Material utilisation has been playing a fundamental role in economic development, but meanwhile, it may have environmental and social consequences. Given China’s rapid economic growth, understanding China’s material utilisation patterns would inform decisions for researchers and policymakers. However, fragmented data from multiple statistical sources hinder us from comprehensively portraying China’s material utilisation dynamics. This study harmonised China-specific official statistics and constructed a China economy-wide material flow accounts database. This database covers hundreds of materials and more than 30 years (1990–2020) from thousands of data sources, which is comprehensive, long-term, up-to-date, and publicly accessed. This database would provide insights into the historical metabolic dynamics of China’s economy with elaboration on the production, consumption, and end-of-life disposal of materials. This database also allows for international analyses since it is developed based on an internationally standardised analytical framework. Furthermore, this study would benefit studies on policy impact evaluation, environmental pressure assessment, and sustainable development strategies.  That is, the utilisation of materials has been playing a fundamental role in economic growth and well-being, but at the same time, it may have environmental and social consequences. China has been rapidly developing with a massive number of materials utilised. Understanding China’s historical material utilisation patterns are essential for formulating appropriate policies for achieving global Sustainable Development Goals (SDGs)2, which necessitates a detailed quantitative picture of material production, consumption, and end-of-life disposal.Materials significantly contribute to sustainable development in a direct and indirect way3, which depicts how the national economy system receives external materials and energy  from and releases wastes  to the environment. This framework was initially established in 2000 by the European Commission Statistical System (Eurostat) and was improved and adopted as an internationally standardised environmental-economic system accounting method by the Statistical Commission of the United Nations (UN) in 2012. The latest version of this framework was released in 20184. Based on this analytical framework, several EW-MFA indicators were derived to depict the metabolic processes5, which aid in developing effective strategies6. For example, the SDG targets for resource productivity (SDG 8.4) and sustainable use of natural resources (SDG 12.2) can be evaluated with EW-MFA results of the domestic material consumption. Previous EW-MFA studies on China’s economy revealed amounts, components, efficiencies, etc. of material utilisation for particular purposes, such as, monitoring the progress of the circular economy between 1995 and 20157, analysing policy effects between 1990 and 2002 for sustainable development5 and between 2000 and 2010 for circular economy8, formulating the 13th Five-Year plan strategies (2016–2020) based on historical patterns from 1992 to 20149. However, a long time-series profile of China’s rapid growth since the 1990s has yet to be identified.There have been several attempts to explore national material utilisation. Some studies focused on developing and improving the statistical accounting framework for quantifying material utilisation in terms of flows. The economy-wide material flow analysis (EW-MFA) is the origin and most widely used10, Eurostat foreign trade statistics, the United States geological survey (USGS), etc., were used in the EW-MFA of the EU. Before being applied to estimates, these original statistics need to be adjusted and aggregated. However, due to an incomplete understanding of data gathering and pre-processing procedures, uncertainties and biases may exist in further data interpretation3. For example, the USGS uses the weight unit of metal content to record the quantity of mineral production, whereas the China statistical department uses the gross ore unit, resulting in considerable differences in values. So far, existing data compilation tools are country/area-specific  or year/period-specific , which are inapplicable to the China’s statistics. In addition to the differences between multinational datasets, uncertainties may arise, for example, from inevitable artificial errors during data preparation. However, it is time-consuming and labour-intensive to assess reliability related to data collection procedures since original statistics are fragmented over multiple databases and have to be processed before applications.Other studies have focused on harmonising data compilation procedures for EW-MFA application, which are essential for comparative analyses across multiple nations and periods. Typically, estimates of national material utilisation are based on a variety of data sources, which were gathered using various statistical criteria. For instance, agricultural statistics from the UN Food and Agriculture Organization (FAO)Quantifying the in-use material stocks to evaluate the progress of the circular economy.Analysing the sustainability of material use for any substances or materials in China to evaluate the progress toward the SDGs.Compilating the Physical Input-Output Tables (PIOT) to aid in the analysis of the effects of policy on specific economic activities.In this study, we first identify system boundaries, material types, materials, and processes related to China’s economy based on the internationally-standardised analytical framework , examine the reporting standards, material coverages, and time periods of official China-specific statistics, apply them to characterise China’s material utilisation patterns between 1990 and 2020, and analyse uncertainties from data collection and estimation procedures. This dataset covers hundreds of materials that have been extracted, emitted, imported, exported, and stored in China for more than thirty years. This dataset can be applied to the studies, including but not limited to:The first step is to define an economy, i.e., the economic  territory of a country in which the activities and transactions of producer and consumer units are resident. Additionally, the period is a total of thirty-one years, from 1990 to 2020, for the following reasons: (1) statistics before 1990 are of poor quality and are insufficient to allow us to conduct analyses; and (2) so far, statistics have just recently been updated to cover the year of 2020. Furthermore, the analytical framework (hereinafter referred to as China EW-MFA) is developed to explore material utilisation and its environmental consequences within China’s economy.(1) Input of extracted resources: domestic natural resources are extracted from the environment to the economy through human-controlled means. (2) Output of domestic processed materials: after being processed by manufacturers, materials are released from the economy into the environment in the form of by-products and residues, which can be classified by their destinations  and pathways (dissipative use and losses). (3) Input and (4) output by cross-border trade: by imports and exports, materials are transported between China’s economy and the economies of the rest of the world. (5) Input and (6) output of balancing items (BI): sometimes, materials identified in the output processes are not considered by inputs, which needs to be balanced. For example, the utilisation of fossil energy materials by combustion causes the emission of carbon dioxide (CO2) into the air, which is identified as system output, but requirements of oxygen (O2) as system input are not counted. (7) Additions to the system: within the economy, materials would have been added to the economy in the form of buildings, infrastructures, durable goods, and household appliances, which are referred to as the net additions to stock (NAS).The general structure of China EW-MFA is depicted in Fig. 2, methane (CH4), dinitrogen oxide (N2O)), air pollutants , black carbon (BC)), and toxic contaminants of mercury (Hg) in divalent, gaseous elemental, and particulate forms. Those released into the water are inorganic matters (of nitrogen (N), phosphorus (P), Arsenic (As), and four heavy metals of lead (Pb), mercury (Hg), cadmium (Cd), and chromium (Cr)) and organic matters of cyanide, petroleum, and volatile phenol. Materials released into the land are waste disposal in uncontrolled landfills, which are illegal in China. Some materials are dissipated by application, for example, fertilisers, compost, sewage sludge being applied to agricultural land, and pesticides being used to cultivate crops. Some would be unintentionally dissipated from abrasion, corrosion, erosion, and leakages. Materials (in red boxes) are BI, which includes the input of O2 and output of water vapour in the fossil energy material combustion process, the input of O2 and output of water vapour and CO2 in the respiration process of human and cultivated livestock, input and output of water in imported and exported beverages, and the output of water from domestically extracting crops.The last step is to specify the materials concerned in each process. Four types  Material of water is not included since its flow volume is more substantial than others, which needs to be independently analysed; (2) Activities of foreign tourists, cross-border transfer of emissions through natural media, etc. are excluded. (3) To be clear, we refer to a data cell as a specific flow process of a specific substance in a specific year, e.g., the number of cereals domestically extracted in 2020.Based on our China EW-MFA, we first analyse accessibility, reliability, completeness, rules of redistribution, etc., for each data source 16 during 1990–1994 are unavailable, which is estimated based on the relationship between the production yield (PYield) and areas (AVegetables), as shown in Eq. PYield is assumed to remain constant at 27.04 thousand tonnes per thousand hectares from 1990 to 1995, derived by dividing vegetable production  by areas  in 1995.Nuts in crops: One of them is chestnuts. The chestnut production in 2020 is unavailable, which is assumed to be the same as in 2019.Crop residues in biomass residues: They are referred to as that harvested production of crops that do not reach the market to be sold but are instead employed as raw materials for commercial purposes such as energy generation and livestock husbandry. This number (Wcrop residues) can be calculated by first determining the number of crop residues available from primary crop production (Wcrop) and the harvest factor (Pharvest factor), and then using the recovery rate (Precovery rate) to determine the number of crop residues used by the economy, as shown in Eq. 18, which are adopted in this study, i.e., wheat (1.1 for Pharvest factor and 0.463 for Precovery rate), maize , rice , sugar cane , beetroots , tuber , pulse , cotton , fibre crops , silkworm cocoons , and oil-bearing crops .Roughage of grazed biomass and fodder crops in biomass residues: In China, the grazed biomass for roughage includes annual forage and perennial forage, whereas fodder crops comprise straw feed, processed straw feed, and all other fodder crops. However, information19 on grazed biomass production is only accessible from 2006 to 2018, whereas fodder crop statistics are only available from 2015 to 2017. Equation WDomestic production) in requirement (WRoughage requirement) has remained constant, while the proportion (PSupply fraction) of grazed biomass and fodder crop in domestic roughage production has been unchanged. The requirement (WRoughage requirement) is determined by the quantity of livestock (QLivestock) and their annual feeding amount . PAnnual intake (in tonnes per head per year) has been localised for each type of livestock4, with 4.5 for live cattle and buffaloes, 0.5 for sheep and goats, 3.7 for horses, and 2.2 for mules and asses.Timber in wood: As illustrated in Eq. 16 is reported in volume units of cubic metres (VTimber), which need to be converted into mass units (WTimber) via density (PDensity). The parameter PDensity is assumed to be 0.58 tonnes per cubic metre, calculated by averaging 0.52 for coniferous types and 0.64 for non-coniferous ones4.Non-ferrous metals in metal ores: Non-ferrous metal statistics are derived from two sources. China statistics20 are measured in gross ore  but are only available from 1999 to 2017, whereas the USGS statistics21 cover the period of 1990 to 2020 but they are measured in metal or concentrate content . Therefore, USGS statistics need to be converted with an empirical unit conversion factor (PUnit conversion factor) before being applied to estimate unavailable statistics reported by China, as shown in Eq. Non-metallic minerals: The official China-specific information on non-metallic mineral domestic production is available between 1999 and 201720, the rest of which could be estimated from USGS statistics (1990–2020)21. Also, two differences in reporting standards are observed resulting from the material coverages and reporting units. China statistics contain eighty-eight materials in mineral ores, whereas the USGS only includes twenty in the concentrate unit. Therefore, a conversion factor is developed in this estimation, as shown in Eq. Coal in fossil energy materials: Coal, mined in China, includes raw coal, peat, stone coal, and oil shale. Except for raw coal, statistics for the rest are only available from 1999 to 201720. The unavailable data  is estimated using Eq. PSupply fraction) of peat, stone coal, and oil shale in raw coal production  remains constant, so the 1999 proportion is applied to all years before that (earlier years of 1990–1998), while the 2017 proportion is used to the recent years between 2018 and 2020. For example, PSupply fraction for oil shale production was assumed to be 0.014% during 1990–1999, calculated by dividing raw coal production  by oil shale production (179) in 1999. PSupply fraction in the earlier and the recent years are 0.007% and 0.001% for peat, 0.203% and 0.031% for stone coal, and 0.014% and 0.067% for oil shale.The output of processed materials by releaseMaterials released into the air: In China, thirteen materials are released into the air, as shown in Fig. 2) is reported in China environmental statistical yearbooks23, while the rest is specified in the EDGAR24. However, in EDGAR, statistics for recent years have not yet been updated, which are estimated with the value in the most recent year in our database. For example, nitrous oxide (NOx) records are only available for the years prior to 2016, with 26,365 thousand tonnes in 2015 and 26,837 in 2014. As a result of the observed decreasing trend in NOx emissions, NOx emission data for 2016–2020 is estimated to be 26,000 thousand tonnes. This estimate may be subjective due to constraints, but it would be aligned with European statistics, allowing for international comparisons. Data can be updated after the EDGAR statistics have been updated.Materials released into the water: Ten principal materials have been found in China wastewater  that are nitrogen (N), phosphorus (P), organic pollutants of petroleum, volatile phenol and cyanide, heavy metals of mercury (Hg), lead (Pb), cadmium (C·d), and the hexavalent chromium (Cr6+), and arsenic (As). Many statistics23 have been of poor quality . Given that the statistics of pollutants in industrial wastewater cover more periods and contain fewer abnormal observations, the total material emissions can be approximated from those of industrial wastewater. Equations WIndustrial materials) are first identified using material mass concentration (PConcentration) and the weight of industrial wastewater , and then the materials in total wastewater  are identified using the proportion (PContribution) of materials in industrial wastewaters  to the total. The assumption is that PConcentration and PContribution change gradually between years, which enables to use linear interpolation method to estimate unavailable parameters. Consider cyanide: its PConcentration was 23.61 (1‰ ppm) in 2005 and 37.31 in 2002, which was assumed to be 28.18 in 2004 and 32.74 in 2003. PConcentration was assumed to be 100% throughout the years for cyanide because all cyanide emissions in China are driven by industrial wastewater discharges. Later, the total material emissions can be derived by dividing the industrial wastewater mass by PConcentration.Materials released to theland: This is zero because uncontrolled landfills are illegal in China.Materials dissipated by organic fertiliser use: In China, manure is the primary organic fertiliser, which is excreted by pigs, dairy cows, calves, sheep, horses, asses, mules, camels, chickens, and other animals. As shown in Eq. WManure) is estimated through the amounts of raised livestock , the weight of daily manure production , the number of days they are raised , and the moisture content of their manure  for each type of animal. These parameters are region-specific, which have been localised by Chinese scholars27 and listed in Table Materials dissipated by mineral fertiliser use: The mineral fertilisers used in China are four types, i.e., nitrogen (N), phosphorus (P), potash (K), and compound. Their usage (WFertiliser usage) is measured in nutrient mass , which needs to be converted into the gross mass by dividing their nutrient content (PNutrient content). Equation PNutrient content is localised by the Ministry of Agriculture and Rural Affairs of China28 as 29%, 22%, 35%, and 44% for N- bearing, P- bearing, K-bearing, and compound fertilisers, respectively.Materials dissipated by sewage sludge: Sewage sludge is the residue generated by municipal wastewater treatment. As demonstrated in Eq. Wss, dissipation) is the untreated amount of production , represented by the parameter of Pss, dissipation rate. Sewage sludge production  statistics are only available for the years 2006–202029, and data for the remaining years can be estimated using Eq. Pss, production rate represents the relationship between sewage sludge production  and wastewater treatment , and in Eq. Pww, treatment efficiency represents the relationship between the quantity of treated wastewater  and the treatment capacity . In this estimation, three assumptions are made. The first is to estimate Www, treatment, Pww, treatment efficiency is assumed to be unchanged at 63% during 1990–2001, given it has been increasing from 63% in 2002 to ~80% in recent years. The second is that, in order to estimate Wss, production, Pss, production rate is assumed to be unchanged at 3.5 between 1990 and 2005, suggesting 3.5 tonnes of sewage sludge are generated by processing 10,000 cubic metres of wastewater. This assumption is determined by that Pss, production rate is approximately 3.5 during 2006–2010 while declines sharply and stabilises at around two during 2011–2020. The last is, to estimate the Wss,dissipation, Pss,dissipation rate is assumed to be 5% between 1990 and 2005, given it has been around 5% during 2006–2020.Materials dissipated by composting: Composting is a natural process that uses microbes to turn organic materials into other products, which are then used for fertilising and entering the environment. In China, composting has been used to treat two materials: feces and municipal waste, whose quantities (WComposting) were only available from 2003 to 201029. The unavailable data can be estimated using Eq. WComposting) is proportionally related to the fresh weight of all treated materials , the proportion treated by composting (PComposting rate), and the dry content (PDry matter). Considering that China’s composting capacity has been declining since 2001 due to the implementation of waste incineration power generation technologies30, Pcomposting rate is assumed to be the same as it was in 2003 (9.5%) between 1990 and 2002, and 1.5% in 2010 between 2011 and 2020. The parameter of PDry matter is 50%4.The input and output by cross-border trade. Statistics of imports and exports have been gathered since 1962 and stored in the UN Comtrade database31. However, the data quality issue of outliers, and missing values, especially in weight, is reportedly identified. In our previous work, we addressed these issues, and an improved database32 is provided. Details about our estimation methods can be found in publications35. As UN Comtrade lists 5,039 different commodity types (in 6-digit HS0 commodity code), yet only 18 material types are specified in the China EW-MFA, UN Comtrade statistics need to be aligned to the China EW-MFA framework. Therefore, we compared each commodity and each material type between them and established a correspondence table to map UN Comtrade commodity types onto our EW-MFA material types. For example, non-ferrous metal materials of China EW-MFA include commodities, such as copper ores and concentrates (260300 HS0 code), silver powder (710610), manganese, articles thereof, and waste or scrap (811100), etc., whereas biomass residues include cereal straw and husks (121300), lucerne meal and pellets (121410), and other fodder and forage products (121410). This correspondence table between HS0 and EW-MFA classification for imports and exports is provided in Supplementary File The input of balancing itemsO2required for combustion: In BI, requirements for materials can be abstracted as equalling exogenous demands minus intrinsic supplies (Eq. 2 requirements by the combustion process: (1) demanding exogenous oxygen to oxidise elements  released into the air, (2) demanding exogenous oxygen to oxidise the hydrogen embedded in fossil energy materials, and (3) providing intrinsic oxygen embedded in fossil energy materials. The first part can be estimated via Eq. WEmissions) of CO2, N2O, NOx, CO, and SO2 by their oxygen content (POxygen content). For the second  via molar mass conversion factor (PMass conversion factor). PMass conversion factor equals 7.92, derived by the molar mass of one oxygen (16 g/mol) divided by that of two hydrogen atoms (2 × 1.01 g/mol). The last is the intrinsic supplies from fossil fuel materials, which is identified via Eq. WFossil fuel materials) by their oxygen content (POxygen content). The parameters in this estimation are presented in Table DMC), which equals domestic extraction (DE) plus imports (IM) and minus exports (EX).lies Eq. . Three pcond Eq. , the oxyals, and  providinO2required for respiration: O2 is required by the metabolic activities of living organisms, the majority of which are humans and livestock. Bacteria are another sort of organism, which are not included in this estimation because their O2 requirements are too small to be quantified. The respiration-required O2 is related to the total quantity (QOrganisms) and their respiration activity by organism types, as shown in Eq. PRespiration requirement coefficient), which is the average quantity of O2 that each organism utilises to maintain the metabolic activity, as listed in Table Water required for thedomesticproduction of exported beverages: The exported beverages are produced domestically using domestically extracted materials, especially a large amount of water. The weight of water is considered in the output by cross-border trade but is not included in the domestic extraction input. The resulted imbalance can be identified by specifying the water weight in beverages, i.e., multiplying the traded beverage weight  by a parameter of the water content (PWater content), as given in Eq. 32, with PWater content of 85% for the first and 90% for the latter4.The output of balancing items.Water vapour from combustion: Water vapour emissions by domestically combusting fossil fuel materials are contributed by two paths. The direct evaporation of embedded water is the first path . The PMoisture content for each type of fossil fuel material is listed in Table PMass conversion factor), as given in Eq. PMass conversion factor equals 8.92 by dividing the molar mass of water (18.02 g/mol) by that of two hydrogen atoms (2 × 1.01 g/mol).path Eq. , which cWater vapour and CO2from respiration: Respiration activities of organisms will produce water vapour and