Long-term trends in the burden of alcoholic cardiomyopathy in China based on Global Burden of Disease 2021

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This study aimed to characterize the temporal trends of alcohol cardiomyopathy (ACM) burden in China during 1990–2021. Methods The epidemiological data utilized in this investigation were sourced from the Global Burden of Disease (GBD) 2021 database. Temporal trends in ACM prevalence and mortality rates were analyzed through join-point regression modeling, with quantification of temporal changes expressed as average annual percentage change (AAPC) metrics. Simultaneously, age-period-cohort (APC) analysis was implemented to evaluate the independent contributions of aging effects, temporal variations, and generational influences. Furthermore, we developed an extended autoregressive integrated moving average (ARIMA) framework to project disease burden patterns through 2036, providing a 15-year forecast of epidemiological trends. Results The age-standardized prevalence and mortality rates in both sexes changed from 0.52 (95% CI: 0.43, 0.62) to 1.56 (95% CI: 1.24, 1.92) and from 0.05 (95% CI: 0.02, 0.12) to 0.10 (95% CI: 0.02, 0.15) per 100 000 people in China from 1990 to 2021. The age-standardized disability-adjusted life-years(DALYs) rate, years lived with disability(YLDs) rate and years of life lost(YLL ) rate is increasing from 1990 to 2021. AAPC in age-standardized prevalence and mortality rates for ACM in China were 3.70 (95% CI: 3.50, 3.90), and 1.90 (95% CI: 1.70, 2.10). The effects of age, period, and cohort on prevalence and mortality rates differed. Conclusions The increasing age-standardized prevalence, mortality, DALYs, YLDs and YLL rates is gradually increasing between 1990 and 2021 in China. The burden of ACM in China will be a major public health challenge, given the country’s large population base and aging population. Health sciences/Cardiology Health sciences/Health occupations alcoholic cardiomyopathy Global Burden of Disease Burden China Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Alcohol-induced cardiomyopathy (ACM) represents a distinct subtype of dilated cardiomyopathy (DCM) that develops exclusively from prolonged excessive alcohol consumption in the absence of other etiological factors. The diagnostic criteria for ACM encompass three essential components: (1) sustained heavy alcohol consumption exceeding 80 g/day for at least five years; (2) echocardiographic evidence of left ventricular dilation (> 2 SD above normal) accompanied by impaired systolic function (LVEF < 50%); and (3) systematic exclusion of alternative DCM etiologies, including hypertensive, valvular, and ischemic heart diseases [1] . Initially characterized in the late 19th century, this alcohol-related myocardial pathology manifests through ventricular chamber enlargement, diminished contractile function, and either normal or reduced ventricular wall dimensions [2,3] . As a major contributor to non-ischemic cardiomyopathy, alcohol abuse accounts for approximately one-tenth of all DCM cases worldwide[4]. Substantial experimental evidence has elucidated the direct myocardial toxicity of ethanol and its metabolic byproducts [5] .In high-income countries, ACM has emerged as a predominant cause of left ventricular dysfunction and represents the leading etiology of mortality among non-ischemic cardiomyopathy patients in the United States. Population-based studies estimate that ACM contributes to 3.8–47% of non-ischemic cardiomyopathy cases across different populations [6,7] . The World Health Organization's 2018 Global Status Report on Alcohol and Health revealed that more than half (56%) of the global population aged ≥ 15 years consumes alcoholic beverages [8] . Genetic polymorphisms in alcohol metabolism pathways, particularly ALDH2 deficiency prevalent in Asian populations, contribute to individual susceptibility through impaired acetaldehyde clearance, resulting in characteristic flushing reactions [9] .A striking gender disparity characterizes ACM epidemiology, with males demonstrating significantly higher disease prevalence than females. Hospitalization data indicate a 9:1 male-to-female ratio in ACM cases [10] . This pronounced gender imbalance likely results from multiple interacting factors, including sociocultural influences (e.g., differential reporting of alcohol consumption patterns), physiological variations (e.g., sex-specific differences in ethanol metabolism and tolerance thresholds), and potential limitations in current diagnostic criteria that fail to account for biological sex differences. Emerging evidence suggests that females may develop ACM at lower cumulative alcohol exposure levels and shorter durations of heavy drinking compared to their male counterparts [11,12] . Methods Data sources The Global Burden of Disease Study 2021 presents a comprehensive evaluation of mortality patterns, utilizing cause-specific mortality indicators and years of life lost metrics. This updated analysis encompasses 288 distinct causes of death, stratified by age and gender, across 204 geographical regions from 1990 through 2021. Notably, this revision extends the temporal scope beyond the previously reported data spanning 1990–2019. The study incorporates multiple analytical models to estimate disease burden and injury-related outcomes, thereby enhancing the precision of epidemiological assessments [13] . Joinpoint regression analysis (dup: abstract ?) Developed by Kim et al. (2000) [14] , the join-point regression methodology represents an advanced analytical framework for evaluating temporal patterns in ACM burden. This statistical approach employs segmented linear modeling to quantify disease rate fluctuations, utilizing least squares estimation to minimize potential biases associated with conventional trend analysis methods. The computational algorithm optimizes the residual sum of squares between model-derived predictions and empirical observations, enabling automatic detection of significant trend inflection points. All statistical computations were performed using the Joinpoint Regression Program (version 4.9.1.0). To quantify temporal changes, we calculated the average annual percentage change (AAPC) as a composite measure, with statistical significance determined through comparison against a null hypothesis of zero change. Throughout the analysis, results were considered statistically significant at the conventional threshold of P < 0.05. Age-period-cohort analysis The age-period-cohort (APC) framework represents a fundamental analytical tool in epidemiological and sociological research for examining temporal patterns in disease prevalence and mortality. This modeling approach, predominantly employing Poisson regression, enables simultaneous assessment of age-specific effects, temporal variations, and generational influences. A critical methodological challenge stems from the inherent collinearity among the three temporal dimensions, resulting in model identifiability problems that impede the precise estimation of individual component effects [15] . Multiple statistical strategies have been developed to mitigate this issue, including the implementation of intrinsic estimators [16] , application of regularization techniques [17] , and development of specialized estimation functions [18] , each presenting distinct advantages and constraints. The current investigation adopted the comprehensive APC methodology developed by Carstensen [19,20] , which incorporates the Lexis diagram framework for enhanced temporal visualization. All statistical analyses were performed using the Epi package (version 2.46) within the R statistical environment (version 4.2.0). Autoregressive integrated moving average model The autoregressive integrated moving average (ARIMA) framework integrates autoregressive processes with moving average components to analyze temporal data patterns. This statistical approach is predicated on the fundamental assumption that sequential observations represent time-dependent stochastic variables, with their inherent temporal dependencies systematically characterized through the model's structure. Such methodological foundation enables the prediction of future observations based