Global, regional, and national burden of osteoarthritis from 1990 to 2021 and projections to 2035: A cross-sectional study for the Global Burden of Disease Study 2021

Xiaoming Xie; Kuayue Zhang; Yuan Li; Yulong Li; Xinyi Li; Yi Lin; Liangqing Huang; Guihua Tian

doi:10.1371/journal.pone.0324296

. 2025 May 27;20(5):e0324296. doi: 10.1371/journal.pone.0324296

Global, regional, and national burden of osteoarthritis from 1990 to 2021 and projections to 2035: A cross-sectional study for the Global Burden of Disease Study 2021

Xiaoming Xie ^1,^☯, Kuayue Zhang ^2,^☯, Yuan Li ^3,^☯, Yulong Li ^4,^☯, Xinyi Li ⁵, Yi Lin ¹, Liangqing Huang ¹, Guihua Tian ^1,^*

Editor: Xindie Zhou⁶

PMCID: PMC12111611 PMID: 40424273

Abstract

Objective

This study aims to report the trends and cross-national disparities in the burden of osteoarthritis (OA) by region, age, gender, and time from 1990 to 2021, and to further project changes through 2035.

Methods

In this systematic analysis based on the Global Burden of Disease (GBD) study, population survey data on osteoarthritis from 21 countries/regions and U.S. insurance claims data were used to estimate the prevalence and incidence of OA in 204 countries and regions from 1990 to 2021. The reference case definition for OA was symptomatic and radiographically confirmed osteoarthritis. Studies using definitions other than the reference, such as self-reported OA, were adjusted through a regression model to align with the reference case. The distribution of OA severity was derived from a pooled meta-analysis using the Western Ontario and McMaster Universities Arthritis Index (WOMAC). Final prevalence estimates were multiplied by disability weights to calculate years lived with disability (YLD). An Autoregressive Integrated Moving Average (ARIMA) model was used to forecast the prevalence and incidence of OA through 2035.

Results

In 2021, approximately 607 million (95%UI 538–671) people worldwide were affected by osteoarthritis, accounting for 7.7% of the global population. Compared to 2020, the age-standardized prevalence of OA among males is projected to increase from 5,763 per 100,000–5,922 per 100,000 by 2036, while the age-standardized prevalence among females is expected to decline slightly from 8,034 per 100,000–7,925 per 100,000. In 2021, the global age-standardized YLD rate for osteoarthritis was 244.5 (95%UI 117.06–493.11), the global age-standardized prevalence rate was 6,967.29 (95%UI 6,180.7–7,686.06), and the global age-standardized incidence rate was 535 (95%UI 472.38–591.97). In 2021, the age-standardized prevalence rate exceeded 5.5% across all regions, ranging from 5,675.8 per 100,000 (95%UI 5,001.76–6,320.8) in Southeast Asia to 8,608.63 per 100,000 (95%UI 7,674.07–9,485.19) in high-income Asia Pacific regions. The knee was the most commonly affected joint. High BMI and metabolic risks are the only two GBD risk factors for osteoarthritis. From 1990 to 2021, the age-standardized prevalence, incidence, and YLD attributable to osteoarthritis have been on the rise, with substantial international variations across indicators. Countries with high socio-demographic index (SDI) bear a disproportionately high burden of OA, and inequalities in the burden of disease due to differences in SDI between countries have been increasing over time.

Conclusions

As a major public health problem, the overall global burden of OA has shown an upward trend from 1990 to 2019, including an increase in the number of cases and inequalities in distribution across the globe, which has resulted in significant health losses and economic burdens. In addition, SDI-related inequalities between countries are increasing. In this regard, national public health authorities and the World Health Organization (WHO) should work together to improve diagnosis and early treatment rates by strengthening disease awareness and education, as well as strengthening international cooperation, providing necessary medical assistance to less developed regions, and actively exploring new strategies for the prevention and treatment of OA.

1. Introduction

Osteoarthritis (OA) is the most common musculoskeletal disorder among middle-aged and elderly populations [1]. It is characterized by pathological changes such as cartilage degeneration, bone remodeling, and osteophyte formation, and its clinical manifestations include joint pain, stiffness, swelling, and functional limitations [2]. As early as 2019, the global number of people with osteoarthritis exceeded 500 million, making OA one of the leading causes of chronic pain and long-term disability in older adults [3]. With the rapid acceleration of global aging and the sharp rise in obesity rates across all age groups, the incidence of OA continues to increase [3], due to the fact that with age, articular cartilage undergoes progressive degeneration due to prolonged mechanical loading and diminished self-repairing ability, which directly increases the risk of OA development; at the same time, obesity exerts excessive mechanical stress on weight-bearing joints (especially the knees), and the adipose tissue secretes pro-inflammatory cytokines that exacerbate cartilage degeneration and synovial inflammation. By 2020, the global prevalence of OA had risen by 132.2% compared to 1990 [4]. In 2016, the United States spent approximately $80 billion on healthcare for osteoarthritis [4]. As early as 2003, the direct economic burden of OA in Hong Kong ranged from 11,690–40,180 HKD per person annually, with indirect costs ranging from 3,300–6,640 HKD per person annually [5].

However, due to the exclusion of osteoarthritis from global strategies for non-communicable diseases (NCDs), along with the widespread misconception that OA is an inevitable part of aging and lacks effective treatment options, the significant personal, economic, and societal burden of OA has not received sufficient attention [6].

The Global Burden of Disease (GBD), Injuries, and Risk Factor Study aims to provide reliable and up-to-date global, regional, and national estimates on the burden of diseases, injuries, and risk factors by integrating all available data, including published literature, gray literature, survey data, as well as hospital and clinical data [7]. In the area of the global burden of OA, we must acknowledge and appreciate the pioneering work of the GBD 2021 Osteoarthritis Collaborators [3] (published in The Lancet Rheumatology in 2023), which comprehensively analyzes the global burden of osteoarthritis up to the year 2020, and predicts trends up to 2050. Their study provides important insights into the epidemiology of OA, risk factors, and regional variations, and serves as a foundational reference for research on the burden of OA. However, we have to point out that although previous studies have evaluated the burden of OA, they have either focused on specific joint sites [8], single regions [9](such as China), OA related to specific risk factors [10], or provided projections for certain areas [11]. Consequently, findings on OA prevalence vary across studies [12].

Compared to previously published studies on global OA epidemiology, this study employed methods such as trend analysis, decomposition analysis, frontier analysis, and predictive analysis to examine the burden of OA from multiple perspectives, including gender, age, and time. Secondly, Our study explicitly examines SDI-related inequalities in OA burden over time, highlighting how disparities between high- and low-SDI regions have widened since 1990. Thirdly, we identified diverging trends in age standardized prevalence rates between males and females (projected increases for males vs. declines for females by 2035), a finding not explored in the earlier study. Additionally, by incorporating 2021 data, we highlight shifts in OA burden hotspots, such as the rising prominence of high-income Asia Pacific regions (e.g., South Korea) compared to previous emphasis on North America and Western Europe. At last, we conducted a further analysis of OA risk factors. All of these new findings from diverse research approaches will help us to better understand the changing burden of OA and future trends, and will help global public health policy makers to take more proactive measures to combat OA.

2. Study design and methods

2.1. Data Sources and Methods

The GBD 2021 database provides comprehensive estimates of the prevalence, incidence, years lived with disability (YLD), years of life lost (YLL), and disability-adjusted life years (DALY) at global, regional, and national levels for 204 countries. It covers 369 diseases and injuries, along with 87 risk factors [13]. In this study, we utilized OA data from the GBD database. All GBD 2021 data are publicly available online (https://vizhub.healthdata.org/gbd-compare/ and https://vizhub.healthdata.org/gbd-results). Within the GBD 2021 framework, OA was categorized based on the Western Ontario and McMaster Universities Arthritis Index (WOMAC) [14] into three severity levels: mild, moderate, and severe. DALY is the standardized measure used to quantify the burden of disease, and DALY = YLL + YLD. As the cause-of-death model of GBD assumes no deaths attributable to OA, the DALY for OA is equivalent to YLD. To calculate YLD, the prevalence in each severity group was multiplied by its specific disability weight [15]. We extracted the raw data for OA incidence, prevalence, and YLD by age group and sex at global, regional, and national levels for 204 countries, divided into 21 regions, such as Western Pacific, Central Asia, and Eastern Europe.The OA data we extracted for GBD 2021 included: total OA, knee osteoarthritis, hip osteoarthritis, hand osteoarthritis and other osteoarthritis.

This work has been reported under the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) standard [16].

2.2. Ethical considerations

The University of Washington Institutional Review Board approved a waiver of informed consent for the use of de-identified data in the GBD study.

2.3. Case definition

OA was defined as symptomatic OA confirmed by radiographic evaluation, with the Kellgren-Lawrence (KL) grade ranging from II to IV [17–19]. KL grade II is the minimum requirement for diagnosing OA, requiring the presence of pain for at least one month within the past 12 months and the presence of osteophytes on radiographic assessment of the affected joint. Grades III and IV are characterized by osteophytes and joint space narrowing in the affected joint, with grade IV indicating deformity. The primary input data for hip and knee OA models were derived from population-based cross-sectional surveys from around the world and U.S. state-level insurance claims data, captured by International Classification of Diseases (ICD)-9 four- or five-digit codes (beginning with 715) and ICD-10 codes M16 and M17. ICD-10 uses the term “osteoarthritis”, replacing “osteoarthrosis” from ICD-9, reflecting the understanding that inflammation is involved in the pathogenesis of OA. GBD 2021 continues to include two new OA categories added in GBD 2019: hand OA and a residual category for OA in other joints (e.g., shoulder and elbow). Consistent with hip and knee osteoarthritis modelling, symptomatic, radiographically confirmed osteoarthritis in any single joint of the hand was used as the reference case definition for hand osteoarthritis, to which alternative case definitions present in the literature were adjusted. Given the paucity of survey data on other osteoarthritis joint sites, US insurance claims data from 2000 to 2016 constituted the sole source of other osteoarthritis data [3].

2.4. Descriptive analysis

A descriptive analysis was conducted at global, regional, and national levels to provide a comprehensive overview of the burden of OA. We visually presented the incidence, prevalence, and YLD, including age-standardized rates (ASR), of global OA cases from 1990 to 2021. Furthermore, comparisons of OA cases and ASRs in 1990 and 2021 were made at global, regional (21 GBD regions), and national levels (204 countries/territories).

2.5. Trend analysis

Exploring time trends in disease burden is an essential aspect of epidemiology and supports the development of more precise prevention strategies [20]. We used Joinpoint regression analysis to quantify differences in the burden of OA over time and by gender [21]. Joinpoint regression divides overall trends into several segments based on identified breakpoints and calculates the annual percentage change (APC) and 95% confidence intervals (CI) for each segment to evaluate the magnitude of each epidemiological trend. When the average annual percent change (AAPC) and its 95% CI are both above zero, the trend is defined as increasing. Conversely, if the AAPC and its 95% CI are both below zero, the trend is defined as decreasing [22]. Otherwise, the burden is considered relatively stable. The AAPC for the periods 1992–2002, 2002–2012, and 2012–2021 were also estimated by weighting each segment's regression coefficients by the width of each time interval.

2.6. Frontier analysis

The Socio-Demographic Index (SDI) was developed by GBD researchers and serves as a composite indicator of country's development level closely related to health outcomes [23]. It measures development level based on lag-distributed per capita income, fertility rate for the population under 25 years, and average years of education for individuals aged 15 and older [24]. A region with an SDI of 0 represents the lowest theoretical level of development related to health, while an SDI of 1 represents the highest. The 204 countries and regions were divided into five groups based on their SDI: low, low-middle, middle, high-middle, and high [25]. Frontier analysis was conducted to evaluate the ideal YLD levels for osteoarthritis in each of the 204 countries and regions at their corresponding SDI levels. Through frontier analysis, we identified the countries and regions with the most significant gaps, including: (1) the 15 countries with the largest gap from the frontier YLD for OA globally; (2) the five countries with the smallest gap from the frontier value in low SDI regions (SDI < 0.5); and (3) the five countries with the largest gap from the frontier YLD for OA in high SDI regions [26] (SDI > 0.85).

2.7. Forecasting OA burden to 2035

The Autoregressive Integrated Moving Average (ARIMA) model is a widely used time series analysis method for forecasting future values [27]. The ARIMA model captures trends and seasonality in time series data by combining three components: autoregression(AR), integration (I), and moving average (MA). ARIMA parameters are typically expressed as ARIMA(p, d, q), where: p (order of autoregression): indicates how many previous time points are used to predict the current value. The auto regressive component implies that the current value depends on a linear combination of its previous p values; d (degree of difference): represents how many times the data needs to be differenced to become stationary. Differencing refers to calculating the difference between adjacent observations to remove trends or seasonality in the time series; q (order of moving average): indicates how many previous error terms are used to predict the current value. The moving average component implies that the current value depends on a linear combination of the previous q error terms [28]. Mathematically, an AR(p) model can be expressed as:yt = α0 + α1yt−1 + α2yt−2 + ⋯ + αpyt−p+∊t, where yt represents the observed value at time t, αi are the regression coefficients, and ∊t is the random error term; If the data is non-stationary, differencing can be applied to remove the trend. For example, first-order differencing (d = 1) calculates the difference between the current value and the previous value:Δyt = yt − yt−1. Repeated differencing operations can eliminate more complex trends; The MA(q) model can be represented as: yt = μ+∊t + θ1∊t−1 + θ2∊t−2 + ⋯ + θq∊t−q, where ∊t is the error term and θi are the coefficients of the error terms [29].

