The BioSUD Biobank: A genomic resource for Substance Use Disorders in Italy

doi:10.21203/rs.3.rs-5116564/v1

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The BioSUD Biobank: A genomic resource for Substance Use Disorders in Italy

https://doi.org/10.21203/rs.3.rs-5116564/v1

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Substance Use Disorders (SUDs) are a significant public health concern with complex etiologies involving genetic, environmental, and psychological factors. Here we present BioSUD, a biobank that, by integrating genomic data with comprehensive phenotypic assessments, including sociodemographic, psychosocial, and addiction-related variables, was developed to investigate the etiology of SUDs within the Southern Italian population. We assessed a cohort of 1,806 participants (1,508 controls and 298 individuals with SUD diagnosis), revealing a marked male predominance in both groups. Genomic analyses of the newly generated genotypes showed a predominantly Southern Italian ancestry for the BioSUD cohort. Moreover, multiple genotype analysis highlights the presence of different ancestries, suggesting a complex history of Southern Italian groups, contributing to a relatively high level of genetic variation that may be not captured by translational studies based on groups with predominantly Northern European ancestries. From a social and psychological perspective, individuals with SUDs exhibited lower socioeconomic status, increased exposure to adverse experiences, and compromised familial and peer relationships relative to controls. These results show that the BioSUD cohort is valuable for studying SUDs-associated complex behavioral traits.

Substance Use Disorders (SUDs)

Genomic Analysis

Southern Italian Population

Addiction

BioSUD

Biobank

Substance Use Disorders (SUDs) are characterized by a complex pattern of substance use leading to significant impairment or distress, according to the Diagnostic and Statistical Manual of Mental Disorders − 5th Edition, Text Revision (DSM-5, TR) [1]. These disorders encompass various issues related to substance abuse and addiction, including tolerance, withdrawal, and unsuccessful attempts to control or reduce substance use.

SUDs pose significant threats to health and mortality in populations worldwide [2]. In 2021, the United Nations Office on Drugs and Crime (UNODC) [3] reported 296 million global drug users, with 29.5% suffering from SUD, marking a 45% increase over the past decade. The Last European Drug Report [4] indicated that over 29% of Europeans aged 15 to 64 have used drugs at least once in their lifetime. In Italy, substance use among 15 to 19-year-olds surged to 27.9% in 2023, involving ~ 1 million students. In 2022, the European Union witnessed an estimated mortality rate of 22.5 deaths per million among 15 to 64-year-olds [5]. Data collected by law enforcement and prefectures in Italy show a slight increase in drug-related deaths in 2022 compared to the previous year [6].

SUD etiology is influenced by neurological, genetic, and sociocultural factors [7, 8]. From a genetic perspective, SUDs are characterized by a heritable component, as observed by a series of familial patterns identified in twin and family studies [9]. Heritability estimates on different SUDs typically range between 50–60% and suggest a high degree of polygenicity [10, 11].

Genome-wide association studies (GWAS) have pinpointed specific genomic loci associated with various SUDs [12–14], alcohol use disorder (AUD), nicotine dependence, cocaine, and opioid use disorder (CUD and OUD, respectively). Linkage disequilibrium (LD) score regression methods revealed positive genetic correlations between smoking, cannabis use, major depression, and risk-taking behaviors [14, 15] Though often limited in robustness and reproducibility, candidate gene studies have explored genetic variations in dopaminergic, serotonergic, and opioid receptors, GABAergic, and nicotinic cholinergic system genes in various SUDs [16–19].

From a psychological perspective, current addiction models suggest that initial substance use is driven by positive reinforcement, which can lead to automated processes and inflexible, compulsive behaviors that resist negative consequences [20, 21]. Persistent substance use significantly impacts the Prefrontal Cortex (PFC), which is crucial for many executive functions (EFs [22, 23], including inhibitory control, working memory, and attention [24, 25]. These impairments lead to broader cognitive deficits in individuals with SUDs, affecting areas beyond substance-related reward [26–28]. Different GWAS have provided evidence for a genetic correlation between SUDs and risk-taking behavior, impaired executive functioning, and negative affectivity [15]. However, the magnitude of the shared genetic contribution of SUDs to these phenotypes remains an open question, requiring further investigation [29].

Moreover, Northern European ancestry groups are the most frequently studied populations in GWAS, so there is a need to investigate more diverse populations. The Italian population, for instance, exhibits substantial genetic heterogeneity [30, 31], highlighting the importance of its inclusion in SUD studies.

In recent decades, there has been a global surge in the establishment of biobanks. These large-scale resources collect and store numerous biological samples from thousands of participants. In addition, they link each sample to comprehensive observational data and detailed questionnaire responses, significantly enriching the research potential [32, 33]. Despite their relatively recent inception, biobanks have already revolutionized biomedicine, especially in association studies, and are expected to yield exceptional insights shortly [34]. However, for specific traits, such as SUDs, most biobanks lack sufficient data, with research primarily focused on alcohol and tobacco addiction or abuse, together with lifetime usage data for other substances.

Given the high societal costs and the evolving understanding of the complexities behind SUDs, it is crucial to thoroughly investigate this intricate interplay to improve the efficacy of SUD prevention, diagnosis, and personalized interventions [35]. Here, we present a novel Biobank for the Study of Substance Use Disorders in Southern Italy (BioSUD), with the primary goal of unraveling the genetic underpinnings of substance addiction disorders and establishing links between genetic and environmental factors and responses to medical treatments. The outcomes of our research are expected to form the basis of genotype-informed treatments for SUDs. The implications extend beyond individual patients, fostering a positive impact in the broader society and the scientific community.

Recruitment

The BioSUD initiative aims to provide a genomic resource for studying phenotypic traits associated with SUDs. We strive to collect and analyze 3,000 individuals, of whom 1,500 have a diagnosis of SUD. On the 1st of February 2024, the cohort included 1,806 participants, of whom 1,508 individuals served as control participants, comprising 1046 males and 462 females, recruited exclusively at a single blood donor center (Centro Trasfusionale of the University General Hospital, Bari, Italy) and 298 case participants, 278 males and 20 females, collected from several private therapeutic centers and public structures (detailed below).

