Exposure to ambient air pollution and metabolic dysfunction-associated fatty liver disease: Findings from over 2.7 million adults in Northwestern China.

Ambient air pollutants exposures may lead to aggravated Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD). However, there is still a scarcity of empirical studies that have rigorously estimated this association, especially in regions where air pollution is severe. To fill in the literature gap, we conducted a cross-sectional study involving 2711,207 adults living in five regions of southern Xinjiang Uyghur Autonomous Region in 2021. Using a Space-Time Extra-Trees model, we assessed the four-year (2017-2020) average concentrations of particulate matter with aerodynamic diameter ≤1 µm (PM1), particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5), particulate matter with aerodynamic diameter ≤10 µm (PM10), ozone (O3), sulfur dioxide (SO2), and carbon monoxide (CO), and then assigned these values to the participants. Generalized linear mixed models were employed to examine the relationships between air pollutants and the prevalence of MAFLD, with adjustment for multiple confounding factors. The odds ratios and 95% confidence intervals of MAFLD were 2.002 (1.826-2.195), 1.133 (1.108-1.157), 1.034 (1.027-1.040), 1.077 (1.023-1.134), 2.703 (2.322-3.146) and 1.033 (1.029-1.036) per 10 µg/m3 increase in the 4-year average PM1, PM2.5, PM10, O3, SO2 and CO exposures, respectively. The robustness of the findings was confirmed by a series of sensitivities. In summary, long-term exposure to ambient air pollutants was associated with increased odds of MAFLD, particularly in males and individuals with unhealthy lifestyles.

Design and development of a disease-specific clinical database system to increase the availability of hospital data in China.

Purpose: In order to meet restrictions and difficulties in the development of hospital medical informatization and clinical databases in China, in this study, a disease-specific clinical database system (DSCDS) was designed and built. It provides support for the full utilization of real world medical big data in clinical research and medical services for specific diseases. Methods: The development of DSCDS involved (1) requirements analysis on precision medicine, medical big data, and clinical research; (2) design schematics and basic architecture; (3) standard datasets of specific diseases consisting of common data elements (CDEs); (4) collection and aggregation of specific disease data scattered in various medical business systems of the hospital; (5) governance and quality improvement of specific disease data; (6) data storage and computing; and (7) design of data application modules. Results: A DSCDS for liver cirrhosis was created in the gastrointestinal department of a 3A grade hospital in China and had more than nine data application modules. Based on this DSCDS, a series of clinical studies are being carried out, such as retrospective or prospective cohorts, prognostic studies using multimodal data, and follow-up studies. Conclusion: The development of the DSCDS for liver cirrhosis in this paper provides experience and reference for the design and development of DSCDSs for other specific diseases in China; it can even expand to the development of DSCDSs in other countries if they have the demand for DSCDS and the same or better medical informatization foundation. DSCDS has more accurate, standard, comprehensive, multimodal and usable data of specific diseases than the general clinical database system and clinical data repository (CDR) and provides a credible data foundation for medical research, clinical decision-making and improving the medical service quality of specific diseases. Supplementary Information: The online version contains supplementary material available at 10.1007/s13755-023-00211-4.