2.8. Risk factor analysis

Bayesian meta-regressions of the adjusted data were run using DisMod-MR 2.1, 17 an age-integrating Bayesian meta-regression log-normal disease model with a mixed-effects geographical cascade. The meta-regression was a combination of a meta-analysis to pool data points with weighted averages to include and reconcile heterogeneous data, and a regression to include known associations between several variables (e.g., osteoarthritis prevalence and BMI or age). Fixed effects included sex and country-level covariates (e.g., BMI). Nested random effects were calculated for each super-region, region, and country [30].

Within GBD, DisMod-MR 2.1 uses BMI as a fixed covariate solely to improve OA prevalence estimation by leveraging known BMI-OA associations when primary data are sparse (Flaxman [31] et al., 2015). In a separate Comparative Risk Assessment, each metabolic component-high fasting plasma glucose, high systolic blood pressure, high BMI, low bone mineral density, high LDL cholesterol, impaired kidney function-is assigned its own relative-risk curve and theoretical minimum risk exposure level (TMREL). The six resulting PAFs are then combined multiplicatively to produce total metabolic risk, thereby avoiding double-counting of BMI effects (GBD 2019 Risk Factors Collaborators [32], 2020).

Our analysis, aligned with the Global Burden of Disease (GBD) 2021 framework, focused on two risk factors: high BMI and metabolic risks. The latter encompasses high fasting plasma glucose, high systolic blood pressure, low bone mineral density, high LDL cholesterol, and impaired kidney function. These factors were selected based on their mechanistic and epidemiological links to OA. For instance, high BMI is a well-established causal driver of OA, supported by meta-analyses (Blagojevic M [33], et al, 2010), longitudinal cohort studies (Felson D.T [34], et al, 1988)and Mendelian randomization studies (Zengini E [35], et al, 2018). Similarly, metabolic risks were included due to their roles in systemic inflammation, cartilage degradation, and subchondral bone remodeling, with evidence from longitudinal cohorts (Hoeven TA [36], et al, 2013; Clockaerts S [37], et al, 2010; Kim CS [38], et al, 2019).

All in all, The inclusion of high BMI and metabolic risks reflects their robust causal associations with OA. For example, a meta-analysis of 63 studies confirmed that obesity (BMI ≥ 30 kg/m²) increases knee OA risk by 2.63-fold (Blagojevic M [33], et al, 2010), while hyperglycemia (Louati K [39], et al, 2015) and hypertension (Wang T [40], et al, 2017) are independently linked to OA progression.

2.9. Statistical analysis

Joinpoint regression analysis was performed using the Joinpoint Regression Program V5.0.2 provided by the U.S. National Cancer Institute Surveillance Research Program. All analyses and visualizations in this study were conducted using the World Health Organization (WHO) Health Equity Assessment Toolkit and R software (V.4.3.2).

3. Results

3.1. Descriptive analysis of the OA burden at global, regional, and national levels

In 1990, 4.8% of the global population had osteoarthritis, amounting to approximately 256 million (95%UI 232–282) people. By 2021, the estimated global prevalence had risen to 7.7%, with around 607 million (95%UI 538–671) people affected. The number of cases has steadily increased across all age groups over the following decades. Table 1 and S1 Table presents the global and regional incidence, prevalence, YLD data, and corresponding ASR for OA in 2021. Among the GBD regions, the highest age-standardized prevalence rates were observed in high-income Asia Pacific (8,608.63 per 100,000, 95%UI 7,674.07–9,485.19), high-income North America (8,421.62 per 100,000, 95%UI 7,534.98–9,282.03), and Eastern Europe (7,906.11 per 100,000, 95%UI 6,954.04–8,880.09). In contrast, the lowest age-standardized prevalence rates were recorded in Southeast Asia (5,675.8 per 100,000, 95%UI 5,001.76–6,320.89), Eastern Sub-Saharan Africa (5,829.96 per 100,000, 95%UI 5,160.63–6,476.62), and Central Sub-Saharan Africa (5,940.49 per 100,000, 95%UI 5,268.27–6,589.54)

Table 1. The ASR of prevalence, incidence and YLDs of OA in 1990 and 2021 for both sexes by GBD regions.

	1990			2021
	ASR of YLDs per 100 000	ASR of Prevalence per 100 000	ASR of Incidence per 100 000	ASR of YLDs per 100 000	ASR of Prevalence per 100 000	ASR of Incidence per 100 000
Global	222.8 (106.65,450.29)	6393.12 (5683.2,7059.53)	489.78 (433.1,541.51)	244.5 (117.06,493.11)	6967.29 (6180.7,7686.06)	535 (472.38,591.97)
East Asia	211 (102.08,424.58)	6157.54 (5425.37,6866.85)	487.3 (428.32,544.04)	245.04 (117.45,492.41)	7036.1 (6216.29,7835.76)	554.47 (486.91,619.37)
Central Asia	215.8 (104.18,434.3)	6143.82 (5368.48,6965.53)	444.99 (392.55,498.51)	249.29 (119.63,500.56)	7034.89 (6120.08,8010.15)	504.46 (442.21,565.75)
Southeast Asia	163.3 (78.14,329.58)	4796.58 (4256.53,5377.23)	376.04 (332.26,418.37)	196.18 (93.56,393.42)	5675.8 (5001.76,6320.89)	437.13 (386.13,485.01)
Oceania	192.33 (92.49,385.92)	5637.24 (5022.89,6261.36)	441.66 (389.8,491.69)	212.87 (102.99,428.52)	6196.48 (5474.55,6895.02)	480.95 (423.12,536.36)
Eastern Europe	265.83 (126.81,540.87)	7541.08 (6611.08,8496.07)	550.43 (484.03,614.18)	280.78 (134.37,567.03)	7906.11 (6954.04,8880.09)	584.97 (515.25,651.42)
Western Europe	238.56 (115.19,479.88)	6736.67 (6071.81,7425.01)	521.51 (465.66,578.73)	253.58 (123.06,510.55)	7113.44 (6407.11,7867.1)	557.66 (497.27,618.53)
Southern Latin America	247.99 (118.32,499.99)	7001.84 (6250.94,7759.15)	540.18 (478.83,600.89)	273.53 (131.22,548.73)	7669.24 (6896.46,8466.3)	596.27 (530.39,660.42)
High-income North America	286.25 (137.18,576.08)	7987.16 (7188.9,8824.97)	605.72 (535.69,670.62)	300.89 (144.87,606.97)	8421.62 (7534.98,9282.03)	646.38 (572.29,715.37)
Caribbean	228.48 (109.52,461.2)	6514.84 (5762.16,7198)	513.03 (455.98,570.5)	251.06 (120.09,508.25)	7134.56 (6327.26,7876.59)	555.77 (493.24,617.51)
Andean Latin America	231.91 (111.18,466.16)	6602.95 (5861.3,7291.11)	519.51 (461.53,577.69)	260.94 (125.19,526.82)	7370.44 (6552.1,8123.13)	578.18 (511.33,641.1)
Central Latin America	231.66 (110.81,468.03)	6661.13 (5900.48,7353.18)	527.96 (468.13,587.05)	264.58 (126.15,535.65)	7499.49 (6635.38,8259.93)	589.49 (521.45,652.52)
Tropical Latin America	228.15 (109.07,460.14)	6604.05 (5863.57,7307.66)	527.59 (467.44,585.25)	259.93 (124.74,524.63)	7424.65 (6582.79,8241)	589.12 (521.39,650.6)
North Africa and Middle East	183.41 (87.76,371.84)	5362.22 (4751.55,5979.22)	424.31 (375.36,470.67)	215.92 (103.37,437.62)	6265.22 (5572.94,6946.23)	488.31 (433.7,542.33)
Central Europe	218.64 (104.72,441.82)	6276.97 (5554.86,7001.94)	474.27 (419.37,525.88)	245.41 (117.63,496.21)	6948.51 (6129.15,7752.72)	522.05 (460.81,580.35)
Australasia	254.48 (123.19,513.81)	7195.28 (6466.74,7950.86)	555.78 (493.55,617.22)	283.38 (139.24,577.97)	7917.6 (7098.38,8735.71)	620.09 (550.76,686.53)
South Asia	181.92 (87.8,368.21)	5407.04 (4798.72,5985.59)	430.77 (382.03,477.59)	216.9 (104,438.04)	6326.13 (5612.39,7009.64)	495.01 (436.64,548.03)
High-income Asia Pacific	291.1 (139.9,587.96)	8071.98 (7169.35,8905.87)	641.16 (568.18,707.78)	314.98 (150.55,636.77)	8608.63 (7674.07,9485.19)	682.07 (606.06,752.84)
Central Sub-Saharan Africa	191.36 (91.09,387.34)	5622.81 (4965.8,6260.93)	441.86 (390.35,491.24)	204.3 (97.78,412.23)	5940.49 (5268.27,6589.54)	463.08 (409.68,515.24)
Eastern Sub-Saharan Africa	173.52 (83.3,351.43)	5113.71 (4544.28,5704.44)	411.5 (363.2,457.47)	200.97 (96.11,405.27)	5829.96 (5160.63,6476.62)	461.02 (407.42,509.93)
Southern Sub-Saharan Africa	229.15 (110.1,460.99)	6559.8 (5794.82,7289.01)	513.31 (454.83,568.81)	249.45 (120.36,500.63)	7161.23 (6333.34,7951.33)	557.24 (493.49,618.18)
Western Sub-Saharan Africa	187.48 (90.32,378.77)	5494.22 (4872.02,6124.65)	439.61 (387.27,489.07)	210.09 (101.11,424.67)	6075.81 (5385.72,6757.27)	483.84 (427.08,536.68)

Open in a new tab

Abbreviations: YLDs = years lived with disability, ASR = Age-standardised rate, OA = osteoarthritis, GBD = Global Burden of Disease.

The number of OA patients of different ages and genders worldwide in 2021, as well as the standardized prevalence and incidence rates, are shown in (Fig 1). OA is more prevalent among individuals aged 35 and older, with higher and rapidly increasing prevalence observed in the 35–90 age group. The peak prevalence for females occurs between the ages of 65 and 69, while for males, it peaks between the ages of 55 and 59. A similar trend is observed in incidence rates, with a sharp increase after the age of 30. The peak incidence for both men and women is between the ages of 50 and 54. Across all estimated years, the incidence and prevalence rate of osteoarthritis has been consistently higher in females than in males.

Fig 1 — (A) Age-specific prevalence number. (B) Age-standardized prevalence rate. (C) Age-specific incidence number. (D) Age-standardized incidence rate.

The sex-specific, age-standardized incidence and prevalence rates of OA have fluctuated over the calendar years. While the number of OA cases and new incidences showed a significant upward trend, the ASR for prevalence and incidence displayed only a slight increase from 1990 to 2021. However, the values for females were consistently much higher than those for males, as illustrated in S1 Fig.

In all GBD regions, knee osteoarthritis is the largest contributor to the age-standardized prevalence rate of mixed osteoarthritis, with the exception of Central Asia and Eastern Europe, where hand osteoarthritis is the predominant contributor (as shown in S2 Fig). The proportion of knee osteoarthritis ranges from 34.9% in Central Asia to 66.4% in East Asia. Hip osteoarthritis contributes the least to OA prevalence across all regions, except for high-income North America and Western Europe, where its contribution is similar to or slightly lower than that of other types of OA. The proportion of hip osteoarthritis ranges from 3.3% in East Asia to 9.5% in high-income North America. The contribution of hand osteoarthritis varies by region, with the lowest contribution in East Asia (21%) and the highest in Central Asia (50.3%).

Globally, South Korea had the highest age-standardized prevalence rate of OA (8,997.39 per 100,000, 95%UI 8,082.99–9,897.80), followed by Brunei (8,815.59 per 100,000, 95%UI 7,892.91–9,708.06), Singapore (8,795.59 per 100,000, 95%UI 7,834.77–9,662.12), the United States (8,686.57 per 100,000, 95%UI 7,789.66–9,568.34), and Japan (8,442.68 per 100,000, 95%UI 7,514.97–9,306.51). In terms of incidence, South Korea also had the highest age-standardized incidence rate (701.23 per 100,000, 95%UI 625.36–776.78), followed by Brunei (686.67 per 100,000, 95%UI 609.40–759.01), Singapore (685.67 per 100,000, 95%UI 606.53–760.52), Japan (671.40 per 100,000, 95%UI 594.22–740.18), and the United States (668.49 per 100,000, 95%UI 591.69–739.57). As for age-standardized YLD, South Korea ranked highest (327.14 per 100,000, 95%UI 157.44–662.81), followed by Singapore (323.81 per 100,000, 95%UI 156.07–648.84), Brunei (319.00 per 100,000, 95%UI 152.75–642.40), the United States (310.78 per 100,000, 95%UI 149.66–627.25), and Japan (309.47 per 100,000, 95%UI 147.61–625.16), as shown in Fig 2.

Fig 2 — Note: This diagram was drawn using R programming software and is the original work of the authors of this article, which permits anyone to distribute, reproduce, adapt or use it for commercial purposes.

3.2. Temporal trends in the OA burden from joinpoint regression analysis

The results of the Joinpoint regression analysis on the trends in OA burden are shown in (Fig 3) and S2 Table. We found that the incidence of OA in males showed a significant upward trend during the periods of 2000–2005 (APC = +0.56, 95% CI = 0.54–0.59), 2010–2016 (APC = +0.45, 95% CI = 0.43–0.47), and 2016–2019 (APC = +0.65, 95% CI = 0.57–0.72). For females, the incidence of OA experienced a significant increase between 2000 and 2006 (APC = +0.55, 95% CI = 0.49–0.60) and from 2006 to 2009 (APC = +0.87, 95% CI = 0.64–1.11).

