Cardiometabolic Polygenic Risk Scores and Osteoarthritis Outcomes: A Mendelian Randomization Study Using Data From the Malmö Diet and Cancer Study and the UK Biobank

Abstract

Objective: To investigate the causal role of cardiometabolic risk factors in osteoarthritis (OA) using associated genetic variants.

Methods: We studied 27,691 adults from the Malmö Diet and Cancer Study (MDCS) and replicated novel findings among 376,435 adults from the UK Biobank. Trait-specific polygenic risk scores for low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol levels, triglyceride levels, body mass index (BMI), fasting plasma glucose (FPG) levels, and systolic blood pressure (BP) were used to test the associations of genetically predicted elevations in each trait with incident OA diagnosis (n = 3,559), OA joint replacement (n = 2,780), or both (total OA; n = 4,226) in Mendelian randomization (MR) analyses in the MDCS, and with self-reported and/or hospital-diagnosed OA (n = 65,213) in the UK Biobank. Multivariable MR, MR-Egger, and weighted median MR were used to adjust for potential pleiotropic biases.

Results: In the MDCS, genetically predicted elevation in LDL cholesterol level was associated with a lower risk of OA diagnosis (odds ratio [OR] 0.83 [95% confidence interval (95% CI) 0.73-0.95] per 1SD increase) and total OA (OR 0.87 [95% CI 0.78-0.98]), which was supported by multivariable MR for OA diagnosis (OR 0.84 [95% CI 0.75-0.95]) and total OA (0.87 [95% CI 0.78-0.97]), and by conventional 2-sample MR for OA diagnosis (OR 0.86 [95% CI 0.75-0.98]). MR-Egger indicated no pleiotropic bias. Genetically predicted elevation in BMI was associated with an increased risk of OA diagnosis (OR 1.65 [95% CI 1.14-2.41]), while MR-Egger indicated pleiotropic bias and a larger association with OA diagnosis (OR 3.25 [1.26-8.39]), OA joint replacement (OR 3.81 [95% CI 1.39-10.4]), and total OA (OR 3.41 [95% CI 1.43-8.15]). No associations were observed between genetically predicted HDL cholesterol level, triglyceride level, FPG level, or systolic BP and OA outcomes. The associations with LDL cholesterol levels were replicated in the UK Biobank (OR 0.95 [95% CI 0.93-0.98]).

Conclusion: Our MR study provides evidence of a causal role of lower LDL cholesterol level and higher BMI in OA.

Figure 1

One‐sample conventional Mendelian randomization analyses of genetically predicted elevations in cardiometabolic traits and osteoarthritis (OA) outcomes in the Malmö Diet and Cancer Study (MDCS). The odds ratios (ORs) and 95% confidence intervals (95% CIs) for OA outcomes per genetically predicted 1SD increase in levels of cardiometabolic traits determined using respective polygenic risk scores are shown. Fitted values predicted by the polygenic risk score for each trait were used as predictors of incident OA outcomes in the MDCS in a 2‐stage least squares regression analysis. Genetically predicted elevation in low‐density lipoprotein cholesterol (LDLC) level was associated with a lower risk of OA diagnosis and total OA, genetically predicted elevation in body mass index (BMI) was associated with a higher risk of OA diagnosis, and genetically predicted elevation in systolic blood pressure (SBP) was associated with a lower risk of all OA outcomes. High‐density lipoprotein cholesterol (HDLC) level, triglycerides (TG), and fasting plasma glucose (FPG) were not associated with OA outcomes.

Figure 2

Association between genetic predisposition for elevated low‐density lipoprotein cholesterol (LDLC) and osteoarthritis (OA) in the UK Biobank. Odds ratios (ORs) and 95% confidence intervals (95% CIs) for OA using 2‐sample Mendelian randomization (MR) analyses in the UK Biobank are shown. Conventional MR estimates were obtained by analyzing polygenic risk scores for LDL cholesterol created from single‐nucleotide polymorphisms (SNPs) identified in genome‐wide association studies (GWAS) and gene‐specific SNPs related to OA in the UK Biobank weighted by SNP–LDL cholesterol associations in the Global Lipids Genetics Consortium 28. In the conventional MR analysis, for “GWAS threshold,” the polygenic risk score for LDL cholesterol was created using SNPs that were previously associated with LDL cholesterol at the GWAS significance level (P < 5 × 10⁻⁸) in the Global Lipids Genetics Consortium 28. For “GWAS restricted,” the polygenic risk score for LDL cholesterol was created using SNPs that were previously associated with LDL cholesterol at the GWAS significance level (P < 5 × 10⁻⁸) and were not associated with either HDL cholesterol level or triglycerides (P > 0.05) in the Global Lipids Genetics Consortium 28. Two‐sample sensitivity MR analyses were performed using MR‐Egger 34, weighted median MR 35, and multivariable MR 32 using summary SNP exposure data from the Global Lipids Genetic Consortium 28 and summary SNP outcome data from the UK Biobank. Genes for the gene‐specific analyses (HMGCR,PCSK9,LDLR, and NPC1L1) were mainly selected due to the fact that they encode for LDL cholesterol–lowering targets. Gene‐specific analyses indicated a lower risk of OA by LDLR‐mediated higher LDL cholesterol level. A similar trend was observed with the HMGCR instrument, although it did not reach statistical significance. MR indicated that a genetically predicted elevation in LDL cholesterol level decreases the risk of OA.