Unveiling the BMI Risk Threshold for Osteoarthritis: Multi‐Database Causal and Nonlinear Evidence

Shaohong Yu; Li Zeng; Lei Yang; Yangjiang Ou; Chenglin Yang; Xiaoru Lin; Jingyi Shao; Qing Guan; Guobo Li; Xiuquan Lin

doi:10.1111/dom.70892

. 2026 May 31;28(8):7104–7115. doi: 10.1111/dom.70892

Unveiling the BMI Risk Threshold for Osteoarthritis: Multi‐Database Causal and Nonlinear Evidence

Shaohong Yu ¹, Li Zeng ², Lei Yang ³, Yangjiang Ou ⁴, Chenglin Yang ³, Xiaoru Lin ³, Jingyi Shao ⁵, Qing Guan ⁶, Guobo Li ^7,^✉, Xiuquan Lin ^8,^✉

PMCID: PMC13341413 PMID: 42219229

ABSTRACT

Objective

To characterize the nonlinear relationship between BMI and osteoarthritis (OA), and to identify BMI thresholds that inform precise prevention strategies.

Methods

This multi‐database study integrated Global burden of disease 2021, National Health and Nutrition Examination Survey 2007–2018, and Genome‐Wide Association Studies. A generalized additive model was performed to visualize the BMI‐OA relationship, adjusting for multiple confounders. We applied segmented logistic regression models to identify potential threshold effects and used Mendelian randomization to estimate the causal effects of BMI on OA subtypes.

Results

From 1990 to 2021, the age‐standardized prevalence and years lived with disability rates for OA were highest in regions with high SDI. OA prevalence rose nonlinearly with BMI, with breakpoints at 24.00 and 41.58 kg/m². Each unit increase in BMI was associated with higher odds of OA between 24.00 and 41.58 kg/m² (OR = 1.022, 95% CI: 1.003–1.041) and above 41.58 kg/m² (OR = 1.055, 95% CI: 1.022–1.090). Women and individuals aged ≥ 45 years exhibited a higher susceptibility to knee osteoarthritis. BMI was causally associated with knee osteoarthritis (OR = 1.63, 95% CI 1.50–1.77) and hip osteoarthritis (OR = 1.54, 95% CI 1.40–1.70).

Conclusions

These findings suggest that OA risk awareness and weight‐management strategies should begin before BMI reaches the high range, particularly among individuals with BMI exceeding 24.00 kg/m².

Keywords: body mass index, global burden of disease, mendelian randomization, national health and nutrition examination aurvey, osteoarthritis

Abbreviations

BMI: body mass index
CDC: Centers for Disease Control and Prevention
GAM: generalized additive model
GBD: Global Burden of Disease
GWAS: Genome‐Wide Association Studies
HES: hospital event statistics
HOA: hip osteoarthritis
IVW: Inverse Variance Weighting
KOA: knee osteoarthritis
MEC: Mobile Examination Center
MR: Mendelian Randomization
NCHS: National Center for Health Statistics
NHANES: National Health and Nutrition Examination Survey
OA: osteoarthritis
PAF: population attributable fraction
SDI: Socio‐demographic Index
SEV: Summary Exposure Value
WHO: World Health Organization
YLDs: years lived with disability

1. Introduction

Osteoarthritis (OA) is a chronic joint disease characterized by progressive degeneration of articular cartilage, hyperosteogeny, and alterations in joint structure [1]. The disease commonly affects weight‐bearing joints, such as the knee and hip, leading to pain and functional impairments. These symptoms significantly impair patients' daily functioning and quality of life and impose a substantial socioeconomic burden [2]. In the United States, for example, direct medical expenditures related to OA reached approximately $101.8 billion in 2019, and the overall economic burden exceeded $303.5 billion [3]. The World Health Organization (WHO) has classified OA as one of the fastest‐growing major public health issues globally and the second leading cause of disability [4]. Therefore, identifying modifiable key risk factors for OA is crucial for slowing disease progression and alleviating its societal burden [5].

Global Burden of Disease (GBD) 2021 identifies body mass index (BMI) as the only clearly quantifiable and modifiable risk factor for OA. In recent years, global obesity prevalence and mean BMI have persistently increased; according to pooled analyses by the Non‐Communicable Disease (NCD) Risk Factor Collaboration, these trends extend through 2022 [6]. A meta‐analysis has shown that for every 5 kg/m² increase in BMI, the risk of knee osteoarthritis (KOA) increases by 35% [7]. This association may be mediated by increased mechanical load due to weight gain and obesity‐related metabolic disturbances, such as insulin resistance and chronic inflammation, contributing to cartilage degradation [8]. Therefore, in the context of the global obesity epidemic, further investigating whether a causal and non‐linear dose–response relationship exists between BMI and OA is crucial for identifying high‐risk populations, characterizing potential threshold effects, and developing more targeted prevention strategies.

