Plant-based diet and risk of arthritis: a nationwide cohort study of the Chinese elderly population†

Haohao Zhang‡ ^a, Junhao Liang‡^a, Yaxin Han^a, Jiajing Tian^a, Yahui Tu^a, Rui Fan^ab, Wenli Zhu^ab, Zhaofeng Zhang*^abc and Haifeng Zhao*^de
aDepartment of Nutrition and Food Hygiene, School of Public Health, Peking University, Haidian District, Beijing 100191, People's Republic of China. E-mail: zhangzhaofeng@bjmu.edu.cn; Fax: +86-10-82801575
^bBeijing's Key Laboratory of Food Safety Toxicology Research and Evaluation, Beijing 100191, People's Republic of China
^cInstitute of Medical Technology, Peking University Health Science Center, Beijing 100191, People's Republic of China
^dNutritional and Food Sciences Research Institute, Department of Nutrition and Food Hygiene, School of Public Health, Shanxi Medical University, Taiyuan 030001, People's Republic of China. E-mail: haifengzao75@163.com; Tel: +86-351-3985907
^eMOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, School of Public Health, Shanxi Medical University, Taiyuan 030001, People's Republic of China

First published on 24th July 2025

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Background

In the contemporary global context, the phenomenon of population aging is becoming increasingly pronounced and poses significant challenges. World population prospects 2024 predicts that by the late 2070s, the number of individuals aged 65 years and older will reach 2.2 billion globally, outnumbering the number of children (under 18). Elderly individuals experiencing a decline in functional and exercise capacity are susceptible to various chronic ailments and multimorbidity, with arthritis emerging as the major cause of disability among this demographic.^1,2 Arthritis encompasses a collection of chronic conditions that affect the bones, joints, and adjacent soft tissues. Arthritis patients exhibit a heightened susceptibility to developing diabetes and cardiovascular disease compared to individuals without the condition.³ Furthermore, the economic ramifications of arthritis are substantial, encompassing expenses related to medical interventions, ongoing care, and the impairment or loss of occupational and functional capacities resulting from the disease.

The most prevalent forms of arthritis include rheumatoid arthritis (RA) (ICD-11: FA20), osteoarthritis (OA) (ICD-11: FA0Z), gouty arthritis (ICD-11: FA25), and polymyalgia rheumatica (ICD-11: FA22). According to the global burden of disease study 2021, as of 2020, the global population comprises approximately 595 million individuals with OA, 17.6 million with RA, and 55.8 million with gout. Furthermore, the incidence and prevalence of arthritis are progressively increasing over time. Globally, the total number of OA cases has increased by 132.2%, the prevalence of RA has increased by 14.1%, and the prevalence of gout has increased by 22.5% compared to those in 1990. It is projected that by 2050, 31.7 million people will have RA globally, the total number of prevalent cases of gout will reach 95.8 million, and cases of OA in the knee will increase by 74.9%, the hand by 48.6%, the hip by 78.6%, and other types of OA by 95.1%.^4–6 Arthritis has emerged as a prominent contributor to disability, particularly among the elderly, and has assumed a significant role in global public health.

Despite the identification of potential signaling pathways, the current lack of a definitive pharmaceutical intervention for arthritis can be attributed to its multifaceted etiology. Extensive research has revealed a correlation between prevalent forms of arthritis and the release of specific inflammatory mediators.⁷ Notably, myeloid cells, particularly macrophages, assume a pivotal role in the progression of arthritis from a preclinical to a clinical state. Upon activation, macrophages secrete cytokines such as interleukin-1 (IL-1), tumor necrosis factor-α (TNF-α), and granulocyte-macrophage colony-stimulating factor. Substantial therapeutic benefits can be achieved through the inhibition of these cytokines.⁸ Currently, the management of arthritis predominantly relies on non-steroidal anti-inflammatory drugs, glucocorticoids, and artificial joint replacement. Nevertheless, individuals subjected to prolonged administration of these medications or surgical intervention not only endure the associated adverse effects but also shoulder a significant financial strain.