on historical temporal patterns [21] . A critical prerequisite for implementing ARIMA analysis is the stationarity of the time series, which necessitates the maintenance of consistent statistical properties throughout the observation period, including time-invariant mean values (ideally approximating zero) and constant variance. Furthermore, the analyzed sequence should demonstrate stochastic behavior, devoid of deterministic patterns or systematic trends. Statistical analysis Within the Global Burden of Disease 2021 (GBD 2021) framework, two key epidemiological metrics were examined: (1) ACM prevalence, quantified as the ratio of affected individuals to the total population, and (2) ACM mortality, expressed as age-standardized deaths per 100,000 population. Standardization of prevalence and mortality rates was achieved through application of the world population as the reference standard. Temporal analysis was structured into quinquennial intervals (1992–2021), with exclusion of 1990–1991 data due to incomplete temporal coverage. The analytical framework incorporated 17 discrete age categories (15–19 to 95 + years) and 20 generational cohorts (1895–1999 and 2000–2004 birth years). Reference values for age, period, and cohort parameters were established at their respective arithmetic means [22, 23] . Model selection was implemented through the auto.arima() function, with optimization guided by minimization of the Akaike information criterion (AIC) [24] . Temporal trend analysis was conducted using Joinpoint Regression Software, while data management and preprocessing were executed utilizing the Epi package within the R statistical computing environment. Results In China, there were 1860 (95% CI: 318, 2987) deaths due to ACM. Age-standardized rates in terms of prevalence (ASPR), mortality (ASMR), DALYs, YLDs, and YLL of ACM in 2021 were 1.56 cases (95% CI: 1.24, 1.92) per 100,000, 0.10 deaths (95% CI: 0.02, 0.15) per 100,000, 3.43 DALYs (95% CI: 0.67, 5.41) per 100,000, 0.14 YLDs (95% CI: 0.09, 0.21) per 100,000, and 3.29 YLL (95% CI: 0.53, 5.31) per 100,000 (Table 1 ). The all-age numbers and age-standardized rates for males and females are presented in Table 1 . It is clear that men have a higher disease burden than women (Table 1 ). For a comparison of the burden of ACM in China and globally, please refer to Supplementary Table 1. Table 1 All-age cases and age-standardized prevalence, deaths, YLL, YLDs, and DALYs rates in 2021 for ACM in China. measure All-age cases Age-standardized rates per 100 000 people Total Male Female Total Male Female Prevalence 28103.36 (22175.42,34990.68) 24892.85 (19393.96,31197.76) 3210.51 (2629.59,3924.22) 1.56 (1.24,1.92) 2.71 (2.15,3.35) 0.37 (0.30,0.44) Deaths 1860.96 (317.62,2986.8) 1669.87 (167.92,2759.66) 191.09 (15.92,316.92) 0.10 (0.02,0.15) 0.18 (0.02,0.29) 0.02 (0.002,0.03) DALYs 64745.92 (12595.45,102876.08) 60030.63 (7939.79,98046.93) 4715.29 (689.87,7731.06) 3.43 (0.67,5.41) 6.31 (0.85,10.31) 0.50 (0.08,0.82) YLDs 2606.99 (1697.04,3832.25) 2309.21 (1498.56,3409.45) 297.78 (194.59,430.15) 0.14 (0.09,0.21) 0.25 (0.16,0.37) 0.03 (0.02,0.05) YLL 62138.93 (9970.45,100710.71) 57721.42 (5725.75,96165.59) 4417.51 (394.55,7425.39) 3.29 (0.53,5.31) 6.06 (0.58,10.07) 0.47 (0.04,0.79) DALYs = disability-adjusted life-years, YLDs = years lived with disability, YLL = years of life lost. ACM is more prevalent in people over the age of 35, and it increases quickly between the ages of 25 and 49. Males are most affected between the ages of 45 and 49, after the age of 80, age-standardized prevalence rate of females are increasing. After the age of 70, the mortality rate increased dramatically. Surprisingly, between the age of 15 and 89, men had higher prevalence and mortality rates than women (Fig. 1 ). Age-standardized DALYs, YLDs, and YLL rates showed similar trends by sex and age group (Supplementary Fig. 1). The sex-specific, age-standardized prevalence and mortality rates for ACM fluctuated by calendar year. The disease prevalence is generally increasing for men in 1990–2015, and prevalence gradually decrease after 2015, meanwhile, the disease prevalence is generally increasing for women in 1990–2005,while there is no significantly increase for females in 2005–2021 (Fig. 2 A). The disease mortality for males is gradually increase, however, The disease mortality for females began to decrease after 2010(Fig. 2 B). The male age-standardized DALYs are generally decreasing after 2015, the female age-standardized DALYs are generally decreasing after 2009(Fig. 2 C). Age-standardized YLDs and YLL rates showed similar trends by sex and age group (Supplementary Fig. 2). Joinpoint regression analysis Joinpoint regression analyses of the age-standardized prevalence rates for ACM in China from 1990 to 2021 are shown in Fig. 3 . We found the disease prevalence trend to significantly increase from 2000 to 2005 in both male (APC = + 12.10 (2000–2005), 95% CI: 11.50, 12.70) (Fig. 3 A) and female (APC = + 15.40 (2000–2004), 95% CI: 14.10, 16.70) populations (Fig. 3 B). Joinpoint regression analyses of the age-standardized DALYs, YLD, YLL and mortality rates in both sexes are shown in Supplementary Fig. 3 to 6. The AAPCs in ACM prevalence and mortality rates over three decades was showed in Table 2 . Age-standardized prevalence and mortality rates for ACM in China increased by 3.70 (95% CI: 3.50, 3.80) and 1.90 (95% CI: 1.70,2.10) from 1990 to 2021. Surprisingly, females had a lower AAPC of prevalence and mortality rates than males (Table 2 ). Table 2 Joinpoint regression analysis: trends in age-standardized prevalence and mortality rates (per 100 000 persons) among both sexes, males, and females in China, 1990–2021. Gender ASPR ASMR Both Period APC(95%CI) AAPC(95%CI) Period APC(95%CI) AAPC(95%CI) 1990–1995 0.70 (0.40, 1.10) 3.70(3.50,3.80) 1990–1992 -1.70 (-3.70, 0.30) 1.90 (1.70, 2.10) 1995–2000 3.60 (3.10, 4.00) 1992–1996 3.70 (2.70, 4.70) 2000–2004 13.70(12.90,14.60) 1996–2005 2.80 (2.50, 3.00) 2004–2014 4.20 (4.00, 4.30) 2005–2010 6.50 (5.90, 7.10) 2014–2021 -0.20(-0.40,0.00) 2010–2014 0.50 (-0.30, 1.20) 2014–2021 -1.40(-1.60, -1.20) Male 1990–1996 0.70 (0.50, 1.00) 3.70(3.50,3.90) 1990–1992 -1.50 (-3.90, 0.90) 2.40 (2.20, 2.70) 1996–2000 3.40 (2.60, 4.30) 1992–1996 4.10 (2.80, 5.30) 2000–2005 12.10(11.50,12.7) 1996–2004 2.00 (1.70, 2.40) 2005–2015 4.30 (-4.20, 4.50) 2004–2010 8.10 (7.60, 8.60) 2015–2021 -0.70 (-1.10, -0.4) 2010–2014 1.80 (0.90, 2.70) 2014–2021 -1.10 (-1.30, -0.90) Female 1990–1995 2.00 (1.50, 2.60) 3.20(3.00,3.50) 1990–1993 -1.20 (-2.90, 0.60) -0.4 (-0.80, 0.00) 1995–2000 8.20 (7.40, 9.00) 1993–2004 4.70 (4.40, 4.90) 2000–2004 15.40(14.10,16.70) 2004–2010 -1.30 (-2.00, -0.60) 2004–2013 -0.10 (-0.30, 0.20) 2010–2013 -6.10 (-5.50, -6.80) 2013–2021 -0.90(-1.20, -0.70) 2013–2017 -5.50 (-2.30, -3.30) 2017–2021 -2.30 (-3.30, -1.30) AAPC, average annual percent change presented for full period; APC, annual percent change; CI, confidence interval. The effects of age, period, and cohort on prevalence and mortality rates The prevalence rate increased rapidly between the ages of 30 and 40 years, and after 85 years also showed a quickly-increasing trend. The ACM mortality increased from the ages of 15–19 years, peaking at the ages of 95–99 years. After controlling the effects of period and cohort, the risk of ACM in the 90–94 years group was 236.8 times the risks in the 15–19 years [ exp (( αage(85–89 ) – αmean )–( αage(15–19 ) – αmean )) = exp ( αage(85–89 ) – αage(15–19 ) ) = 49.73/0.21]. The period-based trends of ACM prevalence were unsteady. The RR value increased from 0.54 (95% CI 0.52–0.57) in 1994 to 1.41 (95% CI 1.36–1.47) in 2014. The birth cohort of each age group showed that the ACM prevalence in the early period was lower than that in the later period. The cohort risk was significantly lower in the early birth cohort (RRcohort(1905–1909) = 0.38, 95% CI 0.25–0.58) and increased in the recent cohorts (RRcohort(2000–2004) = 16.60, 95% CI 14.24–19.35). The period-based mortality rate had a turning point in 2014. The risk peaked in 2014 [RRperiod(2014) = 1.48, 95%CI 1.40–1.57] and then decreased afterwards [RRperiod(2019) = 1.39, 95% CI 1.30–1.48]. Similarly, the early birth cohort has a similiar effect on ACM