Although previous studies have consistently demonstrated a nonlinear positive association between elevated BMI and the risk of osteoarthritis [9, 10, 11, 12, 13], the dose–response pattern and potential intervention thresholds of this relationship remain insufficiently characterized. Furthermore, many studies have been restricted to single databases, lacking cross‐validation across multiple data sources. Therefore, this study integrates the GBD database, the National Health and Nutrition Examination Survey (NHANES) database, and the Genome‐Wide Association Studies (GWAS) database to conduct a comprehensive analysis of the relationship between BMI and OA. The aim is to validate the impact of BMI on OA in adults and explore potential threshold effects within this relationship. Additionally, by utilizing the latest GWAS database, this study provides genetic evidence from a larger sample to assess the causal effect of BMI on OA and its sub‐types. The findings are expected to provide high‐quality epidemiological evidence for precise prevention and control strategies for OA, thereby supporting global efforts to address the burden of OA.

2. Materials and Methods

2.1. Data Collection and Study Population

2.1.1. Global Burden of Disease (GBD) Database

The GBD database collects detailed data on 370 diseases and injuries across 204 countries and regions from 1990 to 2021, integrating demographic data at both national and regional levels. The GBD study employs a systematic analytical approach that synthesizes various epidemiological data to calculate the relative risk and population attributable fraction (PAF) for diseases associated with BMI and provides data standardization along with uncertainty interval estimation. In this study, the prevalence and years lived with disability (YLDs) data for OA and its subtypes were sourced from the Global Health Data Exchange online platform (https://vizhub.healthdata.org/gbd‐results/). The study was approved by the Institutional Review Board of the University of Washington, and as the study used anonymized publicly available data, informed consent was waived.

2.1.2. National Health and Nutrition Examination Survey (NHANES) Database

The NHANES is a nationwide survey project led by the National Center for Health Statistics (NCHS), designed to systematically assess the health and nutrition status of the U.S. population across all age groups. The survey gathers detailed individual‐level data through face‐to‐face interviews, physical examinations, and laboratory tests. This study utilized data from the 2007–2018 NHANES cycles (https://www.cdc.gov/nchs/nhanes/). The participant selection process for the NHANES database is outlined in the flowchart (Figure 3). Of the initial 59 842 participants, the following individuals were excluded: those with missing BMI or OA history data; individuals < 20 years old; those with a BMI > 60 kg/m². Missing covariate data were addressed using multiple imputation. The final NHANES study population consisted of 32 783 individuals. BMI data was obtained from the NHANES body measures examination variable BMXBMI, which was calculated from measured weight and height collected in the Mobile Examination Center (MEC). The NHANES protocol has been approved by the Centers for Disease Control and Prevention (CDC). Before participation in the NHANES study, all participants provided informed consent, with parental or guardian consent required for minors.

Flow chart of the study population selection process.

2.1.3. NHGRI‐EBI GWAS Catalogue Database

The GWAS Catalogue (https://www.ebi.ac.uk/gwas) is a public database that archives large‐scale genetic studies, providing support for disease mechanism research and precision medicine by identifying genetic loci associated with diseases and phenotypes through GWAS. The GBD 2021 report identifies high BMI as a risk factor for KOA and hip osteoarthritis (HOA). In this study, the causal relationship between BMI and OA was further validated using Mendelian Randomization (MR). According to the retrieved datasets [14, 15], the GWAS summary data for BMI (GCST008025), KOA (GCST90566800), and HOA (GCST90566798) include 49 335, 1 316 500 (172 256 cases, 1 144 244 controls), and 1 152 707 participants (97 328 cases, 1 055 379 controls), respectively. All data used in this study were obtained from public sources, and the original research teams have received appropriate ethical approval.

