We performed genome-wide DNA methylation analysis on bisulfite-treated genomic DNA using the Illumina Infinium EPIC BeadChip (850 K) according to the manufacturer's protocol at the Beaumont Health Genomics Core Facility. The detailed methodology has been previously described (25, 26). To ensure the integrity of our microarray experiments, sample placement on arrays was randomized for both cases and controls age and gender (27). The EPIC array encompasses over 850,000 individual CpG sites across the genome at a single-nucleotide resolution, encompassing multiple genes linked to pain. Following scanning with Illumina iScan scanners, intensity data (iDAT) files were imported into Illumina's Genome Studio methylation analysis package program.

The IDAT files containing data were normalized using Genome Studio software's functional normalization method to ascertain Cytosine methylation levels (represented as ß-values) for each CpG site. Before analysis, CpG-probes with missing ß-values were excluded. Differential methylation was evaluated by comparing the ß-values of cytosines at individual CpG loci between NOWS and control groups. To minimize confounding variables, probes targeting sex chromosomes, non-specific regions, and CpG sites within 10 base pairs of SNPs were eliminated (28–30). Additionally, SNPs with a minor allele frequency of ≤0.05 were exclusively considered for subsequent analysis.

Distinguishing methylated CpG sites between individuals with NOWS and controls was determined using predetermined cutoff criteria with a false discovery rate (FDR) threshold of p < 0.05. When multiple CpG sites were found within a gene, the one with the highest area under the curve (AUC) receiver operating characteristic (ROC) ranking and the lowest p-value was selected. The calculation of p-values for methylation disparities between the case and control groups at each locus followed previously established methods (25, 31). Both raw p-values and FDR-adjusted p-values, corrected for multiple testing using the Benjamini-Hochberg method, were computed. Additionally, the AUC for combinations of loci was computed using the “ROCR” package (version 3.5.0) in the ‘R’ programming environment.

Pain-associated genes were identified through a comprehensive analysis using an integrated genetic database. This database combines data from genome-wide association studies (GWAS), gene expression profiles, and curated literature on pain phenotypes (32, 33). The focus was on genes with robust GWAS evidence linking them to pain perception or chronic pain, genes documented in known pain pathways, and those associated with opioid use, particularly in the context of chronic pain management or opioid-induced hyperalgesia. Detailed information on these genes, including their functions, pathways, and relevant SNPs or mutations, was carefully extracted and cross-referenced with multiple databases to ensure accuracy and relevance, particularly in the context of opioid use, addiction, and chronic pain. This comprehensive approach highlights the genetic underpinnings that link opioid use disorder with pain-related pathways and addiction susceptibility. Additionally, to identify potential genes implicated in pain, relevant review articles were consulted and reviewed (34–38). These articles offered valuable insights by synthesizing existing research, with a particular focus on genes involved in pain perception, chronic pain, and opioid use. A total of 897 pain-associated genes were retrieved including genes from the Human Pain Genetics Database (https://diatchenko.lab.mcgill.ca/hpgdb/), a hand-curated resource of genetic associations with human pain phenotypes (Supplementary Table S1). Genes overlapping between this database and published literature were included only once.

Supplementary Table S2 presents details on several key genes and their biological roles associated with pain. The table categorizes these genes based on their involvement in various physiological and pathological processes, including diabetes, telomere maintenance, glucose metabolism, aging, and suicide, among others. Each entry provides information on the gene's function and its relevance to the context of pain.

The heatmap was created with the Complex Heatmap module (v1.6.0) in the R package (v3.2.2). It displays the distribution of methylated CpG sites among pain-associated CpGs, where each site represents a single data point. This analysis aims to visually represent methylation patterns across these regions. Hierarchical clustering of the samples was performed using Ward's method (39). Significantly different methylated CpG sites between NOWS and control groups were determined using FDR P-values ≤0.05. The area under the receiver operating characteristic (AUC-ROC) was calculated based on methylation levels at the most significant CpG loci.

We performed a PPI network analysis on the 111 differently methylated genes implicated in pain perception in NOWS using the STRING database (version 12.0). Parameters set for the analysis included a maximum false discovery rate (FDR) of 0.05 to focus on statistically significant interactions, a minimum interaction strength of 0.01 to include relevant but not necessarily strong interactions, and a minimum count of 2 to be displayed in the network, ensuring that only genes with at least one connection were included. Notably, the MALAT1 gene (lncRNA), identified as a noncoding gene, was excluded from this analysis due to its inability to encode protein and hence participate in PPIs.