mortality (Figs. 4 ) (Supplementary Fig. 7and 8). The RR increased gradually before the 1995–1999 birth cohort and then displayed a downward trend (Supplementary Table 2). Prediction of ACM prevalence and mortality in the next fifteen years The ARIMA model was used to quantitatively depict the trends of ACM prevalence and mortality over the following 15 years. The optimized model was chosen to be (3,1,0) for males of ACM prevalence with an AIC value of − 128.2 after being filtered by the auto.arima() function, The optimized model was chosen to be (0,2,2) for females of ACM prevalence with an AIC value of -246.19 after being filtered by the auto.arima() function. By repeating the aforementioned steps, the ARIMA model (2,1,0) for ACM males of mortality rate (AIC = − 291.72) was developed, and the ARIMA model (2,0,0) for ACM females of mortality rate (AIC = − 381.24) was developed. For males, the ACM prevalence was expected to decrease from 2.53% in 2022 to 1.36% in 2036. The predicted mortality rate also kept growing over the next decade, decreasing from 0.173 per 100 000 in 2022 to 0.168 per 100 000 in 2036. For females, the ACM prevalence was expected to increase from 0.37% in 2022 to 0.42% in 2036. The predicted mortality rate also kept growing over the next decade, increasing from 0.02 per 100 000 in 2022 to 0.03 per 100 000 in 2036(Figs. 5 ). Discussion Globally, Eastern Europe has been identified as the region with the highest levels of alcohol-related health damage and mortality [25,26,27] . More than 90% of countries in the region consumed more than two litres of anhydrous alcohol per capita per year [28] . Although an international symposium on “The Ongoing Alcohol Burden in Central and Eastern Europe” was held in June 2017 to outline the issue of alcohol-related mortality in a range of Eastern European countries and linked it to alcohol control initiatives, the sustainability of alcohol restriction policies and measures was of concern due to persistent socio-political, social divisions and highly volatile election outcomes in Eastern Europe [29,30,31] . China had a long history of alcohol production and consumption.. In recent decades, the consumption of alcohol in China has grown rapidly compared to other countries, which is closely related to the trend of socioeconomic development and increased alcohol production in China [32] . Statistics from the World Health Organization show that the per capita consumption of pure alcohol in China was 1.03 L in 1970 and had risen to 5.17 L in 1996 and 5.91 L in 2011 [33] . This study examines the trends in the burden of ACM in China over the last 30 years. To our knowledge, this is the first analysis of the epidemiological trends of ACM in China using the joinpoint analysis combined with the apc model. From 1990 to 2021, in the age-standardized prevalence rates, mortality rates, DALYs, YLDs and YLL increased totally, However, the global age-standardized prevalence and mortality of alcoholic cardiomyopathy are decreasing, which indicates that the disease burden of alcoholic cardiomyopathy in China is increasing, and the possible reasons are as follows: 1. Over the past 30 years, as China's economy has boomed and living standards have improved, nightlife has become more plentiful and demand for alcohol has increased. 2. Chinese People's life, work pressure is increasing, people use alcohol to relieve their pressure, 3. Since ancient times, wine has been an indispensable substance in the creation of many Chinese literati, and even today, wine table culture is very popular in China. In terms of gender, we can see that men under the age of 90 years have a much higher overall burden of disease such as morbidity and mortality from alcoholic cardiomyopathy than women of the same age. Jointpoint analysis showed that the disease burden of alcohol-related morbidity and mortality increased year by year in early China, while the disease burden of ACM tended to decrease in late China, it may be because with the development of medical treatment, science and technology, and people's awareness of disease, the level of attention to their own health. APC model analysis shows that, Age effects have a greater impact on ACM prevalence and mortality, with its prevalence being concentrated in youths and mortality risk increasing with age. In the cohort effect, the early birth cohort had a relatively low risk of prevalence and a high risk of mortality. In China, the mortality rate of ACM after the age of 75 years is increasing rapidly. Monitoring disease epidemics and predicting trends is an important link for disease prevention and control. According to the ARIMA model, over the next 15 years, the prevalence and mortality of alcoholic cardiomyopathy in men may gradually decrease, while the prevalence and mortality in women may gradually increase, as follows: for males, ACM prevalence and mortality are expected to fall to 1.36% and 0.168 per 100 000 within the year 2036, however, for females, ACM prevalence and mortality are likely to increase to 0.42% and 0.03 per 100 000 within the year 2036. Alcohol consumption is closely related to public health [34] . Our study improved the understanding about levels and trends of the ACM in China, and hoped to appropriately guide efforts of improving cardiovascular health in early stage at macro geographical scale. The Chinese government should revise the current industrial policies that encourage and support the development of the alcohol industry by strengthening taxation, regulating the production of various types of alcoholic beverages, reducing alcohol concentration, strengthening retail management [35] , preventing illegal alcoholic beverages and reducing informal alcohol, and revising the Alcohol Advertising Regulations to regulate advertising, sponsorship and promotional activities and restrict new media such as the Internet. Conclusions This study estimated the temporal trends of ACM prevalence and mortality in China from 1990 to 2021. We discovered that the prevalence and mortality of ACM have increased slowly over the past 30 years, and growth trend is expected to decline over the next 15 years according to ARIMA model. Interestingly, this trend varies by gender. We believe effective strategies and increased awareness are essential to improve the current situation of ACM in China, thereby improving the quality of life in ACM patients and preventing avoidable deaths. Abbreviations GBD Global Burden of Disease ACM Alcohol cardiomyopathy AAPC average annual percentage change ARIMA autoregressive integrated moving average DALYs disability-adjusted life-years YLDs years lived with disability YLL years of life lost LVEF left ventricular ejection fraction ALDH2 aldehyde dehydrogenase 2 APC Age-period-cohort ASPR Age-standardized rates in terms of prevalence ASMR Age-standardized rates in terms of mortality Declarations Acknowledgements The authors sincerely thank the GBD team for allowing us to access their comprehensive database. Competing interests The authors declare that they have no competing interests. Authors' contributions Dezhong Yang conceptualised and designed the study and analysed the data；Bike Bie has directly accessed and verified the underlying data reported in this manuscript. All authors had full access to all data and accept responsibility to submit for publication.Bike Bie drafted the manuscript for publication. 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Annu Rev Sociol. 2006;32:245 − 70. 10.1146/annurev.soc.32.061604.123144 Jastramskis M, Kuokštis V, Baltrukevičius M.Retrospective voting in Central and Eastern Europe: Hyper-accountability, corruption or socio-economic inequality? Party Polit. 2021;27:667 − 79. 10.1177/1354068819880320 Rupnik J.Explaining Eastern Europe: the crisis of liberalism. J Democracy. 2018;29:24–38. 10.1353/jod.2018.0042 Cochrane J, Chen H, Conigrave KM, Hao W. Alcohol use in China. Alcohol Alcohol. 2003 Nov-Dec;38(6):537 − 42. doi: 10.1093/alcalc/agg111. organization Wh. Global status report on alcohol and health 2011. Global status report on alcohol and health 2011: World Health Organization; 2011. p. 1-298. Alcohol and Public Policy Group. Alcohol: no ordinary commodity–a summary of the second edition. Addiction. 