Development of a model for predicting the 4-year risk of symptomatic knee osteoarthritis in China: a longitudinal cohort study

Knee osteoarthritis (KOA) is among the most common chronic diseases leading to disability worldwide, carrying a substantial and increasing health burden [1, 2]. The prevalence of symptomatic KOA and radiographic KOA in patients over 60 years of age ranges from 10.0 to 16.0% and 35.0 to 50.0% [3–7], respectively. Approximately 250 million people have KOA worldwide, with a twofold increased prevalence in men and a threefold increased prevalence in women in the USA over the past 20 years [5]; symptomatic KOA affects approximately 15.1 million individuals in the US population [8]. The estimated number of individuals over 60 years old suffering from symptomatic KOA reached 37.35 million in China [9]. The years lived with disability (YLDs) caused by osteoarthritis ranked tenth in China in 2016 [10] and fourth in South Korea in 2015 [11]. Osteoarthritis had the fifth greatest relative increase in total YLDs from data of six Nordic countries from 1990 to 2015 [12]. KOA was the first leading among osteoarthritis, accounting for 87% YLDs of osteoarthritis [13]. The increasing prevalence of KOA has increased the socioeconomic burden for affected individuals and healthcare systems [14].

To date, there are no effective therapeutic strategies for KOA. Prediction models for KOA aim to synthesize multiple factors to comprehensively predict the incident risk and may allow for early detection and prevention [15]. The Nottingham KOA model was an early model for the prediction of 12-year KOA risk in middle-aged adults, including easily obtainable factors such as age, sex, family history, body mass index (BMI), occupational risk, and history of knee injury [16]; however, it was developed using data from only two communities in the UK, rather than a random sample of the general population, limiting its validity in other populations [17]. Several studies have developed prediction models based on genomic data [18–21] or radiographic/clinical biomarkers [22] such as hip α-angle and spinal bone mineral density. However, use of these models is limited due to their high cost or complexity [23].

Primary risk factors for incident KOA include advanced age, female gender, overweight/obesity, knee injury, and smoking [24–27]. Smoking decreases the risk of KOA, while the other factors increase the risk. Although physical activity [28, 29], occupational factors [24], ethnicity, and genetics [25] have also been associated with the incidence and/or progression of KOA, previous studies have reported inconsistent results due to methodological differences. Other potential risk factors for the development of KOA include metabolic syndrome [30–32], waist circumference [33], and depressive symptoms [24, 34], although findings regarding these factors remain controversial. Previous studies have reported a dual association between osteoarthritis and certain comorbidities (e.g., hypertension, ischemic heart disease, diabetes) [24, 25], suggesting that these comorbidities can influence the incidence and progression of KOA. Existing risk models of KOA have failed to include these potential risk factors, and there are currently no models for predicting KOA risk in the Chinese population. In this study, we aimed to develop a model for predicting the 4-year risk of KOA based on survey data obtained via a random, nationwide sample of Chinese individuals. This model would consider the potential risk factors.

In CHARLS2011, physical activity measures were available for 3684 participants, while blood samples were available for 11,847 participants. Complete KOA data were available in CHARLS2011 and CHARLS2015 for 9204 of these participants. Seven participants were excluded because they declined to undergo body measurements assessments, rendering over 50% of participant’s variables (including measurements of weight, height, waist circumference, assessments of depressive symptoms, physical activity, ADLs/IADLs, or the blood biomarkers) inaccessible. Among them, one patient was diagnosed with KOA in 2011, and one developed KOA in 2015. Among the remaining 9197 participants, an additional 1004 were excluded because they were diagnosed with KOA at baseline (CHARLS2011). Thus, data from a total of 8193 patients were included when developing the model. Among the 8193 included participants, 815 developed symptomatic KOA in the following 4 years. The overall 4-year cumulative incidence of symptomatic KOA was 9.95%, with 7.62% and 13.77% in males and females respectively.

The mean age was 58.82 years (standard deviation (SD) ± 9.01 years), and 4251 patients were female (51.89%). At baseline, 23.31% participants had difficulty with ADLs/IADLs, while 17.08% were diagnosed with metabolic syndrome, and 44.66% reported one or two chronic comorbidities. A history of hip fracture was reported by 252 (3.08%) participants. Other baseline characteristics are summarized in Table 1, along with the number of missing values for each variable.

We developed and internally validated a model for predicting the 4-year risk of symptomatic KOA among the Chinese population, based on data from the CHARLS cohort. An easy-to-use clinical score model was developed to identify individuals’ risk of developing KOA. The model included ten convenient and accessible variables, including age, sex, and waist circumference, which are most commonly included in previous KOA prediction models. Besides we also included the other controversial or new predictors of KOA, which were first time tested in risk model of KOA. To our knowledge, this is the first model for predicting KOA risk in the Chinese population, and our results suggest that this model can be used to aid in the prevention of KOA.

