Abstract
This study evaluates the global burden of gout in young adults across 204 countries, focusing on incidence, prevalence, disability-adjusted life years (DALYs), and risk factors. Data were extracted from the 2019 global burden of disease study. We analyzed gout burden using numbers, rates, and estimated annual percentage change to assess trends over time, stratified by sex, age, geographical region, and the sociodemographic development index (SDI). From 1990 to 2021, the global incidence, prevalence, and DALYs associated with gout exhibited a marked increase. The incidence rate rose from 1.94 million to 3.89 million cases, while the prevalence rate increased from 9.14 million to 19.24 million. Males consistently demonstrated higher incidence, prevalence, and DALYs compared to females, though similar upward trends were observed in both sexes. Age specific analyses showed that the gout burden increased with age, peaking in the 50 to 54 age group. Notably, there is a growing burden among younger individuals. High body mass index emerged as the leading risk factor for gout. This study highlights a growing global burden of gout, with an alarming shift towards younger age groups and a significant rise in females. High body mass index remains the predominant modifiable risk factor. These findings emphasize the need for targeted interventions to mitigate the increasing impact of gout, particularly among younger populations and females.
Keywords: global burden of disease, gout, high body mass index, risk factor
1. Introduction
Gout is a common chronic inflammatory arthritis and metabolic disorder characterized by hyperuricemia, joint pain, inflammatory arthritis, and complications affecting the kidneys and urinary system.[1–3] As the disease progresses, episodes of gout become longer and more frequent.[4] Furthermore, gout has been associated with an increased risk of cardiovascular events.[5,6] According to the global burden of disease (GBD) 2019 study, gout has seen significant global growth, with high body mass index (BMI) and renal dysfunction identified as major risk factors.[7] In recent years, rapid global economic development has led to improved living conditions and changes in dietary habits. Along with an aging population, these factors have contributed to the rising prevalence of gout and hyperuricemia, placing a substantial burden on society.[8–10] Additionally, the high cost of gout treatment imposes a significant financial strain on patients and their families.
Most previous studies have focused on the overall burden of gout across all age groups,[7] often overlooking the impact on young and middle-aged individuals. This age group represents a vital part of society, facing unique challenges related to education, stress, and lifestyle factors, making them more susceptible to gout.
In this study, we used the latest data from GBD 2021 and applied epidemiological models to assess the global burden of gout from 1990 to 2021. We comprehensively analyzed the trends in gout among young and middle-aged populations across 204 countries and regions, examining factors such as time, gender, age, geographical location, and sociodemographic development levels, along with key risk factors. Our goal is to better understand the impact of gout on the health of young populations and to provide insights for medical intervention, helping to alleviate the growing burden of gout in this demographic.
2. Data source
This study adheres to the GATHER checklist (https://www.who.int/publications/m/item/gather-checklist). The GBD study in 2024 evaluated the incidence, mortality, and disability-adjusted life years (DALYs) of 371 diseases across 204 countries and regions, covering 811 locations, from 1990 to 2021.[11] GBD 2021 used the M10 code from version 10 of the International Classification of Diseases and the corresponding ICD-9-CM diagnosis code (274: gout) to identify cases of gout in medical claims data.[12] Data on the incidence, prevalence, and DALYs related to gout from 2009 to 2021, along with their breakdown by age, gender, country, and region, were retrieved from the Global Health Data Exchange Query Tool (http://ghdx.healthdata.org/gbd-results-tool). Additionally, data on DALYs attributable to different risk factors were also obtained. The study conducted data analysis and disease modeling based on this collected information, focusing on gout cases with an age of onset between 10 and 54 years. These were further divided into 7 age groups: 10 to 24, 25 to 29, 30 to 34, 35 to 39, 40 to 44, 45 to 49, and 50 to 54. The methodologies applied in this study were based on previously published literature on disease burden estimation from GBD.[11,13] The study used publicly available data from the GBD database. The GBD database was approved by the University of Washington in Seattle, WA, which waived informed consent because only de-identified and aggregated data were utilized.
3. Risk estimation
In GBD 2021, risk factor exposure data were modeled using spatiotemporal Gaussian process regression or DisMod-MR 2.1.[14] Quantitative relative risk estimates were generated for each risk-outcome pair. These estimates were then matched with corresponding exposure estimates to calculate the population attributable fraction for each risk-outcome pair. The PAF was multiplied by the outcome rate to determine the attributable years lived with disability, years of life lost, and DALYs.[15] The specific calculation process can be found in previous studies.
