DNA Methylation profiles in girls with anorexia nervosa and functional hypothalamic amenorrhea: a pilot study

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While genome-wide and twin studies suggest a strong genetic component, recent findings emphasize the significant role of environmental factors, including stress and social pressures, in its development. Epigenetic mechanisms, particularly DNA methylation, may mediate these interactions, influencing metabolism and reproductive health. Despite these advances, research on cohorts of young adolescents remains limited. Therefore, this study aims to investigate the epigenetic alterations associated with AN in adolescent females with functional hypogonadotropic hypogonadism, compared to normal-weight pubertal controls. Results: Examination of methylome changes associated with AN in 10 girls revealed 87 differentially methylated regions (DMRs) compared to 11 healthy girls. The majority of these DMRs exhibited hypomethylation in the AN group (69%) and harbored genes, such as Clusterin (CLU), regulating hypothalamic feeding pathways, and MKRN3, involved in pubertal timing regulation. Moreover, we showed that methylation levels in members of AN group differed from those in the control group for 2072 CpG sites (62.40% hypomethylated) located within genes previously associated with metabolic processes (e.g., LEPR and NR1H3) and age of menarche (e.g., GHR, MKL2, GAB2, CTBP2, and DLK1). Additionally, some CpGs located in genes such as CDH7, associated with hypogonadotropic hypogonadism. Interestingly, a total of 92 differentially methylated genes encoding zinc-finger (ZNF) proteins, including MKRN3, exhibited distinct methylation profiles between the two groups. Ingenuity Pathway Analysis of all CpGs highlighted pathways related to metabolism and body mass (e.g. leptin, and sirtuin signaling), neuronal signaling (e.g., semaphorin), and several pathways potentially associated with puberty (e.g., estrogen and GNRH signaling). Conclusion: Most of the observed changes concerned hypomethylation of DMR and CpG sites in the AN group. These epigenetic changes mainly affected transcriptional repressors, such as ZNFs, and genes associated with energy deprivation, metabolism, inflammation and pubertal development with consequent changes in the methylation status of key signalling pathways such as GnRH, leptin and insulin. In conclusion, distinct methylation profiles were observed in the AN and control groups suggesting that epigenetic modifications may serve as potential biomarkers of metabolic dysfunction and pubertal arrest, underlining their interconnected role in the pathophysiology of the disease. Anorexia epigenetics methylation amenorrhea puberty Figures Figure 1 Figure 2 Figure 3 Background Anorexia nervosa (AN) is a severe eating disorder characterized by an obsessive desire to lose weight and a disrupted body image, leading to extreme caloric restriction and severe malnutrition, which, in the most severe cases, can be fatal [1]. This condition is often associated with high rates of morbidity and comorbidities, including anxiety, depression, and other mental disorders, [2]. Although AN primarily affects young women, it can impact individuals of all genders and ages and the precise aetiology remains partly unknown. Recent advances in genome-wide association studies (GWAS) have identified several AN-associated loci that show significant genetic correlations with psychiatric disorders and metabolism regulating factors probably contributing to the pathophysiology of the disease [3]. Furthermore, family and twin studies confirmed heritability estimates of 50–60%, highlighting the significant role of genetic factors in this disorder [4]. On the other hand, environmental factors, such as social pressures, trauma and stress play a crucial role in the risk of developing AN. A particularly critical period is puberty, during which body dissatisfaction and low self-esteem intersect with hormonal changes, increasing vulnerability to eating disorders. Moreover, it is well established that AN significantly impacts normal pubertal development, contributing to delay reproductive maturation through metabolic imbalances [5-7]. Girls with AN frequently experience either primary or secondary amenorrhea, which is closely linked to the severe caloric restriction and significant weight loss typical of this condition resembling a functional form of hypogonadotropic hypogonadism [8, 9]. This dysfunction is not merely a clinical symptom but also an indicator of the profound metabolic and endocrine alterations caused by AN, with repercussions including long-term effects on bone and reproductive health. In addition to genetic and environmental factors, epigenetics has emerged over the past decade as a crucial area of research for understanding eating disorders [10]. Epigenetic mechanisms provide an additional level of gene regulation that bridges genetic background and environmental influences. Distinct DNA methylation profiles have been observed in individuals with AN, although global findings are sometimes conflicting. One study, using DNA from lymphocytes of 29 individuals with an average age of 22 years and with restrictive or binge/purge types of AN, found slight hypermethylation compared to controls [11]. Two other studies, one on adults with AN and the other on adolescents, reported lower global methylation levels [12, 13]. Large-scale epigenome-wide DNA methylation studies have advanced understanding of these methylation patterns, revealing significant changes in genes involved in metabolism, neurotransmission, and neuroplasticity [11, 14-16]. These epigenetic modifications are associated with altered hormone levels linked to delayed reproductive maturation and the onset of amenorrhea. Furthermore, pubertal development itself appears to have a distinctive methylation profile under both physiological and pathological conditions, such as precocious puberty [17] or delayed puberty [18]. These findings have significant diagnostic potential and could facilitate the development of biomarkers to monitor disease activity and treatment effectiveness. Despite these advances, research on cohorts of young adolescents remains limited. Greater attention is needed for studies exploring epigenetic and hormonal changes during this critical developmental window, particularly in relation to pubertal development alterations. Such investigations are essential, as early intervention could mitigate long-term consequences for reproductive and bone health. In this context, the present study aims to investigate epigenetic alterations associated with anorexia nervosa in a cohort of young female adolescents with functional hypogonadotropic hypogonadism, compared to a control group of normal weight pubertal adolescents. Results Population Clinical and laboratory characteristics of the entire cohort are shown in Table 1. Subject with anorexia nervosa (AN) presented a similar mean age and pubertal stage than pubertal controls (CTP). A statistically significant difference in BMI SDS existed between AN patients and controls (-1.71 ± 0.71 and 0.32 ± 1.03, respectively; p = <0.001) as expected. Significantly lower levels of LH and 17-beta-estradiol were found in AN patients (p= .009 and .008, respectively). No difference in FSH levels between the two groups existed (Table 1). In AN patients 4 presented with primary amenorrhea while 6 had secondary amenorrhea with an initially normal onset of menarche (mean age at menarche 11.25± 0.69) and a mean amenorrhea period of 1.3 years (±0.98 SDS). Upon completing the specific questionnaires EAT26 (Children-Eating Attitude Test) and CDI (Children’s Depression Inventory), positive scores were obtained for eating behaviour disorder (Score 48 > 19), and the presence of pervasive depressive traits (P=30, cut off 19) (Table 2). AN CTP p-value Number 10 11 Age years 13.64 ±1.91 13.88±1.96 .784 BMI, SDS -1.71± 0.71 0.32 ± 1.03 <0.001 Height, SDS -0.28 ± 0.68 -1.44± 1.07 . 