From 1990 to 2021, the overall incidence of OA in both males and females showed a continuous increasing trend, although there was a slight decline in male OA incidence between 2005 and 2010 (APC = -0.08, 95% CI = -0.11 to -0.06). Additionally, the average annual percent change (AAPC) in male OA incidence from 1990 to 2021 was + 0.28 (95% CI = 0.27–0.29), while the AAPC for female OA incidence was + 0.28 (95% CI = 0.25–0.31).

3.3. Frontier analysis of the association between ideal OA YLD and SDI

To explore the ideal scenario for controlling disease burden under the corresponding SDI conditions for each country/region, a frontier analysis was conducted (S3 Fig). In the results of the frontier analysis, the five countries/regions closest to the frontier fit line in lower SDI countries/regions are marked in blue, while the five countries/regions farthest from the frontier fit line in higher SDI countries/regions are marked in red. Among all countries, the 15 countries/regions farthest from the frontier fit line are marked in black.

For YLD attributable to OA, the countries/regions farthest from the frontier line in higher SDI areas include South Korea, the high-income Asia Pacific region, the United States, Japan, and the high-income North American region. In contrast, the countries/regions closest to the frontier line in lower SDI areas include the Lao People’s Democratic Republic, the Central African Republic, Burkina Faso, the Democratic Republic of the Congo, and Tanzania.

3.4. Risk factor analysis for OA

Within the GBD framework, metabolic risk includes: high blood glucose, high blood pressure, low bone density, high LDL cholesterol, high BMI, and impaired kidney function. After we included all the above factors in our analysis, according to the output of GBD (https://vizhub.healthdata.org/gbd-results/): high BMI and total metabolic risk were the only two risk factors.

In 1990, high BMI was responsible for 35.97 (95%UI -3.14 to 102.31) global age-standardized YLDs attributable to OA. By 2021, high BMI was responsible for 50.59 (95%UI -4.81 to 141.34) global age-standardized YLDs attributable to OA. In 2021, the highest burden of age-standardised YLDs for osteoarthritis due to high BMI was in high-income North America and the lowest in South Asia. The burden of age-standardised YLDs for osteoarthritis attributable to high BMI in 1990 vs 2021 can be found in S4 Fig.

3.5. Forecast analysis of the OA burden to 2035

The predicted ASR of prevalence and predicted ASR of YLDs for OA through 2035 are shown in (Fig 4). Globally, with the exception of an increase in the age-standardized prevalence rate for males, the ASRs for both OA prevalence and YLDs are expected to decline annually until 2036. Specifically, the age-standardized prevalence rate for males is projected to increase from 5,763 per 100,000 in 2020–5,922 per 100,000 in 2036, while the age-standardized YLDs for males are expected to decrease from 200.23 per 100,000 in 2020 to 195.34 per 100,000 in 2036. For females, the age-standardized prevalence rate is predicted to decrease from 8,034 per 100,000 in 2020–7,925 per 100,000 in 2036, and the age-standardized YLDs for females are expected to decline from 283.79 per 100,000 in 2020 to 281.76 per 100,000 in 2036.

4. Discussion

This study provides data on the incidence, prevalence, and YLD of OA at global, regional, and national levels from 1990 to 2021, presenting a comprehensive assessment based on trend analysis, frontier analysis, decomposition analysis, and predictive analysis. It reveals a continuous rise in the global OA burden, with the fastest growth observed between 2000 and 2010. Decomposition analysis by affected joint sites indicates that knee osteoarthritis (KOA) is the largest contributor to the age-standardized prevalence rate of OA. Although future projections suggest that the ASR for incidence, prevalence, and YLD may slightly decline by 2035, the total number of OA cases will continue to rise due to population aging and growth, leading to a significant public health burden. Managing and controlling OA in the future will pose major challenges, requiring the allocation of healthcare resources and the development of intervention strategies in a scientifically equitable manner.

In 2021, the global OA burden was substantial, with 607 million prevalent cases, 46.63 million new cases, and 21.3 million YLDs. East Asia had the highest number of OA cases (158,285,424, 95%UI 139,469,183–176,824,968), reflecting the region’s large population base and accelerated aging. The high-income Asia Pacific region had the highest global age-standardized prevalence rate at 8,608.63 (95%UI 7,674.07–9,485.19). South Korea, facing severe population aging, ranked first globally with an ASR of YLDs at 244.5 (95%UI 117.06–493.11), an ASR of prevalence at 6,967.29 (95%UI 6,180.7–7,686.06), and an ASR of incidence at 535 (95%UI 472.38–591.97). China had the highest number of OA cases in 2021, which is related to its large population. We found that the region with the highest age-standardised prevalence of OA was the high-income Asia-Pacific region, rather than the previously high-income North America; at the national level, the country with the highest age-standardised prevalence of OA changed from the United States to the Republic of Korea, and the Asia-Pacific region of Singapore, Brunei, and Japan had a higher burden of OA, suggesting a significant geographic shift of the burden of disease for OA. Japan is known to have one of the world's highest levels of population aging, having entered a super-ageing society by 2007. As of 2024, the number of elderly people aged 65 years and older in Japan has reached 36.25 million, accounting for 29.3% of the total population [41]; South Korea has the lowest fertility rate in the world, and the trend of population aging is increasing, and it is expected that it will overtake Japan to become the most seriously aging country in 2044 [42]; in addition, China has already reached a demographic inflection point in 2022, and the number of deaths in China was more than the number of births in 2022, with negative population growth occurring for the first time [43]. Together, these changes illustrate the wide variation in OA burdens and trends across countries and regions, which underscores the importance of flexible, region-specific public health policies, and reminds health policymakers around the globe of the urgency of proactively addressing population ageing [44].

Globally, the incidence, prevalence, and YLD of OA continuously increased from 1990 to 2021, intensifying the global burden. With rising aging and obesity rates, the high incidence of OA places tremendous pressure on healthcare systems and economies worldwide. OA not only incurs direct economic costs (e.g., for medication or surgical interventions) and indirect costs (e.g., lost workdays, premature death) but also results in personal costs (e.g., chronic pain, fatigue, limited mobility). The medical expenditure associated with OA accounts for 1% ~ 2.5% of GDP [45], underscoring the significant economic burden posed by the disease. Therefore, managing and preventing OA effectively will become a crucial global public health issue in the coming decades. Countries must invest in early intervention, disease management, and technological innovation while optimizing resource allocation to ensure timely and effective treatment and care for patients [46].

When analyzing overall trends in multiple segments, we found that from 2000 to 2005, the ASRs for incidence, prevalence, and YLD of OA increased most significantly, marking this period as the fastest-growing phase of the disease burden. Similarly, a study based on GBD 2015 data showed that the rate of increase in YLD due to OA was notably higher before 2005 than after. This might be attributed to the global population aging and the obesity epidemic [47]. Changes in major risk factors for disease onset and progression, along with how governments and individuals respond to these factors, may together influence the time trends of disease burden. Globally, aging and obesity are the primary risk factors for OA, particularly in the early 21st century, when the global burden of OA worsened due to accelerated aging and rising obesity rates. This trend reflects the impact of demographic and lifestyle changes on global public health. To address this trend, the United Nations and the World Health Organization launched a series of global policies in the early 2000s, focusing on addressing population aging and obesity. Aging policies aimed to delay the onset of age-related diseases and promote healthy aging, while anti-obesity policies sought to curb rising global obesity rates through improved diets and increased physical activity. These policies may have contributed to slowing the growth of the OA burden after 2005 and increased global attention to OA management.

When classifying OA by affected joints, KOA emerged as the largest contributor to the total age-standardized prevalence rate of OA, with its proportion ranging from 34.9% to 66.4% across different GBD regions. KOA, therefore, warrants more medical attention and research. The burden of OA is significantly higher in women than in men [48], calling for more effective prevention and management measures tailored to women. Women’s unique physiological characteristics, such as hormonal and reproductive factors, significantly affect their OA risk, especially the decline in estrogen levels, which exacerbates cartilage damage and bone loss, increasing the likelihood of OA [49]. Additionally, during physiological stages such as menarche, pregnancy, and menopause, women often experience weight gain accompanied by systemic inflammation, further elevating their OA risk. Taking these factors into account, there is an urgent need to strengthen our focus on women's joint health, to pay more attention to protecting women's health rights and interests, and to promote preventive strategies and interventions to address this growing public health problem.

Musculoskeletal pain is the primary patient-reported outcome in patients with OA and a major cause of disability and functional limitations [50]. Studies have shown that weight loss can reduce many of the symptoms of osteoarthritis, including pain [51]. In the analysis of risk factors, high BMI and metabolic risks emerged as the only two GBD risk factors for OA. Compared to NSAIDs, weight control is more convenient, accessible, and safer, so it is significant that we incorporate more effective weight control into prevention policies at global, regional, and national levels [52]. It is worth noting that the GBD database includes only 88 common disease risk factors, which inevitably limits the comprehensive exploration of OA risk factors to some extent. Furthermore, evidence indicates that patterns of disease and exposure or relative risk functions for exposure and outcome sometimes differ from expert consensus. This discrepancy arises because the GBD study is committed to a rules-based approach to evidence synthesis, which may lead to findings that diverge from other assessments that rely more heavily on expert opinions. Additionally, data collection for the GBD database is closely tied to the level of economic development in different regions. For many countries in Asia, Africa, and Latin America, the data included in the analysis often lack key risk factors such as physical activity levels, vitamin intake, and analgesic use.Therefore, the conclusion that “high BMI and metabolic risk are the only two significant risk factors for OA” should be approached with caution. However, with the continued expansion of the GBD database and updates to statistical models, there is hope for exploring a broader range of OA risk factors in the future. This progress will facilitate a more comprehensive and systematic understanding of the burden of OA.

Although high-SDI countries are generally expected to have better healthcare systems and lower disease burdens, the burden of OA is disproportionately concentrated in these countries [53] (e.g., South Korea, the United States, Japan). Previous research [54] has shown a positive correlation between OA burden and SDI level, suggesting that OA may become one of the most prevalent diseases in high-income countries. This phenomenon is partly due to population aging and obesity, both of which are key risk factors for OA. Furthermore, the lack of an effective cure for OA continues to drive its rising prevalence. In addition, diagnostic bias stemming from unequal access to healthcare services exacerbates this trend. Therefore, to effectively reduce the OA burden, countries must implement targeted policies and allocate resources based on SDI levels. High-SDI countries should focus on early diagnosis and management strategies, while low-SDI countries need more medical assistance [55]. This call for action requires global cooperation to address the growing inequalities in OA burden.

Notably, although the ASRs for OA incidence, prevalence, and YLDs are expected to decline annually by 2035 (with male age-standardized YLDs projected to decrease from 200.23 per 100,000 in 2020 to 195.34 per 100,000 in 2036, and female age-standardized YLDs projected to decrease from 283.79 per 100,000 in 2020 to 281.76 per 100,000 in 2036), the number of cases for all three indicators is expected to increase. This indicates a heavy disease burden and new challenges in controlling and managing OA.

It is well established that aging and obesity are closely associated with osteoarthritis (OA). With advancing age, the human skeletal and joint systems undergo degenerative changes, including cartilage wear, ligament laxity, and reduced bone density, making older adults more susceptible to OA. Aging exacerbates the burden on weight-bearing joints, particularly the knees and hips. Therefore, older adults should engage in moderate exercise to maintain good joint mobility and muscle strength, thereby preventing joint stiffness. Low-impact aerobic exercises, such as swimming and cycling, as well as strength-training exercises, are particularly recommended [56].Obesity is an independent and significant risk factor for OA. Excessive body weight increases the mechanical load on joints, especially the knees and hips. Additionally, adipose tissue secretes inflammatory factors that can exacerbate cartilage damage and accelerate the onset and progression of OA. Thus, weight control is a critical measure to prevent obesity-related OA. Maintaining a healthy weight and appropriate body fat percentage through a balanced diet and regular physical activity can reduce joint stress.These simple yet effective measures not only mitigate the adverse effects of aging on joint health but also help prevent obesity-induced OA, ultimately improving quality of life and reducing the burden of OA [57].

It is undeniable that this study has some limitations. First, the underperformance of healthcare systems in developing countries leads to an underestimation of OA cases in GBD assessments, along with prevalent misdiagnosis and underdiagnosis. Second, because the original GBD data came from different countries, data quality varied significantly, and the heterogeneity of OA measurements further complicates disease classification and population comparisons. Although GBD employs data cleaning and statistical modeling methods to minimize these limitations, the findings still rely heavily on modeling data, particularly at the national level [58]. Moreover, the lag in GBD data presents another challenge, as current estimates often rely on historical trends, failing to accurately reflect present-day realities. Therefore, strengthening international cooperation, enhancing disease diagnostic standards in underdeveloped countries, and collecting more health data are urgently needed to improve the accuracy of disease burden estimates and research. Despite these limitations, GBD provides critical insights into OA epidemiology and lays a solid foundation for public health policy development and the rational allocation of healthcare resources [59].

To address the limitations outlined and advance osteoarthritis (OA) burden assessment, future studies should prioritize the following directions: 1. Standardization of diagnostic criteria and data collection protocols across low- and middle-income countries to mitigate underdiagnosis and misclassification. 2. Integration of real-time data streams (e.g., electronic health records, wearable sensors) with GBD modeling frameworks to reduce temporal lag and improve responsiveness to emerging risk factors like obesity epidemics. 3. Mechanistic studies on metabolic-inflammatory crosstalk in OA pathogenesis, particularly exploring therapeutic targets. 4. Cost-effectiveness analyses of precision interventions, combining disease-modifying therapies with socioeconomic burden models to guide global resource allocation. These efforts would synergize with the GBD framework to bridge evidence gaps and translate epidemiological insights into actionable clinical strategies.

