Abstract
BACKGROUND AND AIMS: There is growing evidence that tobacco smoking causes depression, but it is unclear which constituents of tobacco smoke (e.g. nicotine, carbon monoxide) may be responsible. We used Mendelian randomisation (MR) to measure the independent effect of nicotine on depression, by adjusting the effect of circulating nicotine exposure [via nicotine metabolite ratio (NMR)] for the overall effect of smoking heaviness [via cigarettes per day (CPD)] to account for the non-nicotine constituents of tobacco smoke.
DESIGN: Univariable MR and multivariable MR (MVMR) were used to measure the total and independent effects of genetic liability to NMR and CPD on major depressive disorder (MDD). Our primary method was inverse variance weighted (IVW) regression, with other methods as sensitivity analyses.
SETTING AND PARTICIPANTS: For the exposures, we used genome-wide association study (GWAS) summary statistics among European ancestry individuals for CPD (n = 143 210) and NMR (n = 5185). For the outcome, a GWAS of MDD stratified by smoking status was conducted using individual-level data from UK Biobank (n = 35 871-194 881).
MEASUREMENTS: Genetic variants associated with NMR (n = 6) and CPD (n = 53).
FINDINGS: Univariable MR-IVW indicated a causal effect of CPD on MDD [odds ratio (OR) = 1.13, 95% confidence interval (CI) = 1.04-1.23, P = 0.003] but no clear evidence for an effect of NMR on MDD (OR = 0.98, 95% CI = 0.97-1.00, P = 0.134). MVMR indicated a causal effect of CPD on MDD when accounting for NMR (IVW: OR = 1.19, 95% CI = 1.03-1.37, P = 0.017; Egger: OR = 1.13, 95% CI = 0.89-1.43, P = 0.300) and weak evidence of a small effect of NMR on MDD when accounting for CPD (IVW: OR = 0.98, 95% CI = 0.96-1.00, P = 0.057; Egger: OR = 0.98, 95% CI = 0.96-1.00, P = 0.038).
CONCLUSIONS: The role of nicotine exposure in risk of depression cannot be entirely dismissed. However, the causal effect of tobacco smoking increasing depression risk appears to be largely independent of circulating nicotine exposure, which implies the role of alternative causal pathways.
| Original language | English |
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| Journal | Addiction |
| Early online date | 11 Feb 2025 |
| DOIs | |
| Publication status | E-pub ahead of print - 11 Feb 2025 |
Data Availability Statement
Data and code required to replicate the analysis findings reported in the article are provided on GitHub at: https://doi.org/10.5281/zenodo.12187311. Primary data from the UK Biobank resource are accessible upon application (https://www.ukbiobank.ac.uk/). The summary level data sets used for the exposures in MR analyses are available online; GSCAN (https://conservancy.umn.edu/items/ca7ed549-636b-41c0-ae79-97c57e266417); Buchwald and colleagues (https://www.ebi.ac.uk/gwas/efotraits/EFO_0007794).Funding
This work is primarily supported by a Society for the Student of Addiction PhD studentship awarded to C.B. The funding body had no role in the design of the study; the collection, analysis and interpretation of data; or writing this manuscript. R.E.W. is funded by a postdoctoral fellowship from the South-Eastern Norway Regional Health Authority (2020024). This work is supported by the Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol (MC_UU_00032/7). J.K. receives funding from the Innovative Medicines Initiative 2 Joint Undertaking under Grant 777394 for the project AIMS-2-TRIALS. This work was supported by Cancer Research UK (Grant C18281/A29019) who funded the salary of J.K.
| Funders | Funder number |
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| Medical Research Council |