The World Health Organization is aligning its efforts with the Global Strategy and Action Plan on Ageing and Health 2016–2020, as well as the United Nations Decade of Healthy Ageing (2021–2030), in order to promote healthy ageing worldwide. The government of China introduced the 14th Five-Year Plan for Healthy Ageing. However, the advancement of healthy ageing policies is impeded by the absence of effective coping strategies for arthritis. Additionally, the limited availability of medical resources has shifted the current focus towards dietary interventions. The modulation of systemic inflammation through dietary patterns has been observed to yield positive health outcomes in various chronic diseases.^9–11 A study conducted by Jun Li et al., involving a follow-up period of 5291518 person-years, suggests that reducing the inflammatory potential of dietary patterns could be an effective approach to managing cardiovascular disease.¹² Mazzucca et al. conducted a prospective analysis to examine the effect of different food intakes on RA onset through the use of the UK Biobank.¹³ However, it is important to acknowledge that the existing body of research on arthritis and dietary patterns has certain limitations.^14–16 Most studies have relied on cross-sectional designs, which may limit the ability to establish causality. Additionally, the small sample sizes and the inclusion of populations from diverse regions with varying genetic backgrounds may restrict the generalizability of the findings to a global context.

The plant-based diet encompasses a vegetarian dietary approach that incorporates a wide range of foods such as fruits, vegetables, legumes, grains, nuts, and beans, among others.¹⁷ This dietary pattern has been extensively investigated for its numerous anti-inflammatory components, which have shown promise in various disease contexts.¹⁸ Notably, research conducted in China has demonstrated the potential of plant-based diets in mitigating the adverse effects of PM2.5 on cognitive decline and reducing the risk of all-cause mortality.^19,20 Building upon these findings, this study aims to investigate whether a plant-based dietary pattern, utilizing a nationally representative database from China, can serve as a preventive measure against arthritis. Our objective was to discover a straightforward and practical approach to implementing a method that would enhance healthy life expectancy, thereby attaining the ambitious objective of promoting healthy aging.

Methods

Study population

The Chinese Longitudinal Healthy Longevity Survey is an extensive prospective cohort study that commenced in 1998. The CLHLS encompasses a representative sample of China, specifically focusing on 23 provinces dominated by the Han population, including autonomous regions and municipalities. Within each province, 50% of cities and counties were randomly selected for participation. A more comprehensive account of the sampling design can be found elsewhere.^21,22 The CLHLS study received approval from the Biomedical Ethics Committee of Peking University (IRB00001052-13074), and all participants or their legal representatives provided written informed consent.

Our study used the 2008 waves of the CLHLS and their follow-up waves in 2011, 2014 and 2018. We excluded participants who were younger than 65 years old, missed values for arthritis, missed dietary patterns and covariates, and had arthritis at baseline. In total, 10059 participants were included in the analysis. More details on participant inclusion and exclusion can be found in ESI Fig. S1.†

Dietary assessment

The plant-based diet pattern consists of higher consumption of plant-based foods and lower consumption or exclusion of animal-based foods.²³ We obtained dietary information through a simplified food frequency questionnaire, which has been demonstrated to be reliable and valid in previous studies.^24–26 We constructed an adapted PDI, which was pioneered by Satija et al.¹⁷ The food groups included whole/refined grain, vegetable, fresh fruit, vegetable/animal oil, garlic, nut, tea, beans, salt-preserved vegetable, sugar, egg, milk, meat and fish. Garlic, nuts, tea, beans, salt-preserved vegetables and sugar were scored 1, 2, 3, 4 or 5 with increasing frequency of consumption. Vegetable and fresh fruit were scored 1, 2, 4 or 5 with increasing the frequency of consumption. Egg, milk, meat and fish were scored 1, 2, 3, 4 or 5 with decreasing frequency of consumption. Whole/refined grain and vegetable/animal oil were scored 1 or 5. The total score ranged from 16 to 76 theoretically. The population was divided into low PDI and high PDI groups according to the median PDI score. High PDI indicated greater plant-based diet quality.