02 Tanner Stage, breast 2.5 ± 0.60 3 ± 0.76 .184 Tanner stage, pubic hair 2.7 ± 0.42 3± 0.59 .352 LH, IU/L 1.08 ± 1.06 5.2 ± 2.50 . 009 FSH; IU/L 6.9 ±2.53 5.14 ± 2.48 .403 17β-estradiol, pg/mL 16.68 ±8.05 38.78 ± 9.43 .008 Table 1 Clinical and laboratory characteristics of patients with AN. The parameters are expressed as mean ± standard deviation. . The parameters are expressed as mean ± standard deviation, except for Tanner stage reported as median (min and max value). Mann-Whitney U Test Calculator and T-Test Calculator for 2 Independent Means were used as appropriate to calculate p-value for AN versus control group using t-test. Age of onset (years) 12.42±1.91 Cumulative illness duration (months) 14.5±17.43 Child Behavior Checklist (CBCL) 68.33±2.51 Eating Attitudes Test ( EAT) 36.25±9.42 Child depression inventory (CDI) 2.03±0.5 Secondary amenorrhea (%) 60% Age at menarche (years) 11.25±0.76 Months of menses presence before secondary amenorrhea (months) 32.4±0.6 D uration of amenorrhoea (months) 15.6 ± 1.1 Table 2: Clinical features in patients with AN. Differentially methylated genomic regions in AN compared to healthy pubertal girls To assess global DNA methylation changes that occur in girls with anorexia nervosa (AN) and healthy pubertal control (CTP), we first identify differentially methylated regions (DMRs). This analysis revealed 87 DMRs (with an FDR 5%), of which 68.97% were hypomethylated in girls with anorexia who presented primary or secondary amenorrhea. These 87 DMRs are mainly included the promoter regions of genes involved in immune and inflammatory pathways, such as LTA , in the hypothalamic feeding regulatory pathways, such as CLU , as well in genes involved in the onset of puberty, such as MKRN3 (Additional file 1). We also applied the epigenetic Landscape in Silico Deletion Analysis (LISA) to identify enriched transcription factors potentially regulating genes associated with our DMRs. Among the top twenty significant transcription factors (Additional file 2), we found the progesterone receptor (PR) and estrogen receptor (ESR1) genes, which mediate the effects of sex hormones involved in the reproductive system and menstrual cycle regulation. Additionally, we also identified DNMT1 (DNA Methyltransferase 1), SFMBT1 (Scm-like with Four MBT Domains 1), and DNMT3B (DNA Methyltransferase 3 Beta) which are involved in DNA methylation processes and chromatin remodelling affecting gene expression. Differences between groups at individual CpG sites Studies on methylation frequently identify a predominance of differentially methylated sites in regulatory regions, attributing to these a significant impact on gene expression. However, the role of differentially methylated CpG sites in regions distant from the promoter remains uncertain. We decided to consider only CpG sites mapped to transcription start sites or promoter regions, as they theoretically might influence the regulation of their respective genes. Based on this consideration the exploration of methylation levels in isolated CpG sites identified 2072 differentially methylated probes (DMPs) mapping onto 1896 genes (Additional file 3) . The comparison between girls with anorexia (AN) and pubertal controls (CTP), reported a significant proportion of differentially methylated CpGs (FDR 10%) located in intergenic regions (IGRs) and within the body of genes. Specifically, approximately 27% of these DMPs were located in promoter regions, including TSS1500 (10.2%), TSS200 (6.03%), 5'UTR (8.05%) and the first exon (2.47%) (Figure 1A). Interestingly, the global analysis of DMPs showed the same trend as previously reported in DMRs, with lower methylation levels in the AN group compared to the controls (Figure 1B). The comparison between the AN and control groups revealed several differentially methylated probes related to nutrition. In detail, genes involved in cholesterol and lipid processing (PRKAG2), glucose metabolism (IGF2R, RPTOR and SORBS1) and appetite regulation (GHRL, LEPR and CAMKK2). Others probes mapped onto genes relevant to immune function (such as NOD1, DSE, IRAK3, HHLA2, and NR1H3). Among hypermethylated CpGs sites, there were some located near genes involved in the regulation of anxiety and depression as serotonin degradation signalling (ALDH4A1 and EXT2) (Figure 2). Additionally, we found a different methylation of CpGs sites in genes related to wound healing processes (TNXB) [19], skeletal homeostasis (LRP5)[20], and energy metabolism (FHL2)[21]. Interestingly, as shown in Table 3, 14 DMPs mapped onto genes that have been previously associated with age of menarche: GHR, LEPR, NFAT5, GAB2, ARNTL, EIF4G1, RMI1, PTPRD, RORA, MKL2, MKRN3, CTBP2, WWP2, and DLK1 [22]. Subsequently, considering the list of genes with differentially methylated promoters, we performed a detailed bioinformatics analysis, to search putative transcription factors (TF) which are potentially regulating genes associated with our DMPs. In this way, we detected 180 TFs (FDR <0.05). The top 20 (full list shown in Additional file 2), include the receptor of sexual hormones as AR, ESR1, and PR that are crucial for reproductive processes such as menstrual cycle regulation and pregnancy. Additionally, the CEBPB factor which plays a role in the regulation of genes involved in immune response, metabolism, and differentiation of various cell types including adipocytes and hepatocytes, is also present in the list with a high score. Moreover, among genes with differentially methylated promoters there was an enrichment of genes containing Estrogen Responsive Elements (EREs) (hypergeometric test with p-value < 0.05), suggesting that some of the effects of estrogen signaling in puberty are modified through epigenetic mechanisms. However, we could not find a correlation between beta-values and estrogen levels, no CpGs were found directly correlated with them (data not shown). Finally, to identify pathways that could be modulated by the differentially methylated genes, we performed Ingenuity Pathway Analysis (IPA). Interestingly, our results show differential methylation in signaling pathways that are easily correlated with reduced caloric intake, affecting metabolic processes and body mass. These include the white adipose tissue browning, sirtuin signaling, as well as insulin and leptin signaling. Additionally, IPA showed enrichment in neuronal signaling pathways such as the GnRH and semaphorin which have been previously reported to be associated with pubertal development [23, 24]. Other signaling pathways, as those involved in estrogen and ovarian cancer, present a different methylation status between the AN and control groups (Figure 3). The entire list of differentially methylated pathways can be found in the Additional file 4 CG_ID P.Value adj.P.Val deltaBeta CHR Gene name Feature cg17942553 3.71E-06 0.000133 -0.10629 1 ARNT TSS1500 cg15205441 2.51E-08 4.35E-06 -0.16401 10 CTBP2 5'UTR cg02795603 7.87E-08 9.17E-06 -0.10834 3 EIF4G1 5'UTR cg24252966 2.97E-07 2.29E-05 -0.12395 3 EIF4G1 5'UTR cg04116507 1.07E-08 2.49E-06 -0.12154 11 GAB2 5'UTR cg15466952 1.62E-07 1.51E-05 -0.10346 1 LEPR 5'UTR cg02761778 1.87E-07 1.68E-05 -0.18258 16 NFAT5 5'UTR cg25172788 8.39E-06 0.000238 -0.12531 9 RMI1 5'UTR cg17181046 2.98E-07 2.30E-05 -0.20816 15 RORA TSS1500 cg09789698 5.07E-09 1.53E-06 -0.1106 16 WWP2 5'UTR cg16103098 0.002119 0.012164 0.172079 14 DLK1 TSS1500 cg00215587 0.000396 0.003707 -0.10076 15 MKRN3 5'UTR cg24779990 0.000257 0.002724 -0.14077 5 GHR TSS200 cg11196813 0.002353 0.013115 0.124879 9 PTPRD TSS1500 cg20931294 1.58E-05 0.000372 -0.11603 16 MKL2 5'UTR Table 3: Genes previously associated ti the age of menarche [22]. Figure 3: Ingenuity canonical pathway analysis between AN and CTP. In red are represented the pathways enriched with