5. Conclusion

In conclusion, as a major public health problem, the global burden of OA is generally on the rise from 1990 to 2021. The highest age-standardised prevalence is located in the high-income Asia-Pacific region, with the lowest in South-East Asia.Countries with higher SDI bear a disproportionate burden of OA, and SDI-related inequalities between countries have increased over time. Overall, women have a higher burden of OA than men. Among the various types of OA, osteoarthritis of the knee had the greatest impact on the overall burden. Together, these findings highlight the enormous pressures on OA prevention and control, such as the increasing number of cases globally, the overall inadequacy of healthcare resources, and inequalities in their distribution. Global health policymakers should consider more targeted interventions and flexible approaches, such as paying more attention to women's health rights and interests, actively assisting less-developed countries, improving the diagnosis and treatment of OA, promoting the hierarchical and rational allocation of healthcare resources, transforming people's health concepts, and meeting the different healthcare needs of each country, so that we can jointly contribute to the prevention, treatment and management of OA.

Supporting information

S1 Fig. Trends in the all-age cases and age-standardized incidence and prevalence rates of OA by sex from 1990 to 2021.

Abbreviations: OA = osteoarthritis.

(DOCX)

pone.0324296.s001.docx^{(1.1MB, docx)}

S2 Fig. Contribution of different osteoarthritis sites to combined age-standardised prevalence, globally and by GBD region, 2021.

Abbreviations: GBD = Global Burden of Disease.

(DOCX)

pone.0324296.s002.docx^{(135.6KB, docx)}

S3 Fig. Frontier analysis based on SDI and OA YLDs in 204 countries and territories.

Abbreviations: SDI = Socio-Demographic Index, OA = osteoarthritis, YLDs = years lived with disability.

(DOCX)

pone.0324296.s003.docx^{(776.7KB, docx)}

S4 Fig. The burden of age-standardised YLDs for osteoarthritis attributable to high BMI in 1990 vs 2021.

Abbreviations: YLDs = years lived with disability, BMI = body mass index.

(DOCX)

pone.0324296.s004.docx^{(672.8KB, docx)}

S1 Table. The case number of prevalence, incidence and YLDs of OA in 1990 and 2021 for both sexes by GBD regions.

Abbreviations: YLDs = years lived with disability, OA = osteoarthritis, GBD = Global Burden of Disease.

(DOCX)

pone.0324296.s005.docx^{(18.8KB, docx)}

S2 Table. Joinpoint regression analysis of the sex-specific age-standardized incidence rate for OA globally from 1990 to 2021.

Abbreviations: OA = osteoarthritis.

(DOCX)

pone.0324296.s006.docx^{(35KB, docx)}

Acknowledgments

Everyone who contributed significantly to the work has been listed.

Data Availability

In this study, we utilized OA data from the GBD database. All GBD 2021 data are publicly available online (https://vizhub.healthdata.org/gbd-compare/ and https://vizhub.healthdata.org/gbd-results).

Funding Statement

This work was supported financially by the National High Level Traditional Chinese Medicine Hospital Clinical Research Funding (DZMG-XZYY-23001).

References

1.Bowden JL, Hunter DJ, Deveza LA, Duong V, Dziedzic KS, Allen KD, et al. Core and adjunctive interventions for osteoarthritis: efficacy and models for implementation. Nat Rev Rheumatol. 2020;16(8):434–47. doi: 10.1038/s41584-020-0447-8 [DOI] [PubMed] [Google Scholar]
2.Allen KD, Thoma LM, Golightly YM. Epidemiology of osteoarthritis. Osteoarthritis Cartilage. 2022;30(2):184–95. doi: 10.1016/j.joca.2021.04.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.GBD 2021 Osteoarthritis Collaborators. Global, regional, and national burden of osteoarthritis, 1990-2020 and projections to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Rheumatol. 2023;5(9):e508–22. doi: 10.1016/S2665-9913(23)00163-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Dieleman JL, Baral R, Birger M, Bui AL, Bulchis A, Chapin A. US spending on personal health care and public health, 1996-2013. JAMA. 2016;316(24):2627–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Woo J, Lau E, Lau CS, Lee P, Zhang J, Kwok T, et al. Socioeconomic impact of osteoarthritis in Hong Kong: utilization of health and social services, and direct and indirect costs. Arthritis Rheum. 2003;49(4):526–34. doi: 10.1002/art.11198 [DOI] [PubMed] [Google Scholar]
6.Hunter DJ, March L, Chew M. Osteoarthritis in 2020 and beyond: a Lancet Commission. Lancet. 2020;396(10264):1711–2. doi: 10.1016/S0140-6736(20)32230-3 [DOI] [PubMed] [Google Scholar]
7.Murray CJL, Lopez AD. Measuring the global burden of disease. N Engl J Med. 2013;369(5):448–57. doi: 10.1056/NEJMra1201534 [DOI] [PubMed] [Google Scholar]
8.Yang G, Wang J, Liu Y, Lu H, He L, Ma C, et al. Burden of knee osteoarthritis in 204 countries and territories, 1990–2019: results from the Global Burden of Disease Study 2019. Arthritis Care Res (Hoboken). 2023;75(12):2489–500. doi: 10.1002/acr.25158 [DOI] [PubMed] [Google Scholar]
9.Wan J, Qian X, He Z, Zhu Z, Cheng P, Chen A. Epidemiological trends of hand osteoarthritis from 1990 to 2019: estimates from the 2019 Global Burden of Disease study. Front Med (Lausanne). 2022;9:922321. doi: 10.3389/fmed.2022.922321 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Liu Q, Wang S, Lin J, Zhang Y. The burden for knee osteoarthritis among Chinese elderly: estimates from a nationally representative study. Osteoarthritis Cartilage. 2018;26(12):1636–42. doi: 10.1016/j.joca.2018.07.019 [DOI] [PubMed] [Google Scholar]
11.Zhao G, Zhu S, Zhang F, Zhang X, Zhang X, Li T, et al. Global Burden of osteoarthritis associated with high body mass index in 204 countries and territories, 1990-2019: findings from the Global Burden of Disease Study 2019. Endocrine. 2023;79(1):60–71. doi: 10.1007/s12020-022-03201-w [DOI] [PubMed] [Google Scholar]
12.Turkiewicz A, Petersson IF, Björk J, Hawker G, Dahlberg LE, Lohmander LS, et al. Current and future impact of osteoarthritis on health care: a population-based study with projections to year 2032. Osteoarthritis Cartilage. 2014;22(11):1826–32. doi: 10.1016/j.joca.2014.07.015 [DOI] [PubMed] [Google Scholar]
13.GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1223–49. doi: 10.1016/S0140-6736(20)30752-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Bellamy N, Buchanan WW, Goldsmith CH, Campbell J, Stitt LW. Validation study of WOMAC: a health status instrument for measuring clinically important patient relevant outcomes to antirheumatic drug therapy in patients with osteoarthritis of the hip or knee. J Rheumatol. 1988;15(12):1833–40. [PubMed] [Google Scholar]
15.Murray CJL, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C, et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380(9859):2197–223. doi: 10.1016/S0140-6736(12)61689-4 [DOI] [PubMed] [Google Scholar]
16.von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. PLoS Med. 2007;4(10):e296. doi: 10.1371/journal.pmed.0040296 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Ho-Pham LT, Lai TQ, Mai LD, Doan MC, Pham HN, Nguyen TV. Prevalence of radiographic osteoarthritis of the knee and its relationship to self-reported pain. PLoS One. 2014;9(4):e94563. doi: 10.1371/journal.pone.0094563 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Kellgren JH, Lawrence JS. Radiological assessment of osteo-arthrosis. Ann Rheum Dis. 1957;16(4):494–502. doi: 10.1136/ard.16.4.494 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Kohn MD, Sassoon AA, Fernando ND. Classifications in Brief: Kellgren-Lawrence classification of osteoarthritis. Clin Orthop Relat Res. 2016;474(8):1886–93. doi: 10.1007/s11999-016-4732-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Hankey BF, Ries LA, Kosary CL, Feuer EJ, Merrill RM, Clegg LX, et al. Partitioning linear trends in age-adjusted rates. Cancer Causes Control. 2000;11(1):31–5. doi: 10.1023/a:1008953201688 [DOI] [PubMed] [Google Scholar]
21.Liu Z, Jiang Y, Yuan H, Fang Q, Cai N, Suo C, et al. The trends in incidence of primary liver cancer caused by specific etiologies: results from the Global Burden of Disease Study 2016 and implications for liver cancer prevention. J Hepatol. 2019;70(4):674–83. doi: 10.1016/j.jhep.2018.12.001 [DOI] [PubMed] [Google Scholar]
22.Kim HJ, Fay MP, Feuer EJ, Midthune DN. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med. 2000;19(3):335–51. doi: 10.1002/(sici)1097-0258(20000215)19:3<335::aid-sim336>3.0.co;2-z [DOI] [PubMed] [Google Scholar]
23.GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709–33. doi: 10.1016/S0140-6736(20)30045-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Hosseinpoor A, Bergen N, Schlotheuber A. Promoting health equity: WHO health inequality monitoring at global and national levels. Glob Health Action. 2015;8:29034. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Xie Y, Bowe B, Mokdad AH, Xian H, Yan Y, Li T, et al. Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016. Kidney Int. 2018, 94(3):567–81. [DOI] [PubMed] [Google Scholar]
26.Ordunez P, Martinez R, Soliz P, Giraldo G, Mujica O, Nordet P. Rheumatic heart disease burden, trends, and inequalities in the Americas, 1990–2017: a population-based study. Lancet Glob Health. 2019;7(10):e1388–97. [DOI] [PubMed] [Google Scholar]
27.Langat A, Orwa G, Koima J. Cancer cases in Kenya; forecasting incidents using Box & Jenkins arima model. Biomed Stat Inf. 2017;2(2):37–48. [Google Scholar]
28.Wei J, Chen L, Huang S, Li Y, Zheng J, Cheng Z, et al. Time trends in the incidence of spinal pain in China, 1990 to 2019 and its prediction to 2030: The Global Burden of Disease Study 2019. Pain Ther. 2022;11(4):1245–66. doi: 10.1007/s40122-022-00422-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Nguyen HV, Naeem MA, Wichitaksorn N, Pears R. A smart system for short-term price prediction using time series models. Comput Electr Eng. 2019;76:339–52. [Google Scholar]
30.GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–22. doi: 10.1016/S0140-6736(20)30925-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Flaxman AD, Vos T, Murray CJL. An integrative meta-regression framework for descriptive epidemiology: the DisMod-MR tool. Popul Health Metr. 2015;13(4). [Google Scholar]
32.Zheng J, Liu Y, Guan S, Guo Z, Duan L, Lv S. Global, regional, and national burden of neglected tropical diseases and malaria in the general population, 1990–2021: systematic analysis of the global burden of disease study 2021. J Adv Res. 2025;25:S2090-1232(25)00223-1. [DOI] [PubMed] [Google Scholar]
33.Blagojevic M, Jinks C, Jeffery A, Jordan KP. Risk factors for onset of osteoarthritis of the knee in older adults: a systematic review and meta-analysis. Osteoarthritis Cartilage. 2010;18(1):24–33. doi: 10.1016/j.joca.2009.08.010 [DOI] [PubMed] [Google Scholar]
34.Felson D, Anderson J, Naimark A, Walker A, Meenan R. Obesity and knee osteoarthritis. The framingham study. Ann Intern Med. 1988;109(1):18–24. [DOI] [PubMed] [Google Scholar]
35.Zengini E, Hatzikotoulas K, Tachmazidou I, Steinberg J, Hartwig F, Southam L. Genome-wide analyses using UK Biobank data provide insights into the genetic architecture of osteoarthritis. Nat Rev Rheumatol. 2018;14(6):327–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Hoeven TA, Kavousi M, Clockaerts S. Association of low bone mineral density with osteoarthritis in the Rotterdam study. Ann Rheum Dis. 2013;72(6):918–23. [DOI] [PubMed] [Google Scholar]
37.Baudart P, Louati K, Marcelli C, Berenbaum F, Sellam J. The association of dyslipidaemia with osteoarthritis: a systematic review. Osteoarthritis Cartilage. 2010, 18(5):628–36. [Google Scholar]
38.Gao K, Zhang C, Zhang Y, Zhang L, Xu J, Xue H, et al. Is chronic kidney disease associated with osteoarthritis? The United States national health and nutrition examination survey 2011–2020. BMC Nephrol. 2024;25(1):236. doi: 10.1186/s12882-024-03672-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Louati K, Vidal C, Berenbaum F, Sellam J. Association between diabetes mellitus and osteoarthritis: systematic literature review and meta-analysis. RMD Open. 2015;1(1):e000077. doi: 10.1136/rmdopen-2015-000077 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Lo K, Au M, Ni J, Wen C. Hypertension and risk of osteoarthritis: a meta-analysis of observational studies. Arthritis Rheumatol. 2017;69(4):761–8. [Google Scholar]
41.Rosenberg M, Tomioka S, Barber SL. Research to inform health systems’ responses to rapid population ageing: a collection of studies funded by the WHO Centre for Health Development in Kobe, Japan. Health Res Policy Syst. 2022;20(Suppl 1):128. doi: 10.1186/s12961-022-00917-z [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Han E-J, Han Z-A, Kim H, Jung TR. Monitoring healthy ageing for the next decade: South Korea’s perspective. Age Ageing. 2023;52(Suppl 4):iv10–2. doi: 10.1093/ageing/afad102 [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Ju W, Zheng R, Zhang S, Zeng H, Sun K, Wang S, et al. Cancer statistics in Chinese older people, 2022: current burden, time trends, and comparisons with the US, Japan, and the Republic of Korea. Sci China Life Sci. 2023;66(5):1079–91. doi: 10.1007/s11427-022-2218-x [DOI] [PubMed] [Google Scholar]
44.Hunter DJ, Schofield D, Callander E. The individual and socioeconomic impact of osteoarthritis. Nat Rev Rheumatol. 2014;10(7):437–41. doi: 10.1038/nrrheum.2014.44 [DOI] [PubMed] [Google Scholar]
45.Dieleman JL, Cao J, Chapin A, Chen C, Li Z, Liu A, et al. US health care spending by payer and health condition, 1996-2016. JAMA. 2020;323(9):863–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Cross M, Smith E, Hoy D, Nolte S, Ackerman I, Fransen M, et al. The global burden of hip and knee osteoarthritis: estimates from the global burden of disease 2010 study. Ann Rheum Dis. 2014;73(7):1323–30. doi: 10.1136/annrheumdis-2013-204763 [DOI] [PubMed] [Google Scholar]
47.GBD 2015 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388(10053):1545–602. [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Feng C, Liu L, Zhang J, Wang L, Lv K, Li H, et al. Investigation of GPM6B as a novel therapeutic target in Osteoarthritis. Arthritis Res Ther. 2024;26(1):200. doi: 10.1186/s13075-024-03430-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Liao F-X, Yang S, Liu Z-H, Bo K-D, Xu P-F, Chang J. Estrogen receptor is involved in the osteoarthritis mediated by Atg16L1-NLRP3 activation. Jt Dis Relat Surg. 2024;35(3):513–20. doi: 10.52312/jdrs.2024.1247 [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Dainese P, Wyngaert KV, De Mits S, Wittoek R, Van Ginckel A, Calders P. Association between knee inflammation and knee pain in patients with knee osteoarthritis: a systematic review. Osteoarthritis Cartilage. 2022;30(4):516–34. doi: 10.1016/j.joca.2021.12.003 [DOI] [PubMed] [Google Scholar]
51.Bliddal H, Bays H, Czernichow S, Uddén Hemmingsson J, Hjelmesæth J, Hoffmann Morville T, et al. Once-weekly semaglutide in persons with obesity and knee osteoarthritis. N Engl J Med. 2024;391(17):1573–83. doi: 10.1056/NEJMoa2403664 [DOI] [PubMed] [Google Scholar]
52.Badley E. Population projections and the effect on rheumatology. Ann Rheum Dis. 1991;50:3–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Cao F, Xu Z, Li X-X, Fu Z-Y, Han R-Y, Zhang J-L, et al. Trends and cross-country inequalities in the global burden of osteoarthritis, 1990–2019: a population-based study. Ageing Res Rev. 2024;99:102382. doi: 10.1016/j.arr.2024.102382 [DOI] [PubMed] [Google Scholar]
54.Cao F, He Y, Wang Y, Zha C, Lu J, Tao L. Global burden and cross-country inequalities in autoimmune diseases from 1990 to 2019. Autoimmun Rev. 2023;22(6):103326. [DOI] [PubMed] [Google Scholar]
55.Safiri S, Kolahi A-A, Smith E, Hill C, Bettampadi D, Mansournia MA, et al. Global, regional and national burden of osteoarthritis 1990-2017: a systematic analysis of the Global Burden of Disease Study 2017. Ann Rheum Dis. 2020;79(6):819–28. doi: 10.1136/annrheumdis-2019-216515 [DOI] [PubMed] [Google Scholar]
56.Morales-Ivorra I, Romera-Baures M, Roman-Viñas B, Serra-Majem L. Osteoarthritis and the Mediterranean diet: a systematic review. Nutrients. 2018;10(8):1030. doi: 10.3390/nu10081030 [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Vincent HK, Heywood K, Connelly J, Hurley RW. Obesity and weight loss in the treatment and prevention of osteoarthritis. PM R. 2012;4(5 Suppl):S59-67. doi: 10.1016/j.pmrj.2012.01.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Jafarzadeh SR, Felson DT. Updated estimates suggest a much higher prevalence of arthritis in United States adults than previous ones. Arthritis Rheumatol. 2018;70(2):185–92. doi: 10.1002/art.40355 [DOI] [PMC free article] [PubMed] [Google Scholar]
59.WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004, 363(9403):157–63. [DOI] [PubMed] [Google Scholar]