We also constructed a healthful plant-based diet index (hPDI) to explore whether a healthful plant-based diet had a greater influence on arthritis. Based on the plant-based diet index, salt-preserved vegetables and sugar were scored 5, 4, 3, 2 or 1. Refined grain was scored 1 and whole grain was scored 5. The total score ranged from 16 to 80. The population was also divided into two groups. More details on the construction and scoring of the PDI and hPDI can be found in ESI Table S1.†

Arthritis assessment

The definition of arthritis was based on self-report. During each survey wave, for every surviving respondent, they would be asked: “Are you suffering from arthritis?”. For deceased interviewees, their close family member would be asked: “Did elder suffer from any of the following diseases?”. If the participant answered yes, he or she was defined as suffering from arthritis. If the participant answered no, he or she was defined as not suffering from arthritis at that time.

Covariates

The covariates included age (years), sex (male/female), resident type (city and town/rural), educational attainment, financial status, exercise status (exercise/do not exercise), smoke status (smoke/do not smoke), drink status (drink/do not drink), body mass index (BMI), comorbidity (0/1/2/≥3 coexisting diseases). Participants’ age, sex and resident type were collected at baseline. Educational attainment was assessed by the number of years of schooling and categorized as <1 year and ≥1 year. Financial status was assessed by the total income of the family in the last year before the survey and categorized as <10000 Yuan and ≥10000 Yuan. Exercise, smoke and drink statuses represented whether participants exercised, smoked or drank at the time of interview at baseline. BMI was categorized as abnormal (<18.5, >24) or normal (18.5–24). We defined comorbidity as the total number of diseases that participants suffered at baseline. Comorbidity (including hypertension, diabetes, cardiovascular disease, respiratory diseases, tuberculosis, cataract, glaucoma, cancer, gastric or duodenal ulcer, Parkinson's disease, bedsores, dementia, epilepsy, cholecystitis or cholelith disease, dyslipidemia, chronic nephritis, mammary gland hyperplasia, hepatitis) ranged from 0 to 18 and was grouped as 0, 1, 2 and ≥3.

Statistical analysis

Baseline characteristics of the study population were summarized using descriptive statistics. Differences in these characteristics were then compared using the Wilcoxon test for continuous variables and the chi-square test for categorical variables.

Follow-up ended on the date of suffering from arthritis, death, loss to follow-up, or end of study period. We used the Kaplan–Meier cumulative incidence curve and the Schoenfeld residuals method to test the assumption for the proportional hazards model. Cox proportional hazard models were used to assess the association between plant-based diet patterns and the risk of developing arthritis during the follow-up. The participants were stratified by PDI scores and individual food groups to explore the association between the plant-based diet and arthritis and the association between individual foods and arthritis. Using the low PDI group as a reference, we calculated the hazard ratios (HRs) and 95% confidence intervals (CIs) of arthritis across different PDI categories. For all outcomes, models without any adjusted covariates (model-not adjusted), models adjusted only for age and sex (model 1-adjusting age and sex), models adjusted for age, sex, resident type, educational attainment, financial status (model 2-further adjusting sociological factors), models adjusted for age, sex, exercise status, smoking status, drinking status, BMI, comorbidity (model 3-further adjusting demographic variables) and models adjusted for age, sex, resident type, educational attainment, financial status, exercise status, smoking status, drinking status, BMI, comorbidity (model 4-multivariate adjustment) were constructed. We also used restricted cubic splines, which were fitted with 4 knots, to explore the nonlinearity between PDI and arthritis. The Wald test was used to assess whether the observed relationships were linear or nonlinear.

