Global, regional, and national burden of cardiomyopathy (including alcoholic cardiomyopathy and others) from 1990 to 2021: An analysis of data from the global burden of disease study 2021 and forecast to 2040

Haoyang Chen; Ruifeng Liang; Yanzhang Tian

doi:10.1371/journal.pone.0341687

. 2026 Jan 30;21(1):e0341687. doi: 10.1371/journal.pone.0341687

Global, regional, and national burden of cardiomyopathy (including alcoholic cardiomyopathy and others) from 1990 to 2021: An analysis of data from the global burden of disease study 2021 and forecast to 2040

Haoyang Chen ^1,², Ruifeng Liang ^1,^*, Yanzhang Tian ^2,^*

Editor: Claudio Alberto Dávila-Cervantes³

PMCID: PMC12858021 PMID: 41615926

Abstract

Background

Cardiomyopathy is a disease that can lead to severe cardiac symptoms and has seen an increasing number of cases in recent years. This study aims to analyze the incidence, mortality, and disability-adjusted life years of cardiomyopathy (including alcoholic cardiomyopathy and other cardiomyopathy) globally, as well as in different regions and countries, from 1990 to 2021, at different gender, age, and socio-demographic index levels.

Methods

All data relevant to the burden of disease analysis in this study were obtained from the 2021 Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), encompassing alcoholic cardiomyopathy (AC) and other cardiomyopathy (OC). The overall burden of total cardiomyopathy (TC) was evaluated by integrating data about AC and OC, with 95% confidence intervals calculated based on the 95% uncertainty interval (UI) width divided by the standardized error value, determined by 1.96 × 2. Temporal patterns and trends in age-standardized prevalence rates (ASPR), mortality rates (ASDR), and disability-adjusted life years (ASR_DALYs) for global TC, as well as for AC and OC burden calculations, their estimated annual percentage changes (EAPCs) were calculated, assessed, and visualized. The analysis was categorized by gender, 20 age groups, 21 GBD regions, 204 countries/regions, and 5 socio-demographic index (SDI) regions. The burden of disease prediction model was subjected to Bayesian age-period-cohort (BAPC) modeling to derive predictions for the period from 2022 to 2040. All statistical analyses and mappings were conducted using the R statistical package version 4.4.3.

Results

In 2021, the global burden of TC remains considerable, with a total of 4752361.3, including AC: 528429 and OC: 4223932.2; the ASPRs (per 100,000 persons) were 59.5, 6.2 and 53.3; the ASDR (per 100,000 persons) were 4.5, 0.7 and 3.7; the age-standardized DALYs were 129.8, 25.3 and 104.5. In the aspect of AC, a considerable disparity in age-standardized prevalence, mortality, and DALYs was observed between the regional, national, and gender levels. The predictive results indicated that from 2021 to 2040, the ASPR of TC and OC showed a general increasing trend, while that of AC showed a decreasing trend. The ASDR and ASR_DALYs of TC, OC, and AC showed a general decreasing trend.

Conclusions

Globally, there has been an observed increase in the ASPR of TC and OC, while AC presented a decreasing trend, with significant regional, age, and gender variations in future trends. Although future projections in this study suggested a decline in ASDR and ASR_DALYs of TC, AC, and OC, there is a need to continue controlling the burden of disease in TC, AC, and OC studies to respond to the corresponding epidemiological trends and reduce the burden of them to some extent.

Introduction

The history of research on cardiomyopathy can be traced back to a hundred years ago [1]. Cardiomyopathy comprises disorders characterized by structural-functional impairment of the myocardium, manifesting as diverse clinical presentations ranging from asymptomatic ventricular dysfunction and exercise-induced dyspnea to severe outcomes (heart failure, sudden death). This condition arises from multiple etiologies, including genetic predisposition, obesity, type 2 diabetes, hypertension, and excessive alcohol consumption [2–9]. AC is a cardiovascular disease caused by excessive alcohol consumption (typically defined as more than 80 grams per day for 5 years or longer) and belongs to an acquired dilated cardiomyopathy (DCM) [10,11] Although many studies have led to innovations and breakthroughs in the treatment of cardiomyopathy, limitations remain, which require traditional treatment options such as invasive surgery, like heart transplantation [2,5].^.

Previous studies have shown that in 2019, childhood myocarditis and cardiomyopathy affected 121,259 boys and 77,216 girls worldwide [12]. Another study, using the GBD2019 database, found an estimated 710,000 AC cases and 3.73 million OC cases across the entire global population [13] In 2021, cardiomyopathy and myocarditis ranked 9th in Central, Eastern, and Central Asia combined (7th in Eastern Europe) among the top ten Level 3 causes by DALYs rate [14]. A review in 2023 indicates that the annual sudden cardiac death (SCD) incidence rate due to cardiomyopathy is between 0.15% and 0.7% globally, significantly higher than that in other age and gender-matched populations [15]. In addition, endomyopathy (CMPs) due to cardiac damage and damage to cardiomyocytes, including exacerbation of underlying CMPs or emergence of new CMPs, was common among patients suffering from COVID-19 during the novel coronavirus pandemic [16]. Although many recent epidemiological studies of cardiomyopathy have been conducted in different regions or populations, and corresponding control measures have been proposed, the global burden of cardiomyopathy is still not optimistic [17–19]. Previous studies have also found a significant association between extreme temperature (high or low) and risk of cardiomyopathy and exacerbation of symptoms in patients with cardiomyopathy [20,21].

Despite the Global Burden of Disease (GBD) studies providing extensive data on various cardiovascular diseases, a comprehensive analysis specifically for cardiomyopathy (including AC, among others) as a comprehensive category is still lacking. Neither the overall trends of TC, AC, and OC diseases from 1990 to 2021 have been integrated, nor have future disease prediction studies been conducted. This study aims to fill this gap by systematically organizing the global, regional, and national burden of cardiomyopathy as an independent disease entity from 1990 to 2021 using data from the GBD database. It will analyze and compare the age-standardized incidence rates (ASPRs), age-standardized mortality rates (ASIRs), and age-standardized DALYs for TC, AC, and OC, and predict disease trends from 2022 to 2040 by dividing age into 20 five-year age groups (<5 years, 5–9 years, 10–14 years, …, 90–94 years, and ≥95 years) using the Bayesian age-period-cohort (BAPC) model. By synthesizing data scattered within the GBD framework, this study provides a unique integrated assessment, establishing a critical benchmark for understanding the total public health burden of all forms of cardiomyopathy. This is crucial for informed policy planning and resource allocation, which typically target disease groups rather than isolated ICD codes.

Methods

Data source and collection

GBD 2021 provides and systematically analyses comprehensive estimates of the global burden of 371 diseases and injuries, 288 causes of death, and 88 risk factors for 204 countries and territories from 1990 to 2021. These analyses were achieved by using sophisticated statistical models such as the Cause of Death Envelope Model (CODEm) framework, MR-BRT, and DisMod-MR 2.1 to adjust for bias due to differences in data sources, definitions, and measurement methods, ensuring internal consistency of estimates across regions, ages, genders and years [14,22,23].

This study used data on the disease burden of AC and OC from GBD 2021 from 1990 to 2021, obtained using the Global Health Data Exchange (GHDx) results tool. Epidemiological data related to AC and OC were established according to the 9th and 10th editions of the International Classification of Diseases (ICD-9 and ICD-10), where AC corresponds to the disease code 425.5 (ICD-9) or I42.6 (ICD-10), and OC corresponds to the codes 425.0–425.18, 425.3, and 425.8–425.9 (ICD-9) or I42.0–I42.5 and I42.7 (ICD-10). TC was evaluated by integrating data about AC and OC, with 95% confidence intervals calculated based on the 95% uncertainty interval (UI) width divided by the standardized error value, determined by 1.96 × 2.

In addition, the University of Washington Institutional Review Board waived the requirement for informed consent when accessing GBD data [24]. This study adhered to the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) [25].

Sociodemographic index

The socio-demographic index (SDI), developed by researchers from the Global Burden of Disease Study (GBD), uses data on fertility, education levels, and per capita income to quantify the level of development of a country or region. The indicator is divided into five levels (low, low-middle, middle, high-middle, and high), ranging from 0 to 1, and a higher SDI indicates a greater level of socio-economic development [26]. This index was used in this study to explore the relationship between the burden of cardiomyopathy and socio-economic development levels.

Statistical analysis

For statistical analysis of trends in prevalence, mortality, and DALYs for TC, AC, and OC, we need to calculate estimated annual percentage change (EAPC) using their corresponding ASRs. Its calculation formula is as follows:

A S R = \frac{\sum_{i = 1}^{A} a_{i} w_{i}}{\sum_{i = 1}^{A} w_{i}} \times 100, 000

(a_i: the age-specific rate in the ith age group; w: the number of people in the corresponding ith age group among the standard population; A: the number of age groups).

The required ASR is already included in the disease data obtained from GBD 2021. This study needs to use a regression model that describes a pattern of age-standardized ratios for a specific period to calculate the EAPC value [27]. The model equation is as follows:

Y = α + β X + ε

Where Y represents ln(ASR), X represents the calendar year, α is the intercept term, β represents the slope or trend, and $ε$ is the error term. The EAPC annual percentage change calculation formula can be expressed as:

E A P C = 100 \times (e x p (β) - 1)

Similarly, the 95% confidence interval (CI) of EAPC can also be calculated from the above linear regression model. If the lower limit of EAPC and its 95% CI are > 0, the ASR is considered to have an increasing trend. Conversely, if the EAPC and its upper limit of 95% CI are both<0, it is considered that ASR has a downward trend. If neither condition is met, the ASR is considered stable. This study used Spearman correlation to assess the correlation between different SDI level regions and cardiomyopathy ASPR, ASDR, and age-standardized DALYs.

The Bayesian age-period-cohort (BAPC) model demonstrates unique comprehensive advantages when processing the GBD database, with its core lying in adopting a hierarchical Bayesian framework to model global, regional, and national data as an integrated system. Through “information leverage,” it significantly enhances the robustness of estimates for sparse data, such as small countries and rare diseases. The model can simultaneously decompose age, period, and cohort effects, providing in-depth attributional analysis for changes in disease burden. It leverages complete Bayesian inference to quantify uncertainty for all predictions comprehensively. Meanwhile, its inherent smooth prior ensures that trend predictions align with epidemiological common knowledge, avoiding unreasonable fluctuations. Ultimately, it generates estimates for all countries and diseases under unified model standards, ensuring the fairness and comparability of global disease burden rankings, making it a robust, scientifically sound, and practical tool for global public health policy making [28,29]. TheBAPC model was used in this study to predict the trend of cardiomyopathy from 2022 to 2040 by dividing age into 20 five-year age groups (<5 years, 5–9 years, 10–14 years,..., 90–94 years, and ≥95 years). The BAPC model was specified as:

η_{a p} = μ + α_{a} + β_{p} + γ c + ε_{a p}

Where ηₐₚ represents the log-rate for age group a and period p, μ is the intercept, αₐ denotes age effects, βₚ represents period effects, γc represents cohort effects (with cohort c = p – a), and εₐₚ is the error term. BAPC’s usage comes from the R BAPC package.

All of the above data analysis processing calculations were performed using the R software (version 4.4.3), and all analysis results with P < 0.05 were considered statistically significant.

Ethics statement

Since the GBD study used publicly accessible data that did not contain any confidential or identifiable patient information, the Washington University Institutional Review Board reviewed and approved the exemption for informed consent. (https://www.healthdata.org/research-analysis/gbd).

Results

Global trends

Prevalence.

Globally, TC, AC, and OC in 2021 were estimated to be 4752361.3 cases (95% UI: 3937775.4–5566947.2), 528429 cases (95% UI: 4,39582.3–639167.4), and 4223932.2 cases (95% UI: 3417357.4–5034257.6), all of which increased in prevalence compared with 1990, with percentage increases of 79.9%, 48.24%, and 84.83%, respectively. The ASPR per 100,000 persons for TC, AC, and OC were 59.5 (95% UI: 49.4–69.6), 6.2 (95% UI: 5.1–7.4), and 53.3 (95% UI: 43.6–63.8), respectively. Compared with 1990, the ASPR per 100,000 persons increased for both total cardiomyopathy and OC, with EAPCs of 0.16 (95% UI: 0.15–0.18) and 0.27 (95% UI: 0.25–0.29), respectively, but the ASPR per 100,000 persons for AC showed a decreasing trend, with an EAPC of −0.59 (95% UI: −0.83 to −0.35) (Table 1, S1 Table, S4 Table, Fig 1).