The control samples were collected between March and October 2021 during their blood donation process following the acquisition of the written informed consent. Before the sample collection, they were informed about the aims and rationale of the project, and an informative document was handed out.

All the cases met the standardized diagnostic criteria for SUDs according to the International Classification of Diseases, 11th Revision (ICD-11) [36], or the DSM-5 TR (1). Eligible participants meeting these criteria were recruited from private (N = 71) and public (N = 227) healthcare facilities in Apulia, a region of Southern Italy. Specifically, the private center samples were collected from the Therapeutic Community Emmanuel Onlus - Sector Dependencies (Lecce) and the Therapeutic Community "Fratello Sole" - Social Cooperative (Gioia del Colle, BA). The samples from public Institutions were gathered at SerD of Bari (BA), Bitonto (BA), Brindisi (BR), Campi Salentina (LE), Castellaneta (TA), Casarano (LE), Foggia (FG), Francavilla (TA), Galliano del Capo (LE), Gallipoli (LE), Giugliano (LE), Grumo Appula (BA), Lecce (LE), Maglie (LE), Manduria (TA), Martina Franca (TA), Nardò (LE), Ostuni (BR), Poggiardo (LE), San Cesario di Lecce (LE), San Pietro Vernotico (BR), Taranto (TA), Ugento (LE) and the SerD in the Brindisi Prison (BR).

Different engagement strategies were employed to encourage volunteer participation in the cases. In all facilities, the BioSUD members presented the project separately to staff and participants, using multimedia tools such as presentations and short demonstrative videos. After the presentation, the volunteers' willingness to participate in the study was recorded. Written consent and blood samples were collected during scheduled routine examinations in subsequent weeks to reduce participant burden. A dedicated medical professional or psychologist was on-site to oversee the process, including obtaining written consent and aiding with the questionnaire (detailed below). Healthcare professionals, including doctors and specialized nurses on the research team, collected blood samples. After transport to the BioSUD lab facilities, all the blood samples were processed as described above within 72 hours. The questionnaire data from both the cases and the controls was subsequently entered into Excel and processed with R Studio, version 4.5.0 [37].

Sampling

Blood samples from controls were collected by a specialized nurse and from cases by healthcare professionals, including physicians and specialized nurses affiliated with the research team. Specifically, after the written consent was returned, venous blood (8ml) was drawn using a Vacutainer K2 EDTA and kept refrigerated until arrival at the processing laboratory at the University of Bari. Within 72 hours from collection, all samples underwent centrifugation at 0.8 x g for 15 minutes at a 45-degree angle. Following stratification, 1 mL of plasma was stored at -80°C, while the remaining sample was preserved at -20°C for subsequent DNA extraction and analyses.

DNA extraction and genotyping

DNA was extracted from 250µL of the layer of nucleated blood cells obtained after centrifugation during the initial processing. The Qiagen DNA Blood Mini Kit was used per the manufacturer's protocol. The quality and concentration of the extracted DNA were evaluated using the nanodrop spectrophotometer. Samples with a concentration of at least 30ng/µl and a 260/280 ratio higher than 1.6 were selected for DNA genotyping, performed at the Institute of Genomics of Tartu, Estonia, using the Illumina Global Screening Array (GSA, Illumina Inc.). All the samples with a quality call rate lower than 97% were discarded. To date, 1,279 samples have been genotyped.

Dataset

The newly generated genotypes were combined with publicly available datasets comprising 4551 individuals from 140 different populations, 107 of which are Eurasian [38–49], using –bmerge of PLINK version 1.9 [50]. Before merging each dataset, we removed all markers and individuals with more than 5% missingness. The resulting dataset includes 3188,788 autosomal variants from 5,830 individuals. Further filtering steps are detailed in the following sections.

Principal Component Analysis (PCA)

To explore the genetic variation of the BioSUD cohort, we performed a Principal Component Analysis. We kept only the Eurasian populations from the complete dataset; loci and individuals with missingness rates higher than 5% were discarded. After pruning for SNPs with high linkage disequilibrium score (indep-pairwise 200 50 0.4), 69 359 SNPs and 3,530 samples remained, and the PLINK files were converted to EIGENSTRAT format using convertf (version 5722).

Principal Component Analysis (PCA) was performed using SmartPCA (version 16000) from the EIGENSOFT package [51]. Specifically, we projected the BioSUD samples into the principal component space inferred from all other Eurasian individuals (using the poplistname option). Outliers were automatically removed with the numoutlieriter, numoutlierevec, and outliersigmathresh options set to default parameters. This process led to removing 101 samples, reducing the sample size to 3,429 individuals. After visual inspection of the PCA plot, we identified and removed three additional BioSUD participants falling outside the genetic variability of the cohort, likely due to ancestral discrepancies (Fig. S1 – Supplementary Materials). Thus, the final dataset was composed of 3,426 individuals.

ROH estimation

The --homozyg function in PLINK was exploited to detect Runs Of Homozygosity (ROHs) containing at least 50 SNPs. The minimum ROH length was set at 1,500 Kb to exclude short ROH due to Linkage Disequilibrium (LD). ROHs were detected by scanning along genotypes for each BioSUD cohort and all other individuals sharing the same bulk of SNPs.

Admixture analysis

To increase the number of SNPs analyzed while maintaining a proper sample size for the population, the analysis was confined to data from the 1000 Genomes Project [38] and Raveane et al. [52] The resulting dataset comprised 2697 individuals: 1408 from European (Finnish: FIN; Central Europeans: CEU; British from England and Scotland: GBR; Iberians from Spain: IBS; Italians: ITA; Tuscans: TSI), African (Luhya from Webuye, Kenya: LWK; Yoruba from Nigeria: YRI), and Asian (Gujarati Indians from Houston: GIH; Dai Chinese: CDX; Japanese from Tokyo: JPT) populations, and 1279 from the BioSUD sample.

We carried out an ADMIXTURE analysis to further explore the genetic similarity of the BioSUD samples with other populations.

