Abstract
Background
The epidemiology and burden of adult acne have not been comprehensively evaluated. This study aims to analyze the global disease burden of adult acne from 1990 to 2021 and explore potential risk factors.
Methods
With Global Burden of Disease (GBD), we got all the incidence, prevalence, disability-adjusted life years (DALYs), and corresponding age-standardised rate (ASR) of acne vulgaris (25 + years). The data were stratified by three dimensions of distribution and the Socio-Demographic Index (SDI). We calculated the estimated annual percentage change (EAPC) to evaluate trends, and assessed health inequality, then employed Bayesian age-period-cohort (BAPC) analysis for forecasting. Mendelian Randomization (MR) analysis was utilized for identifying potential risk factors, the Global Dietary Database (GDD) for verification.
Results
From 1990 to 2021, global incidence of adult acne cases increased by 66.6%, reaching 20,290,350 (95% UI: 19,253,788–21,344,142); prevalence rose by 66.4%; DALYs’ by 66.2%. Projections suggest continued increases through 2030. Although the global ASRs began to decline in the twenty-first century, the lower SDI regions showed a contrasting upward trend. In all regions, the greatest increases in the EAPC of ASRs were observed in Western Sub-Saharan Afric, whereas the largest declines occurred in the High-Income Asia Pacific region,1990–2021. A negative correlation was found between the ASR of adult acne and countries’ SDIs (p < 0.01). Notably, females experienced a higher burden of adult acne than males in various dimensions. MR results suggested that intake of sugar added to coffee is associated with an increased incidence of acne, consistent with the findings of the relationship between sugar-sweetened beverage (SSB) intakes and the ASR of adult acne across countries.
Conclusion
The disease burden of adult acne demonstrates significant heterogeneity across regions and genders, with obviously greater severity in underdeveloped areas and among females. Meanwhile, it should pay attention to the parallel issue of nutritional inequality.
Keywords: Adult acne, Post-adolescent acne, Global burden of disease (GBD), Global dietary database (GDD), Sugar-sweetened beverage (SSB) intakes, Mendelian randomization (MR)
Introduction
Adult acne has traditionally been defined as acne vulgaris over age 25, and also known as post-adolescent acne [1, 2]. It exhibits a higher prevalence of inflammatory lesions and fewer comedones compared to adolescent acne. Unlike adolescent acne, which typically manifests on the forehead, nose, and upper cheeks, adult acne predominantly occurs on the chin, jawline, and neck [3]. Moreover, its etiology is more complex. While adolescent acne is primarily driven by hormonal changes, adult acne is influenced by a multitude of factors, including stress, diet, hormonal imbalances, and the use of certain medications [4, 5].
In recent years, there has been a gradual increase in the number of adults affected by new-onset or persistent Acne Vulgaris [6]. Noticeably, the economic burden of adult acne is substantial, encompassing direct costs associated with medical treatments—ranging from prescription medications to, in some cases, laser intervention procedures—as well as indirect costs, such as reduced work productivity due to the disease [7]. Psychologically, adult acne can have profound effects on an individual’s mental health and well-being [8]. It is associated with an increased risk of depression, anxiety, and lowered self-esteem, particularly among those experiencing moderate to severe acne [9]. However, despite growing awareness of adult acne, there are no long-term global trends reported on its epidemiology.
Therefore, we aim to analyze the trends in incidence, prevalence, and disability-adjusted life years (DALYs) of adult acne from 1990 to 2021 using the GBD database. Additionally, we will assess dietary risk factors associated with adult acne in conjunction with the Global Dietary Database (GDD). We hope that the interpretation of these epidemiological estimates will aid healthcare professionals in developing new preventive and therapeutic approaches, thereby reducing the global burden of adult acne.
Methods
Data sources
The GBD 2021 database assessed the global burden of 371 diseases, injuries, and conditions across 21 regions and 204 countries from 1990 to 2021, along with 88 risk factors [10]. We obtained annual incidence numbers, prevalence cases, numbers of DALYs, and their corresponding 95% uncertainty intervals (UIs) for Acne Vulgaris, stratified by sex, age (25 +), region, and country from 1990 to 2021 [11]. Based on previous studies, GBD 2021 also calculated the Socio-Demographic Index (SDI) for each region and country, which is a composite measure of the socio-economic status influencing health outcomes in those areas. The SDI is categorized into five quintiles: low, low-middle, middle, high-middle, and high. To explore the correlation between dietary factors and adult acne across different countries, we obtained data on adult sugar-sweetened beverage (SSB) intakes for 2018 from the GDD (https://globaldietarydatabase.org/data-download) and previous literature [12]. This study is based on publicly available data and does not require ethical approval. This study is based on publicly available data and does not require ethical approval.
Mendelian randomization (MR) analysis
The summary data for acne (finn-b-L12_ACNE) were obtained from the FinnGen biobank, while the intake of sugar added to coffee (ukb-e-100370_AFR) was sourced from the UKBiobank. Single nucleotide polymorphisms (SNPs) with a genome-wide significant p-value (P < 5 × 10–6) and a minor allele frequency (MAF) greater than 0.01 were considered potential instrumental variables (IVs) [13]. SNPs were filtered based on linkage disequilibrium (LD) criteria (r2 < 0.001, kb = 10,000). The strength of the IVs was tested using the F-statistic (F > 10) to ensure a strong association between the IVs and the exposure factor. We also removed palindromic SNPs and harmonized the exposure outcome dataset to avoid allele coding distortion.