2.2. OA Diagnosis

In the GBD 2021 database, OA diagnosis is based on radiographic confirmation, using the Kellgren‐Lawrence grading scale, and includes only symptomatic OA cases with grades 2–4 [16, 17]. HOA corresponds to ICD‐10 code M16, while KOA corresponds to ICD‐10 code M17. The ICD‐10 terminology replaces the ICD‐9 term ‘osteoarthrosis’ with ‘osteoarthritis’ to emphasize the inflammatory component of the disease. In NHANES, OA diagnosis is based on self‐reported questionnaires. Participants aged 20 and older were classified as having OA if they answered ‘Yes’ to the question ‘Doctor ever said you had arthritis’ and further specified ‘Osteoarthritis or degenerative arthritis’ when asked ‘Which type of arthritis was it?’ [18]. In GWAS, OA cases are selected based on hospital event statistics (HES) using ICD‐10 codes for KOA and HOA to ensure diagnostic accuracy [19]. Although case definitions differed across databases, each data source contributed complementary evidence at a distinct level of inference: GBD provided radiographically anchored estimates of OA burden, NHANES captured physician‐diagnosed OA as reported in a nationally representative community sample, and GWAS identified hospital‐coded OA in clinically ascertained populations. Therefore, the BMI threshold derived from NHANES should be interpreted as a population‐level risk‐acceleration point rather than a universal clinical cutoff, while the consistency of findings across GBD, NHANES, and GWAS strengthens the overall robustness of the observed association between BMI and OA.

2.3. Variable Description

The primary exposure variable in this study was BMI. In the NHANES database, height and weight were measured through standardized physical examination procedures conducted at the Mobile Examination Center. BMI was calculated as weight in kilograms divided by height in meters squared. In our analysis, BMI was treated as a continuous variable.

Established risk factors for OA confirmed by previous studies served as the covariates in this study, including demographic information (sex, age, race, education level, marital status, ratio of family income to poverty), physical examination data (waist circumference), and questionnaire data (alcohol status, smoking status, physical activity, hypertension, hyperlipidemia, diabetes, cardiovascular diseases) [20, 21, 22, 23]. All covariates were collected through standardized household interviews and in‐person measurements. The outcome variable was OA status, defined according to self‐reported physician diagnosis (yes/no).

To ensure data quality, a comprehensive assessment of missing data was conducted, and the proportions of missing data for key variables in the overall NHANES population and stratified by osteoarthritis status are presented in eTable 1. Missing covariates were handled using multiple imputation. In the revised analysis, the imputation model explicitly included osteoarthritis status, BMI, and the covariates used in the multivariable models.

2.4. Statistical Analysis

For GBD 2021, this study explores the trends in the association of high BMI with OA prevalence and YLDs rates across different geographic regions and time periods. All prevalence and YLDs rates were age and sex standardized using the GBD world standard population weights to account for demographic differences. To further investigate the relationship between the Socio‐demographic Index (SDI) and the Summary Exposure Value (SEV) of high BMI, Pearson's correlation coefficient was used to investigate the correlation between the SEV of high BMI and SDI. Stratified analyses were conducted by age and sex to examine the distribution of OA.

For NHANES, continuous variables were expressed as mean ± standard deviation, whereas categorical variables were presented as numbers and percentages. Between group comparisons were performed using Pearson's chi squared test and Welch's two sample t‐test. To assess the nonlinear relationship between BMI and OA prevalence, we first fitted a generalized additive model (GAM) with adjustment for covariates [24]. Subsequently, a segmented logistic regression model was applied to further identify potential BMI‐related breakpoints. BMI was modelled as a piecewise linear variable with two unknown breakpoints, and the breakpoint locations and segment specific regression coefficients were estimated simultaneously using an iterative maximum likelihood procedure. The effects within different BMI intervals were expressed as odds ratios and 95% confidence intervals (CI) for each 1 kg/m² increase in BMI [25, 26].

This study explores the causal relationship of BMI with KOA and HOA using two‐sample Mendelian randomization. Instrumental variables (IVs) were derived from large‐scale GWAS and selected based on their genome‐wide significant association with BMI (p < 5 × 10⁻⁸) and independence from each other (r ² < 0.001). The F‐statistics of the included SNPs ranged from 29.13 to 133.77, all exceeding the conventional threshold of 10, indicating adequate instrument strength. The exact F‐statistics and R ² values for all included SNPs are provided in eTable 2. The primary analysis was conducted using inverse‐variance weighted (IVW) estimation, supplemented by sensitivity analyses using weighted median, MR‐Egger, weighted mode, and simple mode estimations [27]. Additionally, a leave‐one‐out analysis was performed to assess the robustness of the results. After cross‐referencing with summary statistics for KOA and HOA, 11 IVs were extracted.

All statistical tests were two‐tailed, with a significance level set at p < 0.05. All statistical analyses and graphical representations were performed using R software (version 4.4.2).