Table 1. 1990–2021 Global and regional prevalence trends in total cardiomyopathy burden.

location	Total Cardiomyopathy Prevalence (95% UI)
location	Number_1990	ASR per 100,000_1990	Number_2021	ASR per 100,000_2021	EAPC_95% CI
Global	2641734.7 (2242999.5–3040469.9)	57.5 (49.1–65.9)	4752361.3 (3937775.4–5566947.2)	59.5 (49.4–69.6)	0.16 (0.15–0.18)
High SDI	1065034.8 (912775.6–1217294)	116.6 (100.6–132.6)	1448054.1 (1211937.4–1684170.9)	107.5 (91.3–123.8)	−0.23 (−0.26 to −0.21)
High-middle SDI	541598.1 (469519–613677.3)	56.6 (48.5–64.7)	859609.3 (727513.8–991704.7)	58.8 (50.1–67.5)	0.27 (0.22–0.33)
Middle SDI	424884.9 (347858.7–501911.1)	29.8 (24.9–34.7)	952037.3 (769074–1135000.5)	40.1 (32.7–47.5)	1.04 (0.95–1.12)
Low-middle SDI	357429.6 (290946.5–423912.8)	39.7 (33–46.4)	825217.2 (651492.1–998942.4)	49 (39.4–58.6)	0.66 (0.64–0.69)
Low SDI	249561.6 (188805.7–310317.4)	64.2 (49.6–78.8)	661916 (501148.2–822683.8)	74.9 (57.7–92.2)	0.52 (0.5–0.53)
Andean Latin America	10665.2 (8415.8–12914.6)	30.2 (24.6–35.8)	21897.6 (17313.3–26481.8)	34.4 (27.4–41.5)	0.58 (0.43–0.73)
Australasia	24329.9 (20751.3–27908.5)	120 (102.2–137.8)	48266.7 (40623.4–55909.9)	130.2 (108.4–152)	0.54 (0.4–0.67)
Caribbean	16648.7 (13749.6–19547.8)	50.2 (41.7–58.6)	35419.5 (30080.8–40758.1)	72 (61–83)	1.36 (1.28–1.44)
Central Asia	20808.9 (17130.6–24487.2)	31.9 (26.9–36.9)	56642.7 (47216.5–66068.8)	63.3 (52.7–73.9)	2.95 (2.7–3.2)
Central Europe	119930.2 (100631.6–139228.8)	95.1 (80–110.2)	197756.6 (160987.5–234525.7)	118.1 (99.7–136.6)	0.95 (0.8–1.09)
Central Latin America	60432 (48625.7–72238.2)	37.6 (31.4–43.7)	103546.7 (83865.3–123228)	42.2 (34.3–50.2)	0.37 (0.3–0.44)
Central Sub-Saharan Africa	31054.5 (21950.4–40158.5)	72.5 (52.2–92.8)	87713.5 (61109–114318)	80.2 (58.2–102.3)	0.31 (0.28–0.33)
East Asia	106692.4 (84299.3–129085.5)	10 (8.1–11.9)	300085 (236306.1–363863.9)	19.7 (15.5–24)	2.73 (2.44–3.03)
Eastern Europe	219806.7 (190015.3–249598.1)	94.8 (82.2–107.4)	323184.5 (277536.5–368832.5)	136 (116.9–155)	1.45 (1.32–1.59)
Eastern Sub-Saharan Africa	158254.2 (119410–197098.4)	111.9 (84–139.8)	402362.8 (307368.7–497357)	126.7 (94.9–158.5)	0.31 (0.28–0.34)
High-income Asia Pacific	145218.4 (121835.2–168601.6)	83.6 (69.2–98)	207491 (166585.4–248396.5)	84.3 (69.9–98.7)	0.23 (0.16–0.3)
High-income North America	475379.1 (400685.8–550072.4)	162.9 (137.7–188)	579544.2 (483536.9–675551.6)	135.4 (113.3–157.5)	−0.79 (−0.85 to −0.74)
North Africa and Middle East	108680.1 (85520–131840.1)	30.5 (25.1–35.8)	225005.7 (176441.3–273570.1)	37.5 (29.9–45.1)	0.69 (0.64–0.75)
Oceania	1050.5 (806.9–1294.2)	20.9 (16.6–25.3)	2706.6 (2047.3–3365.8)	24.2 (18.4–29.9)	0.52 (0.49–0.56)
South Asia	279720.1 (228209.4–331230.8)	34 (28.1–39.8)	718523.1 (573392.8–863653.4)	44.8 (36.3–53.4)	1.05 (0.99–1.11)
Southeast Asia	73091.5 (58987.9–87195.2)	23.5 (19.2–27.8)	164367.4 (130971.2–197763.5)	26.8 (21.5–32.2)	0.28 (0.24–0.33)
Southern Latin America	49050.3 (40705.4–57395.2)	102.8 (84.6–121)	73399.4 (58930.2–87868.7)	99.7 (81.3–118.2)	−0.15 (−0.25 to −0.06)
Southern Sub-Saharan Africa	33160 (24481.4–41838.5)	75.4 (56.6–94.2)	59624.9 (44507.5–74742.4)	79.9 (59.6–100.2)	0.11 (0.02–0.2)
Tropical Latin America	144439.9 (121194.1–167685.8)	121 (101.1–141)	284686.2 (233197.1–336175.3)	122.7 (99.9–145.5)	−0.15 (−0.23 to −0.07)
Western Europe	465077.8 (389851.4–540304.2)	105.9 (90.9–120.8)	594168.1 (485269.9–703066.3)	96.5 (82–111.1)	−0.22 (−0.28 to −0.16)
Western Sub-Saharan Africa	98244.3 (72557.9–123930.7)	67.5 (50.2–84.8)	265969.2 (191434.1–340504.4)	71.5 (52.3–90.8)	0.14 (0.12–0.16)

Open in a new tab

Fig 1 — (A) Total cardiomyopathy. (B) Alcoholic cardiomyopathy. (C) Other cardiomyopathy.

Mortality.

Globally, TC, AC, and OC in 2021 were estimated to be 370273.6 cases, 64011.4 cases, and 306262.2 cases, all of which were elevated compared to the number of deaths in 1990, with percentage increases of 45.65%, 35.98%, and 47.85%, respectively. The ASDR per 100,000 persons for TC, AC, and OC were 4.5, 0.7, and 3.7, respectively. Compared with 1990, the ASDR per 100,000 persons for TC, AC, and OC all decreased, with EAPCs of −1.63, −1.72, and −1.62 (Table 2, S2 Table, S5 Table, Fig 1).

Table 2. 1990–2021 Global and regional mortality trends in total cardiomyopathy burden.

location	Total Cardiomyopathy Deaths (95% UI)
location	Number_1990	ASR per 100,000_1990	Number_2021	ASR per 100,000_2021	EAPC_95% CI
Global	254222.2 (230367.4–278077)	7.3 (6.6–7.9)	370273.6 (340965.8–399581.3)	4.5 (4.1–4.8)	−1.63 (−1.81 to −1.45)
High SDI	84440.6 (80107.9–88773.3)	7.9 (7.5–8.3)	80709.5 (73718.9–87700.2)	3.7 (3.4–3.9)	−2.68 (−2.78 to −2.57)
High-middle SDI	81962.7 (77354–86571.5)	10.5 (9.7–11.2)	119684.4 (111748.5–127620.4)	6.6 (6.1–7)	−1.47 (−2 to −0.93)
Middle SDI	35158.9 (29924–40393.8)	3.8 (3.3–4.4)	67447.4 (61573.4–73321.3)	2.8 (2.5–3)	−1.31 (−1.5 to −1.12)
Low-middle SDI	33596.9 (23365.6–43828.2)	5.5 (3.8–7.3)	67734 (56333.2–79134.9)	5.1 (4.2–6)	−0.27 (−0.32 to −0.21)
Low SDI	18584.6 (12794–24375.3)	7.6 (4.8–10.5)	34113 (24726.4–43499.6)	6.5 (4.6–8.3)	−0.47 (−0.6 to −0.34)
Andean Latin America	518.9 (442.9–594.9)	2 (1.7–2.3)	645 (522.5–767.5)	1.1 (0.9–1.3)	−1.79 (−2.15 to −1.42)
Australasia	1585.8 (1492.9–1678.7)	7.2 (6.8–7.6)	1656.7 (1507.2–1806.2)	3.1 (2.8–3.3)	−2.42 (−2.86 to −1.98)
Caribbean	1231.7 (1010.6–1452.7)	4.5 (3.7–5.3)	2951.5 (2516–3386.9)	5.6 (4.7–6.5)	1.09 (0.88–1.31)
Central Asia	2194.4 (1912.2–2476.7)	4.5 (3.9–5.1)	10887.5 (9172.8–12602.1)	13 (11.1–14.9)	4.64 (3.55–5.74)
Central Europe	22385.8 (20931.9–23839.8)	17.6 (16.4–18.8)	27188.7 (24842.7–29534.6)	12.1 (11–13.2)	−1.28 (−1.54 to −1.02)
Central Latin America	2175 (2073.1–2276.9)	2.4 (2.2–2.5)	4262.8 (3765.8–4759.8)	1.8 (1.5–2)	−1.48 (−1.66 to −1.3)
Central Sub-Saharan Africa	3627.1 (2128.8–5125.5)	15.7 (8–23.4)	7529.2 (3661.5–11396.9)	14.2 (6.3–22.1)	−0.32 (−0.35 to −0.29)
East Asia	7077 (3667.5–10486.5)	0.9 (0.4–1.3)	17439.7 (12990.8–21888.6)	1 (0.7–1.2)	0.4 (0.06–0.75)
Eastern Europe	30455.5 (28638.8–32272.2)	12.2 (11.5–12.9)	69697.7 (64590–74805.5)	23.3 (21.6–25)	1.88 (0.69–3.09)
Eastern Sub-Saharan Africa	5024 (3627.9–6420.1)	4.7 (3.5–5.9)	8818.6 (6107.6–11529.6)	4 (2.9–5.1)	−0.59 (−0.68 to −0.5)
High-income Asia Pacific	10532.3 (9761.9–11302.7)	6.1 (5.6–6.6)	9990.2 (8540.6–11439.8)	1.8 (1.6–2)	−3.29 (−3.88 to −2.7)
High-income North America	30154.2 (28619–31689.3)	8.7 (8.3–9.1)	30262 (27889.1–32634.9)	4.7 (4.4–5.1)	−2.55 (−2.74 to −2.35)
North Africa and Middle East	6745.3 (4646.1–8844.5)	3 (1.7–4.3)	8788.9 (6140.3–11437.4)	2 (1.3–2.7)	−1.27 (−1.33 to −1.22)
Oceania	150.9 (92.2–209.5)	4.2 (2.5–5.9)	379.9 (231.5–528.3)	4.2 (2.5–5.9)	0.06 (0.02–0.1)
South Asia	27341.1 (15413.3–39268.8)	4.8 (2.6–7)	67758.2 (51893.1–83623.2)	5 (3.9–6.2)	0.36 (0.25–0.48)
Southeast Asia	8249.9 (6499.1–10000.7)	3.9 (3.1–4.8)	17143.3 (14539–19747.6)	3.2 (2.7–3.6)	−0.99 (−1.18 to −0.8)
Southern Latin America	6680.3 (6095.6–7265)	15.5 (14.1–16.9)	7429.7 (6754.7–8104.6)	8.3 (7.6–9.1)	−2.07 (−2.25 to −1.88)
Southern Sub-Saharan Africa	3754.3 (2987–4521.5)	15.1 (11.6–18.6)	6524.8 (5783.1–7266.5)	12.8 (11.3–14.2)	−0.64 (−0.93 to −0.35)
Tropical Latin America	13390 (12826.6–13953.3)	15.6 (14.8–16.5)	17353 (16070.4–18635.6)	6.9 (6.4–7.5)	−3.23 (−3.52 to −2.93)
Western Europe	58911.2 (54467–63355.3)	10.4 (9.6–11.1)	35954.8 (32311.8–39597.8)	3.4 (3.1–3.7)	−3.92 (−4.23 to −3.61)
Western Sub-Saharan Africa	12037.6 (8504.9–15570.2)	13.9 (9.5–18.3)	17611.5 (13804.8–21418.3)	8.7 (7–10.3)	−1.79 (−1.93 to −1.66)

Open in a new tab

Disability-adjusted life years

Globally, the estimated DALYs for TC, AC, and OC in 2021 were 10690818.8, 2185527.6 and 8505291.2, all increased compared to 1990 DALYs, with percentage increases of 41.76%, 39.94%, and 42.23%, respectively. The ASR_DALYs per 100,000 persons for TC, AC, and OC were 129.8, 25.3, and 104.5, respectively. Compared with 1990, ASR_DALYs per 100,000 persons decreased for TC, AC, and OC, with EAPCs of −1.07, −1.44, and −0.97 (Table 3, S3 Table, S6 Table, Fig 1).

Table 3. 1990–2021 Global and regional DALYs trends in total cardiomyopathy burden.