hypermethylated CpGs while, in green, the hypomethylated ones. The figure shows only part of the total pathways. The total list of pathways is available in additional file section. ZNF Methylation Although the role of Zinc finger (ZNF) genes in pubertal process is well-established, there is, currently, no evidence of their involvement in eating disorders. Interestingly, our methylation profiling analysis identified numerous Zinc finger genes among those differentially methylated. In fact, 92 DMPs were mapped near ZNF genes. Some of these, such as ZNF148, which regulates the GABA transporter, and ZNF445 and ZNF365, which are involved in methylation processes and genomic stability, respectively, were previously reported by our group in another cohort of patients with pubertal disorders [17]. Additionally, other ZNF genes, such as ZNF574, ZNF30, ZMIZ2, and ZFAND2B, have been associated with the development of estrogen-dependent tumors, including breast and ovarian cancers. Notably, several differentially methylated ZNF genes are associated with metabolic syndrome, such as ZNF664, or with conditions like hypocholesterolemia and diabetes, such as ZNF860. Others, like ZFAT, are involved in adipocyte differentiation and body fat distribution. Furthermore, some genes, including ZBTB43 and ZNF331, are linked to changes induced by caloric restriction. Discussion Over the past two decades, numerous studies have highlighted the role of a complex interplay between genetic, biological, psychological and environmental factors (such as stress, diet, trauma, or environmental exposures) in the development of anorexia nervosa (AN) [25]. In addition, epigenetics has emerged as a critical mechanism, regulating gene expression in response to environmental stimuli without altering the DNA sequence [10, 26]. Despite the complexity and the high comorbidity of AN with other psychiatric disorders, the underlying risk factors and specific biomarkers remain poorly understood. This knowledge gap has become even more pressing due to the recent surge in eating disorder diagnoses, hospitalizations, and worsening of symptoms, phenomena exacerbated by the Covid-19 pandemic and associated lockdown measures [27, 28]. In this research landscape, our study aimed to analyze DNA methylation profiles in peripheral blood leukocytes from adolescent girls diagnosed with AN and amenorrhea, comparing them to those of healthy pubertal controls. Specifically, our study identified 87 regions and 2,072 differentially methylated CpG sites, with a higher percentage of reduced methylation in girls with AN. This finding aligns with existing literature that associates DNA hypomethylation with energy deprivation and chronic stress [29, 30] , both of which are key features of AN. Nutritional deficiencies, including a lack of methyl donors such as folates [31, 32], due to prolonged fasting in AN may contribute to the hypomethylation observed in these patients. This potential link underlines how severe caloric restriction can lead to molecular alterations that can affect gene expression and hormonal balance. Echoing previous studies [11, 16], we have reported significant epigenetic changes in genes central to metabolic, nutritional and immune functions. Longitudinal studies by Steiger [16] highlighted the dynamic nature of DNA methylation in subjects with active AN and those in remission. Indeed, nutritional intake and body mass index (BMI) appear to directly influence methylation with potentially reversible changes and with the remission that often leads to normalization of methylation levels. Nevertheless, data reported in the literature about global methylation in anorexic patients are often conflicting, likely due to the dynamic nature of the methylation process. For easier and more accurate comparison, it would be necessary to know the stage of the disease and the duration of malnutrition. Steiger himself reported that the severity of malnutrition (reflected by a low BMI) or the duration of malnutrition exposure (indicated by the chronicity of the disease) is generally associated with lower methylation levels [15]. However, differences between our cohort and Steiger's, as well as variations in the methodologies used, probably contribute to the difficulty of comparing data. The two studies differ significantly in their experimental design (longitudinal versus cross-sectional) and in the composition of the cohorts, which primarily varies in terms of age, pharmacological treatments, and other environmental factors (as smoke). Additionally, genomic methylation profiles were assessed using different methods: while Steiger combined the analysis of two distinct arrays, the Infinium Human Methylation 450 BeadChips and the Infinium MethylationEPIC BeadChips, considering only the probes in common between the two methods, we used exclusively the MethylationEPIC BeadChips (850K) kit. Despite the differences, there is therefore considerable overlap between our data and Steiger's findings, particularly with regard to differentially methylated genes associated with nutritional regulation and metabolism, such as RPTOR, SORBS1 and IGF2R (involved in glucose and insulin metabolism), CAMKK2 and GHRL (related to appetite control) or PRKAG2 (related to body mass and lipid metabolism). In particular, a lower methylation of PRKAG2 has been associated to a higher BMI in a community sample [33]. In addition, we identified genes linked to inflammation and immune response, such as NOD1, DSE, IRAK3 and HHLA2, as well energy metabolism, such as FHL2. We also confirmed that genes such as TNXB and NR1H3, which have previously been associated with anorexia nervosa [11, 14], included differentially methylated sites. The TNXB gene (Tenascin XB) encodes an extracellular matrix glycoprotein with anti-adhesive properties, essential for maintaining tissue integrity and functionality [34]. However, no direct correlation between TNXB and processes such as body weight regulation, malnutrition, or anorexia nervosa has been reported to date. Nonetheless, the extracellular matrix plays a pivotal role in tissue metabolism, particularly in adipose tissue [35], and could indirectly influence processes relevant to anorexia nervosa . Investigating the epigenetic profile of TNXB in patients with anorexia nervosa may provide valuable insights into potential mechanisms of metabolic adaptation or tissue vulnerability associated with this condition. Additionally, an intriguing finding involves the hypermethylation of the NR1H3 gene, first described by Booij in 2015 [11]. This gene, besides being a key regulator of macrophage function, is critically involved in transcriptional programs governing lipid homeostasis and inflammatory processes [36]. Knockout mouse models have also highlighted its central role in regulating cholesterol homeostasis [37] suggesting that NR1H3 may be relevant to understanding the molecular mechanisms underlying hunger, systemic inflammation, and lipid balance, which are potentially implicated in the pathophysiology of anorexia nervosa. In addition to methylation differences in genes associated with anorexia, we also investigated methylation status of genes associated to pubertal development. Amenorrhea, and the consequent delay in puberty, is a hallmark symptom of AN in adolescent girls [38] and is closely tied to disruptions in the endocrine signals that regulate normal pubertal progression [39, 40]. One of the most striking findings of this study was the different methylation of the MKRN3 gene, a crucial regulator of puberty [41, 42]. MKRN3 is known to inhibit the secretion of gonadotropin-releasing hormone (GnRH), thereby delaying the onset of puberty. The hypomethylation observed in the MKRN3 gene in the AN (anorexia nervosa) group suggest a potential loss of epigenetic repression, which might contribute to the functional hypothalamic hypogonadism seen in these