3. Results

As shown in Table 1, in both 1990 and 2021, high‐SDI regions consistently ranked highest for YLDs and prevalence across the global and five SDI regions. In 1990, the age‐standardized prevalence and age‐standardized YLDs rate in high‐SDI regions reached their highest values, at 7371.33 per 100 000 (95% UI: 6609.68–8130.17) and 262.74 per 100 000 (95% UI: 126.00–529.15), respectively. Notably, the United States had the highest absolute number of cases and YLDs, with 25 007 735.07 (95% UI: 22 549 194.96–27 580 854.86) and 900 534.50 (95% UI: 432 395.48–1 819 200.41), and its age‐standardized prevalence and YLDs rates ranked among the top five globally. By 2021, this pattern had not changed substantially: the age‐standardized prevalence in high‐SDI regions remained at 7897.27 per 100 000 (95% UI: 7067.13–8689.88) and the age‐standardized YLDs rate at 283.13 per 100 000 (95% UI: 136.04–570.53), still at the highest levels, with the United States continuing to rank among the top. The results for each country are visualized in map form eFigure 1.

TABLE 1.

The changes in age‐standardized prevalence and age‐standardized YLDs of OA globally and in five SDI regions in 1990 and 2021.

	Age‐standardized Prevalence rate per 100 000			Age‐standardized YLDs rate per 100 000
	1990	2021	Percentage change 1990–2021	1990	2021	Percentage change 1990–2021
Global	6393.12 (5683.20, 7059.53)	6967.29 (6180.70, 7686.06)	0.09 (0.08, 0.10)	222.80 (106.65, 450.30)	244.501 (117.061, 493.106)	0.10 (0.09, 0.10)
Low SDI	5080.55 (4507.60, 5674.54)	5605.58 (4967.54, 6230.60)	0.10 (0.09, 0.12)	170.90 (82.56, 345.11)	190.93 (91.63, 384.26)	0.12 (0.11, 0.13)
Low‐middle SDI	5326.73 (4722.18, 5924.73)	6106.25 (5419.32, 6763.20)	0.15 (0.13, 0.16)	180.18 (86.93, 364.19)	209.35 (100.40, 422.62)	0.16 (0.15, 0.18)
Middle SDI	6066.13 (5375.33, 6744.29)	6903.80 (6123.00, 7643.11)	0.14 (0.12, 0.16)	208.27 (100.47, 419.68)	240.40 (115.09, 483.99)	0.15 (0.13, 0.17)
High‐middle SDI	6557.50 (5802.43, 7265.76)	7120.38 (6297.96, 7879.76)	0.09 (0.07, 0.10)	228.99 (109.20, 462.13)	250.58 (119.78, 503.69)	0.09 (0.08, 0.11)
High SDI	7371.33 (6609.68, 8130.17)	7897.27 (7067.13, 8689.88)	0.07 (0.07, 0.08)	262.74 (126.00, 529.15)	283.13 (136.04, 570.53)	0.08 (0.07, 0.09)

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The results of the comparative analysis across time periods (1990 vs. 2021) indicated that (Figure 1) among the five different SDI regions, SEV for high BMI in high‐SDI regions consistently remained the highest. Furthermore, in all SDI regions, the age‐standardized exposure rate for high BMI in 2021 showed a significant increase compared to 1990.

Age‐standardized summary exposure value for high body mass index in five SDI regions in 1990 and 2021.

From 1990 to 2021, there were significant differences in the age‐standardized YLDs rates for OA across global and five SDI regions (eFigure 2). Overall, age‐standardized YLDs for KOA accounted for a larger proportion of OA‐related disability than HOA. Over time, the age‐standardized YLDs rates for OA have shown an upward trend, but the increase was relatively gradual in high and middle‐high SDI regions. Among these, the rise in OA was primarily driven by KOA, while the trend for HOA remained relatively flat.

Age‐stratified analyses from 1990 to 2021 revealed that the increases in the prevalence and YLDs rates of OA, HOA, and KOA were predominantly concentrated in individuals aged 45 and older, with a significant upward trend in disease burden as age increased (Figure 2). Specifically, HOA was more prevalent in those aged 65 years and older, while KOA had the highest prevalence in the 45–64 age group; the distribution of YLDs across age groups mirrored the prevalence patterns. Gender‐stratified analyses showed that females generally had higher age‐standardized prevalence estimates than males for overall OA, with a similar pattern observed for KOA; in contrast, male and female prevalence estimates for HOA were broadly comparable, with substantial overlap in their confidence intervals. For age‐standardized YLD rates, females also tended to have higher estimates than males for overall OA and KOA, whereas the male and female trajectories for HOA were similar over time, with largely overlapping uncertainty intervals, suggesting a less pronounced sex disparity (eFigure 3).