We performed ten independent repetitions (using time as a starting point for randomization with the –seed option) for each K value ranging from two to ten using the ADMIXTURE software tool (version 1.3; [53]. The “optimal” number of K was inferred using the cross-validation (CV) procedure with the –cv option, and the lowest CV error was observed at K = 7 and 8 (Fig. S2 – Supplementary Materials). We first conducted an ADMIXTURE analysis excluding the BioSUD data. After obtaining the initial results, we projected the BioSUD data onto the resulting ADMIXTURE profiles (-P flag) to integrate and analyze their genetic structure within the established framework.

Questionnaire

Each participant filled out a tailored questionnaire to assess various aspects of substance use, including nicotine, alcohol, cocaine, heroin, cannabis, and other substances (Tables S1 and S2 - Supplementary Materials). This comprehensive tool delves into the frequency, amounts, and patterns of substance use, leveraging the DSM-5 TR checklist to assess potential SUDs (APA, 2022). Additionally, the questionnaire investigates behaviors associated with substance use, emphasizing psychosocial elements such as family dynamics, peer groups, substance accessibility, and social environments experienced in adolescence.

To accommodate the specific challenges and time constraints often encountered by individuals struggling with addiction outside of structured rehabilitation settings, the questionnaire administered to the 'Emmanuel' group and control participants was longer (233 items) than the one given to other patients (165 items). This adjustment aimed to optimize data collection efficiency without compromising essential information. The survey encompasses three main sections: sociodemographic, psychosocial, and substance use (Fig. 1).

The sociodemographic section collects a broad spectrum of information about participants, considering the potential influence of diverse life aspects on the study's outcomes. Critical demographic details, such as gender, age, education, marital status (including the number of children), place of residence, place of birth, income, employment status, self-reported health, and family background (parents, siblings, and other caregivers) were included.

The survey's psychosocial section focuses on life events affecting psychological and social well-being. It explores experiences such as early separations from parents, parental divorce, and relocations, scrutinizing potential impacts on individuals (Tables S1 and S2 - Supplementary Materials). Closely tied to the substance use section, our exploration extended to the exposure to substances in the social context, encompassing both family and peers. In addition, we investigated substance accessibility in the cities where participants lived and the perceived level of safety. Furthermore, this section explored adverse events across life stages, including grief, accidents, illness, violent crimes, sexual abuse, and other painful events. Participants reported occurrences in age categories (< 14, 14–18, 18–25, > 25 years), and the overall incidence was quantified by calculating cumulative events across categories.

In the relationship quality subsection, participants self-reported their quality estimates of relationships with fathers, mothers, siblings, and peers on a scale ranging from "Very Poor" to "Very Good". This categorical representation was converted into a 5-point numerical scale (1 to 5). Cumulative scores across family members were aggregated and later reclassified into categories: 1–3 as "Very Poor," 4–6 as "Poor," 7–9 as "Average", 10–12 as "Good", and 13–15 as "Very Good" for analytical purposes.

The substance use section explores substance consumption, encompassing nicotine, alcohol, cannabis, cocaine, heroin, and other substances (e.g., amphetamines, MDMA, ecstasy, hallucinogens, etc.). We tailored questions for each substance to identify potential use disorders. We utilized the DSM-5 TR checklist [1] to thoroughly examine substance use across various categories, which aligned with our research goals. The exposure subsection explores family, peer, and accessibility influences on substance use, considering social settings and craving behaviors. Subjective feelings, including relief, reward, and obsession, are measured. A Visual Analogue Scale (VAS) was also used for the craving assessment. Positive responses to substance consumption-related questionnaire items were assigned a value of 1 and summed to create a 'family substance consumption' score. This score was then categorized into five ordinal levels: 'None' (0 positive responses), 'Low' (1–2 positive responses), 'Average' (3–4 positive responses), 'High' (5–6 positive responses), and 'Very High' (more than seven positive responses), based on average and standard deviation.

Figure 1: Overview of Questionnaire Sections: Sociodemographic, Psychosocial, and Substance Use Assessment Variables.

*Areas investigated in Controls and Emmanuel questionnaire only.

The genetic variation of the BioSUD cohort

Principal Component Analysis

To genetically characterize the BioSUD samples, we first projected the BioSUD individuals onto the PCA space inferred from 2,150 Eurasian individuals (Fig. 2a). The PCA showed that BioSUD samples form a cluster largely overlapping with individuals from Southern Italy and partially overlapping with the ones from Central Italy. On the contrary, it differed from individuals from Northern Italy and Sardinia. Within Eurasia, the BioSUD samples appeared more similar to Balkan populations and Corsicans than to Iberians. Indeed, on the west side of Europe, the Iberian populations seemed to be close to Northern Italian populations and Central Europeans. At the same time, on the west side of Europe, Iberians looked closer to Northern Italians than to BioSUD samples and Southern Italy. Central European populations were closer to Northern Italians than to the BioSUD cohort.

Runs Of Homozygosity

To infer the homozygosity pattern within the BioSUD cohort compared to worldwide populations, we performed an ROH analysis for genomic segments extending more than 1,500 kb and encompassing at least 50 SNPs. Our results showed that the BioSUD cohort has an average of 7.64 ROH segments for an average total length of 18,132.644 kb (Standard Deviation (SD) = 21,627.211), which is comparable with those inferred for the other European populations (ITA = 21 637.46, IBS = 22,599.718, GBR = 23,427.666). However, we inferred a large SD for the BioSUD population, possibly due to 53 individuals showing a total ROH length ranging from 32,796 kb to 349,267 kb.

Moreover, when comparing the median of the total ROH length of the BioSUD cohort with the ones from European populations, the BioSUD resulted in the lower one (BioSUD = 14,940.7 kb; ITA = 16,892 kb; IBS = 18,337.8 kb; GBR = 19,915.7 kb; FIN = 30,780.85 kb), suggesting being the most heterozygous population among the analyzed ones (Fig. 2b). When extending this comparison to the CDX and YRI populations, only the latter showed a higher heterozygosity rate than the BioSUD cohort (CDX = 33,066.85, YRI = 9,379.32).

Admixture Analysis

We performed an Admixture for K from 2 to 10 for ten random iterations to infer population structure (Fig. S2 - Supplementary Materials). Due to the lower error, cross-validation showed 7 and 8 as the optimal K values (Fig. S3 - Supplementary Materials).

