Evaluation of circulating lymphocyte subsets in children with attention-deficit hyperactivity disorder

Introduction

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental condition of childhood, characterized by developmentally inappropriate inattention, hyperactivity, and impulsivity that impair academic, social, and family functioning (1). Its worldwide prevalence is estimated at approximately 5.3% in children and adolescents, with rates as high as 12.4% reported in school-age populations in Turkey (2,3). Symptoms typically emerge before age 12 and persist into adolescence and adulthood in up to two-thirds of cases. Genetic factors account for roughly 70–80% of the variance in ADHD risk, while environmental contributors—such as micronutrient status, early feeding practices, and exposure to neurotoxicants—play important modulatory roles (4,5). The core neurobiological underpinnings involve dysregulation of catecholaminergic neurotransmission in prefrontal and striatal circuits, although the precise molecular and cellular mechanisms remain incompletely understood (6,7).

In recent years, accumulating evidence has implicated neuroinflammation and broader immune-system dysregulation in ADHD pathophysiology. Epidemiological studies link prenatal and early-life inflammatory exposures to heightened ADHD risk, and comorbidity with atopic and autoimmune disorders is elevated in ADHD cohorts (8,9). Biochemical investigations have revealed aberrant levels of pro- and anti-inflammatory cytokines, oxidative-stress markers, and autoantibodies (e.g., anti-Purkinje, anti-dopamine-transporter) in subsets of patients. Such findings have spurred interest in peripheral immune cell profiles—readily accessible via blood—as potential windows into central neuroinflammatory processes (10,11).

Unlike cytokine assays—which require specialized reagents and laboratory infrastructure—lymphocyte immunophenotyping via flow cytometry offers a rapid, cost-effective, and reproducible method to capture systemic immunoregulatory states (12,13). Alterations in Treg frequency may reflect central neuroimmune modulation (e.g., delayed myelination, microglial activation) that contributes to attentional and executive-function deficits (14). Moreover, peripheral lymphocyte profiles can be longitudinally tracked to assess treatment response or developmental course.

Among peripheral immune cells, lymphocyte subpopulations—and particularly regulatory T cells (Tregs)—have emerged as promising candidate biomarkers for ADHD activity. Tregs (phenotypically CD3⁺CD4⁺CD25⁺FoxP3⁺) maintain immune homeostasis by suppressing excessive inflammatory responses (14). In a case-control flow-cytometry study of 49 drug-naïve children with combined-type ADHD versus 35 age- and gender-matched healthy controls (HCs), peripheral Treg levels were significantly elevated in the ADHD group. In logistic-regression modeling—including age, attention and hyperactivity indices, and Treg percentage—elevated Treg levels were independently associated with increased odds of ADHD, and the full model correctly classified 83.3% of cases (14). This suggests that Treg quantification may have clinical utility both for supporting diagnosis and for monitoring immune-related activity in ADHD.

In this study, the peripheral blood lymphocyte profiles of medication-naïve children with ADHD were analyzed, aiming to elucidate distinct immunophenotypic characteristics and evaluate the diagnostic potential of these immune markers for ADHD. Through comprehensive immunophenotyping of 42 newly diagnosed ADHD patients and 45 age-matched HCs, our investigation reveals significant alterations in lymphocyte subset distributions among ADHD children, including elevated CD8⁺ T-cell proportions/counts, increased regulatory T-cell frequencies, and disrupted naive/memory T-cell equilibrium. Furthermore, diagnostic models integrating hematological parameters and immunophenotypic markers demonstrate high discriminatory accuracy. These findings not only offer novel insights into the immunopathological mechanisms underlying ADHD but also lay the groundwork for future development of immune-based biomarkers and therapeutic strategies tailored to ADHD. We present this article in accordance with the STARD reporting checklist (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-362/rc).

Methods

Subjects

A total of 42 drug-naïve children and adolescents, aged 4 to 16 years, newly diagnosed with attention-deficit/hyperactivity disorder (ADHD) according to DSM-5 criteria, were recruited from the department of Child and Adolescent Psychiatry in both the Third and The Fifth People’s Hospital of Ganzhou (15). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by The Fifth People’s Hospital of Ganzhou Ethics Committee Board (No. GZWY-EC-2025006). Written informed consent was obtained from the parents or legal guardians of all participants before any study procedures were initiated. None of the participants had received prior pharmacological treatment. Symptom severity was assessed using the Conners’ Teacher Rating Scale (CTRS) and the Turgay DSM-IV-Based Child and Adolescent Behavior Disorders Screening and Rating Scale (T-DSM-IV-S). Each patient also underwent a structured psychiatric interview. Specifically, the Schedule for Affective Disorders and Schizophrenia for School-Aged Children—Present and Lifetime Version (K-SADS-PL) was used to screen for any comorbid DSM-IV axis I psychiatric conditions. A HC group of 45 subjects, matched to the ADHD group on age and sociodemographic characteristics, was recruited. Control participants had no history of psychiatric disorders or significant medical conditions. Potential participants (in both ADHD and control groups) were excluded if they met any of the following criteria (16-18):