For hPDI, we also constructed models to calculate the HRs and 95% CIs of arthritis across categories of hPDI, which aimed to determine whether a healthful plant-based diet was more effective than a plant-based diet in preventing arthritis.

To ensure the stability of the results, a sensitivity analysis was performed. First, considering death as a competing risk factor for arthritis outcome, the multi-factor competitive risk model was applied as a sensitivity analysis. Therefore, multi-factor competing risks regression models were calculated. Second, our study aimed to examine the correlation between PDI and arthritis, utilizing various definitions of arthritis, such as hospital diagnosis and a combination of patient-reported symptoms and hospital diagnosis. Third, we conducted a stratified analysis based on demographic factors, including sex, type of residence, educational attainment, financial status, exercise habits, smoking habits, and alcohol consumption.

To explore the interaction between PDI and exercise status, interaction analysis was conducted. Participants were divided into four subgroups based on PDI and exercise status (do not exercise and exercise). Multi-adjusted time-dependent Cox regression was used to examine the interaction. For individual foods, multi-adjusted time-dependent Cox regression was used to calculate HRs and 95% CIs to explore the association between the frequency of each food intake and arthritis. Statistical analysis was performed using R software. Statistical significance was considered at P < 0.05 in two-sided tests.

Results

Characteristics of participants

Table 1 lists the characteristics of 10059 participants without arthritis at baseline. The mean age was 87.2 (standard deviation (SD): 11.3) years old. 44.9% of participants were males, 62.8% of participants were rural residents, and 37.3% of participants had ≥1 year of education. 42.8% of participants had annual revenues less than 10000 Yuan. 34.9% and 31.8% of participants were current smokers and drinkers. 38.8% of participants had regular exercise. The mean BMI was 20.3 (SD: 3.54) kg m⁻². Compared with the low PDI group, participants with higher PDI were more likely to be male, had a lower average age, higher educational attainment levels, and were more likely to live in cities or towns. They also had higher rates of normal BMI, exercise, drinking, and smoking.

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Association of PDI with arthritis

During a median follow-up period of 4.2 years, 1482 participants (14.7%) who were arthritis-free at baseline reported developing arthritis. Of these, 662 were in the low PDI group and 820 were in the high PDI group. The incidence of arthritis was 37.7 per 1000 person-years in the low PDI group and 32.1 per 1000 person-years in the high PDI group (Table 2).

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Based on the unadjusted model, participants with the highest quality of plant-based diet (PDI range: 49–68) were associated with a 17.9% decrease in the risk of arthritis (HR: 0.821, 95% CI: 0.741, 0.909), compared to those with the lowest quality (PDI range: 26–48). In both age- and sex-adjusted analyses and multivariable-adjusted analysis, the association between PDI and the risk of arthritis showed a similar trend but with a slightly diminished magnitude (HR: 0.839, 95% CI: 0.756, 0.931 in model 1; HR: 0.838, 95% CI: 0.755, 0.929 in model 2; HR: 0.841, 95% CI: 0.758, 0.933 in model 3; HR: 0.840, 95% CI: 0.757, 0.932 in model 4). Compared to a plant-based diet, a healthy plant-based diet did not show superiority (ESI Table S3†). The HRs (95% CIs) of arthritis according to continuous PDI are shown in ESI Table S2.† The Kaplan–Meier cumulative incidence curve is shown in ESI Fig. S2.†

Overall, there was no nonlinear relation between PDI and the risk of arthritis (P for non-linearity = 0.93). The risk of arthritis was decreased in participants with higher PDI (Fig. 1). When PDI was below 49, the diet pattern acted as a risk factor for arthritis. In contrast, when PDI was 49 or higher, the diet pattern acted as a protective factor for arthritis.

