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Relationship between serum carotenoids and osteoarthritis or degenerative arthritis: A cross-sectional study using the National Health and Nutrition Examination Survey

Nutrition Journal volume 24, Article number: 25 (2025) Cite this article

Abstract

Background

Carotenoids possess essential antioxidant and anti-inflammatory properties; however, the relationships between carotenoids and osteoarthritis or degenerative arthritis (OA) remain inadequately understood. This study aimed to investigate the correlation between diverse serum carotenoid concentrations and OA in a large American cohort and to examine the influence of various factors on the association between carotenoids and OA.

Methods

Data from the 2001–2006 and 2017–2018 National Health and Nutrition Examination Surveys were utilized. In our analysis, we utilized a directed acyclic graph to identify potential confounding variables. The associations between serum carotenoids (including total carotenoid, trans-lycopene, β-cryptoxanthin, lutein/zeaxanthin, α-carotene, and β-carotene) and OA were comprehensively evaluated via a weighted generalized linear model (GLM) and restricted cubic spline models. Threshold effect analyses were used to identify potential cutoff points, subgroup analyses were used to explore heterogeneity, interaction analyses were used to examine potential modifiers, and sensitivity analyses were used to validate the robustness of the findings.

Results

The weighted GLM results revealed that, overall, the concentrations of various serum carotenoids did not exhibit a significant linear correlation with the probability of OA. Dose‒response curves and threshold effect analysis revealed a significant nonlinear relationship (P for overall = 0.027; P for nonlinearity = 0.019; P for likelihood ratio = 0.0128) between trans-lycopene (threshold effect) and OA, with an inflection point at 19.49 µg/dl. Further subgroup weighted linear regression analysis indicated that when the serum trans-lycopene concentration exceeded 19.49 µg/dl, there was a significant association [odds ratio (OR) = 0.89 (0.80–0.99); P = 0.027] between the per standard deviation trans-lycopene increase and a lower probability of OA after adjusting for other variables. Moreover, individuals with elevated trans-lycopene [0.70 (0.52–0.94); P = 0.018] in the fifth quintile had notably reduced odds of OA compared with those in the first quintile. When the trans-lycopene level is less than 19.49 µg/dl, no correlation exists between the two variables. Linear subgroup and interaction analyses revealed that when the concentration of carotenoids exceeded 19.49 µg/dl, various categorical factors did not significantly influence the relationship between trans-lycopene and OA overall. However, pairwise comparisons revealed that lower serum trans-lycopene concentrations are more closely associated with a greater probability of OA in elderly individuals [OR (95% CI) = 0.270 (0.112–0.654); P = 0.005; P for trend = 0.003] than in younger individuals [0.973 (0.385–2.463); P = 0.954; P for trend = 0.61] (P for interaction = 0.007).

Conclusions

In the American population, trans-lycopene rather than other types of carotenoids may exhibit a significantly negative correlation with OA, displaying a nonlinear pattern with a threshold point of approximately 19.49 µg/dl. This relationship may become more pronounced with increasing age.

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Introduction

Osteoarthritis, also known as degenerative arthritis (OA), has emerged as a predominant and debilitating form of joint degenerative disease, largely due to the increasing aging population. It is estimated to affect at least 36 million adults in the United States and imposes a substantial disease burden. Increased catabolism in the extracellular matrix (ECM) of the articular cartilage leading to cartilage damage is a key factor in the development and progression of OA. Recent investigations have revealed a shift toward viewing OA as a multifaceted ailment with various contributing factors, departing from its classification solely as a degenerative disease [1,2,3]. To delve deeper into the etiology of this condition, numerous studies have explored the predisposing elements of OA. The identified risk factors include sex, advancing age, weight increase, joint mechanics, history of joint surgeries, and genetic predispositions. Recent studies have highlighted the associations between nutritional factors and susceptibility to OA [4, 5]. Carotenoids represent a vital category of micronutrients, constituting lipid-soluble pigments that manifest as orange, yellow, or red hue and function as antioxidants within the human body. Some carotenoids serve as crucial precursors to vitamin A. Among the carotenoids in the bloodstream, more than 95% are β-carotene, α-carotene, β-cryptoxanthin, lutein/zeaxanthin, and lycopene [6, 7]. In addition to their antioxidant properties, carotenoids play key roles in anti-inflammatory, anti-infective, anticancer, and antiapoptotic mechanisms [8,9,10].