2010 May;105(5):769 − 79. Maimon D, Browning CR. Underage drinking, alcohol sales and collective efficacy: Informal control and opportunity in the study of alcohol use. Soc Sci Res. 2012 Jul;41(4):977 − 90.doi: 10.1016/j.ssresearch.2012.01.009. Additional Declarations No competing interests reported. Supplementary Files supplementarymaterials2.26.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6055235","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":422369198,"identity":"654730cd-c034-46d4-973e-71e1b089d72d","order_by":0,"name":"Bike Bie","email":"","orcid":"","institution":"The Third Military Medical University(Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bike","middleName":"","lastName":"Bie","suffix":""},{"id":422369199,"identity":"d2fc98b0-b6a1-417d-8814-fb90a87aedca","order_by":1,"name":"Dezhong Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYDACZjBpw8PGzHwAKpRAlJY0OX72tsQG4rRAwGFjyZ4zhsRpMW/nMXxcwcCcuOFGzvdHN3MOM/Cz5xgw/NyBW4vMYR5jwzMMbEAtuRubc7cdZpDseWPA2HsGtxYJZrY0yQYGHoQWgxs5BsyMbXi1pP9sYJAAOewhWIs9YS3MxxgbGAxA3meE2CJBWMthoMMSQIFsODt3WzqPxJlnBQd78WnhP9j4sYHhPygqH3zO3WYtx9+evPHBTzxawIDxH4LNAyIOENAwCkbBKBgFo4AAAACEl0zaXd5XtAAAAABJRU5ErkJggg==","orcid":"","institution":"The Third Military Medical University(Army Medical University","correspondingAuthor":true,"prefix":"","firstName":"Dezhong","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-02-18 10:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6055235/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6055235/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77634008,"identity":"112ff60a-361e-46a6-86b3-5ed91c8c8c92","added_by":"auto","created_at":"2025-03-03 18:05:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":129242,"visible":true,"origin":"","legend":"\u003cp\u003eAge-specific numbers and age-standardized prevalence and mortality rates of ACM in China, 2021. (A) Age-specific prevalence number. (B) Age-standardized prevalence rate.(C) Age-specific mortality number. (D) Age-standardized mortality rate.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6055235/v1/e3020e5cf194c74e37cf7073.png"},{"id":77634010,"identity":"5a933874-f577-449d-866f-211eb02aa644","added_by":"auto","created_at":"2025-03-03 18:05:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":177659,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in the all-age cases and age-standardized prevalence, mortality, and DALYs rates of ACM by sex from 1990 to 2021. (A) Prevalence number and rate. (B) Mortality number and rate. (C) DALYs number and rate.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6055235/v1/740d3586a779313645231081.png"},{"id":77633994,"identity":"d07d2b6e-3723-4cf5-a151-3a416656213e","added_by":"auto","created_at":"2025-03-03 18:05:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":140559,"visible":true,"origin":"","legend":"\u003cp\u003eJoinpoint regression analysis of the sex-specific age-standardized prevalence rate for ACM in China from 1990 to 2021. (A) Age-standardized prevalence rate for males. (B) Age-standardized prevalence rate for females.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6055235/v1/5be38d543feba132f7228491.png"},{"id":77634738,"identity":"080cfe5d-e1c3-4eb6-b4bc-342f6831a021","added_by":"auto","created_at":"2025-03-03 18:13:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":148159,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence rates of ACM in China. (A) The age-specific prevalence rates of ACM according to time periods; each line connects the age-specific prevalence for a 5-year period. (B) The age-specific prevalence rates of ACM according to birth cohort; each line connects the age-specific prevalence for a 5-year cohort. (C) The period-specific prevalence rates of ACM according to age groups; each line connects the birth cohort-specific prevalence for a 5-yer age group. (D) The birth cohort-specific prevalence rates of ACM according to age groups; each line connects the birth cohort-specific prevalence for a 5-year age group.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6055235/v1/5cb21b7e95cfbf87de93618b.png"},{"id":77634003,"identity":"8f54ebe5-304e-4ca5-ad70-34cd9a9218c8","added_by":"auto","created_at":"2025-03-03 18:05:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":92601,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated period effects for prevalence (A,B) and mortality (C,D) of ACM in China (1990-2036).(A)Estimated period effects for males prevalence of ACM in China (1990-2036). (B)Estimated period effects for females prevalence of ACM in China (1990-2036). (C)Estimated period effects for males mortality of ACM in China (1990-2036).(D)Estimated period effects for females mortality of ACM in China (1990-2036).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6055235/v1/715087a5654041393fd6f39d.png"},{"id":77635186,"identity":"f0cd7a11-1414-4584-ba22-8953d97f7eaf","added_by":"auto","created_at":"2025-03-03 18:21:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1396778,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6055235/v1/fd634414-b552-47e7-be86-98b1b87178ac.pdf"},{"id":77633991,"identity":"34e313bc-338a-4b62-96e9-898c125a31c4","added_by":"auto","created_at":"2025-03-03 18:05:30","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3399732,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterials2.26.docx","url":"https://assets-eu.researchsquare.com/files/rs-6055235/v1/7cddbee77cd207b5cb74e6ad.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long-term trends in the burden of alcoholic cardiomyopathy in China based on Global Burden of Disease 2021","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlcohol-induced cardiomyopathy (ACM) represents a distinct subtype of dilated cardiomyopathy (DCM) that develops exclusively from prolonged excessive alcohol consumption in the absence of other etiological factors. The diagnostic criteria for ACM encompass three essential components: (1) sustained heavy alcohol consumption exceeding 80 g/day for at least five years; (2) echocardiographic evidence of left ventricular dilation (\u0026gt;\u0026thinsp;2 SD above normal) accompanied by impaired systolic function (LVEF\u0026thinsp;\u0026lt;\u0026thinsp;50%); and (3) systematic exclusion of alternative DCM etiologies, including hypertensive, valvular, and ischemic heart diseases\u003csup\u003e[1]\u003c/sup\u003e. Initially characterized in the late 19th century, this alcohol-related myocardial pathology manifests through ventricular chamber enlargement, diminished contractile function, and either normal or reduced ventricular wall dimensions\u003csup\u003e[2,3]\u003c/sup\u003e. As a major contributor to non-ischemic cardiomyopathy, alcohol abuse accounts for approximately one-tenth of all DCM cases worldwide[4]. Substantial experimental evidence has elucidated the direct myocardial toxicity of ethanol and its metabolic byproducts\u003csup\u003e[5]\u003c/sup\u003e.In high-income countries, ACM has emerged as a predominant cause of left ventricular dysfunction and represents the leading etiology of mortality among non-ischemic cardiomyopathy patients in the United States. Population-based studies estimate that ACM contributes to 3.8\u0026ndash;47% of non-ischemic cardiomyopathy cases across different populations\u003csup\u003e[6,7]\u003c/sup\u003e. The World Health Organization's 2018 Global Status Report on Alcohol and Health revealed that more than half (56%) of the global population aged\u0026thinsp;\u0026ge;\u0026thinsp;15 years consumes alcoholic beverages\u003csup\u003e[8]\u003c/sup\u003e. Genetic polymorphisms in alcohol metabolism pathways, particularly ALDH2 deficiency prevalent in Asian populations, contribute to individual susceptibility through impaired acetaldehyde clearance, resulting in characteristic flushing reactions\u003csup\u003e[9]\u003c/sup\u003e.A striking gender disparity characterizes ACM epidemiology, with males demonstrating significantly higher disease prevalence than females. Hospitalization data indicate a 9:1 male-to-female ratio in ACM cases\u003csup\u003e[10]\u003c/sup\u003e. This pronounced gender imbalance likely results from multiple interacting factors, including sociocultural influences (e.g., differential reporting of alcohol consumption patterns), physiological variations (e.g., sex-specific differences in ethanol metabolism and tolerance thresholds), and potential limitations in current diagnostic criteria that fail to account for biological sex differences. Emerging evidence suggests that females may develop ACM at lower cumulative alcohol exposure levels and shorter durations of heavy drinking compared to their male counterparts\u003csup\u003e[11,12]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eThe Global Burden of Disease Study 2021 presents a comprehensive evaluation of mortality patterns, utilizing cause-specific mortality indicators and years of life lost metrics. This updated analysis encompasses 288 distinct causes of death, stratified by age and gender, across 204 geographical regions from 1990 through 2021. Notably, this revision extends the temporal scope beyond the previously reported data spanning 1990\u0026ndash;2019. The study incorporates multiple analytical models to estimate disease burden and injury-related outcomes, thereby enhancing the precision of epidemiological assessments\u003csup\u003e[13]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eJoinpoint regression analysis (dup: abstract ?)