Involvement of adiponectin in the biology of attention deficit hyperactivity disorder

Involvement of adiponectin in the biology of attention deficit hyperactivity disorder | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Involvement of adiponectin in the biology of attention deficit hyperactivity disorder Nagahide Takahashi, Toshiki Iwabuchi, Tomoko Nishimura, Akemi Okumura, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8283198/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract We investigated the involvement of adiponectin in the development of attention-deficit/hyperactivity disorder (ADHD), using a multi-pronged approach integrating cord blood adiponectin concentrations, polygenic risk scores derived from publicly available GWAS summary statistics, and Mendelian randomization. This study aimed to evaluate both upstream genetic influences and downstream adiponectin-level associations. Using a combination of longitudinal cord blood data (N = 531) and genome-wide association studies of ADHD (N = 55,374) and adiponectin (N = 46,434), we examined observational, genetic, and causal associations linking adiponectin with ADHD. Lower cord blood adiponectin levels were associated with higher ADHD symptoms at ages 5–6 and 8–9, and predicted membership in the increasing-symptom trajectory group. Gene-set analysis demonstrated pathway-level overlap between ADHD and adiponectin regulation. Genetic correlation analysis showed a significant negative correlation between ADHD and adiponectin levels. Mendelian randomization provided stronger evidence for a causal effect of ADHD on adiponectin levels. Polygenic risk score analysis also demonstrated a negative association between genetic liability for ADHD and cord blood adiponectin levels. Additionally, lower adiponectin levels were found to be associated with elevated inflammatory markers in the cord blood samples obtained from the cohort study, which may provide a potential explanation for the higher prevalence of metabolic comorbidities observed in individuals with ADHD. These findings suggest that adiponectin is involved in the mechanism of ADHD and targeting adiponectin regulation may offer a novel approach for managing ADHD and its related health risks. ADHD adiponectin obesity GWAS Mendelian Randomization PRS Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Recent studies have highlighted a significant co-occurrence of obesity and ADHD in both children [ 1 , 14 ] and adults [ 11 ]. Evidence suggests that obesity-related genes, such as the FTO gene encoding alpha-ketoglutarate-dependent dioxygenase, may contribute to ADHD risk [ 7 ], and shared genetic risk alleles between obesity and ADHD have been proposed [ 2 ]. Obesity is often accompanied by abnormal circulating levels of adipocytokines, including adiponectin, a hormone secreted by adipose tissue [ 3 , 32 ]. These abnormalities are linked to conditions like type II diabetes and insulin resistance [ 17 , 52 ], and emerging research has also observed altered adiponectin levels in psychiatric disorders such as major depression [ 20 ], schizophrenia [ 8 ], panic disorder [ 46 ], bipolar disorder [ 5 ], and ADHD [ 23 , 27 , 54 ]. Adiponectin is a multifunctional adipokine with insulin-sensitizing[ 38 ] and anti-inflammatory properties [ 48 ], playing a critical role in fatty acid oxidation[ 51 ]. Its expression is regulated by insulin [ 36 ], testosterone [ 25 ], and glucocorticoids [ 39 ]. The hormone's effects are mediated by its receptors, AdipoR1 and AdipoR2, primarily expressed in muscle and liver tissue[ 50 ], as well as in the brain [ 45 ]. A third receptor, T-cadherin, specifically binds the hexameric and high molecular weight (HMW) forms of adiponectin and is highly expressed in the cardiovascular system and brain [ 15 ]. Genome-wide association studies (GWAS) have identified associations between single nucleotide polymorphisms (SNPs) in the CDH13 gene, which encodes T-cadherin, and ADHD [ 19 , 21 ]. Additionally, CDH13 polymorphisms are strongly associated with serum adiponectin levels [ 24 , 49 ]. Reduced adiponectin levels are linked to metabolic and inflammatory disorders such as insulin resistance, type II diabetes, metabolic syndrome, obesity, and dyslipidemia [ 13 ]. Low adiponectin also promotes systemic inflammation, which plays a critical role in cardiovascular diseases like atherosclerosis [ 26 ]. These anti-inflammatory and insulin-sensitizing properties make adiponectin essential for maintaining metabolic homeostasis [ 22 ]. Similarly, neuroinflammation has been implicated in various psychiatric disorders, including ADHD [ 9 ]. For instance, we have previously reported that neonatal inflammation, such as elevated inflammatory cytokines, is associated with ADHD symptoms in children [ 41 ]. Imaging studies further support this by showing highly activated microglia in the brains of individuals with ADHD, suggesting a role for inflammation in its pathophysiology [ 53 ]. The frequent co-occurrence of ADHD with metabolic and inflammatory conditions has been extensively documented, and recent evidence suggests these conditions are genetically correlated [ 6 ]. In addition to its proposed role in metabolic and inflammatory disorders, adiponectin has recently been implicated in the pathophysiology of eating disorders[ 44 ]. Given the increasing recognition of overlapping behavioral and biological features between ADHD and eating disorders, such findings may provide further rationale for investigating adiponectin in ADHD. Moreover, other adipocytokines such as leptin and resistin have been associated with both energy balance and neurodevelopmental processes, suggesting that adiponectin may act as part of a broader network of metabolic signaling molecules involved in ADHD [ 37 ]. However, the mechanisms linking ADHD and adiponectin across observational and genetic levels remain unclear. In this study, we used longitudinal cord blood data together with GWAS summary statistics on adiponectin and ADHD to investigate their associations across observational, pathway-level, and genetic domains, and to evaluate potential causal relationships. Specifically, we hypothesized that (1) lower cord blood adiponectin levels would be associated with increased ADHD symptoms, (2) ADHD and adiponectin would share genetic architecture, and (3) ADHD and adiponectin would show genetic correlation or causal associations supported by multiple genetic analyses. Methods Analytic strategy To test our study hypotheses, we employed a multi-method approach combining observational and genetic analyses. First, we examined associations between cord blood adiponectin levels and ADHD symptoms using longitudinal cohort data. Second, we used gene-set enrichment analysis (GSEA) to identify biological pathways shared between ADHD and adiponectin regulation. Third, we quantified genome-wide shared heritability using linkage disequilibrium score regression (LDSC) and evaluated potential causal effects using Mendelian randomization (MR). Finally, we conducted polygenic risk score (PRS) analysis to examine the association between genetic liability for ADHD and adiponectin levels in the cohort participants. Participants Participants were recruited from the Hamamatsu Birth Cohort for Mothers and Children (HBC Study) which included pairs of mothers and children (N = 531, 267 females and 264 males). Recruitment procedures of HBC study are fully described in our previous paper [ 40 ]. We obtained summary statistics data from large-scale GWAS meta-analysis of serum adiponectin levels, which included participants from the Adiponectin Genetics Consortium (ADIPOGen; n = 29,347), the Asian Genetic Epidemiology Network (AGEN; n = 7,825), and the Metabolic Syndrome in Men Study (METSIM; n = 9,262) [ 35 ]. All adiponectin levels were measured in fasting serum samples from adult participants. The GWAS was conducted as a meta-analysis of summary statistics across the three consortia, and individual-level data were not pooled. Participants for GWAS study of ADHD were recruited from iPSYCH (N = 55,374). Measurement Serum adiponectin cytokines (IL-1beta and IL-6) and CRP in cord blood were measured using measured by enzyme-linked immunosorbent assays (ELISA) according to the manufacturer’s protocol (Funakoshi, Co. Ltd) in the HBC cohort. ADHD symptoms were assessed at ages 6–7 and 8–9 years using the Japanese version of ADHD-Rating Scale (ADHD-RS) [ 43 ] in the participants of HBC study. The symptoms trajectory analysis was performed using the traj program that was developed for Stata [ 16 ]. Model fit was assessed using BIC. Cord blood samples were collected at birth and used to assay adiponectin and inflammatory markers. Serum protein levels of adiponectin in the ADIPOGen consortium, the AGEN consortium and the METSIM study were measured using the SomaScan v4 proteomics platform. ADHD diagnosis was made using ICD10 criteria in the participants of iPSYCH. Gene-set enrichment analysis Using summary data of GWAS of ADHD obtained from PGC ( https://doi.org/10.6084/m9.figshare.14671965 ) and serum adiponectin levels from GWAS catalog ( http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90293001-GCST90294000/GCST90293086 ), we identified genetic pathways specific to ADHD and serum adiponectin levels. Gene mapping of SNPs obtained from the summary data was conducted using positional mapping, eQTL mapping and Chromatin interaction mapping using FUMA ( https://fuma.ctglab.nl ) [ 47 ]. To identify gene-sets associated with specific conditions and diseases, a range of databases was utilized, including MsigDB ( https://www.gsea-msigdb.org/gsea/index.jsp ), which focuses on all Canonical Pathways, such as BioCarta, KEGG and Reactome (MsigDB c2) and GWAS catalog. Using the hypergeometric distribution, a p-value of gene overlap was calculated and corrected for multiple hypothesis testing according to Benjamini and Hochberg using MAGMA gene-set analysis [ 10 ]. Genetic correlation analysis Genetic correlations (r g ) between ADHD and adiponectin were calculated by linkage disequilibrium score regression (LDSC) using GWAS summary data of both conditions. Mendelian Randomization