CO2, whose estimation is similar to that of O2 requirements. As shown in Eq. QOrganisms) and the respiration activity by organism types. The latter is represented by the parameter of respiration emission coefficient (PRespiration emission coefficient), which is specified in Table 2 for each type of organism.Water from imported beverages: The estimation approach is the same as water by the domestic production of exported beverages, as described in Eq. Water in biomass products: Usually, the input of biomass products by domestic extraction16 has been measured in fresh weight, but their corresponding output29 by sewage sludge, composting, etc., are in dry weight, leading to an imbalance in water weight. The water weight in biomass products is calculated by multiplying their domestic extraction amount in fresh weight (WBiomass) by a parameter of moisture content at harvest (PMoisture content), as shown in Eq. PMoisture content by biomass products are presented in Table A few inconsistencies in statistics were noticed, which would result in data incompleteness. For example, the domestic extraction of vegetables has been accounted for and published since 1995, before which statistics are unavailable. The domestically harvested timber has been measured in the volume unit of cubic metres, which needs to be converted into the mass unit via density conversion factor. Therefore, acquired statistics have to be estimated, which are specified in grey colour in Fig. Domestic extraction (DE): is referred to as natural materials that are extracted from the domestic environment and are used in the domestic economy, i.e., the total input of natural materials by extraction.Domestic processed output (DPO): is referred to as materials that are released to the domestic environment after being processed in the domestic economy, i.e., the total output of processed materials by release.Import (IM): is referred to as all goods  that originated from other economies and are further used in the domestic economy. It is calculated as the sum of all imported goods.Export (EX): is referred to as all goods that originated from the domestic economy and are transported to other economies to be used. It is calculated as the sum of all exported goods.Domestic material input (DMI): is referred to as materials that originated from the domestic environment by extraction and other economies and are available (to be used or to be stored) for the domestic economy. It is calculated as the sum of DE plus IM, as shown in Eq. Domestic material consumption (DMC): is referred to as materials that are directly used in the domestic economy after parts of them are exported to other economies. It is calculated as the difference between DMI and EX.Physical trade balance (PTB): is referred to as a surplus or deficit of materials for the domestic economy. It is calculated as the difference between IM and EX.Net additions to stock (NAS): is referred to as materials that remain in the domestic economy. It is calculated by taking BI items into account, as shown in Eq. The above attempts have quantified material inputs and outputs by flows and presented a detailed profile of material utilisation for each material in China’s economy. In order to depict the economy in a more general way, EW-MFA indicators are assessed by aggregating flows by materials or periods as below.36 and at our online website(https://www.macycle.org/china-ewmfa/). Five Excel files in .xlsx format provide detailed information on data compilation, including original statistics, data sources, and estimation methods (using formulas). Being interoperable and reusable, the ready-to-use China EW-MFA data are prepared in two files (EWMFA_Flows.csv and EWMFA_Flows.json). Data records in each file are introduced in brief as follows.Domestic Extraction.xlsx: This file contains original statistics as well as data sources and estimated data related to the quantification of DE. from 1990 to 2020. In addition, the compilation tools for DE estimation as described above are implemented in the file using formulas only. As shown in Fig. DE increased rapidly from 1990 to 2010 and then remained flat from 2011 to 2014, with a slight decrease after 2015. In addition, the percentage of each material changed slightly. The extraction of non-metallic minerals has accounted for the majority, followed by fossil energy materials. However, the gap between them has shrunk, which was almost the same during 2012–2014. This percentage of biomass has been falling, with 20% in 2000 but 15% in 2020. With regard to metal ores, their DE percentage increased slightly from 2000 (7%) to 2015 (16%) but then fell to 10% in 2020.More specifically, Fig. Domestic Processed Output.xlsx: This file contains original statistics as well as data sources and estimated data related to the quantification of DPO from 1990 to 2020. In addition, the compilation tools for data estimation as described above are implemented in the file using formulas only. As indicated in Fig. 2 emissions to the air contribute to the majority (greater than 90%) of the DPO, with 92.4% in 1990 and 98% in 2020, as shown in Fig. 4. As shown in Fig. DPO to water is found to be decreasing from 9.5 in 1991 to 1.2 million tonnes in 2019, suggesting an improved water quality in China. N is the primary material in DPO to water, which is sourced from fertiliser applications and discharges from livestock, domestic and industrial sources37. Sharp decreases in N emissions are observed from 4.61 million tonnes in 2015 to 1.24 in 2016, which can be ascribed to China’s 13th Five Year Plan’s efforts to upgrade urban sewage systems, increase wastewater treatment rates, and reduce fertiliser usage38. However, a rise is observed in 2020, which may be attributed to rising municipal wastewater volumes during the pandemic period and the corresponding increasing emission of N39. The rest materials released into the water are mostly released by industrial wastewater, whose emission has been declining from 0.105 in 1990 to 0.004 (3%) million tonnes in 2020. The emissions by dissipative use .xlsx: It contains flows and derived indicators from 1990 to 2020 for China’s economy and for each material category, which is the result calculated from the above statistics. Indicators are DE, IM, EX, DMI, DMC, NAS, and PTB, for China’s economy, biomass, metal ores, non-metallic minerals, and fossil energy materials respectively, while BI, and DPO for China’s economy. Figure IM in DMI, which represents the import dependency. Both DMI and DMC have experienced remarkable growth, with no significant difference between them. This indicates that most input materials are utilised by China’s economy, and China has been rapidly developing. Moreover, this development has been intensively utilising domestic resources, given that ~90% of DMI is contributed by DE. However, the percentage of IM in DMI has been continuously increasing from 2.5% in 1992 to 18.3% in 2019, indicating that China’s economy is becoming more reliant on imported resources. This greater dependence is also observed with a gradual increase of PTB in Fig. EWMFA_Flows.csv and EWMFA_Flows.json: These two files contain the whole dataset  in an interoperable format, which can be directly applied in studies.A total of seven data files are accessed publicly at figshare repositoryRegarding our study, this database is designed to provide officially verified data that are related to the material flows in China by collecting and compilating China-specific statistics. Therefore, the data quality of this database is ensured by the official statistics’ data quality. Our data validation should be established to verify that any data values are consistent with those officially published statistics. This is verified by manually reviewing any data values repeatedly using a developed data quick-check tool. This tool connects every data cell to its source (in any format), making it easy and convenient for a manual double-check of data consistency to improve the reliability of this study. This tool was developed with the Office Visual Basic for Applications (VBA) and is built into the data collection files. The instructions and source codes for this tool can be found in Supplementary File The major uncertainty of this constructed database is caused by artificial mistakes  during statistic collection procedures. Trying to reduce this, we repeatedly review the data values of this dataset and compare them with the officially-published statistics. So far, this statistic collection procedure still must be done manually, because original statistical publications are unavailable for direct use. The reasons are (1) most original statistical publications are stored in only human-understandable data format, and (2) these formats and also statistical criteria  have been continuously changing. As a result, before statistics are applied, these publications must be compared and converted, both of which must be done manually. So far, this study has integrated compilation methods with a pre-defined data format, allowing the material flows to be determined correspondingly by re-organizing the collected statistics into each data cell. It works like a calculator, with statistics in and material flow results out, which to a large extent has reduced uncertainties in the flow quantification. For future studies, the focus could be put on converting original statistical publications into a data format that a machine can directly understand and process, using technologies, such as pattern recognition and natural language processing. These efforts could reduce human-related uncertainties to a larger extent.PNutrient content for China-used mineral fertilisers has been identified by the China statistical department. However, some parameters have not been localised, for example, the moisture content of fossil fuel materials in China. These parameters were adopted as indicated by the Eurostat4, which could allow international comparisons but may lead to differences between the estimated results and the facts. In addition, uncertainties may inevitably stem from our subjective parameter assumptions, that parameters are assumed to be static or changing linearly during a period. These assumptions may cause under-or overestimation of flows and related indicators. For example, the yield of vegetable production (PYield) is assumed to be unchanged from 1990 to 1994, which may result in small biases in estimations of DE, BI (Output of water in biomass products), and other indicators. However, minor uncertainties are likely to arise because only a small percentage of flows are estimated based on assumptions.In data estimation, parameter uncertainties always exist since parameters are rarely perfectly accurate. In this study, trying to mirror the reality as closely as possible, we used most of the parameters that had been localised as China-specific by previous studies. For example, In this study, uncertainties stem from artificial errors during statistic collection and subjective parameter selection during data compilation.Supplementary File 2Supplementary File 4Supplementary File 3Supplementary File 1