\u003c/h3\u003e\n\u003cp\u003eDeveloped by Kim et al. (2000)\u003csup\u003e[14]\u003c/sup\u003e, the join-point regression methodology represents an advanced analytical framework for evaluating temporal patterns in ACM burden. This statistical approach employs segmented linear modeling to quantify disease rate fluctuations, utilizing least squares estimation to minimize potential biases associated with conventional trend analysis methods. The computational algorithm optimizes the residual sum of squares between model-derived predictions and empirical observations, enabling automatic detection of significant trend inflection points. All statistical computations were performed using the Joinpoint Regression Program (version 4.9.1.0). To quantify temporal changes, we calculated the average annual percentage change (AAPC) as a composite measure, with statistical significance determined through comparison against a null hypothesis of zero change. Throughout the analysis, results were considered statistically significant at the conventional threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003ch3\u003eAge-period-cohort analysis\u003c/h3\u003e\n\u003cp\u003eThe age-period-cohort (APC) framework represents a fundamental analytical tool in epidemiological and sociological research for examining temporal patterns in disease prevalence and mortality. This modeling approach, predominantly employing Poisson regression, enables simultaneous assessment of age-specific effects, temporal variations, and generational influences. A critical methodological challenge stems from the inherent collinearity among the three temporal dimensions, resulting in model identifiability problems that impede the precise estimation of individual component effects\u003csup\u003e[15]\u003c/sup\u003e. Multiple statistical strategies have been developed to mitigate this issue, including the implementation of intrinsic estimators\u003csup\u003e[16]\u003c/sup\u003e, application of regularization techniques\u003csup\u003e[17]\u003c/sup\u003e, and development of specialized estimation functions\u003csup\u003e[18]\u003c/sup\u003e, each presenting distinct advantages and constraints. The current investigation adopted the comprehensive APC methodology developed by Carstensen\u003csup\u003e[19,20]\u003c/sup\u003e, which incorporates the Lexis diagram framework for enhanced temporal visualization. All statistical analyses were performed using the Epi package (version 2.46) within the R statistical environment (version 4.2.0).\u003c/p\u003e\n\u003ch3\u003eAutoregressive integrated moving average model\u003c/h3\u003e\n\u003cp\u003eThe autoregressive integrated moving average (ARIMA) framework integrates autoregressive processes with moving average components to analyze temporal data patterns. This statistical approach is predicated on the fundamental assumption that sequential observations represent time-dependent stochastic variables, with their inherent temporal dependencies systematically characterized through the model's structure. Such methodological foundation enables the prediction of future observations based on historical temporal patterns\u003csup\u003e[21]\u003c/sup\u003e. A critical prerequisite for implementing ARIMA analysis is the stationarity of the time series, which necessitates the maintenance of consistent statistical properties throughout the observation period, including time-invariant mean values (ideally approximating zero) and constant variance. Furthermore, the analyzed sequence should demonstrate stochastic behavior, devoid of deterministic patterns or systematic trends.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWithin the Global Burden of Disease 2021 (GBD 2021) framework, two key epidemiological metrics were examined: (1) ACM prevalence, quantified as the ratio of affected individuals to the total population, and (2) ACM mortality, expressed as age-standardized deaths per 100,000 population. Standardization of prevalence and mortality rates was achieved through application of the world population as the reference standard. Temporal analysis was structured into quinquennial intervals (1992\u0026ndash;2021), with exclusion of 1990\u0026ndash;1991 data due to incomplete temporal coverage. The analytical framework incorporated 17 discrete age categories (15\u0026ndash;19 to 95\u0026thinsp;+\u0026thinsp;years) and 20 generational cohorts (1895\u0026ndash;1999 and 2000\u0026ndash;2004 birth years). Reference values for age, period, and cohort parameters were established at their respective arithmetic means\u003csup\u003e[22, 23]\u003c/sup\u003e. Model selection was implemented through the auto.arima() function, with optimization guided by minimization of the Akaike information criterion (AIC)\u003csup\u003e[24]\u003c/sup\u003e. Temporal trend analysis was conducted using Joinpoint Regression Software, while data management and preprocessing were executed utilizing the Epi package within the R statistical computing environment.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn China, there were 1860 (95% CI: 318, 2987) deaths due to ACM. Age-standardized rates in terms of prevalence (ASPR), mortality (ASMR), DALYs, YLDs, and YLL of ACM in 2021 were 1.56 cases (95% CI: 1.24, 1.92) per 100,000, 0.10 deaths (95% CI: 0.02, 0.15) per 100,000, 3.43 DALYs (95% CI: 0.67, 5.41) per 100,000, 0.14 YLDs (95% CI: 0.09, 0.21) per 100,000, and 3.29 YLL (95% CI: 0.53, 5.31) per 100,000 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The all-age numbers and age-standardized rates for males and females are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. It is clear that men have a higher disease burden than women (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For a comparison of the burden of ACM in China and globally, please refer to Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAll-age cases and age-standardized prevalence, deaths, YLL, YLDs, and DALYs rates in 2021 for ACM in China.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAll-age cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAge-standardized rates per 100 000 people\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevalence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28103.36\u003c/p\u003e \u003cp\u003e(22175.42,34990.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24892.85\u003c/p\u003e \u003cp\u003e(19393.96,31197.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3210.51\u003c/p\u003e \u003cp\u003e(2629.59,3924.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003cp\u003e(1.24,1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003cp\u003e(2.15,3.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003cp\u003e(0.30,0.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeaths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1860.96\u003c/p\u003e \u003cp\u003e(317.62,2986.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1669.87\u003c/p\u003e \u003cp\u003e(167.92,2759.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e191.09\u003c/p\u003e \u003cp\u003e(15.92,316.