The YLDs and prevalence rates of OA, HOA and KOA in three age groups from 1990 to 2021.

The study population included a total of 32 783 participants, of whom 15 881 (48%) were male and 16 902 (52%) were female (Figure 3). Table 2 summarizes these characteristics. OA patients were predominantly characterized by the following traits: female sex, older age, higher education level, better socioeconomic status, non‐Hispanic White, and higher waist circumference index. A higher prevalence of OA was also observed among former smokers, former drinkers, and individuals with no physical activity. Additionally, individuals with hypertension, hyperlipidemia, diabetes, and cardiovascular disease were more likely to develop OA. NHANES data showed significant differences in all variables between individuals with and without OA (p < 0.05).

TABLE 2.

Baseline characteristics of study participants (N = 32 783).

Variable	Overall N = 32 783 ^a	Non‐OA N = 29 367 ^a	OA N = 3416 ^a	p ^b
Gender
Male	15 881 (48%)	14 655 (50%)	1226 (36%)	< 0.001
Female	16 902 (52%)	14 712 (50%)	2190 (64%)
Age (year)	49.67 ± 17.70	48.06 ± 17.44	63.52 ± 13.31	< 0.001
Age category
≥ 65 year	7801 (24%)	6045 (21%)	1756 (51%)	< 0.001
20‐44 year	13 745 (42%)	13 406 (46%)	339 (9.9%)
45–64 year	11 237 (34%)	9916 (34%)	1321 (39%)
Race
Non‐Hispanic White	13 262 (40%)	11 198 (38%)	2064 (60%)	< 0.001
Non‐Hispanic Black	7063 (22%)	6521 (22%)	542 (16%)
Mexican American	4916 (15%)	4640 (16%)	276 (8.1%)
Other race	7542 (23%)	7008 (24%)	534 (16%)
Education level
High school	7475 (23%)	6710 (23%)	765 (22%)	< 0.001
>High school	17 177 (52%)	15 233 (52%)	1944 (57%)
<High school	8131 (25%)	7424 (25%)	707 (21%)
Marital status
Never married	6055 (18%)	5798 (20%)	257 (7.5%)	< 0.001
Widowed/Divorced/Separated	7313 (22%)	6129 (21%)	1184 (35%)
Married/Living with partner	19 415 (59%)	17 440 (59%)	1975 (58%)
Ratio of family income to poverty	2.46 ± 1.62	2.44 ± 1.62	2.65 ± 1.63	< 0.001
BMI (kg/m²)	29.13 ± 6.74	28.93 ± 6.64	30.83 ± 7.35	< 0.001
Waist circumference (cm)	99.44 ± 16.35	98.81 ± 16.21	104.79 ± 16.62	< 0.001
Alcohol status
Never	8901 (27%)	7931 (27%)	970 (28%)	< 0.001
Current	20 640 (63%)	18 655 (64%)	1985 (58%)
Former	3242 (9.9%)	2781 (9.5%)	461 (13%)
Smoking status
Never	18 357 (56%)	16 722 (57%)	1635 (48%)	< 0.001
Current	6680 (20%)	6090 (21%)	590 (17%)
Former	7746 (24%)	6555 (22%)	1191 (35%)
Physical activity
Vigorous	11 577 (35%)	10 773 (37%)	804 (24%)	< 0.001
None	8806 (27%)	7564 (26%)	1242 (36%)
Moderate	12 400 (38%)	11 030 (38%)	1370 (40%)
Hypertension
No	20 937 (64%)	19 605 (67%)	1332 (39%)	< 0.001
Yes	11 846 (36%)	9762 (33%)	2084 (61%)
Hyperlipidemia
No	21 086 (64%)	19 556 (67%)	1530 (45%)	< 0.001
Yes	11 697 (36%)	9811 (33%)	1886 (55%)
Diabetes
No	28 294 (86%)	25 651 (87%)	2643 (77%)	< 0.001
Yes	4489 (14%)	3716 (13%)	773 (23%)
Cardiovascular disease
No	30 028 (92%)	27 219 (93%)	2809 (82%)	< 0.001
Yes	2755 (8.4%)	2148 (7.3%)	607 (18%)

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^{^a}

n (%); Mean ± SD.

^{^b}

Pearson's χ ² test; Welch two sample t‐test.