analysis Two-sample MR analyses with regression analyses were conducted using meta-analysis of adiponectin GWAS summary and iPSYCH, with standard variant harmonization procedures ( https://mrcieu.github.io/TwoSampleMR/ ). We used a P value threshold of 5 × 10⁻⁸ to select SNPs for ADHD diagnosis and adiponectin levels. For the main analysis, we used the Inverse Variance Weighted (IVW) method as the primary approach. Sensitivity analyses were conducted using the Weighted Median, Weighted Mode, Simple Mode, and MR-Egger methods. Heterogeneity across SNPs was assessed using Cochran’s Q statistic in both IVW and MR-Egger models. Potential horizontal pleiotropy was evaluated using MR-Egger intercept and the MR-PRESSO global test. The leave-one-out analysis was performed to evaluate the robustness of the results, and the MR-PRESSO test was used to detect outliers. Polygenic risk score analysis Polygenic Risk Score (PRS) for adiponectin was calculated using PRS-CSx which consider ethnic difference between discovery cohort and targeted cohort [ 30 ]. Genotyping was conducted using a Japonica array designed specifically for single nucleotide polymorphism (SNP) genotyping for a Japanese population. SNPs and individuals were retained as follows: missing data for SNP < 0.02, pairwise identify-by-descent (IBD) 10 − 6 , and minor allele frequency > 0.01. Genotyping imputation was performed using BEAGLE 5.0 on the Japanese population in the 1000 Genome Project phase 3 reference panel. SNPs with an imputation INFO score < 0.7 were excluded. We also excluded SNPs located within the MHC region because of high linkage equilibrium in this region. The number of SNPs analyzed for PRS was 5,606,655. A P-value threshold of 0.05 was used, which is standard in Bayesian PRS methods such as PRS-CS/PRS-CSx [ 12 , 29 ]. To account for population stratification, 4 principal components (PCs) calculated with PLINK 1.9 were used. The criteria for SNP clumping were r 2 > 0.1 within a 2Mb window. PRS scores were calculated with P-value thresholds at 0.05. Standardized PRS scores (mean = 0 and standard deviation = 1) were used for the analyses. For stratified analysis, children were divided into tertiles based on ADHD-PRS (low, medium, high) to evaluate potential gene–environment interaction effects. Structural equation modeling We conducted structural equation modeling (SEM) to examine the pathway linking serum adiponectin levels in cord blood and inflammatory markers (IL-1β, IL-6, CRP). Observed variables were modeled as indicators of latent constructs for systemic inflammation and ADHD symptomatology. The SEM was performed using maximum likelihood estimation in Stata 16, and model fit was evaluated using standard indices: Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), and chi-squared test. Good model fit was defined according to commonly accepted criteria: CFI > 0.95, RMSEA < 0.06, and non-significant χ². Statistical analysis All analyses were conducted using Stata 16 software (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC). Covariates included in all regression and SEM models were child sex, gestational age at birth, birthweight, maternal smoking during pregnancy, and parental age at birth. These covariates were selected a priori based on known associations with both ADHD and metabolic profiles. Ethical consideration The study protocol was approved by the Hamamatsu University School of Medicine and University Hospital Ethics Committee (Ref No. 20–233). Written informed consent was obtained from each caregiver (usually the mother) for her own and her children's participation. This study followed the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) reporting guideline and STROBE-MR guideline. Results Association of adiponectin levels with ADHD symptoms and its trajectories Participant characteristics of HBC study are summarized in Table 1 . Serum adiponectin levels in cord blood were negatively correlated with ADHD symptoms in children both aged 6–7 years (Beta [SE] = -0.072[ 0.034], P = 0.033, Fig. 1 a) and 8–9 years (Beta [SE] = -0.109 [0.035], P = 0.002, Fig. 1 b). Trajectory analysis showed that there are three trajectories in the change of ADHD-RS score from 6–7 years old to 8–9 years old (constantly low, decreasing, and increasing, Fig. 2 ). Reduced cord blood adiponectin was associated with increased risk ratio to belong to a group whose ADHD-RS score increasing (OR [95% CI] = 1.182 [1.139–1.230], P = 0.028, Supplementary Table 1). Table 1 Sample Characteristics of children and their parents in the study n (%) Gender Male 264 (49.7) Female 267 (50.3) Ethnicity Japanese 726 (100) Small for gestational age < 10th percentile 43 (5.9) 10th-100th percentile 683 (94.1) Paternal education < 12 years 62 (8.5) 12 years and longer 664 (91.5) Maternal education < 12 years 38 (5.3) 12 years and longer 684 (94.3) Mean (SD) Birthweight (g) 2940.5 (438.8) Gestational age at birth (weeks) 38.9 (1.6) Paternal age at birth (years) 33.2 (5.8) Maternal age at birth (years) 31.5 (5.1) Household income (million JPY) 6.0 (2.8) IQ (WISC-IV: FSIQ) 101.6(0.5) Abbreviation: SD, Standard Deviation; JPY, Japanese Yen; ADHD-RS, Attention deficit hyperactivity disorder rating scale; IQ, intelligent quotient; WISC-IV, Wechsler Intelligent Scale for Children-IV; FSIQ, Full Scale IQ Gene set enrichment analysis Gene set analysis of GWAS summary of ADHD showed that statistically significant over-lapping genes with GWAS summary of adiponectin levels (the number of over-lapping genes = 14 out of 50, Adjusted P-value = 7.16 x 10 − 3 ). Representative overlapping genes included ADARB1, ADCY2, and ADAM9, which are involved in neurodevelopmental or metabolic regulation. See Supplementary Table 2 for the results of all overlapping pathways. Genetic correlation analysis LD score regression analysis showed that ADHD and adiponectin levels are inversely correlated, but the direction was negative (r g [SE] = -0.31 [0.034], P = 2.148x10 − 20 ). The SNP-based heritability was estimated at h² [SE] = 0.268 [0.011] for ADHD and h² [SE] = 0.152 [0.019] for adiponectin, indicating a moderate heritable component in both traits that may contribute to their observed genetic association. Mendelian randomization analysis MR analysis suggested an association between adiponectin and ADHD. The association of adiponectin with ADHD was marginally significant (IVW, β [SE] = 0.121 [0.053], P = 1.54 × 10⁻³), whereas the association of ADHD with adiponectin was consistently significant across multiple methods (IVW, β [SE] = 0.042 [0.004], P = 1.94 × 10⁻²¹) (Table 2 ). Sensitivity analyses further supported the directional association from ADHD to adiponectin (see Supplementary Table 3 and Supplementary Fig. 1), whereas the association from adiponectin to ADHD was not consistently replicated. The leave-one-out analysis showed that no single genetic variant was driving the causal effect of ADHD on adiponectin (Supplementary Fig. 2). Table 2 Two sample mendelian randomization analysis between adiponectin and ADHD Exposure Outcome SNPs beta SE P Adiponectin ADHD 180 -0.08227 0.026 1.535x10 − 3 ADHD Adiponectin 84 -0.042 0.004 8.967×10 − 12 Abbreviations: ADHD, Attention deficit hyperactivity disorder; SNP, single nucleotide polymorphism, B, standardized beta; SE, standard error; MR-eggar, Mendelian Randomization; IVW, Inverse variance weighted Cochran’s Q test indicated no significant heterogeneity in both the IVW and MR-Egger models. Directional pleiotropy was assessed using the MR-Egger intercept, which did not indicate significant pleiotropic bias (Supplementary Table 4). Additionally, the MR-PRESSO test did not identify any significant outliers, further supporting the robustness of the results. Polygenic risk score analysis between ADHD and adiponectin levels To get additional evidence to show that ADHD is a risk for reduced adiponectin, we conducted polygenic risk score analysis using GWAS summary of ADHD. We demonstrated that ADHD-PRS was negatively associated with adiponectin in cord blood (beta [SE] = -1.176 [0.437], P = 0.007, Fig. 3 ). Adiponectin and inflammation We first conducted univariate linear regression analyses to examine the associations between serum adiponectin levels and individual inflammatory markers in cord blood. Adiponectin was negatively associated with IL-6 (β [SE] = − 0.174 [0.065], p = 0.007), IL-1β (β [SE] = − 0.152 [0.061], p = 0.013), and CRP (β [SE] = − 0.139 [0.062], p = 0.025). These consistent inverse associations provided empirical justification for modeling a latent inflammation factor in a structural equation modeling (SEM) framework. Finally, the suppression of inflammation in cord blood by adiponectin was demonstrated using SEM analysis. The constructed model exhibited a negative correlation between cord blood adiponectin levels and inflammatory markers, including IL-1beta, IL-6, and CRP, as illustrated in Fig. 4 . The SEM model showed acceptable fit (CFI = 0.999, RMSEA = 0.017, χ² = 0.338). Discussion In the present study, we explored the role of adiponectin in the development of ADHD. First, serum adiponectin levels in cord blood were negatively associated with ADHD symptoms in children aged 6–7 and 8–9 years. There were three distinct trajectories in the change of ADHD symptoms severity, and adiponectin was negatively associated with a risk of belonging to a group whose ADHD symptoms getting worse over time. Gene set analysis of the GWAS summary of ADHD indicated that there was an overlap of ADHD and adiponectin levels at GWAS levels. Genetic correlation analysis demonstrated a negative correlation between ADHD and adiponectin levels. MR analyses suggested directional asymmetry, with stronger evidence for an effect of ADHD on adiponectin levels. Lastly, we showed that ADHD-PRS was negatively associated with cord blood adiponectin in our cohort. Taken together, our data demonstrated that adiponectin was dysregulated in subjects with ADHD, and it can be considered that the disease itself has some impact on serum levels of adiponectin. To the best of our knowledge, even cross-sectional data on the association between ADHD and adiponectin are limited [ 