The joint-brain axis: evidence from a Mendelian randomization study of osteoarthritis and cortical structure

doi:10.21203/rs.3.rs-3993034/v1

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The joint-brain axis: evidence from a Mendelian randomization study of osteoarthritis and cortical structure

https://doi.org/10.21203/rs.3.rs-3993034/v1

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Background

Osteoarthritis (OA) is a common joint disease that may affect brain function via a joint-brain axis, but its causal impact on brain cortical structures is unclear.

Method

We used Mendelian randomization (MR), a method that leverages genetic variants as natural experiments, to examine the effects of OA, including knee and hip OA, on cortical surface area (SA) and thickness (TH) of the whole brain and 34 functional regions. We used summary statistics from large-scale genome-wide association studies (GWAS) of OA and brain cortical structures in individuals of European ancestry.

Result

We found that overall OA was causally associated with reduced SA of the isthmus cingulate (beta: -296.2, 95% CI: -439.1, -153.3, P = 4.82e-05), a brain region involved in mood, memory, and spatial orientation. This suggests that OA may influence neuropsychiatric disorders in OA patients. We also detected several suggestive associations between OA and other brain regions.

Conclusion

Our study reveals a novel causal link between OA and brain cortical structures, with implications for understanding and treating cognitive impairment and other disorders in OA patients. Our findings also highlight the importance of considering the joint-brain axis in clinical practice and future research.

Osteoarthritis is a chronic degenerative joint disease characterized by the wear and tear of joint cartilage and its subsequent destruction, resulting in joint inflammation, pain and dysfunction.[1] It is also the most common joint disease worldwide and can be classified into primary and secondary osteoarthritis based on different classification methods. [2] Primary osteoarthritis refers to osteoarthritis without a clear cause, and secondary osteoarthritis results from certain known factors, such as joint injury, metabolic disease, and inflammatory arthritis.[3] The incidence of osteoarthritis increases with age, is higher in women than in men, and varies by race and region.[4, 5] The pathogenesis of osteoarthritis is not fully understood, but may involve the interplay of chondrocytes, cartilage matrix, synovial membrane, bone marrow, periarticular muscles, and multiple tissues and factors such as nerves.[5] The etiology of osteoarthritis is also multifactorial, including genetics, age, gender, obesity, joint injury, occupation, and exercise.[1, 4, 5] Osteoarthritis not only affects the physical and psychological health of individuals, but also places a heavy burden on society and the economy. [4, 5]

Osteoarthritis (OA) is a degenerative joint disease that is often associated with changes in the cerebral cortex, in addition to the peripheral joint injuries that result in cartilage destruction and reduction of joint space. Recent research has focused on the joint-brain axis and its potential implications for OA patients. One study using the arterial spin labeling (ASL) technique found that cerebral blood flow was reduced in OA patients, particularly in the frontal, parietal, and occipital lobes, which are involved in cognitive, emotional, and motor function. These findings suggest that the neurodegenerative process may be occurring in patients with OA.[6]

Pain sensitization is a common feature of OA, where patients experience increased pain response to injurious stimuli. This sensitization may originate from peripheral injury. Pujol et al. investigated the effects of pain sensitization on different functions in the cerebral cortex and found weaker local connectivity in the insula in OA patients using functional magnetic resonance imaging (fMRI). This suggests the existence of different alterations in cortical structures in OA.[7]

While there is growing evidence to suggest a link between osteoarthritis (OA) and cerebral cortex function, the causal relationship between these two conditions remains unclear, given several limitations and challenges. Observational studies, rather than randomized controlled trials, have been primarily employed, leading to potential confounding factors or reverse causality. Furthermore, most studies have relied on correlational analyses, rather than robust causal inference methods, potentially leading to false positives or negatives. Moreover, many studies have focused on single biomarkers or imaging indicators, which may be subject to measurement error or insufficient sensitivity. Finally, the majority of research has been conducted on specific populations or animal models, limiting generalizability. To address these limitations, future research must prioritize high-quality and systematic studies that utilize rigorous methods for causal inference, incorporate a range of biological and clinical endpoints, and include broad and representative samples. By doing so, we can begin to more accurately identify and validate the causal relationship between OA and cerebral cortex function.