Sensitivity analysis

To test the robustness of the association between PDI and arthritis, a series of sensitivity analyses were performed. First, we examined a multi-factor competitive risk model. The result showed that the association between PDI and arthritis remained significant in the adjusted models. Second, similar trends were observed for different definitions of arthritis, including diagnosis by hospital and both self-report and diagnosis by hospital (ESI Tables S4–S6†).

Interaction analysis of PDI and exercise status on arthritis

Results of the interaction analysis between PDI and exercise status on the risk of arthritis are shown in Table 3. Compared with participants with low PDI and no exercise, those with low PDI and exercise showed no significant difference in the risk of arthritis (HR: 0.987, 95% CI: 0.841, 1.16). However, participants with high PDI and no exercise had a significantly lower risk (HR: 0.856, 95% CI: 0.748, 0.980). Additionally, those with high PDI and exercise had a substantially lower risk (HR: 0.808, 95% CI: 0.694, 0.941).

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Stratified analysis

Fig. 2 presents the results of the stratified analyses. In both male and female participants, a higher PDI was correlated with a reduced risk of arthritis, with HRs (95% CIs) of 0.835 (0.707, 0.987) and 0.840 (0.736, 0.960), respectively. Similarly, among rural participants, a higher PDI was associated with a lower risk of arthritis, with an HR (95% CI) of 0.773 (0.676, 0.884). Likewise, in participants with normal BMI, a higher PDI was linked to a decreased risk of arthritis, with an HR (95% CI) of 0.800 (0.696, 0.920). In participants with an educational attainment of less than 1 year, a higher PDI was associated with a lower risk of arthritis, with an HR (95% CI) of 0.773 (0.675, 0.885). There was a stronger association between higher PDI and lower risk of arthritis among participants who exercised, drank alcohol, and smoked. Among participants with fewer than two comorbidities, a higher PDI was found to be significantly associated with a reduced risk of arthritis.

Individual food analyses

Fig. 3 summarizes the risks of developing arthritis stratified by individual food groups. Low consumption of eggs was an independent factor for higher risk of arthritis, with HR (95% CI) of 1.19 (1.05, 1.35). In contrast, some dietary items were found to have protective effects against arthritis, including sugar (HR: 0.82, 95% CI: 0.70, 0.96) and vegetable grease (HR: 0.76, 95% CI: 0.66, 0.88).

Discussion

The aim of this study was to devise a viable and effective dietary intervention approach tailored to the Chinese population for the prevention of arthritis. Our results demonstrated that adopting a high-quality plant-based diet reduced the risk of arthritis by 16.0%, particularly in rural regions. Additionally, adherence to a high PDI diet combined with regular exercise further enhanced the effectiveness of arthritis prevention. These findings provide evidence to support the concept that “food is medicine” and establish a substantial basis for advocating healthy aging. Moreover, these findings have the potential to stimulate governmental initiatives that endorse plant-based dietary approaches, aligning with the goals of the healthy eating campaign and the Elderly Health Improvement Action outlined in the Healthy China 2030 blueprint.

Our findings suggest that a plant-based diet can delay the onset of arthritis symptoms, mainly due to its high content of polyphenols, flavonoids, antioxidant vitamins, and dietary fiber, while its low saturated fat content also plays a role.^27,28 Notably, polyphenols possess the ability to counteract free radicals and inhibit the activation of NF-κB, thereby suppressing inflammatory reactions.^29–31 Additionally, vegetable oil is a valuable source of monounsaturated fatty acids, such as oleic acid. Experimental studies have demonstrated that oral oleic acid can be converted into oleoylethanolamide, which may serve as a promising therapeutic agent for many inflammatory disorders in the small intestine.³² Consequently, this inhibitory effect can impede the transition of arthritis from a state of heightened susceptibility to the manifestation of clinical symptoms.⁸ Conversely, saturated fatty acids derived from animal sources have been observed to elevate the levels of pro-inflammatory cytokines, including TNF-α and IL-6, potentially accelerating the onset or exacerbating the severity of arthritis symptoms.³³