The functional implications of these genes were further investigated through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses via the DAVID v6.8 tool. Enrichment analysis was conducted, focusing on biological processes (BP), cellular components (CC), molecular functions (MF), and metabolic pathways. Significant terms and pathways (p-value < 0.05, Benjamini-Hochberg corrected) were identified to elucidate the biological basis of NOWS pain perception. Results from the GO and KEGG analyses were visualized using bubble plots in R using the ggplot2 package. Each bubble represents a GO term or KEGG pathway, with size indicating gene count and color showing significance, providing an intuitive view of the enriched biological themes.

A Venn diagram analysis was performed to identify unique and shared methylation patterns among genes associated with NOWS-associated pain. Gene lists from four independent analyses were compiled and visualized using InteractiVenn (40). This approach enabled the identification of genes unique to each analysis as well as those common across multiple analyses.

The heatmap reveals two distinct clusters of CpGs, (i.e., NOWS and control) utilizing CpG methylation markers associated with pain sensation. One cluster is characteristic of NOWS patients, while the other corresponds to the control group. This strong observation provides significant backing to the idea that these methylation markers are dependable indicators for discriminating between individuals with NOWS and those without. These results validate the precision and effectiveness of the methylation markers in distinguishing between the two patient groups. Supplementary Figures S1-S4 illustrate four distinct analysis combinations.

The Protein-Protein Interaction (PPI) network analysis, which incorporated 110 nodes after excluding the noncoding MALAT1 gene, revealed significant connectivity among proteins implicated in pain perception in NOWS. The network consisted of 350 edges, which is notably higher than the expected number of edges (154), suggesting a non-random pattern of protein interactions. The average node degree was calculated to be 6.36, indicating a moderate level of interaction per protein, while the average local clustering coefficient was 0.402, reflecting the network's tendency to form clusters. Remarkably, the PPI enrichment p-value was less than 1.0e−16, indicating highly significant enrichment beyond chance. This suggests that the differentially methylated genes in NOWS are interconnected and likely play a substantial role in pain perception. Visual analysis of the PPI network revealed several highly connected nodes that may be key regulators in NOWS pathophysiology (Figure 1).

The GO analysis identified significant terms across Biological Process (BP) (Figure 2), Cellular Component (CC) (Figure 3), and Molecular Function (MF) (Figure 4), that provide insights into the molecular basis of pain perception in NOWS. Key Biological Processes highlighted include the positive regulation of cytosolic calcium ion concentration, calcium ion import across the plasma membrane, and vasodilation, which suggest alterations in ion regulation and vascular function may play a critical role in NOWS-associated pain. Protein phosphorylation and signal transduction were also prominent, indicative of complex signaling pathways involved in pain response. Terms related to cardiac muscle function and nerve development were notably enriched, reflecting the potential influence of these genes on the neurophysiological adaptations in NOWS.

In Cellular Components a significant enrichment in genes associated with the plasma membrane points to the importance of membrane-bound proteins and receptors in nociception. Other notable components included axons, neuronal cell bodies, and various synapse structures, such as glutamatergic synapses, suggesting a focus on neuron-specific structures and interactions. Significant Molecular Functions implicated in the data include scaffold protein binding and calmodulin binding, which may influence intracellular signaling pathways related to pain. The activity of voltage-gated calcium channels and protein kinase activities were also highlighted, suggesting potential targets for modulating neuronal excitability and signal transduction in pain pathways specific to NOWS.

The GO terms identified suggest that the differently methylated genes in NOWS are significantly involved in neural signaling and development, membrane dynamics, and intracellular signaling mechanisms These biological themes provide a molecular framework that may explain the altered perception of pain in NOWS and could help in guiding further research into targeted therapies for pain management in affected neonates (Supplementary Tables S7–S9).

