Using multivariable Mendelian randomization to estimate the causal effect of bone mineral density on osteoarthritis risk, independently of body mass index

Abstract

Objectives Observational analyses suggest that high bone mineral density (BMD) is a risk factor for osteoarthritis (OA); it is unclear whether this represents a causal effect or shared aetiology and whether these relationships are body mass index (BMI)-independent. We performed bidirectional Mendelian randomization (MR) to uncover the causal pathways between BMD, BMI and OA. Methods One-sample (1S)MR estimates were generated by two-stage least-squares regression. Unweighted allele scores instrumented each exposure. Two-sample (2S)MR estimates were generated using inverse-variance weighted random-effects meta-analysis. Multivariable MR (MVMR), including BMD and BMI instruments in the same model, determined the BMI-independent causal pathway from BMD to OA. Latent causal variable (LCV) analysis, using weight-adjusted femoral neck (FN)–BMD and hip/knee OA summary statistics, determined whether genetic correlation explained the causal effect of BMD on OA. Results 1SMR provided strong evidence for a causal effect of BMD estimated from heel ultrasound (eBMD) on hip and knee OA {odds ratio [OR]hip = 1.28 [95% confidence interval (CI) = 1.05, 1.57], p = 0.02, ORknee = 1.40 [95% CI = 1.20, 1.63], p = 3 × 10–5, OR per standard deviation [SD] increase}. 2SMR effect sizes were consistent in direction. Results suggested that the causal pathways between eBMD and OA were bidirectional (βhip = 1.10 [95% CI = 0.36, 1.84], p = 0.003, βknee = 4.16 [95% CI = 2.74, 5.57], p = 8 × 10–9, β = SD increase per doubling in risk). MVMR identified a BMI-independent causal pathway between eBMD and hip/knee OA. LCV suggested that genetic correlation (i.e. shared genetic aetiology) did not fully explain the causal effects of BMD on hip/knee OA. Conclusions These results provide evidence for a BMI-independent causal effect of eBMD on OA. Despite evidence of bidirectional effects, the effect of BMD on OA did not appear to be fully explained by shared genetic aetiology, suggesting a direct action of bone on joint deterioration.

High bone mass and cam morphology are independently related to hip osteoarthritis: findings from the High Bone Mass cohort

“…HBM individuals have been shown to have increased incidence and progression of radiographic features of OA (i.e. osteophytes and joint space narrowing [JSN]) at load bearing joints (knee and hip), as well as an increased prevalence of radiographic OA in non-loadbearing joints (hands) [21][22][23][24]. It is thought that these individuals may have a predisposition to a bone-forming phenotype that may be, at least partially, genetically determined [25].…”

Novel Genetic Loci Associated with Osteoarthritis in Multi-Ancestry Analyses in 484,374 Participants from MVP and the UK Biobank

“…Interestingly, this locus has also been associated with body fat distribution 39 and bone mineral density 40 among other traits like body height [41][42][43] and lipid levels 44 . Being overweight and having high bone mineral density are established risk factors for OA with evidence supporting a direct effect of bone density on joint deterioration 45 and BMI 46 on osteoarthritis. Similarly, variants in 11p15.3, one of the other identified novel loci (MEGA19), is associated with height 47 , BMI 42,48 , lipids 49 , blood pressure 50 and CRP levels 51,52 .…”

A Guide for Understanding and Designing Mendelian Randomization Studies in the Musculoskeletal Field

“…An example was our recent analysis of the causal effect of BMD on OA and vice versa, where we observed evidence for an effect in both directions, although the direction of effect of OA on BMD did not reflect the direction we hypothesized for a true bidirectional effect (ie, if OA‐related reductions in physical activity lead to lower BMD). ( 40 ) Bidirectional MR analyses require summary statistics for SNPs associated with both the exposure and outcome at genomewide significance. Steiger filtering is often performed before analysis to exclude SNPs that explain a greater variance in the outcome variable than the exposure variable for that analysis.…”

“…5, a multivariable MR analysis could be performed including both BMD and BMI as exposures. ( 40 ) However, it is often not possible to identify all common factors contributing to the correlated pleiotropy, and therefore additional MR methods are required.…”

“…An extension to MR that is becoming more popular in the musculoskeletal field is multivariable MR. (1,4,40,(60)(61)(62)(63)(64)(65)(66)(67)(68)(69)(70)(71)(72)(73)(74)(75) When evidence of pleiotropy is observed, and there is a trait believed to explain the pleiotropy, multivariable MR can estimate the direct effect of the exposure on the outcome, independent of the additional trait. (76,77) For example, in our recent analysis, we used multivariable MR to identify a BMI-independent effect of BMD on OA, as we suspected that the pleiotropy we observed in our univariable analyses reflected pleiotropy via the shared causal factor, BMI.…”