To establish a causal relationship between two variables, we employed the powerful technique of Mendelian randomization (MR). By leveraging genetic variation as a natural experiment, MR allows us to estimate the causal effect between an exposure variable (such as OA) and an outcome variable (such as the cerebral cortex) independent of confounding factors or reverse causality.[8, 9] This method has been widely used to confirm the causal relationship between OA and various diseases,[10, 11] but its potential impact on cortical brain structures remains unexplored.

To fill this gap, we conducted a two-sample MR analysis using publicly available summary statistics from large-scale genome-wide association studies (GWAS). Specifically, we investigated the effects of OA, including knee and hip osteoarthritis, on two measures of cortical structure—cortical surface area (SA) and cortical thickness (TH)—as detected by magnetic resonance imaging (MRI) of the human brain. In addition, we performed subgroup analyses to explore potential differences in the effects of OA on different functional areas of the brain.

Our findings shed new light on the existence of a joint-brain axis and provide important insights into the impact of OA on the human brain.

2.1 Data sources for overall OA, knee OA, and HIP OA

We obtained pooled-level GWAS data for overall OA from a meta-analysis of European ancestry participants. OA patients were defined based on UK Biobank data, a large biomedical database and biobank that collects genotype, phenotype, lifestyle data, medical records, and biological samples from 500,000 volunteers in the UK. Individuals were considered to have OA if they had either (1) a physician diagnosis of OA, (2) joint replacement surgery, or (3) joint pain or stiffness and were aged ≥45 years. The overall OA dataset included 327,918 European individuals, comprising 30,727 OA patients and 297,191 controls. [12]

The knee OA and hip OA data were obtained from two cohorts, UK Biobank and arcOGEN, with patient inclusion criteria being hospital-documented or self-reported osteoarthritis of the knee or hip and no hospital-documented or self-reported osteoarthritis of the other joint. The knee OA dataset included 402,994 European individuals, consisting of 24,955 knee OA patients and 378,039 controls. The hip OA dataset included 393,743 European individuals, of which 15,704 were hip OA patients and 378,039 were controls. [13]

2.2 Data source for brain cortex surficial area and cortex thickness

Magnetic resonance imaging (MRI) data from 51,665 individuals were used in this study, obtained from 60 cohorts consisting of 33,992 individuals of European origin and 17,673 individuals of non-European origin (enrolled cohorts were listed in Supplementary Table S4). Only individuals of European origin were analyzed for this study. The MRI data underwent quality control and preprocessing to exclude data with significant abnormalities or missing values, as well as individuals with duplicates or relatives. For the GWAS data on cortical structures, we examined surface area and mean thickness of the whole cerebral cortex, as well as surface area and thickness of 34 cortical regions with known functional specialization. These 34 brain regions were distinguished using a gradient and boundary algorithm that detects points of change in multimodal data on the cerebral cortical surface. We only analyzed the 34 specific brain regions that were corrected for global surface area or thickness as covariates, in order to exclude the confounding effect of global on regional factors and to more accurately identify genetic variants affecting specific brain regions.[14]

Overall, knee OA, and hip OA data were used to determine thickness (TH) and surface area (SA) for the entire cerebral cortex. We analyzed TH and SA of 34 cortical regions with and without global brain-weighted estimates, yielding 138 results. Data that included globally weighted estimates indicated that SA and TH in specific regions were related to SA and TH in the whole brain, whereas data without globally weighted estimates indicated that SA and TH measurements in specific regions were not related to whole brain SA and TH.

2.3. Selection of genetic instruments

To explore the causal relationship between osteoarthritis and cerebral cortical structures, we selected three genetic data sets for osteoarthritis: overall OA, hip OA, and knee OA. To ensure the validity of the instrumental variables, we checked the three basic model assumptions of MR [15]. Firstly, the genetic variants must be strongly associated with the target exposure. Secondly, the genetic variants should not be associated with any confounders of the exposure-outcome relationship. Lastly, the genetic variants can only affect the outcome through the target exposure and not through other pathways. These assumptions ensure that the genetic variants can be used as valid instrumental variables to simulate a randomized controlled trial and thus infer a causal relationship between exposure and outcome.