To develop and validate a predictive nomogram for idiopathic rapid eye movement (REM) sleep behavior disorder (RBD) in a community population in Beijing, China.via a calibration curve. Decision curve analysis (DCA) was performed to evaluate the clinical value of the model.Based on the validated RBD questionnaire-Hong Kong (RBDQ-HK), we identified 78 individuals with possible RBD (pRBD) in 1,030 community residents from two communities in Beijing. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify candidate features and develop the nomogram. Internal validation was performed using bootstrap resampling. The discrimination of the nomogram was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, and the predictive accuracy was assessed p = 0.011], smoking , physical activity (≥4 times/week) , exposure to pesticides , constipation , depression , and daytime somnolence . The nomogram displayed good discrimination, with original AUC of 0.885 , while the bias-corrected concordance index (C-index) with 1,000 bootstraps was 0.876. The calibration curve and DCA indicated the high accuracy and clinical usefulness of the nomogram.From 31 potential predictors, 7 variables were identified as the independent predictive factors and assembled into the nomogram: family history of Parkinson's disease (PD) or dementia [odds ratio (OR), 4.59; 95% confidence interval (CI), 1.35–14.45; This study proposed an effective nomogram with potential application in the individualized prediction for pRBD. Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by loss of normal muscle atonia during the REM sleep, presenting with complex behaviors associated with a nightmare . The golIdiopathic RBD (iRBD) is by far the strongest risk factor for prodromal synucleinopathies , a groupAlthough many predictive factors for iRBD have been proposed, none of them has been integrated into a prediction model. The nomogram has been accepted as a reliable tool to create a simple intuitive graph of a statistical predictive model that quantifies the risk of a clinical event , 17. Nomn = 133), the persons moved before recruitment (n = 39), mobile phone was not connected (n = 55), or deceased (n = 5), resulting in a final source population of 1,068 persons. Among them, there were 38 individuals who missed data for some critical assessments and, thus, were excluded from the study  and 13 community units of a suburb district (Fangshan district). In total, 1,300 persons were invited, of whom 232 were not eligible, mainly because inclusion criteria were not met  . The RBDhttp://www.R-project.org/). Categorical variables were expressed as frequencies and percentages, while continuous variables were described as medians and interquartile ranges (IQRs). Mann-Whitney U tests (for continuous variables with skewness distributions) and the chi-square test  were utilized to compare the differences in characteristics between groups. The R package “glmnet” was used to perform the least absolute shrinkage and selection operator (LASSO) regression and the R package “rms” to establish the nomogram and calibration curve. The R package “pROC,” “plotROC,” and “rmda” were applied to generate the receiver operating characteristic (ROC) and decision curve analysis (DCA). All tests were two-tailed and p < 0.05 was defined as statistically significant  regression with 10-fold cross-validation was used for the most useful predictive factors from the initial dataset to address multiple cross-related covariates and reduce the risk of overfitting the data . It addsp > 0.05 was considered as well calibrated.Model performance is divided into two main categories: discriminative ability and model calibration. The discriminative ability assesses whether the model is able to differentiate between patients with a favorable and an unfavorable outcome. It is expressed by the area under the curve (AUC) of a ROC curve . Model cInternal validation evaluating the stability of a prediction model to random changes in sample composition was performed by the bootstrap resampling, in which regression models were fitted in 1,000 bootstrap replicates, drawn with replacement from the development sample . The modThe clinical usefulness of the nomogram was determined by DCA by quantifying the net benefit to the subjects under different threshold probabilities .Subjects with pRBD were more probable to report a family history of PD or dementia than subjects without pRBD and were more likely to be smokers or alcohol drinkers when compared with subjects without pRBD. Subjects with pRBD had a lower frequency of physical activity than those without. We found a significantly higher occurrence of exposure to pesticides in subjects with pRBD than in those without. We also found an increased prevalence of constipation, depression, and daytime somnolence among subjects with pRBD. On the SCOPA-AUT scores, subjects with pRBD had a higher score than those without. No statistically significant differences were found in other factors between the subjects with and without pRBD.A LASSO regression model with 10-fold cross-validation was employed to select predictive variables among the preliminarily screened factors . A totalp = 0.011], smoking , physical activity (≥4 times/week) , exposure to pesticides , constipation , depression , and daytime somnolence , 4.59; 95% confidence interval (CI), 1.35–14.45; p = 0.980). A calibration curve with 500 bootstrap resamples is presented in p = 0.956).The nomogram is generated and shown in The DCA for the nomogram is presented in The current study has developed a nomogram for predicting pRBD in the community population. It is well discriminated and calibrated for the personal prediction and facilitates individualized treatment. This will improve clinical decisions for clinicians and help patients to get more net benefits. To our knowledge, this study is the first to develop a nomogram for predicting risk for individuals with pRBD. The nomogram visualized the predictive model and made clinical use convenient.Different from the traditional multivariate logistic regression analysis method, this study adopted the LASSO regression analysis, which performed well in reducing the data dimensionality and decreasing multicollinearity between variables, and it has emerged as a powerful tool under the theory of bias-variance tradeoff in the variable selection , 21. ComIn this study, we found that genetic factors, lifestyles, and environmental exposures are strong predictors of pRBD, which are mostly consistent with previous studies but may differ from those previously defined for neurodegenerative synucleinopathies –15, 26. We also identified a strong relationship between pRBD and several other non-motor symptoms of PD. There was a positive association between RBD and comorbid depression , 38. HowIdiopathic RBD, which represents a premonitory symptom of impending neurodegeneration, offers a precious time window, so early identification of pRBD is important to allow neuroprotective and early management strategies to be administered prior to the neurodegenerative diseases. In this study, we used the screened predictors to construct and validate the nomogram model. We found and validated that a combination of family history of PD or dementia, smoking, pesticide exposure, and several non-motor symptoms are useful for predicting pRBD. Since it combines simple and easy-to-collect variables that can easily be gathered in the clinic and communities, the model provides an accurate, non-invasive, low-cost, and rapid method for screening high-risk individuals for pRBD and neurodegenerative disorders. In addition, due to the strength and utility of this nomogram and the easy way to implement, the nomogram is likely to be useful in large population-based primary screening studies.There are several limitations of this study. First, a major limitation is that the diagnosis of pRBD was completely based on a self-report questionnaire but not the formal PSG confirmation , 47, 48.In conclusion, the nomogram we developed can successfully predict pRBD risk for elderly subjects. Future studies with larger numbers of participants will be useful for updating and validating this nomogram to improve predictions of pRBD risk among the community population.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Medical Ethics Committee of Xuanwu Hospital of Capital Medical University ([2020]060). The patients/participants provided their written informed consent to participate in this study.HL designed the study, analyzed the data, interpreted the results, and wrote the manuscript. X-YL, JH, WL (4th author), FX, JZ, RH, HW, LC, JY, XL, YS, and WL (15th author) collected the data. XW, ZW, RK, YL, and JX supervised the study. YD and QY designed the study and supervised the study. CW designed the study, supervised the study, and revised the manuscript. All authors contributed to the article and approved the submitted version.This study was supported by grants from the National Natural Science Foundation (81771212 and 82171412), the Ministry of Science and Technology (2016YFC1306000), and the Special Fund from Key Laboratory of Neurodegenerative Diseases, Ministry of Education of China (PXM2019_026283_ 000002).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Alternaria toxins often co-occur in cereal-based products, but the current risk assessment is commonly conducted for only one type of mycotoxin at a time. Compared to adults, infants and young children are more susceptible to mycotoxins through food consumption, especially with cereal-based food products which are the main source of exposure. This study aimed to perform a probabilistic risk assessment of combined exposure to DON and three major Alternaria toxins, namely including alternariol monomethyl ether (AME), alternariol (AOH), and tenuazonic acid (TeA) through consumption of cereal-based foods for Chinese infants and young children. A total of 872 cereal-based food products were randomly collected and tested for the occurrence of DON and three major Alternaria toxins. The results on mycotoxin occurrence showed the DON, TeA, AOH, and AME was detected in 56.4%, 47.5%, 7.5%, and 5.7% of the samples, respectively. Co-contamination of various mycotoxins was observed in 39.9% of the analyzed samples. A preliminary cumulative risk assessment using the models of hazard index (HI) and combined margin of exposure (MoET) was performed on DON and Alternaria toxins that were present in cereal-based food products for infants and young children in China for the first time. The results showed that only 0.2% and 1.5%, respectively, of individuals exceeded the corresponding reference value for DON and TeA, indicating a low health risk. However, in the case of AME and AOH, the proportion of individuals exceeding the reference value was 24.1% and 33.5%, respectively, indicating the potential health risks. In the cumulative risk assessment of AME and AOH, both HI and MoET values indicated a more serious risk than that related to individual exposure. Further research is necessary to reduce the uncertainties that are associated with the toxicities of the Alternaria toxins and cumulative risk assessment methods.Deoxynivalenol (DON) and emerging  Aspergillus, Fusarium, Penicillium, and Alternaria genera under preferable conditions [Mycotoxins are secondary metabolites that are produced by many fungi, including nditions . Mycotoxnditions ,3.Fusarium spp., is a predominantly detected mycotoxin in cereals  and cereal-based food products globally [Deoxynivalenol (DON), produced by globally ,5,6. DONglobally ,8. Althoglobally .Alternaria spp. found in cereals and cereal-based processed products have also raised concerns in the past few decades [Alternaria toxins are often found co-occurring along with DON in cereal-based food products [Alternaria toxins exert multiple potential toxicities, including genotoxicity, cytotoxicity, and endocrine disruption effects [Alternaria toxins, TeA exerts the highest acute toxicity and can cause hemorrhages in many organs, suppress weight gain, and reduce feed efficiency in animals, whereas AME and AOH were both strongly mutagenic to some bacterial cells [Alternaria toxins have been found in cereal-based baby foods which can cause exposure to an inadequate margin of safety [Alternaria toxins in foods globally because of a lack of data on toxicity and exposure, except for the maximum limit of 500 µg/kg set for TeA in sorghum/millet-based baby foods by the Bavarian Health and Food Safety Authority [Apart from DON, a group of “emerging mycotoxins” that are produced by  decades . Alternaproducts ,12,13. Oproducts . Alternaal cells . Alarminf safety . Howeveruthority .Infants and young children are more susceptible than adults to the toxic effects of exogenous harmful substances owing to their immature organ development, poor detoxification ability, and high food intake per unit of body weight. They are usually regarded as vulnerable subgroups in food safety risk assessments . In theiHI), combined margin of exposure (MoET), relative potency factor/toxic equivalent factor (RPF/TEF), and reference point index (RPI) [Humans are naturally exposed to various chemical hazards with multiple exposure pathways, and different chemical hazards may pose synergized effects on humans. Previous studies mainly focused on single compound exposure assessment for consumers ,19,20,21ex (RPI) ,27,28,29Alternaria toxins in cereal-based food products, and the fact that cereal-based food products are the main food source in the diet of infants and young children after 6 months, the present study aimed to evaluate the occurrence of DON and Alternaria toxins  in cereal-based food products that were marketed in China, and subsequent probabilistic risk assessment of combined exposure to DON and Alternaria toxins in cereal-based food products was performed for Chinese infants and young children.Considering the scarce information on the cumulative risk assessment of DON and Alternaria toxin, and 39.9% (348/872) of the analyzed samples were co-detected with two or more toxins. As shown in The occurrences of DON, AME, AOH, and TeA in cereal-based food products are presented in As for AME and AOH, their positive rates in the samples, 5.7% (50/872) for AME and 7.5% (65/872) for AOH, were much lower. Their mean levels in the food samples were also lower compared with the TeA levels; the mean levels of AME and AOH were 0.23 (0.08–0.37) μg/kg  and 0.66Alternaria toxins, TeA showed the highest positive rate and contamination level. As shown in For Aspergillus, Fusarium, Penicillium, and Alternaria) [Alternaria toxins in cereal-based food products is displayed in Multiple mycotoxins were simultaneously and frequently detected in the cereals as they may be contaminated with multiple fungi  . The co-Alternaria toxins that were analyzed in this study, only the acute reference dose (ARfD) of DON (8 µg/kg bw) is known, so a probabilistic model based on Monte Carlo simulation was used to determine the acute risk for infants and young children resulting from DON exposure by consuming each type of cereal-based food products. The occurrence data and consumption data of each food product were randomly drawn and multiplied, and the population distribution of DON intake per kg body weight was simulated through 10,000 iterations using the @Risk software version 8.0 . The probability of acute exposure exceeding ARfD of 8 μg/kg bw was calculated in the population [A single large dose of mycotoxin can potentially cause acute poisoning. Among the DON and pulation .The result of the acute exposure estimate using the probability method showed almost no possibility of exceeding the ARfD of 8 μg/kg bw by consuming the relevant foods, indicating the low risk of acute poisoning in infants and young children.Alternaria toxins through cereal-based food product consumption, higher exposure was seen in the 1–2 years groups, especially for DON and TeA . As shown in As shown in The estimated mean and 95th percentile TeA intake exposure was 0.121 (0.116–0.125) μg/kg bw/day and 3.499 (3.492–3.505) μg/kg bw/day, respectively, in infants and young children (1–3 years), with 1.5% of the individuals exceeding the TDI (1.5 µg/kg bw/day) of TeA in the LB, MB, and UB scenarios .Alternaria toxins, especially DON and TeA, is common in cereal-based food products. Due to the considerable possibility of exposure to multiple mycotoxins in consumers, the cumulative risks of the relevant multiple mycotoxins were further evaluated in this study. After taking into account the possible mycotoxin combinations that were displayed by the food analytical data, MoA of different mycotoxins, and the application conditions of different models, the cumulative risks of DON and TeA co-exposure were characterized using an HI model, while for AOH–AME co-exposure scenario, both HI and MoET models were applied.In terms of the combined exposure of mycotoxins, we found that the co-occurrence of DON and the HQ) and HI that were derived from the probabilistic risk assessment of individual and combined exposure to DON, TeA, AME, and AOH in cereal-based food products among infants and young children in China. This study highlighted that all HQs or HIs were <1 (0.0–0.60) for DON, TeA, and DON and TeA at the mean, median, 90th, and 95th exposure levels under LB, MB, and UB scenarios, and only 1.5% of the individuals whose Hi values were greater than I under UB, indicating a low health risk to the individual DON and TeA exposure and the combined DON–TeA exposure for infants and young children in China. Among AME, AOH, and AME and AOH, the His of AME and AOH were greater than 1 even at P90 under LB scenario, and 13.2% of the individuals showed HI values greater than 1, indicating that the cumulative health risks of AME–AOH co-exposure require further attention. In addition, the His of the cumulative AME–AOH exposure were higher than the HQs of individual exposure to AME or AOH , and 100% of the individuals exhibited MoET values that were lower than 10,000, suggesting serious health risks to infants and young children.Alternaria toxin contamination. DON is a predominant mycotoxin in cereals, and its high incidence and contamination level have been reported in many studies [Alternaria toxins, the contamination of AOH and AME in various food categories were frequently detected [Alternaria toxin, which was consistent with previous studies [In the present study, 872 cereal-based food products were analyzed for DON and emerging  studies ,32,33. Adetected . The ana studies ,35.Ideally, mycotoxin contamination in cereal-based complementary foods for infants and young children is expected to be lesser than that in common cereals; however, the available data in this study indicated that contamination rates in cereal-based complementary foods has a worryingly higher positive rate. For DON, the positive rates in cereal-based complementary foods and common cereals were 60.7% and 43.5%, respectively . The higAlternaria toxins was reported in previous studies [The co-occurrence of DON with emerging  studies ,38. Babi studies  reportedAs for the estimation of acute exposure to DON, this study revealed that there was almost no possibility of exceeding the ARfD of 8 μg/kg bw by consuming the relevant foods, indicating the low risk of acute poisoning in infants and young children. On the contrary, a similar study  reportedThe estimated mean exposure of DON in this study was similar to that of a previous report that was based on an investigation in Spain . In thatAlternaria toxins after a risk assessment [Although the estimated exposure to individual AME and AOH seemed low, about a quarter of individuals exceeded the TTC value. Considering the possible serious outcomes of AME and AOH exposure, attention should be paid to them. EFSA also considered them as the two most concerning sessment . In addisessment  reportedPrevious published data related to the dietary risk assessment of TeA based on the TTC value are limited. EFSA (2011)  reportedAlternaria mycotoxin exposure across the European population, which is consistent with the results of this study.Cereal-based products are an important contributor to mycotoxin exposure globally . Wang etAlternaria toxins that were present in cereal-based food products for infants and young children in China for the first time. In this study, the risk characterization of DON and Alternaria toxins suggested a low health risk due to the combined DON and TeA exposure to infants and young children in China. However, the combined exposures to AME and AOH indicated a more serious risk than that which was related to individual exposure for infants and young children in China. However, this study has some limitations as follows: the foods that were included in the exposure assessment of the Alternaria toxins were cereal-based food products only; however, these mycotoxins also occur in other food categories such as oilseeds, vegetables, and fruits [Alternaria toxins and their complex mechanism of toxicity, their adverse toxicological effects have not been fully unraveled; hence, this study adopted a preliminary approach for individual and cumulative risk assessments. In the future, as more studies will be performed to determine the below minimum detectable limits (BMDLs) with more reliable MoEs or reference values, a more accurate assessment will be performed to characterize the health risks of exposure to the individual Alternaria toxin and the cumulative risks of their combined exposure with other mycotoxins.To the best of our knowledge, a cumulative risk assessment was performed on DON and d fruits ,45,46, aAlternaria toxins and the cumulative risk assessment methods, further research is needed on the health risks in infants and young children who are getting exposed to single or multiple mycotoxins via cereal-based food products. Although the toxicity data of TeA are limited, its acute toxicity and median lethal dose (LD50) in mice seem to be similar to that of DON (46–78 mg/kg bw), which is worth noticing [Given the uncertainties that are involved in the present study, especially those regarding the toxicities of the noticing . MoreoveMycotoxins in cereal-based food products mainly come from the raw materials, and mycotoxins are difficult to destroy during processing . TherefoAlternaria toxins  in this study. The risk assessment results showed that the health risks that were posed by DON and TeA, and their combined exposure are low despite their considerable prevalence in both cereal-based complementary foods and common cereals. However, the exposures of AOH and AME, individual or combined, from cereal-based food products indicate a potentially unacceptable risk for Chinese infants and young children. Further research is necessary to reduce the uncertainties that are associated with the toxicities of the Alternaria toxins and cumulative risk assessment methods.Considering the prevalence of mycotoxin contamination in cereal-based food products and the vulnerability of infants and young children to these exogenous hazards, we assessed the individual and cumulative health risks of exposure to DON and three major In this study, we included major cereal-based food products that are consumed by infants and young children to obtain representative exposures. This included common cereals of rice, wheat flour, millet, maize, and cereal-based complementary foods of rice flour, biscuits, and noodles for infants and young children. In 2021, 872 food samples, including 656 pre-packaged complementary foods  and 216 common cereals , were collected from the markets  of 18 provinces across China using a stratified random sampling method. Every batch of collected food was thoroughly mixed to obtain a homogeneous sample. All the samples were ground to a fine powder and stored in plastic containers at −20 °C until mycotoxin analysis.Alternaria toxins in the samples. We used an AB Sciex 6500 Qtrap tandem mass spectrometer  coupled with a Shimadzu LC-30AD quaternary pump , SIL-30AC autosampler, and CTO-20AC column oven. All the reagents were HPLC or analytical grade. The standards of DON, 3-ADON, 15-ADON, AME, AOH, and TeA were purchased from Romer labs .The food samples were analyzed to detect the presence of DON, 3-acetylated DON (3-ADON), 15-acetylated DON (15-ADON), AME, AOH, and TeA based on the method that was reported by Ji et al. (2022) . High-peThe limit of detection (LOD) and limit of quantification (LOQ) was 1.0 µg/kg and 3.0 µg/kg, respectively, for DON, 3-ADON, 15-ADON and TeA, whereas 0.3 µg/kg and 1.0 µg/kg, respectively, for AOH and AME.Food consumption data of infants and young children were obtained from a nationwide survey of Chinese residents that was conducted by the China National Center for Food Safety Risk Assessment (CFSA) in 2015. In this survey, a multi-stage stratified cluster random sampling method was applied and a validated 24 h dietary recall questionnaire was used to collect the diet information on three discrete days in 15 provinces . The data were recorded through interviewing with the parents or main supporters of the babies face-to-face