Two-sample MR analysis was employed to estimate the potential bidirectional causal relationship between acne and the intake of sugar in coffee. The random effects inverse variance weighted (IVW) method was used as the primary assessment approach, as it provides robust causal estimates in the absence of directional pleiotropy. Additionally, we utilized the weighted mode and MR-Egger methods for supplementary analyses and examined horizontal pleiotropy and heterogeneity to ensure the robustness of the results.
Statistical analyses
The computation of the age-standardized rate (ASR) is derived from the following formula:
Here, αi denotes the age-specific rate within the ith age bracket, while wicorresponds to the population count (or weighting) for that same age bracket within the GBD’s standard demographic. N signifies the total count of age brackets considered [14].
For the derivation of the Estimated Annual Percentage Change (EAPC), linear regression analysis is applied. The broom package is instrumental in refining and organizing the regression outcomes, a step that is essential for maintaining the precision of our analytical procedures. To ensure the seamless execution of these analyses, the broom and dplyr packages have been duly installed and integrated into the analytical framework.
To calculate the Average Annual Percent Change (AAPC), we first fitted segmented regression models to the annual incidence rates of the outcome variable. These models allow for the estimation of separate linear trends within defined segments, with breakpoints determined through an iterative procedure based on maximum likelihood estimation. The breakpoints indicating changes in trends were empirically identified using the "segmented" package in R. Additionally, global trends were stratified by age group, sex, and SDI, reporting both regional and national trends. Trend forecasts for the next 10 years were based on Bayesian age-period-cohort (BAPC) modeling. Smooth curve fitting methods were employed to analyze the relationship between age-standardized incidence rate (ASIR) of adult acne and adult SSB intakes in various countries. All analyses were conducted using the statistical software packages R (version 4.3) and EmpowerStats (version 4.2). A p-value of < 0.05 was considered statistically significant.
Results
Burden of adult acne by time
From 1990 to 2021, the overall number of individuals affected by adult acne globally exhibited a continuous upward trend (Fig. 1A, B, C), indicating an increased disease burden. The incidence cases rose from 12,178,028 (95% UI: 11,513,474–12,835,449) in 1990 to 20,290,350 (95% UI: 19,253,788–21,344,142) in 2021. Similarly, prevalence cases increased from 29,693,841 (95% UI: 28,303,069–31,323,515) in 1990 to 49,401,399 (95% UI: 47,256,278–51,921,233) in 2021. The numbers of DALYs rose from 625,821 (95% UI: 393,597–982,826) in 1990 to 1,040,339 (95% UI: 655,127–1,624,216) in 2021 (Table 1).
Fig. 1.
Joinpoint regression analysis of adult acne burden at the global level during 1990 to 2021. A Incidence numbers. B Prevalence numbers. C Numbers of DALYs. D Age-standardized incidence rate. E Age-standardised prevalence rate. F Age-standardised DALYs rate
Table 1.
Incidence, prevalence and DALYs for adult acne (25 + year) in 1990 and 2021 and their temporal trends from 1990 to 2021
| characteristics | Incidence | Prevalence | DALYs | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Cases | ASIR (per 100,000 population) | Cases | ASPR (per 100,000 population) | Cases | ASDR (per 100,000 population) | ||||||||||
| 1990 (95% UI) | 2021 (95% UI) | 1990 (95% UI) | 2021 (95% UI) | EAPC (95% CI) | 1990 (95% UI) | 2021 (95% UI) | 1990 (95% UI) | 2021 (95% UI) | EAPC (95% CI) | 1990 (95% UI) | 2021 (95% UI) | 1990 (95% UI) | 2021 (95% UI) | EAPC (95% CI) | |