27 , 54 ], and there have been no longitudinal studies addressing this topic so far. Consequently, the present research has provided novel insights by exploring not only the early-life association between cord blood adiponectin and later ADHD symptoms, but also the developmental course of these symptoms across childhood. In particular, cord blood adiponectin predicted membership in the increasing-symptom trajectory between ages 6–7 and 8–9, suggesting that intrauterine metabolic or inflammatory status may influence the unfolding of ADHD symptom patterns across childhood. These findings partially explain why the prevalence of obesity, type II diabetes and cardio-vascular diseases is high in subjects with ADHD compared to healthy controls. Therefore, it may be important to find methods to regulate serum adiponectin levels (in this case, increase) in ADHD subjects to reduce physical comorbidities. The present findings, together with previous literature, raise important questions about the interplay between adiponectin and other metabolic signaling molecules in ADHD. Adiponectin does not act in isolation; it is part of a broader adipocytokine network that includes leptin, resistin, ghrelin, and others, all of which have been implicated in neurodevelopmental regulation and feeding behavior. Given the known heterogeneity of BMI among individuals with ADHD, including both underweight and overweight profiles, future research should explore whether distinct adipokine patterns characterize different ADHD subtypes or symptom clusters. Moreover, the potential bidirectional influence between adiponectin and appetite-regulating hormones could help explain overlapping pathophysiological features between ADHD and eating disorders, as well as metabolic comorbidities frequently observed in this population [ 37 , 44 ]. Animal models with reduced adiponectin signaling by knockout of AdipoR2 (adiponectin receptor 2) have been reported to exhibit hyperactivity compared to wild-type littermates [ 31 ]. Conversely, it has been shown that hyperactivity is reduced in transgenic mice overexpressing adiponectin. These lines of evidence suggest that dysregulated adiponectin signaling may affect ADHD symptoms [ 34 ], although the exact mechanism has not been elucidated. Furthermore, reduced cord blood levels of adiponectin were shown to increase inflammatory cytokines. Since we have previously shown that there is a gene-environmental interaction between genetic risk for ADHD and inflammatory cytokines on the severity of ADHD symptoms [ 41 ], it may also be important to keep a decent levels of serum adipokines to prevent the progress of the illness. Unfortunately, no medication currently approved for ADHD has not been reported to increase serum levels of adiponectin, except one small clinical trial which showed methylphenidate increased adiponectin in 20 children with ADHD [ 33 ]. However, several clinical trials and animal study showed that administration of melatonin can increase serum levels of adiponectin. For example, a double-blind, placebo-controlled trial involving 70 subjects with metabolic syndrome found that melatonin supplementation (6 mg/day for 12 weeks) significantly increased adiponectin levels[ 4 ]. Furthermore, supplementation of melatonin has been repeatedly increasing serum levels of adiponectin in mice [ 18 , 28 ]. Therefore, supplementation of melatonin may have benefit on controlling ADHD symptoms via regulation of adiponectin levels. This is also in line with our previous studies which showed that reduced melatonin may be underlying the basis of ADHD [ 42 ]. It should be acknowledged that a couple of limitations are associated with this study. Firstly, the adiponectin levels were measured using umbilical cord blood; therefore, a temporal discrepancy exists between the adiponectin levels and the symptoms of ADHD. Postnatal environmental and developmental factors may modulate the long-term associations between neonatal adiponectin levels and ADHD symptoms, and this should be considered when interpreting the results. Furthermore, isoform-specific measures of adiponectin (e.g., high molecular weight vs. total levels) were not available in the present study, which limits the ability to interpret pathways specifically involving T-cadherin binding. Secondly, while previous genome-wide association studies have implicated CDH13, a gene encoding T-cadherin, as being associated with both ADHD and adiponectin levels, our study did not include locus-specific analyses or SNP-level evaluation of CDH13. Although CDH13-linked SNPs may have contributed to polygenic scores or gene set enrichment results in our analyses, the lack of direct interrogation of this locus represents a limitation. Future studies incorporating targeted gene-level tests or fine-mapping analyses may help elucidate the specific role of CDH13 in the adiponectin–ADHD pathway. Lastly, although the PRS study demonstrated a significant association, the replication of these findings in independent cohorts is necessary for validation. In conclusion, the present study suggests that adiponectin is involved in the mechanism of ADHD, which can be a potential target of managing ADHD symptoms and related comorbidities. Declarations Data availability statement: The data generated for this study are subject to the following licenses/restrictions: Privacy and Confidentiality of Participants. Requests to access these datasets should be directed to NT, [email protected] Author Contributions: Concept and design: Takahashi, Tsuchiya Acquisition, analysis, or interpretation of data: Takahashi, Okumura, Harada, Iwabuchi, Nishimura Drafting of the manuscript: Takahashi, Tsuchiya Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Takahashi, Rahman Obtained funding: Tsuchiya Administrative, technical, or material support: Okumura, Harada, Iwabuchi, Nishimura Supervision: Tsuchiya, Nishimura Disclosure of interest: The authors declare no competing interest in this work. Declaration of generative AI in scientific writing: None Funding/Support: This work was supported by grants from the Ministry of Education, Culture, Sports, Science, and Technology in Japan (grant number 19H03582 to KJT). Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Acknowledgments The authors are grateful to all the study participants. The authors thank Dr. Tetsuo Kato of the Kato Maternity Clinic, Drs. K Sugihara, M Sugimura, K Takeuchi, K Suzuki, Y Murakami, Y Kohmura, Y Miyabe, K Hirai, Y Nakamura, R Koizumi, H Murakami, Y Kobayashi-Kohmura, K Muramatsu-Kato, Prof. N Kanayama, and all attending obstetricians of Hamamatsu University School of Medicine for their full support with participant enrolment. The authors also thank the chief midwife, Ms. Kiyomi Hinoki, and all midwives and staff at the maternity clinic of Hamamatsu University School of Medicine for their support with participant enrolment. We thank the HBC Study team, including former members, Ms. Emi Higashimoto, Noriko Kodera, Atsuko Nakamura, Yumeno Kugizaki, Yukiko Suzuki, Riyo Takabayashi, Maiko Honda, Hiroko Muraki, Makiko Narumiya, Eriko Sato, Drs. Yuka Omori, Hanae Tainaka, Asako Tokizawa, Damee Choi, Takanobu Horikoshi, Keisuke Wakusawa, Yosuke Kameno, Daisuke Kurita, Kiyokazu Takebayashi, Masamichi Yokokura, Tomoyasu Wakuda, Ryosuke Asano, Thanseem Ismail, Keiko Iwata, Yasuhide Iwata, Emiko Kawai, Masayoshi Kawai, Yujin K Kuroda, Kaori Matsumoto, Hideo Matsuzaki, Tsuruko Mori, Kyoko Nakaizumi, Kazuhiko Nakamura, Anitha A Pillai, Yui Seno, Chie Shimmura, Shiro Suda, Genichi Sugihara, Katsuaki Suzuki, Kohei Yamada, Shigeyuki Yamamoto, Yujiro Yoshihara, Yusaku Endoh, Koichi Hirano, Teruhiko Suzuki, Professor Norio Mori, Professor Nori Takei, Professor Hitoshi Kuwabara, Professor Hidenori Yamasue, Professor Masatsugu Tsujii, and Professor Toshirou Sugiyama, for data collection and management, and administrative and academic supports. The authors are grateful to Ms. Yukiko Akizuki, Tomoko Takahashi, Kumi Kusakabe, for administrative supports. 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J Pineal Res 49:342–348 Ruan Y, Lin Y, Feng YA, Chen C-Y, Lam M, Zeng J, Fullard JF, Jonas L, Hauberg ME, Ernst J, Werge T, Wu W (2022) Improving polygenic prediction in ancestrally diverse populations. Nat Genet 54:573–580 Ruan Y, Lin YF, Feng YA, Chen CY, Lam M, Guo Z, He L, Sawa A, Martin AR, Qin S, Huang H, Ge T (2022) Improving polygenic prediction in ancestrally diverse populations. Nat Genet 54:573–580 Ruiz M, Devkota R, Bergh PO, Nik AM, Blid Sköldheden S, Mondejar-Duran J, Tufvesson-Alm M, Bohlooly YM, Sanchez D, Carlsson P, Henricsson M, Jerlhag E, Borén J, Pilon M (2024) Aging AdipoR2-deficient mice are hyperactive with enlarged brains excessively rich in saturated fatty acids. Faseb j 38:e23815 Ryo M, Nakamura T, Kihara S, Kumada M, Shibazaki S, Takahashi M, Nagai M, Matsuzawa Y, Funahashi T (2004) Adiponectin as a biomarker of the metabolic syndrome. 