Previous studies have explored the correlation between carotenoids and OA owing to the considerable involvement of oxidative stress mechanisms in OA [11]. Nonetheless, the literature is limited, and the findings are inconclusive [12,13,14,15,16,17,18,19]. Variables such as ethnicity might affect the relationship between carotenoids and OA, thereby contributing to this inconsistency in results. Hence, this study used data from four survey cycles of the National Health and Nutrition Examination Survey (NHANES) to investigate the associations between carotenoids and OA in a large American adult population. Furthermore, this study aimed to analyze how various factors affect the relationship between carotenoids and OA.

Materials and methods

Research population

The study incorporated data from 40,763 participants in the NHANES, which were collected during two time periods: 2001–2006 (n = 31,509) and 2017–2018 (n = 9,254). Participants with missing data on OA or serum carotenoids (n = 26,785) were excluded, resulting in a final sample size of 13,978 participants. Covariates with missing values exceeding 20% (n = 2,795) were subsequently excluded from the analysis. Multiple imputations via chained equations were then performed on the remaining covariates to address any residual missing data, maintaining the final sample size at 13,978 participants (Fig. 1). The primary analysis was conducted via Dataset A, which had the highest Cronbach’s alpha coefficient, ensuring optimal internal consistency. This dataset retained a final sample size of 13,978 participants.

Fig. 1
figure 1

Sample selection flowchart from the 2001–2006 and 2017–2018 cycles of the National Health and Nutrition Examination Survey (NHANES). OA: osteoarthritis

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Exposure variables and outcomes

In epidemiological research, self-reported OA often serves as the basis for case identification [20]. The NHANES inquired whether the patients had ever been diagnosed with arthritis by a healthcare professional. The respondents who answered “yes” were categorized as arthritis cases, and those who answered “osteoarthritis or degenerative arthritis” were identified as patients with OA.

Five carotenoids [α-carotene, β-carotene (trans-β-carotene and cis-β-carotene), β-cryptoxanthin, combined lutein/zeaxanthin, and trans-lycopene] were quantified via high-performance liquid chromatography with photodiode array detection. Additional information on measurements and quality control procedures can be accessed at the following links: [https://wwwn.cdc.gov/nchs/data/nhanes/2001-2002/labmethods/l06vit_b_met_aecar.pdf, https://wwwn.cdc.gov/nchs/data/nhanes/2003-2004/labmethods/l45vit_c_met_vitae_carotenoids.pdf, https://wwwn.cdc.gov/nchs/data/nhanes/2005-2006/labmethods/vitaec_d_met_aecar.pdf, and https://wwwn.cdc.gov/nchs/data/nhanes/2017-2018/labmethods/VITAEC-J-MET-508.pdf]. The concentrations of the five serum carotenoids were summed to obtain the total carotenoid concentration. Serum carotenoid concentrations from 2003–2004 were standardized across the other three NHANES cycles via the regression formulas provided by the NHANES.

Covariates

Our study outcomes might have been affected by factors such as demographic variables, OA-related comorbidities, and smoking and drinking habits. In accordance with the literature and prior knowledge, we chose to consider participants’ age, sex, education level, race, poverty income ratio (PIR), body mass index (BMI) [21], drinking [22] or smoking habits and serum cotinine concentration [23], OA comorbidity (i.e., presence of osteoporosis) [24], history of hypertension [25] or diabetes [26], moderate or vigorous physical activity engagement [27], survey cycle, and serum concentrations of calcium, phosphorus, vitamin D [28], uric acid [29], urea nitrogen [30], and total cholesterol [31] as preliminary candidate covariates. Specifically, to account for potential confounding effects related to smoking habits, we considered serum cotinine levels as a candidate factor, as they may better represent exposure to secondhand smoke. Given the significant temporal gap between the two study cycles (2006–2017), it is plausible that the epidemiological characteristics of the population and certain testing methodologies employed by the NHANES may have undergone considerable changes. Therefore, we also included cycles 2001–2006 and 2017–2018 as potential confounding factors. Additionally, we have included confounding factors that have been frequently considered in recent studies, such as total cholesterol, as candidate variables, and research has indicated that OA is also a disease associated with cholesterol metabolism disorders [31]. On the basis of the correlation or causal relationships between OA (Table 1), various types of carotenoids (Tables S1 (a-f)), and a range of candidate covariates, we ultimately identified 15 potential covariates considered in this study from a pool of 21 candidates, guided by insights from the Directed Acyclic Graph (DAG) (Fig. S1): age, sex, race, education level, NHANES cycle, PIR, cigarette use, alcohol use, moderate or vigorous physical activity engagement, serum calcium and phosphorus, and history of osteoporosis, hypertension and diabetes.