| Global | 12,178,027.48 (11,513,473.90–12835448.05) | 20,290,349.49 (19,253,787.41–21,344,141.79) | 471.48 (445.75–496.93) | 435.57 (413.32–458.19) | − 0.27 (− 0.30–0.24) | 29,693,840.34 (28,303,068.40–31323514.39) | 49,401,398.12 (47,256,277.61–51,921,232.13) | 1149.60 (1095.76–1212.70) | 1060.49 (1014.44–1114.58) | − 0.28 (− 0.31–0.24) | 625,820.65 (393,596.27–982,825.62) | 1,040,338.86 (655,126.00–1624215.59) | 24.23 (15.24–38.05) | 22.33 (14.06–34.87) | − 0.27 (− 0.31–0.24) |
| High SDI | 3,922,084.63 (3,721,455.24–4,134,977.45) | 4,913,224.81 (4,680,632.21–5,146,271.52) | 701.21 (665.34–739.27) | 617.30 (588.08–646.58) | − 0.44 (− 0.48–0.39) | 9,832,187.46 (9,406,957.87–10,293,369.88) | 12,179,642.68 (11,716,782.77–12,670,619.47) | 1757.85 (1681.82–1840.30) | 1530.26 (1472.10–1591.95) | − 0.51 (− 0.55–0.47) | 206,242.49 (129,492.38–323,911.13) | 254,414.50 (160,842.06–397469.29) | 36.87 (23.15–57.91) | 31.96 (20.21–49.94) | − 0.52 (− 0.56–0.48) |
| High-middle SDI | 2,631,480.92 (2,484,902.92–2,772,304.98) | 3,891,016.29 (3,691,341.23–4,082,133.22) | 441.68 (417.08–465.32) | 420.33 (398.76–440.98) | − 0.16 (− 0.23–0.09) | 6,476,994.09 (6,154,994.04–6839245.99) | 9,631,868.06 (9,207,344.21–10,094,689.60) | 1087.14 (1033.09–1147.94) | 1040.50 (994.64–1090.50) | − 0.13 (− 0.20–0.05) | 137,013.08 (85,947.23–215,983.15) | 203,565.87 (127,153.40–317128.98) | 23.00 (14.43–36.25) | 21.99 (13.74–34.26) | − 0.13 (− 0.20–0.05) |
| Middle SDI | 3,337,535.07 (3,145,687.64–3,526,903.14) | 5,907,614.42 (5,605,266.91–6,206,918.24) | 427.86 (403.27–452.14) | 388.12 (368.26–407.78) | − 0.37 (− 0.42–0.31) | 7,961,278.34 (7,559,472.81–8,441,927.52) | 14,285,767.07 (13,628,325.58–15,064,979.97) | 1020.61 (969.10–1082.23) | 938.55 (895.36–989.74) | − 0.31 (− 0.36–0.25) | 168,576.69 (105,745.85–265,845.04) | 302,113.74 (189,912.82–470,806.00) | 21.61 (13.56–34.08) | 19.85 (12.48–30.93) | − 0.31 (− 0.37–0.25) |
| Low-middle SDI | 1,574,428.13 (1,478,740.97–1,674,166.11) | 3,610,327.68 (3,401,615.58–3,827,972.88) | 337.78 (317.25–359.18) | 367.68 (346.42–389.85) | 0.29 (0.26–0.32) | 3,726,663.75 (3,510,542.37–3,973,470.89) | 8,608,556.46 (8,135,933.18–9,155,753.21) | 799.53 (753.16–852.48) | 876.71 (828.57–932.43) | 0.34 (0.29–0.40) | 78,473.89 (49,783.97–123,486.95) | 181,463.34 (113,883.19–285,519.18) | 16.84 (10.68–26.49) | 18.48 (11.60–29.08) | 0.35 (0.30–0.41) |
| Low SDI | 704,676.94 (658,121.06–750125.72) | 1,955,707.72 (1,826,251.40–2088344.54) | 393.45 (367.45–418.82) | 455.99 (425.80–486.91) | 0.50 (0.48–0.52) | 1,677,774.85 (1,583,912.11–1,785,225.67) | 4,665,807.42 (4,394,383.77–4,965,298.85) | 936.77 (884.36–996.76) | 1087.86 (1024.58–1157.69) | 0.52 (0.49–0.54) | 35,114.32 (21,942.16–54,844.07) | 98,153.59 (61,850.59–154,122.32) | 19.61 (12.25–30.62) | 22.89 (14.42–35.93) | 0.54 (0.51–0.57) |
| Andean Latin America | 50,439.90 (46,552.91–54,624.70) | 119,276.40 (110,643.51–128,599.87) | 326.31 (301.17–353.39) | 325.71 (302.13–351.17) | − 0.04 (− 0.06–0.02) | 121,716.08 (112,546.69–131,723.37) | 288,677.52 (268,088.15–309,383.00) | 787.42 (728.10–852.17) | 788.29 (732.06–844.83) | − 0.03 (− 0.06–0.00) | 2574.50 (1557.94–4060.68) | 6091.69 (3705.79–9620.08) | 16.66 (10.08–26.27) | 16.63 (10.12–26.27) | − 0.02 (− 0.06–0.01) |
| Australasia | 82,746.82 (77,362.70–88370.08) | 136,759.03 (127,927.14–145,236.61) | 667.55 (624.11–712.91) | 637.48 (596.31–676.99) | − 0.19 (− 0.25–0.12) | 187,795.85 (175,269.02–200968.46) | 310,580.29 (292,050.34–331,488.49) | 1515.02 (1413.96–1621.29) | 1447.71 (1361.34–1545.17) | − 0.17 (− 0.23–0.12) | 3924.98 (2427.17–6297.28) | 6476.58 (4060.70–10223.65) | 31.66 (19.58–50.80) | 30.19 (18.93–47.66) | − 0.18 (− 0.23–0.13) |
| Caribbean | 46,764.40 (43,418.81–50,394.09) | 75,610.67 (70,657.84–80,769.83) | 278.90 (258.94–300.54) | 266.16 (248.73–284.32) | − 0.21 (− 0.24–0.18) | 106,592.49 (99,134.72–115,517.38) | 171,235.36 (160,002.88–184,434.88) | 635.70 (591.22–688.93) | 602.78 (563.24–649.24) | − 0.22 (− 0.25–0.19) | 2255.64 (1390.90–3606.13) | 3612.99 (2237.38–5743.28) | 13.45 (8.30–21.51) | 12.72 (7.88–20.22) | − 0.23 (− 0.26–0.21) |