This study examined 2 potential risk factors for gout: high BMI and impaired kidney function. For information on the definitions of these risk factors, the estimated overall PAF, and the attributable burden for each risk factor, please refer to previous research.[16]
4. Statistical analysis
We used the number of cases and the incidence rate to describe the incidence, mortality, and DALYs of gout. The incidence rate is reported per 1,00,000 population, with the uncertainty interval (UI) representing the 2.5th and 97.5th percentiles. To evaluate the time trends of gout, we calculated the estimated annual percentage change (EAPC) of the age-standardized incidence rate. The EAPC value and its 95% confidence interval (CI) were obtained through a linear regression model. A declining trend in incidence is indicated when both the EAPC and its 95% CI upper bound are below 0. Conversely, an increasing trend is observed when both the EAPC and its 95% CI lower bound exceed 0.[17] We used the number of cases and the incidence rate to describe the incidence, mortality, and DALYs of gout. The incidence rate is reported per 1,00,000 population, with the UI representing the 2.5th and 97.5th percentiles. To evaluate the time trends of gout, we calculated the EAPC of the age-standardized incidence rate. The EAPC value and its 95% CI were obtained through a linear regression model. A declining trend in incidence is indicated when both the EAPC and its 95% CI upper bound are below 0. Conversely, an increasing trend is observed when both the EAPC and its 95% CI lower bound exceed 0. In this study, the R software package (version 4.4.2; Vienna, Austria) was used for the drawing of the figures.
5. Results
5.1. Global trends in gout burden
In 2021, the global number of gout cases among individuals aged 10 to 54 reached 38,92,893 (95% UI: 27,78,566–52,15,925). This included 30,76,465 cases (95% UI: 21,98,123–40,99,562) in males and 8,16,429 cases (95% UI: 5,79,087–11,08,392) in females. Compared to 1990, the number of cases has doubled, having previously been 19,43,589 (95% UI: 14,01,286–25,85,217).
In 2021, the incidence rate, prevalence rate, and DALYs for gout were 76.93 (95% UI: 54.91–103.08), 380.31 (95% UI: 271.16–501.99), and 12.45 (95% UI: 7.65–18.98), respectively. Since 1990, the incidence rate has increased from 56.19 (95% UI: 40.51–74.75) to 76.932 (95% UI: 54.911–103.079), while the prevalence rate remained stable at 380.31 (95% UI: 271.16–501.99). DALYs, however, rose from 8.69 (95% UI: 5.41–13.07) to 12.452 (95% UI: 7.653–18.975), showing a similar upward trend.
From 1990 to 2021, the EAPC in age-standardized gout metrics was 1.175 (95% CI: 1.130–1.219) for incidence, 1.375 (95% CI: 1.314–1.436) for prevalence, and 1.364 (95% CI: 1.301–1.427) for DALYs.
Higher levels of the social demographic index (SDI) were associated with increased gout incidence, prevalence, and DALYs. In regions with high SDI, these values were 122.98 (95% UI: 89.16–163.20), 745.743 (95% UI: 545.527–987.567), and 24.167 (95% UI: 15.174–36.782), respectively. Additionally, as SDI decreased, gout incidence, prevalence, and DALYs also declined, with the lowest values observed in regions with low SDI.
Regionally, the burden of gout was highest in high-income North America, with incidence, prevalence, and DALYs of 173.04 (95% UI: 127.47–225.70), 1196.06 (95% UI: 898.73–1545.68), and 38.36 (95% UI: 24.09–57.26), respectively (Tables 1 and S1, Supplemental Digital Content, https://links.lww.com/MD/Q809).
Table 1.
Incidence rates, prevalence rates, DALY rates of gout by sex, SDI, and region in 1990 to 2021.
