Cross-Disorder Genomics Data Analysis Elucidates a Shared Genetic Basis Between Major Depression and Osteoarthritis Pain

Causal Factors for Osteoarthritis: A Scoping Review of Mendelian Randomization Studies

OBJECTIVE

Mendelian randomization (MR) has increasingly been utilized as a tool for establishing causal relations between modifiable exposures and osteoarthritis (OA). The goal of this review was to summarize available MR studies of OA that evaluate the causal role of modifiable risk factors on OA.

METHODS

This review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Extension for Scoping Reviews model. We performed a literature search for relevant studies published before December 2021 across multiple databases using the search terms "osteoarthritis" and ("Mendelian randomization" or "polygenic risk score"). We reported the MR estimates of causal associations between exposures and OA and then assessed methodologic quality of abstracted studies according to their efforts to validate the three key MR assumptions.

RESULTS

Our search identified 45 studies reporting on 141 exposure-association analyses. All studies performed a formal instrumental variable analysis to estimate the causal effect of exposure on OA. Causal associations (P < 0.05) were reported in 60 of these analyses representing 36 unique publications, and MR-Egger sensitivity analyses were performed in 45 of these analyses. MR studies provided support for causal associations of OA with increased levels of adiposity, coffee consumption, bone mineral density, and sleep disturbance, and decreased levels of serum calcium and low-density lipoprotein cholesterol.

CONCLUSION

These results highlight the potential benefits of weight reduction and improvement of sleep quality to reduce the risk of OA and call for a better understanding of the relations of coffee consumption and serum calcium to OA risk.

Pain-phenotyping in osteoarthritis: Current concepts, evidence, and considerations towards a comprehensive framework for assessment and treatment

Objectives

Pain as central symptom of osteoarthritis (OA) needs to be addressed as part of successful treatment. The assessment of pain as feature of disease or outcome in clinical practice and drug development remains a challenge due to its multidimensionality and the plethora of confounders. This article aims at providing insights into our understanding of OA pain-phenotypes and suggests a framework for systematic and comprehensive assessments.

Methods

This narrative review is based on a search of current literature for various combinations of the search terms "pain-phenotype" and "knee OA" and summarizes current knowledge on OA pain-phenotypes, putting OA pain and its assessment into perspective of current research efforts.

Results

Pain is a complex phenomenon, not necessarily associated with tissue damage. Various pain-phenotypes have been described in knee OA. Among those, a phenotype with high pain levels not necessarily matching structural changes and a phenotype with low pain levels and impact are relatively consistent. Further subgroups can be differentiated based on patient reported outcome measures, assessments of comorbidities, anxiety and depression, sleep, activity and objective measures such as quantitative sensory testing.

Conclusions

The complexity of both OA as disease and pain in OA prompt the definition of a set of variables that facilitate assessments comparable across studies to maximize our understanding of pain, as central concern for the patient.

Bidirectional causal relationship between psychiatric disorders and osteoarthritis: A univariate and multivariate Mendelian randomization study

BACKGROUND

Observational studies have shown associations between psychiatric disorders and osteoarthritis (OA). However, the causal impact of different psychiatric disorder types on specific sites of osteoarthritis remains unclear. This study aimed to comprehensively understand the potential causal associations between psychiatric disorders and osteoarthritis using Mendelian randomization (MR) analysis.

METHODS

We collected data from genome-wide association studies of knee osteoarthritis (KOA) (n = 403,124), hip osteoarthritis (HOA) (n = 393,873), osteoarthritis of the knee or hip (KHOA) (n = 417,596), as well as three psychiatric disorders: bipolar disorder (n = 41,917), major depressive disorder (n = 170,756), and schizophrenia (n = 76,755) among European populations. We applied bidirectional univariate and multivariate MR analyses, including inverse variance weighted, Mendelian randomization-Egger, weighted median, simple mode, and weighted mode. We considered p < .05 as a criterion for identifying potential evidence of association. Bonferroni correction was used for multiple tests.

RESULTS

Our univariate MR analysis results demonstrated that bipolar disorder is a protective factor for KOA (OR = 0.90, 95% CI = 0.83 to 0.97, p = 0.0048) and may also be protective for KHOA (p = 0.02). Conversely, major depression has a positive causal effect on both KOA (OR = 1.27; 95% CI = 1.08 to 1.49; p = 0.0036) and KHOA (OR = 1.24; 95% CI = 1.12 to 1.37; p = 3.62×10-05 ). Furthermore, our analysis suggested that KHOA may be a risk factor for major depression (OR = 1.06; 95% CI = 1.00 to 1.12; p = 0.0469) in reverse MR. After adjusting smoking (OR = 1.46; 95% CI = 1.19 to 1.65; p = 0.0032) and body mass index (OR = 1.44; 95% CI = 1.09 to 1.81; p = 8.56×10-04 ), the casual association between major depression and KHOA remained.