Initially, we selected independent SNPs that were strongly associated with brain cortical structure (p-values < 5e-08), but the number of such SNPs was too low for our analysis. Thus, we relaxed the threshold to 5e-06 to screen our instrumental variables. We then performed a clustering procedure with R2<0.001 and window size=10000kb on European ancestral individuals from the 1000 Genomes Project to exclude SNPs with strong linkage disequilibrium (LD). Subsequently, we removed SNPs that had a weaker association with brain cortical structure (p-value > 5e-06). We applied MR Pleiotropy Residual Sum and Outlier (MR PRESSO) to correct for the presence of horizontal pleiotropy. This method uses genome-wide summary statistics to perform linear regression analysis on multiple instrumental variables, then calculates the residual sum of squares and compares it with the data from random simulations to detect the presence of horizontal pleiotropy [16]. After finding horizontal pleiotropy, we improved the accuracy of causal inference by correcting for the effect of outliers. The specific analysis process can be seen in the figure 1.

2.4. Mendelian randomization analyses

Three MR analysis methods were utilized in our study to address heterogeneity and pleiotropy issues: random-effect inverse-variance weighted (IVW), MR Egger, and weighted median. We excluded SNPs associated with brain cortical structures and outliers detected by MR-PRESSO before using IVW as the primary outcome. IVW is the most established and widely used method because it provides the most efficient and accurate causal estimates if there is no horizontal pleiotropy of the instruments [17]. It regresses the exposure effect on the outcome effect without an intercept term, using the inverse of the outcome variance as the weight. The instruments must satisfy the three core assumptions of MR, i.e., no horizontal pleiotropy, to obtain correct causal estimates. Weighted median is another method that only requires that most of the instruments (more than 50%) satisfy the three core assumptions of MR to obtain correct causal estimates. MR Egger is a method that can detect and correct for horizontal pleiotropy and the intercept term can be used to assess its magnitude. We further assessed horizontal pleiotropy for significant estimates using MR Egger intercept test and funnel plot analysis. Cochran's Q test was used to detect heterogeneity, and funnel plots were used to visualize possible directional pleiotropy. To exclude potential confounding factors, such as body mass index, obesity, waist-to-hip ratio, smoking, etc., we searched PhenoScanner and removed any SNPs that showed genome-wide association with any of these factors [18].

2.5 Statistic

The statistical analyses were conducted using TwoSampleMR (version 1.0.3) and MRPRESSO (version 6.1) in R (version 0.4.25). The global level tests were considered significant at a two-sided P value of 0.05. For region-level analyses, we corrected for multiple testing using the Bonferroni method for 408 MR estimates, resulting in a significance threshold of P < 1.22 × 10^-4. We also considered results with a P value < 0.05 as nominally significant.

2.6 Ethical statement

This study did not involve human or animal experiments, but only used existing genetic data. We used genetic data from UK Biobank and ENIGMA Consortium, which were collected and used with the informed consent of the participants and in accordance with the data sharing and confidentiality principles. UK Biobank has obtained Research Tissue Bank (RTB) approval from the North West Multi-centre Research Ethics Committee (MREC), so we did not need to apply for separate ethical review. We cited the sources of these data and followed the usage rules of UK Biobank and ENIGMA Consortium.

We selected 67, 88, and 35 SNPs to predict the genetic risk of hip OA, knee OA, and overall OA(listed in Supplementary Table S1-3), respectively, ensuring that all genetic instruments possessed strong predictive power with F-statistics greater than 10[19]. Utilizing these genetic instruments, we conducted MR studies investigating the association between OA and both weighted and unweighted global SA/TH, as well as 34 functional gyri. While no causal relationship between OA and global SA/TH was observed (Figure 2 and Table 1), several gyrus were found to be influenced by OA (Figure 2, Figure 3 and Table 1).