patients [43]. This observation aligns with the clinical manifestation of primary or secondary amenorrhea in anorexic girls and arise the possibility that disrupted pubertal progression in AN could involve epigenetic mechanisms. However, although these findings are consistent with previous studies that have highlighted how delayed puberty and amenorrhea are consequences of profound metabolic and endocrine disruptions induced by severe caloric restriction and weight loss, further studies are needed to verify whether the altered methylation of MKRN3 translates into changes in its expression and to clarify its role in the pathophysiology of delayed puberty associated with AN. Similarly, we have identified differential methylation in several other genes related to pubertal timing and reproductive health, including GHR, LEPR, GAB2 and DLK1, suggesting a potential role in delayed menarche in anorexia [22] . In particular, the DLK1 (Delta-Like 1 Homolog) gene, part of the non-canonical Notch signalling pathway, is a key regulator of pubertal development, metabolism and adipocyte differentiation [44-46]. Although there is no direct evidence associating DLK1 to anorexia, it is possible its indirect implication in metabolic and eating disorders affecting energy balance. Additionally, our study revealed that differentially methylated CpGs were enriched in genes involved in estrogen and GnRH signaling, further supporting the notion that anorexia disrupts the delicate hormonal balance necessary for normal pubertal development [47]. These pathways are known to play a crucial role in reproductive health, and their epigenetic deregulation in AN could provide insights into the observed delay in menarche and ongoing reproductive dysfunction in affected individuals. In fact, the search for transcription factors targeting DMPs revealed higher scores for hormone receptors such as progesterone, androgens and estrogens, suggesting that epigenetics may modulate hormone action and response. As reported by Tremolizzo et al. [12], steroid hormone levels in AN patients were inversely correlated with overall DNA methylation in the blood. The correlation analysis between estrogen levels and DMPs in our cohort did not yield significant results, probably due to the cross-sectional nature of the study and the limited number of the samples. However, a significant number of DMPs were identified within or in close proximity to genes containing high-affinity estrogen-responsive elements. Such genes could be involved in pubertal timing and endocrine development, confirming the findings of previous longitudinal studies on normal pubertal transition. [48, 49]. An innovative aspect of our study is the identification of an epigenetic signature associated with zinc finger proteins (ZNFs), known for their crucial role in transcriptional regulation and chromatin remodeling, fundamental for genomic stability and key processes such as pubertal development and metabolism [50]. Specifically, we identified 92 ZNF genes with differential methylation in patients with anorexia nervosa (AN). The protein products of many of the identified ZNF genes appear to be involved in critical biological pathways. For instance, ZNF664 is implicated in the development of metabolic syndrome [51, 52], ZFAT is involved in adipocyte differentiation and fat distribution [53], while ZBTB43 and ZNF331 play a role in the cellular response to caloric restriction [54, 55]. Among these, ZNF148 is particularly noteworthy as it participates in neurotransmission processes underlying pubertal timing by regulating the GABA transporter (SLC6A1) and in metabolism by modulating the insulin receptor (IRS2) [56]. Similarly, ZNF445 and ZFP57 are involved in genomic imprinting and metabolic pathways, as evidenced by ZNF445’s role in Temple syndrome and the regulation of imprinting regions such as the MEG3/DLK1 locus [57-59]. Furthermore, genome-wide association studies (GWAS) have identified SNPs near ZNF genes associated with age at menarche, reinforcing their role in pubertal regulation [50], which may be disrupted in pathological conditions such as anorexia nervosa. Although there is no direct evidence linking ZNF genes to eating disorders, the differential methylation observed here suggests potential short- and/or long-term consequences of these epigenetic modifications in the disorder. Additionally, the analysis of canonical pathways revealed an enrichment of differentially methylated genes involved in key developmental processes related to anorexia and pubertal development, including neuronal signaling, energy metabolism, and the browning of white adipocytes. These pathways include the sirtuin signaling pathway, which has been shown to play a crucial role not only in neuronal migration and the metabolic regulation of pubertal development, but especially in energy balance and energy sensing at the central level [60, 61]. Other noteworthy differentially methylated pathways involve semaphorins and GnRH. Notably, intercellular communication within the GnRH neural network is partially mediated by semaphorin signaling, which is essential for the development of hypothalamic circuits and the regulation of GnRH release by circulating sex steroids [62]. Insufficient semaphorin signaling contributes to certain forms of reproductive disorders in humans, such as hypogonadotropic hypogonadism [63] , defective neuroendocrine control of the ovarian cycle in adults [64], and obesity [65]. Furthermore, we reported that the G protein-coupled receptor (GPCR) signaling pathway exhibits differential methylation between individuals with anorexia nervosa (AN) and healthy pubertal controls. GPCRs play crucial roles in neurosensory and endocrine systems, influencing processes like insulin secretion, β-cell expansion, and glucose homeostasis [66], and are potential therapeutic targets for metabolic disorders such as type 2 diabetes. GPCRs mediate actions of major neurotransmitters, including serotonin and noradrenaline, which are implicated in psychiatric disorders like the depression associated with AN [67]. Two other key pathways here reported are leptin and insulin. It has been shown that, in underweight anorexic patients, plasma levels of leptin are strongly reduced and correlated with decreased body mass index (BMI) and body fat content, as are those of insulin [68]. In addition, pathways that modulate energy metabolism, such as AMP-activated protein kinase (AMPK) and white adipose tissue browning, also showed different methylation status. In fact, in the study by Bredella et al. [69], the link between brown adipose tissue (BAT), bone mineral density (BMD), and Pref-1 (DLK1) levels in young women, including subjects with anorexia nervosa (AN), remitted anorexics, and those with normal weight, was analyzed. Women with active BAT showed higher BMD and lower Pref-1 levels than those without BAT. These results suggest that BAT may positively influence bone health, with implications for conditions such as AN. Although the present analysis of methylation patterns in anorexia nervosa (AN) provides valuable insights, we acknowledge several limitations as the small cohort size, which may affect the statistical significance of the results, and the absence of RNA samples that prevented the validation of gene expression changes associated with the detected methylation variations. Secondly, the use of peripheral tissues such as blood instead of disease-relevant tissues (e.g. brain, adipose tissue or gonads) introduces another limitation although ethical and logistical constraints make it difficult to study such tissues in humans. However, studies on blood or saliva have been shown to reflect epigenetic trends observed in other tissues, thus supporting their utility as surrogates in many contexts [70]. However, future efforts would be needed to integrate multi-omics datasets, such as ChIP-Seq or