• History of seizure disorder, intellectual disability, autism spectrum disorder (ASD), or other organic brain pathology.
• Current or past diagnosis of psychotic disorder, conduct disorder, or any DSM-IV axis I psychiatric disorder (in addition to ADHD for the patient group).
• Cardiovascular risk factors (such as hypertension or hyperlipidemia) or any acute or chronic systemic illness.
• Use of any medication within the month preceding enrollment, or active tobacco use.
• Abnormal complete blood count (CBC) results indicating hematologic disorders (e.g., anemia or polycythemia; leukopenia or leukocytosis [including neutropenia, neutrophilia, lymphopenia, or lymphocytosis]; or thrombocytopenia or thrombocytosis).

The control group consisted of 45 healthy individuals with no current or prior psychiatric or significant medical history and matched to the ADHD cohort in terms of key sociodemographic variables. Subjects were excluded if they had a diagnosis of epilepsy, intellectual disability, ASD, psychotic disorders, conduct disorder, organic brain pathology, hypertension, hypercholesterolemia, or any acute/chronic physical illness. Further exclusion criteria included recent medication use (within the past month), tobacco use, anemia, polycythemia, lymphopenia, lymphocytosis, neutropenia, neutrophilia, thrombocytopenia, or thrombocytosis, as determined via CBC analysis (Table 1).

Sample size: the sample size was estimated based on the prior reported studies (14,19).

Clinical and data collection

A semi-structured clinical form was utilized to document sociodemographic (such as age, sex, family history, and education) and relevant clinical history. Patient medical records were reviewed to corroborate the reported history and to obtain additional information regarding birth circumstances, developmental milestones, and previous hospitalizations.

Laboratory analysis

Venous blood samples were drawn from all participants into K2EDTA anticoagulant tubes (BD Biosciences, USA) and processed within one hour. CBC analyses were performed using an automated hematology analyzer (Sysmax XN-9000) following the manufacturer’s protocol. The CBC provided absolute counts of neutrophils, lymphocytes, monocytes, and platelets for each sample.

Reagents for antibody staining

Fluorochrome-conjugated monoclonal antibodies against human lymphocyte surface markers were obtained from commercial suppliers. A BD Biosciences (San Jose, CA, USA) Multitest™ 6-color TBNK reagent (Cat. 662995) was used to enumerate major lymphocyte subsets (CD3⁺ T cells, CD4⁺ T cells, CD8⁺ T cells, CD19⁺ B cells, CD56⁺ NK cells) and CD4/CD8 subpopulations by providing both percentages and absolute counts. Additional BD Biosciences antibodies included anti-CD3, anti-CD4, anti-CD8, anti-CD25, anti-CD28, and anti-CD45RA (various catalog numbers). PE-conjugated anti-CD127 and APC-conjugated anti-CD45RO antibodies were purchased from Jiangxi Celgene Biotechnology (China) for identifying regulatory and memory T-cell subsets. Red blood cells were lysed using BD FACSLysing Solution (BD Biosciences) according to the manufacturer’s instructions. All antibodies were titrated in pilot experiments, and staining was performed on EDTA-K2 anticoagulated whole blood.

Flow cytometry analysis

For each sample, aliquots of whole blood were incubated with the antibody panel at 4 ℃ for 15–30 minutes in the dark. Following staining, erythrocytes were eliminated by adding BD FACS Lysing Solution, incubating for 10 minutes, and washing cells with phosphate-buffered saline. Stained leukocytes were then analyzed on a BD FACSCanto™ I flow cytometer using BD FACSDiva software (version 8.02) and FlowJo sofeware (Version: V10). Lymphocytes were gated using CD45 fluorescence in combination with side scatter (SSC) to improve lymphocyte purity. Within the lymphocyte gate, T cells were identified as CD3⁺ cells, with CD4⁺ helper and CD8⁺ cytotoxic subsets delineated by CD4 and CD8 markers. B cells (CD19⁺) and NK cells (CD3–CD56⁺) were also quantified. Regulatory T cells were defined as the CD4⁺CD25⁺CD127 low population. Naïve CD4⁺ T cells were gated as CD45RA⁺CD4⁺CD3⁺, and effector/memory CD4⁺ T cells as CD45RO⁺CD4⁺CD3⁺. The BD Multitest TBNK reagent and Trucount tubes were used to calculate absolute cell counts and percentages of each subset. At least 10,000 lymphocyte events were collected per sample to ensure statistical reliability.

