Back

Causal association of the brain structure with the risk of knee osteoarthritis: A large-scale genetic correlation study

Ruan, Z.; Lin, S.; Zhao, S.; Long, H.

2023-12-30 orthopedics
10.1101/2023.12.20.23300318 medRxiv
Show abstract

ObjectivesObservational studies have shown the association between knee osteoarthritis (KOA) and neurological disorders with alterations in brain imaging-derived phenotypes (BIDPs). This study aimed at investigating whether alterations in brain structure are correlated with the occurrence of KOA. MethodsBased on the summary data from two large scale genome-wide association studies (GWASs), we performed a bidirectional two-sample Mendelian randomization (MR) analysis using single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to determine the potential causal relationships between KOA and BIDPs. ResultsWe identified the genetic correlations of 152 BIDPs with KOA using linkage disequilibrium score regression. MR analysis revealed that increased volume but decreased intensity-contrast of bilateral nucleus accumbens (NAc), as well as increased left paracentral area was positively causally associated with KOA risk. For the IDPs of structural connectivity, we identified causal associations between multiple increased DTI parameter indicators of corticospinal tract (CST) and KOA. Inversely, KOA was positively correlated with the thickness and intensity-contrast of the rostral anterior cingulate, as well as the intensity-contrast of caudal anterior cingulate, insula cortex, and the grey matter volume of pallidum. ConclusionOur study supported bidirectional causal associations between KOA and BIDPs, which may provide new insights into the interaction of KOA with structural alterations in the nervous system.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.