RNA-Seq, and broader and more defined patient cohort selection criteria to minimize confounding factors, paving the way for more reproducible and comparable results. Despite these limitations, epigenetic studies in anorexia nervosa remain a crucial avenue to advance our understanding of this disorder. The identification of specific epigenetic signatures not only sheds light on the complex interplay between genetic predispositions and environmental influences, but also promises practical applications in the clinical setting. These signatures could serve as reliable biomarkers to diagnose more accurately anorexia, as well as for other eating disorders, follow its progression and assess the risks of remission or relapse. Additionally, epigenetic profiles may provide valuable insights into developmental alterations, such as those affecting pubertal maturation, which are closely associated with anorexia and its long-term health consequences. This capability is particularly valuable in addressing the dynamic and multifaceted nature of the disease. Moreover, the reversibility of some epigenetic changes with nutritional and therapeutic interventions underlines their potential as indicators of treatment response. Beyond clinical applications, these studies also offer an unprecedented opportunity to uncover new mechanisms underlying the disorder, paving the way for innovative therapeutic approaches targeting both metabolic and pubertal pathways. By harnessing this knowledge, clinicians could adopt a more personalized approach to the management of AN, tailoring treatments to patients' unique epigenetic profiles and paving the way for the development of innovative therapeutic strategies. Future efforts should focus on refining these biomarkers and expanding their application through longitudinal studies and the integration of multi-omics, thus maximizing their potential to transform both research and clinical practice in the field of eating disorders. Methods Population A total of 10 girls (mean age 13.625± 1.79) with an active diagnosis of Anorexia Nervosa (AN) according to DSM-IV-TR and functional hypogonadotropic hypogonadism were enrolled at the Division of Child and Adolescent Psychiatry (eating disorder service for developmental age) of the University of Campania Luigi Vanvitelli, Naples. All patients presented with AN restrictive subtype (AN-R). Functional hypogonadotropic hypogonadism is defined as reduced levels of FSH, LH and 17-besta-estradiol or inappropriately low levels compared to clinical pubertal stage in absence of organic lesion and thyroid disfunction. Primary amenorrhea was defined as the absence of menarche five years after initial breast development, Secondary amenorrhea has been defined as the cessation of previously regular menses for three consecutive months or previously irregular menses for six months. All the patients were not under medications when DNA methylation analysis "was performed, except for one patient who had started aripiprazole six months earlier. Eleven age-comparable (mean age 13.49 ± 1.48), pubertal, drugfree control (CR) females without history of psychiatric or neurological disorders were also recruited. Pubertal controls were selected among patients who were referred to Paediatric Endocrinology Units of the University of Campania “Luigi Vanvitelli,” Naples on suspicion of poor growth and were therefore considered healthy. Inclusion criteria for healthy controls were: BMI between 10-85th according to WHO growth chart 2006, pubertal stage, no history of menses irregularity chronical drug assumption, and cycles irregularity if menarche already occurred. Exclusion criteria for both AN and CR controls were: - haematological or hepatic disorders, - cancer -recent infections or surgery Alcohol abuse was denied by all recruited participants. Following ethics committee approval of the study, parents have written informed consent, as well as all recruited subjects gave informed written consent to participate, before undergoing the study procedures. Clinical parameters Weight, height and BMI standard deviations (SDs) were calculated using WHO growth chart 2006 for sex and age [71] . Clinical examinations were performed in all patients and controls to assess pubertal stage according to Tanner classification. The Italian version of Eating Attitudes Test ( EAT-26) was administered to all AN subject as a standardized self-report measure of symptoms and concerns characteristic of eating disorders and the CDI 2 for comprehensive multi-rater assessment of depressive symptoms [72]. The Italian version of the Child Behavior Checklist (CBCL) for children and adolescents aged 6 to 18 years has been administred to all AN subjects to assess behavioral and emotional problems [73]. The Child depression Inventory (CDI) test for in children and adolescents between the ages of 7 and 17 was performed to measure the cognitive, affective, and behavioral signs of depression [74]. Laboratory studies All blood samples were drawn between 8:00 and 8:30 a.m. from an antecubital vein, clotted, centrifuged, and serum was stored at −20 °C until analyses were performed. FSH and LH concentrations were determined by immunochemiluminometric assay (ICMA) with inter-assay coefficient of variation less than 5%. Serum 17-β-estradiol was measured by using competitive chemiluminescent immunoassay using coated magnetic particles with inter-assay coefficient of variation less than 5%. Samples preparation and genome-wide DNA methylation analysis Genomic DNA was extracted from peripheral blood leukocytes using standard procedures. DNA quality and quantity were assessed by NanoDrop (Thermo Fisher Scientific) and electrophoresis on 1% agarose gel. Microarray analyses were performed by Genomix4life S.R.L. (Baronissi, Salerno, Italy). DNA concentration in each sample was assayed with a Qubit fluorometer. Bisulfite converted DNA (250 ng) was used for analysis of whole-genome methylation using the MethylationEPIC BeadChip (Illumina, San Diego, CA, USA). In brief, bisulfite converted DNA was whole-genome amplified for 20 h followed by end-point fragmentation. Fragmented DNA was precipitated, denatured and hybridized to the BeadChips for 20 h at 48 °C. The BeadChips were washed and the hybridized primers were extended and labeled before scanning the BeadChips using the Illumina iScan system. Data analysis was performed as described in Casarotto et al [75]. CpGs beta values were obtained using ChAMP on R (v. 4.2.0). Values of blood cell composition were obtained from the patients and missing values were imputed with a random forest model. Singular Value Decomposition was not able to detect the blood cell composition as factor that account for a substantial fraction of variation, for this reason no corrections were performed. Only CpGs with a detection p-value 0.1) and FDR values < 0.05. Further analyses were performed on the CpGs located within promoter regions, which are defined as CpGs annotated in the TSS1500, TSS200, 1 Exone, and 5’UTR. DMR analysis was performed with ChAMP using the BumpHunter model with default parameters. DMR with FDR values > 0.05 and |Δβ| 1.3 were considered significant. List of regions with high affinity EREs in the human genome were downloaded from the “MOUSE AND HUMAN ERE DATABASES” (http://mapageweb.umontreal.ca/maders/eredatabase) and intersected with the genomic coordinates of the Differentially Methylated Positions (DMPs). Hypergeometric test with p-value < 0.05 was used to assess the enrichment of ERE in our DMPs. Transcription factors were found using “epigenetic Landscape In Silico deletion Analysis” (LISA - http://lisa.cistrome.org/) with default parameters. All the statistics, pie charts, and heatmaps were obtained using R. Abbreviations ALDH4A1 – Aldehyde Dehydrogenase 4 Family Member A1 AMPK – AMP-Activated Protein Kinase AN – Anorexia Nervosa ARNTL – Aryl Hydrocarbon Receptor