location	Total Cardiomyopathy DALYs (95% UI)
location	Number_1990	ASR per 100,000_1990	Number_2021	ASR per 100,000_2021	EAPC_95% CI
Global	7541688.4 (6684606.4–8398770.5)	175.1 (157.8–192.4)	10690818.8 (9864323.4–11517314.2)	129.8 (119.7–140)	−1.07 (−1.37 to −0.76)
High SDI	1990081.7 (1922446.5–2057716.9)	196.3 (189.7–203)	1669168.3 (1568982.4–1769354.2)	97 (91.8–102.3)	−2.5 (−2.6 to −2.41)
High-middle SDI	2109501.8 (2001931.2–2217072.4)	225.9 (213.4–238.3)	3511891.8 (3285172.2–3738611.4)	204.4 (191.6–217.3)	−0.43 (−1.24 to 0.39)
Middle SDI	1286944.1 (1080708.5–1493179.6)	100.1 (85.4–114.8)	1960925.6 (1792691.2–2129160.1)	77.4 (71–83.9)	−1.07 (−1.22 to −0.92)
Low-middle SDI	1322396.6 (922910.5–1721882.6)	152.1 (107.3–196.9)	2159235.9 (1821876.6–2496595.1)	135.7 (114.3–157)	−0.33 (−0.37 to −0.3)
Low SDI	821628.9 (594886–1048371.9)	216.6 (150.1–283.2)	1375930.4 (1018126.2–1733734.7)	180.8 (133–228.6)	−0.59 (−0.68 to −0.49)
Andean Latin America	26399.7 (20933.6–31865.8)	71.7 (60.2–83.2)	23695.3 (19320.5–28070)	38.1 (31–45.1)	−1.81 (−2.17 to −1.45)
Australasia	41535.6 (39504.6–43566.5)	190.8 (181.6–199.9)	38791.9 (36038.2–41545.5)	88.1 (81.7–94.6)	−2.16 (−2.57 to −1.76)
Caribbean	47803.3 (35100.5–60506.1)	151.6 (117.7–185.5)	91041.8 (73173.2–108910.4)	180.8 (141.8–219.8)	0.96 (0.76–1.16)
Central Asia	79366.9 (69767.4–88966.4)	141.6 (123.9–159.3)	359966.1 (301501.7–418430.5)	386.1 (325.6–446.5)	4.34 (3.2–5.49)
Central Europe	459793.1 (433966.2–485620)	340.1 (321–359.1)	536149.8 (485549.7–586749.8)	268 (241.3–294.7)	−0.79 (−0.95 to −0.63)
Central Latin America	92469.2 (87637.3–97301.2)	70.9 (67.8–74.1)	138930.2 (122284.5–155575.8)	56.5 (49.4–63.6)	−1.03 (−1.15 to −0.91)
Central Sub-Saharan Africa	157250.9 (102616.7–211885.1)	417.3 (240.6–594.1)	285028.7 (150953.3–419104.2)	361.4 (177–545.8)	−0.44 (−0.48 to −0.41)
East Asia	343948.3 (200681.7–487214.9)	32.5 (18.8–46.3)	475009.8 (368812.6–581207.1)	28.2 (22.3–34.1)	−0.63 (−0.89 to −0.38)
Eastern Europe	1058289.5 (989083.8–1127495.2)	416 (389.6–442.4)	2377494.5 (2203109.2–2551879.9)	864.2 (803–925.5)	1.97 (0.67–3.28)
Eastern Sub-Saharan Africa	280297.1 (192018.3–368575.8)	169.8 (126.3–213.4)	443195.4 (302142.8–584247.9)	142.7 (101.5–183.8)	−0.57 (−0.65 to −0.5)
High-income Asia Pacific	255970.7 (242395.5–269545.9)	141.4 (133–149.8)	177106.4 (159462.2–194750.6)	49.3 (45.3–53.2)	−3.07 (−3.45 to −2.68)
High-income North America	799841.7 (770994.3–828689)	249.4 (240.7–258)	697089 (662671.2–731506.7)	132.1 (125.7–138.5)	−2.56 (−2.74 to −2.38)
North Africa and Middle East	390044.9 (227333.4–552756.5)	112 (76.7–147.2)	349328.7 (270034.8–428622.5)	64.7 (48.5–80.8)	−1.7 (−1.77 to −1.64)
Oceania	7448.7 (4418.1–10479.2)	139.2 (85.7–192.8)	18288.6 (11463.6–25113.6)	147.5 (92–203)	0.26 (0.22–0.3)
South Asia	1046248.1 (613893.7–1478602.5)	133.2 (76.8–189.5)	2038330.7 (1565071.9–2511589.6)	130.9 (100.7–161)	0.09 (0.02–0.16)
Southeast Asia	253196.7 (200020.5–306372.9)	85.4 (67.6–103.2)	474712.8 (391648.3–557777.4)	73.7 (61.8–85.6)	−0.72 (−0.85 to −0.58)
Southern Latin America	165960 (153648–178272)	359.5 (332.2–386.8)	154446.8 (143937.1–164956.4)	186.3 (173.7–198.9)	−2.25 (−2.46 to −2.04)
Southern Sub-Saharan Africa	112147.1 (96350.3–127943.9)	341.4 (277.6–405.1)	188419.5 (167318.6–209520.3)	301.9 (268.2–335.7)	−0.44 (−0.64 to −0.23)
Tropical Latin America	418325.8 (403123.2–433528.3)	394.2 (379.3–409.1)	484332.7 (459528.6–509136.7)	193.8 (183.9–203.7)	−2.84 (−3.12 to −2.57)
Western Europe	1055547.2 (999574.8–1111519.5)	201.5 (191.3–211.7)	635347.9 (586453.2–684242.7)	77.8 (72.6–83)	−3.36 (−3.6 to −3.12)
Western Sub-Saharan Africa	449804 (326575.7–573032.3)	347.6 (244.5–450.6)	704112.3 (533489–874735.6)	225.3 (177.5–273.1)	−1.64 (−1.78 to −1.51)

Open in a new tab

Socio-demographic index regions

Total cardiomyopathy.

For TC in 2021, ASPR per 100,000 persons peaked in High SDI regions (107.5), while Middle SDI regions showed the lowest (40.1), though Low SDI regions (74.9) ranked second only to High SDI. EAPC for ASPR per 100,000 persons (−0.23) remained negative exclusively in High SDI regions, contrasting with positive EAPC values across other SDI strata. ASDR (6.6) and ASR_DALYs (204.4) per 100,000 persons both peaked in High-middle SDI regions; Low SDI regions reported second-highest values (ASDR 6.5; ASR_DALYs 180.8), whereas Middle SDI regions recorded minima (ASDR 2.8; ASR_DALYs 77.4). Notably, all three metrics exhibited minimum EAPC values in High SDI regions: ASDR per 100,000 persons (−2.68) and ASR_DALYs per 100,000 persons (−2.5) (Table 1–Table 3, Fig 1A). In 2021, TC cases demonstrated younger age distributions in lower SDI regions but shifted to older distributions in higher SDI regions, with age-related disparity widening as age groups advanced (Fig 2A). What’ more, ASPR, ASDR, and ASR_DALYs of TC showed negative correlations with SDI at 0.5, shifted to positive correlations at 0.75, and reverted to negative correlations with further SDI increases (Fig 3).

Fig 2 — (A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

Fig 3 — (A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

Alcoholic cardiomyopathy

In 2021, ASPR per 100,000 persons for AC peaked in High SDI regions (14.2), followed by High-middle SDI regions; Low-middle SDI regions recorded the lowest (1.4). Highest ASDR (2.4) and ASR_DALYs (88.3) per 100,000 persons were both observed in High-middle SDI regions. Minimum EAPC for ASPR (−0.58) occurred in High-middle SDI regions, whereas ASDR and ASR_DALYs showed minima in High SDI regions (−2.08 and −1.74) (S1–S3 Tables, Fig 1B). In 2021, AC cases were predominantly distributed in High SDI regions and High-middle SDI regions, with age-related concentration increasing among middle-aged and elderly populations; male patients outnumbered females by notable margins (S1 Fig). For AC’s ASPR, ASDR, and ASR_DALYs, we observed a non-significant association with SDI until 0.6, followed by a strong positive correlation at 0.75 that shifted to negative with further SDI increases (S3 Fig).

Other cardiomyopathy

In 2021, ASPR per 100,000 persons for OC peaked in High SDI regions (93.3), followed by Low SDI regions (73). ASDR (6.4) and ASR_DALYs (178.2) per 100,000 persons both peaked in Low SDI regions. Notably, Middle SDI regions exhibited near-minimal values: ASPR (38.3) and ASDR (2.6), with ASR_DALYs at 72.8—slightly higher than High SDI regions’ 72.7. Minimum EAPC values for all three indicators occurred in High SDI regions: ASPR (−0.29), ASDR (−2.82), and ASR_DALYs (−2.73) (S4–S6 Tables, Fig 1C). In 2021, OC cases predominantly involved younger populations concentrated in lower SDI regions, contrasting with TC’s similar elderly predominance in higher SDI regions; yet male middle-aged/elderly for OC showed notable fewer cases compared to TC, whereas female distributions exhibited no significant difference (S2 Fig). For the ASPR, ASDR, and ASR_DALYs of OC, we observed a negative correlation with SDI at 0.5, which then turns into a sustained positive correlation until 0.75, after which the ASDR and ASR_DALYs metrics turn back to a negative trend as SDI rises further (S4 Fig).

21 GBD regions

From the results of the study, it is easy to see that the disease trends of TC, AC, and OC had significant differences among 21 GBD regions in 2021.

Total cardiomyopathy

For TC, in 2021, among 21 GBD regions, regions with higher TC ASPR per 100,000 persons included Eastern Europe (136), High-income North America (135.4), Australasia (130.2), and Eastern Sub-Saharan Africa (126.7). EAPC for ASPR per 100,000 persons was negative in only four regions, with the maximum value of 2.95 in Central Asia and the minimum value of −0.79 in High-income North America. TC had higher ASDR per 100,000 persons in Eastern Europe (23.3), Central Sub-Saharan Africa (14.2), Southern Sub-Saharan Africa (12.8), and Central Europe (12.1). EAPC for ASDR per 100,000 persons was positive in six regions and negative in the rest, with the maximum value of 4.64 in Central Asia and the minimum value of −3.92 in Western Europe. Additionally, TC had the largest ASR_DALYs per 100,000 persons in Eastern Europe (864.2), and EAPC for ASR_DALYs per 100,000 persons was positive in five regions and negative in the rest, with the maximum value of 4.34 in Central Asia and the minimum value of −3.36 in Western Europe. It is worth noting that TC had the highest ASPR per 100,000 persons, ASDR per 100,000 persons, and ASR_DALYs per 100,000 persons in Eastern Europe. In contrast, TC had the lowest ASPR per 100,000 persons (19.7), ASDR per 100,000 persons (1.0), and ASR_DALYs per 100,000 persons (28.2) in East Asia. Additionally, Central Asia had the highest EAPC for the above three indicators of TC, while Western Europe had the lowest EAPC for ASDR per 100,000 persons and ASR_DALYs of TC (Table 1–Table 3 , Fig 3).

Alcoholic cardiomyopathy

According to the research results, it can be seen that, similar to TC, AC had the highest ASPR per 100,000 persons (62.5), ASDR per 100,000 persons (13.1), and ASR_DALYs per 100,000 persons (510.5) in Eastern Europe. On the other hand, AC had the lowest ASPR per 100,000 persons (0), ASDR per 100,000 persons (0), and ASR_DALYs per 100,000 persons (0.1) in Andean Latin America, with the lowest ASDR per 100,000 persons also found in Central Sub-Saharan Africa (0), Eastern Sub-Saharan Africa (0), Southern Sub-Saharan Africa (0), and North Africa and Middle East (0). The EAPCs for ASPR per 100,000 persons were negative in eight regions and positive in the rest; the EAPCs for ASDR per 100,000 persons were positive in five regions and negative in the rest; the EAPCs for ASR_DALYs per 100,000 persons were positive in five regions and negative in the rest. Notably, the EAPC values for the three indicators were the highest in the Caribbean, with values of 5.65, 5.28, and −6.69, respectively; the EAPC values for the three indicators were the lowest in Southern Latin America, with values of −3.88, −6.69, and −6.72 (S1–S3 Tables, S3 Fig).

Other cardiomyopathy

For OC, in 2021, Eastern Sub-Saharan Africa had the highest ASPR per 100,000 persons (124.1), and Central Sub-Saharan Africa had the highest ASDR per 100,000 persons (14.2) and ASR_DALYs per 100,000 persons (361.2). Notably, like TC, East Asia had the lowest ASPR per 100,000 persons (18.1), ASDR per 100,000 persons (0.9), and ASR_DALYs per 100,000 persons (24.6). Additionally, for OC, the EAPCs for ASPR per 100,000 persons were negative in only two regions and positive in the rest; the EAPCs for ASDR per 100,000 persons were positive in five regions and negative in the rest; the EAPCs for ASR_DALYs per 100,000 persons were positive in four regions and negative in the rest. Among these, High-income North America had the lowest EAPC for ASPR per 100,000 persons (−0.84); Central Asia had the highest EAPC for ASPR per 100,000 persons (3.15) and the highest EAPC for ASDR per 100,000 persons (5.09); Western Europe had the lowest EAPC for ASDR per 100,000 persons (−4.05) and the lowest EAPC for ASR_DALYs per 100,000 persons (−3.45); Eastern Europe had the highest EAPC for ASR_DALYs per 100,000 persons (5.39) (S4–S6 Tables, S4 Fig).

Additionally, this study analyzed the global and regional (5 SDI/21 GBD) distributions of AC/OC-attributable prevalence, deaths, and DALYs for TC in 1990 and 2021. Globally, OC accounted for >50% of cardiomyopathy-related prevalence, deaths, and DALYs, with stable proportions over time except in Eastern Europe, where OC prevalence/division ratios diverged significantly from global patterns. In Eastern Europe, AC prevalence/division ratios exceeded OC for all three indicators (Fig 4).

Fig 4 — (A) 1990 prevalence. (B) 1990 deaths. (C) 1990 DALYs. (D) 2021 prevalence. (E) 2021 deaths. (F) 2021 DALYs.

National trends

Total cardiomyopathy.

In 2021, global TC incidence (ASPR) ranged 8.3–188.6/100,000, with Montenegro (188.6), Ethiopia (182.9), and Poland (182.5) reporting highest rates versus Cook Islands (8.3), Jordan (14.2), and Thailand (15.2) (S7 Table, Fig 5A, Fig 6A). Mortality (ASDR) followed similar patterns (0.4–39.3/100,000), peaking in Kazakhstan (39.3), Latvia (27.9), and Montenegro (27.7) while Cook Islands (0.4), Jordan (0.4) and Thailand (0.7) remained lowest (S8 Table, Fig 5B, Fig 6B). DALYs (ASR_DALYs) showed steeper gradients (13.2–981.6/100,000), with Latvia (981.6), the Russian Federation (966.1), and Ukraine (727.2) reporting extremes, contrasting with Jordan (13.2), Cook Islands (13.5), and Kuwait (19.9) (S9 Table, Fig 5C, Fig 6C). Notably, Cook Islands, Jordan, and Thailand consistently presented the lowest values across all indicators, while extreme maxima predominantly occurred in high SDI nations, except Ethiopia (981.6) (Fig 6).

Fig 5 — (A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

Fig 6 — (A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

Alcoholic cardiomyopathy

In 2021, ASPR per 100,000 persons for AC globally ranged 0–65.7, with highest values in Russian Federation (65.7), Latvia (62.2), and Ukraine (60.9); lowest values remained 0 across seven countries including Ecuador and Tajikistan (S10 Table, S5A Fig, S7A Fig). Corresponding ASDR per 100,000 persons ranged 0–14.5, peaking at 14.5 in Latvia, 13.9 in Russian Federation, and 13.6 in Ukraine, while 43 countries (including Ecuador/Tajikistan) reported 0 (S11 Table, S5B Fig, S7B Fig). ASR_DALYs per 100,000 persons spanned 0–575.3, with Latvia (575.3), Russian Federation (542.4), and Ukraine (521.2) showing maxima; four countries (Cook Islands, Tajikistan, Egypt, Malaysia) recorded minima at 0, 0, 0, and 0 respectively (S12 Table, S5C Fig, S7C Fig). Notably, all three indicators’ extreme maxima consistently occurred in the Russian Federation, Latvia, and Ukraine, with values rising proportionally to SDI elevation (S7 Fig).