Table 1 Baseline characteristics of the study participants stratified by osteoarthritis or degenerative arthritis (OA) based on dataset A
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We categorized races as Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, and other races (including multiracial). Age was categorized as young (20–45 years), middle-aged (45–65 years), or older (> 65 years). Educational level was classified as less than high school, high school diploma (including a GED), or higher than high school. Smoking habits were defined on the basis of whether an individual smoked more than 100 cigarettes (frequently smoking or rarely smoking), and drinking habits were based on whether an individual consumed more than 4 or 5 glasses of alcohol per day (frequently drinking or rarely drinking). Osteoporosis, history of hypertension or diabetes, and regular engagement in moderate or vigorous physical activity were categorized as yes or no. The survey cycles were categorized as 2001–2006 and 2017–2018. Detailed information on calcium and phosphorus was obtained from the NHANES laboratory test data. Data with missing values exceeding 20% were excluded for the covariates related to OA and carotenoids. Additionally, logarithm-Ln transformations were applied to nonnormally distributed numerical covariates during the analysis.

Statistical analysis

National estimates were efficiently computed through weighted analyses following the Centers for Disease Control and Prevention guidelines for adjusting for the oversampling of minority subgroups. We assigned weights to the data from 2001–2006 and 2017–2018 on the basis of the NHANES guidelines, with the final weight set at 1/4 × Wtmec2yr. All the statistical analyses considered the impact of these weights. Owing to the nonnormal distribution of continuous data, the baseline characteristics of the participants are presented as weighted medians (interquartile ranges) for continuous variables and as unweighted absolute values (weighted percentages) for categorical variables. Weighted χ2 and Wilcoxon or Kruskal‒Wallis tests were separately conducted to determine the statistical significance of categorical variables and continuous variables with a nonnormal distribution. Logarithm-Ln transformation was applied to skewed serum carotenoid levels to approximate a normal distribution. In the multivariable regression analyses, the serum carotenoid levels were categorized into five groups (weighted quintile) for data analysis. A weighted generalized linear model (GLM) was used to estimate the odds ratio (OR) and 95% confidence interval (95% CI) to elucidate the associations between carotenoids and OA. Simultaneously, we calculated the OR and P values corresponding to the per-standard deviation (SD) by standardizing the independent variable data to a mean of 0 and an SD of 1. Five weighted logistic regression models were developed to adjust for covariates. The crude model was not adjusted. Model 1 was adjusted for sex, age, race, education level and family income. Model 2 was built upon Model 1, incorporating indicators closely related to OA, such as a history of osteoporosis, serum calcium and phosphorus levels, and a history of hypertension and diabetes. Model 3, an extension of Model 2, was further adjusted for lifestyle factors, including drinking or smoking habits and regular engagement in moderate or vigorous physical activity. The fully adjusted model (Model 4) was derived from Model 3, with additional adjustments for the NHANES cycle. Logarithm-Ln transformation was also applied to all nonnormally distributed numerical covariates during regression analysis.