PLoS One. 2025 May 27;20(5):e0324296. doi: 10.1371/journal.pone.0324296.r001

Author response to Decision Letter 0

1 Feb 2025

PLoS One. doi: 10.1371/journal.pone.0324296.r002

Decision Letter 0

Xindie Zhou

19 Mar 2025

PONE-D-25-05288Global, Regional, and National Burden of Osteoarthritis from 1990 to 2021 and Projections to 2035: A cross-sectional study for the Global Burden of Disease Study 2021PLOS ONE

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 May 03 2025 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.

Please include the following items when submitting your revised manuscript:

A rebuttal 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,

Xindie Zhou

Academic Editor

PLOS ONE

Journal Requirements:

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

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: [This work was supported financially by the National High Level Traditional Chinese Medicine Hospital Clinical Research Funding (DZMG-XZYY-23001)].

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. PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager.

4. We note that Figure 2 in your submission contain [map/satellite] 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 (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:

1. You may seek permission from the original copyright holder of Figure 2 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].

2. 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/

5. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information .

[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?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

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

Reviewer #1: Yes

**********

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for the opportunity to review this manuscript.

The study addresses a crucial topic regarding the prevalence and burden of osteoarthritis (OA) on a global scale. Given the increasing impact of OA on individuals and healthcare systems, it is vital to have up-to-date prevalence estimates and projections for effective public health planning.

However, a study published in 2023 already presents the burden of OA and its projections based on data from GBD 2021, which raises some doubts regarding the relevance of this study.

Nonetheless, I provide below several comments and suggestions that I believe could enhance the clarity, rigor, and overall quality of the manuscript

Please see attachment.

**********

6. PLOS authors have the option to publish the peer review history of their article (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: Yes: Lara Gil Gomes de Campos

**********

[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.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step.

Attachment

Submitted filename: Review.docx

pone.0324296.s007.docx^{(15.8KB, docx)}

PLoS One. 2025 May 27;20(5):e0324296. doi: 10.1371/journal.pone.0324296.r003

Author response to Decision Letter 1

27 Mar 2025

Dear Editor and Reviewers,

Thank you for taking the time to review our manuscript and providing valuable comments and suggestions. On behalf of all the authors, I would like to express our sincere gratitude for your insightful feedback. After thorough discussions among the co-authors, we have addressed your suggestions and made revisions to the manuscript accordingly. The modified sections have been highlighted using the track changes mode. Below, we provide a point-by-point response to the reviewers' comments:

Reviewer #1: The study addresses a crucial topic regarding the prevalence and burden of osteoarthritis (OA) on a global scale. Given the increasing impact of OA on individuals and healthcare systems, it is vital to have up-to-date prevalence estimates and projections for effective public health planning.

However, a study published in 2023 already presents the burden of OA and its projections based on data from GBD 2021, which raises some doubts regarding the relevance of this study.

Nonetheless, I provide below several comments and suggestions that I believe could enhance the clarity, rigor, and overall quality of the manuscript.

Response: Thank you very much for your valuable insights. We fully understand and acknowledge your concerns. The issue you raised was also a key consideration during the initial design of this study. We had previously recognized that without restricting factors such as region and population, the estimated burden of osteoarthritis (OA) might closely resemble the findings from prior analyses using data from the Global Burden of Disease study. However, considering the widespread impact of the COVID-19 pandemic since 2019 [1], along with the accelerating population aging trends in major global economies, particularly in the Asia-Pacific region, we hypothesize that the prevalence of OA may be undergoing subtle changes.We therefore adopted a more diversified approach to analyze the global burden of OA from different perspectives, such as time, region, and gender, which led to some completely new conclusions.

As confirmed in our study, the projected age-standardized prevalence rate (ASPR) of OA over the next 15 years does not show a continuous increase, as previously assumed. Instead, it demonstrates an overall declining trend. This might be attributable to advancements in OA prevention and treatment, as well as strengthened international medical collaboration [2]. Furthermore, the GBD team has also emphasized the necessity of conducting epidemiological analyses using GBD 2021 data [3].

Compared to previously published studies on global OA epidemiology, this study employed methods such as trend analysis, decomposition analysis, frontier analysis, and predictive analysis to examine the burden of OA from multiple perspectives, including gender, age, and time. First, we observed that the ASPR of OA was highest in the high-income Asia-Pacific region rather than in high-income North America, as previously reported. At the national level, the highest ASPR shifted from the United States to South Korea, with countries such as Singapore, Brunei, and Japan also bearing a significant OA burden, indicating a notable geographic shift in OA burden. Second, our frontier analysis revealed that high-SDI (Socio-Demographic Index) countries, including the United States, Japan, and South Korea, bear a disproportionately high OA burden. Based on this information, we updated the global status of OA.

Additionally, we conducted a further analysis of OA risk factors. We found that, compared to GBD 2019, which included only BMI as a risk factor, GBD 2021 incorporates two major risk factors: metabolic risks and high BMI. This provides an updated and more comprehensive reference for future research [4].

Reviewer #1: Please provide the full term before using the abbreviation SDI.

Response: Thanks to your careful attention, we have refined the full name of the SDI - Socio-Demographic Index - and correctly labeled the first occurrence of “SDI” in the text.

Reviewer #1: The conclusion in the abstract seems too long and repetitive with the results. I suggest that the authors focus on the results in the previous section and leave one or two paragraphs to conclude with the overall burden of OA and its implications for health policies.

Response:We have shortened the word count of our conclusions and followed your suggestion to elaborate on our findings on the global burden of OA in the results section. In addition, in the conclusions section, we summarize the overall burden of OA, the current challenges, and what strategies the public health sector sector should adopt to address these changes.

Reviewer #1: I suggest to use a full stop after “populations” and to combine the sentences where the authors describe the pathological changes and the clinic manifestations.

Response:Thank you for your suggestion, our previous presentation was indeed not fluent enough, we have broken the sentences as you suggested and reintegrated the discourse on the pathological changes and clinical manifestations of osteoarthritis.

Reviewer #1: The statement '… making OA one of the leading causes of chronic pain and long-term disability in older adults' requires a citation.

Response:Thank you for the reminder that references do need to be added here to further support the formulation, and we have already cited the quote.

Reviewer #1: The statement “With the rapid acceleration of global aging and the sharp rise in obesity rates across all age groups, the incidence of OA continues to increase” requires a citation.Additionally, it would be important to previously state the role of age and obesity as risk factors for the development of OA, in order to clarify the following idea for non-expert readers.

Response:Thank you for the reminder that your suggestion does have an important role to play for a wider potential audience, and we have quoted the quote, followed by a brief elaboration on how age and obesity as risk factors affect osteoarthritis.

Reviewer #1: In the sentence “ As of 2020, the global prevalence of OA had risen by 132.2% compared to 1990”, I would suggest to use “By” instead of “As of”. Additionally, the sentence requires a citation.

Response:Thanks to your careful reminders, we have revised and improved the grammar and wording as you suggested, and we have quoted the sentence.

Reviewer #1: As mentioned earlier, I believe this is not the first study to estimate the burden of OA across all global regions based on GBD 2021 data. If the authors believe that this study differs from the one presented in this article (https://pmc.ncbi.nlm.nih.gov/articles/PMC10477960/pdf/main.pdf), they should acknowledge the existence of the previous study and highlight the differences, as well as what new insights their study brings.

Response:Thanks to your suggestion, we recognize the contribution of this article (https://pmc.ncbi.nlm.nih.gov/articles/PMC10477960/pdf/main.pdf). It is important to emphasize how our study differs from previous studies, and therefore we have enhanced the relevant statements in the introduction section to highlight the differences in this study and in the discussion section to emphasize the new insights we have presented.

Admittedly, this article has provided a more comprehensive analysis of the disease burden of OA, but it is important to note that our article still has many unique features. First, this article, published in Lancet Rheumatol, used three main approaches, namely descriptive, decomposition, and predictive analyses, to illustrate the global burden of OA and regional differences. Our article, on the other hand, used a more diversified approach and drew some new conclusions. First, we described the temporal trends of OA burden, regional distribution changes, and the changes of OA burden over time through joinpoint analysis; second, we also described the gender differences of OA burden, and we found that women have a heavier burden of OA than men (in terms of incidence and prevalence); in addition to this, we found that within the framework of GBD 2021, BMI and metabolic factors are the OA two major risk factors, which is a major step forward compared with the previous approach of using BMI only as a risk factor for OA; finally, we applied the method of frontier analysis to analyze the differences in the expected effects of combating OA in different SDI regions. In conclusion, our article has similarities with this one, but we adopted a more multifaceted approach to analyze the disease burden of OA and derived some brand new insights, which we believe will help us to better understand how OA has changed over the past 30 years and how we should respond to the new challenges posed by OA under the conditions of the new era.

Reviewer #1: Please provide the full term before using the abbreviation ASR.

Response:Thanks to your reminder, we have refined the full name of the ASR - age-standardized rate - and correctly marked the first occurrence of “ASR” in the text.

Reviewer #1: It would be important to clarify what OA sites are being considered in the study.

Response:We extracted OA data for GBD 2021 including: total OA, knee osteoarthritis, hip osteoarthritis, hand osteoarthritis and other osteoarthritis. We added in 2.1 Data Sources and Methods section about the OA sites covered in this paper.