| Central Asia | 69,379.59 (63,393.03–75608.29) | 115,243.20 (106,326.82–125,419.19) | 218.71 (199.84–238.35) | 212.41 (195.97–231.16) | − 0.03 (− 0.07–0.01) | 174,996.18 (161,436.96–191,032.93) | 286,357.73 (266,076.20–309718.95) | 551.66 (508.91–602.21) | 527.79 (490.41–570.85) | − 0.01 (− 0.06–0.04) | 3711.86 (2301.71–5791.90) | 6095.40 (3734.83–9589.69) | 11.70 (7.26–18.26) | 11.23 (6.88–17.68) | − 0.01 (− 0.06–0.05) |
| Central Europe | 111,980.90 (106,135.31–117,624.09) | 111,913.44 (106,935.63–116,444.77) | 145.60 (138.00–152.93) | 130.57 (124.76–135.85) | − 0.27 (− 0.38–0.17) | 275,729.75 (263,694.19–287,682.82) | 265,321.74 (256,115.22–274,823.31) | 358.50 (342.85–374.04) | 309.55 (298.81–320.63) | − 0.39 (− 0.51–0.27) | 5867.68 (3690.37–9186.55) | 5649.02 (3529.17–8950.60) | 7.63 (4.80–11.94) | 6.59 (4.12–10.44) | − 0.38 (− 0.50–0.26) |
| Central Latin America | 194,853.78 (182,281.52–208,597.22) | 403,312.41 (381,245.55–427,087.66) | 295.43 (276.37–316.27) | 275.55 (260.47–291.79) | − 0.26 (− 0.28–0.23) | 442,406.15 (416,028.99–471,502.25) | 912,750.51 (861,706.27–968,392.03) | 670.76 (630.77–714.87) | 623.60 (588.73–661.62) | − 0.26 (− 0.29–0.24) | 9363.47 (5764.67–14,751.27) | 19,293.85 (12,094.88–30,316.16) | 14.20 (8.74–22.37) | 13.18 (8.26–20.71) | − 0.27 (− 0.29–0.24) |
| Central Sub-Saharan Africa | 86,821.85 (79,021.22–93,949.38) | 258,609.22 (238,178.64–280,703.50) | 454.31 (413.49–491.61) | 507.10 (467.04–550.42) | 0.36 (0.33–0.38) | 206,349.11 (190,993.36–225,126.13) | 612,558.69 (562,923.11–669,953.95) | 1079.76 (999.41–1178.02) | 1201.15 (1103.82–1313.69) | 0.35 (0.31–0.39) | 4295.67 (2660.62–6601.49) | 12,837.22 (7965.97–20,344.50) | 22.48 (13.92–34.54) | 25.17 (15.62–39.89) | 0.38 (0.33–0.42) |
| East Asia | 3,152,748.10 (2,982,982.60–3319919.39) | 4,647,440.75 (4,426,858.58–4,853,913.61) | 507.30 (479.98–534.20) | 442.08 (421.10–461.72) | − 0.50 (− 0.57–0.43) | 7,598,981.81 (7,211,376.29–8,027,477.61) | 11,490,107.08 (10,972,979.77–12,008,769.40) | 1222.73 (1160.37–1291.68) | 1092.97 (1043.78–1142.31) | − 0.41 (− 0.48–0.34) | 161,523.95 (100,721.18–254,980.64) | 244,029.81 (152,706.04–379289.05) | 25.99 (16.21–41.03) | 23.21 (14.53–36.08) | − 0.41 (− 0.48–0.34) |
| Eastern Europe | 255,584.07 (240,951.87–270,735.79) | 245,021.84 (232,890.21–257,748.74) | 177.14 (167.00–187.64) | 162.53 (154.48–170.97) | − 0.05 (− 0.15–0.04) | 636,668.55 (603,851.51–675,463.78) | 598,410.16 (571,032.19–630,369.14) | 441.27 (418.52–468.16) | 396.93 (378.77–418.13) | − 0.04 (− 0.15–0.06) | 13,481.05 (8405.10–21376.77) | 12,649.55 (7954.37–19,759.73) | 9.34 (5.83–14.82) | 8.39 (5.28–13.11) | − 0.04 (− 0.14–0.07) |
| Eastern Sub-Saharan Africa | 319,109.98 (296,708.22–340,967.18) | 906,228.74 (845,027.79–970,566.67) | 504.09 (468.70–538.62) | 575.74 (536.86–616.62) | 0.45 (0.43–0.46) | 802,430.55 (753,490.44–855,795.96) | 2,295,038.56 (2,153,251.49–2,453,653.09) | 1267.58 (1190.27–1351.88) | 1458.08 (1368.00–1558.85) | 0.47 (0.45–0.48) | 16,789.96 (10,527.66–26,207.99) | 48,258.94 (30,402.28–75,672.70) | 26.52 (16.63–41.40) | 30.66 (19.32–48.08) | 0.50 (0.48–0.51) |
| High-income Asia Pacific | 814,516.39 (770,980.71–857,669.34) | 891,802.72 (851,006.37–933,643.26) | 746.26 (706.37–785.80) | 614.65 (586.53–643.49) | − 0.61 (− 0.70–0.53) | 1,982,410.58 (1,882,259.70–2084242.63) | 2,172,542.28 (2,076,448.85–2,276,714.94) | 1816.28 (1724.53–1909.58) | 1497.36 (1431.13–1569.16) | − 0.60 (− 0.67–0.52) | 41,818.69 (26,355.40–65227.27) | 45,798.52 (28,496.26–71,740.41) | 38.31 (24.15–59.76) | 31.57 (19.64–49.44) | − 0.60 (− 0.68–0.52) |