KEGG pathway analysis of the 111 differently methylated genes identified their involvement in 48 pathways (FDR p-value <0.05), with a primary focus on neuronal function and substance use—both highly relevant to pain (Supplementary Table S10). Notably, the cAMP and MAPK signaling pathways, which exhibited high gene counts, play crucial roles in cellular responses to external stimuli and pain modulation. The neuroactive ligand-receptor interaction pathway, also enriched with a substantial number of genes, suggests alterations in neurotransmitter dynamics that may influence pain perception. Additionally, the enrichment of multiple disease-related pathways, including cancer and various infections, underscores the broader impact of epigenetic modifications on cellular stress and immune responses, which may have implications for pain regulation and opioid withdrawal (Figure 5).

The Venn diagram analysis revealed distinct and overlapping methylation patterns, illustrating the complex epigenetic landscape of NOWS-associated pain (Figure 6). Each analysis identified a unique set of differentially methylated genes, while several genes exhibited overlapping methylation changes, suggesting their involvement in shared biological pathways. Notably, the PLCE1 gene was consistently methylated across all analyses, highlighting it as a core epigenetic marker for NOWS-related pain. These findings reveal key regulatory networks with therapeutic potential. See Supplementary Table S11 for the full gene list.

We identified ten dysregulated autophagy-related genes—BDNF, LMX1B, ESR2, ULK4, ATG5, BECN1, MAPK3, PIK3C3, CTSS, and ULK1—that affect neuronal development, pain sensitivity, and long-term health. BDNF is essential for neuronal survival and plasticity (43). LMX1B and ESR2 support serotonergic neuron development and pain modulation. Dysregulation of LMX1B increases pain sensitivity and mood disorder risk, while ESR2 affects pain thresholds and mood via estrogen signaling, underscoring their role in the complex interplay of pain and mood regulation (44). ULK4 and ULK1 are critical enzymes that initiate autophagy, dysregulation of ULK1 or ULK4 impairs autophagy, causing damaged proteins to accumulate in neurons. This can lead to neurodegeneration, increased cellular stress, inflammation, and heightened pain sensitivity, raising the risk of chronic pain (45). ATG5 is vital for autophagosome formation; abnormal expression can impair autophagy, causing neuroinflammation and chronic pain (46). BECN1 regulates autophagy initiation; dysregulation may result in neuron damage and an increased risk of neurodegenerative diseases (47). MAPK3, part of the MAPK/ERK pathway, affects pain perception and neural plasticity, impacting cognitive function (48). PIK3C3 is involved in autophagy and endocytic trafficking; dysfunction may lead to neuroinflammation and chronic pain (49). CTSS, linked to neuropathic pain, may increase neuroinflammation and affect immune function (50).

Dysregulation of cytokine genes, such as IL23R, can alter pain perception through immune modulation (51); while NR3C1, encoding the glucocorticoid receptor, affects pain pathways and inflammation, with its dysregulation potentially leading to inflammatory pain (52). Reduced NR3C1 expression is associated with chronic cocaine use and linked to anxiety disorders and depression (53); MAPK3 (ERK1) is key in the MAPK pathway, facilitating pain signal transmission and modulating the nociceptive pathway, crucial for pain perception regulation (48); TNF and IL-1β, key pro-inflammatory cytokines, play crucial roles in glial cell function, aging, obesity, depression, and pain modulation by heightening oxidative stress, exacerbating neuroinflammation, and increasing pain perception (54); NGF promotes neuropathic pain through nerve sensitization and sprouting (55); IL-18 and its receptor IL18R1 may contribute to pain signaling, diverging from their traditional roles in the immune system (56); CXCL8 promotes inflammatory pain through neutrophil recruitment (57); BDNF, a neurotrophin, is crucial in numerous pain conditions, especially neuropathic pain (58), and is linked to obesity, type 2 diabetes mellitus, and other facets of metabolic syndrome (59); TRPV4, a cation channel, plays a role in nociception and pain sensation (60); The lncRNA MALAT1 is significantly upregulated in the spinal cord and microglia, making it a key player in pain regulation (61), Its modulation of miR-129-5p and HMGB1 further highlights its role in neuropathic pain mechanisms (62). Estrogen receptors ERα and ERβ, encoded by ESR1 and ESR2, are essential for pain modulation and metabolic functions, including glucose metabolism and insulin sensitivity with their dysregulation affecting both pain sensitivity and overall metabolic processes (63). Variants in ESR1 and ESR2 genes affect menopause timing and symptom severity, with ESR1 influencing the onset and ESR2 affecting symptoms like hot flashes and bone density (64). In women with opioid addiction, these variants are linked to irregular menstruation, early menopause, worsened symptoms, and increased anxiety, depression, and chronic pain (65, 66); CAMK4, a kinase in calcium signaling, is linked to pain processing and synaptic plasticity (67); OSM, a cytokine, contributes to inflammatory and neuropathic pain (68); while TGFBR2, a receptor for TGF-β, is involved in pain-related cellular processes (69). Also, RUNX1, a transcription factor, affects sensory neuron development and pain processing (70).