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003cp\u003e(0.02,0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003cp\u003e(0.02,0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003cp\u003e(0.002,0.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDALYs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64745.92\u003c/p\u003e \u003cp\u003e(12595.45,102876.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60030.63\u003c/p\u003e \u003cp\u003e(7939.79,98046.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4715.29\u003c/p\u003e \u003cp\u003e(689.87,7731.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003cp\u003e(0.67,5.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.31\u003c/p\u003e \u003cp\u003e(0.85,10.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003cp\u003e(0.08,0.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYLDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2606.99\u003c/p\u003e \u003cp\u003e(1697.04,3832.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2309.21\u003c/p\u003e \u003cp\u003e(1498.56,3409.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e297.78\u003c/p\u003e \u003cp\u003e(194.59,430.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003cp\u003e(0.09,0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003cp\u003e(0.16,0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003cp\u003e(0.02,0.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYLL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62138.93\u003c/p\u003e \u003cp\u003e(9970.45,100710.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57721.42\u003c/p\u003e \u003cp\u003e(5725.75,96165.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4417.51\u003c/p\u003e \u003cp\u003e(394.55,7425.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003cp\u003e(0.53,5.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003cp\u003e(0.58,10.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003cp\u003e(0.04,0.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDALYs\u0026thinsp;=\u0026thinsp;disability-adjusted life-years, YLDs\u0026thinsp;=\u0026thinsp;years lived with disability, YLL\u0026thinsp;=\u0026thinsp;years of life lost.\u003c/p\u003e \u003cp\u003eACM is more prevalent in people over the age of 35, and it increases quickly between the ages of 25 and 49. Males are most affected between the ages of 45 and 49, after the age of 80, age-standardized prevalence rate of females are increasing. After the age of 70, the mortality rate increased dramatically. Surprisingly, between the age of 15 and 89, men had higher prevalence and mortality rates than women (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Age-standardized DALYs, YLDs, and YLL rates showed similar trends by sex and age group (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe sex-specific, age-standardized prevalence and mortality rates for ACM fluctuated by calendar year. The disease prevalence is generally increasing for men in 1990\u0026ndash;2015, and prevalence gradually decrease after 2015, meanwhile, the disease prevalence is generally increasing for women in 1990\u0026ndash;2005,while there is no significantly increase for females in 2005\u0026ndash;2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The disease mortality for males is gradually increase, however, The disease mortality for females began to decrease after 2010(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The male age-standardized DALYs are generally decreasing after 2015, the female age-standardized DALYs are generally decreasing after 2009(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Age-standardized YLDs and YLL rates showed similar trends by sex and age group (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eJoinpoint regression analysis\u003c/h3\u003e\n\u003cp\u003eJoinpoint regression analyses of the age-standardized prevalence rates for ACM in China from 1990 to 2021 are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. We found the disease prevalence trend to significantly increase from 2000 to 2005 in both male (APC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;12.10 (2000\u0026ndash;2005), 95% CI: 11.50, 12.70) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and female (APC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;15.40 (2000\u0026ndash;2004), 95% CI: 14.10, 16.70) populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Joinpoint regression analyses of the age-standardized DALYs, YLD, YLL and mortality rates in both sexes are shown in Supplementary Fig.\u0026nbsp;3 to 6. The AAPCs in ACM prevalence and mortality rates over three decades was showed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Age-standardized prevalence and mortality rates for ACM in China increased by 3.70 (95% CI: 3.50, 3.80) and 1.90 (95% CI: 1.70,2.10) from 1990 to 2021. Surprisingly, females had a lower AAPC of prevalence and mortality rates than males (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eJoinpoint regression analysis: trends in age-standardized prevalence and mortality rates (per 100 000 persons) among both sexes, males, and females in China, 1990\u0026ndash;2021.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eASPR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eASMR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPC(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAAPC(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAPC(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAAPC(95%CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u0026ndash;1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70 (0.40, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.70(3.50,3.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1990\u0026ndash;1992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.70 (-3.70, 0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.90 (1.70, 2.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1995\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.60 (3.10, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1992\u0026ndash;1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.70 (2.70, 4.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u0026ndash;2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.70(12.90,14.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1996\u0026ndash;2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.80 (2.50, 3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2004\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.20 (4.00, 4.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2005\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.50 (5.90, 7.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.20(-0.40,0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2010\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50 (-0.30, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2014\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.40(-1.60, -1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70 (0.50, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.70(3.50,3.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1990\u0026ndash;1992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.50 (-3.90, 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.40 (2.20, 2.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1996\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.40 (2.60, 4.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1992\u0026ndash;1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.10 (2.80, 5.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u0026ndash;2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.10(11.50,12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1996\u0026ndash;2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00 (1.70, 2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2005\u0026ndash;2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.30 (-4.20, 4.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2004\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.10 (7.60, 8.