| Categories | Incidence | Prevalence | DALY | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Rates in 1990 (95% UI) |
Rates in 2021 (95% UI) |
1990–2021 EAPC (95% CI) |
Rates in 1990 (95% UI) |
Rates in 2021 (95% UI) |
1990–2021 EAPC (95% CI) |
Rates in 1990 (95% UI) |
Rates in 2021 (95% UI) |
1990–2021 EAPC (95% CI) |
|
| Global | 56.194 (40.514–74.745) | 76.932 (54.911–103.079) | 1.175 (1.130–1.219) | 264.226 (189.085–353.984) | 380.310 (271.159–501.998) | 1.375 (1.314–1.436) | 8.694 (5.408–13.074) | 12.452 (7.653–18.975) | 1.364 (1.301–1.427) |
| Sex | |||||||||
| Male | 88.023 (63.507–116.903) | 119.921 (85.683–159.801) | 1.164 (1.116–1.212) | 419.824 (302.127–560.586) | 605.642 (436.221–801.894) | 1.389 (1.318–1.461) | 13.796 (8.537–20.796) | 19.818 (12.237–30.176) | 1.381 (1.308–1.454) |
| Female | 23.405 (16.626–31.729) | 32.726 (23.212–44.429) | 1.241 (1.195–1.286) | 103.942 (73.515–142.226) | 148.591 (104.506–203.852) | 1.344 (1.294–1.395) | 3.438 (2.080–5.260) | 4.877 (2.921–7.625) | 1.323 (1.272–1.375) |
| SDI | |||||||||
| High SDI | 82.008 (59.150–109.916) | 122.980 (89.155–163.202) | 1.387 (1.312–1.463) | 442.801 (317.402–602.246) | 745.743 (545.527–987.567) | 1.839 (1.716–1.962) | 14.482 (9.008–21.951) | 24.167 (15.174–36.782) | 1.812 (1.689–1.936) |
| High-middle SDI | 66.129 (47.798–88.249) | 108.166 (75.456–145.800) | 1.827 (1.764–1.890) | 305.403 (218.458–404.936) | 528.605 (372.213–701.987) | 2.040 (1.966–2.113) | 10.070 (6.259–15.327) | 17.365 (10.440–26.938) | 2.033 (1.959–2.107) |
| Middle SDI | 54.589 (39.275–72.243) | 83.253 (59.198–111.575) | 1.591 (1.528–1.655) | 243.968 (174.911–327.486) | 391.549 (277.909–521.328) | 1.799 (1.728–1.870) | 8.068 (4.970–12.212) | 12.870 (7.892–19.605) | 1.782 (1.711–1.853) |
| Low SDI | 34.149 (24.506–45.947) | 36.913 (26.497–49.491) | 0.295 (0.238–0.351) | 146.290 (104.497–199.364) | 157.921 (112.890–214.493) | 0.295 (0.231–0.360) | 4.783 (2.942–7.337) | 5.190 (3.238–7.992) | 0.316 (0.252–0.381) |
| Low-middle SDI | 37.969 (27.261–50.700) | 49.207 (35.227–65.785) | 0.899 (0.845–0.953) | 165.249 (117.681–223.934) | 217.715 (154.382–293.881) | 0.965 (0.907–1.024) | 5.433 (3.391–8.301) | 7.144 (4.410–11.053) | 0.970 (0.912–1.028) |
| Region | |||||||||
| Andean Latin America | 24.807 (17.983–33.139) | 41.276 (29.324–55.366) | 1.750 (1.717–1.783) | 111.903 (78.835–154.189) | 193.206 (137.552–261.276) | 1.884 (1.845–1.922) | 3.739 (2.283–5.793) | 6.421 (3.821–10.114) | 1.846 (1.806–1.886) |
| Australasia | 95.635 (70.025–128.313) | 137.544 (98.892–187.399) | 1.051 (0.948–1.154) | 504.270 (364.089–666.546) | 777.649 (547.536–1065.275) | 1.321 (1.187–1.456) | 16.409 (10.109–24.682) | 25.236 (14.847–39.823) | 1.315 (1.182–1.447) |
| Caribbean | 24.695 (17.545–33.038) | 37.628 (26.732–50.759) | 1.401 (1.356–1.447) | 112.758 (79.074–154.516) | 178.509 (126.728–242.512) | 1.544 (1.486–1.602) | 3.757 (2.233–5.856) | 5.920 (3.598–9.173) | 1.523 (1.462–1.583) |
| Central Asia | 39.101 (28.357–51.360) | 56.731 (40.580–76.284) | 1.511 (1.388–1.634) | 172.029 (123.214–232.260) | 257.134 (182.072–347.435) | 1.651 (1.518–1.786) | 5.670 (3.449–8.589) | 8.477 (5.292–13.125) | 1.636 (1.505–1.768) |
| Central Europe | 41.631 (29.635–56.464) | 60.213 (42.020–81.893) | 1.196 (1.169–1.222) | 185.210 (131.745–250.698) | 274.215 (192.696–374.607) | 1.259 (1.226–1.292) | 6.081 (3.724–9.431) | 8.987 (5.324–13.930) | 1.265 (1.235–1.295) |
| Central Latin America | 19.156 (13.943–25.760) | 30.947 (21.954–41.668) | 1.587 (1.576–1.598) | 87.455 (62.088–120.051) | 147.022 (104.188–202.337) | 1.710 (1.695–1.724) | 2.917 (1.750–4.461) | 4.874 (3.009–7.619) | 1.681 (1.666–1.697) |