| High-income North America | 1,263,253.69 (1,201,919.46–1,330,429.54) | 1,629,038.66 (1,566,609.14–1,690,128.96) | 709.48 (675.03–747.21) | 635.03 (610.70–658.85) | − 0.30 (− 0.41–0.18) | 3,034,699.02 (2,915,690.25–3,160,138.08) | 3,858,655.85 (3,749,951.43–3,968,066.53) | 1704.37 (1637.53–1774.82) | 1504.18 (1461.81–1546.83) | − 0.45 (− 0.56–0.35) | 63,440.34 (39,858.84–99,573.14) | 79,877.51 (50,551.40–125460.61) | 35.63 (22.39–55.92) | 31.14 (19.71–48.91) | − 0.48 (− 0.58–0.37) |
| North Africa and Middle East | 473,278.44 (439,209.53–509,728.66) | 1,309,462.65 (1,225,329.54–1,399,209.20) | 359.73 (333.83–387.43) | 389.30 (364.29–415.98) | 0.35 (0.26–0.43) | 1,128,150.70 (1,050,074.35–1,220,606.05) | 3,120,717.51 (2,923,708.97–3,360,824.96) | 857.48 (798.14–927.76) | 927.78 (869.21–999.16) | 0.34 (0.26–0.42) | 23,768.82 (14,962.74–37,127.30) | 65,685.17 (40,683.13–103,306.82) | 18.07 (11.37–28.22) | 19.53 (12.09–30.71) | 0.34 (0.26–0.43) |
| Oceania | 11,389.21 (10,537.85–12,295.63) | 29,278.48 (27,120.10–31597.21) | 443.41 (410.27–478.70) | 465.23 (430.94–502.08) | 0.12 (0.09–0.14) | 25,295.28 (23,313.01–27385.34) | 64,688.02 (60,039.18–70,060.67) | 984.81 (907.64–1066.18) | 1027.89 (954.02–1113.26) | 0.10 (0.07–0.13) | 535.71 (319.44–840.21) | 1367.35 (851.78–2178.98) | 20.86 (12.44–32.71) | 21.73 (13.53–34.62) | 0.10 (0.07–0.13) |
| South Asia | 1,371,879.94 (1,295,526.74–1,451,198.43) | 3,268,749.34 (3,095,072.95–3,446,772.80) | 303.25 (286.38–320.79) | 329.58 (312.07–347.53) | 0.33 (0.27–0.39) | 3,280,881.79 (3,108,919.42–3,483,434.74) | 7,863,377.54 (7,475,671.91–8,304,123.96) | 725.24 (687.23–770.01) | 792.86 (753.76–837.30) | 0.42 (0.31–0.53) | 68,945.73 (43,826.09–108030.30) | 165,500.95 (104,521.82–259,852.98) | 15.24 (9.69–23.88) | 16.69 (10.54–26.20) | 0.43 (0.32–0.54) |
| Southeast Asia | 1,005,661.00 (941,809.23–1,069,610.39) | 1,976,581.12 (1,867,431.66–2,089,020.28) | 500.22 (468.46–532.03) | 478.91 (452.47–506.15) | − 0.16 (− 0.19–0.14) | 2,392,767.44 (2,256,296.08–2564714.85) | 4,742,834.79 (4,488,722.66–5,042,305.33) | 1190.18 (1122.30–1275.71) | 1149.16 (1087.59–1221.71) | − 0.14 (− 0.17–0.11) | 50,675.23 (31,806.98–79,080.27) | 100,575.83 (62,598.64–157,137.76) | 25.21 (15.82–39.34) | 24.37 (15.17–38.07) | − 0.14 (− 0.16–0.11) |
| Southern Latin America | 189,946.41 (175,325.53–203,812.13) | 321,408.03 (298,099.97–343,380.16) | 726.26 (670.36–779.28) | 748.83 (694.53–800.02) | 0.12 (0.08–0.17) | 441,929.00 (412,268.82–480,321.68) | 745,309.30 (696,996.35–797,566.89) | 1689.72 (1576.32–1836.52) | 1736.46 (1623.90–1858.21) | 0.13 (0.10–0.16) | 9272.48 (5819.31–14,367.83) | 15,570.60 (9586.32–24,710.04) | 35.45 (22.25–54.94) | 36.28 (22.33–57.57) | 0.12 (0.09–0.15) |
| Southern Sub-Saharan Africa | 99,011.21 (92,540.11–105,216.72) | 207,863.01 (195,477.29–219,847.70) | 472.14 (441.28–501.73) | 491.35 (462.07–519.68) | 0.18 (0.15–0.21) | 241,626.09 (227,730.10–258043.51) | 505,338.33 (477,413.29–538,412.95) | 1152.20 (1085.94–1230.49) | 1194.52 (1128.51–1272.70) | 0.17 (0.14–0.21) | 5072.74 (3145.68–7956.33) | 10,508.72 (6548.79–16,536.25) | 24.19 (15.00–37.94) | 24.84 (15.48–39.09) | 0.15 (0.12–0.19) |
| Tropical Latin America | 181,295.76 (171,375.36–191,495.06) | 347,554.54 (330,015.41–364,761.82) | 263.05 (248.66–277.85) | 242.99 (230.73–255.02) | − 0.24 (− 0.27–0.22) | 403,831.22 (382,448.67–426,894.44) | 767,952.73 (731,925.37–807,303.10) | 585.94 (554.91–619.40) | 536.91 (511.72–564.42) | − 0.27 (− 0.29–0.24) | 8503.59 (5331.69–13,406.79) | 16,157.92 (10,042.57–25,424.47) | 12.34 (7.74–19.45) | 11.30 (7.02–17.78) | − 0.26 (− 0.29–0.24) |
| Western Europe | 2,106,906.25 (1,994,134.27–2,222,876.49) | 2,425,776.71 (2,303,580.44–2,554,975.44) | 823.84 (779.74–869.19) | 755.42 (717.37–795.65) | − 0.38 (− 0.43–0.32) | 5,529,257.72 (5,261,883.00–5808722.79) | 6,299,162.30 (6,018,831.38–6,582,128.92) | 2162.04 (2057.49–2271.32) | 1961.64 (1874.34–2049.76) | − 0.42 (− 0.48–0.37) | 115,742.61 (72,667.88–182,245.71) | 131,482.07 (83,085.37–204,514.76) | 45.26 (28.41–71.26) | 40.95 (25.87–63.69) | − 0.43 (− 0.48–0.37) |