Figure 4 illustrated the association between BMI and OA prevalence based on NHANES data. After adjustment for covariates, the GAM‐fitted smoothed curve showed a nonlinear positive association between BMI and OA prevalence. Further analysis using a segmented logistic regression model identified two BMI breakpoints at 24.00 kg/m² and 41.58 kg/m². Below 24.00 kg/m², BMI was not significantly associated with OA (OR = 1.008, 95% CI: 0.955–1.063, p = 0.7790). Within the BMI range of 24.00 to 41.58 kg/m², each 1 kg/m² increase in BMI was associated with 2.2% higher odds of OA (OR = 1.022, 95% CI: 1.003–1.041, p = 0.0197). At BMI values above 41.58 kg/m², the association became more pronounced, with each 1 kg/m² increase in BMI associated with 5.5% higher odds of OA (OR = 1.055, 95% CI: 1.022–1.090, p = 0.0011) (Table 3).

The nonlinear relationship between BMI and OA after controlling for confounding factors.

TABLE 3.

Segmented logistic regression analysis of the association between BMI and osteoarthritis in NHANES.

BMI	OR (95% CI)	p
Effect 1: BMI < ψ₁	1.008 (0.955, 1.063)	0.7790
Effect 2: ψ₁ ≤ BMI < ψ₂	1.022 (1.003, 1.041)	0.0197
Effect 3: BMI ≥ ψ₂	1.055 (1.022, 1.090)	0.0011

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Note: OR is expressed per 1 kg/m² increase in BMI. Model: Adjusted for age, sex, race/ethnicity, education, smoking, alcohol consumption, hypertension, and diabetes. ψ₁ = 24.00 kg/m²; ψ₂ = 41.58 kg/m².

Further stratified analysis indicated that OA prevalence was significantly higher among individuals with low physical activity compared to those with moderate or vigorous activity, and these individuals were more sensitive to changes in BMI. Moreover, the prevalence of OA was consistently higher among former drinkers than current drinkers. In the middle‐aged and elderly population (≥ 45 years), OA risk progressively increased with rising BMI (Figure 5).

Analysis of BMI and the prevalence of OA in different populations after controlling for confounding factors. (A) Age category; (B) Alcohol status; (C) Physical activity.

Eleven SNPs were included as instrumental variables to assess the causal relationship of BMI with HOA and KOA. The F‐statistics of all included SNPs were greater than 10, indicating the absence of weak instrumental variables (IVs). As shown in Figure 6, BMI was positively correlated with both HOA and KOA (OR = 1.54, 95% CI: 1.40–1.70 vs. OR = 1.63, 95% CI: 1.50–1.77).

Forest plot of Mendelian randomization analysis. *Note:* The associations of body mass index with risk of HOA and KOA.

eFigure 4A shows that the 11 SNPs exhibit a linear relationship with both HOA and KOA, with a positive slope. This finding is consistent across all five methods used. eFigure 4B presents the 11 SNPs associated with HOA and KOA from the Inverse Variance Weighting (IVW) analysis (p = 3.71e‐18 vs. p = 8.93e‐31), showing a positive correlation. This suggests that an increase in BMI is associated with an increased risk of HOA and KOA. Furthermore, the results demonstrate low dependency on individual SNPs. eFigure 4C shows that, after excluding individual SNPs, the overall confidence interval remains entirely to the right of zero. eFigure 4D illustrates that the 11 SNPs are evenly distributed on both sides of the IVW.

4. Discussion

This study is the first large‐scale investigation integrated GBD 2021, NHANES, and Mendelian randomization to provide a multi‐layered assessment of the association between BMI and OA. We found a nonlinear relationship between BMI and OA prevalence and identified two BMI breakpoints at 24.00 kg/m²and 41.58 kg/m². These findings suggest that the OA risk associated with BMI may begin to increase before individuals reach the conventional obesity threshold, highlighting the need for earlier weight management. In addition, high BMI appeared to be an important modifiable contributor to the global burden of OA, particularly knee OA, with variation across population subgroups.

High BMI is significantly associated with 20 health outcomes, including OA [28]. This study, using a nationally representative sample, is the first to employ a nonlinear model to reveal a significant dose–response relationship between BMI and OA risk after adjusting for covariates such as age and gender. In contrast to the conventional focus on obesity [10, 29], our findings suggest that the risk curve begins to rise more clearly at a much earlier stage. The first breakpoint was identified at 24.00 kg/m², indicating that individuals near the upper limit of the normal BMI range or at the beginning of the overweight range may already enter a stage of more apparent OA risk increase. The second breakpoint at 41.58 kg/m² further suggests that OA risk may accelerate again among individuals with extremely high BMI. This may indicate a shift in the growth pattern of disease risk from slow accumulation to sustained acceleration. Given that WHO data indicate global obesity rates have nearly doubled over the past three decades, with more than 1 billion individuals affected by 2022 [30], the findings of this study provide crucial threshold references for the precise prevention and control of obesity‐related OA, addressing the gap in previous research regarding the quantification of OA risk during the overweight stage.