Other cardiomyopathy

In 2021, ASPR per 100,000 persons for OC globally ranged 13.7–179.9, peaking in Ethiopia (179.9), Montenegro (159.8), and France (148); lowest values remained 13.7 in Kyrgyzstan, 14.1 in Jordan, and 14.6 in Thailand (S13 Table, S6A Fig, S8A Fig). Corresponding ASDR per 100,000 persons ranged 0.4–38.2, with Kazakhstan (38.2), Montenegro (23.5), and North Macedonia (20.5) reporting maxima; Jordan (0.4), Cook Islands (0.4), and Kuwait (0.6) showed minima (S14 Table, S6B Fig, S8B Fig). ASR_DALYs per 100,000 persons spanned 13.1–1083.3, peaking in Kazakhstan (1083.3), Turkmenistan (480.1), and Dominica (438.6); Jordan (13.1), Cook Islands (13.5), and Kuwait (16.5) maintained consistent minima across ASDR and ASR_DALYs (S15 Table, S6C Fig, S8C Fig). Excluding outlier nations (e.g., Kazakhstan, Ethiopia, and Montenegro), these OC metrics exhibited no significant associations with SDI trends.

Age and sex

In 2021, ASPR for TC remained stable in 0–59 age groups but increased with age (peaking in older cohorts), while ASDR and ASR_DALYs consistently rose across all > 5 age strata. Males exhibited pronounced dominance over females in all three indicators within the same age groups, except females predominated in ≥80 age groups (Fig 2). For AC, males showed significantly higher prevalence, mortality, and DALYs than females at all ages, with mortality/DALYs peaking in 55–64 age groups then declining (S1 Fig). OC’s age/sex patterns were similar to TC’s trends (S2 Fig).

Risk factors

This study quantified temperature-attributable mortality and DALYs for TC, AC, and OC in 2021 globally and across 5 SDI/21 GBD regions. Except North Africa and the Middle East, low temperature contributed more to all three diseases: globally, it accounted for 11.8% (TC), 6.4% (AC), 5.3% (OC) of deaths and 10.8% (TC), 6.2% (AC), 4.6% (OC) of DALYs. In High SDI regions showed maximum contributions, low temperature caused 15.6% (TC), 7.8% (AC/OC) deaths and 14.3% (TC), 7.3% (AC), 7% (OC) DALYs, while Low SDI regions showed minimal contributions (deaths: 6.1% (TC), 4.2% (AC), 2.8% (OC); DALYs: 5.2% (TC), 2.9% (AC), 2.4% (OC)). Central Europe exhibited the highest low temperature impacts: 13.4% (TC), 9.5% (AC/OC) deaths, and 13.2% (TC), 9.3% (AC), 9.2%(OC) DALYs (Fig 7).

Fig 7 — (A) Total cardiomyopathy. (B) Alcoholic cardiomyopathy. (C) Other cardiomyopathy.

Forecasts for future disease trends of TC, AC, and OC

TC Predictions.

Global ASPR will continue rising to 60.1/100,000 by 2040 (males: 73.8, females: 47.9), ASDR will decline to 3.4 (males: 4.8, females: 2.1), and ASR_DALYs will rise temporarily in 2022 before falling to 100.5 by 2040 (males: 146.9, females: 58.6), with males consistently higher across all metrics. What’s more, the male ASPR growth rate exceeds that of females till 2040 (Fig 8).

Fig 8 — (A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

AC Predictions.

Global ASPR will decline to 3.6/100,000 by 2040 (males: 6.0, females: 1.4); ASDR temporarily rises in 2022 before dropping to 0.5 (males: 0.8, females: 0.2); ASR_DALYs follows the ASDR pattern, dropping to 16.1 by 2040 (males: 25.8, females: 6.8) (S9 Fig).

OC Predictions.

Global ASPR will rise moderately (57.3/100,000 by 2040; males: 69.1, females: 47.1); ASDR maintains a decline to 3.0 (males: 4.1, females: 2.1); ASR_DALYs continue decreasing to 84.6 (males: 119.3, females: 53.5) (S10 Fig).

Sensitive analysis

To assess the robustness of the Bayesian Age-Period-Cohort (BAPC) model’s predictions, we conducted a sensitivity analysis using the autoregressive integrated moving average (ARIMA) time series model. For each key outcome indicator (prevalence, mortality, and DALYs), we fitted ARIMA models using the auto.arima function from the R language forecasting package (version 8.21.1) based on age-standardized rate data from 1990 to 2021. Model selection was based on minimizing the corrected Akaike Information Criterion (AICc), and predictions along with 95% prediction intervals for the period 2022–2040 were generated. The results of this sensitivity analysis are presented in the supplementary material (S11 Fig). The ARIMA model produces prediction results consistent with those of the BAPC model for the aforementioned indicators on TC, confirming the robustness of the BAPC model.

Discussion

This study utilized GBD 2021 data to quantify the global burden of AC and OC, while leveraging AC/OC prevalence, mortality, DALYs profiles to derive TC-related epidemiological metrics (prevalence, mortality, DALYs) across 204 countries, 5 SDI strata, and 21 GBD regions. Stratified by year, age, gender, socioeconomic development (SDI), and geography, we computed age-standardized rates globally and regionally. From 1990 to 2021, TC, AC, and OC exhibited upward trends in prevalence, mortality, and DALYs, aligning with historical GBD findings [14]. Notably, TC/OC age-standardized prevalence rates (ASPR) increased globally, whereas OC’s ASPR and TC/AC/OC’s age-standardized mortality/DALYs (ASDR/ASR_DALYs) demonstrated divergent declines.

Regional analysis revealed convergent epidemiological patterns for TC and OC, with both diseases demonstrating their lowest age-standardized prevalence (ASPR), mortality (ASDR), and DALYs (ASR_DALYs) in Middle SDI regions. However, distinct divergences emerged: TC, AC, and OC exhibited their steepest declining trends in ASPR/ASDR/ASR_DALYs across High SDI and High-middle SDI strata, while paradoxically, TC/AC displayed peak ASPR in High SDI regions, whereas TC/AC’s highest ASDR/ASR_DALYs shifted to High-middle SDI regions, and OC’s ASDR/ASR_DALYs burdens peaked in Low SDI regions. Notably, three indicators for TC ranked second highest in Low SDI regions, similar to regional dominance for OC. Crucially, all three diseases followed inverted N-shaped trajectories, with ASPR/ASDR/ASR_DALYs first decreasing, then increasing as regional SDI indices rose to some extent, but decreasing lastly generally. These SDI disparities may be associated with intertwined healthcare resource allocation, socioeconomic stratification, risk factor exposures, and data reliability. High SDI regions, despite abundant healthcare resources, may face modern risk exposures (e.g., sedentary lifestyles, pollution) that paradoxically correlate with elevated ASPR through overdiagnosis and delayed mortality, suggesting potential needs for targeted chronic disease management and cardiomyopathy prevention in high-risk demographics. Conversely, High SDI regions appear to achieve EAPC stabilization/decrease through integrated healthcare optimization, risk mitigation strategies, and robust public health frameworks, indicating possible effective burden mitigation. High-middle SDI regions may grapple with resource maldistribution, creating a “high burden-low efficiency” paradox where fragmented healthcare access is associated with amplified ASDR/DALY burdens. Low SDI regions appear to face a vicious cycle where inadequate health literacy and preventive care are correlated with delayed diagnosis until advanced disease stages, potentially exacerbating the ASPR/ASDR/DALY triple burdens. Middle SDI regions, undergoing epidemiological transition, exhibit intermediate burden profiles shaped by partial healthcare improvements and residual risk exposures. These findings highlight potential considerations for proactive policy interventions, including primary healthcare expansion and health education campaigns, to potentially optimize resource allocation and address these patterns, thereby potentially curbing future disease burdens. This SDI-stratified analysis underscores the need for tailored strategies addressing unique regional epidemiological profiles [30,31].

Regional analysis of 21 GBD regions revealed distinct cardiomyopathy burden patterns: Sub-Saharan Africa exhibited elevated OC prevalence (ASPR), mortality (ASDR), and DALYs (ASR_DALYs), which may be attributable to fragmented primary/secondary prevention systems and healthcare inefficiencies amid lifestyle-driven epidemiological transitions [32]. Conversely, Eastern Europe showed peak TC/AC burdens, corroborating national-level findings where Russia, Ukraine, and Latvia reported extreme AC maxima for all three indicators. WHO Regional Office data (2019) revealed Europe’s highest alcohol consumption per capita (15 + years) at 15.2L pure alcohol, with Eastern Europe demonstrating a declining prevalence trend stabilized within ±3% over two decades. Disparities persisted between European income tiers: low/middle-income regions exhibited 4.8L higher consumption than high-income counterparts, aligning with our observed regional alcohol burden patterns [33]. Despite the WHO’s 2030 targets to curb harmful alcohol use, European policy implementation appears inadequate—alcohol warning labels are scarce outside Western Europe, and public awareness campaigns are lacking, suggesting challenges in harm reduction strategies [34–36]. In contrast, East Asia demonstrated the lowest TC/OC burdens, reflecting regional heterogeneity in socioeconomic development and health literacy [37]. This underscores the need for region-specific interventions targeting cardiomyopathy risk trajectories. Notably, East Asian governments have implemented multifaceted policies: Japan and China integrate cardiovascular-risk food labeling, taxation on tobacco/alcohol, and population-wide health promotion initiatives emphasizing physical activity and dietary modifications [38]. These proactive measures contrast sharply with Eastern Europe’s policy inertia, highlighting a correlation between structured public health frameworks and potentially mitigated cardiomyopathy burdens.

This study revealed disproportionately elevated ASPR, ASDR, and ASR_DALYs for TC/OC in the < 5 age cohort compared to adolescent groups, with infantile cardiomyopathy exhibiting distinct epidemiological divergence—1-year-olds showed 3.2-fold higher incidence rates than older children (1–18 years), which may reflect genetic predisposition prevalence [39]. Post-5 age groups demonstrated age-related ascension of TC/OC indicators, potentially amplified by shifting disease spectra amid global population aging [40]. Gender disparities were most pronounced for AC, where males demonstrated elevated prevalence rates, aligning with male-predominant alcohol consumption patterns documented in the 2019 AC GBD study [41]. Although the ratio of male to female per capita alcohol consumption varies due to cultural and economic factors, WHO global alcohol consumption data revealed a persistent male dominance in drinking patterns, with the ratio typically ranging from 1.2:1 to nearly 2:1 in 2019 [33]. This male-dominant pattern suggests a potential association between behavioral risks and disease manifestation, particularly in high-risk cardiomyopathy phenotypes.

In risk factor studies, the results showed that ASPR, ASDR, and ASR_DALYs for cardiomyopathy were attributable to both low and high temperature indices. Existing literature includes many related studies on the risk factors of low temperature and high temperature. Current evidence suggests that high or low ambient temperature can cause abnormal body temperature, which may be associated with the deterioration of existing cardiomyopathy or stress cardiomyopathy [20,42,43]. Ambient temperature fluctuations may modulate alcohol-related behavioral risk exposure frequencies, potentially influencing AC disease trends. Current GBD data remains inconclusive on this ambient temperature-alcohol consumption relationship, necessitating deeper investigation.

We are familiar with the global pandemic of COVID-19 that occurred from 2019 to 2021, which may have affected the analysis of the global trends in cardiomyopathy. Additionally, many studies have discussed the changes in the prevalence of cardiomyopathy caused by COVID-19 and its pandemic. As shown in this study, high burdens of cardiomyopathy continue in Eastern Europe and other regions, consistent with reports of severe impacts of COVID-19 in these areas [44]. Some studies suggest that complications of COVID-19 can manifest in various cardiovascular ways, including myocardial injury, arrhythmias, cardiac arrest, heart failure, and coagulation abnormalities [44,45]. Furthermore, the infection rate, hospitalization rate, and mortality rate of SARS-CoV-2 in diabetic populations are higher than in the general population, making subgroups with cardiomyopathy in diabetic patients potentially more vulnerable to severe prognosis difficulties and worse disease outcomes after contracting COVID-19 [46]. These studies highlight the potential impact of COVID-19 on the prevalence and severity of cardiomyopathy during the pandemic, emphasizing the importance of considering healthcare facilities and related health policies after the pandemic.

In this study, we calculated the disease burden of TC using data from AC and OC in the 2021 GBD database. This analysis has clear limitations. First, our data sources all rely on GBD 2021, although the GBD study strives for the greatest reliability and comparability in the data included, it is inevitably subject to reporting delays or inaccuracies, inconsistent data collection or source quality, classification errors, and country-specific coding biases. For example, in low-income countries, cardiomyopathy may not receiv sufficient attention, possibly leading to underestimation of the disease burden in these regions. Additionally, the classification of OC is very vague, the case definitions of cardiomyopathy and its subtypes may vary across different sources and study time spans, which may introduce heterogeneity, and the GBD database does not further categorize cardiomyopathy types, making it impossible to conduct targeted analysis of related disease indicators based on specific types of cardiomyopathy. Furthermore, when using GBD databases from different years for analysis, changes in data sources and processing methods may lead to partial differences in our results compared to past GBD studies on cardiomyopathy. Moreover, the GBD database does not query incidence data for AC and OC, which is also a significant reason for the incompleteness of our study content. What’s more, our predictive approach, although based on robust statistical models, cannot account for unforeseen changes in future risk factors, diagnostic techniques, or treatment methods that may significantly alter the disease trend. Finally, the COVID-19 pandemic’s impact on healthcare institutions may have affected GBD data collection for cardiomyopathy-related indicators, which could be a factor in the increased burden of cardiomyopathy. However, since GBD classifies COVID-19 as a separate disease, we cannot explore the specific impact of this risk factor on changes in cardiomyopathy indicators.

Conclusion

In conclusion, the disease burden of cardiomyopathy in the world is still not optimistic, and there are many kinds of cardiomyopathy, and its epidemic characteristics in different regions of the world also have significant differences, so corresponding disease control measures need to be taken according to different geographical differences, cultural differences and economic differences. Similarly, the prevalence of cardiomyopathy has been effectively controlled in some regions, and these successful experiences should be used to formulate health policies. For alcohol use, countries should introduce relevant control policies, especially in Eastern European countries such as Russia. In future analysis of cardiomyopathy, it is needed to further classify OC from congenital or acquired cardiomyopathy to achieve not only treatment or better prognosis, but also prevention of more occurrence of cardiomyopathy at the genes, environment, and lifestyle levels level, and more research on the disease burden of cardiomyopathy attributed to other risk factors, to effectively control the future burden of cardiomyopathy.