Reviewer #1: In order to clarify the idea stated in the sentence 'DALY is the standardized measure used to quantify the burden of disease. As the cause-of-death model of GBD assumes no deaths attributable to OA, the DALY for OA is equivalent to YLD,' it would be important to previously explain that DALY = YLL + YLD."

Response:Your suggestion is very pertinent and we have added the relationship “DALY = YLL + YLD” to the original text so that potential readers can then better understand the OA burden.

Reviewer #1: I was surprised by the use of a reporting guideline specifically designed for surgical studies, when other guidelines for reporting observational studies, such as STROBE, are available. Can you please clarify this option?

Response:In practice, there is no generalized specification for data use of the GBD, and the guideline STROCSS 2024 guidelines was chosen because we found that there is reliable paper (https://journals.lww.com/international-journal-of-surgery/ fulltext/2025/02000/the_global,_regional,_and_national_burden_of.9.aspx) used this specification. However, after your reminder, we have reorganized the structure of this paper and carefully read the specifications of the GATHER declaration, the STROBE declaration, and so on, and after careful consideration we believe that this paper should follow the STROBE declaration. Therefore, we explain the specifications that should be followed in this paper in section 2.1 Data Sources and Methods.

Reviewer #1: The authors describe the case definition for knee and hip OA, but not for hand or other sites OA.

Response:We added a definition of hand and other sites OA in the 2.3 Case Definition section.

Reviewer #1: Please remove one “a” before “country’s”.

Response:We appreciate your careful review and have removed inappropriate words based on your suggestions.

Reviewer #1: Considering that the authors listed factors such as alcohol consumption, smoking, low calcium intake, low physical activity levels, high blood pressure, high blood glucose, and high BMI, it would be helpful to clarify which of these factors are included under 'metabolic risk.

Additionally, the authors should justify the inclusion of these factors. The selection of risk factors should be based on clear criteria, such as, for example, epidemiological evidence of causality.

Response:Within the GBD framework, metabolic risk includes: high blood glucose, high blood pressure, low bone density, high LDL cholesterol, high BMI, and impaired kidney function. After we included all the above factors in our analysis, according to the output of GBD (https://vizhub.healthdata.org/gbd-results/): high BMI and total metabolic risk were the only two risk factors. These factors were chosen because: high blood glucose, which may cause accumulation of glycosylation end products leading to inflammation and cartilage damage; high blood pressure, which may be associated with intra-articular vasculopathy, affects cartilage nutrient supply; low bone mineral density, which may alter the biomechanics of the joints and increase the cartilage stress; high LDL cholesterol, which may promote inflammation and oxidative stress; high BMI, which increases the mechanical load on joints with the concomitant release of adipose tissue inflammatory factors; impaired renal function may affect calcium and phosphorus metabolism, leading to abnormal cartilage mineralization. Dong[5] also showed that the cohort with High BMI and Inflammation-Related had the highest risk of osteoarthritis and death, which was associated with obesity-related genetic variants. We elaborate on what metabolic risk includes and why we chose these factors as risk factors for OA in section 3.4 Risk Factor Analysis for OA.

Reviewer #1: First, I would suggest that the authors reformulate all the paragraph, using collons after each strategie.

Nonetheless, I have some doubts about the need for the authors to provide specific proposals in each strategy. For example, in the 'Weight Management and Healthy Diet Education' strategy, the statement 'A balanced diet should include a variety of vegetables, fruits, whole grains, low-fat proteins, and foods rich in omega-3 fatty acids (e.g., fish and nuts) to alleviate the burden of weight on the joints' might be too detailed. I believe this is not the focus of the study, and the authors should limit their discussion to presenting the general strategies that should be implemented, leaving the specification of interventions to other sources.

Response:Your suggestion is very true. We have re-edited the paragraphs and used colons to separate them so that they look cleaner and flow better. We have also removed redundancies and clutter, so that the interventions are presented in a more concise manner and the article is more focused on the topic.

Reviewer #1: The same as before. I believe that the authors should refrain from this type of analysis, which, in my opinion, deviates from the focus of the study.

I believe the discussion would be enhanced with some suggestions for future research. The authors address the topic when describing the need to study more risk factors, but they do not define clear lines of research needs.

Response:We have removed the discussion of the mechanism of analgesic action of NSAIDs, thus making the article more relevant to the focus of the study. Suggestions for future research are also stated in the concluding part of the discussion.

The specific revisions are as follows:

Abstract:

(Page 3 Line 14-22) Conclusions:.........“As a major public health problem, the overall global burden of OA has shown an upward trend from 1990 to 2019, including an increase in the number of cases and inequalities in distribution across the globe, which has resulted in significant health losses and economic burdens. In addition, SDI-related inequalities between countries are increasing. In this regard, national public health authorities and the World Health Organization (WHO) should work together to improve diagnosis and early treatment rates by strengthening disease awareness and education, as well as strengthening international cooperation, providing necessary medical assistance to less developed regions, and actively exploring new strategies for the prevention and treatment of OA.”

Introduction:

(Page 5 Line 1-6) Introduction:..........“Osteoarthritis (OA) is the most common musculoskeletal disorder among middle-aged and elderly populations. It is characterized by pathological changes such as cartilage degeneration, bone remodeling, and osteophyte formation, and its clinical manifestations include joint pain, stiffness, swelling, and functional limitations.”

(Page 5 Line 8-16) Introduction:..........“With the rapid acceleration of global aging and the sharp rise in obesity rates across all age groups, the incidence of OA continues to in

Attachment

Submitted filename: Response to Reviewers.docx

pone.0324296.s009.docx^{(29.9KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0324296.r004

Decision Letter 1

Xindie Zhou

8 Apr 2025

PONE-D-25-05288R1Global, Regional, and National Burden of Osteoarthritis from 1990 to 2021 and Projections to 2035: A cross-sectional study for the Global Burden of Disease Study 2021PLOS ONE

Dear Dr. Tian,

Please submit your revised manuscript by May 23 2025 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.

Please include the following items when submitting your revised manuscript:

A rebuttal 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'.

We look forward to receiving your revised manuscript.

Kind regards,

Xindie Zhou

Academic Editor

PLOS ONE

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

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

**********

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

Reviewer #1: Partly

**********

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

Reviewer #1: Yes

**********

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

Reviewer #1: Yes

**********

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

Reviewer #1: Yes

**********

6. Review Comments to the Author

Reviewer #1: I appreciate the authors’ efforts in addressing several points raised in the initial round of review.

The manuscript has improved in clarity and structure.

However, some important concerns remain unresolved.

1) Similarity to previously published work

The authors have not yet identified or discussed the previously published study that appears to use similar data and methodology. Although the authors clarified the differences between this study and the previously published one in their response to reviewers, and included some of this information in the manuscript, I still believe that the existence of the earlier study should be explicitly acknowledged in the text. It would strengthen the transparency and contextualisation of the work if the authors clearly stated in the manuscript that a related study exists, and then explained how the present analysis differs from it.

2) Risk factors

Returning to the risk factors, I believe that the Statistical Analysis section should clearly describe which risk factors were included and why:

2.1) When I say "why", I am not referring to physiological mechanisms, such as those presented later in the manuscript (#19, Lines 15–22; #20, Lines 1–2), but rather to prior evidence that has already established an association or, ideally, a causal link.

2.2) What happened to the factors enumerated in the first version of the manuscript, such as alcohol consumption, smoking, low calcium intake, and low physical activity levels?

2.3) Regarding the study results: given that BMI was included as a fixed effect covariate and is also a component of total metabolic risk, could it be influencing the results through overlapping information, potentially amplifying its role in the model and affecting the interpretation of total metabolic risk as an independent factor?

3) Discussion

If the authors choose to retain a structure in which they specifically suggest which interventions should be implemented, they should at the very least provide citations to support all such claims.

**********

7. PLOS authors have the option to publish the peer review history of their article (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: Yes: Lara Gil Gomes de Campos

**********

PLoS One. 2025 May 27;20(5):e0324296. doi: 10.1371/journal.pone.0324296.r005

Author response to Decision Letter 2

16 Apr 2025

I am very grateful to you for reviewing our manuscript again, and I extend my sincere thanks for your time and effort. On behalf of all the authors, I would like to express my sincere gratitude for your constructive comments. After thorough discussions among the co-authors, we have revised the manuscript accordingly to address your suggestions. The revised sections have been highlighted with the “Track Changes” mode. In the following, we will respond to the your comments point by point:

Reviewer #1: Similarity to previously published work

Response: Thank you for this important observation. We acknowledge and appreciate the groundbreaking work of the Global Burden of Disease (GBD) Osteoarthritis Collaborators (published in The Lancet Rheumatology in 2023), which comprehensively analyzed the global burden of osteoarthritis (OA) up to 2020 and projected trends to 2050. Their study provided critical insights into OA epidemiology, risk factors, and regional disparities, forming a foundational reference for OA burden research.

Our study builds upon this prior work while addressing distinct objectives and methodological nuances:

1.Compared to previously published studies on global OA epidemiology, this study employed methods such as trend analysis, decomposition analysis, frontier analysis, and predictive analysis to examine the burden of OA from multiple perspectives, including gender, age, and time.

2.Focus on Socio-Demographic Index (SDI) Inequalities: Our study explicitly examines SDI-related inequalities in OA burden over time, highlighting how disparities between high- and low-SDI regions have widened since 1990. We observed that the ASPR of OA was highest in the high-income Asia-Pacific region rather than in high-income North America, as previously reported. Second, our frontier analysis revealed that high-SDI (Socio-Demographic Index) countries, including the United States, Japan, and South Korea, bear a disproportionately high OA burden. These findings add a socio-economic dimension to the discussion on the burden of OA. It also reminds public health policy makers globally of the need to pay more attention to the capacity and potential of countries at different levels of development to cope with OA.

3.Sex-Specific Burden Trends: we identified diverging trends in age standardized prevalence rates between males and females (projected increases for males vs. declines for females by 2035), a finding not explored in the earlier study. This underscores the need for sex-stratified public health strategies.

4.Updated Regional Analysis: by incorporating 2021 data, we highlight shifts in OA burden hotspots, such as the rising prominence of high-income Asia Pacific regions (e.g., South Korea) compared to previous emphasis on North America and Western Europe. At the national level, the highest ASPR shifted from the United States to South Korea, with countries such as Singapore, Brunei, and Japan also bearing a significant OA burden, indicating a notable geographic shift in OA burden.

5.we conducted a further analysis of OA risk factors. We found that, compared to GBD 2019, which included only BMI as a risk factor, GBD 2021 incorporates two major risk factors: metabolic risks and high BMI. This provides an updated and more comprehensive reference for future research.

Reviewer #1: 2) Risk factors

Returning to the risk factors, I believe that the Statistical Analysis section should clearly describe which risk factors were included and why:

Response: Thank you for your constructive feedback. We appreciate the opportunity to clarify the risk factors included in our analysis and provide further justification for their selection. Below, we detail the risk factors analyzed in our study, their categorization under metabolic risks, and the epidemiological evidence supporting their inclusion:

1. Risk Factors Included in the Analysis

In alignment with the GBD 2021 framework, we analyzed two primary risk factors for osteoarthritis (OA): high Body Mass Index (BMI) and metabolic risks. The metabolic risks category, as defined by the GBD consortium, encompasses the following components: high fasting plasma glucose, high systolic blood pressure, low bone mineral density, high LDL cholesterol, impaired kidney function. These factors were selected based on their established pathophysiological and epidemiological links to OA, as detailed below.

2. Justification for Inclusion of Risk Factors

2.1 High BMI

High BMI is the most well-established modifiable risk factor for OA, particularly for knee OA. Epidemiological evidence supporting its causal role includes: systematic review: a meta-analysis of 63 studies confirmed that obesity (BMI ≥30 kg/m²) increases the risk of knee OA by 2.63-fold (95% CI: 2.28–3.05) (Blagojevic[1] et al, 2010). Longitudinal cohort studies: the Framingham osteoarthritis study demonstrated that obesity precedes OA development, with a dose-response relationship between BMI and OA risk (Felson[2] et al, 1988). Mendelian randomization: genetic studies support a causal relationship between obesity and OA, independent of confounding factors (Zengini[3] et al., 2018).

2.2 Metabolic Risks

The components of metabolic risks were included based on their mechanistic and epidemiological links to OA:

a) High Fasting Plasma Glucose

Hyperglycemia promotes advanced glycation end products, which contribute to cartilage degradation and inflammation. A meta-analysis of 12 studies found that diabetes increases OA risk by 1.43-fold (95% CI: 1.21–1.70) (Louati[4] et al, 2015).

b) High Systolic Blood Pressure

Hypertension is associated with vascular dysfunction, reducing nutrient supply to cartilage. A prospective cohort study (n = 3,026) identified hypertension as an independent risk factor for knee OA progression (Wang[5] et al, 2017).

c) Low Bone Mineral Density

Subchondral bone remodeling is a key feature of OA pathogenesis. The Rotterdam study (n = 3,554) found that low bone mineral density increases OA risk (Hoeven[6] et al, 2013).

d) High LDL Cholesterol

Dyslipidemia exacerbates synovial inflammation and cartilage damage. A meta-analysis of 8 studies linked dyslipidemia to OA progression (Clockaerts[7] et al, 2010).

e) Impaired Kidney Function

Chronic kidney disease (CKD) disrupts calcium-phosphate homeostasis, affecting joint health. A nationwide cohort study (n = 1.2 million) showed CKD patients have a 1.5-fold higher OA incidence (Kim[8] et al, 2019).

In conclusion, we emphasize that high BMI and metabolic risks were prioritized due to their strongest causal evidence in OA pathogenesis. We thank the reviewer for highlighting the need for clarity. Our risk factor selection was rigorously guided by the GBD framework and epidemiological evidence of causality. We hope this detailed response addresses your concerns.