This breakpoint pattern may be biologically plausible and may reflect two established pathophysiological pathways through which increasing BMI drives OA: increased mechanical loading and metabolic‐inflammatory responses [30]. On the one hand, overweight and obesity significantly increase biomechanical load on the joints, thereby accelerating cartilage degeneration [31]. According to the 2021 OA Clinical Guidelines, for every 1 kg increase in body weight, the pressure on the knee joint during walking increases by 3–4 kg. On the other hand, pro‐inflammatory cytokines (such as IL‐6) secreted by visceral adipose tissue elevate inflammatory markers via the circulatory system [32]. Research by Chow et al. has shown a significant positive correlation between serum IL‐6 levels and OA risk [33]. More importantly, mechanical stress can activate the NF‐κB signaling pathway, which, in conjunction with inflammatory factors, upregulates the expression of matrix‐degrading enzymes [34], creating a ‘stress‐inflammation’ vicious feedback loop that ultimately leads to a multiplicative increase in OA risk. This study further confirms, through Mendelian randomization analysis, that an increase in BMI is causally associated with KOA and HOA, providing genetic evidence supporting the aforementioned mechanisms.

These findings collectively indicate that the association between BMI and OA is underpinned by both biomechanical loading and metabolic‐inflammatory mechanisms [35], with an early risk increase becoming apparent over BMI 24.00 kg/m² and a further acceleration occurring in the extreme high‐BMI range. Although current public health interventions often prioritize individuals with obesity [36, 37], our findings suggest that OA related risk awareness and weight‐management strategies should begin earlier, particularly among individuals whose BMI approaches or exceeds 24.00 kg/m². Early screening, lifestyle modification, and individualized interventions at this stage may more effectively curb the onset and progression of OA and mitigate its long‐term disease burden.

Further analysis revealed that physical inactivity and alcohol abstinence are two key behavioural factors that modify the association between BMI and OA. Physical inactivity may directly increase OA risk through multiple pathways, including reduced cartilage nourishment, weakened muscular support, and the accumulation of systemic inflammation [38]. In contrast, the persistently higher OA risk among former drinkers is unlikely to reflect a protective effect of alcohol itself, but may instead be related to differences in metabolic or lifestyle profiles between abstainers and current drinkers. In addition, the ‘sick quitter’ effect—where individuals stop drinking due to deteriorating health—may contribute to reverse causation and residual confounding [39, 40]. These findings not only highlight the multifactorial pathogenesis of OA but also emphasize the importance of primary prevention and clinical management targeting these modifiable risk factors, providing a crucial scientific basis for developing precise public health intervention strategies.

It is noteworthy that this study also found a significant age‐ and sex‐related disparity in the association between BMI and OA, particularly with stronger associations observed in individuals aged ≥ 45 years and in women. The peak incidence of KOA occurs in the 45–64 age group, while HOA peaks in individuals aged ≥ 65 years. This discrepancy may be explained by several factors: during midlife, activity levels are relatively high, but cartilage repair capacity begins to decline [41]. Combined with the mechanical load imposed by the high prevalence of obesity during this period, this leads to an increased risk of cumulative mechanical damage in weight‐bearing knee joints. In contrast, the structural stability of the hip joint, as a ball‐and‐socket joint, may delay early degeneration [42]. However, sarcopenia in the elderly, which causes dynamic instability, along with bone metabolic disorders and calcification imbalance, may eventually accelerate the degeneration process in the later stages of life [43].

Additionally, this study found that women exhibited a significantly higher prevalence of overall OA and KOA, but no such trend was observed for HOA. This finding aligns with previous research consensus [11] and may be attributed to gender‐specific anatomical and biomechanical factors (such as a larger Q‐angle and relatively weaker muscle strength) [44], the loss of oestrogen protection after menopause [9], and the amplified inflammatory response associated with obesity [45]. In contrast, the progression of HOA is more influenced by age‐related sarcopenia and calcification imbalance, and thus, no significant gender difference was observed. Based on these findings, OA prevention efforts should focus on women and middle‐aged to older adults, particularly those with BMI exceeding 24.00 kg/m², rather than waiting until obesity has already developed. Health education and weight management programs targeting this group are essential to reduce obesity rates and decrease knee joint load. For HOA, preventive measures should be extended to the elderly population as a whole, with an emphasis on screening and intervention for sarcopenia in older adults and the promotion of bone health measures to slow the progression of hip joint degeneration.