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2015\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.70 (-1.10, -0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2010\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.80 (0.90, 2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2014\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.10 (-1.30, -0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u0026ndash;1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00 (1.50, 2.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.20(3.00,3.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1990\u0026ndash;1993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.20 (-2.90, 0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.4 (-0.80, 0.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1995\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.20 (7.40, 9.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1993\u0026ndash;2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.70 (4.40, 4.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u0026ndash;2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.40(14.10,16.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2004\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.30 (-2.00, -0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2004\u0026ndash;2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.10 (-0.30, 0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2010\u0026ndash;2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.10 (-5.50, -6.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2013\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.90(-1.20, -0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2013\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.50 (-2.30, -3.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2017\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.30 (-3.30, -1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAAPC, average annual percent change presented for full period; APC, annual percent change; CI, confidence interval.\u003c/p\u003e\n\u003ch3\u003eThe effects of age, period, and cohort on prevalence and mortality rates\u003c/h3\u003e\n\u003cp\u003eThe prevalence rate increased rapidly between the ages of 30 and 40 years, and after 85 years also showed a quickly-increasing trend. The ACM mortality increased from the ages of 15\u0026ndash;19 years, peaking at the ages of 95\u0026ndash;99 years. After controlling the effects of period and cohort, the risk of ACM in the 90\u0026ndash;94 years group was 236.8 times the risks in the 15\u0026ndash;19 years [\u003cem\u003eexp\u003c/em\u003e((\u003cem\u003eαage(85\u0026ndash;89 ) \u0026ndash; αmean\u003c/em\u003e)\u0026ndash;(\u003cem\u003eαage(15\u0026ndash;19 )\u003c/em\u003e \u0026ndash; \u003cem\u003eαmean\u003c/em\u003e))\u0026thinsp;=\u0026thinsp;\u003cem\u003eexp\u003c/em\u003e(\u003cem\u003eαage(85\u0026ndash;89 )\u003c/em\u003e \u0026ndash; \u003cem\u003eαage(15\u0026ndash;19 )\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;49.73/0.21]. The period-based trends of ACM prevalence were unsteady. The RR value increased from 0.54 (95% CI 0.52\u0026ndash;0.57) in 1994 to 1.41 (95% CI 1.36\u0026ndash;1.47) in 2014. The birth cohort of each age group showed that the ACM prevalence in the early period was lower than that in the later period. The cohort risk was significantly lower in the early birth cohort (RRcohort(1905\u0026ndash;1909)\u0026thinsp;=\u0026thinsp;0.38, 95% CI 0.25\u0026ndash;0.58) and increased in the recent cohorts (RRcohort(2000\u0026ndash;2004)\u0026thinsp;=\u0026thinsp;16.60, 95% CI 14.24\u0026ndash;19.35). The period-based mortality rate had a turning point in 2014. The risk peaked in 2014 [RRperiod(2014)\u0026thinsp;=\u0026thinsp;1.48, 95%CI 1.40\u0026ndash;1.57] and then decreased afterwards [RRperiod(2019)\u0026thinsp;=\u0026thinsp;1.39, 95% CI 1.30\u0026ndash;1.48]. Similarly, the early birth cohort has a similiar effect on ACM mortality (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) (Supplementary Fig.\u0026nbsp;7and 8). The RR increased gradually before the 1995\u0026ndash;1999 birth cohort and then displayed a downward trend (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of ACM prevalence and mortality in the next fifteen years\u003c/h2\u003e \u003cp\u003eThe ARIMA model was used to quantitatively depict the trends of ACM prevalence and mortality over the following 15 years. The optimized model was chosen to be (3,1,0) for males of ACM prevalence with an AIC value of \u0026minus;\u0026thinsp;128.2 after being filtered by the auto.arima() function, The optimized model was chosen to be (0,2,2) for females of ACM prevalence with an AIC value of -246.19 after being filtered by the auto.arima() function. By repeating the aforementioned steps, the ARIMA model (2,1,0) for ACM males of mortality rate (AIC = \u0026minus;\u0026thinsp;291.72) was developed, and the ARIMA model (2,0,0) for ACM females of mortality rate (AIC = \u0026minus;\u0026thinsp;381.24) was developed. For males, the ACM prevalence was expected to decrease from 2.53% in 2022 to 1.36% in 2036. The predicted mortality rate also kept growing over the next decade, decreasing from 0.173 per 100 000 in 2022 to 0.168 per 100 000 in 2036. For females, the ACM prevalence was expected to increase from 0.37% in 2022 to 0.42% in 2036. The predicted mortality rate also kept growing over the next decade, increasing from 0.02 per 100 000 in 2022 to 0.03 per 100 000 in 2036(Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eGlobally, Eastern Europe has been identified as the region with the highest levels of alcohol-related health damage and mortality\u003csup\u003e[25,26,27]\u003c/sup\u003e. More than 90% of countries in the region consumed more than two litres of anhydrous alcohol per capita per year\u003csup\u003e[28]\u003c/sup\u003e. Although an international symposium on \u0026ldquo;The Ongoing Alcohol Burden in Central and Eastern Europe\u0026rdquo; was held in June 2017 to outline the issue of alcohol-related mortality in a range of Eastern European countries and linked it to alcohol control initiatives, the sustainability of alcohol restriction policies and measures was of concern due to persistent socio-political, social divisions and highly volatile election outcomes in Eastern Europe\u003csup\u003e[29,30,31]\u003c/sup\u003e. China had a long history of alcohol production and consumption.. In recent decades, the consumption of alcohol in China has grown rapidly compared to other countries, which is closely related to the trend of socioeconomic development and increased alcohol production in China\u003csup\u003e[32]\u003c/sup\u003e. Statistics from the World Health Organization show that the per capita consumption of pure alcohol in China was 1.03 L in 1970 and had risen to 5.17 L in 1996 and 5.91 L in 2011\u003csup\u003e[33]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study examines the trends in the burden of ACM in China over the last 30 years. To our knowledge, this is the first analysis of the epidemiological trends of ACM in China using the joinpoint analysis combined with the apc model. From 1990 to 2021, in the age-standardized prevalence rates, mortality rates, DALYs, YLDs and YLL increased totally, However, the global age-standardized prevalence and mortality of alcoholic cardiomyopathy are decreasing, which indicates that the disease burden of alcoholic cardiomyopathy in China is increasing, and the possible reasons are as follows: 1. Over the past 30 years, as China's economy has boomed and living standards have improved, nightlife has become more plentiful and demand for alcohol has increased. 2. Chinese People's life, work pressure is increasing, people use alcohol to relieve their pressure, 3. Since ancient times, wine has been an indispensable substance in the creation of many Chinese literati, and even today, wine table culture is very popular in China. In terms of gender, we can see that men under the age of 90 years have a much higher overall burden of disease such as morbidity and mortality from alcoholic cardiomyopathy than women of the same age. Jointpoint analysis showed that the disease burden of alcohol-related morbidity and mortality increased year by year in early China, while the disease burden of ACM tended to decrease in late China, it may be because with the development of medical treatment, science and technology, and people's awareness of disease, the level of attention to their own health. APC model analysis shows that, Age effects have a greater impact on ACM prevalence and mortality, with its prevalence being concentrated in youths and mortality risk increasing with age. In the cohort effect, the early birth cohort had a relatively low risk of prevalence and a high risk of mortality. In China, the mortality rate of ACM after the age of 75 years is increasing rapidly. Monitoring disease epidemics and predicting trends is an important link for disease prevention and control. According to the ARIMA model, over the next 15 years, the prevalence and mortality of alcoholic cardiomyopathy in men may gradually decrease, while the prevalence and mortality in women may gradually increase, as follows: for males, ACM prevalence and mortality are expected to fall to 1.36% and 0.168 per 100 000 within the year 2036, however, for females, ACM prevalence and mortality are likely to increase to 0.42% and 0.03 per 100 000 within the year 2036.\u003c/p\u003e \u003cp\u003eAlcohol consumption is closely related to public health\u003csup\u003e[34]\u003c/sup\u003e. Our study improved the understanding about levels and trends of the ACM in China, and hoped to appropriately guide efforts of improving cardiovascular health in early stage at macro geographical scale. The Chinese government should revise the current industrial policies that encourage and support the development of the alcohol industry by strengthening taxation, regulating the production of various types of alcoholic beverages, reducing alcohol concentration, strengthening retail management\u003csup\u003e[35]\u003c/sup\u003e, preventing illegal alcoholic beverages and reducing informal alcohol, and revising the Alcohol Advertising Regulations to regulate advertising, sponsorship and promotional activities and restrict new media such as the Internet.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study estimated the temporal trends of ACM prevalence and mortality in China from 1990 to 2021. We discovered that the prevalence and mortality of ACM have increased slowly over the past 30 years, and growth trend is expected to decline over the next 15 years according to ARIMA model. Interestingly, this trend varies by gender. We believe effective strategies and increased awareness are essential to improve the current situation of ACM in China, thereby improving the quality of life in ACM patients and preventing avoidable deaths.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGBD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Global Burden of Disease\u003c/p\u003e\n\u003cp\u003eACM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Alcohol cardiomyopathy\u003c/p\u003e\n\u003cp\u003eAAPC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;average annual percentage change\u003c/p\u003e\n\u003cp\u003eARIMA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;autoregressive integrated moving average\u003c/p\u003e\n\u003cp\u003eDALYs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; disability-adjusted life-years\u003c/p\u003e\n\u003cp\u003eYLDs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; years lived with disability\u003c/p\u003e\n\u003cp\u003eYLL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; years of life lost\u003c/p\u003e\n\u003cp\u003eLVEF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; left ventricular ejection fraction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eALDH2 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; aldehyde dehydrogenase 2\u003c/p\u003e\n\u003cp\u003eAPC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Age-period-cohort\u003c/p\u003e\n\u003cp\u003eASPR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Age-standardized rates in terms of prevalence\u003c/p\u003e\n\u003cp\u003eASMR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Age-standardized rates in terms of mortality\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely thank the GBD team for allowing us to access their comprehensive database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDezhong Yang conceptualised and designed the study and analysed the data；Bike Bie has directly accessed and verified the underlying data reported in this manuscript. All authors had full access to all data and accept responsibility to submit for publication.Bike Bie drafted the manuscript for publication. All authors approved the final draft and consented to the submission of the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the GBD 2021: https://ghdx.healthdata.org/gbd-2021;\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDom\u0026iacute;nguez F, Adler E, Garc\u0026iacute;a-Pav\u0026iacute;a P. Alcoholic cardiomyopathy: an update. Eur Heart J. 2024 Jul 9;45(26):2294\u0026ndash;2305.doi: 10.1093/eurheartj/ehae362.\u003c/li\u003e\n\u003cli\u003eGeorge A, Figueredo VM. Alcoholic cardiomyopathy: a review. 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Soc Sci Res. 2012 Jul;41(4):977\u0026thinsp;\u0026minus;\u0026thinsp;90.doi: 10.1016/j.ssresearch.2012.01.009.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"alcoholic cardiomyopathy, Global Burden of Disease, Burden, China","lastPublishedDoi":"10.21203/rs.3.rs-6055235/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6055235/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlcoholic cardiomyopathy remains an important cause of death from cardiovascular disease in China. This study aimed to characterize the temporal trends of alcohol cardiomyopathy (ACM) burden in China during 1990\u0026ndash;2021.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe epidemiological data utilized in this investigation were sourced from the Global Burden of Disease (GBD) 2021 database. Temporal trends in ACM prevalence and mortality rates were analyzed through join-point regression modeling, with quantification of temporal changes expressed as average annual percentage change (AAPC) metrics. Simultaneously, age-period-cohort (APC) analysis was implemented to evaluate the independent contributions of aging effects, temporal variations, and generational influences. Furthermore, we developed an extended autoregressive integrated moving average (ARIMA) framework to project disease burden patterns through 2036, providing a 15-year forecast of epidemiological trends.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe age-standardized prevalence and mortality rates in both sexes changed from 0.52 (95% CI: 0.43, 0.62) to 1.56 (95% CI: 1.24, 1.92) and from 0.05 (95% CI: 0.02, 0.12) to 0.10 (95% CI: 0.02, 0.15) per 100 000 people in China from 1990 to 2021. The age-standardized disability-adjusted life-years(DALYs) rate, years lived with disability(YLDs) rate and years of life lost(YLL ) rate is increasing from 1990 to 2021. AAPC in age-standardized prevalence and mortality rates for ACM in China were 3.70 (95% CI: 3.50, 3.90), and 1.90 (95% CI: 1.70, 2.10). The effects of age, period, and cohort on prevalence and mortality rates differed.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe increasing age-standardized prevalence, mortality, DALYs, YLDs and YLL rates is gradually increasing between 1990 and 2021 in China. The burden of ACM in China will be a major public health challenge, given the country\u0026rsquo;s large population base and aging population.\u003c/p\u003e","manuscriptTitle":"Long-term trends in the burden of alcoholic cardiomyopathy in China based on Global Burden of Disease 2021","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-03 18:05:24","doi":"10.21203/rs.3.rs-6055235/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"714c82cc-fdbf-4f9d-b20a-03e1dff60cb3","owner":[],"postedDate":"March 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45023226,"name":"Health sciences/Cardiology"},{"id":45023227,"name":"Health sciences/Health occupations"}],"tags":[],"updatedAt":"2025-03-03T18:05:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-03 18:05:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6055235","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6055235","identity":"rs-6055235","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}