Supporting information

S1 Fig. The age-specific numbers and ASRs of alcoholic cardiomyopathy by SDI regions in 2021.

(A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

(DOCX)

pone.0341687.s001.docx^{(6.3MB, docx)}

S2 Fig. The age-specific numbers and ASRs of other cardiomyopathy by SDI regions in 2021.

(A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

(DOCX)

pone.0341687.s002.docx^{(6.3MB, docx)}

S3 Fig. Socio-demographic index trends with ASRs of alcoholic cardiomyopathy in 21 GBD regions in 2021.

(A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

(DOCX)

pone.0341687.s003.docx^{(4.1MB, docx)}

S4 Fig. Socio-demographic index trends with ASRs of other cardiomyopathy in 21 GBD regions in 2021.

(A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

(DOCX)

pone.0341687.s004.docx^{(4.1MB, docx)}

S5 Fig. ASRs of alcoholic cardiomyopathy in 204 countries and territories in 2021.

(A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

(DOCX)

pone.0341687.s005.docx^{(9.8MB, docx)}

S6 Fig. ASRs of other cardiomyopathy in 204 countries and territories in 2021.

(A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

(DOCX)

pone.0341687.s006.docx^{(9.8MB, docx)}

S7 Fig. Socio-demographic index trends with ASRs of alcoholic cardiomyopathy in 204 countries and territories in 2021.

(A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

(DOCX)

pone.0341687.s007.docx^{(7.9MB, docx)}

S8 Fig. Socio-demographic index trends with ASRs of other cardiomyopathy in 204 countries and territories in 2021.

(A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

(DOCX)

pone.0341687.s008.docx^{(7.1MB, docx)}

S9 Fig. Global disease trends predictions of alcoholic cardiomyopathy from 2021 to 2040.

(A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

(DOCX)

pone.0341687.s009.docx^{(8MB, docx)}

S10 Fig. Global disease trends predictions of other cardiomyopathy from 2021 to 2040.

(A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

(DOCX)

pone.0341687.s010.docx^{(7.9MB, docx)}

S11 Fig. Global disease trends predictions of total cardiomyopathy from 2021 to 2040 using ARIMA.

(DOCX)

pone.0341687.s011.docx^{(1.6MB, docx)}

S1 Table. 1990–2021 Global and regional prevalence trends in alcoholic cardiomyopathy burden.

(DOCX)

pone.0341687.s012.docx^{(19.1KB, docx)}

S2 Table. 1990–2021 Global and regional mortality trends in alcoholic cardiomyopathy burden.

(DOCX)

pone.0341687.s013.docx^{(18.8KB, docx)}

S3 Table. 1990–2021 Global and regional DALYs trends in alcoholic cardiomyopathy burden.

(DOCX)

pone.0341687.s014.docx^{(19.2KB, docx)}

S4 Table. 1990–2021 Global and regional prevalence trends in other cardiomyopathy burden.

(DOCX)

pone.0341687.s015.docx^{(19.4KB, docx)}

S5 Table. 1990–2021 Global and regional mortality trends in other cardiomyopathy burden.

(DOCX)

pone.0341687.s016.docx^{(19.2KB, docx)}

S6 Table. 1990–2021 Global and regional DALYs trends in other cardiomyopathy burden.

(DOCX)

pone.0341687.s017.docx^{(19.4KB, docx)}

S7 Table. 1990–2021 Global and national prevalence trends in total cardiomyopathy burden.

(DOCX)

pone.0341687.s018.docx^{(50.2KB, docx)}

S8 Table. 1990–2021 Global and national mortality trends in total cardiomyopathy burden.

(DOCX)

pone.0341687.s019.docx^{(47.5KB, docx)}

S9 Table. 1990–2021 Global and national DALYs trends in total cardiomyopathy burden.

(DOCX)

pone.0341687.s020.docx^{(51.9KB, docx)}

S10 Table. 1990–2021 Global and national prevalence trends in alcoholic cardiomyopathy burden.

(DOCX)

pone.0341687.s021.docx^{(47.2KB, docx)}

S11 Table. 1990–2021 Global and national mortality trends in alcoholic cardiomyopathy burden.

(DOCX)

pone.0341687.s022.docx^{(44.6KB, docx)}

S12 Table. 1990–2021 Global and national DALYs trends in alcoholic cardiomyopathy burden.

(DOCX)

pone.0341687.s023.docx^{(48.3KB, docx)}

S13 Table. 1990–2021 Global and national prevalence trends in other cardiomyopathy burden.

(DOCX)

pone.0341687.s024.docx^{(50KB, docx)}

S14 Table. 1990–2021 Global and national mortality trends in other cardiomyopathy burden.

(DOCX)

pone.0341687.s025.docx^{(48.4KB, docx)}

S15 Table. 1990–2021 Global and national DALYs trends in other cardiomyopathy burden.

(DOCX)

pone.0341687.s026.docx^{(48.5KB, docx)}

S1 Data. The dataset used in the article.

(ZIP)

pone.0341687.s027.zip^{(32.4MB, zip)}

Acknowledgments

Thanks to the GBD 2021 database for data provision.

Data Availability

All relevant data are within the paper and Supporting Information files.

Funding Statement

Our research received funding from the Shanxi Provincial Department of Human Resources and Social Security (grant no. 20210002) and the Shanxi Provincial Department of Science and Technology (grant no. 202104041101024). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.McKenna WJ, Maron BJ, Thiene G. Classification, epidemiology, and global burden of cardiomyopathies. Circ Res. 2017;121(7):722–30. doi: 10.1161/CIRCRESAHA.117.309711 [DOI] [PubMed] [Google Scholar]
2.Huang S, Li J, Li Q, Wang Q, Zhou X, Chen J, et al. Cardiomyopathy: pathogenesis and therapeutic interventions. MedComm (2020). 2024;5(11):e772. doi: 10.1002/mco2.772 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Ho CY, Day SM, Ashley EA, Michels M, Pereira AC, Jacoby D, et al. Genotype and lifetime burden of disease in hypertrophic cardiomyopathy: insights from the sarcomeric human cardiomyopathy registry (SHaRe). Circulation. 2018;138(14):1387–98. doi: 10.1161/CIRCULATIONAHA.117.033200 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Goli R, Li J, Brandimarto J, Levine LD, Riis V, McAfee Q, et al. Genetic and phenotypic landscape of peripartum cardiomyopathy. Circulation. 2021;143(19):1852–62. doi: 10.1161/CIRCULATIONAHA.120.052395 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Parikh VN, Day SM, Lakdawala NK, Adler ED, Olivotto I, Seidman CE, et al. Advances in the study and treatment of genetic cardiomyopathies. Cell. 2025;188(4):901–18. doi: 10.1016/j.cell.2025.01.011 [DOI] [PubMed] [Google Scholar]
6.Ritchie RH, Abel ED. Basic mechanisms of diabetic heart disease. Circ Res. 2020;126(11):1501–25. doi: 10.1161/CIRCRESAHA.120.315913 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Fuentes-Antrás J, Picatoste B, Ramírez E, Egido J, Tuñón J, Lorenzo Ó. Targeting metabolic disturbance in the diabetic heart. Cardiovasc Diabetol. 2015;14:17. doi: 10.1186/s12933-015-0173-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Del Franco A, Menale S, Chiti C, Biagioni G, Tomberli A, Zampieri M, et al. The evolving paradigm and current perception of hypertrophic cardiomyopathy: Implications for management. Prog Cardiovasc Dis. 2023;80:8–13. doi: 10.1016/j.pcad.2023.08.002 [DOI] [PubMed] [Google Scholar]
9.Zegkos T, Panagiotidis T, Parcharidou D, Efthimiadis G. Emerging concepts in arrhythmogenic dilated cardiomyopathy. Heart Fail Rev. 2021;26(5):1219–29. doi: 10.1007/s10741-020-09933-z [DOI] [PubMed] [Google Scholar]
10.Fernández-Solà J. The effects of ethanol on the heart: alcoholic cardiomyopathy. Nutrients. 2020;12(2):572. doi: 10.3390/nu12020572 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Domínguez F, Adler E, García-Pavía P. Alcoholic cardiomyopathy: an update. Eur Heart J. 2024;45(26):2294–305. doi: 10.1093/eurheartj/ehae362 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Ba H, Zhang D, Guan S, Zheng J. Global burden of myocarditis and cardiomyopathy in children and prediction for 2035 based on the global burden of disease study 2019. Front Cardiovasc Med. 2023;10:1173015. doi: 10.3389/fcvm.2023.1173015 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Wang M, Yi J, Chen Z. Global, regional, and national burden of childhood cardiovascular disease: trends from 1990 to 2021. Front Pediatr. 2024;12:1495238. doi: 10.3389/fped.2024.1495238 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.GBD 2021 Diseases and Injuries Collaborators. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2133–61. doi: 10.1016/S0140-6736(24)00757-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Polovina M, Tschöpe C, Rosano G, Metra M, Crea F, Mullens W, et al. Incidence, risk assessment and prevention of sudden cardiac death in cardiomyopathies. Eur J Heart Fail. 2023;25(12):2144–63. doi: 10.1002/ejhf.3076 [DOI] [PubMed] [Google Scholar]
16.Omidi F, Hajikhani B, Kazemi SN, Tajbakhsh A, Riazi S, Mirsaeidi M, et al. COVID-19 and cardiomyopathy: a systematic review. Front Cardiovasc Med. 2021;8:695206. doi: 10.3389/fcvm.2021.695206 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Zhang Y, Liu M, Li P, Ma L, Yin L, Cui Y, et al. The burden and management competency of cardiomyopathies in China: a nationwide survey study. The Lancet Regional Health - Western Pacific. 2024;46:101062. doi: 10.1016/j.lanwpc.2024.101062 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Tan K, Foo R, Loh M. Cardiomyopathy in Asian cohorts: genetic and epigenetic insights. Circ: Genomic and Precision Medicine. 2023;16(5):496–506. doi: 10.1161/circgen.123.004079 [DOI] [PubMed] [Google Scholar]
19.Guevara Moctezuma EI, Smith Doria S, Ortiz JV, Teixeira de Sousa DR, Mwangi VI, do Nascimento Couceiro K, et al. Chronic Chagas Cardiomyopathy in the Brazilian Amazon region: clinical characteristics and regional distinctiveness. Front Public Health. 2023;11:1284639. doi: 10.3389/fpubh.2023.1284639 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Zhang Z, Wang Y, Zhou Y, Pei J, Zhao G, Dong P, et al. Association between diurnal temperature range and risk of cardiomyopathy-induced hospitalisation in Henan, China: a time-series study. Risk Manag Healthc Policy. 2025;18:279–90. doi: 10.2147/RMHP.S502132 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Bois JP, Adams JC, Kumar G, Ommen SR, Nishimura RA, Klarich KW. Relation between temperature extremes and symptom exacerbation in patients with hypertrophic cardiomyopathy. The American Journal of Cardiology. 2016;117(6):961–5. doi: 10.1016/j.amjcard.2015.12.046 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.GBD 2021 Causes of Death Collaborators. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2100–32. doi: 10.1016/S0140-6736(24)00367-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.GBD 2021 Risk Factors Collaborators. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the global burden of disease study 2021. Lancet. 2024;403(10440):2162–203. doi: 10.1016/S0140-6736(24)00933-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Institute of Health Metrics and Evaluation (IHME). GBD results. https://vizhub.healthdata.org/gbd-results/. 2021.
25.Stevens GA, Alkema L, Black RE, Boerma JT, Collins GS, Ezzati M, et al. Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement. PLoS Med. 2016;13(6):e1002056. doi: 10.1371/journal.pmed.1002056 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Institute of Health Metrics and Evaluation (IHME). Global burden of disease study 2021 (GBD 2021) Socio-Demographic Index (SDI) 1950-2021. 2023. https://ghdx.healthdata.org/record/global-burden-disease-study-2021-gbd-2021-socio-demographic-index-sdi-1950-2021
27.Clegg LX, Hankey BF, Tiwari R, Feuer EJ, Edwards BK. Estimating average annual per cent change in trend analysis. Stat Med. 2009;28(29):3670–82. doi: 10.1002/sim.3733 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Pearson-Stuttard J, Guzman-Castillo M, Penalvo JL, Rehm CD, Afshin A, Danaei G, et al. Modeling future cardiovascular disease mortality in the united states: national trends and racial and ethnic disparities. Circulation. 2016;133(10):967–78. doi: 10.1161/CIRCULATIONAHA.115.019904 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Kiyoshige E, Ogata S, O’Flaherty M, Capewell S, Takegami M, Iihara K, et al. Projections of future coronary heart disease and stroke mortality in Japan until 2040: a Bayesian age-period-cohort analysis. Lancet Reg Health West Pac. 2022;31:100637. doi: 10.1016/j.lanwpc.2022.100637 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Menacho KD, Ramirez S, Perez A, Dragonetti L, Perez de Arenaza D, Katekaru D, et al. Improving cardiovascular magnetic resonance access in low- and middle-income countries for cardiomyopathy assessment: rapid cardiovascular magnetic resonance. Eur Heart J. 2022;43(26):2496–507. doi: 10.1093/eurheartj/ehac035 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Vervoort D, Swain JD, Pezzella AT, Kpodonu J. Cardiac Surgery in Low- and Middle-Income Countries: A State-of-the-Art Review. Ann Thorac Surg. 2021;111(4):1394–400. doi: 10.1016/j.athoracsur.2020.05.181 [DOI] [PubMed] [Google Scholar]
32.Fundikira LS, Chillo P, Mutagaywa R, Kamuhabwa A, Kwesigabo G, Asselbergs FW, et al. Risk factors and prevalence of dilated cardiomyopathy in sub-saharan africa: a systematic review. Glob Heart. 2022;17(1):76. doi: 10.5334/gh.1166 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Global status report on alcohol and health and treatment of substance use disorders. Geneva: World Health Organization; 2024. [Google Scholar]
34.Jasilionis D, Leon DA, Pechholdová M. Impact of alcohol on mortality in Eastern Europe: Trends and policy responses. Drug Alcohol Rev. 2020;39(7):785–9. doi: 10.1111/dar.13167 [DOI] [PubMed] [Google Scholar]
35.Burki T. Poor progress on reducing alcohol consumption in Europe. Lancet Oncol. 2024;25(9):1120. doi: 10.1016/S1470-2045(24)00435-2 [DOI] [PubMed] [Google Scholar]
36.The Lancet Regional Health-Europe. Why is alcohol so normalised in Europe?. Lancet Reg Health Eur. 2024;46:101119. doi: 10.1016/j.lanepe.2024.101119 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Dalakoti M, Lin NHY, Yap J, Cader A, Dipanker P, Lee D, et al. Primary Prevention of Cardiovascular Disease in Asia: Challenges: A Narrative Review. JACC Adv. 2025;4(4):101670. doi: 10.1016/j.jacadv.2025.101670 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Dalakoti M, Lin NHY, Yap J, et al. Primary prevention of cardiovascular disease in asia: opportunities and solutions: a narrative review. JACC Adv. 2025;4(4):101676. doi: 10.1016/j.jacadv.2025.101676 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Lipshultz SE, Sleeper LA, Towbin JA, Lowe AM, Orav EJ, Cox GF, et al. The incidence of pediatric cardiomyopathy in two regions of the United States. N Engl J Med. 2003;348(17):1647–55. doi: 10.1056/nejmoa021715 [DOI] [PubMed] [Google Scholar]
40.Xi J-Y, Zhao J-G, Li X-Q, Yan B, Bai J-J, Xiang Y-N, et al. Quantifying the loss of healthy life expectancy due to population ageing: health benefit estimation from a global perspective. BMJ Glob Health. 2025;10(5):e018194. doi: 10.1136/bmjgh-2024-018194 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Zhang J, Liu H, Bu X, Lu Q, Cheng L, Ma A, et al. The burden of alcoholic cardiomyopathy in China and different regions around the world. J Glob Health. 2022;12:04041. doi: 10.7189/jogh.12.04041 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Watanabe C, Nishina N, Kawai C. Stress-induced cardiomyopathy accompanied by heat stroke. J Cardiol Cases. 2015;12(1):16–9. doi: 10.1016/j.jccase.2015.03.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Al Houri HN, Jomaa S, Jabra M, Alhouri AN, Latifeh Y. Pathophysiology of stress cardiomyopathy: A comprehensive literature review. Ann Med Surg (Lond). 2022;82:104671. doi: 10.1016/j.amsu.2022.104671 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Isath A, Malik AH, Goel A, Gupta R, Shrivastav R, Bandyopadhyay D. Nationwide analysis of the outcomes and mortality of hospitalized COVID-19 patients. Curr Probl Cardiol. 2023;48(2):101440. doi: 10.1016/j.cpcardiol.2022.101440 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Kwenandar F, Japar KV, Damay V, Hariyanto TI, Tanaka M, Lugito NPH, et al. Coronavirus disease 2019 and cardiovascular system: A narrative review. Int J Cardiol Heart Vasc. 2020;29:100557. doi: 10.1016/j.ijcha.2020.100557 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Dimnjaković J, Buble T, Ivanko P, Pristaš I, Brborović O, Brborović H. Association of anti-diabetic drugs and COVID-19 outcomes in patients with diabetes mellitus type 2 and cardiomyopathy. Sci Rep. 2024;14(1):7227. doi: 10.1038/s41598-024-57871-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0341687.r001