This study utilized weighted GLM regression with a restricted cubic spline model to explore the nonlinear correlation between carotenoids and OA and to create a dose‒response curve. The number of knots was determined via the minimum Akaike information criterion method (the number of konts: total carotenoid 3, trans-lycopene 3, β-cryptoxanthin 3, lutein/zeaxanthin 3, α-carotene 3, and β-carotene 3; Fig. 2). Two-piecewise threshold effect analysis was performed to assess the nonlinear relationship between carotenoids and OA comprehensively. We jointly assessed the nonlinear relationships and inflection points between various carotenoids and OA by analyzing dose‒response curves and threshold effects. Additionally, subgroup and interaction analyses were performed across multiple factors, such as race, age group, drinking or smoking habits, sex, educational level, hypertension, diabetes, osteoporosis, survey cycle, and moderate or vigorous physical activity engagement. These analyses were conducted via weighted GLMs, and an interaction diagram was generated. Sensitivity analyses were conducted to validate the robustness of the results. We employed an alternative imputation strategy to address the missing values: missing covariate values were imputed with median values across the five imputed datasets (dataset1–dataset5) to maximize the use of the five datasets. This resulted in a new imputed dataset (dataset B) with a final sample size of 13,978 for additional sensitivity analysis. Specifically, we utilize the mi function from the mi package in R to address missing values in the data, including those in continuous variables. The mi package implements imputation for continuous variable missingness by constructing a linear regression model based on a Gaussian distribution. In contrast to the strategy employed for dataset A, which involves selecting the most reliable dataset from five datasets generated through multiple imputations for analysis, dataset B capitalizes on all five imputed datasets by using the median of the corresponding data from these datasets to fill in the missing values. This approach to handling missing data effectively reduces the bias introduced by the randomness inherent in multiple imputations. All the statistical analyses were performed via R software versions 4.2.3 and DAGitty versions 3.1. All tests were two-tailed, and significance was defined as P values less than 0.05.

Fig. 2
figure 2

Nonlinear dose‒response relationships between carotenoids and osteoarthritis or degenerative arthritis (OA) based on dataset A. All covariates included in this study were adjusted. Test for P for overall and P for nonlinearity based on the Wald test

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Results

Participant characteristics at baseline

The baseline characteristics of individuals with and without OA are presented in Table 1. The analysis was based on 13,978 adult participants (aged over 20 years), including males (48.7%), older individuals (aged over 65 years; 13.5%), and middle-aged individuals (aged between 45–65 years; 31.5%). Among them, 1,575 participants were diagnosed with OA. The participants with OA were more likely to be older, female, non-Hispanic White, obese, and smokers and to have conditions such as hypertension, diabetes, osteoporosis, and physical inactivity and to have a survey cycle from 2017–2018. Additionally, participants with OA presented elevated serum vitamin D, uric acid, urea nitrogen, total cholesterol, α-carotene, and β-carotene levels, as well as lower total carotenoid, trans-lycopene, and β-cryptoxanthin concentrations. The baseline characteristics of individuals categorized by quintiles of various types of carotenoids are presented in Table S1 (a-f).

Associations between Serum Carotenoid Concentrations and OA

In the fully adjusted model (Model 4), after accounting for all relevant factors, various types of carotenoids did not exhibit a significant linear correlation with OA (Table 2). The dose‒response relationship (Fig. 2) and threshold effect (Table 3) analysis revealed significant nonlinear relationships (threshold effect; infection point = 19.49 µg/dl) between trans-lycopene (P overall = 0.027; P for nonlinearity = 0.019; P for likelihood ratio test = 0.0128) and OA. When the serum trans-lycopene concentration exceeded 19.49 µg/dl, a consistent inverse association was observed between Ln(trans-lycopene) and OA [OR = 0.60(0.41–0.86); P = 0.007] (Table 3), which suggests that for every 1.72-fold increase in trans-lycopene concentration, there is a 40% lower prevalence of OA. Further subgroup weighted linear regression analysis indicated that when the serum trans-lycopene concentration surpassed the inflection point, there was a significant association [OR = 0.89 (0.80–0.99); P = 0.027] between the per-SD trans-lycopene increase and a lower probability of OA after adjusting for other variables. Compared with the lowest quintile, the highest quintile of trans-lycopene [OR = 0.70 (0.52–0.94); P = 0.018] was significantly associated with a reduced probability of OA, and these associations remained stable across various models. The P for trend analysis indicated a statistically significant decreasing trend in OA risk with increasing trans-lycopene levels (Table 4). However, when the serum trans-lycopene concentration was below the inflection point, no significant linear correlation between carotenoids and OA was observed (Table 5).