Nuclear Translocator Like BAT – Brown Adipose Tissue BMD – Bone Mineral Density BMI – Body Mass Index BMR – Basal Metabolic Rate CAMKK2 – Calcium/Calmodulin Dependent Protein Kinase Kinase 2 CBCL – Child Behavior Checklist CDH7 – Cadherin 7 CDI – Child Depression Inventory CLU – Clusterin CpG – Cytosine-phosphate-Guanine CTBP2 – C-Terminal Binding Protein 2 DMP – Differentially Methylated Position DMR – Differentially Methylated Region DNMT1 – DNA Methyltransferase 1 DNMT3B – DNA Methyltransferase 3 Beta DSE – Dermatan Sulfate Epimerase DLK1 – Delta Like Non-Canonical Notch Ligand 1 EAT-26 – Eating Attitudes Test EIF4G1 – Eukaryotic Translation Initiation Factor 4 Gamma 1 ERE – Estrogen Responsive Element ESR1 – Estrogen Receptor 1 EXT2 – Exostosin Glycosyltransferase 2 FDR – False Discovery Rate FHL2 – Four And A Half LIM Domains 2 FSH – Follicle Stimulating Hormone GAB2 – GRB2 Associated Binding Protein 2 GHR – Growth Hormone Receptor GHRL – Ghrelin And Obestatin Prepropeptide GnRH – Gonadotropin-Releasing Hormone GPCR – G Protein-Coupled Receptor GWAS – Genome-Wide Association Study HHLA2 – HERV-H LTR-Associating 2 ICMA – Immunochemiluminometric Assay IGF2R – Insulin Like Growth Factor 2 Receptor IPA – Ingenuity Pathway Analysis IRAK3 – Interleukin 1 Receptor Associated Kinase 3 LEPR – Leptin Receptor LH – Luteinizing Hormone LTA – Lymphotoxin Alpha LRP5 – LDL Receptor Related Protein 5 MKL2 – Megakaryoblastic Leukemia Translocation 2 MKRN3 – Makorin Ring Finger Protein 3 NFAT5 – Nuclear Factor Of Activated T-Cells 5 NOD1 – Nucleotide Binding Oligomerization Domain Containing 1 NR1H3 – Nuclear Receptor Subfamily 1 Group H Member 3 PR – Progesterone Receptor PRKAG2 – Protein Kinase AMP-Activated Non-Catalytic Subunit Gamma 2 PTPRD – Protein Tyrosine Phosphatase Receptor Type D RMI1 – RecQ Mediated Genome Instability 1 RORA – RAR Related Orphan Receptor A RPTOR – Regulatory Associated Protein Of MTOR Complex 1 SFMBT1 – Scm Like With Four MBT Domains 1 SORBS1 – Sorbin And SH3 Domain Containing 1 TSS – Transcription Start Site TNXB – Tenascin XB UTR – Untranslated Region WWP2 – WW Domain Containing E3 Ubiquitin Protein Ligase 2 ZNF30 – Zinc Finger Protein 30 ZNF148 – Zinc Finger Protein 148 ZNF331 – Zinc Finger Protein 331 ZNF365 – Zinc Finger Protein 365 ZNF445 – Zinc Finger Protein 445 ZNF574 – Zinc Finger Protein 574 ZNF664 – Zinc Finger Protein 664 ZNF860 – Zinc Finger Protein 860 ZFAND2B – Zinc Finger AN1-Type Containing 2B ZFAT – Zinc Finger And AT-Hook Domain Containing ZFP57 – Zinc Finger Protein 57 ZBTB43 – Zinc Finger And BTB Domain Containing 43 ZMIZ2 – Zinc Finger MIZ-Type Containing 2 Declarations Ethics approval and consent to participate This study involving Human Participants was approved by the ethics committee of “Luigi Vanvitelli” University (Protocol number 23.26-20200008943) and was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments. Consent for publication Not applicable. Availability of data and materials Raw methylation data and the normalized beta-values are available on ArrayExpress (E-MTAB-14823). Competing interests The authors declare that they have no competing interests. Funding This work was supported by a grant (390) funded by “VALERE: VAnviteLli pEr la RicErca” program of University of Campania “L. Vanvitelli.” Authors’ contributions A.G. conceptualized the idea of the manuscript and also coordinated the research process and reviewed drafts. S.P. and D.P. performed genetic analysis. S.P. analyzed and interpreted the data and prepared the draft manuscript. D.P. performed methylation and bioinformatic analysis. M.C., F.S. and M.G.G. enrolled the patients, performed the clinical examinations and supervised the paper; M.G.G. E.M.d.G. supervised the whole work and administered the project. F.A. and G.R.U. enrolled the controls and took care of the clinical data and data analysis. 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Burlington VT: University of Vermont Department of psychiatry. 1991. Ahlen J, Ghaderi A. Evaluation of the Children's Depression Inventory-Short Version (CDI-S). Psychological assessment. 2017;29(9):1157-66. Casarotto M, Lupato V, Giurato G, Guerrieri R, Sulfaro S, Salvati A, et al. LINE-1 hypomethylation is associated with poor outcomes in locoregionally advanced oropharyngeal cancer. Clinical epigenetics. 2022;14(1):171. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1DMRDCAvsCTP.xlsx Additionalfile2TFenrichedDCAtoCTP.xlsx Additionalfile3DMPDCAvsCTP.xlsx Additionalfile4IPADCAvsCTP.xlsx Cite Share Download PDF Status: Posted Version 1 posted 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. 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Palumbo","email":"data:image/png;base64,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","orcid":"","institution":"Department of Women's and Children's Health and General and Specialized Surgery, University of Campania \"Luigi Vanvitelli","correspondingAuthor":true,"prefix":"","firstName":"Stefania","middleName":"","lastName":"Palumbo","suffix":""},{"id":466473062,"identity":"b0c5ba36-3c41-44fd-ae43-0eda94d0d669","order_by":1,"name":"Domenico Palumbo","email":"","orcid":"","institution":"Laboratory of Molecular Medicine and Genomics, Department of Medicine, Surgery and Dentistry SMS, University of Salerno","correspondingAuthor":false,"prefix":"","firstName":"Domenico","middleName":"","lastName":"Palumbo","suffix":""},{"id":466473063,"identity":"b8842d31-64f2-4d4e-bf44-0845e3ac5773","order_by":2,"name":"Grazia Cirillo","email":"","orcid":"","institution":"Department of Women's and Children's Health and General and Specialized Surgery, University of Campania \"Luigi Vanvitelli","correspondingAuthor":false,"prefix":"","firstName":"Grazia","middleName":"","lastName":"Cirillo","suffix":""},{"id":466473064,"identity":"97d03db9-6ded-4149-9ccd-7550c4985c89","order_by":3,"name":"Francesca Aiello","email":"","orcid":"","institution":"Department of Women's and Children's Health and General and Specialized Surgery, University of Campania \"Luigi Vanvitelli","correspondingAuthor":false,"prefix":"","firstName":"Francesca","middleName":"","lastName":"Aiello","suffix":""},{"id":466473065,"identity":"98d4d38c-cd2a-481f-81fc-3e074f6c02fd","order_by":4,"name":"Giuseppina Rosaria Umano","email":"","orcid":"","institution":"Department of Women's and Children's Health and General and Specialized Surgery, University of Campania \"Luigi Vanvitelli","correspondingAuthor":false,"prefix":"","firstName":"Giuseppina","middleName":"Rosaria","lastName":"Umano","suffix":""},{"id":466473066,"identity":"6985c53d-da8f-486f-b4d4-02029c003b23","order_by":5,"name":"Maria Gloria Gleijeses","email":"","orcid":"","institution":"Clinic of Child and Adolescent Neuropsychiatry, Department of Mental Health, Physical and Preventive Medicine, University of Campania \"Luigi Vanvitelli","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Gloria","lastName":"Gleijeses","suffix":""},{"id":466473067,"identity":"333770d8-d7ab-42fe-9f36-cd263b5a6b0e","order_by":6,"name":"Emanuele Miraglia del Giudice","email":"","orcid":"","institution":"Department of Women's and Children's Health and General and Specialized Surgery, University of Campania \"Luigi Vanvitelli","correspondingAuthor":false,"prefix":"","firstName":"Emanuele","middleName":"Miraglia del","lastName":"Giudice","suffix":""},{"id":466473068,"identity":"7e459435-6e8b-47b7-a596-277023e1293a","order_by":7,"name":"Marco Carotenuto","email":"","orcid":"","institution":"Clinic of Child and Adolescent Neuropsychiatry, Department of Mental Health, Physical and Preventive Medicine, University of Campania \"Luigi Vanvitelli","correspondingAuthor":false,"prefix":"","firstName":"Marco","middleName":"","lastName":"Carotenuto","suffix":""},{"id":466473069,"identity":"fa3c0900-7f19-40f5-9577-e307b1f3507e","order_by":8,"name":"Filomena Salerno","email":"","orcid":"","institution":"Clinic