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J Biol Chem 270:26746–26749 Scott SR, Millwood SN, Manczak EM (2023) Adipocytokine correlates of childhood and adolescent mental health: A systematic review. Dev Psychobiol 65:e22379 Shehzad A, Iqbal W, Shehzad O, Lee YS (2012) Adiponectin: regulation of its production and its role in human diseases. Horm (Athens) 11:8–20 Sukumaran S, Dubois DC, Jusko WJ, Almon RR (2012) Glucocorticoid effects on adiponectin expression. Vitam Horm 90:163–186 Takagai S, Tsuchiya KJ, Itoh H, Kanayama N, Mori N, Takei N (2016) Cohort Profile: Hamamatsu Birth Cohort for Mothers and Children (HBC Study). Int J Epidemiol 45:333–342 Takahashi N, Nishimura T, Harada T, Okumura A, Iwabuchi T, Rahman MS, Kuwabara H, Takagai S, Usui N, Makinodan M, Matsuzaki H, Ozaki N, Itoh H, Nomura Y, Newcorn JH, Tsuchiya KJ (2023) Interaction of genetic liability for attention deficit hyperactivity disorder (ADHD) and perinatal inflammation contributes to ADHD symptoms in children. Brain Behav Immun Health 30:100630 Takahashi N, Nishimura T, Okumura A, Harada T, Iwabuchi T, Rahman MS, Liu P-H, Chuang G-T, Chang Y-C, Nomura Y, Tsuchiya KJ (2024) Association between genetic risk of melatonin secretion and attention deficit hyperactivity disorder. Psychiatry Res Commun 4:100188 Tani I, Okada R, Ohnishi M, Nakajima S, Tsujii M (2010) Japanese version of home form of the ADHD-RS: An evaluation of its reliability and validity. Res Dev Disabil 31:1426–1433 Tarchi L, Garella R, Ricca V, Castellini G, Squecco R (2024) The Role of Adiponectin and Other Adipokines in Eating Disorders. In, p 1–27 Thundyil J, Pavlovski D, Sobey CG, Arumugam TV (2012) Adiponectin receptor signalling in the brain. Br J Pharmacol 165:313–327 Unsal C, Hariri AG, Yanartas O, Sevinc E, Atmaca M, Bilici M (2012) Low plasma adiponectin levels in panic disorder. J Affect Disord 139:302–305 Watanabe K, Taskesen E, van Bochoven A, Posthuma D (2017) Functional mapping and annotation of genetic associations with FUMA. Nat Commun 8:1826 Wolf AM, Wolf D, Rumpold H, Enrich B, Tilg H (2004) Adiponectin induces the anti-inflammatory cytokines IL-10 and IL-1RA in human leukocytes. Biochem Biophys Res Commun 323:630–635 Wu Y, Li Y, Lange EM, Croteau-Chonka DC, Kuzawa CW, McDade TW, Qin L, Curocichin G, Borja JB, Lange LA, Adair LS, Mohlke KL (2010) Genome-wide association study for adiponectin levels in Filipino women identifies CDH13 and a novel uncommon haplotype at KNG1-ADIPOQ. Hum Mol Genet 19:4955–4964 Yamauchi T, Kamon J, Ito Y, Tsuchida A, Yokomizo T, Kita S, Sugiyama T, Miyagishi M, Hara K, Tsunoda M, Murakami K, Ohteki T, Uchida S, Takekawa S, Waki H, Tsuno NH, Shibata Y, Terauchi Y, Froguel P, Tobe K, Koyasu S, Taira K, Kitamura T, Shimizu T, Nagai R, Kadowaki T (2003) Cloning of adiponectin receptors that mediate antidiabetic metabolic effects. Nature 423:762–769 Yamauchi T, Kamon J, Waki H, Terauchi Y, Kubota N, Hara K, Mori Y, Ide T, Murakami K, Tsuboyama-Kasaoka N, Ezaki O, Akanuma Y, Gavrilova O, Vinson C, Reitman ML, Kagechika H, Shudo K, Yoda M, Nakano Y, Tobe K, Nagai R, Kimura S, Tomita M, Froguel P, Kadowaki T (2001) The fat-derived hormone adiponectin reverses insulin resistance associated with both lipoatrophy and obesity. Nat Med 7:941–946 Yatagai T, Nagasaka S, Taniguchi A, Fukushima M, Nakamura T, Kuroe A, Nakai Y, Ishibashi S (2003) Hypoadiponectinemia is associated with visceral fat accumulation and insulin resistance in Japanese men with type 2 diabetes mellitus. Metabolism 52:1274–1278 Yokokura M, Takebasashi K, Takao A, Nakaizumi K, Yoshikawa E, Futatsubashi M, Suzuki K, Nakamura K, Yamasue H, Ouchi Y (2021) In vivo imaging of dopamine D1 receptor and activated microglia in attention-deficit/hyperactivity disorder: a positron emission tomography study. Mol Psychiatry 26:4958–4967 Yurteri N, Şahin İE (2020) Decreased serum levels of total and high molecular weight adiponectin in treatment-naïve children with ADHD. Anadolu Psikiyatri Dergisi 21:633–640 Additional Declarations No competing interests reported. Supplementary Files Supplementarytable2.xlsx 20251205TakahashiSupplementarymaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Mar, 2026 Reviews received at journal 18 Mar, 2026 Reviews received at journal 07 Mar, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviews received at journal 21 Feb, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviewers invited by journal 20 Feb, 2026 Editor assigned by journal 06 Dec, 2025 Submission checks completed at journal 06 Dec, 2025 First submitted to journal 04 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8283198","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595162023,"identity":"77e0b9c5-0b4d-4cbc-bef4-f19890cd489e","order_by":0,"name":"Nagahide Takahashi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABMklEQVRIie3RsWqDQBjA8U+EcznpmiCNr3By0FCEOvY1FAcXB8GxQwThutTdxwgU2nS7cBAXQboVmiEgOKVToCBtoWlDQ2lM6Fjo/ZeT7/xx6AHIZH80FYi6WZUE1s8q326RgwRtCXJ/QeA7AbzvxU1DrbhfRZE9ADOtSTs5c4Yafl60kzmY5wnE0S45vQpjIycBBYSol5W+d5fqt1ZWNmCVHGi+SwgPXQMT4SUIKNeZ6hKh3xgKE2DlLlDcQaql/7ImowRpq+kbGzlE4OYweQhmH6e4gDD1dSaUscDok5i9fWSJbEwCi6Ewpses8MYCnfQz1mCCp0nnt1RB/YhfbfNILa77T+zCIZVoei2bD8zLdEY7/hh83QL6MeaYcIXRLgHaonPMwUxArTv3ZDKZ7J/1Dl4+Y7f8Xp9hAAAAAElFTkSuQmCC","orcid":"","institution":"National Center of Neurology and Psychiatry","correspondingAuthor":true,"prefix":"","firstName":"Nagahide","middleName":"","lastName":"Takahashi","suffix":""},{"id":595162024,"identity":"7dc71ab3-3e9a-4160-9f6f-30a657e9c162","order_by":1,"name":"Toshiki Iwabuchi","email":"","orcid":"","institution":"Hamamatsu University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Toshiki","middleName":"","lastName":"Iwabuchi","suffix":""},{"id":595162025,"identity":"4123c013-75d5-4aca-a9ad-e72bbb92e3a1","order_by":2,"name":"Tomoko Nishimura","email":"","orcid":"","institution":"Hamamatsu University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tomoko","middleName":"","lastName":"Nishimura","suffix":""},{"id":595162026,"identity":"87b5157b-2a53-4bbd-99d6-30a7acdca73f","order_by":3,"name":"Akemi Okumura","email":"","orcid":"","institution":"Hamamatsu University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Akemi","middleName":"","lastName":"Okumura","suffix":""},{"id":595162027,"identity":"da22ce9b-1dce-41a1-8bbc-bb1dde851c8c","order_by":4,"name":"Taeko Harada","email":"","orcid":"","institution":"Hamamatsu University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Taeko","middleName":"","lastName":"Harada","suffix":""},{"id":595162028,"identity":"0e7e6916-ea12-4de8-b526-6250b6a407e7","order_by":5,"name":"Md Shafiur Rahman","email":"","orcid":"","institution":"Hamamatsu University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Shafiur","lastName":"Rahman","suffix":""},{"id":595162029,"identity":"6b2ac4d5-4038-46a6-aa59-a899e7e74156","order_by":6,"name":"Kenji J. Tsuchiya","email":"","orcid":"","institution":"Hamamatsu University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kenji","middleName":"J.","lastName":"Tsuchiya","suffix":""}],"badges":[],"createdAt":"2025-12-05 01:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8283198/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8283198/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103410743,"identity":"eb0968c3-71ca-44d2-b178-eeb51957a8d6","added_by":"auto","created_at":"2026-02-25 10:59:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":108061,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCord serum adiponectin levels and ADHD symptoms in children\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The x-axis represents levels of adiponectin in the cord blood. The y-axis indicates total scores on the ADHD-RS, measured when participants were aged between 5 and 6 years. The red lines represent the best-fitted lines in the regression analysis.\u003c/p\u003e\n\u003cp\u003eAbbreviations: ADHD-RS, Attention deficit hyperactivity disorder rating scale\u003c/p\u003e\n\u003cp\u003e(B) The x-axis represents levels of adiponectin in the cord blood. The y-axis indicates total scores on the ADHD-RS, measured when participants were aged between 8 and 9 years. The red lines represent the best-fitted lines in the regression analysis.\u003c/p\u003e\n\u003cp\u003eAbbreviations: ADHD-RS, Attention deficit hyperactivity disorder rating scale\u003c/p\u003e","description":"","filename":"20250305TakahashiFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8283198/v1/fd7ccb82c18a2fb78ff82963.png"},{"id":103506908,"identity":"ede9c589-9f6e-402a-8a0f-5b3c0d933e98","added_by":"auto","created_at":"2026-02-26 13:39:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":46726,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrajectories of ADHD symptoms over time\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe x-axis represents child age in months (assessments at 66 and 96 months). The y-axis represents total scores on the Attention Deficit Hyperactivity Disorder Rating Scale (ADHD-RS). Trajectories were estimated using growth mixture modeling (GMM). Dotted lines indicate the 95% confidence interval around each trajectory. Abbreviations: ADHD, Attention deficit hyperactivity disorder\u003c/p\u003e","description":"","filename":"20250305TakahashiFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8283198/v1/ee14ac4d2da12056da0fde3a.png"},{"id":103410741,"identity":"7cbe0576-fdba-4010-a7ba-18d8f14235c8","added_by":"auto","created_at":"2026-02-25 10:59:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":117045,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePolygenic risk score analysis between ADHD-PRS and cord blood adiponectin levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe x-axis represents polygenic risk score of ADHD and the y-axis indicates levels of adiponectin in the cord blood. The red lines represent the best-fitted lines in the regression analysis.