Older age was identified as a risk factor for KOA in our study; the most significant increase in risk was observed in the 60–69 years group. The cumulative incidence of symptomatic KOA gradually increased from 45 years of age, increasing rapidly after 55 years of age, peaking at approximately 65 years of age [47]. After 70 years of age, increases in the cumulative incidence of KOA were no longer significant [47]. Our findings, along with previous, highlight the need to prevent the incident of KOA in individuals between 45 and 70 years of age.

Obesity creates an abnormal loading environment for weight-bearing joints and may contribute to the pathogenesis of KOA [48]. Alternatively, the increased risk of KOA may be caused by the positive energy balance and metaflammation associated with obesity [49]. Although BMI has been illustrated as an important predictor of KOA [50], Wallace et al. (2019) [48] reported that increased abdomen size is associated with a greater risk of radiographic KOA than high BMI. Further studies are required to determine whether BMI, waist circumference, or metabolic syndrome comprehensively influences KOA risk due to mechaflammation and metaflammation. In this study, we analyzed the effects of BMI, waist circumference, and metabolic syndrome on KOA incident in the Chinese population. None of these three factors were a significant predictor of KOA incident; BMI had relatively low significance compared with waist circumference.

We analyzed the likelihood that the damage by BMI on joint tissues and pain symptoms would not reach a significant effect in the short term. Only the 12-year Nottingham KOA model investigated BMI related to symptomatic KOA [16], and the 9-year Rotterdam model [21] and 4-year Chingford model [22] were predictive for radiographic KOA. BMI was also not included in final 4-year Chingford model. Zheng & Chen [50] synthesized that BMI was a significant factor for incident KOA, but the diagnosis of KOA was radiographic KOA or severe KOA or replacement of KOA in 13 of 14 included studies. Among the eight studies with follow-up duration less than 10 years, none focused on symptomatic KOA. Another possible reason was that abnormal waist circumference was much prevalent than abnormal BMI in the Chinese population because body feature is prone to be small in the Asian race compared with the European or American population [51], thus waist circumference was much significant with incident KOA than BMI in this study. The results imply that the sensitivity of index of obesity might vary with race when evaluating the risk of KOA. Additional studies focusing on risk model of KOA are required to verify the significance of BMI with the incident of symptomatic KOA in Chinese population and other ethnic populations.

Another conventional risk factor for KOA was physical activity. In the present study, no significant association was observed between VPA/light physical activity (LPA) and the incident of KOA; however, MPA positively predicted the incident of KOA. The reported associations between physical activity and the incident of KOA were inconsistent, resulting from a variation in assessment methods, activity categories, or populations. Felson et al. [28] reported walking and other recreational activities did not increase the risk of OA in older adults. Results from the Chingford cohort demonstrated that physical activities related to work and sports increase the risk of osteophytes, while walking decreases the risk of osteophytes in middle-aged women [52]; however, all effects were not statistically significant. Findings from the Framingham Heart Study [53] indicate that performing over 2 h/day would increase risk symptomatic KOA (OR, 5.3; 95% CI, 1.2–24) and the association was also significant for radiographic KOA (OR, 1.3 per hour; 95% CI, 1.1–1.6), while the effects of MPA and LPA were insignificant. Given the discrepancy between studies, additional studies should aim to verify the influence of different types of physical activity on the risk of KOA. Such studies should seek to determine the most appropriate type, duration, frequency, and intensity of physical activity for preventing KOA in different populations.

In our model, health-related variables are addressed and our findings provide evidence that these variables contribute essential values to the prediction of symptomatic KOA. Depressive symptoms, comorbidities, and history of hip fracture are psychologically and physiologically objective factors related to KOA. Self-rated health and difficulty with ADL/IADLs were mainly subjective, which was reflected in the patient’s knowledge and ability to cope with disease.

Patients with KOA are prone to be comorbid with depression and other chronic comorbidities, and chronic diseases often exhibit interactions with comorbidities in complex ways [54]. Hence, previous studies have assumed that there may be a potential effect of depression and chronic comorbidities on incident of KOA. Seavey et al. [34] indicated that depressive symptoms represented a risk factor for arthritis incident (OR, 1.72; 95% CI, 1.27–2.35). Jinks et al. [55] also reported that depression was a significant predictor of knee pain (OR, 1.4; 95% CI, 1.1–1.8), where pain is the dominant physical symptom among patients with symptomatic KOA. Our study is the first model involving depression as predictor in a prediction model of symptomatic KOA. Patients with mild or moderate-to-severe depression were two or three times more likely to develop KOA than those without depression. Although a bidirectional causal association has rarely been illustrated either between arthritis and depression or between any other chronic disease and depression, targeted strategies for addressing depressive symptoms may therefore aid in reducing the incident of KOA. We also assessed relationships for 12 main types of comorbidities with incident of KOA. KOA and comorbidities may accelerate the progression of one another [24]. Results showed that patients with comorbidities had a significantly increased risk of developing KOA within 4 years. This addressed the effect of comorbidities in developing KOA in the Chinese population; this might have some value for developing prediction model of KOA in other ethnic groups.