The barplot summarizing the ancestral component proportions for K = 7 is shown in Fig. 2c. The BioSUD samples (Fig. 2c) showed a composition very similar to the Italian populations (Fig. 2c), with the main genetic components being modal in Southern Europeans (yellow in Fig. 2c, Average (A) = 0.764, Median (M) = 0.766, SD = 0.027), Northern Europeans (blue in Fig. 2c, A = 0.100, M = 0.099, SD = 0.024) and South-East Asians (GIH, red in Fig. 2C, A = 0.090, M = 0.091, SD = 0.012). A minor proportion of genetic component is represented by the ones modal in the African populations, with the East African one (LWK) accounting for 1.9% (pink in Fig. 2c, A = 0.019, M = 0.019, SD = 0.009) and the West African one (YRI) for the 1.3% (green in Fig. 2c, A = 0.013, M = 0.012, SD = 0.021).

Although SD values suggested the BioSUD cohort to be highly homogeneous, we could identify three outliers that deviate from the ancestral component proportions of other individuals. Outlier 1 showed 66% of a component modal in YRI (green in Fig. S4 - Supplementary Materials), suggesting a predominantly African ancestry. The questionnaire data confirmed this observation, showing that this individual was born in Nigeria. Outlier 2 exhibited a higher prevalence of ancestries commonly found in the Japanese population compared to the BioSUD cohort (purple in Fig. S4 - Supplementary Materials, 0.18), Northern European (blue in Fig. S4 - Supplementary Materials, 0.16), and South-East Asians (red in Fig. S4 - Supplementary Materials, 0.09). However, considering the additional information on this subject, we could not explain this different profile, which we suspect to be linked to his family history. Outlier 3 had, as expected, the modal component in the Southern Europeans as the major one (yellow in Fig. S4 - Supplementary Materials, 0.52) and additionally showed a higher rate than expected of the components modal in Western Africans and Eastern Africans, accounting respectively for 29.0% (green in Fig. S4 - Supplementary Materials) and 12.7% (pink in Fig. S4 - Supplementary Materials). From the questionnaire data, we hypothesized that this individual had an Italian and an African parent, thus explaining these results.

Descriptive evaluation of the Questionnaire variables

Sociodemographic data and Psychosocial factors

All the 1806 sampled individuals completed the questionnaire. The missingness rate of questions varied from 0–39.6% (A = 11.8%).

A significantly greater number of male participants were included in both the control (1,046 males vs. 462 females) and case groups (278 males vs. 20 females) (Fig. 3a). To mitigate the anticipated overrepresentation of males in the case group, we intentionally oversampled males in the control group. This approach aimed to ensure a comparable gender distribution between the two groups for subsequent analysis.

The overall sample had an average age of 40.69 years (SD = 12.31, range 18–72). The control group had an average age of 40.43 years (SD = 12.66, range 18–72), while the cases had an age of A = 42.10 years (SD = 10.15, range 20–71). The age distribution is summarized in Fig. 3b.

High school was the most frequent level of education among participants (46.1%), followed by a university degree (25.2%), with the remaining participants distributed across middle school (16.0%), post-graduate studies (10.0%), and primary school (1.9%; Fig. 3c). The comparison between the sample data and the 2020 statistics from the Italian National Institute of Statistics (ISTAT) for Apulia reveals disparities in the educational distribution. The control group boasted a higher attendance rate for high school (49.9% vs. 31.9% national average), degree (30.4% vs. 12.4%), and lower middle school rate (5.4% vs. 31.3%) than the Apulian ISTAT average (31.9%). On the contrary, the cases showed a lower rate for high school (31.4%) and degree (3.4%) and a higher middle school completion (55.6%) than the ISTAT data. For employment status, the case group shows a higher proportion of unemployed for over 12 months (37.7% vs. 12.4%) and lower full-time employment (34.8% vs. 58.8%; Fig. 3d).

The results show that participants in the case group were more likely to have experienced adverse events than controls, regardless of differences in group size (Fig. 3e). Controls reported higher rates of bereavement (68.3%) and violent crime victimization (11.3%) if compared with cases (51.2% and 7.3%, respectively). Conversely, serious accidents were considerably higher among cases, with a rate of 15.9% compared to 8.7% among controls. Compared to controls, cases were more likely to report serious illness (2.4% vs. 0.5%) and to have witnessed violent crimes (8.2% vs. 5.9%) and sexual abuse (4.8% vs. 1.1%). Regarding family members' substance use and behaviors (Fig. 3f), results indicated that controls showed a trend in family drug consumption, with 23.9% reporting no use, 56.4% reporting low consumption, 16.7% reporting moderate consumption, and minimal representation in higher categories (2.1% high and 0.9% very high). In contrast, a different pattern emerged among cases, with 6.8% reporting no family substance consumption, 39.6% reporting low consumption, 26.1% reporting average consumption, and a notable presence falling in higher categories (13.0% high and 14.5% very high). Most participants in the control group reported having "Good" or "Very Good" relationships with their families (71.7%), with only a tiny percentage (2.8%) indicating "Poor" or "Very Poor" relationships. In contrast, the case group exhibited a lower proportion of "Good" or "Very Good" family relationships (47.4%) and a significantly higher percentage (21.3%) reporting "Poor" or "Very Poor" quality (Fig. 3g). Similarly, in the control group, a substantial majority reported either "Good" or "Very Good" relationships with peers (76.0%). In comparison, the combined "Poor" and "Very Poor" categories accounted for a relatively small proportion (1.7%). Conversely, the case group demonstrated a lower prevalence of "Good" and "Very Good" relationships with peers (51.2%) and a higher incidence of "Poor" or "Very Poor" categories (10.1%) (Fig. 3h).

Substance use

To assess participants' nicotine use, we inquired about regular smoking, defined as the consumption of at least one unit of tobacco or nicotine-containing products per day. Among controls, 54.7% are non-smokers, 20.1% are former smokers (more than six months before the date of the questionnaire), 3.2% are former smokers (less than six months before the date of the questionnaire), and 22.0% are current smokers. In contrast, the case group exhibits a distinct profile, with 91.3% in the current smoker’s category. (Fig. 3i).