\u003c/p\u003e\n\u003cp\u003eAbbreviations: ADHD-PRS, Attention deficit hyperactivity disorder-polygenic risk score, ADHD-RS, Attention deficit hyperactivity disorder rating scale\u003c/p\u003e","description":"","filename":"20250305TakahashiFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8283198/v1/4de74457a129778c3b0821c8.png"},{"id":103507107,"identity":"269b84b4-6db4-4b86-8c52-dfe1a00e02ab","added_by":"auto","created_at":"2026-02-26 13:40:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":36752,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural equation modeling analysis of adiponectin and inflammation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eObserved measures are represented by squares and latent variables are represented by circles. Single-headed arrows (path) define associations between variables. Numbers represent the standard coefficient. CFI=0.999, Chi2=0.338, RMSEA=0.017 * P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001\u003c/p\u003e\n\u003cp\u003eAbbreviations: IL1b, interleukin 1 beta; MCP-1, monocyte chemoattractant protein-1; IL-6, interleukin 6; CRP, C-reactive protein; ADHD, Attention deficit hyperactivity disorder; CFI, Comparative fit index; RMSEA, root mean squared error of the approximation (RMSEA)\u003c/p\u003e","description":"","filename":"20250305TakahashiFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8283198/v1/7c6ac5b247799c1958c6b1ee.png"},{"id":103510075,"identity":"874e1702-c843-4b60-8fbb-b7afae4fe4c9","added_by":"auto","created_at":"2026-02-26 14:03:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1107213,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8283198/v1/a30852bd-6aca-4e87-b48a-a490bb2a76e1.pdf"},{"id":103507773,"identity":"f7b5d263-d249-434f-b993-fcc362ab8cef","added_by":"auto","created_at":"2026-02-26 13:44:59","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":55865,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8283198/v1/262d57971a151d7fdaf4928d.xlsx"},{"id":103410745,"identity":"d00ecbe6-a1a7-48da-bab6-13ac2077c5a8","added_by":"auto","created_at":"2026-02-25 10:59:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":259288,"visible":true,"origin":"","legend":"","description":"","filename":"20251205TakahashiSupplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8283198/v1/6c5b71c0b1383087dd0697a3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Involvement of adiponectin in the biology of attention deficit hyperactivity disorder","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRecent studies have highlighted a significant co-occurrence of obesity and ADHD in both children [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and adults [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Evidence suggests that obesity-related genes, such as the FTO gene encoding alpha-ketoglutarate-dependent dioxygenase, may contribute to ADHD risk [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and shared genetic risk alleles between obesity and ADHD have been proposed [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Obesity is often accompanied by abnormal circulating levels of adipocytokines, including adiponectin, a hormone secreted by adipose tissue [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These abnormalities are linked to conditions like type II diabetes and insulin resistance [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], and emerging research has also observed altered adiponectin levels in psychiatric disorders such as major depression [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], schizophrenia [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], panic disorder [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], bipolar disorder [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and ADHD [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdiponectin is a multifunctional adipokine with insulin-sensitizing[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and anti-inflammatory properties [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], playing a critical role in fatty acid oxidation[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Its expression is regulated by insulin [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], testosterone [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and glucocorticoids [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The hormone's effects are mediated by its receptors, AdipoR1 and AdipoR2, primarily expressed in muscle and liver tissue[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], as well as in the brain [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. A third receptor, T-cadherin, specifically binds the hexameric and high molecular weight (HMW) forms of adiponectin and is highly expressed in the cardiovascular system and brain [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Genome-wide association studies (GWAS) have identified associations between single nucleotide polymorphisms (SNPs) in the CDH13 gene, which encodes T-cadherin, and ADHD [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, CDH13 polymorphisms are strongly associated with serum adiponectin levels [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eReduced adiponectin levels are linked to metabolic and inflammatory disorders such as insulin resistance, type II diabetes, metabolic syndrome, obesity, and dyslipidemia [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Low adiponectin also promotes systemic inflammation, which plays a critical role in cardiovascular diseases like atherosclerosis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These anti-inflammatory and insulin-sensitizing properties make adiponectin essential for maintaining metabolic homeostasis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Similarly, neuroinflammation has been implicated in various psychiatric disorders, including ADHD [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For instance, we have previously reported that neonatal inflammation, such as elevated inflammatory cytokines, is associated with ADHD symptoms in children [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Imaging studies further support this by showing highly activated microglia in the brains of individuals with ADHD, suggesting a role for inflammation in its pathophysiology [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe frequent co-occurrence of ADHD with metabolic and inflammatory conditions has been extensively documented, and recent evidence suggests these conditions are genetically correlated [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition to its proposed role in metabolic and inflammatory disorders, adiponectin has recently been implicated in the pathophysiology of eating disorders[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Given the increasing recognition of overlapping behavioral and biological features between ADHD and eating disorders, such findings may provide further rationale for investigating adiponectin in ADHD. Moreover, other adipocytokines such as leptin and resistin have been associated with both energy balance and neurodevelopmental processes, suggesting that adiponectin may act as part of a broader network of metabolic signaling molecules involved in ADHD [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, the mechanisms linking ADHD and adiponectin across observational and genetic levels remain unclear. In this study, we used longitudinal cord blood data together with GWAS summary statistics on adiponectin and ADHD to investigate their associations across observational, pathway-level, and genetic domains, and to evaluate potential causal relationships. Specifically, we hypothesized that (1) lower cord blood adiponectin levels would be associated with increased ADHD symptoms, (2) ADHD and adiponectin would share genetic architecture, and (3) ADHD and adiponectin would show genetic correlation or causal associations supported by multiple genetic analyses.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnalytic strategy\u003c/h2\u003e \u003cp\u003eTo test our study hypotheses, we employed a multi-method approach combining observational and genetic analyses. First, we examined associations between cord blood adiponectin levels and ADHD symptoms using longitudinal cohort data. Second, we used gene-set enrichment analysis (GSEA) to identify biological pathways shared between ADHD and adiponectin regulation. Third, we quantified genome-wide shared heritability using linkage disequilibrium score regression (LDSC) and evaluated potential causal effects using Mendelian randomization (MR). Finally, we conducted polygenic risk score (PRS) analysis to examine the association between genetic liability for ADHD and adiponectin levels in the cohort participants.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eParticipants were recruited from the Hamamatsu Birth Cohort for Mothers and Children (HBC Study) which included pairs of mothers and children (N\u0026thinsp;=\u0026thinsp;531, 267 females and 264 males). Recruitment procedures of HBC study are fully described in our previous paper [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe obtained summary statistics data from large-scale GWAS meta-analysis of serum adiponectin levels, which included participants from the Adiponectin Genetics Consortium (ADIPOGen; n\u0026thinsp;=\u0026thinsp;29,347), the Asian Genetic Epidemiology Network (AGEN; n\u0026thinsp;=\u0026thinsp;7,825), and the Metabolic Syndrome in Men Study (METSIM; n\u0026thinsp;=\u0026thinsp;9,262) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. All adiponectin levels were measured in fasting serum samples from adult participants. The GWAS was conducted as a meta-analysis of summary statistics across the three consortia, and individual-level data were not pooled. Participants for GWAS study of ADHD were recruited from iPSYCH (N\u0026thinsp;=\u0026thinsp;55,374).\u003c/p\u003e\n\u003ch3\u003eMeasurement\u003c/h3\u003e\n\u003cp\u003eSerum adiponectin cytokines (IL-1beta and IL-6) and CRP in cord blood were measured using measured by enzyme-linked immunosorbent assays (ELISA) according to the manufacturer\u0026rsquo;s protocol (Funakoshi, Co. Ltd) in the HBC cohort. ADHD symptoms were assessed at ages 6\u0026ndash;7 and 8\u0026ndash;9 years using the Japanese version of ADHD-Rating Scale (ADHD-RS) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] in the participants of HBC study. The symptoms trajectory analysis was performed using the traj program that was developed for Stata [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Model fit was assessed using BIC. Cord blood samples were collected at birth and used to assay adiponectin and inflammatory markers.\u003c/p\u003e \u003cp\u003eSerum protein levels of adiponectin in the ADIPOGen consortium, the AGEN consortium and the METSIM study were measured using the SomaScan v4 proteomics platform. ADHD diagnosis was made using ICD10 criteria in the participants of iPSYCH.