Table 2 Associations of serum carotenoids with OA based on dataset A
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Table 3 Threshold effect analysis of serum carotenoids and OA based on dataset A
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Table 4 Associations of trans-lycopene with OA based on dataset A (trans-lycopene ≥ 19.49 µg/dl)
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Table 5 Associations of trans-lycopene with OA based on dataset A (trans lycopene < 19.49 µg/dl)
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Subgroup and interaction analysis

A significant linear correlation between trans-lycopene and OA was observed only when trans-lycopene levels exceeded a specific threshold. Consequently, we conducted linear interaction analyses for the relationship between trans-lycopene and OA when trans-lycopene levels were greater than 19.49 µg/dl. The findings from the interaction analysis indicated that, overall, various categorical factors did not significantly influence the relationship between trans-lycopene and OA; however, the association was more pronounced in certain subgroups, such as elderly individuals, females, and non-Hispanic whites. Pairwise interaction analysis revealed that higher serum trans-lycopene concentrations in older adults [OR = 0.27 (0.11–0.65); P = 0.005; P for trend = 0.003] were more closely associated with a lower probability of OA than were those in younger individuals [OR = 0.97 (0.39–2.46); P = 0.954; P for trend = 0.61] (P for interaction = 0.007) (Fig. 3). Figure 3 (b) illustrates the impact of age on the relationship between trans-lycopene and OA. Tables 6 and 7 illustrates the linear relationship between trans-lycopene and OA in populations aged over and under 65 years, respectively. (trans-lycopene ≥ infection point). In the elderly population, there was a significant negative correlation between trans-lycopene and OA [(per-SD) OR = 0.72 (0.58–0.90), P = 0.005; highest quintile vs. lowest quintile, OR = 0.30 (0.15–0.60), P = 0.001]. Figure S4 shows that the nonlinear dose‒response relationship between trans-lycopene and OA is predominantly observed in the elderly population.

Fig. 3
figure 3

Subgroup and interaction analyses based on dataset A (trans-lycopene ≥ infection point). a Interaction diagram between OA and trans-lycopene; b influence of age on the relationship between OA and trans-lycopene. OA: osteoarthritis; OP: history of osteoporosis. All covariates (except for grouping variables) included in this study were adjusted in the subgroup analysis. P values were calculated via weighted GLM. Test for P for trend based on variables containing weighted median values for each weighted quartile. The P value for interactions was calculated via ANOVA

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Table 6 Associations of trans-lycopene with OA based on dataset A (trans-lycopene ≥ 19.49 µg/dl and age > 65 years)
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Table 7 Associations of trans-lycopene with OA based on dataset A (trans-lycopene ≥ 19.49 µg/dl and age ≤ 65 years)
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Sensitivity analysis

The results of the multifactorial logistic regression (Table S2), dose‒response curve analysis (Fig. S2), threshold effect evaluation (Table S3; infection point = 19.40 µg/dl), grouped weighted linear regression analysis (Table S4 and Table S5) and subgroup and linear interaction analysis (Fig. S3) of dataset B (imputed with medians from five datasets, n = 13,978) are generally consistent with the main analysis findings. The effective reduction in potential biases due to the randomness of multiple imputations in dataset B renders the analysis results based on this dataset more stable (Table S4 (a) vs. Table 4; Table S5(a) vs. Table 6 (Supplementary Files).

Discussion

Research conclusions regarding the relationship between carotenoids and OA are notably inconsistent. Some cellular and animal studies have underscored the potential value of carotenoids in the prevention and treatment of OA [12, 14, 18]. However, in clinical and epidemiological studies, while some have reported correlations between carotenoids and OA [13, 15], others have failed to establish any significant links between various carotenoids and OA [16, 17, 19]. Therefore, this study aims to further validate the relationship between different carotenoids and OA within a larger population by employing complex sampling designs and multiple statistical approaches while striving to control for confounding factors to achieve a more comprehensive and reliable conclusion. Our study revealed a significant inverse correlation between trans-lycopene, rather than other carotenoids, and OA in the American population. Nonlinear relationship analyses revealed threshold effects between trans-lycopene and OA, with an inflection point at 19.49 µg/dl; when the serum trans-lycopene concentration exceeded the inflection point, the OR (95% CI) for OA was 0.89 (0.80–0.99) per-SD increase in trans-lycopene. Linear interaction analysis suggested that as age increases, the relationship between lower trans-lycopene concentrations and higher OA probability may become more pronounced. In the sensitivity analysis, we obtained similar results via the same analytical methods across datasets with different imputation strategies, thereby demonstrating the robustness of our conclusions.