The case group showed a different pattern of alcohol consumption compared to the controls (Fig. 3j). While a smaller percentage of cases reported drinking alcohol overall than controls (70.5% vs. 81.3%), the former who did drink tended to consume alcohol more frequently, notably "4 times a week or more" (25.7% vs. 6.8%).

Regarding cannabis use, the control group exhibited a low prevalence, with only 8.6% reporting 30 or more times of use and a combined 29.6% for less frequent consumption (less than 29 times; Fig. 3k). The majority (61.7%) of controls never used cannabis. In contrast, the case group displayed a markedly different pattern, with a substantial 78.6% reporting 30 or more times of cannabis use and a combined 11.7% for less frequent consumption (less than 29 times) and only 9.7% reporting never using cannabis.

Regarding cocaine consumption, the control group exhibited a very low prevalence, with only 0.3% reporting 30 or more times of use and a combined 2.6% for less frequent consumption (less than 29 times). The majority (97.2%) of controls never used cocaine (Fig. 3l). In contrast, the case group displayed a markedly different pattern, with a substantial 84.0% reporting 30 or more times of cocaine use. A much smaller percentage (4.5%) of cases reported fewer uses and only 9.7% reported abstinence.

Heroin use differed significantly between cases and controls. While nearly all controls (99.9%) reported no heroin use, only 29.1% of cases never used heroin. A substantial proportion of cases (66.02%) reported using heroin 30 times or more, with the remaining 4.9% reporting less than 29 times use (Fig. S5e - Supplementary Materials). A similar pattern emerged for "other substances." Most controls (97.5%) reported no use, compared to 65.1% of cases. Conversely, 15.5% of cases used other substances 30 times or more, while 19.4% reported less frequent consumption (Fig. S5f - Supplementary Materials).

Figure 3: Frequency distributions and descriptive statistics for questionnaire variables, stratified by Controls and Case groups (represented graphically as red and blue, respectively). From left to right: a. Sex; b. Age; c. Education; d. Employment; e. Adverse events; f. Family substance use; g. Family relationships (Quality); h. Peer relationship (Quality); i. Nicotine use; j. Alcohol use; k. Cannabis use; l. Cocaine use.

Here, we present the first analysis of the genetic, psychosocial, sociodemographic, and SUD behavior variability within the BioSUD cohort, which comprises 1,806 individuals.

When evaluating the genomic variation of the BioSUD cohort, PCA shows that the vast majority of the samples fall within the genetic variability of Southern Italian individuals, with a few samples showing genetic profiles similar to other Italian or Western European regions. Notably, only three samples show genetic profiles compatible with substantial ancestry from different continents. Admixture analysis confirms that the BioSUD cohort has an ancestral composition similar to other Italian groups, with a high proportion of ancestry shared with individuals from Spain and, to a lesser extent, other European groups, confirming a shared demographic and evolutionary history. However, almost all the individuals show a substantial proportion of ancestry component, which is modal in Southern East Asian Individuals and is absent in Iberians. Moreover, a low proportion of ancestry modal in Sub-Saharan African groups is observed. These two ancestries reflect the Italian peninsula's complex demographic history, contributing to the high heterogeneity within the European continent. Recent studies have demonstrated that, within the European continent, Italy harbors the highest degree of genetic variation, with a high heterogeneity across different Italian groups [39, 52]. Here, we confirm this heterogeneity by observing that, on average, Italian individuals have the lowest number of ROHs in Europe. Interestingly, the BioSUD cohort, mainly composed of individuals of Apulian origins, shows a lower number of ROHs when compared to other Italians, suggesting that Southern Italians are among the most diverse groups in Europe. However, further and more comprehensive studies are needed to confirm these results. Nevertheless, the genomic analysis has shown that some of the genomic variability present in Italy might not be well captured in GWAS, which primarily involves individuals with Northern European ancestry, thereby limiting the transferability of these data.

The survey data provide insights into the demographic characteristics of the studied population, specifically gender, age, educational background, and occupational status within the control and case groups.

The pronounced male predominance in substance consumption observed in both the control and cases groups aligns with established trends in substance consumption studies: men exhibit higher rates of alcohol consumption, alcohol-related issues, and alcohol use disorder diagnoses, as well as greater use of illicit substances and higher prevalence rates of substance use disorders [54–57].

Educational backgrounds among participants vary significantly. The control group shows higher rates of high school attendance and degree attainment, while the case group exhibits lower rates in both categories. This pattern aligns with the established link between substance addiction and lower educational achievement [55, 58, 59] However, a comparison with 2020 ISTAT statistics for Apulia suggests potential sampling biases, as the control group's educational attainment exceeds that of the ISTAT data, while the cases display lower attainment. Thus, caution is warranted when interpreting these results and generalizing to the broader population of Apulia should be approached carefully. Additionally, this potential bias should be considered when GWAS are performed.

When delving into psychosocial variables, family substance consumption patterns reveal that controls predominantly report low levels of family substance consumption, while cases show more varied patterns. This variation suggests both genetic [60] and environmental [61, 62] influences contribute to substance use disorders. Unlike their counterparts, individuals with substance use disorders experience a more diverse spectrum of relationship quality in both family and peer contexts, with a higher prevalence of perceived poor relationships [63, 64]. These observations underscore the crucial importance of considering social dynamics in both the assessment and treatment of substance use disorders.

In examining substance use, the control group predominantly consists of non-smokers, whereas the case group exhibits a markedly higher prevalence of current smokers. Individuals with SUDs often face complex challenges related to compulsive substance use, and nicotine, being highly addictive, may become intertwined with other substance use patterns [65–67]. Shared risk factors, common neural pathways, or coping mechanisms may contribute to the increased nicotine consumption observed among those with SUDs. Whereas the control group exhibited a pattern of regular, moderate alcohol consumption, the case group demonstrated a more polarized distribution, with a higher prevalence of abstinence (roughly 29% vs. 19%) and a significant proportion engaging in heavy drinking among those who did consume alcohol. The comparative results of abstinence in cases vs. controls could be explained by the supervised environment represented by private and public healthcare facilities where they have been recruited.