Decision Letter 0

Claudio Dávila-Cervantes

14 Dec 2025

Dear Dr. Tian,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jan 28 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Claudio Alberto Dávila-Cervantes, Ph.D.

Academic Editor

PLOS One

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Thank you for stating the following financial disclosure:

Please state what role the funders took in the study. If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

If this statement is not correct you must amend it as needed.

Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf.

3. We note that Figure 5 in your submission contain map images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.

We require you to either (a) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (b) remove the figures from your submission:

a. You may seek permission from the original copyright holder of Figure 5 to publish the content specifically under the CC BY 4.0 license.

We recommend that you contact the original copyright holder with the Content Permission Form (http://journals.plos.org/plosone/s/file?id=7c09/content-permission-form.pdf) and the following text:

“I request permission for the open-access journal PLOS ONE to publish XXX under the Creative Commons Attribution License (CCAL) CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). Please be aware that this license allows unrestricted use and distribution, even commercially, by third parties. Please reply and provide explicit written permission to publish XXX under a CC BY license and complete the attached form.”

Please upload the completed Content Permission Form or other proof of granted permissions as an ""Other"" file with your submission.

In the figure caption of the copyrighted figure, please include the following text: “Reprinted from [ref] under a CC BY license, with permission from [name of publisher], original copyright [original copyright year].”

b. If you are unable to obtain permission from the original copyright holder to publish these figures under the CC BY 4.0 license or if the copyright holder’s requirements are incompatible with the CC BY 4.0 license, please either i) remove the figure or ii) supply a replacement figure that complies with the CC BY 4.0 license. Please check copyright information on all replacement figures and update the figure caption with source information. If applicable, please specify in the figure caption text when a figure is similar but not identical to the original image and is therefore for illustrative purposes only.

The following resources for replacing copyrighted map figures may be helpful:

USGS National Map Viewer (public domain): http://viewer.nationalmap.gov/viewer/

The Gateway to Astronaut Photography of Earth (public domain): http://eol.jsc.nasa.gov/sseop/clickmap/

Maps at the CIA (public domain): https://www.cia.gov/library/publications/the-world-factbook/index.html and https://www.cia.gov/library/publications/cia-maps-publications/index.html

NASA Earth Observatory (public domain): http://earthobservatory.nasa.gov/

Landsat: http://landsat.visibleearth.nasa.gov/

USGS EROS (Earth Resources Observatory and Science (EROS) Center) (public domain): http://eros.usgs.gov/#

Natural Earth (public domain): http://www.naturalearthdata.com/

4. Please upload a new copy of the figures as the details are not clear. Please follow the link for more information: https://journals.plos.org/plosone/s/figures

5. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: No

**********

2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: No

Reviewer #2: Yes

**********

Reviewer #1: The manuscript "Global, regional, and national burden of cardiomyopathy (including alcoholic

cardiomyopathy and others) from 1990 to 2021: an analysis of data from the global burden of disease study 2021 and forecast to 2040" presents a comprehensive epidemiological assessment of cardiomyopathy using the GBD 2021 dataset. Overall, the topic is timely and relevant, and the manuscript provides useful insights into long-term trends.

From a methodological perspective, the study is well designed. The use of age-standardized rates (ASR), estimated annual percentage change (EAPC), and the Bayesian age–period–cohort (BAPC) model is appropriate for this type of analysis, and the underlying dataset is robust. The conclusions are broadly consistent with the results, although they remain somewhat general. A clearer discussion of the implications for clinical practice or public health policy would enhance the manuscript's impact.

The statistical methods are rigorous, but several descriptions, particularly those involving formulas and confidence intervals, are not fully clear, which may reduce reproducibility. It would also be helpful to elaborate on the rationale for selecting the BAPC model over other forecasting approaches.

The manuscript is written in standard English and is generally easy to follow; however, the text is quite dense, and the amount of numerical detail reduces readability. The Results section, in particular, contains too many values. Or, the Abstract includes a single sentence with more than fifteen numerical values ('...4752361.3 (95% UI: 3937775.4–5566947.2), including AC: 528429 (95% UI: 439582.3–639167.4) and OC: 4223932.2 (95% UI: 3417357.4–5034257.6)...'). A language revision to simplify phrasing and reduce redundancies would be recommended.

Reviewer #2: This manuscript is clearly written and presents descriptive analyses based on publicly available data from the Global Burden of Disease (GBD) 2021 study. The authors have evidently invested considerable effort in compiling and presenting long-term trends and projections. However, despite these strengths, the study suffers from several fundamental conceptual and methodological limitations that substantially limit its scientific contribution and interpretability. These concerns go beyond issues of presentation and cannot be adequately addressed through minor or moderate revision.

First, there is a fundamental mismatch between the stated research objectives and the study design. Although the manuscript is presented as original research, the analyses rely entirely on pre-modelled secondary data obtained from the GBD 2021 database. The study does not introduce new data, novel analytical frameworks, or independent validation. As a result, the work remains largely descriptive and does not adequately support the broader inferential claims made in the manuscript. This represents a conceptual limitation rather than a reporting issue.

Second, the study population is insufficiently defined from an epidemiological perspective. Case identification is based on ICD-9 and ICD-10 codes within the GBD framework; however, the manuscript does not sufficiently address diagnostic heterogeneity, underreporting, or regional variation in disease ascertainment. Without a clearer description of how these issues may affect the estimates, the generalizability and interpretability of the findings are limited.

Third, the analytical contribution of the study is limited. The manuscript largely reproduces standard GBD outputs, including age-standardized rates, estimated annual percentage change, and Bayesian age–period–cohort modelling using existing software packages. The rationale for applying these methods specifically to cardiomyopathy is not clearly justified, and no sensitivity analyses or alternative modelling approaches are presented. Consequently, the robustness of the reported trends and future projections cannot be adequately assessed.

Fourth, the conclusions appear to overinterpret descriptive and model-based results. Several statements imply implications for disease control or policy, despite the absence of causal or risk factor–based analyses. Given the observational and model-dependent nature of the data, such interpretations are not sufficiently supported.

Finally, key methodological details related to model assumptions, uncertainty handling, and reproducibility are insufficiently described. This limits the ability of independent researchers to replicate or critically evaluate the analyses, which is an important consideration for publication in PLOS ONE.

While the manuscript is clearly written and based on a reputable global dataset, the conceptual and methodological limitations outlined above are substantial and, in my view, cannot be resolved through revision alone. I therefore do not recommend the manuscript for publication in its current form.

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: No

Reviewer #2: Yes: Kristian Kurniawan

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures

You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation.

NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications.

PLoS One. 2026 Jan 30;21(1):e0341687. doi: 10.1371/journal.pone.0341687.r002

Author response to Decision Letter 1

23 Dec 2025

Subject: Revised Manuscript Submission: [PONE-D-25-32695] - [Global, regional, and national burden of cardiomyopathy (including alcoholic cardiomyopathy and others) from 1990 to 2021: an analysis of data from the global burden of disease study 2021 and forecast to 2040]

Dear Dr. Claudio Alberto Dávila-Cervantes and Reviewers,

Thank you for the opportunity to revise our manuscript and for the constructive feedback from the editors and reviewers. We sincerely appreciate the editors and reviewers for their insightful comments and constructive suggestions, which have greatly helped us to improve the quality of our work.

We have carefully addressed all the comments and requirements. Our point-by-point responses are detailed below. All changes in the manuscript have been highlighted in red in the ‘Revised Manuscript with Track Changes’ file, and an unmarked version is also provided.

Responses to Journal Requirements:

1. Style Requirements: We have reformatted the entire manuscript according to the PLOS ONE style templates.

2. Financial Disclosure: As the funders had no role in our study, we have included the following statement in our cover letter: “The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”

3. Copyright of Map Figures:

We thank the editor for raising this important point. We wish to provide a comprehensive explanation to clarify that the world maps in our manuscript are fully compliant with the CC BY 4.0 license. The maps in our manuscript were generated using the map_data("world")function from the R mapspackage. This is a critical point: the geographic boundary data within this package is sourced directly from the Natural Earth database (naturalearthdata.com).

• Natural Earth is a public domain resource that is explicitly recommended on your journal's guidelines page for creating CC BY-compatible maps.

• Data from Natural Earth is explicitly dedicated to the public domain, meaning it is free to use, adapt, and publish without restriction, making it perfectly compatible with the CC BY 4.0 license.

Therefore, we confirm that our maps do not utilize any copyrighted services. We believe this explanation and the accompanying caption updates resolve this requirement conclusively.

4. Figure Quality: We have used the LAW compression function to modify the figures formats, sizes, and clarity in the manuscripts and supplementary materials to meet PLOS ONE requirements.

5. Citation of Suggested Works: We have reviewed the literature and ensured that all relevant and necessary citations are included.

Responses to Reviewers' Comments:

Reviewer #1:

We thank the reviewer for the positive assessment and valuable suggestions for enhancement. The reviewer found the methodology sound and conclusions supported, but suggested improving the readability of the text, deepening the conclusions, and providing a clearer rationale for methodological choices.

Response:

1. Language and Readability: We have thoroughly revised the manuscript, particularly the Abstract and Results sections, to reduce numerical density and improve readability. Detailed data arrays have been moved to supplementary tables, and the text now focuses on summarizing key trends.

2. Methodological Clarity: We have expanded the Methods section and added two references (numbered 28 and 29) [1,2] (Page 9, Lines 175 to Page 10, Lines 196) to provide a clearer rationale for the choice of the BAPC model and to ensure all statistical descriptions are unambiguous and reproducible.

3. Discussion and Implications: The discussion section provides a refined elaboration of the original text, detailing the specific implications of our research findings in the fields of public health and clinical practice, thereby enhancing the impact of the manuscript.

Reviewer #2:

We sincerely thank the reviewer for the thorough, critical, and constructive assessment of our manuscript. The comments have been invaluable in helping us identify the weaknesses in our initial submission and have guided us in significantly strengthening the study's conceptual foundation, methodological rigor, and overall scholarly contribution. We have undertaken a major revision to address all the concerns raised. Our point-by-point responses and the specific actions taken are detailed below.

Comment 1: On the conceptual contribution and the descriptive nature of the study. Reviewer's comment: "There is a fundamental mismatch between the stated research objectives and the study design... the work remains largely descriptive and does not adequately support the broader inferential claims..."

Response: We acknowledge and thank the reviewer for this critical insight. We agree that our initial manuscript did not sufficiently articulate the unique value of performing a focused assessment on the broader category of "cardiomyopathy (including AC and OC)" within the GBD framework. To address this:

•Action taken (Introduction revised): We have entirely rewritten the Introduction section (specifically, the final paragraph outlining the study aims on Page 5, Lines 95 to Page 6, Lines 120) to more clearly position our work. We now state that while GBD data is publicly available, our study provides the first comprehensive analysis to specifically consolidate, trend, and forecast the burden of cardiomyopathy (as a collective group of diseases) across time, geography, and socio-demographic indices. We emphasize that this synthesis adds value by moving beyond isolated disease metrics to provide a unified view of the total public health burden attributable to all forms of cardiomyopathy, which is crucial for health policy planning that often addresses disease groups rather than individual ICD codes.