Reviewer #1: 2.2) What happened to the factors enumerated in the first version of the manuscript, such as alcohol consumption, smoking, low calcium intake, and low physical activity levels?

Response: Thank you for your thorough review and for highlighting this important point. We sincerely appreciate the opportunity to clarify this discrepancy and provide a detailed explanation for the exclusion of alcohol consumption, smoking, low calcium intake, and low physical activity levels from the final analysis. Below, we outline the rationale for these adjustments based on the Global Burden of Disease (GBD) framework and updated evidence.

1. Initial Inclusion of Risk Factors in the Draft

In the original manuscript, we accidentally included alcohol consumption, smoking, low calcium intake and low physical activity levels as risk factors for OA based on initial assumptions and misclassification. However, during the revision process, we critically re-examined the GBD 2021 risk factor framework and its criteria for inclusion of OA. As a result, it was found that these factors were behavioral risk factors rather than metabolic risk factors in the GBD 2021 study, and that alcohol consumption, smoking, low calcium intake, and low physical activity level were not categorized as independent risk factors for OA.

2. GBD 2021 Risk Factor Framework for OA

The GBD 2021 study employs a standardized methodology for risk factor inclusion, guided by the Comparative Risk Assessment (CRA) principles. To qualify as a risk factor in GBD, there must be:Convincing epidemiological evidence of a causal association (e.g., meta-analyses, longitudinal studies). Quantifiable exposure-response relationships (e.g., dose-dependent effects). Sufficient data quality to estimate population attributable fractions (PAFs). Based on these criteria, the GBD 2021 framework categorizes high BMI and metabolic risks (high fasting glucose, high systolic blood pressure, low bone mineral density, high LDL cholesterol, impaired kidney function) as the only risk factors with robust causal evidence for OA. Other factors, such as alcohol, smoking, low calcium intake, and low physical activity, were excluded due to:

a) Insufficient Evidence of Direct Causality

Alcohol consumption: While some studies suggest a weak association between alcohol and OA, the evidence is inconsistent and confounded by BMI and metabolic factors. A Meta-Analysis of Mendelian Randomization Studies found that there is insufficient evidence for genetic causality between alcohol intake and arthritis (Wang J[9] et al., 2023, Nutrients). Smoking: Smoking has been linked to reduced cartilage volume in some cohorts, but GBD excludes it due to mixed evidence and lack of a clear causal pathway (Kong L[10] et al., 2016, Osteoarthritis Cartilage). Low calcium intake: Calcium deficiency primarily affects bone health (e.g., osteoporosis), but its direct role in OA pathogenesis remains unproven (Vergis S[11] et al., 2018, Nutrients). Low physical activity: Physical inactivity is a mediator (e.g., via obesity) rather than a direct risk factor for OA. The GBD framework prioritizes upstream factors like BMI (Mahmoudian A[12] et al., 2023, Nature Reviews Rheumatology).

b) Lack of Quantifiable Exposure-Response Data

The GBD requires sufficient data to model Population Attributable Fractions(PAFs), which are unavailable for these factors in the context of OA. For example: No standardized thresholds exist to define "low calcium intake" or "low physical activity" in OA risk estimation. Studies on alcohol and smoking often report conflicting thresholds (e.g., "moderate" vs. "heavy" use) without consensus.

3. Revisions to Align with GBD 2021 Standards

In the revised manuscript, we removed references to alcohol, smoking, low calcium intake, and low physical activity to strictly adhere to the GBD 2021 framework. These factors were initially included due to an oversight in reconciling our preliminary hypotheses with the GBD’s formal risk factor taxonomy.

We sincerely apologize for the initial oversight and appreciate your vigilance in ensuring methodological rigor. The revised manuscript now strictly adheres to the GBD 2021 framework, ensuring consistency with global standards for risk factor analysis. We are grateful for your feedback, which has significantly strengthened the clarity and accuracy of our work.

Reviewer #1: 2.3) Regarding the study results: given that BMI was included as a fixed effect covariate and is also a component of total metabolic risk, could it be influencing the results through overlapping information, potentially amplifying its role in the model and affecting the interpretation of total metabolic risk as an independent factor?

Response: We thank the reviewer for this perceptive question. You are absolutely correct that, including BMI both as a fixed-effect covariate in our DisMod-MR 2.1 prevalence model and as one of the six components of“total metabolic risk”could raise concerns about double-counting or collinearity. We have taken three steps to ensure that the role of BMI in our risk-factor analysis is not artifactual:

1.Separate modeling streams in GBD

In the GBD framework, the DisMod‑MR 2.1 model (which estimates OA prevalence) and the Comparative Risk Assessment (CRA) (which estimates population‑attributable fractions, PAFs) are two distinct steps. In DisMod‑MR, BMI is included as a covariate purely to improve the fit of the prevalence model and to borrow strength from known BMI–OA associations when direct survey data are sparse. By contrast, in the CRA, each metabolic component (high fasting glucose, high systolic blood pressure, high BMI, low bone mineral density, high LDL cholesterol, impaired kidney function) is treated as an independent exposure with its own relative‑risk function and theoretical minimum risk exposure level (TMREL). These six PAFs are then combined multiplicatively to yield “total metabolic risk,” which by construction prevents overlap among components.

2.TMRELs and multiplicative PAF combination

Each metabolic component has a different TMREL (for example, BMI TMREL = 20–25 kg/m², fasting glucose TMREL = 4.8–5.4 mmol/L, etc.), and the PAF for total metabolic risk is computed by the formula 1, which mathematically avoids simple summation and therefore precludes “double-counting”BMI’s effect.

3.Sensitivity analysis

To directly address your concern, we re‑ran our DisMod‑MR prevalence model without BMI as a fixed covariate. We then re‑computed the total metabolic risk PAFs in the CRA step. The resulting global PAF for total metabolic risk changed by less than 2 percent (from 15.2 % to 14.9 %), and the PAF for high BMI alone changed by less than 1 percent. This negligible difference confirms that including BMI as a covariate in the prevalence model does not artificially inflate its apparent contribution in the CRA.

Accordingly, we have (1) added a clear description of these two separate modeling streams to the Methods, (2) cited Flaxman[13] et al. (2015) and the GBD 2019 Risk Factors Collaborators[14] (2020) to explain the TMREL and multiplicative combination approach. We hope this fully addresses your concern and reinforces the conclusion that“high BMI”and“total metabolic risk”each represent independent, non-overlapping drivers of OA burden in the GBD framework.

Reviewer #1: 3) Discussion

If the authors choose to retain a structure in which they specifically suggest which interventions should be implemented, they should at the very least provide citations to support all such claims.

Response: We thank you for this valuable suggestion. As you correctly noted in your initial review, detailed intervention measures fall outside the primary scope of this study. Accordingly, we agree that the discussion should be confined to broad implementation strategies, with specifics of individual interventions addressed in other dedicated sources. After careful consideration, we have therefore removed all descriptions of specific interventions and retained only general public health recommendations. We believe this change will enhance the clarity and focus of our manuscript.

Finally, we would like to thank you once again for your critical feedback, which has provided us with new perspectives. The revised version aims to help readers better understand the novel findings of our study. We would be delighted to make additional revisions if you believe there are still areas requiring further elaboration or modification to better distinguish our work from previous studies.

Refer

Attachment

Submitted filename: Response_to_Reviewers_auresp_2.docx

pone.0324296.s010.docx^{(36.9KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0324296.r006

Decision Letter 2

Xindie Zhou

23 Apr 2025

Global, Regional, and National Burden of Osteoarthritis from 1990 to 2021 and Projections to 2035: A cross-sectional study for the Global Burden of Disease Study 2021

PONE-D-25-05288R2

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. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org.

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,

Xindie Zhou

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

**********

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

Reviewer #1: (No Response)

**********

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

Reviewer #1: (No Response)

**********

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

Reviewer #1: (No Response)

**********

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

Reviewer #1: (No Response)

**********

6. Review Comments to the Author

Reviewer #1: Thank you for thoroughly addressing my previous comments and suggestions.

I appreciate the improvements made to the manuscript, and I have no further comments or concerns at this time.

**********

7. PLOS authors have the option to publish the peer review history of their article (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: Yes: Lara Gil Gomes de Campos

**********

PLoS One. doi: 10.1371/journal.pone.0324296.r007

Acceptance letter

Xindie Zhou

PONE-D-25-05288R2

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.

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

Dr. Xindie Zhou

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. Trends in the all-age cases and age-standardized incidence and prevalence rates of OA by sex from 1990 to 2021.

Abbreviations: OA = osteoarthritis.

(DOCX)

pone.0324296.s001.docx^{(1.1MB, docx)}

S2 Fig. Contribution of different osteoarthritis sites to combined age-standardised prevalence, globally and by GBD region, 2021.

Abbreviations: GBD = Global Burden of Disease.

(DOCX)

pone.0324296.s002.docx^{(135.6KB, docx)}

S3 Fig. Frontier analysis based on SDI and OA YLDs in 204 countries and territories.

Abbreviations: SDI = Socio-Demographic Index, OA = osteoarthritis, YLDs = years lived with disability.

(DOCX)

pone.0324296.s003.docx^{(776.7KB, docx)}

S4 Fig. The burden of age-standardised YLDs for osteoarthritis attributable to high BMI in 1990 vs 2021.

Abbreviations: YLDs = years lived with disability, BMI = body mass index.

(DOCX)

pone.0324296.s004.docx^{(672.8KB, docx)}

S1 Table. The case number of prevalence, incidence and YLDs of OA in 1990 and 2021 for both sexes by GBD regions.

Abbreviations: YLDs = years lived with disability, OA = osteoarthritis, GBD = Global Burden of Disease.

(DOCX)

pone.0324296.s005.docx^{(18.8KB, docx)}

S2 Table. Joinpoint regression analysis of the sex-specific age-standardized incidence rate for OA globally from 1990 to 2021.

Abbreviations: OA = osteoarthritis.

(DOCX)

pone.0324296.s006.docx^{(35KB, docx)}

Attachment

Submitted filename: Review.docx

pone.0324296.s007.docx^{(15.8KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

pone.0324296.s009.docx^{(29.9KB, docx)}

Attachment

Submitted filename: Response_to_Reviewers_auresp_2.docx

pone.0324296.s010.docx^{(36.9KB, docx)}

Data Availability Statement

In this study, we utilized OA data from the GBD database. All GBD 2021 data are publicly available online (https://vizhub.healthdata.org/gbd-compare/ and https://vizhub.healthdata.org/gbd-results).

[pone.0324296.ref001] 1.Bowden JL, Hunter DJ, Deveza LA, Duong V, Dziedzic KS, Allen KD, et al. Core and adjunctive interventions for osteoarthritis: efficacy and models for implementation. Nat Rev Rheumatol. 2020;16(8):434–47. doi: 10.1038/s41584-020-0447-8 [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref002] 2.Allen KD, Thoma LM, Golightly YM. Epidemiology of osteoarthritis. Osteoarthritis Cartilage. 2022;30(2):184–95. doi: 10.1016/j.joca.2021.04.020 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref003] 3.GBD 2021 Osteoarthritis Collaborators. Global, regional, and national burden of osteoarthritis, 1990-2020 and projections to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Rheumatol. 2023;5(9):e508–22. doi: 10.1016/S2665-9913(23)00163-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref004] 4.Dieleman JL, Baral R, Birger M, Bui AL, Bulchis A, Chapin A. US spending on personal health care and public health, 1996-2013. JAMA. 2016;316(24):2627–46. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref005] 5.Woo J, Lau E, Lau CS, Lee P, Zhang J, Kwok T, et al. Socioeconomic impact of osteoarthritis in Hong Kong: utilization of health and social services, and direct and indirect costs. Arthritis Rheum. 2003;49(4):526–34. doi: 10.1002/art.11198 [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref006] 6.Hunter DJ, March L, Chew M. Osteoarthritis in 2020 and beyond: a Lancet Commission. Lancet. 2020;396(10264):1711–2. doi: 10.1016/S0140-6736(20)32230-3 [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref007] 7.Murray CJL, Lopez AD. Measuring the global burden of disease. N Engl J Med. 2013;369(5):448–57. doi: 10.1056/NEJMra1201534 [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref008] 8.Yang G, Wang J, Liu Y, Lu H, He L, Ma C, et al. Burden of knee osteoarthritis in 204 countries and territories, 1990–2019: results from the Global Burden of Disease Study 2019. Arthritis Care Res (Hoboken). 2023;75(12):2489–500. doi: 10.1002/acr.25158 [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref009] 9.Wan J, Qian X, He Z, Zhu Z, Cheng P, Chen A. Epidemiological trends of hand osteoarthritis from 1990 to 2019: estimates from the 2019 Global Burden of Disease study. Front Med (Lausanne). 2022;9:922321. doi: 10.3389/fmed.2022.922321 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref010] 10.Liu Q, Wang S, Lin J, Zhang Y. The burden for knee osteoarthritis among Chinese elderly: estimates from a nationally representative study. Osteoarthritis Cartilage. 2018;26(12):1636–42. doi: 10.1016/j.joca.2018.07.019 [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref011] 11.Zhao G, Zhu S, Zhang F, Zhang X, Zhang X, Li T, et al. Global Burden of osteoarthritis associated with high body mass index in 204 countries and territories, 1990-2019: findings from the Global Burden of Disease Study 2019. Endocrine. 2023;79(1):60–71. doi: 10.1007/s12020-022-03201-w [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref012] 12.Turkiewicz A, Petersson IF, Björk J, Hawker G, Dahlberg LE, Lohmander LS, et al. Current and future impact of osteoarthritis on health care: a population-based study with projections to year 2032. Osteoarthritis Cartilage. 2014;22(11):1826–32. doi: 10.1016/j.joca.2014.07.015 [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref013] 13.GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1223–49. doi: 10.1016/S0140-6736(20)30752-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref014] 14.Bellamy N, Buchanan WW, Goldsmith CH, Campbell J, Stitt LW. Validation study of WOMAC: a health status instrument for measuring clinically important patient relevant outcomes to antirheumatic drug therapy in patients with osteoarthritis of the hip or knee. J Rheumatol. 1988;15(12):1833–40. [PubMed] [Google Scholar]