Chronic pain frequently leads to opioid and alcohol use, disrupting brain chemistry and causing dependence (71). We identified 22 dysregulated pain-related genes in infants exposed to opioids in utero, affecting neurodevelopment, stress response, and reward pathways, thereby increasing the likelihood of addiction and opioid use later in life. SCN8A affects neuronal excitability, increasing addiction risk (72), while BDNF influences dopamine signaling, with lower levels linked to vulnerability (73). Changes in OPRM1 may alter opioid sensitivity (74), and variants in ANKK1/DRD2 disrupt dopamine signaling, increasing susceptibility to addiction (75). NPY impacts stress responses (76), and MAPK dysregulation affects synaptic plasticity (77). Variants in NR3C1, calcium channels (CACNA1C, CACNA1H, CACNA2D1), and GABAergic genes (ABRG2, GRIA1) further influence addiction pathways. PRKAR1B, NRXN3, SHANK3, and immune genes (TGFBR2, IL1B, TNF) contribute to addiction risk, with autophagy-related genes (BECN1, ULK1, ULK4) may contribute to neurodegeneration, as previously detailed (78–80).

Chronic pain can lead to depression, a major risk factor for suicidality (81). We found multiple deregulated genes involved in pain, such as TNF, IL1B, and IL6ST (82–84), were identified as being associated with both depression and suicide. Gene variations affecting inflammation play a key role in depression, linking chronic pain, mood disorders, and suicide risk. Suicide is also reported in conditions like major depressive disorder, Alzheimer's disease, and Autism Spectrum Disorder. In our study, we identified several key genes associated with suicidal behavior, such as BDNF, KCNJ2, NOS3, CACNA1C, PCSK5, GRIA1, ESR1, ANKK1, DLG2, GABRG2, IGSF9B, PRDM16, and NR3C1. Variations in these genes may contribute to the complex interaction of biological, psychological, and environmental factors that may increase suicide risk in individuals in later life (83, 85–88).

Telomere maintenance genes, essential for preserving telomere length and integrity, have emerged as potential modulators of pain sensitivity (89). As telomeres shorten with age and stress, they are linked to cellular aging, and increased inflammation, and can influence pain perception (90). The dysregulated genes identified in this study, including NOS3, CFTR, ESR1, MAPK3, MTA1, ESR2, PRDM16, IKBKAP, RUNX2, REST, DLG2, PIK3C3, SHMT1, and PLCE1, may impact pain perception by affecting inflammation, oxidative stress, and neuronal excitability pathways.

“Pain can lead to impulsive behavior by affecting decision-making and self-control (91). Those experiencing pain may engage in risky activities, such as overusing opioids, which can result in addiction (1). Pain also amplifies emotional distress and weakens self-regulation, further increasing impulsivity (92). We identified five dysregulated genes associated with impulsivity: BDNF, MAPK10, NRG1, NRXN3, and PRKG1. BDNF, a key biomarker for impulsivity, regulates impulse control by shaping neural circuits for decision-making (93). MAPK10 influences impulsivity through stress pathways and brain regions controlling behavior. NRG1 affects synaptic networks tied to impulse control (94), while NRXN3 variants alter synaptic efficacy, contributing to impulsivity (95). PRKG1 disrupts synaptic plasticity and neurotransmitter release, impacting impulse control (96).