\u003c/p\u003e\n\u003ch3\u003eGene-set enrichment analysis\u003c/h3\u003e\n\u003cp\u003eUsing summary data of GWAS of ADHD obtained from PGC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.6084/m9.figshare.14671965\u003c/span\u003e\u003cspan address=\"10.6084/m9.figshare.14671965\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and serum adiponectin levels from GWAS catalog (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90293001-GCST90294000/GCST90293086\u003c/span\u003e\u003cspan address=\"http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90293001-GCST90294000/GCST90293086\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), we identified genetic pathways specific to ADHD and serum adiponectin levels. Gene mapping of SNPs obtained from the summary data was conducted using positional mapping, eQTL mapping and Chromatin interaction mapping using FUMA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fuma.ctglab.nl\u003c/span\u003e\u003cspan address=\"https://fuma.ctglab.nl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. To identify gene-sets associated with specific conditions and diseases, a range of databases was utilized, including MsigDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/index.jsp\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which focuses on all Canonical Pathways, such as BioCarta, KEGG and Reactome (MsigDB c2) and GWAS catalog. Using the hypergeometric distribution, a p-value of gene overlap was calculated and corrected for multiple hypothesis testing according to Benjamini and Hochberg using MAGMA gene-set analysis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eGenetic correlation analysis\u003c/h3\u003e\n\u003cp\u003eGenetic correlations (r\u003csub\u003eg\u003c/sub\u003e) between ADHD and adiponectin were calculated by linkage disequilibrium score regression (LDSC) using GWAS summary data of both conditions.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMendelian Randomization analysis\u003c/h2\u003e \u003cp\u003eTwo-sample MR analyses with regression analyses were conducted using meta-analysis of adiponectin GWAS summary and iPSYCH, with standard variant harmonization procedures (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mrcieu.github.io/TwoSampleMR/\u003c/span\u003e\u003cspan address=\"https://mrcieu.github.io/TwoSampleMR/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We used a P value threshold of 5 \u0026times; 10⁻⁸ to select SNPs for ADHD diagnosis and adiponectin levels. For the main analysis, we used the Inverse Variance Weighted (IVW) method as the primary approach. Sensitivity analyses were conducted using the Weighted Median, Weighted Mode, Simple Mode, and MR-Egger methods. Heterogeneity across SNPs was assessed using Cochran\u0026rsquo;s Q statistic in both IVW and MR-Egger models. Potential horizontal pleiotropy was evaluated using MR-Egger intercept and the MR-PRESSO global test. The leave-one-out analysis was performed to evaluate the robustness of the results, and the MR-PRESSO test was used to detect outliers.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePolygenic risk score analysis\u003c/h3\u003e\n\u003cp\u003ePolygenic Risk Score (PRS) for adiponectin was calculated using PRS-CSx which consider ethnic difference between discovery cohort and targeted cohort [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Genotyping was conducted using a Japonica array designed specifically for single nucleotide polymorphism (SNP) genotyping for a Japanese population. SNPs and individuals were retained as follows: missing data for SNP\u0026thinsp;\u0026lt;\u0026thinsp;0.02, pairwise identify-by-descent (IBD)\u0026thinsp;\u0026lt;\u0026thinsp;0.2, SNP Hardy Weinberg equilibrium of p\u0026thinsp;\u0026gt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, and minor allele frequency\u0026thinsp;\u0026gt;\u0026thinsp;0.01. Genotyping imputation was performed using BEAGLE 5.0 on the Japanese population in the 1000 Genome Project phase 3 reference panel. SNPs with an imputation INFO score\u0026thinsp;\u0026lt;\u0026thinsp;0.7 were excluded. We also excluded SNPs located within the MHC region because of high linkage equilibrium in this region. The number of SNPs analyzed for PRS was 5,606,655. A P-value threshold of 0.05 was used, which is standard in Bayesian PRS methods such as PRS-CS/PRS-CSx [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. To account for population stratification, 4 principal components (PCs) calculated with PLINK 1.9 were used. The criteria for SNP clumping were r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.1 within a 2Mb window. PRS scores were calculated with P-value thresholds at 0.05. Standardized PRS scores (mean\u0026thinsp;=\u0026thinsp;0 and standard deviation\u0026thinsp;=\u0026thinsp;1) were used for the analyses. For stratified analysis, children were divided into tertiles based on ADHD-PRS (low, medium, high) to evaluate potential gene\u0026ndash;environment interaction effects.\u003c/p\u003e\n\u003ch3\u003eStructural equation modeling\u003c/h3\u003e\n\u003cp\u003eWe conducted structural equation modeling (SEM) to examine the pathway linking serum adiponectin levels in cord blood and inflammatory markers (IL-1β, IL-6, CRP). Observed variables were modeled as indicators of latent constructs for systemic inflammation and ADHD symptomatology. The SEM was performed using maximum likelihood estimation in Stata 16, and model fit was evaluated using standard indices: Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), and chi-squared test. Good model fit was defined according to commonly accepted criteria: CFI\u0026thinsp;\u0026gt;\u0026thinsp;0.95, RMSEA\u0026thinsp;\u0026lt;\u0026thinsp;0.06, and non-significant χ\u0026sup2;.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were conducted using Stata 16 software (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC). Covariates included in all regression and SEM models were child sex, gestational age at birth, birthweight, maternal smoking during pregnancy, and parental age at birth. These covariates were selected a priori based on known associations with both ADHD and metabolic profiles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEthical consideration\u003c/h2\u003e \u003cp\u003e The study protocol was approved by the Hamamatsu University School of Medicine and University Hospital Ethics Committee (Ref No. 20\u0026ndash;233). Written informed consent was obtained from each caregiver (usually the mother) for her own and her children's participation. This study followed the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) reporting guideline and STROBE-MR guideline.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociation of adiponectin levels with ADHD symptoms and its trajectories\u003c/h2\u003e\n \u003cp\u003eParticipant characteristics of HBC study are summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Serum adiponectin levels in cord blood were negatively correlated with ADHD symptoms in children both aged 6\u0026ndash;7 years (Beta [SE] = -0.072[ 0.034], P\u0026thinsp;=\u0026thinsp;0.033, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea) and 8\u0026ndash;9 years (Beta [SE] = -0.109 [0.035], P\u0026thinsp;=\u0026thinsp;0.002, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb). Trajectory analysis showed that there are three trajectories in the change of ADHD-RS score from 6\u0026ndash;7 years old to 8\u0026ndash;9 years old (constantly low, decreasing, and increasing, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Reduced cord blood adiponectin was associated with increased risk ratio to belong to a group whose ADHD-RS score increasing (OR [95% CI]\u0026thinsp;=\u0026thinsp;1.182 [1.139\u0026ndash;1.230], P\u0026thinsp;=\u0026thinsp;0.028, Supplementary Table 1).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSample Characteristics of children and their parents in the study\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e264 (49.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e267 (50.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJapanese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e726 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall for gestational age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;10th percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10th-100th percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e683 (94.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePaternal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;12 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 years and longer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e664 (91.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaternal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;12 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 years and longer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e684 (94.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBirthweight (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2940.5 (438.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGestational age at birth (weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.9 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePaternal age at birth (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.2 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaternal age at birth (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.5 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold income (million JPY)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.0 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIQ (WISC-IV: FSIQ)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101.6(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAbbreviation: SD, Standard Deviation; JPY, Japanese Yen; ADHD-RS, Attention deficit hyperactivity disorder rating scale; IQ, intelligent quotient; WISC-IV, Wechsler Intelligent Scale for Children-IV; FSIQ, Full Scale IQ\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eGene set enrichment analysis\u003c/h2\u003e\n \u003cp\u003eGene set analysis of GWAS summary of ADHD showed that statistically significant over-lapping genes with GWAS summary of adiponectin levels (the number of over-lapping genes\u0026thinsp;=\u0026thinsp;14 out of 50, Adjusted P-value\u0026thinsp;=\u0026thinsp;7.16 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e). Representative overlapping genes included ADARB1, ADCY2, and ADAM9, which are involved in neurodevelopmental or metabolic regulation. See Supplementary Table\u0026nbsp;2 for the results of all overlapping pathways.