CONCLUSION

Our study indicates that major depression is a great risk factor for KHOA, increasing the likelihood of their occurrence. However, further in-depth studies will be required to validate these results and elucidate the underlying molecular mechanisms.

Causal association between subtypes of osteoarthritis and common comorbidities: A Mendelian randomisation study

Objective

To investigate the causal association between Osteoarthritis (OA) and five comorbidities: depression, tiredness, multisite chronic pain, irritable bowel syndrome (IBS) and gout.

Design

This study used two-sample Mendelian Randomisation (MR). To select the OA genetic instruments, we used data from the largest recent genome-wide association study (GWAS) of OA (GO Consortium), with a focus on OA of the knee (62,497 cases, 333,557 controls), hip (35,445 cases, 316,943 controls) and hand (20,901 cases, 282,881 controls). Genetic associations for comorbidities were selected from GWAS for depression (246,363 cases, 561,190 controls), tiredness (449,019 participants), multisite chronic pain (387,649 participants), IBS (53,400 cases, 433,201 controls) and gout (6543 cases, 456,390 controls). We performed a bidirectional MR analysis using the inverse variance weighted method, for both joint specific and overall OA.

Results

Hip OA had a causal effect on multisite chronic pain (per unit change 0.02, 95% CI 0.01 to 0.04). Multisite chronic pain had a causal effect on knee (odd ratio (OR) 2.74, 95% CI 2.20 to 3.41), hip (OR 2.12, 95% CI 1.54 to 2.92), hand (OR 2.24, 95% CI 1.59 to 3.16) and overall OA (OR 2.44, 95% CI, 2.06 to 2.86). In addition, depression and tiredness had causal effects on knee and hand, but not hip, OA.

Conclusions

Apart from Hip OA to multisite chronic pain, other joint OA did not have causal effects on these comorbidities. In contrast, multisite chronic pain had a causal effect on any painful OA.

A Shared Genetic Signature for Common Chronic Pain Conditions and its Impact on Biopsychosocial Traits

The multiple comorbidities & dimensions of chronic pain present a formidable challenge in disentangling its aetiology. Here, we performed genome-wide association studies of 8 chronic pain types using UK Biobank data (N =4,037-79,089 cases; N = 239,125 controls), followed by bivariate linkage disequilibrium-score regression and latent causal variable analyses to determine (respectively) their genetic correlations and genetic causal proportion (GCP) parameters with 1,492 other complex traits. We report evidence of a shared genetic signature across chronic pain types as their genetic correlations and GCP directions were broadly consistent across an array of biopsychosocial traits. Across 5,942 significant genetic correlations, 570 trait pairs could be explained by a causal association (|GCP| >0.6; 5% false discovery rate), including 82 traits affected by pain while 410 contributed to an increased risk of chronic pain (cf. 78 with a decreased risk) such as certain somatic pathologies (eg, musculoskeletal), psychiatric traits (eg, depression), socioeconomic factors (eg, occupation) and medical comorbidities (eg, cardiovascular disease). This data-driven phenome-wide association analysis has demonstrated a novel and efficient strategy for identifying genetically supported risk & protective traits to enhance the design of interventional trials targeting underlying causal factors and accelerate the development of more effective treatments with broader clinical utility. PERSPECTIVE: Through large-scale phenome-wide association analyses of >1,400 biopsychosocial traits, this article provides evidence for a shared genetic signature across 8 common chronic pain types. It lays the foundation for further translational studies focused on identifying causal genetic variants and pathophysiological pathways to develop novel diagnostic & therapeutic technologies and strategies.

The effect of interactions between BMI and sustained depressive symptoms on knee osteoarthritis over 4 years: data from the osteoarthritis initiative

BACKGROUND

To assess the compound effects of BMI and sustained depressive symptoms on changes in knee structure, cartilage composition, and knee pain over 4 years using statistical interaction analyses.

METHODS

One thousand eight hundred forty-four individuals from the Osteoarthritis Initiative Database were analyzed at baseline and 4-year follow-up. Individuals were categorized according to their BMI and presence of depressive symptoms (based on the Center for Epidemiological Studies Depression Scale (threshold≥16)) at baseline and 4-year follow-up. 3 T MRI was used to quantify knee cartilage T₂ over 4 years, while radiographs were used to assess joint space narrowing (JSN). Mixed effects models examined the effect of BMI-depressive symptoms interactions on outcomes of cartilage T₂, JSN, and knee pain over 4-years.