| Central Sub-Saharan Africa | 33.418 (23.855–45.057) | 37.495 (26.818–49.989) | 0.419 (0.388–0.450) | 142.440 (100.930–196.077) | 160.799 (114.536–215.182) | 0.454 (0.418–0.490) | 4.631 (2.797–7.194) | 5.256 (3.195–8.363) | 0.486 (0.449–0.522) |
| East Asia | 79.012 (57.217–105.086) | 140.550 (99.020–189.144) | 2.154 (2.074–2.234) | 359.090 (257.060–477.221) | 685.011 (482.528–907.933) | 2.421 (2.334–2.509) | 11.901 (7.402–18.057) | 22.558 (13.676–34.751) | 2.403 (2.316–2.490) |
| Eastern Europe | 53.646 (38.509–71.247) | 68.824 (48.125–92.795) | 1.037 (0.934–1.140) | 238.366 (169.625–322.793) | 309.424 (219.538–420.764) | 1.087 (0.971–1.204) | 7.757 (4.705–12.019) | 10.072 (6.091–15.595) | 1.103 (0.989–1.217) |
| Eastern Sub-Saharan Africa | 32.041 (23.112–42.916) | 36.252 (25.962–48.707) | 0.443 (0.369–0.518) | 135.814 (97.268–185.404) | 154.188 (110.577–211.329) | 0.452 (0.368–0.537) | 4.454 (2.744–6.961) | 5.076 (3.127–7.932) | 0.469 (0.383–0.555) |
| High-income Asia Pacific | 80.475 (57.229–109.396) | 110.949 (77.717–150.010) | 0.979 (0.941–1.018) | 411.220 (287.549–561.589) | 599.832 (415.540–818.977) | 1.168 (1.131–1.205) | 13.548 (8.171–20.858) | 19.738 (12.008–30.639) | 1.168 (1.131–1.206) |
| High-income North America | 107.145 (77.988–143.020) | 173.041 (127.471–225.695) | 1.749 (1.598–1.901) | 625.311 (454.388–845.632) | 1196.057 (898.734–1545.683) | 2.406 (2.210–2.602) | 20.326 (12.627–30.874) | 38.363 (24.092–57.264) | 2.368 (2.170–2.567) |
| North Africa and Middle East | 38.538 (27.713–51.252) | 62.396 (44.160–84.036) | 1.683 (1.605–1.762) | 167.588 (120.385–226.258) | 281.609 (200.219–380.925) | 1.801 (1.713–1.888) | 5.532 (3.415–8.727) | 9.207 (5.615–14.080) | 1.781 (1.693–1.869) |
| Oceania | 62.419 (45.661–83.014) | 76.227 (54.655–101.909) | 0.642 (0.620–0.664) | 277.849 (203.749–367.669) | 346.896 (249.328–466.610) | 0.714 (0.695–0.733) | 9.109 (5.581–13.954) | 11.362 (6.763–17.167) | 0.712 (0.693–0.731) |
| South Asia | 37.701 (26.970–50.886) | 47.539 (34.030–63.617) | 0.788 (0.725–0.852) | 162.397 (115.120–220.943) | 207.057 (147.347–280.899) | 0.835 (0.767–0.902) | 5.317 (3.266–8.245) | 6.775 (4.164–10.561) | 0.839 (0.773–0.905) |
| Southeast Asia | 55.660 (40.040–74.008) | 90.626 (64.873–120.856) | 1.733 (1.684–1.782) | 245.587 (175.422–330.865) | 419.602 (299.329–561.381) | 1.919 (1.862–1.976) | 8.123 (5.018–12.392) | 13.829 (8.406–20.782) | 1.914 (1.857–1.972) |
| Southern Latin America | 72.579 (52.398–96.981) | 100.721 (73.180–134.965) | 1.001 (0.967–1.035) | 388.932 (274.673–529.178) | 566.840 (406.356–763.551) | 1.176 (1.132–1.220) | 12.713 (7.822–18.994) | 18.414 (11.219–27.542) | 1.168 (1.123–1.212) |
| Southern Sub-Saharan Africa | 43.202 (31.325–57.263) | 60.738 (43.617–82.125) | 1.125 (1.063–1.186) | 185.200 (130.984–250.355) | 265.950 (190.367–353.331) | 1.178 (1.115–1.241) | 6.031 (3.730–9.222) | 8.547 (5.216–13.075) | 1.132 (1.065–1.200) |
| Tropical Latin America | 24.098 (17.221–32.532) | 41.016 (29.001–56.153) | 1.816 (1.778–1.853) | 109.173 (76.938–150.262) | 193.391 (137.030–266.757) | 1.947 (1.908–1.987) | 3.621 (2.199–5.555) | 6.359 (3.853–9.639) | 1.931 (1.889–1.973) |
| Western Europe | 62.273 (44.191–83.884) | 77.990 (55.712–104.910) | 0.790 (0.710–0.870) | 331.161 (228.026–454.307) | 442.483 (305.419–606.078) | 1.030 (0.939–1.121) | 10.841 (6.450–16.687) | 14.467 (8.614–22.379) | 1.026 (0.935–1.118) |
| Western Sub-Saharan Africa | 36.352 (26.100–48.593) | 37.359 (26.901–50.151) | 0.105 (0.087–0.122) | 156.772 (111.640–212.181) | 161.191 (115.257–218.283) | 0.111 (0.091–0.132) | 5.147 (3.139–7.911) | 5.324 (3.278–8.187) | 0.137 (0.117–0.158) |
EAPC is expressed as 95% CIs.