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eGenetic correlation analysis\u003c/h2\u003e\n \u003cp\u003eLD score regression analysis showed that ADHD and adiponectin levels are inversely correlated, but the direction was negative (r\u003csub\u003eg\u003c/sub\u003e [SE] = -0.31 [0.034], P\u0026thinsp;=\u0026thinsp;2.148x10\u003csup\u003e\u0026minus;\u0026thinsp;20\u003c/sup\u003e). The SNP-based heritability was estimated at h\u0026sup2; [SE]\u0026thinsp;=\u0026thinsp;0.268 [0.011] for ADHD and h\u0026sup2; [SE]\u0026thinsp;=\u0026thinsp;0.152 [0.019] for adiponectin, indicating a moderate heritable component in both traits that may contribute to their observed genetic association.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eMendelian randomization analysis\u003c/h2\u003e\n \u003cp\u003eMR analysis suggested an association between adiponectin and ADHD. The association of adiponectin with ADHD was marginally significant (IVW, \u0026beta; [SE]\u0026thinsp;=\u0026thinsp;0.121 [0.053], P\u0026thinsp;=\u0026thinsp;1.54 \u0026times; 10⁻\u0026sup3;), whereas the association of ADHD with adiponectin was consistently significant across multiple methods (IVW, \u0026beta; [SE]\u0026thinsp;=\u0026thinsp;0.042 [0.004], P\u0026thinsp;=\u0026thinsp;1.94 \u0026times; 10⁻\u0026sup2;\u0026sup1;) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Sensitivity analyses further supported the directional association from ADHD to adiponectin (see Supplementary Table 3 and Supplementary Fig. 1), whereas the association from adiponectin to ADHD was not consistently replicated. The leave-one-out analysis showed that no single genetic variant was driving the causal effect of ADHD on adiponectin (Supplementary Fig. 2).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTwo sample mendelian randomization analysis between adiponectin and ADHD\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSNPs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ebeta\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdiponectin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.08227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.535x10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdiponectin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.967\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;12\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAbbreviations: ADHD, Attention deficit hyperactivity disorder; SNP, single nucleotide polymorphism, B, standardized beta; SE, standard error; MR-eggar, Mendelian Randomization; IVW, Inverse variance weighted\u003c/p\u003e\n \u003cp\u003eCochran\u0026rsquo;s Q test indicated no significant heterogeneity in both the IVW and MR-Egger models. Directional pleiotropy was assessed using the MR-Egger intercept, which did not indicate significant pleiotropic bias (Supplementary Table\u0026nbsp;4). Additionally, the MR-PRESSO test did not identify any significant outliers, further supporting the robustness of the results.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003ePolygenic risk score analysis between ADHD and adiponectin levels\u003c/h2\u003e\n \u003cp\u003eTo get additional evidence to show that ADHD is a risk for reduced adiponectin, we conducted polygenic risk score analysis using GWAS summary of ADHD. We demonstrated that ADHD-PRS was negatively associated with adiponectin in cord blood (beta [SE] = -1.176 [0.437], P\u0026thinsp;=\u0026thinsp;0.007, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eAdiponectin and inflammation\u003c/h2\u003e\n \u003cp\u003eWe first conducted univariate linear regression analyses to examine the associations between serum adiponectin levels and individual inflammatory markers in cord blood. Adiponectin was negatively associated with IL-6 (\u0026beta; [SE] = \u0026minus;\u0026thinsp;0.174 [0.065], p\u0026thinsp;=\u0026thinsp;0.007), IL-1\u0026beta; (\u0026beta; [SE] = \u0026minus;\u0026thinsp;0.152 [0.061], p\u0026thinsp;=\u0026thinsp;0.013), and CRP (\u0026beta; [SE] = \u0026minus;\u0026thinsp;0.139 [0.062], p\u0026thinsp;=\u0026thinsp;0.025). These consistent inverse associations provided empirical justification for modeling a latent inflammation factor in a structural equation modeling (SEM) framework. Finally, the suppression of inflammation in cord blood by adiponectin was demonstrated using SEM analysis. The constructed model exhibited a negative correlation between cord blood adiponectin levels and inflammatory markers, including IL-1beta, IL-6, and CRP, as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The SEM model showed acceptable fit (CFI\u0026thinsp;=\u0026thinsp;0.999, RMSEA\u0026thinsp;=\u0026thinsp;0.017, \u0026chi;\u0026sup2; = 0.338).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we explored the role of adiponectin in the development of ADHD. First, serum adiponectin levels in cord blood were negatively associated with ADHD symptoms in children aged 6\u0026ndash;7 and 8\u0026ndash;9 years. There were three distinct trajectories in the change of ADHD symptoms severity, and adiponectin was negatively associated with a risk of belonging to a group whose ADHD symptoms getting worse over time. Gene set analysis of the GWAS summary of ADHD indicated that there was an overlap of ADHD and adiponectin levels at GWAS levels. Genetic correlation analysis demonstrated a negative correlation between ADHD and adiponectin levels. MR analyses suggested directional asymmetry, with stronger evidence for an effect of ADHD on adiponectin levels. Lastly, we showed that ADHD-PRS was negatively associated with cord blood adiponectin in our cohort. Taken together, our data demonstrated that adiponectin was dysregulated in subjects with ADHD, and it can be considered that the disease itself has some impact on serum levels of adiponectin.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, even cross-sectional data on the association between ADHD and adiponectin are limited [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], and there have been no longitudinal studies addressing this topic so far. Consequently, the present research has provided novel insights by exploring not only the early-life association between cord blood adiponectin and later ADHD symptoms, but also the developmental course of these symptoms across childhood. In particular, cord blood adiponectin predicted membership in the increasing-symptom trajectory between ages 6\u0026ndash;7 and 8\u0026ndash;9, suggesting that intrauterine metabolic or inflammatory status may influence the unfolding of ADHD symptom patterns across childhood. These findings partially explain why the prevalence of obesity, type II diabetes and cardio-vascular diseases is high in subjects with ADHD compared to healthy controls. Therefore, it may be important to find methods to regulate serum adiponectin levels (in this case, increase) in ADHD subjects to reduce physical comorbidities.\u003c/p\u003e \u003cp\u003eThe present findings, together with previous literature, raise important questions about the interplay between adiponectin and other metabolic signaling molecules in ADHD. Adiponectin does not act in isolation; it is part of a broader adipocytokine network that includes leptin, resistin, ghrelin, and others, all of which have been implicated in neurodevelopmental regulation and feeding behavior. Given the known heterogeneity of BMI among individuals with ADHD, including both underweight and overweight profiles, future research should explore whether distinct adipokine patterns characterize different ADHD subtypes or symptom clusters. Moreover, the potential bidirectional influence between adiponectin and appetite-regulating hormones could help explain overlapping pathophysiological features between ADHD and eating disorders, as well as metabolic comorbidities frequently observed in this population [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnimal models with reduced adiponectin signaling by knockout of AdipoR2 (adiponectin receptor 2) have been reported to exhibit hyperactivity compared to wild-type littermates [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Conversely, it has been shown that hyperactivity is reduced in transgenic mice overexpressing adiponectin. These lines of evidence suggest that dysregulated adiponectin signaling may affect ADHD symptoms [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], although the exact mechanism has not been elucidated.\u003c/p\u003e \u003cp\u003eFurthermore, reduced cord blood levels of adiponectin were shown to increase inflammatory cytokines. Since we have previously shown that there is a gene-environmental interaction between genetic risk for ADHD and inflammatory cytokines on the severity of ADHD symptoms [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], it may also be important to keep a decent levels of serum adipokines to prevent the progress of the illness.\u003c/p\u003e \u003cp\u003eUnfortunately, no medication currently approved for ADHD has not been reported to increase serum levels of adiponectin, except one small clinical trial which showed methylphenidate increased adiponectin in 20 children with ADHD [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, several clinical trials and animal study showed that administration of melatonin can increase serum levels of adiponectin. For example, a double-blind, placebo-controlled trial involving 70 subjects with metabolic syndrome found that melatonin supplementation (6 mg/day for 12 weeks) significantly increased adiponectin levels[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Furthermore, supplementation of melatonin has been repeatedly increasing serum levels of adiponectin in mice [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Therefore, supplementation of melatonin may have benefit on controlling ADHD symptoms via regulation of adiponectin levels. This is also in line with our previous studies which showed that reduced melatonin may be underlying the basis of ADHD [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt should be acknowledged that a couple of limitations are associated with this study. Firstly, the adiponectin levels were measured using umbilical cord blood; therefore, a temporal discrepancy exists between the adiponectin levels and the symptoms of ADHD. Postnatal environmental and developmental factors may modulate the long-term associations between neonatal adiponectin levels and ADHD symptoms, and this should be considered when interpreting the results. Furthermore, isoform-specific measures of adiponectin (e.g., high molecular weight vs. total levels) were not available in the present study, which limits the ability to interpret pathways specifically involving T-cadherin binding. Secondly, while previous genome-wide association studies have implicated CDH13, a gene encoding T-cadherin, as being associated with both ADHD and adiponectin levels, our study did not include locus-specific analyses or SNP-level evaluation of CDH13. Although CDH13-linked SNPs may have contributed to polygenic scores or gene set enrichment results in our analyses, the lack of direct interrogation of this locus represents a limitation. Future studies incorporating targeted gene-level tests or fine-mapping analyses may help elucidate the specific role of CDH13 in the adiponectin\u0026ndash;ADHD pathway. Lastly, although the PRS study demonstrated a significant association, the replication of these findings in independent cohorts is necessary for validation.