| Western Sub-Saharan Africa | 290,459.80 (271,242.83–309,823.89) | 863,418.52 (806,855.63–924,435.20) | 420.79 (392.95–448.84) | 487.94 (455.97–522.42) | 0.49 (0.47–0.52) | 679,325.00 (637,571.88–726,466.60) | 2,029,781.80 (1,904,271.80–2172930.00) | 984.13 (923.64–1052.43) | 1147.07 (1076.15–1227.97) | 0.51 (0.48–0.54) | 14,255.95 (8883.20–22555.41) | 42,819.18 (26,651.27–66,959.08) | 20.65 (12.87–32.68) | 24.20 (15.06–37.84) | 0.53 (0.51–0.56) |
DALYs, disability-adjusted life years; ASIR, age-standardized incidence rate; ASPR, age-standardized prevalence rate; ASDR, age-standardized DALYs rate; EAPC, estimated annual percentage change
Although the overall ASIR, age-standardized prevalence rate (ASPR), and age-standardized DALYs rate (ASDR) for adult acne globally have shown a decreasing trend since 1998/1999 (Fig. 1D, E, F, Table 1). It is noteworthy that when stratified by SDI, regions with low and low-middle SDI did not experience a decline like those with higher SDI; instead, their rates continued to rise from 1990 to 2021 (Fig. 2D, E, F). Furthermore, during these years, the overall cases of adult acne consistently increased across all SDI regions (Fig. 2A, B, C).
Fig. 2.
Burden of adult acne by SDI regions from 1990 to 2021. A Incidence numbers. B Prevalence numbers. C Numbers of DALYs. D Age- standardised incidence rate; E Age- standardised prevalence rate; F Age-standardised DALYs rate. SDI, socio-demographic index
Burden of adult acne by regions
We conducted a further breakdown of global regions to better assess the burden of adult acne worldwide. In the stratification by SDI quintiles and GBD regions, the ASRs of adult acne showed an increasing trend in areas with low SDI, low-middle SDI, as well as regions like Western Sub-Saharan Africa and Eastern Sub-Saharan Africa. And Western Sub-Saharan Africa experienced the highest increase among the 21 regions. Conversely, regions characterized by higher SDI, including High-Income North America and High-Income Asia Pacific, showed a declining trend in ASRs. The High-Income Asia Pacific reported the largest decrease among the 21 regions (Fig. 3). Figure 4 presents a map illustrating the EAPC of ASRs for adult acne across 204 countries. The country with the highest increase in ASIR is Afghanistan (EAPC 95%:1.26(0.91–1.61)), while the Northern Mariana Islands (EAPC 95%: − 1.52(− 1.79–1.26)) show the greatest decrease. For ASPR and ASDR, Equatorial Guinea(EAPC 95%: 1.40(1.32–1.48); 1.44(1.36–1.52), respectively) records the largest increase, whereas the Northern Mariana Islands (EAPC 95%: − 1.58(− 1.85–1.31); − 1.60(− 1.87–1.32), respectively) again exhibit the most significant decrease.
Fig. 3.
The EAPCs in age-standardised rates of incidence A, prevalence B and DALYs C due to adult acne from 1990 to 20,221 by GBD region and SDI quintile. EAPC, estimated annual percentage change; SDI, Socio-demographic Index
Fig. 4.
The EAPCs in age-standardised rates of incidence A, prevalence B and DALYs C due to adult acne between 1990 and 2019 in 204 countries and territories. EAPC, estimated annual percentage change
Burden of adult acne by population
We stratified the global population by sex and age to explore the burden of adult acne in different demographic groups worldwide. As shown in Fig. 5, among the 21 regions defined by the GBD, the ASIR (Fig. 5A), ASPR (Fig. 5C), and ASDR (Fig. 5E) of adult acne in females were consistently higher than in males in 2021. When grouped by five-year intervals, the incidence (Fig. 5B), prevalence (Fig. 5D), and DALYs (Fig. 5F) of adult acne showed a gradual decline with increasing age. Additionally, it is evident that the disease burden is consistently higher in females across all age groups compared to males.
Fig. 5.
The age-standardised rates of incidence A, prevalence C and DALYs E due to adult acne by sex, across 21 regions, in 2021. And age patterns by sex of the total number and rates of incidence B, prevalence D and DALYs F due to adult acne at the global level in 2021
Burden of adult acne by SDI
Figure 6A illustrates the relationship between trends in the ASIR of adult acne and different SDI regions (as defined by the GBD) from 1990 to 2021. Higher SDI regions, such as Western Europe, had a higher baseline ASIR but exhibited a declining trend, whereas lower SDI regions, such as Eastern Sub-Saharan Africa, had a lower baseline ASIR but showed an increasing trend. This resulted in an atypical hook-shaped trend line (p < 0.01). The correlation between the ASIR of adult acne and SDI across 204 countries in 2021 is detailed in the supplementary information. Figure 6B indicates a negative correlation between the ASIR of EAPC of global adult acne and SDI of regions in 2021 (p < 0.01), suggesting that regions with higher SDI tend to have a negative EAPC, indicating a reduction in disease burden. Health inequality analyses yielded similar results. As shown by the slope index of inequality, the gap in the ASIR between the highest and lowest SDI countries and territories increased from − 70.97 (95% CI: − 157.32 to 15.38) in 1990 to − 118.98 (95% CI: − 206.05 to − 31.90) in 2021 (Fig. 6C). The concentration index was − 0.04 in 1990 rised to 0.02 in 2021(Fig. 6D). These suggest that absolute health inequalities and relative inequalities in adult acne have both increased from 1990 to 2021.
Fig. 6.
Change of age-standardised rates of incidence (ASIR) for adult acne among 21 GBD regions from 1990 to 2021 A. EAPC of ASIR was associated with SDI in 204 countries and territories in 2021 B. Health inequality regression curves for ASIR of adult acne C. Concentration curves for ASIR of adult acne D. EAPC, estimated annual percentage change
Future burden prediction and risk factor identification for adult acne
It is projected that by 2030, the cases of adult acne among adults will continue to rise (Fig. 7). The two-sample MR analysis indicates a positive causal relationship between intake of sugar added to coffee and acne (IVW: OR = 1.079, 95% CI: 1.005–1.158, P = 0.037). Although MR-Egger and Weighted Mode did not show significant p-values, the trends were consistent (Figs. 8A, B). Sensitivity analyses revealed no significant heterogeneity or horizontal pleiotropy (see supplementary information). Figure 8C presents a map from the GDD database depicting SSB intakes among adults globally in 2018, with significantly higher levels observed in countries on the African continent. Moreover, the results from smooth curve fitting analysis suggest a nonlinear association between the ASIR of adult acne and SSB intakes in various countries (P < 0.05).
Fig. 7.
The predictions of global incidence cases for adult acne to 2030. A 25–29 years, B 30–34 years, C 35–39 years, D 40–44 years, E 45–49 years, F 50–54 years, G 55–59 years, H 60–64 year
Fig. 8.
Identification of risk factor for adult acne. Scatter plots A and forest plot B for causal effect of intake of sugar added to coffee on adult acne. C sugar-sweetened beverage (SSB) intakes (8 oz servings/week) in adults across 185 countries in 2018. D The association between age-standardized rates of incidence for adult acne and SSB intakes of adult in global (p < 0.05)
Discussion
Adult acne is a skin condition that persists or emerges after adolescence, challenging the traditional notion that acne is solely a disease of youth [15]. Given the clinical characteristics of adult acne—such as the degree of inflammation and the primary areas of the body affected—as well as its incidence trends (including susceptible populations), it differs from adolescent acne [16]. Additionally, there is a lack of comprehensive global epidemiological and disease burden studies on it. Therefore, the aim of this study is to utilize the GBD database to investigate the global trends in adult acne and explore potential risk factors, thereby filling the existing knowledge gap.
We conducted a comprehensive analysis of the global epidemiological trends and distribution differences in the burden of adult acne based on the GBD 2021 data. Our results indicate that from 1990 to 2021, the incidence cases increased by 66.6%, the prevalence cases increased by 66.4%, and the number of DALYs rose by 66.2%. The region with the highest incident cases currently is Eastern Sub-Saharan Africa. By examining the curves for global ASRs of adult acne, we found that these metrics peaked before the twenty-first century (with AAPC of 0.08, 0.15, and 0.16, respectively). Since the beginning of the twenty-first century, the global ASRs of adult acne have been declining each year, with the most significant reductions occurring between 2015 and 2021 (with AAPC of − 0.73, − 0.64, and − 0.67, respectively). This decline may be related to advancements in global medical technology and improvements in public health, such as the development of new treatment techniques (e.g., phototherapy), the widespread use of new medications (e.g., Accutane, Adapalene), and increased awareness of health issues [17–19].