DALY = disability-adjusted life year, EAPC = estimated annual percentage change, SDI = sociodemographic index, UI = uncertainty interval.
5.2. Temporal trends from 1990 to 2021 and regional disparities
From 1990 to 2021, the global incidence, prevalence, and DALYs of gout have shown a consistent upward trend (Fig. 1). The fastest growth was observed in high-middle social demographic index (SDI) regions, where the EAPCs for incidence, prevalence, and DALY were 1.827 (95% CI: 1.764–1.890), 2.040 (95% CI: 1.966–2.113), and 2.033 (95% CI: 1.959–2.107), respectively.
Figure 1.
Temporal trend of incidence, prevalence, and disability-adjusted life years (DALYs) rates for the burden of gout in young and middle-aged people by globally and SDI from 1990 to 2021. (A) Incidence rate. (B) Prevalence rate. (C) DALYs rate. DALY = disability-adjusted life year, SDI = sociodemographic index.
Regarding gender differences, males had significantly higher gout incidence, prevalence, and DALYs compared to females. However, the gender gap in growth trends was minimal. The EAPC for males was 1.164 (95% CI: 1.116–1.212) for incidence, 1.389 (95% CI: 1.318–1.461) for prevalence, and 1.381 (95% CI: 1.308–1.454) for DALY. For females, the EAPC was 1.241 (95% CI: 1.195–1.286) for incidence, 1.344 (95% CI: 1.294–1.395) for prevalence, and 1.323 (95% CI: 1.272–1.375) for DALY.
In terms of SDI regions, the High-middle SDI regions exhibited the most rapid growth rates for both males and females. In males, the EAPC was 1.792 (95% CI: 1.724–1.861) for incidence, 2.013 (95% CI: 1.927–2.098) for prevalence, and 2.010 (95% CI: 1.924–2.096) for DALY. In females, these values were slightly higher, with an EAPC of 1.863 (95% CI: 1.803–1.924) for incidence, 2.042 (95% CI: 1.983–2.100) for prevalence, and 2.018 (95% CI: 1.956–2.079) for DALY (Table 2).
Table 2.
Temporal trend of incidence, prevalence, and DALYs rates of gout for 21 regions by sex, 1990 to 2021.
| Categories | Incidence | Prevalence | DALY | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Rates in 1990 (95% UI) | Rates in 2021 (95% UI) | 1990–2021 EAPC (95% CI) |
Rates in 1990(95% UI) | Rates in 2021 (95% UI) |
1990–2021 EAPC (95% CI) |
Rates in 1990 (95% UI) |
Rates in 2021 (95% UI) |
1990–2021 EAPC (95% CI) |
|
| High SDI | |||||||||
| Female | 28.901 (20.634–38.884) | 37.112 (26.763–49.829) | 1.038 (0.952–1.124) | 136.168 (95.735–184.326) | 167.454 (118.220–226.159) | 1.016 (0.905–1.126) | 4.493 (2.739–6.822) | 5.482 (3.275–8.417) | 0.981 (0.873–1.090) |
| Male | 133.508 (96.156–178.681) | 204.072 (148.075–270.842) | 1.412 (1.329–1.496) | 740.157 (530.275–999.250) | 1291.865 (948.249–1699.679) | 1.913 (1.774–2.051) | 24.168 (14.983–36.529) | 41.812 (26.258–63.433) | 1.888 (1.749–2.028) |
| High-middle SDI | |||||||||
| Female | 26.342 (18.715–35.608) | 44.282 (30.968–60.960) | 1.863 (1.803–1.924) | 116.472 (82.263–159.829) | 206.444 (143.220–280.783) | 2.042 (1.983–2.100) | 3.870 (2.297–6.077) | 6.783 (3.929–10.764) | 2.018 (1.956–2.079) |
| Male | 104.737 (75.548–139.282) | 168.513 (117.700–226.535) | 1.792 (1.724–1.861) | 488.735 (350.857–644.523) | 832.928 (590.646–1103.130) | 2.013 (1.927–2.098) | 16.087 (9.960–24.561) | 27.362 (16.609–42.252) | 2.010 (1.924–2.096) |
| Middle SDI | |||||||||
| Female | 23.730 (16.858–32.130) | 38.154 (26.967–51.752) | 1.692 (1.630–1.754) | 104.261 (73.756–141.060) | 176.488 (124.405–241.293) | 1.868 (1.806–1.930) | 3.458 (2.076–5.192) | 5.794 (3.461–9.100) | 1.841 (1.778–1.903) |
| Male | 84.252 (61.079–111.622) | 126.976 (90.332–169.579) | 1.585 (1.515–1.654) | 378.263 (271.904–502.637) | 600.044 (426.706–795.721) | 1.802 (1.721–1.883) | 12.499 (7.689–18.729) | 19.730 (12.240–30.120) | 1.788 (1.707–1.869) |
| Low SDI | |||||||||
| Female | 17.020 (11.935–23.055) | 18.802 (13.317–25.630) | 0.352 (0.279–0.425) | 72.225 (50.507–100.748) | 79.863 (56.728–109.988) | 0.358 (0.277–0.439) | 2.371 (1.407–3.654) | 2.622 (1.576–4.147) | 0.370 (0.290–0.450) |
