Exploring the Shared Genetic Basis of Age at Menopause and Osteoarthritis

OBJECTIVE: The relationship between age at menopause and osteoarthritis (OA) remains unclear. METHODS: The genetic commonalities between age at menopause and OA were investigated using data from publicly accessible genome-wide association studies. A variety of methods were employed in this exploration, such as linkage disequilibrium score regression (LDSC), high-definition likelihood, LAVA, pleiotropic analysis under composite null hypothesis, and summary-databased Mendelian randomization. Additionally, causal relationships were probed through the application of Mendelian randomization techniques. RESULTS: , as determined by LDSC. Then, a number of single nucleotide polymorphisms associated with both OA and the age at menopause were identified to exhibit pleiotropic effects. Age at menopause had a causal effect on OA. In addition, six potentially functional genes, C7orf73, RP11-148O21.2, BLK, PAM16, MAPK3, and DBF4B, have been identified to exhibit associations with both age at menopause and OA. CONCLUSION: This study's results provide a deeper comprehension of the genetic underpinnings and complex interplay between age at menopause and OA. It contributes novel perspectives for the discovery of shared genetic-based therapeutic targets, highlighting the value of understanding their common genetic framework for developing future treatments.

Authors

Institutions

Publication Details

Journal
ACR Open Rheumatology
Published
2026-06-30
DOI
https://doi.org/10.1002/acr2.90087
Primary Topic
Osteoarthritis Treatment and Mechanisms
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring the Shared Genetic Basis of Age at Menopause and Osteoarthritis

Jing Zhou, Panpan Long, Bingru Luo, Lili Huang
ACR Open Rheumatology
Osteoarthritis Treatment and Mechanisms
article

Exploring the Shared Genetic Basis of Age at Menopause and Osteoarthritis

Jing Zhou, Panpan Long, Bingru Luo, Lili Huang
article en

Abstract

OBJECTIVE: The relationship between age at menopause and osteoarthritis (OA) remains unclear. METHODS: The genetic commonalities between age at menopause and OA were investigated using data from publicly accessible genome-wide association studies. A variety of methods were employed in this exploration, such as linkage disequilibrium score regression (LDSC), high-definition likelihood, LAVA, pleiotropic analysis under composite null hypothesis, and summary-databased Mendelian randomization. Additionally, causal relationships were probed through the application of Mendelian randomization techniques. RESULTS: , as determined by LDSC. Then, a number of single nucleotide polymorphisms associated with both OA and the age at menopause were identified to exhibit pleiotropic effects. Age at menopause had a causal effect on OA. In addition, six potentially functional genes, C7orf73, RP11-148O21.2, BLK, PAM16, MAPK3, and DBF4B, have been identified to exhibit associations with both age at menopause and OA. CONCLUSION: This study's results provide a deeper comprehension of the genetic underpinnings and complex interplay between age at menopause and OA. It contributes novel perspectives for the discovery of shared genetic-based therapeutic targets, highlighting the value of understanding their common genetic framework for developing future treatments.

ACR Open RheumatologyVol. 8(7)
Hunan Normal University (CN), Changsha Hospital for Maternal and Child Health Care (CN)
Partnerships for the goals
Openalex Percentile: Top 7%
Osteoarthritis Treatment and Mechanisms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.