However, stratification by region revealed that low SDI and low-middle SDI areas exhibit trends opposite to the global development trend, with increasing ASRs of adult acne each year, while other higher SDI regions show a consistent decline. The ASRs trend results across the 21 regions align with this finding, with Western Sub-Saharan Africa experiencing the greatest increase in adult acne ASRs from 1990 to 2021, while High-Income Asia Pacific saw the largest decrease. Furthermore, among 204 countries, Afghanistan had the highest increase in ASIR, while Northern Mariana Islands had the largest decrease. Equatorial Guinea showed the greatest rise in ASPR and ASDR, with Northern Mariana Islands again showing the largest decrease. Our results clearly indicate the imbalance in the burden of adult acne across different global regions.
Acne vulgaris is not a fatal disease but rather a disfiguring condition. In regions with lower SDI scores, the economic status is typically lower, often accompanied by more limited healthcare resources and poorer infrastructure [20]. These factors can intensify the disease burden of adult acne. In such areas, people may be more concerned with meeting basic needs like food and shelter than with their appearance. In contrast, in regions with higher SDI scores, residents generally have a higher level of education and health awareness, and place greater emphasis on their physical appearance [21–23]. Our Spearman’s Correlation analysis reveals a negative correlation between the EAPC of ASIR of adult acne and regional SDI scores. This means that in areas with higher SDI scores, the disease burden of adult acne is decreasing annually, which is consistent with our aforementioned findings.
In terms of demographics, the results indicate that compared with males, period, the number of cases and rates of adult acne were higher in females across most regions and age groups. This suggests a greater disease burden among women. Previous studies have also highlighted gender differences, indicating that women are generally more susceptible to acne, particularly in adulthood, which aligns with our findings [24]. These gender differences may stem from a combination of biological and health factors. Various reasons for elevated androgen levels in women can lead to the development of acne, including conditions such as polycystic ovary syndrome and medications like first-generation progestins that exhibit androgen-like effects [25, 26]. Additionally, women’s frequent exposure to cosmetics due to personal habits or occupations may contribute to skin inflammation, thereby increasing the risk of developing acne [27, 28].
In this study, we projected the incidence of adult acne up to 2030. The results indicate a continued upward trend across all age groups during the late adolescent years. This suggests that the global burden of diagnosing and treating adult acne remains substantial, highlighting the need for enhanced prevention efforts. Consequently, we explored the risk factors associated with adult acne. The development of acne is multifactorial, involving both intrinsic factors and external stimuli, with dietary behaviors being a prominent area of research [29, 30]. A cross-sectional study conducted in France, which surveyed 24,452 participants from 2018 to 2019, found that the consumption of milk, sugary drinks, and fatty or sugary products appeared to correlate with current acne in adults [30]. Another study involving 2,467 Thai adolescents and adults identified that the consumption of chocolate > 100 g/week is associated with an increased severity of acne (aOR: 1.29; 95% CI: 1.07–1.56) [31]. These findings were further corroborated in our research. Our two-sample MR analysis suggests a genetic causal relationship between the intake of sugar added to coffee and acne. Additionally, we used the GBD database to analyze the consumption of SSB intakes among adults in major countries worldwide in 2018. Importantly, we found a non-linear association between the ASIR of adult acne and SSB intakes across countries, with an overall upward trend in the curve. What people eat and drink is one of the most critical determinants of health and health equity [32]. Therefore, reducing the proportion of sugary beverages in the diet and improving dietary structure may be significant measures to alleviate the global burden of adult acne and narrow health disparities worldwide.
Limitations
This study did not conduct a comprehensive analysis of all potential factors influencing the burden of adult acne. Similar to other GBD studies, variations in data quality and coverage of adult acne may introduce bias, particularly in regions with less accurate or incomplete reporting [33]. And the database does not include environmental factors and psychosocial influences across different regions worldwide, but this is also a valuable avenue for further exploration [34]. Moreover,due to data limitations, the age for SSB intakes in adults is not strictly defined as 25 + .
Conclusion
With the global population increasing, cases of adult acne have continued to rise from 1990 to 2030, resulting in a heavy global disease burden. The burden of adult acne demonstrates significant heterogeneity across regions and genders, with notably greater severity in underdeveloped areas and among females. Notably, there is non-linear association between the ASIR of adult acne and SSB intakes across countries, with an overall upward trend. Therefore, we call for regional governments and the international community to work together to strengthen healthcare systems and improve dietary structures in impoverished areas, thereby mitigating global health inequities.
Acknowledgements
Not applicable.
Author contributions
W. Y. and X. H. designed the study and do for conceptualization and investigation. S. P. performed the formal and data analysis. J. D. writed the manuscript. F. Y. and L. L. did for Methodology. X. W., X. L., Y. L. and Z. C. collated the data and literature. J.D. and S. P. contributed equally to this work. All authors reviewed the manuscript and agree to submit the article for publication.
Funding
Talent Highland Construction Project for Medical Beauty and Wellness Industry of CPC Guilin Municipal Party Committee (2022). Key Research and Development Plan Project of Guilin Municipal Bureau of Science and Technology (20220116-2).
Availability of data and materials
The data are available in the Supporting Information of this article, and also can be downloaded from Internet datasets.
Declarations
Ethical approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Shixiong Peng and Jia Deng have contributed equally to this work.
Contributor Information
Wenjie Yan, Email: ywj716@qq.com.
Xi Huang, Email: 1352215665@qq.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data are available in the Supporting Information of this article, and also can be downloaded from Internet datasets.