Statistical analysis

All statistical analyses were conducted using GraphPad Prism (v10.0) and IBM SPSS Statistics (v20.0). Data distribution was assessed with the Kolmogorov-Smirnov test. Normally distributed continuous variables are presented as mean ± standard deviation (SD); non-normal data are expressed as median (interquartile range). Two-group comparisons (ADHD vs. control) employed Student’s t-test for parametric data or the Mann-Whitney U test for nonparametric data. Categorical variables were compared by the chi-square test. Correlations between lymphocyte subsets and ADHD severity scores were evaluated by Pearson’s correlation (for normally distributed variables) or Spearman’s rank correlation (for non-normal distributions). A two-sided P value <0.05 was considered statistically significant. Receiver operating characteristic (ROC) curve analysis was performed to assess the discriminatory power of immune biomarkers for ADHD. The area under the ROC curve (AUC) and corresponding sensitivity and specificity at the optimal cutoff were calculated to identify markers with potential diagnostic value.

Results

Participant demographics

A total of 42 patients diagnosed with ADHD (mean age 9.60 years, range 5–14 years; 39 male, 3 female; mean body mass index (BMI) 17.08 kg/m²) and 45 HC children (mean age 9.95 years, range 4–15 years; 37 male, 8 female; mean BMI 16.65 kg/m²) were enrolled. No significant differences in sex distribution, age, or BMI were observed between the ADHD and HC groups (Table 1). Participants’ sociodemographic characteristics are summarized in Table 1.

Hematological parameters alterations

CBC analysis revealed that white blood cell (WBC) count, red blood cell (RBC) count, platelet (PLT) count, hemoglobin concentration, and lymphocyte count did not differ significantly between ADHD and control groups (Figure 1A-1H, Table 2). In contrast, ADHD patients exhibited a modestly elevated neutrophil count. Conversely, monocyte count and the neutrophil-to-lymphocyte ratio (NLR) were significantly lower in the ADHD group than in controls (Figure 1A-1H, Table 2). Next, ROC analysis was performed on neutrophil count, monocyte count, and NLR. Monocyte count and NLR AUC above 0.60 (Figure 1I-1K), indicating moderate discriminative power. These results are consistent with Önder et al. (19), who reported NLR as a predictive biomarker for ADHD. Finally, a logistic regression model combining neutrophil count, monocyte count, and NLR achieved an AUC of 0.8487 (P<0.001) (Figure 1L-1M).

Figure 1 Comparative analysis of hematological parameters between the ADHD and HC groups. (A) WBC counts, (B) absolute neutrophil counts, (C) lymphocyte counts, (D) monocyte counts, (E) NLR, (F) RBC counts, (G) hemoglobin concentrations, and (H) PLT counts. ROC curves for (I) neutrophil counts, (J) monocyte counts, and (K) NLR. (L) ROC analysis and (M) parameter covariance plots for logistic regression modeling. Data are expressed as mean ± standard deviation, with individual data points representing participants (technical duplicates averaged). Statistical comparisons employed one-way analysis of variance with Tukey’s post-hoc tests. *, P<0.05; ***, P<0.001; ns, non-significant. ADHD, attention-deficit/hyperactivity disorder; AUC, area under the curve; HC, healthy control; NLR, neutrophil-to-lymphocyte ratio; PLT, platelet; RBC, red blood cell; ROC, receiver operating characteristic; WBC, white blood cell.