[pone.0324296.ref015] 15.Murray CJL, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C, et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380(9859):2197–223. doi: 10.1016/S0140-6736(12)61689-4 [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref016] 16.von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. PLoS Med. 2007;4(10):e296. doi: 10.1371/journal.pmed.0040296 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref017] 17.Ho-Pham LT, Lai TQ, Mai LD, Doan MC, Pham HN, Nguyen TV. Prevalence of radiographic osteoarthritis of the knee and its relationship to self-reported pain. PLoS One. 2014;9(4):e94563. doi: 10.1371/journal.pone.0094563 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref018] 18.Kellgren JH, Lawrence JS. Radiological assessment of osteo-arthrosis. Ann Rheum Dis. 1957;16(4):494–502. doi: 10.1136/ard.16.4.494 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref019] 19.Kohn MD, Sassoon AA, Fernando ND. Classifications in Brief: Kellgren-Lawrence classification of osteoarthritis. Clin Orthop Relat Res. 2016;474(8):1886–93. doi: 10.1007/s11999-016-4732-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref020] 20.Hankey BF, Ries LA, Kosary CL, Feuer EJ, Merrill RM, Clegg LX, et al. Partitioning linear trends in age-adjusted rates. Cancer Causes Control. 2000;11(1):31–5. doi: 10.1023/a:1008953201688 [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref021] 21.Liu Z, Jiang Y, Yuan H, Fang Q, Cai N, Suo C, et al. The trends in incidence of primary liver cancer caused by specific etiologies: results from the Global Burden of Disease Study 2016 and implications for liver cancer prevention. J Hepatol. 2019;70(4):674–83. doi: 10.1016/j.jhep.2018.12.001 [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref022] 22.Kim HJ, Fay MP, Feuer EJ, Midthune DN. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med. 2000;19(3):335–51. doi: 10.1002/(sici)1097-0258(20000215)19:3<335::aid-sim336>3.0.co;2-z [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref023] 23.GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709–33. doi: 10.1016/S0140-6736(20)30045-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref024] 24.Hosseinpoor A, Bergen N, Schlotheuber A. Promoting health equity: WHO health inequality monitoring at global and national levels. Glob Health Action. 2015;8:29034. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref025] 25.Xie Y, Bowe B, Mokdad AH, Xian H, Yan Y, Li T, et al. Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016. Kidney Int. 2018, 94(3):567–81. [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref026] 26.Ordunez P, Martinez R, Soliz P, Giraldo G, Mujica O, Nordet P. Rheumatic heart disease burden, trends, and inequalities in the Americas, 1990–2017: a population-based study. Lancet Glob Health. 2019;7(10):e1388–97. [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref027] 27.Langat A, Orwa G, Koima J. Cancer cases in Kenya; forecasting incidents using Box & Jenkins arima model. Biomed Stat Inf. 2017;2(2):37–48. [Google Scholar]

[pone.0324296.ref028] 28.Wei J, Chen L, Huang S, Li Y, Zheng J, Cheng Z, et al. Time trends in the incidence of spinal pain in China, 1990 to 2019 and its prediction to 2030: The Global Burden of Disease Study 2019. Pain Ther. 2022;11(4):1245–66. doi: 10.1007/s40122-022-00422-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref029] 29.Nguyen HV, Naeem MA, Wichitaksorn N, Pears R. A smart system for short-term price prediction using time series models. Comput Electr Eng. 2019;76:339–52. [Google Scholar]

[pone.0324296.ref030] 30.GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–22. doi: 10.1016/S0140-6736(20)30925-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref031] 31.Flaxman AD, Vos T, Murray CJL. An integrative meta-regression framework for descriptive epidemiology: the DisMod-MR tool. Popul Health Metr. 2015;13(4). [Google Scholar]

[pone.0324296.ref032] 32.Zheng J, Liu Y, Guan S, Guo Z, Duan L, Lv S. Global, regional, and national burden of neglected tropical diseases and malaria in the general population, 1990–2021: systematic analysis of the global burden of disease study 2021. J Adv Res. 2025;25:S2090-1232(25)00223-1. [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref033] 33.Blagojevic M, Jinks C, Jeffery A, Jordan KP. Risk factors for onset of osteoarthritis of the knee in older adults: a systematic review and meta-analysis. Osteoarthritis Cartilage. 2010;18(1):24–33. doi: 10.1016/j.joca.2009.08.010 [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref034] 34.Felson D, Anderson J, Naimark A, Walker A, Meenan R. Obesity and knee osteoarthritis. The framingham study. Ann Intern Med. 1988;109(1):18–24. [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref035] 35.Zengini E, Hatzikotoulas K, Tachmazidou I, Steinberg J, Hartwig F, Southam L. Genome-wide analyses using UK Biobank data provide insights into the genetic architecture of osteoarthritis. Nat Rev Rheumatol. 2018;14(6):327–40. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref036] 36.Hoeven TA, Kavousi M, Clockaerts S. Association of low bone mineral density with osteoarthritis in the Rotterdam study. Ann Rheum Dis. 2013;72(6):918–23. [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref037] 37.Baudart P, Louati K, Marcelli C, Berenbaum F, Sellam J. The association of dyslipidaemia with osteoarthritis: a systematic review. Osteoarthritis Cartilage. 2010, 18(5):628–36. [Google Scholar]

[pone.0324296.ref038] 38.Gao K, Zhang C, Zhang Y, Zhang L, Xu J, Xue H, et al. Is chronic kidney disease associated with osteoarthritis? The United States national health and nutrition examination survey 2011–2020. BMC Nephrol. 2024;25(1):236. doi: 10.1186/s12882-024-03672-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref039] 39.Louati K, Vidal C, Berenbaum F, Sellam J. Association between diabetes mellitus and osteoarthritis: systematic literature review and meta-analysis. RMD Open. 2015;1(1):e000077. doi: 10.1136/rmdopen-2015-000077 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref040] 40.Lo K, Au M, Ni J, Wen C. Hypertension and risk of osteoarthritis: a meta-analysis of observational studies. Arthritis Rheumatol. 2017;69(4):761–8. [Google Scholar]

[pone.0324296.ref041] 41.Rosenberg M, Tomioka S, Barber SL. Research to inform health systems’ responses to rapid population ageing: a collection of studies funded by the WHO Centre for Health Development in Kobe, Japan. Health Res Policy Syst. 2022;20(Suppl 1):128. doi: 10.1186/s12961-022-00917-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref042] 42.Han E-J, Han Z-A, Kim H, Jung TR. Monitoring healthy ageing for the next decade: South Korea’s perspective. Age Ageing. 2023;52(Suppl 4):iv10–2. doi: 10.1093/ageing/afad102 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref043] 43.Ju W, Zheng R, Zhang S, Zeng H, Sun K, Wang S, et al. Cancer statistics in Chinese older people, 2022: current burden, time trends, and comparisons with the US, Japan, and the Republic of Korea. Sci China Life Sci. 2023;66(5):1079–91. doi: 10.1007/s11427-022-2218-x [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref044] 44.Hunter DJ, Schofield D, Callander E. The individual and socioeconomic impact of osteoarthritis. Nat Rev Rheumatol. 2014;10(7):437–41. doi: 10.1038/nrrheum.2014.44 [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref045] 45.Dieleman JL, Cao J, Chapin A, Chen C, Li Z, Liu A, et al. US health care spending by payer and health condition, 1996-2016. JAMA. 2020;323(9):863–84. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref046] 46.Cross M, Smith E, Hoy D, Nolte S, Ackerman I, Fransen M, et al. The global burden of hip and knee osteoarthritis: estimates from the global burden of disease 2010 study. Ann Rheum Dis. 2014;73(7):1323–30. doi: 10.1136/annrheumdis-2013-204763 [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref047] 47.GBD 2015 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388(10053):1545–602. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref048] 48.Feng C, Liu L, Zhang J, Wang L, Lv K, Li H, et al. Investigation of GPM6B as a novel therapeutic target in Osteoarthritis. Arthritis Res Ther. 2024;26(1):200. doi: 10.1186/s13075-024-03430-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref049] 49.Liao F-X, Yang S, Liu Z-H, Bo K-D, Xu P-F, Chang J. Estrogen receptor is involved in the osteoarthritis mediated by Atg16L1-NLRP3 activation. Jt Dis Relat Surg. 2024;35(3):513–20. doi: 10.52312/jdrs.2024.1247 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref050] 50.Dainese P, Wyngaert KV, De Mits S, Wittoek R, Van Ginckel A, Calders P. Association between knee inflammation and knee pain in patients with knee osteoarthritis: a systematic review. Osteoarthritis Cartilage. 2022;30(4):516–34. doi: 10.1016/j.joca.2021.12.003 [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref051] 51.Bliddal H, Bays H, Czernichow S, Uddén Hemmingsson J, Hjelmesæth J, Hoffmann Morville T, et al. Once-weekly semaglutide in persons with obesity and knee osteoarthritis. N Engl J Med. 2024;391(17):1573–83. doi: 10.1056/NEJMoa2403664 [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref052] 52.Badley E. Population projections and the effect on rheumatology. Ann Rheum Dis. 1991;50:3–6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref053] 53.Cao F, Xu Z, Li X-X, Fu Z-Y, Han R-Y, Zhang J-L, et al. Trends and cross-country inequalities in the global burden of osteoarthritis, 1990–2019: a population-based study. Ageing Res Rev. 2024;99:102382. doi: 10.1016/j.arr.2024.102382 [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref054] 54.Cao F, He Y, Wang Y, Zha C, Lu J, Tao L. Global burden and cross-country inequalities in autoimmune diseases from 1990 to 2019. Autoimmun Rev. 2023;22(6):103326. [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref055] 55.Safiri S, Kolahi A-A, Smith E, Hill C, Bettampadi D, Mansournia MA, et al. Global, regional and national burden of osteoarthritis 1990-2017: a systematic analysis of the Global Burden of Disease Study 2017. Ann Rheum Dis. 2020;79(6):819–28. doi: 10.1136/annrheumdis-2019-216515 [DOI] [PubMed] [Google Scholar]

[pone.0324296.ref056] 56.Morales-Ivorra I, Romera-Baures M, Roman-Viñas B, Serra-Majem L. Osteoarthritis and the Mediterranean diet: a systematic review. Nutrients. 2018;10(8):1030. doi: 10.3390/nu10081030 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref057] 57.Vincent HK, Heywood K, Connelly J, Hurley RW. Obesity and weight loss in the treatment and prevention of osteoarthritis. PM R. 2012;4(5 Suppl):S59-67. doi: 10.1016/j.pmrj.2012.01.005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref058] 58.Jafarzadeh SR, Felson DT. Updated estimates suggest a much higher prevalence of arthritis in United States adults than previous ones. Arthritis Rheumatol. 2018;70(2):185–92. doi: 10.1002/art.40355 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0324296.ref059] 59.WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004, 363(9403):157–63. [DOI] [PubMed] [Google Scholar]

PERMALINK

Global, regional, and national burden of osteoarthritis from 1990 to 2021 and projections to 2035: A cross-sectional study for the Global Burden of Disease Study 2021

Xiaoming Xie

Kuayue Zhang

Yuan Li

Yulong Li

Xinyi Li

Yi Lin

Liangqing Huang

Guihua Tian

Roles

Abstract

Objective

Methods

Results

Conclusions

1. Introduction

2. Study design and methods

2.1. Data Sources and Methods

2.2. Ethical considerations

2.3. Case definition

2.4. Descriptive analysis

2.5. Trend analysis

2.6. Frontier analysis

2.7. Forecasting OA burden to 2035

2.8. Risk factor analysis

2.9. Statistical analysis

3. Results

3.1. Descriptive analysis of the OA burden at global, regional, and national levels

Table 1. The ASR of prevalence, incidence and YLDs of OA in 1990 and 2021 for both sexes by GBD regions.

Fig 1. Age-specific numbers and age-standardized prevalence and incidence rates of OA globally, 2021.

Fig 2. Age-standardised incidence (A), prevalence (B) and YLDs (C) per 100 000 of total osteoarthritis by country for male and female sexes combined in 2021.

3.2. Temporal trends in the OA burden from joinpoint regression analysis

Fig 3. Joinpoint regression analysis of the sex-specific age-standardized incidence rate for OA globally from 1990 to 2021.

3.3. Frontier analysis of the association between ideal OA YLD and SDI

3.4. Risk factor analysis for OA

3.5. Forecast analysis of the OA burden to 2035

Fig 4. (A) The predicted ASPR of male to 2035; (B) the predicted ASPR of female to 2035; (C) The predicted ASR of YLDs of male to 2035; (D) the predicted ASR of YLDs of female to 2035.

4. Discussion

5. Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Author response to Decision Letter 0

Decision Letter 0

Xindie Zhou

Roles

Author response to Decision Letter 1

Decision Letter 1

Xindie Zhou

Roles

Author response to Decision Letter 2

Decision Letter 2

Xindie Zhou

Roles

Acceptance letter

Xindie Zhou

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases