Two "key" genes could help doctors identify arthritis earlier in patients and help those suffering from the bone disease osteoporosis.
Rheumatoid arthritis, which affects 17 million people worldwide, is caused by immune cells attacking the joints, leading to painful swelling and damage to the cartilage and bone.
People with rheumatoid arthritis often develop osteoporosis, a more serious condition, from the bone damage caused by immune cells and as a side-effect of certain drugs.
Researchers in Taiwan identified two genes linked to both arthritis and osteoporosis that could serve as diagnostic tools and potential targets for treatments.
They said both diseases centre on one of the key mechanisms used to keep the rest of the body in check.
Apoptosis - or programmed cell death - is a crucial tool the immune system uses to remove malfunctioning or unneeded cells.
But it can lead to immune cells mistakenly targeting some at random, with often disastrous results.
Study author Dr Hao-Ju Lo, of Da-Chien General Hospital, Taiwan, said: "In rheumatoid arthritis, excessive apoptosis of bone-forming cells contributes to joint destruction and inflammation. This same process also leads to weakened bones in osteoporosis, emphasising the need to manage both conditions simultaneously."
Because of its central role, the research team set out to find genes involved with apoptosis that were closely linked to both diseases. They gathered genomes from arthritis and osteoporosis sufferers and used computers to sift the data.
Dr Lo said they applied machine learning techniques to identify two key genes - ATXN2L and MMP14 - that play significant roles in both diseases.
The team's findings, published in the journal APL Bioengineering, showed they are "significantly associated" with the progression of both rheumatoid arthritis and osteoporosis.
Dr Lo said they plan to "explore how targeting these genes could improve treatment outcomes", adding: "Our future research may also involve developing personalised therapies, leveraging AI and machine learning to predict which patients are most at risk for osteoporosis."
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