| Male | 51.359 (36.859–69.215) | 55.226 (39.646–73.760) | 0.284 (0.230–0.339) | 220.709 (157.958–298.571) | 236.850 (169.468–319.990) | 0.283 (0.220–0.346) | 7.206 (4.453–11.121) | 7.786 (4.880–11.912) | 0.307 (0.244–0.371) |
| Low-middle SDI | |||||||||
| Female | 18.423 (12.948–25.079) | 24.728 (17.335–33.591) | 1.025 (0.961–1.089) | 79.369 (55.961–110.366) | 108.585 (76.218–149.875) | 1.099 (1.028–1.171) | 2.610 (1.552–4.003) | 3.566 (2.108–5.569) | 1.094 (1.024–1.164) |
| Male | 56.995 (40.926–76.133) | 73.362 (52.412–98.083) | 0.876 (0.824–0.929) | 248.839 (178.443–335.386) | 325.400 (233.064–437.846) | 0.941 (0.885–0.997) | 8.179 (5.099–12.487) | 10.674 (6.648–16.408) | 0.948 (0.893–1.004) |
EAPC is expressed as 95% CIs.
CI = confidence interval, DALY = disability-adjusted life year, EAPC = estimated annual percentage change, SDI = sociodemographic index, UI = uncertainty interval.
At the regional level, in 2021, high-income North America bore the highest burden of gout. However, East Asia showed the fastest growth, with EAPCs of 2.154 (95% CI: 2.074–2.234) for incidence, 2.421 (95% CI: 2.334–2.490) for prevalence, and 2.316 (95% CI: 2.234–2.398) for DALY. Western Sub-Saharan Africa, on the other hand, had the lowest gout burden (Fig. 2).
Figure 2.
Temporal trend of incidence, prevalence, and disability-adjusted life years (DALYs) Rates of gout globally and for 21 regions by SDI, 2009–2021. DALY = disability-adjusted life year, SDI = sociodemographic index.
5.3. Regional disparities, gender differences, and age disparities
The 2021 data highlight significant gender and regional disparities in the incidence, prevalence, and DALYs associated with gout. Overall, males have a much higher burden of gout compared to females, but the distribution varies across different regions.
In East Asia, females are disproportionately affected by gout, carrying the heaviest burden with an incidence of 58.91 (95% CI: 40.99–80.45), a prevalence of 280.37 (95% CI: 193.79–377.04), and a DALY of 9.21 (95% CI: 5.38–14.51). The second-highest burden on females is observed in the high-income North America region. Conversely, the burden on females is lowest in Eastern Sub-Saharan Africa.
For males, the highest burden is seen in high-income North America, with an incidence of 216.27 (95% CI: 152.89–290.93), a prevalence of 1060.32 (95% CI: 753.15–1408.02), and similarly high DALY values. The second-highest burden for males is found in the Australia region, while the lowest is in Central Latin America (Fig. 3).
Figure 3.
Incidence, prevalence, and disability-adjusted life years (DALYs) rates for gout by region and sex, 2021. DALY = disability-adjusted life year.
Age also plays a crucial role in gout distribution. The incidence of gout increases with age for both genders, with the highest incidence observed in the 50 to 54 age group (Fig. 4). This trend suggests that gout is more prevalent in middle-aged and older populations.
Figure 4.
Difference in age specific of incidence, prevalence, and disability-adjusted life years (DALYs) between men and women. (A) Incidence rate. (B) Prevalence rate. (C) DALYs rate. DALY = disability-adjusted life year.
In summary, while males generally experience a heavier burden of gout, East Asia is an exception where females bear the highest burden. Additionally, gout becomes more common with advancing age across both genders.
5.4. Different countries and regions
Between 1990 and 2021, global trends in gout incidence, prevalence, and DALY have shown substantial variation, with some countries experiencing significant increases while others have seen declines.
The Maldives has recorded the largest increase in the burden of gout globally. The annual percentage change (EAPC) for the incidence rate in the Maldives is 3.01 (95% CI: 2.63–3.39), for the prevalence rate is 3.12 (95% CI: 2.73–3.52), and for DALYs is 3.14 (95% CI: 2.75–3.54). These increases are the highest among all countries worldwide, indicating a rapidly growing burden of gout in the Maldives over this period.
