Preadult polytoxicomania-strong environmental underpinnings and first genetic hints

Abstract

Considering the immense societal and personal costs and suffering associated with multiple drug use or "polytoxicomania", better understanding of environmental and genetic causes is crucial. While previous studies focused on single risk factors and selected drugs, effects of early-accumulated environmental risks on polytoxicomania were never addressed. Similarly, evidence of genetic susceptibility to particular drugs is abundant, while genetic predisposition to polytoxicomania is unexplored. We exploited the GRAS data collection, comprising information on N~2000 deep-phenotyped schizophrenia patients, to investigate effects of early-life environmental risk accumulation on polytoxicomania and additionally provide first genetic insight. Preadult accumulation of environmental risks (physical or sexual abuse, urbanicity, migration, cannabis, alcohol) was strongly associated with lifetime polytoxicomania (p = 1.5 × 10^-45; OR = 31.4), preadult polytoxicomania with OR = 226.6 (p = 1.0 × 10^-33) and adult polytoxicomania with OR = 17.5 (p = 3.4 × 10^-24). Parallel accessibility of genetic data from GRAS patients and N~2100 controls for genome-wide association (GWAS) and phenotype-based genetic association studies (PGAS) permitted the creation of a novel multiple GWAS-PGAS approach. This approach yielded 41 intuitively interesting SNPs, potentially conferring liability to preadult polytoxicomania, which await replication upon availability of suitable deep-phenotyped cohorts anywhere world-wide. Concisely, juvenile environmental risk accumulation, including cannabis and alcohol as starter/gateway drugs, strongly predicts polytoxicomania during adolescence and adulthood. This pivotal message should launch more effective sociopolitical measures to prevent this deleterious psychiatric condition.

Fig. 1. Environmental influence on behavioral phenotypes.

A Associations of preadult illicit drug use with violent aggressive behavior as measured by an aggression proxy (developed in Mitjans et al. [16]). Columns in dark colors denote preadult users of the respective drug; light colors indicate non-users of this specific drug. Note that drug use shown is not exclusive, i.e., a person that consumed opioids might also have used inhalants and thus appear in both columns as user; a person that used cocaine but not ecstasy will appear as non-user for ecstasy. Group “No drug ever” refers to lifetime non-consumption of any illicit drug. Left side: N numbers. Right side: P values (Chi²-test, two-sided) comparing users and non-users. Note that in this panel, cannabis, alcohol, nicotine and caffeine are not considered. B Step-wise increase in aggression proxy with the number of drug classes used in early life. Colors in bars represent respective drug classes. C Associations of single preadult environmental risk factors and lifetime polytoxicomania. Columns in dark colors denote individuals exposed to respective risk factor, light colors refer to individuals not exposed to this specific risk (not necessarily devoid of any risk at all). Note that risks shown are not exclusive, i.e., many individuals carry more than 1 risk factor. D–F Accumulation of preadult environmental risk factors leads to stepwise increase in lifetime polytoxicomania (D), exclusive preadult polytoxicomania (E) and polytoxicomania appearing exclusively later in life (F). Colors indicate respective risk factors (B), (D–F): n numbers below bars, (A), (B) on left side. Chi²-test p values (two-sided) on top of graph (B), (D–F) or right side (A), (B); Cochran–Armitage test p values (two-sided) (B, C), (E) underneath in italics. OR: Odds Ratio.

Fig. 2. Environmental risk load and behavioral consequences.

A Risk factor distribution in the 3 different consumption groups: non-polytoxicomanic individuals (lifetime), exclusively adult polytoxicomanic, and exclusively preadult polytoxicomanic individuals, shows a clearly elevated risk load in (preadult) polytoxicomanic individuals. B Relation of number of preadult environmental risk factors and drug consumption behavior with age at schizophrenia onset. Mean±SEM. C Stair pattern increase in percentage of individuals who attempted to commit suicide with number of preadult environmental risk factors; legend of risk factors as in (Fig. 1D). D Distribution of auto- and heteroaggressive behavior shows that autoaggression (suicide attempts) increases with the number of risk factors only in individuals who show heteroaggressive behavior as well. E Not all behavioral traits increase with environmental risk: No association of preadult environmental risk factors and autistic features in adulthood as quantified by PAUSS (developed in Kästner et al. [32]). Colors indicate respective risk factors. Mean±SEM (C), (E): n numbers below bars. Chi²-test (C) or one-way ANOVA (E) p values (two-sided) on top of graph; two-sided Cochran–Armitage (C) and Jonckheere–Terpstra trend tests (E) underneath in italics.

Fig. 3. Novel multiple GWAS–PGAS approach.

A Overview of the analysis design. GWAS set 1 to obtain SNP set 1: 4 GWAS contrasting polytoxicomanic versus non-polytoxicomanic individuals (including healthy individuals) with slightly varied phenotype definitions (details in (B)) to identify SNPs that show consistent associations (p < 0.01) in all 4 GWAS. These SNPs are considered polytoxicomania and/or schizophrenia-associated. GWAS set 2 to obtain SNP set 2: SNPs associated exclusively with schizophrenia, but not polytoxicomania, resulting from GWAS 5 and/or 6 (p < 0.05) are subtracted from SNP set 1 (polytoxicomania and/or schizophrenia-associated), yielding the final set of polytoxicomania-relevant SNPs. B Diagram showing exact phenotype definitions and sample sizes per group for GWAS 1–6. C Detailed workflow of the novel GWAS–PGAS approach including clumping procedure to reduce number of SNPs in the final set.

Fig. 4. Intersection of SNP results before and after clumping.

A Intersection raw results before clumping of associated SNPs from all 6 GWAS. Black bars represent the number of intersecting SNPs below p value threshold 0.05. Dots indicate the respective GWAS for which the number of intersecting SNPs was calculated. Columns with single dots indicate SNPs unique to the corresponding GWAS. GWAS 1-4 and GWAS 5-6 show strong overlap within each other. Importantly, as indicated by the red bar, the largest intersection size when overlapping 4 GWAS is between GWAS 1-4 (polytoxicomania and/or schizophrenia GWAS). These SNPs show no association in GWAS 5-6. The novel approach applied to raw GWAS results would thus yield 3539 SNPs. Colored bars on the left indicate the number of associated SNPs per individual GWAS, i.e., the set size. B Intersections of associated SNPs from all 6 GWAS building on clumped SNP results and considering LD linkage neighbor signals. Black bars represent the number of intersecting SNPs below the given p value threshold on the left. Dots indicate the respective GWAS for which the number of intersecting SNPs was calculated. Columns with single dots indicate SNPs unique to the corresponding GWAS. Again, the intersection of SNPs associated in GWAS 1-4, but not GWAS 5-6, is larger than for any other combinations of 4 GWAS and yields now a strongly reduced set of 41 polytoxicomania-associated SNPs (red bar). Colored bars on the left indicate the number of associated SNPs per individual GWAS, i.e., the set size.