Lycopene, a carotenoid variant, is currently a widely utilized dietary component. Lycopene exists primarily in its all-trans isomeric state in fruits and vegetables such as tomatoes, watermelons, and pink grapefruit; however, serum and tissue samples contain various cis-isomers. Among these isomeric variations, 5-cis lycopene is the most stable and potent antioxidant [32]. A previous animal study demonstrated that trans-lycopene can inhibit the onset and progression of OA in murine models [12]. Additionally, lower serum lycopene levels have been linked to increased pain and physical limitations in individuals with primary knee OA in a case‒control study [13]. Other studies have not reported a correlation between these two factors [15,16,17, 19]. Together, our findings and the relevant literature indicate that lycopene has the potential to reduce the risk of OA through various mechanisms. First, lycopene is a potent antioxidant that neutralizes free radicals in the body, thereby reducing oxidative stress, which is believed to play a significant role in the onset and progression of OA [11]; consequently, lycopene may help protect joint tissues by lowering oxidative stress. Second, lycopene can inhibit the production of proinflammatory cytokines, such as tumor necrosis factor and interleukin-1β, which are critical in the inflammatory response associated with OA [3]. Lycopene may contribute to reducing joint pain and swelling by alleviating inflammation. Third, lycopene can diminish adverse factor-induced damage to chondrocytes, promoting chondrocyte proliferation and cartilage repair by reducing apoptosis [12, 33, 34]. Fourth, some studies suggest that lycopene may increase bone density and reduce bone loss, which is crucial for preventing the progression of OA and the associated risk of osteoporosis [24, 35, 36]. Additionally, trans-lycopene can modulate various cellular signaling pathways, such as the nuclear factor erythroid-2-related factor 2 pathway, to alleviate intervertebral disc degeneration under oxidative stress and reduce the degradation of the ECM in chondrocytes. [37]

Interestingly, our research indicates that the relationship between lower serum trans-lycopene concentrations and higher OA probability may become more pronounced with age. We hypothesize that this phenomenon may be related to several factors: OA is primarily regarded as an "age-related disease," which is significantly influenced by the increase in oxidative stress and enhanced inflammatory responses associated with aging [38]. Furthermore, advancing age may lead to a decline in the regenerative and reparative capabilities of chondrocytes [1]. Additionally, older adults frequently experience a decrease in bone density and osteoporosis, which are closely linked to the onset of OA [24]. These factors play crucial roles in the pathogenesis of age-related OA, whereas in younger populations, OA is often attributed to trauma [38], with different forms of inflammation and primary pathogenic mechanisms than age-related OA. Consequently, trans-lycopene may have a more pronounced relationship with age-related OA because of its potent antioxidant and anti-inflammatory properties, as well as its potential to support cartilage health and enhance bone density. Moreover, in older adults, variations in dietary habits, physical activity levels, and other lifestyle factors may affect the absorption and utilization of trans-lycopene [39], resulting in inherently lower serum concentrations of trans-lycopene (Table S1(b)). Therefore, increasing serum levels of trans-lycopene in older individuals may be more beneficial in reducing the risk of OA.

Additionally, through the analysis of pairwise interactions, we also found that the relationship between trans-lycopene and OA may be more pronounced in other Hispanic populations than in the Mexican cohort (P for interaction = 0.027). Nevertheless, since the association between trans-lycopene and OA in other Hispanic groups was not statistically significant [OR (95% CI) = 0.103 (0.004–2.501)] and considering the relatively small sample size of other Hispanic populations (n = 392), biased results may have arisen. Therefore, we conclude that this interaction lacks biological significance. Although all are antioxidants and anti-inflammatory agents, our findings indicate that among the five primary carotenoids, only trans-lycopene and age-related OA are significantly correlated. This may be attributed to the superior capacity of lycopene for in vitro free radical scavenging compared with that of other carotenoids [32], as well as its relatively strong chondroprotective effects and ability to increase bone density. Furthermore, our study revealed a threshold effect between carotenoids and OA, which may be linked to the bioavailability of lycopene in vivo [40]. At lower concentrations, it is possible that the bioactive levels are insufficient to exert a protective effect on articular cartilage.

Our findings indicate that there is no relationship between trans-lycopene and OA in the overall population when linear models are used. This may be attributed to several factors. First, the dose‒response curve suggests a nonlinear relationship between trans-lycopene and OA in this study, indicating that linear models may not adequately capture this relatively complex relationship. Second, further linear subgroup and interaction analyses revealed that, in addition to the presence of a nonlinear relationship, the linear association between trans-lycopene and OA may be more pronounced in the elderly population (Fig. 3 and Tables 6 and 7). Given that the proportion of younger individuals is substantial in our overall cohort (Table 1), this could also contribute to the lack of clarity in their relationship within the linear model. Furthermore, in this study, we applied a natural logarithm transformation to the carotenoid concentrations during the statistical analysis, which enhanced the convergence of our independent variable data distribution. Consequently, the conclusions drawn in this study are more conservative than those derived from analyses without logarithmic transformation (Table 2 vs. Table S6).