RESULTS

The BMI-depressive symptoms interaction was significantly associated with knee pain (p < 0.001) changes over 4 years, but not with changes in cartilage T₂ (p = 0.27). In women, the BMI-depressive symptoms interaction was significantly associated with JSN (p = 0.01). In a group-based analysis, participants with obesity and depression had significantly greater 4-year changes in knee pain (coeff._{(obesity + depression vs. no_obesity + no_depression)} = 4.09, 95%CI = 3.60-4.58, p < 0.001), JSN (coeff. = 0.60, 95%CI = 0.44-0.77, p < 0.001), and cartilage T₂ (coeff. = 1.09, 95%CI = 0.68-1.49, p < 0.001) than participants without depression and normal BMI.

CONCLUSIONS

The compound effects of obesity and depression have greater impact on knee pain and JSN progression compared to what would be expected based on their individual effects.

Phenotype-Genotype analysis of caucasian patients with high risk of osteoarthritis

Background: Osteoarthritis (OA) is a common cause of disability and pain around the world. Epidemiologic studies of family history have revealed evidence of genetic influence on OA. Although many efforts have been devoted to exploring genetic biomarkers, the mechanism behind this complex disease remains unclear. The identified genetic risk variants only explain a small proportion of the disease phenotype. Traditional genome-wide association study (GWAS) focuses on radiographic evidence of OA and excludes sex chromosome information in the analysis. However, gender differences in OA are multifactorial, with a higher frequency in women, indicating that the chromosome X plays an essential role in OA pathology. Furthermore, the prevalence of comorbidities among patients with OA is high, indicating multiple diseases share a similar genetic susceptibility to OA. Methods: In this study, we performed GWAS of OA and OA-associated key comorbidities on 3366 OA patient data obtained from the Osteoarthritis Initiative (OAI). We performed Mendelian randomization to identify the possible causal relationship between OA and OA-related clinical features. Results: One significant OA-associated locus rs2305570 was identified through sex-specific genome-wide association. By calculating the LD score, we found OA is positively correlated with heart disease and stroke. A strong genetic correlation was observed between knee OA and inflammatory disease, including eczema, multiple sclerosis, and Crohn's disease. Our study also found that knee alignment is one of the major risk factors in OA development, and we surprisingly found knee pain is not a causative factor of OA, although it was the most common symptom of OA. Conclusion: We investigated several significant positive/negative genetic correlations between OA and common chronic diseases, suggesting substantial genetic overlaps between OA and these traits. The sex-specific association analysis supports the critical role of chromosome X in OA development in females.

Major Depressive Disorder: Existing Hypotheses about Pathophysiological Mechanisms and New Genetic Findings

Major depressive disorder (MDD) is a common mental disorder generally characterized by symptoms associated with mood, pleasure and effectiveness in daily life activities. MDD is ranked as a major contributor to worldwide disability. The complex pathogenesis of MDD is not yet understood, and this is a major cause of failure to develop new therapies and MDD recurrence. Here we summarize the literature on existing hypotheses about the pathophysiological mechanisms of MDD. We describe the different approaches undertaken to understand the molecular mechanism of MDD using genetic data. Hundreds of loci have now been identified by large genome-wide association studies (GWAS). We describe these studies and how they have provided information on the biological processes, cell types, tissues and druggable targets that are enriched for MDD risk genes. We detail our understanding of the genetic correlations and causal relationships between MDD and many psychiatric and non-psychiatric disorders and traits. We highlight the challenges associated with genetic studies, including the complexity of MDD genetics in diverse populations and the need for a study of rare variants and new studies of gene-environment interactions.

Depression in Osteoarthritis: Current Understanding

Depression, one of the most common comorbidities with osteoarthritis (OA), affects patient prognosis and quality of life. It also increases the overall burden of disease. This subgroup of patients has not been effectively managed in clinical settings. The study aimed to direct physicians' attention to the co-occurrence of depression and OA. Therefore, this review summarizes the relevant literature published over the past 10 years. The focus is on the prevalence of and risk factors for depression in OA, the effects of depression on OA development and treatment response, comorbidity mechanisms, screening, and non-pharmacological treatment. The research on the etiology of depression has been driven largely by epidemiological studies. Recent studies have shown that high levels of pain, poor levels of function, high numbers of OA sites, and slow gait might be associated with depression. However, the pathophysiology of OA and depression comorbidities remains unclear. In addition to immune inflammation and structural changes in the brain, which have been documented in brain imaging studies, psychosocial factors may also play a role. The evidence indicates that depression can be treated with early intervention; however, adjustments may need to be made for individuals with comorbid depression in OA. It is recommended that health care providers pay more attention to depressive symptoms in patients with OA. Clinicians should develop and implement an individualized and comprehensive treatment plan for patients based on a mental health assessment and in teams with other professionals to optimize treatment outcomes.