Controls mostly abstained from cannabis and cocaine, while cases showed more frequent and intense substance use, especially cannabis (78.6%). Heroin and other substance use were nearly absent among controls (roughly 99.9%), while consumption rates were significantly higher in the case group. Further research is needed to understand the factors influencing these patterns and the complex dynamics of diverse substances used in SUDs [68, 69]. Together, these results highlight the BioSUD cohort as a valuable resource for studying complex behavioral traits associated with SUDs. This cohort has the potential to be used in future GWAS and in studies exploring the relationship between genetic and environmental factors in defining SUD phenotypes.

Data Availability Statement
The datasets generated and analyzed during the current study *will be* available in the European Genome-phenome Archive (EGA) repository, *https://ega-archive.org/*. For more information, contact [email protected]

Contributions

All authors contributed to the manuscript's conception, design, data acquisition, analysis, interpretation, drafting, and revision. They also approved the final version and agreed to be accountable for all aspects of the work.

Specific contributions:

● F.M., M.V.: Conceptualization, methodology, formal analysis, data curation, writing – review & editing, visualization.

● A.T., A.O., C.C.: Data curation.

● M.Q., A.O., M.C., V.A., V.L., S.D., A.L., S.D., D.L.: Data sampling.

● R.M.R., L.D.G., A.D., D.C., F.P.P., M.M.: Investigation, sample processing.

● R.M.R., L.D.G., A.D., F.P.: Writing – original draft preparation.

● F.A., C.R.C.: Writing – review & editing, visualization.

Competing Interests

The authors declare no conflict of interest. The funders had no role in the study's design, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

Ethics Approval

Ethical approval for this study was obtained from the Ethics Committee of the Department of Brindisi (CE 149/19), Taranto (CE 150/19), Bari (CE 0019544), Lecce (CE 47/2020), in compliance with the Declaration of Helsinki. Informed consent was secured from all participants before enrolment, with explicit disclosure of their right to opt out without specifying a reason.

Fundings

We acknowledge financial support under the #NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006) – A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022) (L.D.G. and M.V.).

We acknowledge financial support under the National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.1, Call for tender No. 104 published on 2.2.2022 by the Italian Ministry of University and Research (MUR), funded by the European Union – NextGenerationEU– Project Title Telomere-to-telomere sequencing: the new era of Centromere and neocentromere eVolution (CenVolution) – CUP H53D23003260006 - Grant Assignment Decree No. 1015 adopted on 07/07/2023 by the Italian Ministry of Ministry of University and Research (MUR) (M.V.) and Project Title SUDWAY: Substance Use Disorders through Whole genome, psychological and neuro-endophenotypes AnalYsis CUP H53D23003310006- Grant Assignment Decree No. 1015 adopted on 7 July 2023 by the Italian Ministry of Ministry of University and Research (MUR) (F.M). F.M. was supported by Fondazione con il Sud (2018-PDR-01136).

This work was supported by the Italian Ministry of University and Research (MUR) grant PRIN 2020 (project code 2020J84FAM, CUP H93C20000040001) to F.A.

