inverse association, with an adjusted β (95% CI) of −1.16 (−2.12, −0.21) for the highest quartile, and a nonlinear association was observed (P < 0.05). Conclusion Long-term cumulative TG and RC were associated with increased peripheral arterial stiffness but not central arterial stiffness, and cumulative HDL-C was negatively associated with both peripheral and central arterial stiffness. These findings underscore the importance of long-term TG and RC control along with maintaining adequate HDL-C levels.
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2026, 39(7): 745-757.
doi: 10.3967/bes2026.061
Objective Traditional disease prevention strategies that rely on fixed parameters and macro-level models struggle to capture the diversity of individual behaviors and environmental complexities. Indoor spaces with high population densities and poor ventilation, such as schools and hospitals, are particularly vulnerable to pathogen transmission. The coronavirus disease (COVID-19) pandemic highlighted the need for precise intervention strategies. Methods We developed a spatial-individual agent-based model that integrates fine-grained spatiotemporal dynamics, where transmission risk is quantified by the exact distance and duration of contact. This model was applied to a high-resolution case study of a university dormitory floor to evaluate various testing frequencies, scopes, and isolation intensities. Results Simulations showed that a dormitory-wide isolation policy outperformed individual restrictions by protecting uninfected rooms. Counter-intuitively, every-three-day class-based testing lowered infection risks compared to daily class-based testing by minimizing high-density interactions. In spatially constrained environments, stricter isolation reduces the overall outbreak duration but increases the contact transmission rate among individuals sharing the same enclosed space. Conclusion Epidemic control in high-density environments requires balancing testing frequency and isolation stringency based on spatial constraints. Under strict isolation, frequent testing is vital for breaking transmission chains. In less restrictive settings, moderately reducing the testing frequency minimizes unnecessary contact. These findings provide data-driven guidance for optimizing public health policies on campuses.
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2026-6 Contents
(30 day view times: 21)
2026, 39(6): 1-2.
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Wenxuan Zhao,
Yu Wang,
Changzhen Xiang,
Chenfeng Li,
Chen Chen,
Jiaonan Wang,
Jianlong Fang,
Feng Lu,
Kai Chen,
Shilu Tong,
Jie Ban,
Xiaoming Shi
2026, 39(7): 758-768.
doi: 10.3967/bes2026.062
Objective City-specific tools for assessing and warning about respiratory disease risks are underdeveloped, limiting effective public health response. This study aimed to develop and validate a novel city-specific prediction framework (WHAair-LSTM) for forecasting daily respiratory outpatient visits by integrating a composite air pollution health index. Methods Based on over 223.7 million hospital visits across multiple megacities, we constructed and validated a five-level morbidity-driven composite air pollution index (WHAair) for each city using city-specific exposure-response relationships. An LSTM model was built using WHAair, temperature, humidity, and historical visit data to predict next-day visits. The proposed modeling framework was developed with city-level data, and it was externally validated using datasets from other cities. Results Higher WHAair levels were significantly associated with increased outpatient visits. The model demonstrated excellent predictive performance (Beijing: R2 = 0.963, RMSE = 53.5) and effectively captured visit surges. Excluding WHAair degraded model accuracy (ΔRMSE = +44.1%). The framework maintained robust performance in external validation, confirming its transferability. Conclusion The WHAair-LSTM framework provides a scalable and practical tool for city-level respiratory disease early warning by bridging environmental monitoring with clinical practice.
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