Major Psychiatric Disorders, Substance Use Behaviors, and Longevity

Abstract

Importance: Observational studies suggest that major psychiatric disorders and substance use behaviors reduce longevity, making it difficult to disentangle their relationships with aging-related outcomes.

Objective: To evaluate the associations between the genetic liabilities for major psychiatric disorders, substance use behaviors (smoking and alcohol consumption), and longevity.

Design, settings, and participants: This 2-sample mendelian randomization (MR) study assessed associations between psychiatric disorders, substance use behaviors, and longevity using single-variable and multivariable models. Multiomics analyses were performed elucidating transcriptomic underpinnings of the MR associations and identifying potential proteomic therapeutic targets. This study sourced summary-level genome-wide association study (GWAS) data, gene expression, and proteomic data from cohorts of European ancestry. Analyses were performed from May 2022 to November 2023.

Exposures: Genetic susceptibility for major depression (n = 500 199), bipolar disorder (n = 413 466), schizophrenia (n = 127 906), problematic alcohol use (n = 435 563), weekly alcohol consumption (n = 666 978), and lifetime smoking index (n = 462 690).

Main outcomes and measures: The main outcome encompassed aspects of health span, lifespan, and exceptional longevity. Additional outcomes were epigenetic age acceleration (EAA) clocks.

Results: Findings from multivariable MR models simultaneously assessing psychiatric disorders and substance use behaviorsm suggest a negative association between smoking and longevity in cohorts of European ancestry (n = 709 709; 431 503 [60.8%] female; β, -0.33; 95% CI, -0.38 to -0.28; P = 4.59 × 10-34) and with increased EAA (n = 34 449; 18 017 [52.3%] female; eg, PhenoAge: β, 1.76; 95% CI, 0.72 to 2.79; P = 8.83 × 10-4). Transcriptomic imputation and colocalization identified 249 genes associated with smoking, including 36 novel genes not captured by the original smoking GWAS. Enriched pathways included chromatin remodeling and telomere assembly and maintenance. The transcriptome-wide signature of smoking was inversely associated with longevity, and estimates of individual smoking-associated genes, eg, XRCC3 and PRMT6, aligned with the smoking-longevity MR analyses, suggesting underlying transcriptomic mediators. Cis-instrument MR prioritized brain proteins associated with smoking behavior, including LY6H (β, 0.02; 95% CI, 0.01 to 0.03; P = 2.37 × 10-6) and RIT2 (β, 0.02; 95% CI, 0.01 to 0.03; P = 1.05 × 10-5), which had favorable adverse-effect profiles across 367 traits evaluated in phenome-wide MR.

Conclusions: The findings suggest that the genetic liability of smoking, but not of psychiatric disorders, is associated with longevity. Transcriptomic associations offer insights into smoking-related pathways, and identified proteomic targets may inform therapeutic development for smoking cessation strategies.

Figure 1.. Study Overview

Presented is the study flow diagram outlining the data sources (categorized by their primary roles in the analyses), methods, and follow-up analyses. dlFPC indicates dorsolateral prefrontal cortex; EAA, epigenetic age acceleration; GTEx, genotype-tissue expression; GWAS, genome-wide association study; IVW, inverse variance weighted; MR, mendelian randomization; MVMR, multivariable MR; nACHr, neuronal nicotinic acetylcholine receptor; pQTL, protein quantitative trait locus; ROSMAP, Religious Orders Study and Rush Memory and Aging Project; SNV, single-nucleotide variant; TCGA, The Cancer Genome Atlas; TWAS, transcriptome-wide association study. ^aMeta-analysis GWAS or QTL study. ^bSingle-cohort GWAS or QTL study.

Figure 2.. Mendelian Randomization (MR) Results of Association of Neuropsychiatric Disorders and Substance Use Behaviors With Human Longevity

A, Estimated associations reported are MR estimates with 95% CIs. Multivariable MR (MVMR) results reported are MR estimates from the final 2 MVMR models incorporating all psychiatric disorders, smoking behavior, and 1 of the alcohol consumption exposures simultaneously. B, Points plotted are the associations statistics for the 5 variants comprising the CHRNA5-CHRNA3-CHRNB4 gene cluster smoking instrument. Regression lines shown correspond to the main inverse variance–weighted (IVW) and complementary MR methods. C, Single-nucleotide variants (SNVs) are colored by their linkage disequilibrium (LD) R², and the labeled SNV rs8042849 is the candidate causal SNV shared between smoking behavior and the multivariate longevity outcome. SVMR indicates single-variable MR.

Figure 3.. Mendelian Randomization (MR) Results of the Association of Neuropsychiatric Disorders and Substance Use Behaviors With Epigenetic Age Acceleration (EAA)

Associations reported are MR estimates with 95% CIs. Multivariable MR (MVMR) results reported are MR estimates from the final 2 MVMR models incorporating all psychiatric disorders, smoking behavior, and 1 of the alcohol consumption exposures simultaneously. SVMR indicates single-variable MR.

Figure 4.. Transcriptomic Imputation Results Finding Genes Associated With Smoking Behavior and Comparison With Human Longevity

Panel A presents the FUSION transcriptome-wide association study (TWAS) identifying genes associated with lifetime smoking behavior. The y-axis shows the TWAS z scores for smoking behavior for each gene-tissue feature analyzed and the x-axis shows the genomic position of the gene. Highlighted genes are the high-confidence smoking-associated genes that surpassed correction for multiple comparisons (P = 8.31 × 10⁻⁷) and further demonstrated evidence of colocalization (posterior probability >0.8) with smoking behavior. Panel B presents the comparison of the high-confidence smoking-associated TWAS features (gene-tissue combinations) with the multivariate longevity outcome that were directionally consistent with the mendelian randomization analyses (ie, had an inverse association with smoking and longevity) and further had TWAS P < .05 for longevity (eMethods in Supplement 1). The y-axis shows the TWAS z scores for smoking behavior and longevity for each gene-tissue feature analyzed.

Figure 5.. Results of Cis-Instrument Mendelian Randomization (MR) Screen for Brain Proteins Associated With Smoking Behavior

A, Labeled proteins are those that surpassed false discovery rate correction for multiple comparisons and also demonstrated evidence of colocalization with smoking behavior (PP.H4 > 0.8; eMethods in Supplement 1). B, There were 27 cortical proteins that colocalized with lifetime smoking, including the cis-MR estimates, colocalization PP.H4 value, and druggability tiers (eMethods in the Supplement). C and D, LY6H and FIT2 were 2 of the proteins that surpassed false discovery rate correction for multiple comparisons and also demonstrated evidence of colocalization with smoking behavior, plotted here against 367 diseases and biomarkers (eMethods in Supplement 1; eTable 1 in Supplement 2). The red dotted lines indicated the adjusted P value threshold of 1.36 × 10⁻⁴ (0.05/367 outcomes tested), and labeled outcomes in (C) surpassed correction for multiple comparisons. The direction of the MR estimates for each of these labeled outcomes (schizophrenia, irritable mood, and mean time to correctly identify matches) suggested a beneficial association directionally consistent with the direction that would be therapeutically beneficial to reduce smoking behavior (eg, for LY6H, this corresponds to inhibition).