Comment 2: On the study of population and generalizability.

Reviewer's comment: "The study population is insufficiently defined from an epidemiological perspective... diagnostic heterogeneity, underreporting, or regional variation..."

Response: This is a valid limitation of any study using GBD data. We now explicitly acknowledge and discuss this.

•Action taken (New Limitations subsection): As strongly suggested by the reviewer's comments, we have added a Refinement of the limitations section in the Discussion part to more accurately reflect the limitations presented in this paper (Page 42, Lines 603 to Page 43, Lines 627). In this section, we openly discuss:

1. The inherent limitations of the GBD data, including potential misclassification and under-ascertainment in regions with weak vital registration systems.

2. The fact that our analysis is based on modeled estimates rather than raw data, and thus is subject to the assumptions and limitations of the GBD modeling framework.

Comment 3: On the analytical contribution, robustness, and sensitivity analysis. Reviewer's comment: "The analytical contribution of the study is limited... no sensitivity analyses or alternative modelling approaches are presented. Consequently, the robustness of the reported trends and future projections cannot be adequately assessed."

Response: This is a crucial point, and we have taken substantial steps to enhance the analytical rigor and transparency of our work.

•Action taken (Sensitivity analysis performed): To directly address the concern about robustness, we have conducted a comprehensive sensitivity analysis for our forecasts. Specifically, we complemented the primary Bayesian Age-Period-Cohort (BAPC) model with an alternative, well-established time-series forecasting method: the Autoregressive Integrated Moving Average (ARIMA) model.

Results added: We have added a new subsection within the Results section titled "Sensitivity Analysis" (Page 36, Lines 476 to Lines 487). This section details the ARIMA model specification, including describing how we use AMIRA to perform forecasting of TC from 2022 to 2040.

Results presented: The results of this sensitivity analysis, comparing the projections from the BAPC and ARIMA models for key metrics (e.g., global TC’s ASPR), are now presented in a new Supplementary Figure (S11 Fig.). We note that the projections from both models showed consistent trends, which significantly strengthens the credibility of our findings.

•Action taken (Methodological justification enhanced): We have expanded the Methods section and added two references (numbered 28 and 29) [1,2] to provide a clearer rationale for selecting the BAPC model over other approaches (Page 9, Lines 175 to Page 10, Lines 196). We explain that the BAPC model is particularly suited for long-term epidemiological forecasts as it explicitly accounts for the separate effects of age, time period, and birth cohort, which are critical drivers of chronic disease trends.

Comment 4: On overinterpretation of results. Reviewer's comment: "The conclusions appear to overinterpret descriptive and model-based results... such interpretations are not sufficiently supported."

Response: We agree with the reviewer that our initial discussion sometimes overstepped the boundaries of what descriptive data can support. We have carefully revised the tone and content.

•Action taken (Language calibrated): We have systematically gone through the Discussion and Conclusion sections and replaced any language that implied causation or direct policy prescription with more cautious, descriptive, and suggestive language. For example, we changed phrases like "our findings show that policy must..." to "the observed rising trends suggest that future health planning may need to be considered...".

•Action taken (Focus on explanation): We have reframed the Discussion to focus on explaining the possible reasons behind the observed trends (e.g., citing literature on improved diagnostics, aging populations, changes in risk factors) rather than making definitive claims about their implications for direct intervention.

Comment 5: On reproducibility and methodological detail. Reviewer's comment: "Key methodological details... are insufficiently described. This limits the ability of independent researchers to replicate or critically evaluate the analyses..."

Response: We have taken concrete steps to ensure the full reproducibility of our study.

•Action taken (Methods expanded): We have added significantly more detail to the Methods section. This includes specifying the software and packages used and explicitly defining how BAPC modle were calculated.

•Action taken (Code and data availability): In accordance with PLOS ONE's data policy, and to ensure full transparency, we can transparently upload R code publicly. The GBD data itself is publicly available from the IHME website (as originally stated).

We believe that the extensive revisions undertaken—including the addition of sensitivity analyses, enhanced methodological description, a refined discussion, and a clear statement of limitations—have fundamentally addressed the concerns raised. The manuscript is now substantially stronger, more rigorous, and more transparent. We are deeply grateful for your time and insightful comments, which have been instrumental in improving our work.

Sincerely,

M.D. Yanzhang Tian

Institution: Department of Biliary and Pancreatic Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University

Address: No. 99, Longcheng Street, Taiyuan, Shanxi, China

E-mail: 15802872565@163.com

References

1 Pearson-Stuttard J, Guzman-Castillo M, Penalvo JL, et al. Modeling Future Cardiovascular Disease Mortality in the United States: National Trends and Racial and Ethnic Disparities. Circulation. 2016;133(10):967-978. doi:10.1161/CIRCULATIONAHA.115.019904

2 Kiyoshige E, Ogata S, O'Flaherty M, et al. Projections of future coronary heart disease and stroke mortality in Japan until 2040: a Bayesian age-period-cohort analysis. Lancet Reg Health West Pac. 2022;31:100637. doi:10.1016/j.lanwpc.2022.100637

Attachment

Submitted filename: Response to Reviewers.docx

pone.0341687.s028.docx^{(24.1KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0341687.r003

Decision Letter 1

Claudio Dávila-Cervantes

11 Jan 2026

Global, regional, and national burden of cardiomyopathy (including alcoholic cardiomyopathy and others) from 1990 to 2021: an analysis of data from the global burden of disease study 2021 and forecast to 2040

PONE-D-25-32695R1

Dear Dr. Tian,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support .

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Claudio Alberto Dávila-Cervantes, Ph.D.

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

Reviewer #1: The authors have adequately addressed all the points raised in the previous review round. The revision improves the clarity and coherence of the manuscript, and the responses provided are appropriate and satisfactory. I also appreciate the authors' effort in incorporating the requested changes and strengthening the overall quality of the work.

Reviewer #2: I would like to thank the authors for their thorough and thoughtful revision of the manuscript and for the detailed, point-by-point responses to the reviewers’ comments. The revised version demonstrates a clear effort to improve conceptual clarity, methodological transparency, and interpretative balance.

In particular, the revised Introduction more clearly positions the study as a descriptive global assessment based on GBD 2021 estimates, which improves alignment between the research objectives and the analytical approach. The expanded discussion of limitations related to diagnostic heterogeneity, underreporting, and regional variability is appropriate and strengthens the interpretability of the findings.

The analytical methods employed are consistent with established approaches used in global burden of disease research, and the inclusion of an additional sensitivity analysis provides reassurance regarding the stability of the observed temporal trends. While further methodological extensions could be explored in future work, the current analyses are suitable for the aims as stated.

I also appreciate the authors’ efforts to moderate the language in the Discussion and Conclusions. The implications are now framed more cautiously and appropriately reflect the descriptive and model-based nature of the data, avoiding overstatement of causal or policy conclusions.

Overall, I believe that the revised manuscript provides a clear, well-documented, and methodologically sound overview of the global burden and temporal trends of cardiomyopathy. Within the scope and publication criteria of PLOS ONE, the study represents a useful contribution to the literature.

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: No

Reviewer #2: Yes: Kristian Kurniawan

**********

PLoS One. doi: 10.1371/journal.pone.0341687.r004

Acceptance letter

Claudio Dávila-Cervantes

PONE-D-25-32695R1

PLOS One

Dear Dr. Tian,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS One. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

* All relevant supporting information is included in the manuscript submission,

* There are no issues that prevent the paper from being properly typeset

You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps.

Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

You will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing.

If we can help with anything else, please email us at customercare@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Mr. Claudio Alberto Dávila-Cervantes

Academic Editor

PLOS One

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. The age-specific numbers and ASRs of alcoholic cardiomyopathy by SDI regions in 2021.

(A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

(DOCX)

pone.0341687.s001.docx^{(6.3MB, docx)}

S2 Fig. The age-specific numbers and ASRs of other cardiomyopathy by SDI regions in 2021.

(A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

(DOCX)

pone.0341687.s002.docx^{(6.3MB, docx)}

S3 Fig. Socio-demographic index trends with ASRs of alcoholic cardiomyopathy in 21 GBD regions in 2021.

(A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

(DOCX)

pone.0341687.s003.docx^{(4.1MB, docx)}

S4 Fig. Socio-demographic index trends with ASRs of other cardiomyopathy in 21 GBD regions in 2021.

(A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

(DOCX)

pone.0341687.s004.docx^{(4.1MB, docx)}

S5 Fig. ASRs of alcoholic cardiomyopathy in 204 countries and territories in 2021.

(A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

(DOCX)

pone.0341687.s005.docx^{(9.8MB, docx)}

S6 Fig. ASRs of other cardiomyopathy in 204 countries and territories in 2021.

(A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

(DOCX)

pone.0341687.s006.docx^{(9.8MB, docx)}

S7 Fig. Socio-demographic index trends with ASRs of alcoholic cardiomyopathy in 204 countries and territories in 2021.

(A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

(DOCX)

pone.0341687.s007.docx^{(7.9MB, docx)}

S8 Fig. Socio-demographic index trends with ASRs of other cardiomyopathy in 204 countries and territories in 2021.

(A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

(DOCX)

pone.0341687.s008.docx^{(7.1MB, docx)}

S9 Fig. Global disease trends predictions of alcoholic cardiomyopathy from 2021 to 2040.

(A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

(DOCX)

pone.0341687.s009.docx^{(8MB, docx)}

S10 Fig. Global disease trends predictions of other cardiomyopathy from 2021 to 2040.

(A) ASPRs. (B) ASDRs. (C) ASR_DALYs.

(DOCX)

pone.0341687.s010.docx^{(7.9MB, docx)}

S11 Fig. Global disease trends predictions of total cardiomyopathy from 2021 to 2040 using ARIMA.

(DOCX)

pone.0341687.s011.docx^{(1.6MB, docx)}

S1 Table. 1990–2021 Global and regional prevalence trends in alcoholic cardiomyopathy burden.

(DOCX)

pone.0341687.s012.docx^{(19.1KB, docx)}

S2 Table. 1990–2021 Global and regional mortality trends in alcoholic cardiomyopathy burden.

(DOCX)

pone.0341687.s013.docx^{(18.8KB, docx)}

S3 Table. 1990–2021 Global and regional DALYs trends in alcoholic cardiomyopathy burden.

(DOCX)

pone.0341687.s014.docx^{(19.2KB, docx)}

S4 Table. 1990–2021 Global and regional prevalence trends in other cardiomyopathy burden.

(DOCX)

pone.0341687.s015.docx^{(19.4KB, docx)}

S5 Table. 1990–2021 Global and regional mortality trends in other cardiomyopathy burden.

(DOCX)

pone.0341687.s016.docx^{(19.2KB, docx)}

S6 Table. 1990–2021 Global and regional DALYs trends in other cardiomyopathy burden.

(DOCX)

pone.0341687.s017.docx^{(19.4KB, docx)}

S7 Table. 1990–2021 Global and national prevalence trends in total cardiomyopathy burden.

(DOCX)

pone.0341687.s018.docx^{(50.2KB, docx)}

S8 Table. 1990–2021 Global and national mortality trends in total cardiomyopathy burden.

(DOCX)

pone.0341687.s019.docx^{(47.5KB, docx)}

S9 Table. 1990–2021 Global and national DALYs trends in total cardiomyopathy burden.

(DOCX)

pone.0341687.s020.docx^{(51.9KB, docx)}

S10 Table. 1990–2021 Global and national prevalence trends in alcoholic cardiomyopathy burden.

(DOCX)

pone.0341687.s021.docx^{(47.2KB, docx)}

S11 Table. 1990–2021 Global and national mortality trends in alcoholic cardiomyopathy burden.

(DOCX)

pone.0341687.s022.docx^{(44.6KB, docx)}

S12 Table. 1990–2021 Global and national DALYs trends in alcoholic cardiomyopathy burden.

(DOCX)

pone.0341687.s023.docx^{(48.3KB, docx)}

S13 Table. 1990–2021 Global and national prevalence trends in other cardiomyopathy burden.

(DOCX)

pone.0341687.s024.docx^{(50KB, docx)}

S14 Table. 1990–2021 Global and national mortality trends in other cardiomyopathy burden.

(DOCX)

pone.0341687.s025.docx^{(48.4KB, docx)}

S15 Table. 1990–2021 Global and national DALYs trends in other cardiomyopathy burden.

(DOCX)

pone.0341687.s026.docx^{(48.5KB, docx)}

S1 Data. The dataset used in the article.

(ZIP)

pone.0341687.s027.zip^{(32.4MB, zip)}

Attachment

Submitted filename: Response to Reviewers.docx

pone.0341687.s028.docx^{(24.1KB, docx)}

Data Availability Statement

All relevant data are within the paper and Supporting Information files.