of Child and Adolescent Neuropsychiatry, Department of Mental Health, Physical and Preventive Medicine, University of Campania \"Luigi Vanvitelli","correspondingAuthor":false,"prefix":"","firstName":"Filomena","middleName":"","lastName":"Salerno","suffix":""},{"id":466473070,"identity":"f65ea938-c397-4b8f-a2c9-156f212368cf","order_by":9,"name":"Anna Grandone","email":"","orcid":"","institution":"Department of Women's and Children's Health and General and Specialized Surgery, University of Campania \"Luigi Vanvitelli","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Grandone","suffix":""}],"badges":[],"createdAt":"2025-04-01 10:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6352266/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6352266/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84015813,"identity":"2988b973-55c2-4cf5-97c4-323e67dbb43b","added_by":"auto","created_at":"2025-06-05 17:53:51","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22165,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the differentially methylated CpG sites identified for each comparison along the several genomic features. A) pie chart shows the percentage of DMPs for each genomic localization. On the right (B), boxplot shows the beta-values distribution per group (red for CTP, light blue for AN).\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6352266/v1/179410c29e5c624db2f52307.jpg"},{"id":84016075,"identity":"9fb6fdb8-89c4-44de-a57d-e19589fe4199","added_by":"auto","created_at":"2025-06-05 18:01:51","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45852,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially methylated genes between DCAs and healthy pubertal controls divided by macro-areas of interest.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6352266/v1/6509e7f9adb361dba539d2b7.jpg"},{"id":84015815,"identity":"021b3f47-418f-4511-b980-df6b3441c736","added_by":"auto","created_at":"2025-06-05 17:53:51","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":49484,"visible":true,"origin":"","legend":"\u003cp\u003eIngenuity canonical pathway analysis between AN and CTP. In red are represented the pathways enriched with hypermethylated CpGs while, in green, the hypomethylated ones. The figure shows only part of the total pathways. The total list of pathways is available in additional file section.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6352266/v1/11cc5e8a1a681a1f38bc3116.jpg"},{"id":85542681,"identity":"604844a2-ed0d-4ac4-8ffb-94e9376258c2","added_by":"auto","created_at":"2025-06-27 07:09:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1536156,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6352266/v1/ce592c53-06ac-4212-9fb6-e6cc646bd4b9.pdf"},{"id":84015816,"identity":"de64b90a-3f63-4aa0-9b35-214ebf414b34","added_by":"auto","created_at":"2025-06-05 17:53:51","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17261,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1DMRDCAvsCTP.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6352266/v1/33ab1409c1cf1facd819fd38.xlsx"},{"id":84015817,"identity":"0875928f-cb56-4991-8139-360b9beffcf6","added_by":"auto","created_at":"2025-06-05 17:53:51","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":24488,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2TFenrichedDCAtoCTP.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6352266/v1/e0b4cc0dc3f740f8be9fb8d0.xlsx"},{"id":84015819,"identity":"d5d7a7b9-9a4b-4690-a6f2-092a723dd659","added_by":"auto","created_at":"2025-06-05 17:53:51","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":366605,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3DMPDCAvsCTP.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6352266/v1/e4a174043858f019d1590996.xlsx"},{"id":84016076,"identity":"e292c69d-51e7-4d05-91b1-625e02448d22","added_by":"auto","created_at":"2025-06-05 18:01:51","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":22133,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile4IPADCAvsCTP.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6352266/v1/f1045e1d4b050e25e821ea62.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"DNA Methylation profiles in girls with anorexia nervosa and functional hypothalamic amenorrhea: a pilot study ","fulltext":[{"header":"Background","content":"\u003cp\u003eAnorexia nervosa (AN) is a severe eating disorder characterized by an obsessive desire to lose weight and a disrupted body image, leading to extreme caloric restriction and severe malnutrition, which, in the most severe cases, can be fatal [1]. This condition is often associated with high rates of morbidity and comorbidities, including anxiety, depression, and other mental disorders, [2]. Although AN primarily affects young women, it can impact individuals of all genders and ages and the precise aetiology remains partly unknown. Recent advances in genome-wide association studies (GWAS) have identified several AN-associated loci that show significant genetic correlations with psychiatric disorders and metabolism regulating factors probably contributing to the pathophysiology of the disease [3]. Furthermore, family and twin studies confirmed heritability estimates of 50–60%, highlighting the significant role of genetic factors in this disorder [4]. On the other hand, environmental factors, such as social pressures, trauma and stress play a crucial role in the risk of developing AN. A particularly critical period is puberty, during which body dissatisfaction and low self-esteem intersect with hormonal changes, increasing vulnerability to eating disorders. Moreover, it is well established that AN significantly impacts normal pubertal development, contributing to delay reproductive maturation through metabolic imbalances [5-7]. Girls with AN frequently experience either primary or secondary amenorrhea, which is closely linked to the severe caloric restriction and significant weight loss typical of this condition resembling a functional form of hypogonadotropic hypogonadism [8, 9]. This dysfunction is not merely a clinical symptom but also an indicator of the profound metabolic and endocrine alterations caused by AN, with repercussions including long-term effects on bone and reproductive health. In addition to genetic and environmental factors, epigenetics has emerged over the past decade as a crucial area of research for understanding eating disorders [10]. Epigenetic mechanisms provide an additional level of gene regulation that bridges genetic background and environmental influences. Distinct DNA methylation profiles have been observed in individuals with AN, although global findings are sometimes conflicting. One study, using DNA from lymphocytes of 29 individuals with an average age of 22 years and with restrictive or binge/purge types of AN, found slight hypermethylation compared to controls [11]. Two other studies, one on adults with AN and the other on adolescents, reported lower global methylation levels [12, 13]. Large-scale epigenome-wide DNA methylation studies have advanced understanding of these methylation patterns, revealing significant changes in genes involved in metabolism, neurotransmission, and neuroplasticity [11, 14-16]. These epigenetic modifications are associated with altered hormone levels linked to delayed reproductive maturation and the onset of amenorrhea. Furthermore, pubertal development itself appears to have a distinctive methylation profile under both physiological and pathological conditions, such as precocious puberty [17] or delayed puberty [18].\u003c/p\u003e\n\u003cp\u003eThese findings have significant diagnostic potential and could facilitate the development of biomarkers to monitor disease activity and treatment effectiveness. Despite these advances, research on cohorts of young adolescents remains limited. Greater attention is needed for studies exploring epigenetic and hormonal changes during this critical developmental window, particularly in relation to pubertal development alterations. Such investigations are essential, as early intervention could mitigate long-term consequences for reproductive and bone health.