Association between plasma metabolites and osteoarthritis

Abstract

BACKGROUND: In recent years, metabolic disorders have been confirmed to be closely related to the onset of osteoarthritis, but the causal relationship between plasma metabolites and osteoarthritis has not been systematically elucidated. OBJECTIVE: To explore the causal relationship between 1,400 plasma metabolites and 9 types of osteoarthritis using two-sample Mendelian randomization. METHODS: A genome-wide association study of 1,400 metabolites was used as the exposure. Nine types of arthritis, namely any-site osteoarthritis, early osteoarthritis, knee and/or hip osteoarthritis, knee osteoarthritis, hip osteoarthritis, spinal osteoarthritis, finger osteoarthritis, hand osteoarthritis, and thumb osteoarthritis, were set as the outcomes. Single nucleotide polymorphisms were used as instrumental variables, and sensitive single nucleotide polymorphisms were selected for Mendelian randomization analysis. The inverse variance weighted method was used as the main analysis approach. Meanwhile, four methods, namely MR-Egger, weighted median, simple mode, and weighted mode, were employed for cross-validation. MR-PRESSO, Cochran's Q test, and other methods were used for sensitivity and pleiotropy analyses. The false discovery rate method was used for further correction. RESULTS AND CONCLUSION: Mendelian randomization analysis showed that finger osteoarthritis, hand osteoarthritis, hip osteoarthritis, and spinal osteoarthritis had no results meeting FDR < 0.05. Any-site osteoarthritis, early osteoarthritis, knee and/or hip osteoarthritis, knee osteoarthritis, and thumb osteoarthritis were significantly causally associated with multiple metabolites. Metabolites such as glycine, serine, higenamine, and sulfate were closely related to multiple osteoarthritis types. Compared with some non-weight-bearing joint osteoarthritis (e.g., finger and hand osteoarthritis), plasma metabolites showed stronger sensitivity with weight-bearing joint osteoarthritis (e.g., knee and hip osteoarthritis). This study provides a theoretical basis for metabolic intervention strategies for osteoarthritis in the Chinese population and offers a methodological paradigm for mechanistic research on complex diseases in China.

1. Introduction

Osteoarthritis is the most common degenerative musculoskeletal disease and a leading cause of chronic pain and progressive functional disability in the elderly. It can affect multiple synovial joints, with the knee being most frequently affected, followed by the spine (especially cervical and lumbar), hand joints (such as distal interphalangeal joints), ankle, and hip. With the acceleration of global population aging, the high prevalence of osteoarthritis and its resulting progressive disability have become major challenges in global public health. According to the latest data from the Global Burden of Disease Study 2021, the global number of osteoarthritis patients has reached 344 million, and since 1990, the age-standardized prevalence of osteoarthritis has increased by 114%, with related disability-adjusted life years increasing by 115%.

In recent years, significant breakthroughs have been made in the pathological mechanisms of osteoarthritis. Accumulating evidence suggests that osteoarthritis is not only a degenerative joint disease but also a systemic disease closely related to metabolic disorders. This shift in understanding is based on two main findings: on one hand, osteoarthritis often coexists with metabolic diseases such as obesity and diabetes; on the other hand, metabolomics studies have continuously revealed characteristic metabolite alterations and metabolic pathway abnormalities in osteoarthritis patients. In metabolomics research, blood, synovial fluid, articular cartilage, and subchondral bone are common biological sample sources. Among these, peripheral blood is considered an ideal source for early diagnostic biomarkers of osteoarthritis due to its abundance of detectable metabolites, ease of collection, and suitability for large-scale population screening.