by well-trained investigators. A total of 19,480 infants and young children were surveyed to obtain data on food consumption. Infants that do not consume cereal-based food products were filtered out, and a total of 16,163 individuals were assessed for exposure in this study.sEXP is the daily intake of a certain mycotoxin from all the cereal foods that were involved for a certain individual s (μg/kg bw), Fs,i is the consumption of food i (kg/d), Ci is the mean level of the mycotoxin in food i (μg/kg), and BW is the body weight of the individual s (kg). When the daily exposures for all 16,163 individuals were obtained, the statistics data, including the mean, P50, P90, and P95 values, were calculated from the distribution of exposures in the population [Exposure assessments for mycotoxins were estimated using a probabilistic model. The target population of this study was infants and young children aged 0–3 years in China. Their mycotoxin exposures from cereal-based food products were estimated based on data about the consumption of the relevant cereal-based food products and the mean levels of the relevant mycotoxins in these cereal-based food products. The probabilistic models were used for exposure assessment, in which the variables were described as distributions instead of point estimates to obtain the overall distribution of the population. The following formula was adopted to calculate the dietary intake (exposure):pulation .After calculating the total exposure from all the related foods for each individual, distribution fitting was performed using the software @Risk. Monte Carlo simulations were performed through 10,000 iterations and the best fit function based on the lowest Akaike’s information criterion was applied to obtain the statistics of the mean, median, P90, P95, maximum, and the probability of exceeding the reference values in the lower, medium and upper bound scenarios .Alternaria toxins, their toxicity data are not sufficient yet. Therefore, the TTC value was used as a reference dose for AME, AOH, and TeA. The TTC value of 0.0025 μg/kg bw/day was adopted for AME and AOH, and the TTC value of 1.5 μg /kg bw/day was adopted for TeA [Risk characterization was performed by comparing the outputs of exposure with the reference dose values. As for DON, a traditional mycotoxin, widely accepted acute and chronic HBGVs, namely ARfD and TDI, have been established by the Joint FAO/WHO Expert Committee on Food Additives (JECFA) and EFSA based on a comprehensive review of its acute and chronic toxicity data. ARfD and TDI are 8 μg/kg bw/d and 1 μg/kg bw/d, respectively ,49. Howe for TeA .HI and MoET models were used in this study to assess the cumulative risks. The HI model is based on the concentration addition concept and provides a measure of the total risk based on the sum of the individual risks of each component. It applies to chemical mixtures in which the components have the same mode of action (MoA) or act on the same target  [HI. The same is true for AOH and AME, both of which exert genotoxicity in vivo through similar MoA and were also applied to the HI model.Different methodologies are available for cumulative risk characterization. Based on available toxicological data and application conditions, the n level) . As prevHI) was calculated as the sum of the hazard quotient (HQ) of a group of substances under study. HQ is the ratio of the outputs of exposure to the reference dose values. The calculation formula is as follows:ijHQ is the hazard quotient of the individual i that is exposed to substance j; ijExp is the daily intake of a substance j by the individual i (μg/kg bw/day), and jRfD is the reference dose of the substance j (μg /kg bw/day).The hazard index  for each mycotoxin that is involved, as expressed by the following formula:ijMoE is the MoE of the individual i that is exposed to substance j; jPoD is the point of departure for the substance j (μg/kg), Expij is the daily exposure of the individual i to the substance j (μg /kg bw/day), iMoET is the total MoE of the individual i that is exposed to the mixture that is composed of n mycotoxins. A higher MoE indicates a lower risk. Although no established criteria have been set for the magnitude of an acceptable MoE, an MoE or MoET of 10,000 for genotoxic and carcinogenic chemicals that was derived from animal experiments was considered of low concern for public health by EFSA [MoE or MoET below 10,000 was regarded as unacceptable.Alternatively, the  by EFSA . Hence, Alternaria toxins, very few studies were available that could derive a BMDL for AOH and AME. Therefore, no widely acknowledged BMDL has been established yet, and only a 28-day in vivo study in Sprague-Dawley rats showing a BMDL10 value of 0.0336 μg/kg bw/day for AME was reported [10 value was provisionally assumed for AOH and AME for the cumulative risk assessment in the present study.As the existing toxicological data are insufficient for reported . Given tIn this study, three different scenarios were included to facilitate the exposure assessment in relation to the treatment of left-censored data (testing results below LOD). During data analysis, the left-censored data were substituted for 0, half of LOD, and LOD, which was regarded as the lower bound (LB), middle bound (MB), and upper bound (UB), respectively .


To understand the trend of equalization in maternal services and to guide policy-makers regarding resource allocation and public health policy in China.Twelve indicators, including maternal services needs, utilization, and resource allocation, were collected from China Health Statistical Year Book 2010 and 2020. WHO’s comprehensive evaluation model and the non-integral Rank Sum Ratio (RSR) method were used to analyze, rank, and categorize maternal services of 31 provinces  in China.All provinces  are grouped into relative balance areas, low input areas, resource shortage areas, overutilization areas, and resource waste areas. In 2019, there were 18 provinces  in the relative balanced area, and more than one-half had achieved equal development. Compared to 2009, the resource shortage area decreased from three to zero, and the resource waste area increased from four to six. Among the provinces  with a type change compared with 2009, eight changed to a relative balance areas, and four showed an improvement.Under the policy guidance of promoting the equalization of public health services, maternal services are gradually realized. However, several provinces  still have problems such as the mismatch between resource input and health needs, resource waste, over-utilization, etc. Therefore, specific policies should be formulated according to the actual types to promote the transformation into equalization regions. Maternal health is decisive for the country, and maternal mortality is one of the most important indicators to measure the development of social and economic, and social equity . Since 2In 1996, World Health Organization (WHO) and Swedish International Development Cooperation Agency (SIDA) proposed that equity in health services means that all members of society have the same access to health services, without differences based on social privileges . TherefoIn 2009, promoting equalization of Basic Public Health services was one of the five major tasks of China’s health care reform, aiming to ensure that all residents in urban and rural areas could access the services and focus on equity , 18. TheThe study aimed to understand the relationship among maternal service needs, utilization, and resource allocation of 31 provinces  in China in 2009 and 2019, using a multi-index comprehensive evaluation method. Also, from the perspective of equity and efficiency to understand the changing trends and differences in the region, and provide guidance for policymakers regarding resource allocation and public health policy.http://www.nhc.gov.cn/mohwsbwstjxxzx/tjtjnj/new_list.shtml) [The data that included maternal services needs, utilization, and resource allocation was retrieved from the China Health Statistics Yearbook 2010 and 2020 (t.shtml) . Twelve 1 ~ X3 are maternal mortality rate , the proportion of obstetric hemorrhage in maternal deaths (%), and pregnancy-induced hypertension.The three maternal services needs indicators X4 ~ X8 in the utilization of maternal services are.in maternal deaths(%). The five indicators Xregistration rate (%), system management rate (%), prenatal checkup rate (%), postpartum visitrate (%), and hospital delivery rate (%). The maternal system management rate refers to the.number of women who have received early pregnancy checkups, antenatal checkups, sterile.9 ~ X12 are the number of medical institutions , the number of practicing (assistant) physicians (1/1000), and the number of registered nurses (1/1000), the number of obstetrics and gynecology beds . Combined with the responsibilities of maternal health care and the desirability of indicators, the institutions includecomprehensive hospitals, primary medical institutions, maternal and child health hospitals (stations). Health technicians are involved physicians and nurses in maternal and child health hospitals (stations).delivery, and postpartum visits within 28 days after delivery to the number of live births in a given area during the year. Four resource allocation indicators XThe fundamental theory of the RSR method is that a dimensionless statistical indicator is calculated from an n × m matrix using rank conversion. After this calculation, the distribution of RSR using parametric statistical methods. Generally, the RSR indicator ranges from zero (worst) to one (best) and follows a normal distribution. Additionally, the status (worst/best) uses the RSR order or a set of ordinal classifications to evalute .In our study, the RSR method was used to rank and classify 31 provinces in China in terms of maternal service needs, utilization, and resource allocation, respectively. All indicators used the same weight, and considering that the integer rank method would lose the original data information, the non-integer rank RSR was adopted to overcome the disadvantage of losing the quantitative information of the original data.Rank the indicators of maternal services in each province, the high-quality ranked in ascending,and the low-quality indicator ranked in descending order, such asThe detailed processes are as follows:n = 31, Except for maternal mortality rate, the other is high quality in the study.2.Calculate the value of RSR, the equation as followsWhere R is the rank of each maternal services indicator of 31 provinces in China, X is the original value, Rij is the rank of indicators in maternal services needs, utilization, and resource allocation, m is the number of indicator in each dimension, n is the number of provinces, i = 1,2∙∙∙n, j = 1,2∙∙∙m.3.Determine the distribution of RSR. Sort the RSR from small to large; calculate the downwardcumulative frequency P (average rank/n*100%) according to the cumulative frequency and average rank; then convert it into Probit.4.Calculate the regression equation. The value of RSR as the dependent variable and Probit as theindependent variable to fit the linear regression equations of maternal services needs, utilization, and resource allocation, then calculate the fitted RSR of each region.5.P < 0.05Grading and sorting. Concerning the commonly used three-grading table, the Probit critical values are substituted into the regression equation to calculate the RSR critical values for grading, and variance analysis is used to compare the differences among groups. SNK-q is used for pairwise comparison; the statistically significant level is set at Where Based on the investigation of health services in many countries and regions, the WHO proposed to combine health service needs, utilization, and resource input, and grade the sample mean of the three categories of indicators to form eight evaluation types from A to H , as showType A and H indicate appropriate allocation of resources. The high resource utilization is types B and F, types C and E mean low and over resource