[pone.0341687.ref001] 1.McKenna WJ, Maron BJ, Thiene G. Classification, epidemiology, and global burden of cardiomyopathies. Circ Res. 2017;121(7):722–30. doi: 10.1161/CIRCRESAHA.117.309711 [DOI] [PubMed] [Google Scholar]

[pone.0341687.ref002] 2.Huang S, Li J, Li Q, Wang Q, Zhou X, Chen J, et al. Cardiomyopathy: pathogenesis and therapeutic interventions. MedComm (2020). 2024;5(11):e772. doi: 10.1002/mco2.772 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref003] 3.Ho CY, Day SM, Ashley EA, Michels M, Pereira AC, Jacoby D, et al. Genotype and lifetime burden of disease in hypertrophic cardiomyopathy: insights from the sarcomeric human cardiomyopathy registry (SHaRe). Circulation. 2018;138(14):1387–98. doi: 10.1161/CIRCULATIONAHA.117.033200 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref004] 4.Goli R, Li J, Brandimarto J, Levine LD, Riis V, McAfee Q, et al. Genetic and phenotypic landscape of peripartum cardiomyopathy. Circulation. 2021;143(19):1852–62. doi: 10.1161/CIRCULATIONAHA.120.052395 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref005] 5.Parikh VN, Day SM, Lakdawala NK, Adler ED, Olivotto I, Seidman CE, et al. Advances in the study and treatment of genetic cardiomyopathies. Cell. 2025;188(4):901–18. doi: 10.1016/j.cell.2025.01.011 [DOI] [PubMed] [Google Scholar]

[pone.0341687.ref006] 6.Ritchie RH, Abel ED. Basic mechanisms of diabetic heart disease. Circ Res. 2020;126(11):1501–25. doi: 10.1161/CIRCRESAHA.120.315913 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref007] 7.Fuentes-Antrás J, Picatoste B, Ramírez E, Egido J, Tuñón J, Lorenzo Ó. Targeting metabolic disturbance in the diabetic heart. Cardiovasc Diabetol. 2015;14:17. doi: 10.1186/s12933-015-0173-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref008] 8.Del Franco A, Menale S, Chiti C, Biagioni G, Tomberli A, Zampieri M, et al. The evolving paradigm and current perception of hypertrophic cardiomyopathy: Implications for management. Prog Cardiovasc Dis. 2023;80:8–13. doi: 10.1016/j.pcad.2023.08.002 [DOI] [PubMed] [Google Scholar]

[pone.0341687.ref009] 9.Zegkos T, Panagiotidis T, Parcharidou D, Efthimiadis G. Emerging concepts in arrhythmogenic dilated cardiomyopathy. Heart Fail Rev. 2021;26(5):1219–29. doi: 10.1007/s10741-020-09933-z [DOI] [PubMed] [Google Scholar]

[pone.0341687.ref010] 10.Fernández-Solà J. The effects of ethanol on the heart: alcoholic cardiomyopathy. Nutrients. 2020;12(2):572. doi: 10.3390/nu12020572 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref011] 11.Domínguez F, Adler E, García-Pavía P. Alcoholic cardiomyopathy: an update. Eur Heart J. 2024;45(26):2294–305. doi: 10.1093/eurheartj/ehae362 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref012] 12.Ba H, Zhang D, Guan S, Zheng J. Global burden of myocarditis and cardiomyopathy in children and prediction for 2035 based on the global burden of disease study 2019. Front Cardiovasc Med. 2023;10:1173015. doi: 10.3389/fcvm.2023.1173015 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref013] 13.Wang M, Yi J, Chen Z. Global, regional, and national burden of childhood cardiovascular disease: trends from 1990 to 2021. Front Pediatr. 2024;12:1495238. doi: 10.3389/fped.2024.1495238 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref014] 14.GBD 2021 Diseases and Injuries Collaborators. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2133–61. doi: 10.1016/S0140-6736(24)00757-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref015] 15.Polovina M, Tschöpe C, Rosano G, Metra M, Crea F, Mullens W, et al. Incidence, risk assessment and prevention of sudden cardiac death in cardiomyopathies. Eur J Heart Fail. 2023;25(12):2144–63. doi: 10.1002/ejhf.3076 [DOI] [PubMed] [Google Scholar]

[pone.0341687.ref016] 16.Omidi F, Hajikhani B, Kazemi SN, Tajbakhsh A, Riazi S, Mirsaeidi M, et al. COVID-19 and cardiomyopathy: a systematic review. Front Cardiovasc Med. 2021;8:695206. doi: 10.3389/fcvm.2021.695206 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref017] 17.Zhang Y, Liu M, Li P, Ma L, Yin L, Cui Y, et al. The burden and management competency of cardiomyopathies in China: a nationwide survey study. The Lancet Regional Health - Western Pacific. 2024;46:101062. doi: 10.1016/j.lanwpc.2024.101062 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref018] 18.Tan K, Foo R, Loh M. Cardiomyopathy in Asian cohorts: genetic and epigenetic insights. Circ: Genomic and Precision Medicine. 2023;16(5):496–506. doi: 10.1161/circgen.123.004079 [DOI] [PubMed] [Google Scholar]

[pone.0341687.ref019] 19.Guevara Moctezuma EI, Smith Doria S, Ortiz JV, Teixeira de Sousa DR, Mwangi VI, do Nascimento Couceiro K, et al. Chronic Chagas Cardiomyopathy in the Brazilian Amazon region: clinical characteristics and regional distinctiveness. Front Public Health. 2023;11:1284639. doi: 10.3389/fpubh.2023.1284639 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref020] 20.Zhang Z, Wang Y, Zhou Y, Pei J, Zhao G, Dong P, et al. Association between diurnal temperature range and risk of cardiomyopathy-induced hospitalisation in Henan, China: a time-series study. Risk Manag Healthc Policy. 2025;18:279–90. doi: 10.2147/RMHP.S502132 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref021] 21.Bois JP, Adams JC, Kumar G, Ommen SR, Nishimura RA, Klarich KW. Relation between temperature extremes and symptom exacerbation in patients with hypertrophic cardiomyopathy. The American Journal of Cardiology. 2016;117(6):961–5. doi: 10.1016/j.amjcard.2015.12.046 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref022] 22.GBD 2021 Causes of Death Collaborators. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2100–32. doi: 10.1016/S0140-6736(24)00367-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref023] 23.GBD 2021 Risk Factors Collaborators. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the global burden of disease study 2021. Lancet. 2024;403(10440):2162–203. doi: 10.1016/S0140-6736(24)00933-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref024] 24.Institute of Health Metrics and Evaluation (IHME). GBD results. https://vizhub.healthdata.org/gbd-results/. 2021.

[pone.0341687.ref025] 25.Stevens GA, Alkema L, Black RE, Boerma JT, Collins GS, Ezzati M, et al. Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement. PLoS Med. 2016;13(6):e1002056. doi: 10.1371/journal.pmed.1002056 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref026] 26.Institute of Health Metrics and Evaluation (IHME). Global burden of disease study 2021 (GBD 2021) Socio-Demographic Index (SDI) 1950-2021. 2023. https://ghdx.healthdata.org/record/global-burden-disease-study-2021-gbd-2021-socio-demographic-index-sdi-1950-2021

[pone.0341687.ref027] 27.Clegg LX, Hankey BF, Tiwari R, Feuer EJ, Edwards BK. Estimating average annual per cent change in trend analysis. Stat Med. 2009;28(29):3670–82. doi: 10.1002/sim.3733 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref028] 28.Pearson-Stuttard J, Guzman-Castillo M, Penalvo JL, Rehm CD, Afshin A, Danaei G, et al. Modeling future cardiovascular disease mortality in the united states: national trends and racial and ethnic disparities. Circulation. 2016;133(10):967–78. doi: 10.1161/CIRCULATIONAHA.115.019904 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref029] 29.Kiyoshige E, Ogata S, O’Flaherty M, Capewell S, Takegami M, Iihara K, et al. Projections of future coronary heart disease and stroke mortality in Japan until 2040: a Bayesian age-period-cohort analysis. Lancet Reg Health West Pac. 2022;31:100637. doi: 10.1016/j.lanwpc.2022.100637 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref030] 30.Menacho KD, Ramirez S, Perez A, Dragonetti L, Perez de Arenaza D, Katekaru D, et al. Improving cardiovascular magnetic resonance access in low- and middle-income countries for cardiomyopathy assessment: rapid cardiovascular magnetic resonance. Eur Heart J. 2022;43(26):2496–507. doi: 10.1093/eurheartj/ehac035 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref031] 31.Vervoort D, Swain JD, Pezzella AT, Kpodonu J. Cardiac Surgery in Low- and Middle-Income Countries: A State-of-the-Art Review. Ann Thorac Surg. 2021;111(4):1394–400. doi: 10.1016/j.athoracsur.2020.05.181 [DOI] [PubMed] [Google Scholar]

[pone.0341687.ref032] 32.Fundikira LS, Chillo P, Mutagaywa R, Kamuhabwa A, Kwesigabo G, Asselbergs FW, et al. Risk factors and prevalence of dilated cardiomyopathy in sub-saharan africa: a systematic review. Glob Heart. 2022;17(1):76. doi: 10.5334/gh.1166 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref033] 33.Global status report on alcohol and health and treatment of substance use disorders. Geneva: World Health Organization; 2024. [Google Scholar]

[pone.0341687.ref034] 34.Jasilionis D, Leon DA, Pechholdová M. Impact of alcohol on mortality in Eastern Europe: Trends and policy responses. Drug Alcohol Rev. 2020;39(7):785–9. doi: 10.1111/dar.13167 [DOI] [PubMed] [Google Scholar]

[pone.0341687.ref035] 35.Burki T. Poor progress on reducing alcohol consumption in Europe. Lancet Oncol. 2024;25(9):1120. doi: 10.1016/S1470-2045(24)00435-2 [DOI] [PubMed] [Google Scholar]

[pone.0341687.ref036] 36.The Lancet Regional Health-Europe. Why is alcohol so normalised in Europe?. Lancet Reg Health Eur. 2024;46:101119. doi: 10.1016/j.lanepe.2024.101119 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref037] 37.Dalakoti M, Lin NHY, Yap J, Cader A, Dipanker P, Lee D, et al. Primary Prevention of Cardiovascular Disease in Asia: Challenges: A Narrative Review. JACC Adv. 2025;4(4):101670. doi: 10.1016/j.jacadv.2025.101670 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref038] 38.Dalakoti M, Lin NHY, Yap J, et al. Primary prevention of cardiovascular disease in asia: opportunities and solutions: a narrative review. JACC Adv. 2025;4(4):101676. doi: 10.1016/j.jacadv.2025.101676 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref039] 39.Lipshultz SE, Sleeper LA, Towbin JA, Lowe AM, Orav EJ, Cox GF, et al. The incidence of pediatric cardiomyopathy in two regions of the United States. N Engl J Med. 2003;348(17):1647–55. doi: 10.1056/nejmoa021715 [DOI] [PubMed] [Google Scholar]

[pone.0341687.ref040] 40.Xi J-Y, Zhao J-G, Li X-Q, Yan B, Bai J-J, Xiang Y-N, et al. Quantifying the loss of healthy life expectancy due to population ageing: health benefit estimation from a global perspective. BMJ Glob Health. 2025;10(5):e018194. doi: 10.1136/bmjgh-2024-018194 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref041] 41.Zhang J, Liu H, Bu X, Lu Q, Cheng L, Ma A, et al. The burden of alcoholic cardiomyopathy in China and different regions around the world. J Glob Health. 2022;12:04041. doi: 10.7189/jogh.12.04041 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref042] 42.Watanabe C, Nishina N, Kawai C. Stress-induced cardiomyopathy accompanied by heat stroke. J Cardiol Cases. 2015;12(1):16–9. doi: 10.1016/j.jccase.2015.03.010 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref043] 43.Al Houri HN, Jomaa S, Jabra M, Alhouri AN, Latifeh Y. Pathophysiology of stress cardiomyopathy: A comprehensive literature review. Ann Med Surg (Lond). 2022;82:104671. doi: 10.1016/j.amsu.2022.104671 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref044] 44.Isath A, Malik AH, Goel A, Gupta R, Shrivastav R, Bandyopadhyay D. Nationwide analysis of the outcomes and mortality of hospitalized COVID-19 patients. Curr Probl Cardiol. 2023;48(2):101440. doi: 10.1016/j.cpcardiol.2022.101440 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref045] 45.Kwenandar F, Japar KV, Damay V, Hariyanto TI, Tanaka M, Lugito NPH, et al. Coronavirus disease 2019 and cardiovascular system: A narrative review. Int J Cardiol Heart Vasc. 2020;29:100557. doi: 10.1016/j.ijcha.2020.100557 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0341687.ref046] 46.Dimnjaković J, Buble T, Ivanko P, Pristaš I, Brborović O, Brborović H. Association of anti-diabetic drugs and COVID-19 outcomes in patients with diabetes mellitus type 2 and cardiomyopathy. Sci Rep. 2024;14(1):7227. doi: 10.1038/s41598-024-57871-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Global, regional, and national burden of cardiomyopathy (including alcoholic cardiomyopathy and others) from 1990 to 2021: An analysis of data from the global burden of disease study 2021 and forecast to 2040

Haoyang Chen

Ruifeng Liang

Yanzhang Tian

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Methods

Data source and collection

Sociodemographic index

Statistical analysis

Ethics statement

Results

Global trends

Prevalence.

Table 1. 1990–2021 Global and regional prevalence trends in total cardiomyopathy burden.

Fig 1. Trends in prevalence, incidence, deaths, and disability-adjusted life-years from 1990 to 2021.

Mortality.

Table 2. 1990–2021 Global and regional mortality trends in total cardiomyopathy burden.

Disability-adjusted life years

Table 3. 1990–2021 Global and regional DALYs trends in total cardiomyopathy burden.

Socio-demographic index regions

Total cardiomyopathy.

Fig 2. The age-specific numbers and ASRs of total cardiomyopathy by SDI regions in 2021.

Fig 3. Socio-demographic index trends with ASRs of total cardiomyopathy in 21 GBD regions in 2021.

Alcoholic cardiomyopathy

Other cardiomyopathy

21 GBD regions

Total cardiomyopathy

Alcoholic cardiomyopathy

Other cardiomyopathy

Fig 4. Contribution of alcoholic cardiomyopathy and other cardiomyopathy to total cardiomyopathy prevalence, deaths, and DALYs, both sexes, globally and by region, in 1990 and 2021.

National trends

Total cardiomyopathy.

Fig 5. ASRs of total cardiomyopathy in 204 countries and territories in 2021.

Fig 6. Socio-demographic index trends with ASRs of total cardiomyopathy in 204 countries and territories in 2021.

Alcoholic cardiomyopathy

Other cardiomyopathy

Age and sex

Risk factors

Fig 7. Percentage of ASDRs and ASR_DALYs attributable to risk factors (low temperature and high temperature) in global and 21 GBD regions in 2021.

Forecasts for future disease trends of TC, AC, and OC

TC Predictions.

Fig 8. Global disease trends predictions of total cardiomyopathy from 2021 to 2040.

AC Predictions.

OC Predictions.

Sensitive analysis

Discussion

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Claudio Dávila-Cervantes

Roles

Author response to Decision Letter 1

Decision Letter 1

Claudio Dávila-Cervantes

Roles

Acceptance letter

Claudio Dávila-Cervantes

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases