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This study investigated the genetic and epigenetic mechanisms underlying the comorbidity of five substance dependence diagnoses (SDs; alcohol, AD; cannabis, CaD; cocaine, CoD; opioid, OD; tobacco, TD). A latent class analysis (LCA) was performed on 22,668 individuals from six cohorts to identify comorbid DSM-IV SD patterns. In subsets of this sample, we tested SD-latent classes with respect to polygenic overlap of psychiatric and psychosocial traits in 7659 individuals of European descent and epigenome-wide changes in 886 individuals of African, European, and Admixed-American descents. The LCA identified four latent classes related to SD comorbidities: AD + TD, CoD + TD, AD + CoD + OD + TD (i.e., polysubstance addiction, PSU), and TD. In the epigenome-wide association analysis, SPATA4 cg02833127 was associated with CoD + TD, AD + TD, and PSU latent classes. AD + TD latent class was also associated with CpG sites located on ARID1B, NOTCH1, SERTAD4, and SIN3B, while additional epigenome-wide significant associations with CoD + TD latent class were observed in ANO6 and MOV10 genes. PSU-latent class was also associated with a differentially methylated region in LDB1. We also observed shared polygenic score (PGS) associations for PSU, AD + TD, and CoD + TD latent classes (i.e., attention-deficit hyperactivity disorder, anxiety, educational attainment, and schizophrenia PGS). In contrast, TD-latent class was exclusively associated with posttraumatic stress disorder-PGS. Other specific associations were observed for PSU-latent class (subjective wellbeing-PGS and neuroticism-PGS) and AD + TD-latent class (bipolar disorder-PGS). In conclusion, we identified shared and unique genetic and epigenetic mechanisms underlying SD comorbidity patterns. These findings highlight the importance of modeling the co-occurrence of SD diagnoses when investigating the molecular basis of addiction-related traits.
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http://dx.doi.org/10.1038/s41380-025-03031-y | DOI Listing |
Brief Bioinform
August 2025
School of Computer Science, Xi'an Polytechnic University, 710048, Xi'an, China.
Cancer, with its inherent heterogeneity, is commonly categorized into distinct subtypes based on unique traits, cellular origins, and molecular markers specific to each type. However, current studies primarily rely on complete multi-omics datasets for predicting cancer subtypes, often overlooking predictive performance in cases where some omics data may be missing and neglecting implicit relationships across multiple layers of omics data integration. This paper introduces Multi-Layer Matrix Factorization (MLMF), a novel approach for cancer subtyping that employs multi-omics data clustering.
View Article and Find Full Text PDFJ Behav Med
September 2025
Department of Psychology, University of Wisconsin-La Crosse, La Crosse, WI, USA.
Latent profile analysis (LPA) is in the finite mixture model analysis family and identifies subgroups by participants' responses to continuous variables (i.e., indicators); participants' probable membership in each subgroup is based on the similarity between the subgroup's prototypical responses and the person's unique responses.
View Article and Find Full Text PDFCancer Epidemiol Biomarkers Prev
September 2025
University of Iowa Holden Comprehensive Cancer Center, Iowa City, IA, United States.
Background: Comorbidities may affect incidence and management of cancers. The burden of comorbidities among AIAN cancer patients and survivors is unknown.
Methods: Using SEER-Medicare, we identified AIAN people aged 66+ years diagnosed with female breast, lung, and colorectal cancers (2000-2019), with at least one year of Medicare coverage prior to diagnosis.
J Addict Nurs
September 2025
Annika Norell, PhD, School of Behavioral, Social and Legal Sciences, Örebro University, Örebro, Sweden; Faculty of Health Sciences, Kristianstad University, Kristianstad, Sweden.
Background: Although there is substantial evidence of the negative impact of caffeine use on sleep quality, few studies focus specifically on adolescents' patterns of use. This study aimed to identify patterns of caffeine use among adolescents and analyze their association with sleep quality.
Method: A cross-sectional study was conducted in southern Sweden including 1,404 adolescents aged 15-17 (56.
J Child Psychol Psychiatry
September 2025
Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Background: Prospective studies of autism family history infants primarily report recurrence and predictors of autism at 3 years. Less is known about ADHD family history infants and later childhood outcomes. We characterise profiles of mid-childhood developmental and behavioural outcomes in infants with a family history of autism and/or ADHD to identify potential support needs and patterns of co-occurrence across domains.
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