Changes of lymphocyte subpopulations (T, B, NK, Treg)

Motivated by Çetin et al. (14), who reported elevated regulatory T cell (Treg) frequencies in ADHD, we analyzed a broad panel of T, B, and NK lymphocyte subsets by flow cytometry. The proportions of total T cells, CD4⁺ T cells, B cells, and NK cells did not differ significantly between ADHD and control children (Figure 2A-2M, Table 2). In contrast, both the percentage and absolute count of CD8⁺ T cells were significantly higher in the ADHD cohort (Figure 2A-2M). We also observed that Treg percentages were elevated in ADHD, accompanied by a reduced CD4/CD8 ratio relative to controls (Figure 2A-2M, Table 2). ROC analysis revealed that the AUC exceeded 0.60 for CD8⁺ T cell percentage, CD8⁺ T cell count, Treg percentage, and the CD4/CD8 ratio (Figure 2N-2Q). Incorporating these four variables into a logistic regression model yielded an AUC of 0.8095, indicating a robust predictive signature for ADHD (Figure 2R-2S).

Figure 2 Immunophenotypic profiling of TBNK lymphocyte subsets and Tregs in ADHD versus HC. (A) Flow cytometric gating strategy for TBNK subset analysis and Treg quantification. (B-M) Quantitative assessment of TBNK and Treg populations. ROC curves for (N) CD8⁺ T cell percentage, (O) absolute CD8⁺ T cell count, (P) Treg percentage, and (Q) CD4/CD8 ratio. (R) ROC analysis and (S) parameter covariance plots for logistic regression modeling. Data are presented as mean ± standard deviation, with individual participants represented by data points (technical duplicates averaged). Statistical significance determined via one-way analysis of variance with Tukey’s corrections. *, P<0.05; ***, P<0.001; ns, non-significant. ADHD, attention-deficit/hyperactivity disorder; AUC, area under the curve; HC, healthy control; NK, natural killer; ROC, receiver operating characteristic; SSA, side scatter area; TBNK, T cells, B cells, NK cells; Tregs, regulatory T cells.

Memory and activation phenotypes of CD4⁺ and CD8⁺ T cells

We further evaluated CD4⁺ and CD8⁺ T cell subsets using CD28 and CD45RO markers to distinguish naive/activated phenotypes (Figure 3A-3I). Compared to controls, children with ADHD exhibited significantly higher proportions of naive-like CD4⁺CD45RO⁻ (CD45RA⁺) and CD8⁺CD45RA⁺ T cells, as well as CD8⁺CD28⁻ T cells (Figure 3A-3I). By contrast, the memory-associated subsets CD4⁺CD45RO⁺ and CD8⁺CD45RO⁺ were significantly reduced in the ADHD group (Figure 3A-3I). No significant differences were observed in the frequencies of CD4⁺CD28⁺, CD4⁺CD28⁻, or CD8⁺CD28⁺ T cells between groups (Figure 3A-3I). ROC analysis demonstrated that the AUC exceeded 0.70 for four of the five assessed subsets (CD4⁺CD45RO⁻, CD8⁺CD28⁻, CD4⁺CD45RO⁺, CD8⁺CD45RO⁻). In contrast, the CD8⁺CD45RO⁺ subset had a slightly lower AUC of 0.6804 (Figure 3J-3N, Table 3). Incorporating these five variables into a logistic regression model produced an AUC of 0.9910, reflecting excellent discriminative performance (Figure 3O-3P).

Figure 3 Flow cytometric evaluation of T-cell subpopulation dynamics in ADHD and HC groups. (A) Gating protocol for T-cell subset analysis. (B-I) Comparative analysis of T-cell subset distributions. ROC curves for (J) CD4⁺CD45RO⁺ T cells, (K) CD4⁺CD45RO⁻ T cells, (L) CD8⁺CD28⁻ T cells, (M) CD8⁺CD45RO⁺ T cells, and (N) CD8⁺CD45RO⁻ T cells. (O) ROC analysis and (P) parameter covariance plots for logistic regression modeling. Data are displayed as mean ± standard deviation, with individual participants represented by data points (technical duplicates averaged). Statistical comparisons utilized one-way analysis of variance with Tukey’s post-hoc tests **, P<0.01; ***, P<0.001; ns, non-significant. ADHD, attention-deficit/hyperactivity disorder; AUC, area under the curve; HC, healthy control; ROC, receiver operating characteristic.

Discussion

In this study, we assessed peripheral immune cell profiles in medication-naïve children with ADHD and HCs. We identified distinct alterations in circulating lymphocyte subsets in the ADHD group. These results provide novel insights into the immunological mechanisms that may underlie ADHD and are consistent with emerging evidence linking neuroinflammation and immune dysregulation to neurodevelopmental disorders (NDDs).