Several researchers reported that exercise has a beneficial impact on RA.^34,35 To investigate the relationship between plant-based diet and exercise, we conducted an interaction analysis. The findings indicated that enhancing the quality of a plant-based diet could reduce the risk of arthritis and enhance the synergistic effects of the plant-based diet and regular exercise through multiple mechanisms. Our results provide a scientific basis for the effectiveness of combining diet and exercise in relieving and preventing arthritis symptoms. High PDI diet and exercise are feasible and economic lifestyle interventions that have important implications for individuals with lower economic status or limited access to good healthcare. We suggest that the elderly adopt a high PDI diet combined with exercise to prevent arthritis.

In our study, we conducted a stratified analysis to identify potential factors that may modify the effect of a plant-based diet. Firstly, our findings indicate that individuals residing in rural areas with a high PDI diet experienced a significant reduction in the risk of arthritis; a pattern that was also observed among those with lower levels of education. These disparities may be attributed to the income gap between urban and rural areas, as well as to the unequal distribution of medical resources in rural regions.³⁶ This inequality may lead to the failure in timely diagnosis and treatment of early symptoms of arthritis, thereby exacerbating the disease progression. Additionally, compared to urban regions, rural food supplies are relatively constrained. Against this backdrop, a plant-based diet, as a simple, feasible, and cost-effective intervention, plays a more pronounced role in alleviating arthritis symptoms and delaying disease progression. Similarly, participants with lower levels of education may have a simpler understanding of a healthy diet and place a greater emphasis on the accessibility and affordability of food. Therefore, such participants may be more likely to adopt a plant-based diet as a healthy lifestyle recommendation, thus demonstrating a stronger effect in reducing the risk of arthritis. Secondly, our findings indicate a significant reduction in arthritis risk among individuals with a normal BMI who adhered to a plant-based diet. However, no discernible impact was observed among those classified as underweight, overweight, or obese. This discrepancy in effects across different BMI groups may be attributed to elevated levels of inflammatory factors, which potentially counteract the beneficial effects of a plant-based diet in individuals with obesity.³⁷ Consequently, these results underscore the importance of maintaining a healthy body weight in the elderly population. This notion aligns with the China Healthy Lifestyle for All initiative, which emphasizes the importance of maintaining a healthy body weight. Thirdly, our research indicates that adopting a plant-based diet has a greater efficacy in mitigating the susceptibility to arthritis among individuals who engage in smoking and excessive alcohol consumption. The deleterious effects of smoking and high-dose alcohol intake on inflammatory cytokine levels and inflammation exacerbation are widely recognized.³⁸ Consequently, it is imperative for elderly individuals who partake in smoking and alcohol consumption to embrace a plant-based diet as a preventive measure against the onset of arthritis. Fourth, our study reveals a significant correlation between the adoption of a plant-based diet and decreased arthritis susceptibility among individuals with a limited number of comorbidities. This finding may potentially be attributed to the observed link between comorbidity and inflammation.³⁹ To enhance the preventive measures against arthritis, we recommend that older adults engage in physical fitness activities and incorporate a plant-based diet into their daily regimen.

Our study possessed several notable strengths. (1) It was the first investigation to explore the correlation between PDI and arthritis risk, utilizing a nationally representative sample of older adults with a substantial sample size. (2) We meticulously accounted for various covariates, performed multiple stratified analyses, and conducted sensitivity analyses to guarantee the robustness and reliability of our findings. (3) We presented a locally feasible, economically viable, and socially acceptable approach to dietary interventions for arthritis. This pathway holds potential for effectively promoting health, poverty alleviation and rural revitalization strategies in China, thereby contributing to the goals of the Healthy China 2030 Plan.