\u003c/p\u003e \u003cp\u003eIn conclusion, the present study suggests that adiponectin is involved in the mechanism of ADHD, which can be a potential target of managing ADHD symptoms and related comorbidities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e The data generated for this study are subject to the following licenses/restrictions: Privacy and Confidentiality of Participants. Requests to access these datasets should be directed to NT, [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcept and design: Takahashi, Tsuchiya\u003c/p\u003e\n\u003cp\u003eAcquisition, analysis, or interpretation of data: Takahashi, Okumura, Harada, Iwabuchi, Nishimura\u003c/p\u003e\n\u003cp\u003eDrafting of the manuscript: Takahashi, Tsuchiya\u003c/p\u003e\n\u003cp\u003eCritical revision of the manuscript for important intellectual content: All authors.\u003c/p\u003e\n\u003cp\u003eStatistical analysis: Takahashi, Rahman\u003c/p\u003e\n\u003cp\u003eObtained funding: Tsuchiya\u003c/p\u003e\n\u003cp\u003eAdministrative, technical, or material support: Okumura, Harada, Iwabuchi, Nishimura\u003c/p\u003e\n\u003cp\u003eSupervision: Tsuchiya, Nishimura\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure of interest:\u003c/strong\u003e The authors declare no competing interest in this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI in scientific writing:\u003c/strong\u003e None\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding/Support:\u0026nbsp;\u003c/strong\u003eThis work was supported by grants from the Ministry of Education, Culture, Sports, Science, and Technology in Japan (grant number 19H03582 to KJT).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of the Funder/Sponsor:\u0026nbsp;\u003c/strong\u003eThe funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to all the study participants. The authors thank Dr. Tetsuo Kato of the Kato Maternity Clinic, Drs. K Sugihara, M Sugimura, K Takeuchi, K Suzuki, Y Murakami, Y Kohmura, Y Miyabe, K Hirai, Y Nakamura, R Koizumi, H Murakami, Y Kobayashi-Kohmura, K Muramatsu-Kato, Prof. N Kanayama, and all attending obstetricians of Hamamatsu University School of Medicine for their full support with participant enrolment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors also thank the chief midwife, Ms. Kiyomi Hinoki, and all midwives and staff at the maternity clinic of Hamamatsu University School of Medicine for their support with participant enrolment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe thank the HBC Study team, including former members, Ms. Emi Higashimoto, Noriko Kodera, Atsuko Nakamura, Yumeno Kugizaki, Yukiko Suzuki, Riyo Takabayashi, Maiko Honda, Hiroko Muraki, Makiko Narumiya, Eriko Sato, Drs. Yuka Omori, Hanae Tainaka, Asako Tokizawa, Damee Choi, Takanobu Horikoshi, Keisuke Wakusawa, Yosuke Kameno, Daisuke Kurita, Kiyokazu Takebayashi, Masamichi Yokokura, Tomoyasu Wakuda, Ryosuke Asano, Thanseem Ismail, Keiko Iwata, Yasuhide Iwata, Emiko Kawai, Masayoshi Kawai, Yujin K Kuroda, Kaori Matsumoto, Hideo Matsuzaki, Tsuruko Mori, Kyoko Nakaizumi, Kazuhiko Nakamura, Anitha A Pillai, Yui Seno, Chie Shimmura, Shiro Suda, Genichi Sugihara, Katsuaki Suzuki, Kohei Yamada, Shigeyuki Yamamoto, Yujiro Yoshihara, Yusaku Endoh, Koichi Hirano, Teruhiko Suzuki, Professor Norio Mori, Professor Nori Takei, Professor Hitoshi Kuwabara, Professor Hidenori Yamasue, Professor Masatsugu Tsujii, and Professor Toshirou Sugiyama, for data collection and management, and administrative and academic supports. The authors are grateful to Ms. Yukiko Akizuki, Tomoko Takahashi, Kumi Kusakabe, for administrative supports.\u003c/p\u003e\n\u003cp\u003eThe authors are also grateful to Mr. Charles Davey for their professional editing support in English.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgranat-Meged AN, Deitcher C, Goldzweig G, Leibenson L, Stein M, Galili-Weisstub E (2005) Childhood obesity and attention deficit/hyperactivity disorder: a newly described comorbidity in obese hospitalized children. 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Biochem Biophys Res Commun 257:79\u0026ndash;83\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBahrami M, Cheraghpour M, Jafarirad S, Alavinejad P, Cheraghian B (2019) The role of melatonin supplement in metabolic syndrome. Nutr Food Sci 49:965\u0026ndash;977\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarbosa IG, Rocha NP, de Miranda AS, Magalh\u0026atilde;es PV, Huguet RB, de Souza LP, Kapczinski F, Teixeira AL (2012) Increased levels of adipokines in bipolar disorder. 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Anadolu Psikiyatri Dergisi 21:633\u0026ndash;640\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-child-and-adolescent-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ecap","sideBox":"Learn more about [European Child \u0026 Adolescent Psychiatry](http://link.springer.com/journal/787)","snPcode":"787","submissionUrl":"https://submission.nature.com/new-submission/787/3","title":"European Child \u0026 Adolescent Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"ADHD, adiponectin, obesity, GWAS, Mendelian Randomization, PRS","lastPublishedDoi":"10.21203/rs.3.rs-8283198/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8283198/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"We investigated the involvement of adiponectin in the development of attention-deficit/hyperactivity disorder (ADHD), using a multi-pronged approach integrating cord blood adiponectin concentrations, polygenic risk scores derived from publicly available GWAS summary statistics, and Mendelian randomization. This study aimed to evaluate both upstream genetic influences and downstream adiponectin-level associations. Using a combination of longitudinal cord blood data (N = 531) and genome-wide association studies of ADHD (N = 55,374) and adiponectin (N = 46,434), we examined observational, genetic, and causal associations linking adiponectin with ADHD. Lower cord blood adiponectin levels were associated with higher ADHD symptoms at ages 5–6 and 8–9, and predicted membership in the increasing-symptom trajectory group. Gene-set analysis demonstrated pathway-level overlap between ADHD and adiponectin regulation. Genetic correlation analysis showed a significant negative correlation between ADHD and adiponectin levels. Mendelian randomization provided stronger evidence for a causal effect of ADHD on adiponectin levels. Polygenic risk score analysis also demonstrated a negative association between genetic liability for ADHD and cord blood adiponectin levels. Additionally, lower adiponectin levels were found to be associated with elevated inflammatory markers in the cord blood samples obtained from the cohort study, which may provide a potential explanation for the higher prevalence of metabolic comorbidities observed in individuals with ADHD. These findings suggest that adiponectin is involved in the mechanism of ADHD and targeting adiponectin regulation may offer a novel approach for managing ADHD and its related health risks.","manuscriptTitle":"Involvement of adiponectin in the biology of attention deficit hyperactivity disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-25 10:59:30","doi":"10.21203/rs.3.rs-8283198/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-18T20:35:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-18T18:11:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-07T15:22:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259984609345427622447970496862940631806","date":"2026-02-26T10:08:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-21T11:24:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329303960833457799267506935824281892665","date":"2026-02-21T01:22:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25855650407539208055580325754835599833","date":"2026-02-20T18:05:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-20T14:55:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-06T05:43:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-06T05:41:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Child \u0026 Adolescent Psychiatry","date":"2025-12-05T01:29:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-child-and-adolescent-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ecap","sideBox":"Learn more about [European Child \u0026 Adolescent Psychiatry](http://link.springer.com/journal/787)","snPcode":"787","submissionUrl":"https://submission.nature.com/new-submission/787/3","title":"European Child \u0026 Adolescent Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0aea0cd0-c587-47d2-937f-ffbb90f02822","owner":[],"postedDate":"February 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T06:38:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-25 10:59:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8283198","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8283198","identity":"rs-8283198","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}