As a cornerstone of immunocompetence surveillance, lymphocyte subset analysis provides critical insights into the functional status of cellular immunity (16,20). This diagnostic modality facilitates differential diagnosis of pathological conditions including autoimmune disorders and primary immunodeficiency syndromes (9,21). In pediatric practice, immunophenotyping enables clinicians to elucidate age-related immunoregulatory characteristics (22), thereby informing differential diagnosis and guiding tailored therapeutic interventions for various clinical entities (13). The quantitative and qualitative assessment of T-lymphocyte subsets (CD3⁺, CD4⁺, CD8⁺), B-lymphocytes (CD19⁺), and NK cells (CD56⁺) constitutes a valuable tool for monitoring children disease progression and treatment response in immunocompromised populations (20,23,24). Furthermore, longitudinal evaluation of lymphocyte subset dynamics offers prognostic value in conditions characterized by immunodysregulation, such as teenage hematological malignancies and auto-immune related disorders (17).

The most notable finding was a significantly higher proportion of Tregs in ADHD patients compared to controls. In fact, increased Tregs percentages independently predicted ADHD diagnosis in our logistic regression analysis (14). Tregs are critical for maintaining immune homeostasis by suppressing excessive inflammation. An increase in Tregs could initially appear compensatory, suggesting an anti-inflammatory response (14). However, paradoxical Tregs elevations have been reported in various neuropsychiatric and autoimmune conditions. For example, elevated Tregs may indicate chronic immune stimulation (as seen in some autoimmune disorders) or may reflect dysfunction in Treg suppressive function, leading to excess pro-inflammatory cytokine release. This interpretation is supported by previous reports of elevated systemic inflammatory markers, such as C-reactive protein and interleukin-6, in ADHD (25-27). Importantly, the strong correlation between Treg levels and ADHD symptom severity (as indicated by our ROC analysis, AUC of 0.69) highlights the potential of Tregs as both a diagnostic biomarker and a therapeutic target in ADHD.

We also observed an expansion of CD8⁺ T cells and a corresponding decrease in the CD4/CD8 ratio in ADHD subjects. This pattern is reminiscent of immune profiles seen in chronic viral infections or states of persistent antigenic stimulation. CD8⁺ T cells are cytotoxic effectors, and their elevation suggests sustained immune activation. Importantly, ADHD children had a higher proportion of naive CD8⁺ T cells (CD8⁺CD45RA⁺) accompanied by a reduction in memory CD8⁺ T cells (CD8⁺CD45RO⁺). This shift indicates possible impairment in immune memory formation or altered lymphocyte maturation. This pattern differs from observations in ASD, where increases in CD8⁺ T cell exhaustion markers (e.g., PD-1) have been reported, underscoring that different NDDs can exhibit distinct immune signatures (28,29). The functional implications of these altered CD8⁺ T cell subsets in ADHD merit further investigation, particularly regarding their potential effects on neurotransmitter systems or on blood-brain barrier integrity.

Innate immune markers also differed between groups. ADHD children exhibited a lower NLR than controls, driven by a marked reduction in circulating monocytes and a modest increase in neutrophils (19,30). This profile contrasts with the elevated NLR typically seen in acute inflammatory or stress states (10,31). Monocytes serve as precursors to brain-resident macrophages (microglia), so their peripheral depletion may reflect recruitment into the central nervous system (CNS) (32), a phenomenon reported in neurodegenerative models (1,17). The modest neutrophilia we observed may contribute to systemic oxidative stress, which has been implicated in ADHD pathophysiology.

These immune alterations support a model in which ADHD involves dysregulated neuroimmune crosstalk. Peripheral immune cells such as Tregs and CD8⁺ T cells can influence the CNS by migrating across a compromised blood-brain barrier or through systemic cytokine signaling. For instance, activated T cells produce pro-inflammatory cytokines (e.g., IL-17, TNF-α) that can impair the function of prefrontal cortical regions involved in attention and executive control (33-35). Moreover, the imbalance between naive and memory T cell subsets may reflect altered thymic education or early-life exposures (such as infections) that shape immune development. These mechanisms could, in turn, affect synaptic plasticity and the development of neural circuits relevant to ADHD-related behaviors.