On the other hand, only 6 countries have demonstrated a decrease in the gout burden. Among these, Sweden stands out as the country with the most notable decline. Sweden’s gout incidence rate has decreased by -0.67 (95% CI: −0.97 to −0.38), its prevalence rate has fallen by −0.82 (95% CI: −1.21 to −0.42), and its DALYs has dropped by −0.81 (95% CI: −1.20 to −0.43). These trends reflect successful management or preventive measures in Sweden compared to other nations.
Globally, 105 countries have a gout incidence rate higher than the global average, while 93 countries show a prevalence rate above the global average. Similarly, the DALYs rate is higher than the global average in many regions, highlighting the widespread and growing burden of gout in many parts of the world (Table S2, Figs. 5 and S1, Supplemental Digital Content, https://links.lww.com/MD/Q809).
Figure 5.
The incidence, prevalence, and disability-adjusted life years (DALYs) rates and EAPC for gout in 204 countries and territories in 2021 EAPC, estimated annual percentage changes. (A) Incidence rate. (B) Prevalence rate. (C) DALYs rate. EAPC = estimated annual percentage change, DALY = disability-adjusted life year.
This data reflects a complex global pattern, where some regions are experiencing a worsening gout burden, while a few countries, like Sweden, are achieving improvements in managing the disease.
6. Risk factors
According to the GBD database, the global burden of gout measured in DALYs can be attributed to 1 primary risk factor: metabolic risk factors (36.57%), and 2 secondary risk factors: high BMI (34.91%) and renal impairment (2.58%). At the SDI level, the burden of gout in DALYs, similar to the global level, is primarily associated with high BMI, and is positively correlated with SDI. Over the time period from 1990 to 2021, for the primary risk factor of metabolic risk, the proportion has increased from 26.77% to 36.57%. For the secondary risk factor, the proportion accounted for by high BMI increased from 24.63% to 34.91%, while renal impairment saw a slight decrease from 2.84% to 2.58%. These trends are consistent across different SDI regions (Fig. 6). At the age group level, the risk factors for gout DALYs also increase with age, with the 50 to 54 age group having the highest proportion (Fig. S2, Supplemental Digital Content, https://links.lww.com/MD/Q809). At the regional level, the region with the highest risk of high BMI is high-income North America (49.75%), while the region with the highest risk of renal impairment is Central Asia (5.73%; Fig. S3, Supplemental Digital Content, https://links.lww.com/MD/Q809).
Figure 6.
Temporal trend of gout disability-adjusted life years (DALYs) attributable to risk factors in globally and SDI in 2021. DALY = disability-adjusted life year, SDI = sociodemographic index.
7. Discussion
This study highlights the rising global burden of gout over the past 3 decades, with a particular focus on the population aged 10 to 54 years. The findings indicate significant increases in gout incidence, prevalence, and DALYs at both global and regional levels from 1990 to 2021, stressing the growing public health impact of the condition. This underscores the need for better prevention, screening, diagnosis, and treatment strategies for gout.
The occurrence of gout is significantly associated with age and gender, generally increasing with age and being more prevalent in males than in females, which aligns with other GBD studies.[18,19] However, this study reveals a rapid increase in gout incidence, prevalence, and DALYs among females aged 10 to 54 years, with the incidence in females now surpassing that in males. Previous research has largely focused on males, leaving females underrepresented. Some studies suggest that females tend to develop gout later in life and experience higher rates of upper limb joint involvement compared to males,[20] resulting in more severe consequences for females in other aspects.[21,22] Greater attention to gout management in females is therefore crucial for future prevention and treatment efforts.
In addition, the prevalence of hyperuricemia in children has gradually increased, becoming a global public health issue.[23] The main cause is obesity, as childhood obesity has shown a significant upward trend.[24] Obesity increases fat tissue and insulin resistance, which promotes uric acid production and reduces its excretion, significantly increasing the risk of hyperuricemia. Additionally, the widespread consumption of sugary beverages, especially those containing fructose, promotes uric acid synthesis and further exacerbates hyperuricemia.[25] Moreover, children’s dietary habits, particularly influenced by family, may lead to excessive intake of high-purine foods, raising uric acid levels. Studies show that the prevalence of hyperuricemia in children and adolescents has increased from 0.6% to 50.4%,[26] especially in the context of obesity and excessive fructose consumption. To reduce this trend, it is essential to control children’s weight, limit fructose intake, raise public health awareness, and strengthen early screening measures to effectively prevent hyperuricemia, thus reducing the future risk of gout.
The increasing prevalence of gout in middle-aged individuals is not only due to physiological factors but is also closely related to unhealthy eating habits, lifestyle, and obesity. In modern society, middle-aged individuals tend to consume foods high in purines, fats, and sugars, and the widespread fast-food culture and food processing have exacerbated hyperuricemia, leading to gout. At the same time, due to high work pressure, late-night work, lack of exercise, and excessive alcohol consumption, metabolic disorders are often triggered, further increasing the risk of gout.