Glucose metabolism is crucial for physiological functions, including pain perception, possibly due to glucose level fluctuations (97, 98). Glucose metabolism and pain are bidirectional; elevated glucose levels can reduce pain tolerance, as seen when glucose infusion lowers pain thresholds (98). Diabetic patients often experience heightened pain sensitivity (hyperalgesia), linking altered glucose metabolism to pain perception (98). Conversely, acute severe pain decreases insulin sensitivity by affecting nonoxidative glucose metabolism, indicating that pain influences glucose metabolism (99). The hormonal response to pain, involving stress hormones, underscores the importance of pain management in maintaining metabolic balance (99). The study identified genes like NR3C1, CACNA1C, CACNA1H, ESR1, CFTR, KLF11, EIF2AK3, NOTCH3, GJA1, and STX1A as linked to glucose metabolism and pain sensitivity. NR3C1 mediates glucocorticoids’ anti-inflammatory and analgesic effects, affecting glucose metabolism and pain (100). CACNA1C and CACNA1H, which encode voltage-gated calcium channels, influence neurotransmitter release and pain sensitivity through glucose balance disruption (101). CFTR, linked to cystic fibrosis, causes metabolic irregularities that impact pain (102). KLF11 regulates glucose metabolism and insulin, with variations affecting pain sensitivity and diabetes (103). EIF2AK3, involved in ER stress and glucose metabolism, is associated with chronic pain (104). NOTCH3, a cell signaling receptor, may modulate pain through interactions with metabolic regulators (105). GJA1, a gap junction protein, influences glucose metabolism and pain signaling. STX1A, important for neurotransmitter release, affects pain sensitivity and glucose metabolism (106).

Obesity worsens chronic pain by increasing inflammation and aggravating conditions like rheumatoid arthritis and fibromyalgia, while also raising the risk of metabolic syndrome, heart issues, diabetes, and cancer (107). This bidirectional relationship fuels a cycle where obesity heightens pain and reduces physical function, while chronic pain limits activity, contributing to weight gain and further exacerbating both conditions (108). Our study identifies that dysregulation in genes like NOS3, TNF, BDNF, ESR1, ESR2, LMX1B, ATP1A2, OPRM1, NPY, THRB, NRXN3, SPARC, and MYT1l significantly impacts the interplay between obesity and chronic pain. Earlier discussions covered NOS3, TNF, BDNF, ESR1, and ESR2. LMX1B plays a key role in obesity by regulating adipogenesis and energy metabolism (109). LMX1B mutations linked to nail-patella syndrome (NPS) may also contribute to chronic pain, suggesting heightened pain sensitivity (110); ATP1A2 helps regulate metabolism by maintaining ion gradients across cell membranes and is linked to pain disorders such as neuropathic pain and fibromyalgia. The OPRM1 gene, linked to the mu-opioid receptor, affects obesity and pain, especially in NOWS infants, with prolonged opioid use disrupting appetite and metabolism, leading to weight gain (111); NPY regulates appetite and energy balance, promoting feeding behavior and fat storage, thereby contributing to obesity development; THRB regulates metabolism and energy expenditure through thyroid hormones, affecting metabolic rate and potentially causing obesity if dysregulated. It also influences pain sensitivity and inflammation; NRXN3 affects obesity traits like BMI and adiposity through appetite-regulating neural circuits and is linked to pain sensitivity (112). SPARC regulates tissue remodeling, impacting obesity-related processes and pain modulation. MYT1l, a transcription factor important for neural development, is associated with intellectual disability, autism, and obesity (113).

Circadian rhythms, which regulate body processes over a 24-hour cycle, significantly affect pain perception and management (114). Disrupted rhythms can worsen pain and alter opioid processing, while pain and opioids can also impact circadian rhythms (115, 116). Disruptions affect factors like endogenous opioid levels, sleep, gene expression, and hormonal regulation. We identified 12 genes linking circadian rhythms to pain regulation: ESR1, CACNA1C, KLF11, IL1B, ABCA1, CAMK4, OSM, SHMT1, ARNTL, DOCK4, GNA11, and CRIP2. ESR1 affects females’ circadian systems via estrogen levels. CACNA1C modulates calcium influx, potentially disrupting rhythms. KLF11 influences glucose metabolism, impacting circadian patterns. IL1B's proinflammatory effects affect the SCN, disrupting rhythms. ABCA1 affects lipid signaling, possibly disrupting rhythms (117). CAMK4 synchronizes peripheral clocks. OSM modulates rhythms through inflammatory pathways (68). SHMT1 impacts metabolic processes. ARNTL, the core clock gene, regulates other clock genes, and mutations cause disorders. DOCK4, associated with neuronal development, may regulate rhythms. GNA11 influences light signaling pathways. CRIP2 affects rhythmic gene expression in the heart, potentially disrupting circadian rhythms (118).