Çetin et al. conducted a single-center, cross-sectional case-control study involving 49 children diagnosed with ADHD. Psychopathological assessments were carried out using the K-SADS-PL, and ADHD symptom severity was evaluated via the Distracted Continuous Performance Test. Peripheral blood lymphocyte subsets, including Tregs, were assessed through multiparametric flow cytometry. The findings revealed no significant alterations in the general lymphocyte subpopulations between the ADHD and control groups. This study underscores the potential immunoregulatory involvement in ADHD, highlighting elevated peripheral Treg levels as a possible biomarker linked to disease susceptibility (14). Inagaki et al. elucidated the critical role of sodium-calcium exchanger 3 (NCX3) in dopaminergic neurotransmission by demonstrating its abundant localization within dopaminergic neurons of the ventral tegmental area (VTA)—a principal source of dopaminergic projections to the prefrontal cortex (PFC). Their study revealed that NCX3 knockdown in N27 dopaminergic cells resulted in aberrant dopamine influx, driven by a pronounced interaction between calcium/calmodulin-dependent protein kinase II alpha (CaMKIIα) and the dopamine transporter (DAT). These neurochemical alterations were paralleled by upregulated D1 receptor signaling in the PFC. Collectively, these findings indicate that NCX3 deficiency disrupts dopaminergic homeostasis in the PFC, thereby contributing to the emergence of ADHD-like behavioral phenotypes (36). In a separate investigation, Liu et al. employed bidirectional Mendelian randomization to examine causal links between immune cell phenotypes, inflammatory mediators, and NDDs. Their analysis revealed significant positive associations between 13 immune cell subsets and ASD, including six CD8⁺ T cell types, a CD3⁺ T cell subset, two CD20⁺ B cell populations, one CD38⁺ B cell subtype, and two plasmacytoid dendritic cell (DC) subsets. Nine inflammatory mediators also exhibited causal associations with ASD. Among these, IL-7, IL-2, IL-2 receptor subunit β (IL-2β), and IL-18 receptor 1 (IL-18R1) levels were negatively associated, whereas TNF-α and four other factors were positively associated (20). These results underscore a complex interplay between immune cells, inflammatory mediators, and the pathophysiology of NDDs, suggesting that inflammatory cytokines may act as mechanistic bridges linking immune dysregulation to neurodevelopmental dysfunction.

The diagnostic accuracy of Treg levels (AUC of 0.69) and CD8⁺ T cell proportion (AUC of 0.62) suggests they may serve as valuable adjunctive biomarkers for ADHD, pending validation in larger cohorts. Prospective studies should track immune profiles over time to determine whether these alterations precede ADHD onset or arise as a consequence of disease chronicity. Immune-modulating interventions (e.g., omega-3 fatty acid supplementation, probiotic treatments) with anti-inflammatory effects warrant exploration in ADHD, particularly for patients exhibiting pronounced immune dysregulation. Future research should account for common comorbid conditions (such as allergies or autoimmune disorders) to isolate immune signatures specific to ADHD.

Our study has limitations. The cross-sectional design precludes causal inferences, and excluding medicated patients may limit the generalizability of our findings to treated ADHD populations. In addition, the absence of functional immune assays (such as cytokine profiling or direct measurement of Treg suppressive function) means that key mechanistic questions remain unresolved.

Conclusions

This study systematically evaluated peripheral blood immunophenotypes in pediatric ADHD, revealing significant alterations in lymphocyte subsets, including elevated regulatory T cells, skewed CD8⁺ T-cell expansion, and reduced CD4/CD8 ratios. Hematological analyses demonstrated aberrant NLR and monocyte depletion. Diagnostic models incorporating these immunological parameters achieved moderate discriminatory accuracy, suggesting their potential utility as biomarkers. These findings implicate immune dysregulation in ADHD pathophysiology and advocate for further investigation into neuroimmune interactions in NDDs. These results bridge neurodevelopmental and immunological research, suggesting that immune modulation could represent a novel avenue for ADHD stratification and intervention. Further mechanistic studies are needed to elucidate how peripheral immune signals interact with CNS development to shape ADHD phenotypes.

Acknowledgments

The author would like to thank all colleagues at The Fifth People’s Hospital of Ganzhou and the Third People’s Hospital of Ganzhou.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-362/rc

Data Sharing Statement: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-362/dss

Peer Review File: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-362/prf

Funding: This research was supported by the Ganzhou Science and Technology Plan Project of Jiangxi Province (No. GZ2018ZSF394) and Science and Technology Plan Project of Jiangxi Provincial Health Commission (No. 20197407).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-362/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all Aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by The Fifth People’s Hospital of Ganzhou Ethics Committee Board (No. GZWY-EC-2025006). Written informed consent was obtained from the parents or legal guardians of all participants before any study procedures were initiated.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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