American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders - (5th ed) - Text Revised. DSM-T TR. APA, 2022..
Patnode, C. D. et al. Screening for Unhealthy Drug Use: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force. JAMA 323, 2310–2328, 10.1001/jama.2019.21381 (2020).
Haar, K. et al. Family UNited: piloting of a new universal UNODC family skills programme to improve child mental health, resilience and parenting skills in Indonesia and Bangladesh. Int. J. Ment. Health Syst. 17, 49, 10.1186/s13033-023-00602-w (2023).
Le Gouvernement, D. U. G.-D. & Luxembourg, D. E. NATIONAL DRUG REPORT 2024. http://sante.public.lu/dam-assets/fr/publications/r/rapport-national-drug-2024/rapport-relis2023-anglais.pdf
Review of management and administration in the United Nations Office on Drugs and. Crime (UNODC). https://www.unodc.org/documents/Advocacy-Section/FactsFig.s-10p_WEB2.pdf
European Drug Report 2024: Trends and Developments. https://www.emcdda.europa.eu/publications/european-drug-report/2024_en
European Drug Report 2023: Trends and Developments. [cited 2023 Jun 10]. Available from: https://www.emcdda.europa.eu/publications/european-drug-report/2023_en
European School Survey Project on Alcohol and Other Drugs. Publications Office of the European Union; 2016. 99 p. https://play.google.com/store/books/details?id=fceQAQAACAAJ
Prom-Wormley, E. C., Ebejer, J., Dick, D. M. & Bowers, M. S. The genetic epidemiology of substance use disorder: A review. Drug Alcohol Depend. 180, 241–259, 10.1016/j.drugalcdep.2017.06.040 (2017).
Gelernter, J. & Polimanti, R. Genetics of substance use disorders in the era of big data. Nat. Rev. Genet. 22, 712–729, 10.1038/s41576-021-00377-1 (2021).
Martin, E., Schoeler, T., Pingault, J.-B. & Barkhuizen, W. Understanding the relationship between loneliness, substance use traits and psychiatric disorders: A genetically informed approach. Psychiatry Res. 325, 115218, 10.1016/j.psychres.2023.115218 (2023).
Johnson, E. C. et al. A large-scale genome-wide association study meta-analysis of cannabis use disorder. Lancet Psychiatry 7, 1032–1045, 10.1016/S2215-0366(20)30339-4 (2020).
Liu, M. et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat. Genet. 51, 237–244, 10.1016/S2215-0366(20)30339-4 (2019).
Zhou, H. et al. GWAS including 82,707 subjects identifies functional coding variant in OPRM1 gene associated with opioid use disorder. medRxiv (2019). Preprint at: http://dx.doi.org/10.1101/19007039
Zhou, H. et al. Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits. Nat. Neurosci. 23, 809–818, doi:10.1038/s41593-020-0643-5 (2020).
Filip, M., Smaga, I. & Przegaliński, E. The role of serotonin in nicotine abuse and addiction. Handbook of Behavioral Neuroscience 31, 829–841 (2020).
Kashem, M. A. et al. Long-term daily access to alcohol alters dopamine-related synthesis and signaling proteins in the rat striatum. Neurochem. Int. 61, 1280–1288, 10.1016/j.neuint.2012.08.013 (2012).
Buck, K. J. & Finn, D. A. Genetic factors in addiction: QTL mapping and candidate gene studies implicate GABAergic genes in alcohol and barbiturate withdrawal in mice. Addiction 96, 139–149, 10.1046/j.1360-0443.2001.96113910.x (2001).
Xiao, C., Zhou, C.-Y., Jiang, J.-H. & Yin, C. Neural circuits and nicotinic acetylcholine receptors mediate the cholinergic regulation of midbrain dopaminergic neurons and nicotine dependence. Acta Pharmacol. Sin. 41, 1–9, 10.1038/s41401-019-0299-4 (2020).
Bechara, A. et al. A Neurobehavioral Approach to Addiction: Implications for the Opioid Epidemic and the Psychology of Addiction. Psychol. Sci. Public Interest 20, 96–127, 10.1177/1529100619860513 (2019).
Hardy, L., Mitchell, C., Seabrooke, T. & Hogarth, L. Drug cue reactivity involves hierarchical instrumental learning: evidence from a biconditional Pavlovian to instrumental transfer task. Psychopharmacology 234, 1977–1984, 10.1007/s00213-017-4605-x (2017).
Chanraud, S. et al. Brain morphometry and cognitive performance in detoxified alcohol-dependents with preserved psychosocial functioning. Neuropsychopharmacology 32, 429–438, 10.1038/sj.npp.1301219 (2007).
Miyake, A. & Friedman, N. P. The Nature and Organization of Individual Differences in Executive Functions: Four General Conclusions. Curr. Dir. Psychol. Sci. 21, 8–14, 10.1177/0963721411429458 (2012).
Day, A. M., Kahler, C. W., Ahern, D. C. & Clark, U. S. Executive Functioning in Alcohol Use Studies: A Brief Review of Findings and Challenges in Assessment. Curr. Drug Abuse Rev. 8, 26–40, 10.2174/1874473708666150416110515 (2015).
Durazzo, T. C., Meyerhoff, D. J. & Nixon, S. J. A comprehensive assessment of neurocognition in middle-aged chronic cigarette smokers. Drug Alcohol Depend. 122, 105–111, 10.1016/j.drugalcdep.2011.09.019 (2012).
Brière, M. et al. Decision-Making Measured by the Iowa Gambling Task in Patients with Alcohol Use Disorders Choosing Harm Reduction versus Relapse Prevention Program. Eur. Addict. Res. 25, 182–190, 10.1159/000499709 (2019).
Balconi, M. & Campanella, S. Advances in Substance and Behavioral Addiction: The Role of Executive Functions. (Springer Nature, 2021).
Bechara, A., Noël, X. & Crone, E. Loss of willpower: Abnormal neural mechanisms of impulse control and decision making in addiction. of implicit cognition and addiction 215–232, 10.4135/9781412976237.N15 (2005).
Hatoum, A. S. et al. The addiction risk factor: A unitary genetic vulnerability characterizes substance use disorders and their associations with common correlates. Neuropsychopharmacology 47, 1739–1745, 10.1038/s41386-021-01209-w (2022).
Brisighelli, F. et al. Patterns of Y-STR variation in Italy. Forensic Sci. Int. Genet. 6, 834–839, 10.1016/j.fsigen.2012.03.003 (2012).
Fiorito, G. et al. The Italian genome reflects the history of Europe and the Mediterranean basin. Eur. J. Hum. Genet. 24, 1056–1062, 10.1038/ejhg.2015.233 (2016).
Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209, 10.1038/s41586-018-0579-z (2018).
Leitsalu, L. et al. Cohort Profile: Estonian Biobank of the Estonian Genome Center, University of Tartu. Int. J. Epidemiol. 44, 1137–1147, 10.1093/ije/dyt268 (2015).
Hood, L. & Price, N. The Age of Scientific Wellness: Why the Future of Medicine Is Personalized, Predictive, Data-Rich, and in Your Hands. (Harvard University Press, 2023).
Cozzoli, D. et al. Genomic and Personalized Medicine Approaches for Substance Use Disorders (SUDs) Looking at Genome-Wide Association Studies. Biomedicines 9, 10.3390/biomedicines9121799 (2021).
International Classification of Diseases, Eleventh Revision (ICD-11), World Health Organization (WHO) 2019/2021 https://icd.who.int/browse11. Licensed under Creative Commons Attribution-NoDerivatives 3.0 IGO license (CC BY-ND 3.0 IGO).
R Development Core Team. The R Reference Manual: Base Package. Network Theory; 2024. Available from https://cran.rstudio.com/doc/manuals/r-devel/R-admin.pdf
Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74, 10.1038/nature15393 (2015).