Related studies [56, 57] have demonstrated that rheumatoid arthritis increases the risk of hip fracture due to bone loss induced by chronic inflammation, use of glucocorticoids, and physical inactivity. However, rare studies indicated the association between hip fracture and KOA incident. Given that the knee and hip joints are the two most important weight-bearing joints, we sought to determine whether a history of hip fracture increases the risk of developing KOA. Our findings indicated that a history of hip fracture was associated with a 53% increase in the risk of KOA. Identifying the potential mechanisms underlying this association should be helpful for development of risk model in further studies.

Patient-reported outcome (PRO) has been emphasized in multiple studies because PROs may capture important disease-related information prior to the onset of clinical signs or pathophysiological changes [58]. Silverwood et al. [24] noted that poor self-rated health status was a potential risk factor for KOA in an earlier study, although the association was insignificant. Our model showed that the likelihood of developing KOA increased as health status worsened and impairments in ADLs/IADLs. Self-rated health status and assessments of difficulty with ADLs/IADLs could be significant predictors for incident of symptomatic KOA. Self-ratings of health status comprehensively reflect one’s physical and psychological function, as well as one’s knowledge and ability to cope with diseases and self-efficiency. Most of the existing potential risk factors were pooled from epidemiological analyses or clinicians’ experience. Our findings highlight the need to consider the patient’s perspective, as this may aid in furthering our understanding of KOA while reducing the incidence of the disease. Symptomatic KOA progressively decreases self-care ability, causing knee pain or stiffness. Our results implied that impairments in ADLs/IADLs prior to KOA onset may represent a predictive signal for KOA. Hence, preventive interventions may be useful in reducing the incident of KOA in those who have difficulty with ADL/IADL. Improving ADLs/IADLs might become a new interventional target to prevent KOA.

Preventing KOA or other chronic disease in rural area is the biggest challenge in China because of large population [36]. Our model included resident area as one predictor for KOA aiming to improve the prevention of KOA in rural population. Factors related to the high prevalence in rural areas may be multiple, including limited access to knowledge regarding the prevention of KOA and other chronic diseases, a lack of economic resources for timely treatment of chronic diseases, poor ability to manage one’s health, and earlier impairments in physical function due to strenuous farm work. We hope that our results could promote policies and resources directed toward preventing KOA in Chinese rural areas in future.

We developed an easy-to-use clinical score model to identify risk of symptomatic KOA within 4 years and to identify individuals at high risk. This model involved ten commonly available variables. This simple model involved ten commonly available variables, the assessment of variables, and the calculation of risk score, which are both easily understood and handled in practice. Clinicians or the patients themselves could use this tool to assess the risk of KOA in 4-year term. While the score model showed a good performance in assessing risk of KOA (AUC = 0.713; 95% CI 0.695–0.731), the ability to identify individuals at high risk was moderate using a − 20.5 cut-off. Hence, this model should be improved and adjusted when applied in other populations. Adding other clinical biomarkers would provide further insight.

Limitations of the study include the incomplete data, though which was handed using the imputation method and a bootstrap strategy, may have biased our findings, especially the physical activity with a high percentage missing. Second, while new variables included in the study were significantly associated with the incident of KOA, further studies are required to elucidate the mechanisms underlying these associations. Lastly, our model was internally validated; therefore, external validation in other Chinese populations and different ethnic groups remains necessary.

In the present study, we developed the first model for predicting the 4-year risk of developing symptomatic KOA in China, using longitudinal cohort. Our simple score model may aid in the early identification of individuals at the greatest risk of developing KOA within 4 years in clinical practice or community setting. Such early identification may allow for improved patient education and modification of certain risk factors, which may in turn decrease rates of KOA incident.

We thank the China Health and Retirement Longitudinal Study (CHARLS) team for providing nationally representative data.

All authors were involved in revising the article, and all authors approved the final version to be published. Study conception and design: LMW and SMS. Data analysis and interpretation: LMW, HL, HBC, SDJ, and MQW. Draft of the manuscript: LMW, HL, and SMS.

This work was supported by the National Natural Science Foundation of China (no. 81972158).

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

The Medical Ethics Board Committee of Peking University granted the study an exemption from review.

Not applicable.

The authors declare no competing interests.

Supplementary information accompanies this paper at 10.1186/s13075-021-02447-5.