The limitations of this study are primarily as follows: First, although the data sources are diverse (GBD, NHANES, GWAS), there are differences in population representativeness, data collection methods, and diagnostic criteria across the databases, which may introduce systematic bias. Second, the definition of OA differs among the three databases: GBD relies on radiological diagnosis, NHANES is based on self‐reported questionnaires, and GWAS uses hospital codes. However, analyses conducted when each database was analyzed separately showed consistent results regarding the association between BMI and OA, suggesting that the study's findings are unlikely to be significantly affected by these differences in OA definitions.

High BMI has become the leading modifiable risk factor driving the global burden of OA‐related disability. Consistent evidence from GBD, NHANES, and MR analyses indicates a non‐linear, dose‐dependent relationship between BMI and OA risk, with the most pronounced effects observed in the knee joint, females, and middle‐aged to older populations. These findings suggest that OA‐related weight‐management strategies should be initiated before the development of overweight or obesity, with BMI exceeding 24.00 kg/m² serving as an early risk‐alert point and extremely high BMI indicating a stage of further risk acceleration. This provides evidence for differentiated and earlier OA prevention strategies.

Author Contributions

Design: Shaohong Yu, Lei Yang, Jingyi Shao. Conduct/data collection: Shaohong Yu, Li Zeng, Chenglin Yang, Xiaoru Lin, Qing Guan. Analysis: Lei Yang, Jingyi Shao. Writing manuscript: Shaohong Yu, Li Zeng, Lei Yang, Yangjiang Ou, Chenglin Yang, Xiaoru Lin, Jingyi Shao, Qing Guan, Guobo Li, Xiuquan Lin.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by grants from Fujian Provincial Health Technology Project (2020GGA026), the Scientific and Technological Innovation Startup Fund, Fujian Maternity and Child Health Hospital (Grant number YCXB23‐01).

Ethics Statement

This article does not contain any studies with human participants or animals performed by any of the authors. The data used in this research are publicly available and de‐identified, retrieved from the Global Burden of Disease (GBD) study database, the National Health and Nutrition Examination Survey (NHANES) database, and the Genome‐Wide Association Study (GWAS) Catalogue. Therefore, ethical approval and informed consent were not required.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

eFigure 1. Maps of the age‐standardized prevalence and age‐standardized YLDs of OA in various countries in 1990 and 2021.

eFigure 2. The changes in age‐standardized YLDs of HOA and KOA caused by high body‐mass index globally and in five SDI regions from 1990 to 2021.

eFigure 3. Global trends of age‐standardized prevalence and YLDs of OA, HOA and KOA by gender from 1990 to 2021.

eFigure 4A. Scatter Plot: MR Estimates of BMI on HOA and KOA.

eFigure 4B. Single SNP Plot: MR Estimates of BMI on HOA and KOA.

eFigure 4C. Leave‐one‐out sensitivity analysis: MR Estimates of BMI on HOA and KOA.

eFigure 4D. Funnel plot: MR Estimates of BMI on HOA and KOA.

eTable 1. Proportions of missing data for key variables in the overall NHANES population, stratified by osteoarthritis status.

eTable 2. Characteristics and instrument strength of BMI‐associated SNPs used as instrumental variables.

eTable 3. Distribution of osteoarthritis prevalence across PIR quartiles.

eTable 4. Multiple‐imputation logistic regression of PIR as a continuous variable.

DOM-28-7104-s001.docx^{(782.1KB, docx)}

Acknowledgements

The authors acknowledge the efforts of the Global Burden of Disease (GBD) study, the National Health and Nutrition Examination Survey (NHANES), and the Genome‐Wide Association Study (GWAS) Catalogue in providing high‐quality open resources for researchers.

Yu S., Zeng L., Yang L., et al., “Unveiling the BMI Risk Threshold for Osteoarthritis: Multi‐Database Causal and Nonlinear Evidence,” Diabetes, Obesity and Metabolism 28, no. 8 (2026): 7104–7115, 10.1111/dom.70892.

Handling Editor: Ricahrd Donnelly

Guobo Li and Xiuquan Lin are joint corresponding authors.

Contributor Information

Guobo Li, Email: lgb0703@163.com.

Xiuquan Lin, Email: linxiuquan001@163.com.

Data Availability Statement

The data reported in this paper is available from https://vizhub.healthdata.org/gbd‐results/, https://www.cdc.gov/nchs/nhanes/, and https://www.ebi.ac.uk/gwas.

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