At the SDI level, the occurrence of gout is mainly concentrated in developed regions and countries, with a positive correlation between gout prevalence and SDI,[19] particularly in high-income North America and East Asia. In the United States, the prevalence of gout among adults was 3.9% (9.2 million people) in 2015 to 2016,[27] driven by factors such as greater access to seafood, shellfish, and high-energy diets, which increase gout risk.[28,29] Similarly, in China, improved living standards and changes in dietary patterns have contributed to a continuous rise in the burden of gout.[30] As the burden of gout continues to escalate, it is essential to raise awareness and implement effective strategies to reduce the impact and harm caused by this condition.
At the national-level, the burden of gout is most severe in the Maldives, likely due to the island nation’s geographical environment and dietary habits. However, in recent years, there has been a lack of gout-specific research and national-level data in the Maldives, limiting specific insights into the condition. This gap indicates that the Maldives must take the gout-related disease burden seriously. In contrast, Sweden has experienced a decline in the burden of gout, largely thanks to various interventions and comprehensive guidance for gout prevention, which has improved patient compliance.[31] Some studies have shown that patient adherence to urate-lowering therapy tends to decrease over time,[32] underscoring the need for enhanced national-level interventions. These should include regular monitoring of uric acid levels to facilitate early prevention and treatment, as well as efforts to maintain a balanced diet and engage in moderate exercise to help alleviate the burden of gout. Comprehensive management strategies can more effectively control the onset and progression of gout, ultimately improving patients’ quality of life. This indicates that national-level interventions can significantly reduce the disease burden of gout.
This study identifies metabolic risk as the primary factor contributing to gout, with high BMI and renal impairment as secondary factors, and highlights high BMI as the most significant contributor. High-income regions such as North America, North Africa, and the Middle East have particularly high gout prevalence rates. Increasingly, research points to high BMI as a significant risk factor for gout, and weight loss is seen as an important means of reducing its occurrence.[9,33] There is a strong correlation between high BMI and SDI levels, with changes in dietary patterns leading to an increase in populations with high BMI.[19,34] Therefore, high-SDI regions must strengthen their awareness of gout risks and implement early interventions in individuals with high BMI. Promoting proper diets and appropriate physical activity is essential to reducing the disease burden of gout in these areas.
The purpose of this study is to examine the global burden, trends, and risk factors of gout over the past few decades. However, there are some limitations to the study. Firstly, the gout data is derived from the GBD 2021 report, which uses data from multiple sources. This introduces potential discrepancies between countries, particularly in low- and middle-income regions, where long-term and complex interactions of risk factors, such as kidney function, can significantly influence gout outcomes. These challenges may lead to biased evaluations of the disease’s full impact.
In conclusion, gout is a common disease with a significant impact on global health. Between 1990 and 2021, the global rates of DALYs, incidence, and prevalence of gout have risen considerably, with standardized metrics indicating an upward trend. The research shows notable differences in gout occurrence based on gender and age, with a particularly sharp increase in incidence among younger women. This trend suggests that health management agencies should pay closer attention to key populations, such as women and young people, when planning interventions.
To effectively address the rising prevalence of gout, health management agencies must have access to reliable epidemiological data to support effective monitoring and interventions. This study provides essential epidemiological insights as a reference for public health authorities, with the goal of assisting in the development of more effective strategies to mitigate the health impacts of gout on global populations.
Author contributions
Conceptualization: Ke Shi.
Data curation: Ke Shi.
Formal analysis: Ke Shi.
Methodology: Tongdeng You.
Project administration: Tongdeng You.
Writing – original draft: Ke Shi, Yangyi Guo, Tongdeng You.
Writing – review & editing: Ke Shi, Tongdeng You.
Supplementary Material
Abbreviations:
- BMI
- body mass index
- CI
- confidence interval
- DALY
- disability-adjusted life year
- EAPC
- estimated annual percentage change
- GBD
- global burden of disease
- PAF
- population attributable fraction
- SDI
- sociodemographic development index
- UI
- uncertainty interval.
The study used publicly available data from the Global Burden of Disease (GBD) database. The GBD database was approved by the University of Washington in Seattle, WA, which waived informed consent because only de-identified and aggregated data were utilized.
The authors have no funding and conflicts of interest to disclose.
All data generated or analyzed during this study are included in this published article [and its supplementary information files].
Supplemental Digital Content is available for this article.
How to cite this article: Shi K, Guo Y, You T. Global burden of gout in age groups 10 to 54 years from 1990 to 2021: Trend of the global burden of disease study. Medicine 2025;104:48(e46231).
Contributor Information
Ke Shi, Email: 1366054286@qq.com.
Yangyi Guo, Email: 352572374@qq.com.
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