utilization, respectively. Types D and G indicate the low and over the investment of resources.In China, scholar Chen H and others established the comprehensive evaluation modelof Basic Public Health services, and combined the method of RSR, which is grouped into five categories, including relative balance area , low input area (type B), resource shortage area (type D), overutilization area (type E), and resource waste area (types C and G), which is detailed in Table In our study, we used it for reference to carry out comprehensive evaluation of maternal services.According to the RSR value ranking evaluation of the maternal services in each region, in 2009, the top three maternal health needs were in Xizang, Qinghai, and Xinjiang. The utilization of maternal health care was in Beijing, Zhejiang, and Shandong, and the resource allocations were in Beijing, Ningxia, and Xinjiang. While ten years later, the top three maternal health needs were in Xizang, Tianjin, and Qinghai; maternal health care utilization was in Zhejiang,Tianjin, and Guangxi; resource allocations were in Guizhou, Ningxia, and Qinghai Table .Table 3T1= 0.132Probit-0.37 (r = 0.798), RSR2 = 0.136Probit-0.029 (r = 0.693), RSR3 = 0.125Probit-0.293 (r = 0.964), respectively. In 2019, they were RSR4 = 0.126Probit-0.36 (r = 0.849), RSR5 = 0.113Probit + 0.281 (r = 0.444), RSR6 = 0.130Probit-0.289 (r = 0.921). The results of variance analysis showed that all regression equations were statistically significant (P < 0.05).According to the value of RSR and Probit Table , six regThe Probit critical values of 4 and 6 were substituted into all regression equations to calculate the RSR as the basis of classification, and then calculated the fitted values of RSR in maternal service needs, utilization, and resource allocation of all provinces, and the final results were shown in Table P > 0.05). In 2009, an analysis of variance showed that the statistics value F of maternal service needs, utilization, and resource allocation were 36.424, 36.431, and 36.363, respectively, and 36.565, 36.306, and 36.507. In 2019, there were significant differences in the fitted values of RSR among groups of each dimension (P < 0.05). The SNK-q test showed a significant difference pairwise, and the grading was reasonable.The test of variance consistency showed no significant differences among groups in each dimension  failed to meet the requirements of practicing assistant physicians in 2015 . TherefoTen years later, 10 regions still with low efficiency in maternal services, of which six were resource waste and four were over-utilized areas, resource waste coexists with overutilization. Under the guidance of macro policies, while the whole country was actively implementing the public health services tasks, the control of the efficiency of resource utilization had been neglected, especially for consideration of professional public health institutions. Therefore, it is urgent to integrate resources and optimize the utilization efficiency of health resources. We should combine the system of grading diagnosis and treatment to improve the capacity of primary-level medical institutions, promote the development of appropriate health technologies, and give full play to the role of primary-level medical institutions. Therefore, it is urgent to integrate and optimize the efficiency of resources. The hierarchical diagnosis and treatment and medical association should combine to improve the ability of maternal health, promote the development of appropriate health technology, and give full play to the role of primary medical institutions. Resource input was higher than health needs in four regions, which lead to oversupply, so the cost measurement mechanism should be improved to ensure that resources such as personnel and beds are set up according to demand.Based on the WHO’s comprehensive evaluation model of Health Services, this study evaluated the needs and supply of maternal services from the perspective of equity and efficiency in China. The results showed that the equalization of maternal services was gradually being realized under the guidance of the equalization of Public Health Service policies. However, there were still some problems, such as the mismatch of resource input, health needs and waste, and the over-utilization of resources. So the non-balanced regions should formulate targeted policies in the light of their specific circumstances to promote their transformation to equalization. However, due to the availability of data, it is unable to include all technicians serving maternal, and unable to obtain dedicated public health funding. It is suggested the relevant departments should refine the statistical classification of information as much as possible and make more public resources available.


A large number of observational studies showed that patients with psoriasis have a higher risk of cardiovascular disease (CVD), but most studies did not fully adjust for confounding factors, so it is not clear whether the risk of CVD is directly attributed to psoriasis. We used Mendelian randomization (MR) to evaluate the potential causal relationship between psoriasis and CVD.We used genetic instruments from the genome-wide association study (GWAS) of European descent for psoriasis to investigate its relationship with CVD. Inverse variance-weighted (IVW) MR analyses were used for the primary analysis. In addition, a variety of other methods were used to replicate the analysis.The fixed-effects IVW method indicated that genetic susceptibility to psoriasis was associated with a higher risk of heart failure (HF) , atrial fibrillation (AF) , myocardial infarction (MI) , valvular heart disease (VHD) , and large artery stroke (LAS)  but not with the other two subtypes of ischemic stroke (IS) . Sensitivity analysis found weak evidence of horizontal diversity and heterogeneity to ensure the stability of the results.Our study provided evidence for a potential causal link between psoriasis and CVD. These findings partly suggest that early monitoring of cardiovascular risk in patients with psoriasis is intentional. Cardiovascular disease (CVD) was defined as a group of cardiac and vascular diseases, including ischemic stroke (IS), atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), valvular heart disease (VHD), and so on . In 2020Psoriasis was a chronic inflammatory skin disease characterized by the appearance of well-demarcated red scaly patches on the skin . More thIn recent years, the association between psoriasis and CVD has been paid more and more attention . CompareNotably, whether CVD risk is directly attributable to psoriasis remains to be determined. This is because traditional risk factors for cardiovascular disease  are often risk factors for psoriasis as well . These cConfirmation of a causal association is as challenging as the reverse causation and confounding between psoriasis and the risk of CVD. As a new epidemiological research method, Mendelian randomization (MR) could explain observational bias, which used genetic variation as instrumental variables (IVs) to assess the causal effects of exposure factors on outcomes , 21. In https://r5.finngen.fi/), including 4,510 cases and 212,242 controls. For the outcome dataset, GWAS data for HF were derived from HERMES Alliance , including 1,606 cases and 359,588 controls. The summary dataset for IS was from the MEGASTROKE consortium, including three subtypes: large artery stroke (LAS) , cardioembolic stroke (CES) , and small vessel stroke (SVS)   . An overThis two-sample MR study was conducted to evaluate the causal association between genetic susceptibility to psoriasis and the CVD risks. SNP was used as our IV . The who−8) were selected as IVs. The corresponding linkage disequilibrium was tested to confirm that there were SNPs in a linkage disequilibrium state and that the SNPs were independent by pruning SNPs within a 10,000-kb window with an r2 < 0.001 threshold. To remove potential pleiotropic effects, we retrieved the secondary phenotype of each SNP in PhenoScanner V2 (All SNPs significantly associated with psoriasis (P < 5 × 10anner V2  and the 2R) and F-statistic were used to assess the strength of IVs to avoid weak tool bias (F = R2(N-K-1)/[K(1-R2)]. In this equation, 2R refers to the cumulative explained variance of the selected SNP during exposure, K is the number of SNPs for the final analysis, and N is the number of samples of the selected GWAS. F-statistic greater than 10 was considered to be sufficiently strong for the correlation between IVs and exposure that the results of the MR analysis could avoid being affected by weak tool bias.Variance  method of different models was used as the main analysis method according to heterogeneity . At the Various methods were introduced into this study for sensitivity analysis. Firstly, Cochran’s Q test was used to assess heterogeneity between estimates of individual genetic variants. If the P value was <0.05, the final results of MR referred to the random-effects model of IVW; otherwise, the fixed-effects model . SecondlAll statistical analyses were performed using the “TwoSampleMR (0.5.6),” “MR-PRESSO (1.0),” and “mr.raps” packages in R, Version 4.1.2. All P values were two-sided, and P < 0.05 was deemed as suggestive of significance.−8) and removed linkage disequilibrium (LD) . Subsequently, in the PhenoScanner database and GWAS catalog, we did not find any IVs for psoriasis to be associated with potential confounders. Meanwhile, palindromic SNPs  were excluded. To maintain consistency of SNPs used as IVs across analyses, we only used variants that were available for all examined traits and did not replace missing variants with proxies. Finally, the screened SNPs were included in further analysis (F-statistic >10) (We extracted IVs significantly related to psoriasis from the GWAS study (P < 5 × 10tic >10) Table 1.The statistical results of MR are presented in Compared with the control group, the prevalence of HF in psoriasis patients was 1.04 times , the prevalence of AF was 1.04 times , the prevalence of MI was 1.07 times , the prevalence of VHD was 1.001 times , and the prevalence of LAS was 1.11 times . There was no significant difference in the prevalence of CES  and SVS  between psoriasis patients and the controls. Weighted median, maximum likelihood, MR-PRESSO, and MR-RAPS analysis showed similar results to IVW method. No outliers were identified by MR-PRESSO method, indicating that the results were reliable. Risk calculations were performed based on the LogOR of psoriasis, which may partly explain the low OR values.Sensitivity analyses indicated that there was no underlying heterogeneity and horizontal pleiotropy in the results Table 2 This MR study demonstrated that one unit increase in log odds of psoriasis was associated with higher risks of HF, AF, MI, VHD, and LAS; furthermore, there is no evidence to support an association between psoriasis and the risk of SVS and CES.The relationship between psoriasis and CVD was first described in the 1970s , followeThe exact mechanism by which psoriasis increases the risk of CVD is unclear. Dyslipidemia in patients with psoriasis may contribute to the increased risk of CVD. Studies have shown that patients with psoriasis have elevated low-density lipoprotein, very low-density lipoprotein, and lipoprotein (a), accompanied by a decrease in high-density lipoprotein . Abnorma−4, OR = 1.01) . In a clinical trial, 59% of participants had at least 2 traditional CVD risk factors, while 29% had 3 or more . A recen = 1.01) , which iThere are some strengths in our study. First, our MR analysis explored the causal relationship between psoriasis and a series of CVDs for the first time, and the results were unlikely to be influenced by confounders and reverse causal associations. Second, we used the latest large GWAS dataset, and the exposed data did not overlap with the outcome, which improved the reliability of the results. Third, the IVs of each group were evaluated to ensure that there was no tool bias. Fourth, multiple analytical methods were used to perform repeated analyses with consistent results. Furthermore, sensitivity analysis proved our results to be reliable.However, our study also has some limitations. Firstly, although we used multiple steps to test for pleiotropy, the effect of potential pleiotropy could not be completely ruled out, resulting in an inaccurate assessment of the three hypotheses. Fortunately, multiple methods yielded consistent results and sensitivity analyses found weak evidence for horizontal pleiotropy, minimizing the possibility of pleiotropy bias. Secondly, the vast majority of participants in this MR analysis were from Europe, making it more difficult to explain the causal relationship between psoriasis and CVD in other populations. Thirdly, the OR value is relatively low, which should be interpreted carefully. We look forward to more in-depth research on the potential relationship between the two in the future.In summary, our study provides evidence for a potential causal association between psoriasis and CVD. Combined with evidence from observational studies, early cardiovascular risk assessment and prevention in patients with psoriasis are of interest, which facilitate the introduction of individual-specific treatments as soon as possible. Due to the low OR value, caution is required when generalizing the results.The original contributions presented in the study are included in the article/NG, MK, and AD designed the study and drafted the article. DW, MN, and MK conducted data acquisition. NG, MK, XL, DW, MN, ZH, XZ, YW, and AD performed data analysis and article revision. All authors read and approved the final article.This research was funded by Zhejiang Health Major Science and Technology Program, National Health Commission Scientific Research Fund (WKJ-ZJ-2121), and the National Natural Science Foundation of China (81800210).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background and Objectives: The aim of this study is to investigate the characteristics of gastrointestinal bleeding events associated with BCR-ABL tyrosine kinase inhibitor (TKI) treatment, using the reporting odds ratio (ROR) of the adverse event reports submitted to the Japanese Adverse Drug Event Report database between 2004 and 2020, and to examine the number of reported TKI-related gastrointestinal bleeding cases according to sex and age, as well as the actual number of TKI prescriptions issued in Japan. Materials and Methods: The RORs and 95% confidence intervals (CIs) of gastrointestinal bleeding events related to TKIs were calculated using the data of the 595,121 included cases. Results: Significant gastrointestinal bleeding events were detected for dasatinib  and imatinib . In multiple logistic regression analyses, significant gastrointestinal bleeding events were detected for dasatinib , imatinib , age , reporting year , and male sex . Interaction analysis revealed that the association of gastrointestinal bleeding with dasatinib was affected by age (≥60 years) and sex , with the number and proportion of dasatinib-related gastrointestinal bleeding cases increasing among those aged ≥60 years. Conclusions: Specific TKIs and patient characteristics were associated with gastrointestinal bleeding. Our results aid the prompt identification and treatment of TKI-related gastrointestinal bleeding. Chronic myeloid leukemia (CML) is a clonal disease characterized by the presence of the Philadelphia (Ph+) chromosome and its oncogenic product, BCR-ABL. BCR-ABL is a constitutively active tyrosine kinase present in >90% of patients with CML. Treatment of CML was revolutionized by the introduction of a BCR-ABL tyrosine kinase inhibitor (TKI). The clinical use of TKIs has resulted in notably improved prognosis, response rate, overall survival, and patient outcomes in patients with CML. However, TKI use is associated with various adverse effects (AEs), including hematological and non-hematological AEs ; and the monitoring and management of these AEs are crucial for the success of CML treatment . In partWe used data from the Japan Adverse Drug Report (JADER) database to increase the sample size in this study. JADER is a database collecting information on adverse drug reaction cases reported voluntarily to the Pharmaceuticals and Medical Devices Agency (PMDA). Some previous studies have used JADER to investigate rare AEs ,8,9,10. https://www.mhlw.go.jp/ndb/opendatasite, accessed on 31 December 2021), and the actual number of TKI prescriptions in Japan were examined. The number of reported gastrointestinal bleeding cases related to TKIs according to sex and age was also investigated.The purpose of the current study is to evaluate the relationship between TKIs and gastrointestinal bleeding using the reporting odds ratio (ROR), and to analyze the time-to-onset of hemorrhagic gastrointestinal bleeding. Furthermore, the number of prescription drugs, using data from the 5th National Database of Health Insurance Claims and Specific Health Checkups of Open Data from 2018  v24.0 to extract the AEs and underlying diseases listed in the JADER database. Gastrointestinal bleeding was defined by preferred terms in the Standardized MedDRA Queries (SMQ) suggestive of gastrointestinal hemorrhage (SMQ: 20000108).http://www.pmda.go.jp/) (accessed on 1 December 2021). The JADER dataset consists of four tables containing the following data: (1) patient information, including sex, age, and body weight; (2) patient drug information; (3) AEs and outcomes; (4) medical history and primary illness. These four tables were integrated using the FUND E-Z Backup Archive .The JADER database used in this study collects adverse drug reaction data reported voluntarily to the PMDA. Data recorded from April 2004 to March 2020 in the JADER database were downloaded from the PMDA website  to (d) as follows: (a) individuals who received the drug of interest and exhibited the AE of interest; (b) individuals who received the drug of interest and exhibited other AEs (of no interest); (c) individuals who received other drugs (of no interest) and exhibited the AE of interest; and (d) individuals who received other drugs (of no interest) and exhibited other AEs (of no interest). Next, the crude ROR was calculated using the following equation :Crude ROThe RORs were expressed as point estimates with 95% CIs. The data were analyzed using Fisher’s exact test.We calculated the adjusted ROR based on a previous report . In addip < 0.05 were considered statistically significant.The 0–59-year group was used as a reference to calculate the RORs adjusted for age variations, and the sex = female group was used as a reference to calculate the RORs adjusted for sex variations. Subsequently, the interaction of factors for gastrointestinal bleeding was examined; results with The gastrointestinal bleeding onset time was defined as the period from the date of the first TKI prescription to the date of occurrence of gastrointestinal bleeding. Cases with complete data on age, sex, TKI use , complete information on gastrointestinal bleeding occurrence, and prescription start date were included in the time-to-onset analysis. It is important to choose the optimal length of the evaluation period in order to evaluate the time-to-onset of gastrointestinal bleeding. We chose an analysis period of 365 days after administration, based on a previous study . Median Cases with complete data on age, dasatinib use, and gastrointestinal bleeding occurrence were used in this analysis (160 cases). Cases were classified according to age and outcome. Outcomes recorded in the table of adverse reactions in the JADER database, such as “recovered”, “remission”, “not recovered”, “sequela”, “death”, and “unclear”, were used.p < 0.05. The analyses were performed using JMP 14.0 . A signal was detected when the lower limit of the 95% CI of the adjusted ROR exceeded 1.The results were considered statistically significant at p < 0.001) and imatinib . The crude RORs (95% CI) for dasatinib in the 0–59-year-old group and ≥60-year-old group were 6.30 (4.72–8.28) and 4.07 (3.27–5.02), respectively. The crude RORs (95% CI) for dasatinib in the male group and the female group were 3.43 (2.70–4.33) and 6.15 (4.78–7.81), respectively. The crude ROR (95% CI) for imatinib in the 0–59-year-old group was 1.66 (1.15–2.33), and that in the female group was 1.46 , imatinib , age , reporting year , and sex , age (≥60 years) and dasatinib (p = 0.035), and sex  and dasatinib (p < 0.0001) were also significant.In multivariate logistic regression analysis, significant contributions were observed for dasatinib  , suggestThe pathophysiology of bleeding associated with dasatinib and imatinib therapy remains poorly understood. However, previous in vitro and ex vivo studies have demonstrated that dasatinib treatment resulted in the dose-dependent impairment of platelet activation and aggregation . FurtherThere were differences in the number of reported cases and the time-to-onset of gastrointestinal bleeding between dasatinib and imatinib in this study. In a previous study, dasatinib affected platelet function through the inhibition of the SRC family of kinases  and induced hemostatic defects. Conversely, imatinib did not act on the SRC family of kinases. Furthermore, patients treated with imatinib showed a reduction in thrombus growth compared with that in healthy donors, but the effect of dasatinib was much more pronounced in this aspect . The difOur analysis of the RORs after adjusting for the number of gastrointestinal bleeding cases provided information on the association between gastrointestinal bleeding and sex. Adjusting the ROR for the number of reported gastrointestinal bleeding cases revealed that sex  can influence gastrointestinal bleeding. Dasatinib is mainly metabolized by the cytochrome P450 enzyme CYP3A4 to active metabolites in the liver , and intThe number of gastrointestinal bleeding cases associated with dasatinib were highest among patients aged ≥60 years, despite being prescribed to those under 60 years of age . In a prHelicobacter pylori infection, because JADER is a database where information on adverse drug reaction cases is reported voluntarily, and, as a result, we could not obtain sufficient information. Second, cases in the JADER database are spontaneously reported, hence, reporting bias cannot be ruled out. Furthermore, the classification of TKI-induced gastrointestinal bleeding events was not reported in the JADER database, and taking specific measures against TKI-induced gastrointestinal bleeding was challenging. Only data on patients with side effects are available, and there is concern that research data may target a patient group distinct from those often present in actual clinical practice. Therefore, the results of this study need to be further expanded through cohort studies and randomized controlled trials. Furthermore, the frequency of reported gastrointestinal bleeding events with bosutinib was similar to that of imatinib, and it might be possible that signals associated with gastrointestinal bleeding were not detected due to fewer reports on bosutinib. In addition, we were unable to detect the effects of concomitants with medications related to bleeding due to the small number of studies. Therefore, it is necessary to perform further analyses after accumulating more data.There were several limitations to this study. First, there was no evidence that the reported event was caused by the drug, as such events may also be caused by the natural progression of the disease. In addition, we were unable to examine the association with comorbidities that could cause gastrointestinal bleeding, such as the presence of arteriosclerosis and Gastrointestinal bleeding events associated with TKIs were investigated using the JADER database. We found that specific TKIs and patient characteristics were associated with gastrointestinal bleeding. In particular, the association of gastrointestinal bleeding with dasatinib was affected by sex  and age (≥60 years). These patient characteristics should be considered to avoid the onset of gastrointestinal bleeding when prescribing TKIs.