Raveane, A. et al. Population structure of modern-day Italians reveals patterns of ancient and archaic ancestries in Southern Europe. Sci Adv 5, eaaw3492, 10.1126/sciadv.aaw3492 (2019).
Bergström, A. et al. Insights into human genetic variation and population history from 929 diverse genomes. Science 367, 10.1126/science.aay5012 (2020).
Behar, D. M. et al. The genome-wide structure of the Jewish people. Nature 466, 238–242, 10.1038/nature09103 (2010).
Behar, D. M. et al. No evidence from genome-wide data of a Khazar origin for the Ashkenazi Jews. Hum. Biol. 85, 859–900, 10.3378/027.085.0604 (2013).
Kovacevic, L. et al. Standing at the gateway to Europe--the genetic structure of Western balkan populations based on autosomal and haploid markers. PLoS One 9, e105090, 10.1371/journal.pone.0105090 (2014).
Kushniarevich, A. et al. Genetic Heritage of the Balto-Slavic Speaking Populations: A Synthesis of Autosomal, Mitochondrial and Y-Chromosomal Data. PLoS One 10, e0135820, 10.1371/journal.pone.0135820 (2015).
Li, J. Z. et al. Worldwide human relationships inferred from genome-wide patterns of variation. Science 319, 1100–1104, 10.1126/science.1153717 (2008).
Raghavan, M. et al. Upper Palaeolithic Siberian genome reveals dual ancestry of Native Americans. Nature 505, 87–91, 10.1038/nature12736 (2014).
Tambets, K. et al. Genes reveal traces of common recent demographic history for most of the Uralic-speaking populations. Genome Biol. 19, 139, 10.1186/s13059-018-1522-1 (2018).
Yunusbayev, B. et al. The Caucasus as an asymmetric semipermeable barrier to ancient human migrations. Mol. Biol. Evol. 29, 359–365, 10.1093/molbev/msr221 (2012).
Yunusbayev, B. et al. The genetic legacy of the expansion of Turkic-speaking nomads across Eurasia. PLoS Genet. 11, e1005068, 10.1371/journal.pgen.1005068 (2015).
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7, 10.1186/s13742-015-0047-8 (2015).
Roshyara, N. R. & Scholz, M. fcGENE: a versatile tool for processing and transforming SNP datasets. PLoS One 9, e97589, 10.1371/journal.pone.0097589 (2014).
Raveane, A. et al. Assessing temporal and geographic contacts across the Adriatic Sea through the analysis of genome-wide data from Southern Italy. Genomics 114, 110405 10.1016/j.ygeno.2022.110405 (2022).
Alexander, D. H., Shringarpure, S., Novembre, J. & Lange, K. Admixture 1.3 Software Manual. Los Angeles: UCLA (2015). Available: http://www.vcru.wisc.edu/simonlab/bioinformatics/programs/admixture/admixture-manual.pdf
Brady, K. T. & Randall, C. L. Gender differences in substance use disorders. Psychiatr. Clin. North Am. 22, 241–252, 10.1016/s0193-953x(05)70074-5 (1999).
Grant, B. F. et al. Epidemiology of DSM-5 Drug Use Disorder: Results From the National Epidemiologic Survey on Alcohol and Related Conditions–III. JAMA Psychiatry 73, 39–47, 10.1001/jamapsychiatry.2015.2132 (2016).
Lev-Ran, S., Le Strat, Y., Imtiaz, S., Rehm, J. & Le Foll, B. Gender differences in prevalence of substance use disorders among individuals with lifetime exposure to substances: results from a large representative sample. Am. J. Addict. 22, 7–13, 10.1111/j.1521-0391.2013.00321.x (2013).
Wilsnack, R. W. et al. Gender differences in alcohol consumption and adverse drinking consequences: cross-cultural patterns. Addiction 95, 251–265, 10.1046/j.1360-0443.2000.95225112.x (2000).
Teixidó-Compañó, E. et al. Differences between men and women in substance use: the role of educational level and employment status. Gac. Sanit. 32, 41–47, 10.1016/j.gaceta.2016.12.017 (2018).
Waldron, J. S., Malone, S. M., McGue, M. & Iacono, W. G. A Co-Twin Control Study of the Relationship Between Adolescent Drinking and Adult Outcomes. J. Stud. Alcohol Drugs 79, 635–643, 10.15288/jsad.2018.79.635 (2018).
Palmer, R. H. C. et al. Genetic etiology of the common liability to drug dependence: evidence of common and specific mechanisms for DSM-IV dependence symptoms. Drug Alcohol Depend. 123 Suppl 1, S24–32, 10.1016/j.drugalcdep.2011.12.015 (2012).
Charles, N. E. et al. Altered developmental trajectories for impulsivity and sensation seeking among adolescent substance users. Addict. Behav. 60, 235–241, 10.1016/j.addbeh.2016.04.016 (2016).
Silberg, J., Rutter, M., D’Onofrio, B. & Eaves, L. Genetic and environmental risk factors in adolescent substance use. J. Child Psychol. Psychiatry 44, 664–676, 10.1111/1469-7610.00153 (2003).
Fairbairn, C. E. & Cranford, J. A. A multimethod examination of negative behaviors during couples interactions and problem drinking trajectories. J. Abnorm. Psychol. 125, 805–810, 10.1037/abn0000186 (2016).
Fairbairn, C. E. & Sayette, M. A. A social-attributional analysis of alcohol response. Psychol. Bull. 140, 1361–1382, 10.1037/a0037563 (2014).
Kelly, P. J. et al. Prevalence of smoking and other health risk factors in people attending residential substance abuse treatment. Drug Alcohol Rev. 31, 638–644, 10.1111/j.1465-3362.2012.00465.x (2012).
Lien, L., Bolstad, I. & Bramness, J. G. Smoking among inpatients in treatment for substance use disorders: prevalence and effect on mental health and quality of life. BMC Psychiatry 21, 244, 10.1186/s12888-021-03252-9 (2021).
Mendelsohn, C. P. & Wodak Am, A. Smoking cessation in people with alcohol and other drug problems. Aust. Fam. Physician 45, 569–573 (2016).
García-Pérez, Á., Aonso-Diego, G., Weidberg, S. & Secades-Villa, R. Testing the cannabis gateway hypothesis in a national sample of Spanish adolescents. Addict. Behav. 144, 107751, 10.1016/j.addbeh.2023.107751 (2023).
Williams, J. & van Ours, J. C. Hazardous or not? Cannabis use and early labor market experiences of young men. Health Econ. 29, 1148–1160, 10.1002/hec.4125 (2020).

No competing interests reported.

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You are reading this latest preprint version

The BioSUD Biobank: A genomic resource for Substance Use Disorders in Italy

Status:

Version 1

Abstract

Figures

Introduction

Material and Methods

Recruitment

Sampling

DNA extraction and genotyping

Dataset

Principal Component Analysis (PCA)

ROH estimation

Admixture analysis

Questionnaire

Results

The genetic variation of the BioSUD cohort

Principal Component Analysis

Runs Of Homozygosity

Admixture Analysis

Descriptive evaluation of the Questionnaire variables

Sociodemographic data and Psychosocial factors

Substance use

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

The BioSUD Biobank: A genomic resource for Substance Use Disorders in Italy

Status:

Version 1

Abstract

Figures

Introduction

Material and Methods

Recruitment

Sampling

DNA extraction and genotyping

​​Dataset

Principal Component Analysis (PCA)

ROH estimation

Admixture analysis

Questionnaire

Results

The genetic variation of the BioSUD cohort

Principal Component Analysis

Runs Of Homozygosity

Admixture Analysis

Descriptive evaluation of the Questionnaire variables

Sociodemographic data and Psychosocial factors

Substance use

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

Dataset