Our study has several limitations. The cross-sectional study design hinders establishing a causal relationship between carotenoids and OA, necessitating extensive large-scale longitudinal studies to validate the research findings. Moreover, the use of self-reported forms for OA may introduce specific recall biases, and OA is not categorized according to joint location. Nevertheless, this study has several strengths. Notably, there was a relatively substantial sample size for the correlation between carotenoids and OA. By utilizing intricate sampling data from the NHANES and incorporating suitable weights throughout all the analytical phases, our research outcomes are more indicative of a broader American population. We identified a nonlinear association between carotenoids and OA, including calculating the inflection point. This study also identified an intriguing phenomenon through pairwise interaction analysis, revealing a potential interaction between trans-lycopene and OA among both young and elderly populations. Additionally, vitamin supplementation (through dietary adjustments or formulations) is relatively straightforward and safe. This study further underscores the potential of trans-lycopene in the prevention and treatment of OA, particularly in the elderly population.

Conclusions

Our investigation suggested that trans-lycopene may have a negative nonlinear (threshold effect, inflection point = 19.49 µg/dl) correlation with OA in the American population. The relationship between decreasing serum trans-lycopene concentrations and increasing OA probability may be exacerbated by aging. This was limited by the cross-sectional structure of our data. The protective effect of lycopene on OA requires further validation via large-sample prospective studies.

Data availability

The data that support the findings of this study are openly available in the NHANES at https://www.cdc.gov/nchs/nhanes/index.htm.

Abbreviations

ECM:

Extracellular matrix

OA:

Osteoarthritis or degenerative arthritis

NHANES:

National Health and Nutrition Examination Survey

PIR:

Poverty income ratio

BMI:

Body mass index

DAG:

Directed acyclic graph

GLM:

Generalized linear model

OR:

Odds ratio

SD:

Standard deviation

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Acknowledgements

We thank all the staff of and participants in the NHANES for their contribution

Funding

This work was supported by the General Topic of Nanjing Health Science and Technology Development Project (YKK22200, Nanjing Municipal Health Commission).

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Author notes

  1. Bowen Zhu and Guochun Li contributed equally to this manuscript and share first authorship.

Authors and Affiliations

  1. Hongqiao International Medical Research Institute of Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China

    Bowen Zhu

  2. Department of Clinical Laboratory, Nanjing Central Hospital, Nanjing, People’s Republic of China

    Guochun Li, Kaiwen Wu & Xie Wu

  3. Department of Endocrinology, Nanjing Central Hospital, Nanjing, People’s Republic of China

    Qian Luo

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Contributions

WX and ZBW designed the research. WX, ZBW and LGC completed data collection and analysis. WKW, LQ participated in the discussion. WX wrote a manuscript. ZBW revised the manuscript and confirmed the final draft with LGC. All authors approved the submitted and final version.

Corresponding author

Correspondence to Xie Wu.

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Ethics approval and consent to participate

The NHANES, a comprehensive survey of the national population in the United States, utilizes a sophisticated, multistage, and probabilistic sampling method to provide extensive insights into the nutritional and health status of Americans. The data for this study were sourced from the 2001–2006 and 2017–2018 NHANES cycles because only these cycles contained OA data and serum carotenoid information concurrently. The study was conducted in compliance with the guidelines outlined in the Declaration of Helsinki, and all procedures involving human participants were approved by the National Center for Health Statistics Research Ethics Review Board (Protocol #98–12, Continuation of Protocol #2011–17, Protocol #2018–01). Written informed consent was obtained from all the participants or their proxies.

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The authors declare no competing interests.

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Zhu, B., Li, G., Wu, K. et al. Relationship between serum carotenoids and osteoarthritis or degenerative arthritis: A cross-sectional study using the National Health and Nutrition Examination Survey. Nutr J 24, 25 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12937-025-01087-8

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Keywords

Nutrition Journal

ISSN: 1475-2891

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