Analysis at the functional area level, overall OA was found to significantly reduce the surface area (SA) of the pars isthmus cingulate with global weighting (beta: -296.2, 95% CI: -439.1, -153.3, P=4.82e-05), with no evidence of heterogeneity or pleiotropy (Cochran’s Q test: P=0.68; MR-Egger intercept test: P=0.95).(Table 1) Additionally, several nominally significant gyri, including the supra marginal, pars triangularis, inferior temporal, caudal middle frontal, rostral anterior cingulate, paracentral, frontal pole, precentral, lateral orbitofrontal, medial orbitofrontal, and middle temporal gyri, were identified. To ensure the robustness of our findings, we utilized PhenoScanner to evaluate if any significant estimates were confounded by potential risk factors, and identified rs13135092 and rs2807867 as being associated with cholesterol, blood pressure, BMI, and weight. We subsequently excluded these two SNPs and repeated the MR analysis, which confirmed the previous results and established the pars isthmus cingulate SA as being unaffected by potential confounders for overall OA (beta: -296.2, 95% CI: -426.3, -146.2, P: 3.89e-05)(Table 1).

To ensure the robustness of our findings, we performed various tests to assess horizontal pleiotropy for all significant and nominally significant estimates. Specifically, we used Cochran's Q test, MR-Egger intercept test, leave-one-out analysis, and funnel plots. Notably, all MR-Egger intercept tests had P values greater than 0.05, suggesting no evidence of horizontal pleiotropy. Furthermore, leave-one-out analysis and funnel plots (see Supplementary Figure) confirmed that the estimates were not driven by a single SNP, thus ensuring instrumental validity. While some estimates showed heterogeneity in Cochran's Q test, most of the P values were greater than 0.05, except for a few estimates. However, as we employed random-effect IVW as our primary outcome, heterogeneity is considered acceptable[20].

This study represents the first large-scale MR analysis to comprehensively investigate the causal relationships between knee OA, hip OA, overall OA and cortical structures. By systematically assessing the causal relationship between genetically predicted OA and cortical structures, our findings support the notion that OA may have an impact on the cerebral cortex, consistent with previous observational studies. These findings suggest a pathophysiological interaction between OA and brain function, supporting the existence of a joint-brain axis.

The joint-brain axis has been a topic of research for a long time. In clinical settings, some physicians have observed that patients with OA may experience altered brain function, such as depression, cognitive decline, and brain fog [21–23]. Some experts suggest that chronic inflammation caused by OA may produce cytokines like tumor necrosis factor (TNF), which can affect brain function by altering the communication between neural networks, leading to brain fog[21]. Chronic pain caused by OA can also impair cognitive skills as pain-processing brain regions overlap with those involved in memory and attention. This overlap may lead to competition for limited processing resources, and pain tends to dominate, thereby changing brain function[22]. It is also worth noting that depression is prevalent in OA patients, and this may be related to brain fog[21, 23] .

However, despite the clinical observations, there is a lack of high-quality studies investigating the causal relationship between OA and brain cortical structures or various neurological disorders. Cortical thickness, which is a neuroimaging biomarker for predicting cognitive decline, may be affected by changes in brain structure that can partly explain the pathogenesis of psychiatric disorders[24, 25]. To address this gap in the literature, we utilized MRI to investigate the causal relationship between OA and cortical structure. Our future studies aim to elucidate the mechanisms underlying these changes and their impact on brain function and neuropsychiatric disorders. Ultimately, such research may facilitate early identification of high-risk patients and inform the development of more effective prevention and treatment strategies for cognitive impairment and other disorders in OA patients, with significant clinical implications.

Our study reveals that OA significantly affects the isthmus cingulate, a brain region that plays a crucial role in mood, memory, and spatial orientation. The isthmus cingulate connects the cingulate gyrus to the parahippocampal gyrus and is situated in the medial part of the cerebral hemisphere. Previous research has linked a reduction in the thickness of the isthmus cingulate to depression[26], Alzheimer's disease[27], and cognitive impairment[28]. Furthermore, stressful life events have been shown to decrease the volume of this region[29]. The mechanisms that underlie the association between isthmus cingulate lesions and other disorders remain unclear and require further investigation. It is important to determine whether OA causes these functional changes or whether they are mediated by neuropsychiatric diseases that alter the cortical thickness of the isthmus cingulate.

