Additional file 1 of Body mass index and inflammation in depression and treatment-resistant depression: a Mendelian randomisation study
Creators: name: Karageorgiou, Vasilios
name: Casanova, Francesco
name: O’Loughlin, Jessica
name: Green, Harry
name: McKinley, Trevelyan J.
name: Bowden, Jack
name: Tyrrell, Jessica
Publisher: name: figshare
Year: 2024
Subjects: subject: Medicine
subject: Genetics
subject: FOS: Biological sciences
subject: Neuroscience
subject: Biotechnology
subject: Immunology
subject: FOS: Clinical medicine
subject: Mental Health

description: Additional file 1: Appendix [70–74]. Figure S1. Estimated effects of CRP and BMI on a range of depression-related outcomes in a subset of unrelated individuals (n = 52, 510 completed MHQ and contributed to PHQ9 and CIDI, n = 165, 378 with CPRD data linkage that contributed to GP Diagnosis of MDD and TRD. Figure S2. Sex-Stratified Analysis for all outcomes reported in Fig. 2. Estimates from univariable MR (UV), multivariable (MV), and pleiotropy-robust multivariable MR (GRAPPLE) are reported for females and males separately. Figure S3. MR Analysis with external weights for BMI [Locke2015] and CRP [40]. Figure S4. Age as a Moderator of the causal associations of CRP and BMI with mood outcomes. In the visualisation of the meta-regression slope, if the intercept falls within the confidence region of the age slope, then the result is not statistically significant. Figure S5. SNP-CRP associations and SNP-depression associations for n = 194 SNPs in LD. These genetic associations are then projected to independent genetic components (cis-MR, [51]). Figure S6. Proportion of CRP effect mediated by BMI in males and females. The CRP effect is defined by two different instruments. cisMR: CRP effect is estimated with one SNP as instrument (rs2794520). Figure S7. Tissue Expression for SNPs in Locke et al. Transcript per million (TPM) data, scaled per gene, are presented. Ordering follows the sum of scaled TPM across brain regions. Figure S8. Estimates of the Effect of BMI on the depression outcomes when two different tissue expression-informed instruments are used. In the top panel, the top 20 SNPs of genes that are predominantly expressed in the brain are shown (Brain); in the bottom panel, genes that are expressed in the periphery constitute the instrument. Figure S9. Assumed Data Generated Mechanism for two exposures (X1, X2). Some of the variants that associate with X1 also affect Y directly, thereby violating the third IV assumption. Figure S10. Estimation of the causal effect of two exposures X1 and X2 in the presence of weak instrument bias and pleiotropy. The targets of the estimation of $$\beta _{X1} = 1$$ β X 1 = 1 and $$\beta _{X2} = 0.5$$ β X 2 = 0.5 are visualised with grey dashed lines. CFS: Conditional F statistic; CC: Collider Correction; 2SLS: Two-Stage Least Squares; X1, X2: Exposures 1 and 2 in Figure S9. Figure S11. Directed Acyclic Graph. The exposure X and the mediator M exert two independent effects on Y. Genetically proxying only X can result in an inaccuracy in the estimation as $$\beta _{XY, Univariable}$$ β X Y , U n i v a r i a b l e will be capturing the total effect ( $$\beta _{X2} + \beta _{XM} * \beta _{MY}$$ β X 2 + β XM ∗ β MY ). Figure S12. Uncertainty in estimating the proportion of mediated effects, simulation Results. CFS: Conditional F statistic for the exposure X in Figure S11; CFS $$_M$$ M : Conditional F statistic for the mediator M. Error bars in the coverage and power plots represent the Monte Carlo error for $$s = 6000$$ s = 6000 simulations. BB : Bayesian bootstrap; $$mediat\_package$$ m e d i a t _ p a c k a g e : implementation with the R mediation package; norm: non-parametric bootstrap. Figure S13. Simulation Results ( $$\pi _m$$ π m and 95% Confidence Intervals) for Increasing Prevalence of Binary Outcome.