"Together, these findings highlight immune pathways, neuronal signaling, and aging-related molecular processes as key components of opioid dependence."
BUFFALO, NY — August 12, 2026 — A new research paper was published in Volume 18 of Aging on July 27, 2026, titled " Transcriptomic aging clock analysis identifies key genes in opioid dependence ."
The study was led by first author Hai Duc Nguyen from the Division of Microbiology, Tulane National Biomedical Research Center, Tulane University . The corresponding author is Woong-Ki Kim, who is affiliated with the Division of Microbiology, Tulane National Biomedical Research Center, Tulane University , and the Department of Microbiology and Immunology at Tulane University School of Medicine .
Opioid dependence is a chronic and relapsing condition associated with substantial health and societal burdens. Chronic opioid exposure can affect neuronal signaling, immune function, metabolism, and other biological processes, yet the molecular mechanisms underlying dependence remain incompletely understood. Increasing evidence also suggests that substance use disorders may be associated with molecular changes related to aging, raising questions about how chronic opioid exposure interacts with age-dependent processes in the brain.
To investigate these relationships, the researchers conducted an in silico study integrating three complementary approaches: RNA sequencing-based transcriptomic profiling, transcriptomic aging-clock modeling, and analysis of previously published genome-wide association studies (GWAS). The transcriptomic analysis used a publicly available brain RNA-seq dataset containing 42 samples—21 healthy controls and 21 individuals with opioid dependence—while the genetic analysis incorporated findings from six independent GWAS.
The initial gene-expression analysis identified 161 differentially expressed genes in individuals with opioid dependence compared with healthy controls, including 147 upregulated and 14 downregulated genes. Network analysis highlighted eight highly connected potential hub genes: CCL2, CD44, THBS1, TIMP1, CD163, IL6, IL1B, and MYC. Many of these genes are involved in immune and inflammatory processes, and pathway analysis identified significant enrichment of TNF signaling, inflammatory responses, and cytokine-related functions.
Several of these findings point toward neuroimmune mechanisms that may contribute to opioid dependence. For example, CCL2, IL6, and IL1B participate in inflammatory signaling and microglial activation, while CD163 and CD44 are associated with immune-cell activation and migration. The convergence of these molecular signals supports an emerging view that chronic opioid exposure involves not only neuronal pathways but also substantial changes in inflammatory and immune processes within the brain.
The researchers next examined whether gene-expression patterns differed according to age. Individuals with opioid dependence were divided into younger and older groups. Expression of FAM174B, a gene implicated in cellular homeostasis, was significantly reduced in older individuals with opioid dependence, while ZNF256, which is involved in transcriptional regulation, was significantly increased in younger individuals compared with healthy controls. These differences suggest that the molecular features associated with opioid dependence may vary across different stages of adulthood.
A major component of the study involved developing a transcriptomic aging clock—an algorithm that estimates age from patterns of gene expression. The researchers trained an elastic net regression model exclusively on the 21 healthy control samples and then applied it across the dataset. The model showed moderate predictive performance, with a correlation of r = 0.686 between predicted and chronological age and a mean absolute error of 5.16 years.
The analysis revealed striking differences in age-prediction residuals. Younger individuals with opioid dependence had positive residuals, averaging 14.6 years, whereas older individuals had negative residuals, averaging −7.2 years. However, the researchers caution that these findings should not be interpreted simply as evidence that opioid dependence accelerates aging in younger people while reversing it in older individuals. Instead, the pattern may reflect nonlinear transcriptomic remodeling associated with opioid dependence, as well as differences in brain-cell composition and limitations in age matching and model calibration.
Two genes, PHYH and LUCAT1, were particularly associated with these age-related transcriptional states. PHYH is involved in lipid metabolism, while LUCAT1 has been linked to inflammatory regulation. Although neither is currently considered an established marker of aging or opioid dependence, their associations in this dataset identify them as candidates for further investigation into how chronic opioid exposure may interact with molecular processes related to aging.
"Together, these findings suggest that opioid dependence is associated with age-dependent and nonlinear transcriptomic remodeling rather than uniform effects on biological aging trajectories."
The study also examined genetic susceptibility to opioid dependence by integrating six previously published GWAS involving a combined 362,176 participants, including 27,024 cases and 334,972 controls. The analysis identified 223 unique SNP associations, with 13 reaching genome-wide significance. The strongest association was rs2366929 within ADGRV1, a gene involved in neurological function, while another highly significant variant occurred in OPRM1, which encodes the μ-opioid receptor—the primary molecular target of opioid drugs. Other genome-wide significant loci included variants associated with CNIH3, RGMA, GPRIN3, GAPDHP15, SRP72P1, and CTCF-DT.
Together, the transcriptomic and genetic analyses point toward an interconnected biological picture involving neuroinflammation, neuronal signaling, synaptic function, and age-related molecular processes. The convergence of inflammatory hub genes and genes associated with age-related transcriptional states also raises the possibility that neuroimmune dysregulation may represent an important biological connection between opioid dependence and molecular changes associated with brain aging.
The researchers emphasize several important limitations. The transcriptomic dataset was relatively small, the healthy control group was older on average than the younger opioid-dependence group, and the brain RNA-seq data came from bulk tissue rather than individual cell types. Potential confounding factors—including opioid exposure history, polysubstance use, smoking, medications, medical and psychiatric conditions, and postmortem interval—were also not uniformly available. In addition, the aging clock was modeled using chronological age rather than independently measured biological age.
Overall, the findings suggest that opioid dependence is associated with distinct immune, neuronal, genetic, and age-related transcriptional signatures. Rather than demonstrating a simple acceleration of biological aging, the study points to a more complex pattern in which opioid dependence may alter age-associated gene expression differently across the lifespan. Future studies using larger and better age-matched cohorts, single-cell or spatial transcriptomics, and more robust measures of biological age could help determine how these molecular changes contribute to addiction susceptibility, progression, and long-term neurological consequences.
Paper DOI: https://doi.org/10.18632/aging.206405