Although only one estimate survived the Bonferroni correction, caution should be exercised when interpreting other estimates with a p-value < 0.05 obtained from the IVW analysis. Our study found opposite effects of osteoarthritis (OA) on thickness (TH) and surface area (SA) of the isthmus cingulate. Specifically, OA was significantly associated with reduced SA (beta: -296.2, 95% CI: -439.1, -153.3, P:4.28e-05) and nominally associated with increased TH (beta: 0.15, 95% CI: 0.002, 0.6, P:0.04) of this region. This is consistent with previous observational studies reporting decreased SA and increased TH in the isthmus cingulate[30, 31]. The reduction in SA may reflect abnormalities in neural precursor cell division or migration during embryonic development or degeneration of the brain gyrus and sulcus during aging, which may affect the connectivity and differentiation between brain regions, and reduce the functional specificity and computational capacity of the isthmus cingulate[32]. On the other hand, increased TH may reflect processes such as proliferation of neuronal dendrites or reduction of synapses, which may influence the information transmission and integration between neurons, and enhance the information processing capacity or adaptability of the isthmus cingulate[33]. Decreased SA and increased TH may be the result of interactions or compensations. For example, reduced SA may weaken the connectivity of the isthmus cingulate with other brain regions, whereas increased TH may strengthen the connectivity within the isthmus cingulate, allowing it to adjust its function at both local and global levels. The changes in SA and TH of the isthmus cingulate due to OA may reflect neurobiological alterations at different levels that may relate to the role of this region in pain processing[31]. However, the specific underlying mechanisms require further investigation.

In our study, we have demonstrated the importance of adhering to the three main MR assumptions for robust estimation, including selecting strong genetic instruments, checking for potential confounding factors, assessing directional pleiotropy, and performing sensitivity analyses. However, our study is not without limitations. First, our study only included individuals of European descent, so the generalizability of our findings to other ethnic groups remains unclear. Second, while we have identified changes in cortical structure associated with OA, the underlying mechanisms are still unclear and warrant further investigation. Third, MR analysis is susceptible to genetic and mediator bias, which can be challenging to eliminate entirely.

Despite these limitations, our study has provided insights into the potential causal relationship between OA and brain dysfunction, which could inform future research exploring the association between OA and other neuropsychiatric disorders. In particular, our study highlights the need for further investigation into the specific burden of OA patients and the mechanisms underlying the association between OA and neuropsychiatric disorders. Such research may lead to the development of new treatments to prevent or manage neuropsychiatric disorders in individuals with OA.

This study presents a groundbreaking investigation into the link between OA and cortical structures in the brain, using a robust MR analysis. The results suggest that overall OA is associated with a reduction in the SA of the isthmus cingulate, which could potentially serve as an early diagnostic marker for neuropsychiatric disorders in OA patients. The findings underscore the importance of considering a patient's OA status in neurological assessments when alterations in the isthmus cingulate are observed. Further research is needed to elucidate the underlying mechanisms of this association and develop new treatment strategies to address potential neuropsychiatric comorbidities in OA patients. Overall, this study offers valuable insights into the potential impact of OA on brain function and highlights the need for greater attention to this issue in clinical practice.

Data Availability

Data is provided within the manuscript or supplementary information files.

Ethics approval and consent to participate

Not applicable. We used summary-level data from publicly available GWAS studies which have received ethical approval from their respective institutional review boards and informed consent from all participants. No administrative permissions were required to access the data.

Consent for publication

Not applicable.

Competing interests

The author reports no conflicts of interest in this work.

Author Contributions

Yubiao Yang and Xianhu Zhou designed the research. Jinyu Chen and Jian Hao collected the data. Yubiao Yang and Jian Hao analyzed the data. BoYuan Ma and Derong Liu performed the literature search. Yubiao Yang, Jinyu Chen, Derong Liu, Jun Ma and Jian Hao drafted the article. Xianhu Zhou and Yubiao Yang jointly supervised the study. All authors were involved in writing the paper. All authors contributed to the article and approved the submitted version.

Funding

This research was supported by the National Natural Science Foundation of China (Reference: 81371957).

Acknowledgments

We appreciate all the volunteers who participated in this study. We are grateful to the UK Biobank for releasing the OA GWAS summary statistics and the ENIGMA Consortium for releasing the brain cortex surficial area and cortex thickness GWAS summary statistics.

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Table 1 is available in the Supplementary Files section.

No competing interests reported.

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The joint-brain axis: evidence from a Mendelian randomization study of osteoarthritis and cortical structure

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