Genetic underpinning of the comorbidity between type 2 diabetes and osteoarthritis

Arruda, Ana Luiza; Hartley, April; Katsoula, Georgia; Smith, George Davey; Morris, Andrew P.; Zeggini, Eleftheria

doi:10.1016/j.ajhg.2023.06.010

Cited by 14 publications

(4 citation statements)

References 67 publications

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“…Our method is currently focused on the problem of estimating a causal relationship between the shared exposure and the downstream outcomes without accounting for the direct causality between the outcomes. In our application example, various existing evidence supports the absence of direct causality between T2D and OA [63,64]. However, we show in Appendix C that even if direct causality exists, our method is still applicable, as the clustering of the variants associated with the common exposure are generally robust to the outcome causality.…”

Section: Discussionmentioning

confidence: 81%

Using clustering of genetic variants in Mendelian randomization to interrogate the causal pathways underlying multimorbidity

Liang

Mounier

Apfel

et al. 2023

Preprint

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Mendelian randomization (MR) is an epidemiological approach that uses genetic variants as instrumental variables for estimating the causal effect of a modifiable but likely confounded exposure on an outcome. Standard MR usually assumes that all included genetic variants are valid instruments and there is a single homogeneous causal effect of the exposure on the outcome. We allow violations of both assumptions such that the variants can be divided into clusters identifying distinct causal effects driven by different biological mechanisms and/or horizontal pleiotropy. We adapted the Agglomerative Hierarchical Clustering (AHC) method developed for individual-level data to the summary data MR setting, enabling the detection of such variant clusters using only genome-wide summary statistics. We also extend the method to handle two outcomes and a common exposure to aid investigation of the mechanisms of multimorbidity. We conduct Monte Carlo simulations to evaluate the performance of our `MR-AHC' algorithm compared to the existing MR-Clust method, showing that it is both reliable and computationally efficient: it detects variant clusters with high accuracy and is much faster than MR-Clust. In an applied example, we use our method to analyze the causal effects of high body fat percentage on a pair of well-known multimorbid conditions, type 2 diabetes and osteoarthritis, discovering distinct variant clusters reflecting the heterogeneous shared pathways.

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Section: Discussionmentioning

confidence: 81%

Using clustering of genetic variants in Mendelian randomization to interrogate the causal pathways underlying multimorbidity

Liang

Mounier

Apfel

et al. 2023

Preprint

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“…However, T2D and OA are not the result of a single gene, and many genes or environmental factors may play a role in both(78). Arruda found evidences of colocalization at 18 genomic loci to T2D and OA, and these ndings support enrichment for lipid metabolism and skeletal formation pathways for signals underpinning T2D comorbidities with OA (79). At the current stage, most studies don't have su cient data on phenotype, other potential predictors, clinical and imaging parameters to fully elucidate causal relationships between genes and diseases, so more detailed cohort population studies are needed.…”

Section: Discussionmentioning

confidence: 94%

Identification and validation of the shared signature gene MMP9 and ANGPTL4 and its regulatory mechanisms in Type 2 Diabetes combined with Osteoarthritis

Mao,

Xu,

Wan

et al. 2024

Preprint

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Objective Type 2 Diabetes Mellitus (T2D) and Osteoarthritis (OA) are prevalent diseases significantly impacting patients' quality of life. Increasing evidence suggests a correlation between T2D and OA, yet their molecular mechanisms remain elusive. Our study aims to investigate shared biomarkers and potential molecular mechanisms underlying these diseases. Methods Gene expression profiles for T2D and OA were sourced from the Gene Expression Omnibus (GEO) database.We employed bioinformatics to identify differentially expressed genes (DEGs) common to T2D and OA. Subsequently, functional annotation, and protein-protein interaction (PPI) analyses were conducted on these DEGs, leading to the preliminary identification of hub genes. Further validation of these hub genes' mRNA expression was achieved using external T2D and OA datasets, culminating in identifying pivotal genes. Western blotting and qRT-PCR techniques validated the expression of these key genes in clinical case samples. The accuracy of the key genes as predictive biomarkers for T2D and OA were assessed using the receiver operating characteristic curve (ROC). We developed a network of interactions between transcription factors (TFs) and microRNAs (miRNAs) .Finally, we identified the most promising therapeutic agents through molecular docking. Results We identified 209 shared DEGs between T2D and OA. Functional analysis revealed that these DEGs were predominantly associated with ossification, regulation of leukocyte migration, extracellular matrix (ECM) structural constituent, PI3K/AKT and Wnt signaling pathways. PPI analysis and external datasets validation highlighted MMP9 and ANGPTL4 as pivotal genes in T2D and OA. ANGPTL4 regulates glucose metabolism, osteoclast-mediated bone resorption, cartilage degradation, whereas MMP9 plays a role in ECM degradation and inflammatory responses. Both qRT-PCR and Western blot analyses confirmed high expression levels of pivotal genes in T2D, OA, T2D combined with OA cases.Analysis of TFs-miRNAs interactions identified 7 TFs and one miRNA that collectively regulate pivotal genes. ROC analysis underscored the strong diagnostic potential of MMP9 and ANGPTL4. Raloxifene, Ezetimibe, and S-3304 were also identified as promising therapeutic agents for patients with T2D and OA. Conclusion This study uncovers shared signaling pathways, biomarkers, potential therapeutics, and diagnostic models for T2D and OA. These findings offer novel perspectives on the pathogenesis, diagnosis, and treatment of T2D when co-occurring with OA.

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“…The causal association of HbA1c was exclusively observed with KOA, but not with HOA in a previous MR study [ 30 ]. Moreover, there were inconsistent results about the relationship between T2D and OA, one MR study confirmed that KOA could be the potential risk for T2D [ 31 ], whereas another two MR studies failed to establish any causal relationship between T2D and OA [ 32 , 33 ].…”

Section: Discussionmentioning

confidence: 99%

Phenome-wide causal associations between osteoarthritis and other complex traits through the latent causal variable analysis

Mei,

Zhang,

Chen

et al. 2024

BMC Musculoskelet Disord

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Background Individuals with osteoarthritis present with comorbidities, and the potential causal associations remain incompletely elucidated. The present study undertook a large-scale investigation about the causality between osteoarthritis and variable traits, using the summary-level data of genome-wide association studies (GWAS). Methods The present study included the summary-level GWS data of knee osteoarthritis, hip osteoarthritis, hip or knee osteoarthritis, hand osteoarthritis, and other 1355 traits. Genetic correlation analysis was conducted between osteoarthritis and other traits through cross-trait bivariate linkage disequilibrium score regression. Subsequently, latent causal variable analysis was performed to explore the causal association when there was a significant genetic correlation. Genetic correlation and latent causal variable analysis were conducted on the Complex Traits Genomics Virtual Lab platform (https://vl.genoma.io/). Results We found 133 unique phenotypes showing causal relationships with osteoarthritis. Our results confirmed several well-established risk factors of osteoarthritis, such as obesity, weight, BMI, and meniscus derangement. Additionally, our findings suggested putative causal links between osteoarthritis and multiple factors. Socioeconomic determinants such as occupational exposure to dust and diesel exhaust, extended work hours exceeding 40 per week, and unemployment status were implicated. Furthermore, our analysis revealed causal associations with cardiovascular and metabolic disorders, including heart failure, deep venous thrombosis, type 2 diabetes mellitus, and elevated cholesterol levels. Soft tissue and musculoskeletal disorders, such as hallux valgus, internal derangement of the knee, and spondylitis, were also identified to be causally related to osteoarthritis. The study also identified the putative causal associations of osteoarthritis with digestive and respiratory diseases, such as Barrett’s esophagus, esophagitis, and asthma, as well as psychiatric conditions including panic attacks and manic or hyperactive episodes. Additionally, we observed osteoarthritis causally related to pharmacological treatments, such as the use of antihypertensive medications, anti-asthmatic drugs, and antidepressants. Conclusion Our study uncovered a wide range of traits causally associated with osteoarthritis. Further studies are needed to validate and illustrate the detailed mechanism of those causal associations.

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Genetic underpinning of the comorbidity between type 2 diabetes and osteoarthritis

Cited by 14 publications

References 67 publications

Using clustering of genetic variants in Mendelian randomization to interrogate the causal pathways underlying multimorbidity

Using clustering of genetic variants in Mendelian randomization to interrogate the causal pathways underlying multimorbidity

Identification and validation of the shared signature gene MMP9 and ANGPTL4 and its regulatory mechanisms in Type 2 Diabetes combined with Osteoarthritis

Phenome-wide causal associations between osteoarthritis and other complex traits through the latent causal variable analysis

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