Minerals and electrolytes are essential for nerve function and pain regulation, governing nerve signaling and muscle contractions, while dysregulated metal-associated genes can disrupt DNA methylation and exacerbate chronic pain conditions (119). Calcium ions, crucial for neurotransmitter release and signaling, are disrupted by dysregulated genes such as CACNA1C and CACNA1H, heightening pain sensitivity and altering pain thresholds (120). Potassium channels (e.g., KCNJ2, KCNQ5) regulate neuronal excitability by maintaining membrane potential (121), while sodium channels (e.g., SCN1A, SCN8A) are vital for action potential transmission and pain signaling (122). Disruptions in these channels due to genetic variations or prenatal opioid exposure can lead to abnormal pain signaling. Selenium (PCSK5, BDNF) aids in antioxidant defense (123) and neurotransmitter regulation, potentially reducing chronic pain risk and neurodevelopmental issues from opioid withdrawal. Lithium (BDNF) influences neurogenesis and pain regulation by affecting BDNF levels, impacting the nervous system's adaptation to pain in opioid-exposed infants (124). ATP1A2 encodes a Na+/K + ATPase subunit crucial for ion balance, and disruptions can impact pain processing and heighten the risk of pain or neurological issues such as migraine later in life (125).

Dysregulated transporters like ABCA1, SLC12A5, and ABCC7 (CFTR), critically affect infants exposed to opioids in utero by altering neurotransmitter and ion movement across cell membranes (9). ABCA1 affects lipid metabolism and cholesterol transport. Altered ABCA1 can disrupt lipid balance, increasing inflammation and pain sensitivity, and potentially impacting nervous system development and long-term pain processing (126). SLC12A5 encodes the K + -Cl- cotransporter KCC2, essential for maintaining chloride homeostasis in neurons, which is critical for proper synaptic inhibition and neuronal excitability. Impaired SLC12A5 function can lead to altered pain perception and increased sensitivity, effects that may be aggravated in individuals with a history of opioid exposure (127). ABCC7 (CFTR) regulates chloride ion transport across epithelial membranes, impacting tissue homeostasis and inflammation. While primarily associated with cystic fibrosis, CFTR abnormalities can indirectly influence pain pathways by promoting chronic inflammation and epithelial dysfunction, leading to altered pain sensitivity and management (128).

Our study identified 48 significant canonical pathways (FDR < 0.05). We highlight key findings relevant to pain and infants born after prenatal opioid exposure.

The cAMP signaling pathway is crucial for pain perception and transmission in the nervous system. Activating the cAMP pathway in the central nervous system induces hyperalgesia, whereas inhibiting this pathway alleviates hyperalgesia in inflammatory, non-inflammatory, and neuropathic pain models (129). There were 14 genes identified in this pathway.

The MAPK pathway regulates diverse cellular functions and is crucial for transmitting pain signals from the injury site to the brain, where they are interpreted as pain (48). Thirteen differentially methylated genes have been identified in this pathway.

The AGE-RAGE pathway is critical in diabetic complications, including pain (130). Advanced Glycation End-products (AGEs) accumulate in tissues due to hyperglycemia, activating RAGE and initiating signaling cascades (131). In diabetic pain, this pathway contributes to neuroinflammation, oxidative stress, and nerve damage, which are key factors in diabetic neuropathy. RAGE activation leads to the production of pro-inflammatory cytokines, chemokines, and reactive oxygen species (ROS), exacerbating neuroinflammation and contributing to nerve dysfunction and pain hypersensitivity (132). Eight genes are involved in this pathway. Neurodegeneration pathways in NOWS, like oxidative stress, inflammation, and apoptosis, may alter long-term pain sensitivity and modulation (133). Infants with NOWS might experience increased pain sensitivity or altered pain responses, potentially persisting into childhood and adulthood. Calcium signaling and Alzheimer's disease-related pathways, essential for neural function, are disrupted in NOWS (134, 135). These disruptions, combined with epigenetic changes, may contribute to heightened pain sensitivity and neurological dysfunction in affected infants. The PI3K-Akt pathway, vital for cell survival and growth, significantly influences pain processes. It can drive both adaptive responses that manage pain and maladaptive processes that worsen chronic pain (136). The enrichment of oxytocin signaling and circadian entrainment pathways in NOWS affects both pain perception and broader physiological systems in neonates, necessitating comprehensive care strategies (137, 138).