Prudence was imperative in the interpretation and practical implementation of our findings. Firstly, a limitation was the relatively advanced age of the respondents, which could introduce potential biases such as recall bias, survival bias, and competing risk of mortality. We conducted a sensitivity analysis utilizing a competing-risks model, which yielded consistent results. Secondly, due to the lack of precise details regarding food consumption, we employed a basic scale. It was elucidated that utilizing intake frequency instead of intake itself was a dependable and efficacious approach. Thirdly, it should be noted that the identification of participants afflicted with arthritis relied on self-reporting rather than hospital diagnosis, thus presenting a limitation. Nonetheless, to address this concern, we conducted a comprehensive analysis incorporating multi-adjusted time-dependent Cox regression, employing various definitions of arthritis. Encouragingly, the outcome of this analysis demonstrated the effectiveness of the plant-based diet. Fourthly, due to the observational nature of this study, we controlled for some potential confounders but cannot rule out residual or unmeasured confounding. Nevertheless, during multiple model adjustments, the higher-quality plant-based diet consistently showed potential in reducing arthritis risk. Finally, our study did not differentiate between distinct subtypes of arthritis. Nonetheless, our findings support the notion that a plant-based diet holds advantages for arthritis overall, making it a viable option for dissemination in expansive rural regions.

Conclusion

In summary, our study revealed a notable prevalence of arthritis among the elderly population, with a lower risk observed among individuals with higher PDI scores. Adopting a plant-based dietary regimen could potentially delay the onset of arthritis. This recommendation holds relevance in rural areas, where the uneven distribution of medical resources can be alleviated through the promotion of a plant-based diet. By adopting such dietary patterns, it is plausible to anticipate a reduction in arthritis incidence, an improvement in healthy life expectancy, and the achievement of healthy aging objectives.

Author contributions

Conceptualization, Zhaofeng Zhang and Haifeng Zhao; methodology, Haohao Zhang and Junhao Liang; software, Haohao Zhang and Junhao Liang; validation, Wenli Zhu and Rui Fan; investigation, Yaxin Han and Jiajing Tian; data curation, Haohao Zhang, Rui Fan and Zhaofeng Zhang; writing – original draft preparation, Haohao Zhang; writing–review and editing, Haohao Zhang, Junhao Liang and Zhaofeng Zhang; visualization; Junhao Liang and Yahui Tu; supervision, Wenli Zhu, Haifeng Zhao and Zhaofeng Zhang. All authors have read and agreed to the published version of the manuscript.

Conflicts of interest

The authors declare no conflict of interest.

Abbreviations

CLHLS	Chinese Longitudinal Healthy Longevity Survey
PDI	Plant-based diet index
hPDI	Healthful plant-based diet index
HRs	Hazard ratios
CIs	Confidence intervals
RA	Rheumatoid arthritis
OA	Osteoarthritis
IL-1	Interleukin-1
TNF-α	Tumor necrosis factor-α
SD	Standard deviation
NF-κB	Nuclear factor kappa-light-chain-enhancer of activated B cells
IL-6	Interleukin-6

Ethics approval and consent to participate

The CLHLS study was approved by the Research Ethics Committee of Peking University (IRB00001052-13074), and all participants or their proxy respondents provided written informed consent.

Data availability

Data for this article, including the Chinese longitudinal healthy longevity survey questionnaire data and image data, are available at Figshare at https://figshare.com/articles/dataset/Final_dataset_csv/29086997?file=54600998.

Acknowledgements

The funding agencies had no role in the design of the study; in the collection, analysis, or interpretation of the data; in writing or approving the manuscript; or in the decision to submit the manuscript for publication. The authors acknowledge funding from the National Key Technologies R&D Program of China (No. 2023YFC3604702), the Beijing Municipal Natural Science Foundation (grant 7232238) and the Shanxi Province Higher Education “Billion Project” Science and Technology Guidance Project. The authors also thank the staff and participants of the Chinese Longitudinal Healthy Longevity Survey for their important contributions.

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Footnotes

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d5fo02192h

‡ These authors contributed equally to this paper and should be considered as co-first authors.

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