\u003c/p\u003e\n\u003cp\u003eIn this context, the present study aims to investigate epigenetic alterations associated with anorexia nervosa in a cohort of young female adolescents with functional hypogonadotropic hypogonadism, compared to a control group of normal weight pubertal adolescents.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical and laboratory characteristics of the entire cohort are shown in Table\u0026nbsp;1. Subject with anorexia nervosa (AN) presented a similar mean age and pubertal stage than pubertal controls (CTP). A statistically significant difference in BMI SDS existed between AN patients and controls (-1.71 \u0026plusmn; 0.71 and 0.32 \u0026plusmn; 1.03, respectively; p = \u0026lt;0.001) as expected. Significantly lower levels of LH and 17-beta-estradiol were found in AN patients (p= .009 and .008, respectively). No difference in FSH levels between the two groups existed (Table 1).\u003c/p\u003e\n\u003cp\u003eIn AN patients 4 presented with primary amenorrhea while 6 had secondary amenorrhea with an initially normal onset of menarche (mean age at menarche 11.25\u0026plusmn; 0.69) and a mean amenorrhea period of 1.3 years (\u0026plusmn;0.98 SDS). Upon completing the specific questionnaires EAT26 (Children-Eating Attitude Test) and CDI (Children\u0026rsquo;s Depression Inventory), positive scores were obtained for \u0026nbsp; eating behaviour disorder (Score 48 \u0026gt; 19), and the presence of pervasive depressive traits (P=30, cut off 19) (Table 2).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCTP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e13.64 \u0026plusmn;1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e13.88\u0026plusmn;1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e.784\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, SDS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e-1.71\u0026plusmn; 0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.32 \u0026plusmn; 1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeight, SDS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e-0.28 \u0026plusmn; 0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e-1.44\u0026plusmn; 1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;.\u003c/strong\u003e\u003cstrong\u003e02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTanner Stage, breast\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2.5 \u0026plusmn; 0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e3 \u0026plusmn; 0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTanner stage, pubic hair\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2.7 \u0026plusmn; 0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e3\u0026plusmn; 0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e.352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLH, IU/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1.08 \u0026nbsp; \u0026plusmn; 1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e5.2 \u0026plusmn; 2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFSH; IU/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e6.9 \u0026plusmn;2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e5.14 \u0026plusmn; 2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e.403\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e17\u0026beta;-estradiol, pg/mL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e16.68 \u0026plusmn;8.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e38.78 \u0026plusmn; 9.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 1\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eClinical and laboratory characteristics of patients with AN. The parameters are expressed as mean \u0026plusmn; standard deviation. \u0026nbsp; \u0026nbsp;. The parameters are expressed as mean \u0026plusmn; standard deviation, except for Tanner stage reported as median (min and max value). Mann-Whitney U Test Calculator and T-Test Calculator for 2 Independent Means were used as appropriate to calculate p-value for AN \u0026nbsp;versus control group using t-test.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge of onset (years)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e12.42\u0026plusmn;1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCumulative illness duration (months)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e14.5\u0026plusmn;17.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild Behavior Checklist \u0026nbsp;(CBCL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e68.33\u0026plusmn;2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEating Attitudes Test (\u003c/strong\u003e\u003cstrong\u003eEAT)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e36.25\u0026plusmn;9.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eChild depression inventory (CDI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e2.03\u0026plusmn;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSecondary amenorrhea\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e60%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at menarche\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e11.25\u0026plusmn;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonths of menses presence before secondary amenorrhea (months)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e32.4\u0026plusmn;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003cstrong\u003euration of amenorrhoea\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(months)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e15.6 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2: Clinical features in patients with AN.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferentially methylated genomic regions in AN compared to healthy pubertal girls\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess global DNA methylation changes that occur in girls with anorexia nervosa (AN) and healthy pubertal control (CTP), we first identify differentially methylated regions (DMRs). This analysis revealed 87 DMRs (with an FDR \u0026lt; 0.05 and |\u0026Delta;B| \u0026gt; 5%), of which 68.97% were hypomethylated in girls with anorexia who presented primary or secondary amenorrhea. These 87 DMRs are mainly included the promoter regions of genes involved in immune and inflammatory pathways, such as \u003cem\u003eLTA\u003c/em\u003e, in the hypothalamic feeding regulatory pathways, such as \u003cem\u003eCLU\u003c/em\u003e, as well in genes involved in the onset of puberty, such as \u003cem\u003eMKRN3\u003c/em\u003e (Additional file 1). We also applied the epigenetic Landscape in Silico Deletion Analysis (LISA) to identify enriched transcription factors potentially regulating genes associated with our DMRs. Among the top twenty significant transcription factors (Additional file 2), we found the progesterone receptor (PR) and estrogen receptor (ESR1) genes, which mediate the effects of sex hormones involved in the reproductive system and menstrual cycle regulation. Additionally, we also identified DNMT1 (DNA Methyltransferase 1), SFMBT1 (Scm-like with Four MBT Domains 1), and DNMT3B (DNA Methyltransferase 3 Beta) which are involved in DNA methylation processes and chromatin remodelling affecting gene expression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferences between groups at individual CpG sites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudies on methylation frequently identify a predominance of differentially methylated sites in regulatory regions, attributing to these a significant impact on gene expression. However, the role of differentially methylated CpG sites in regions distant from the promoter remains uncertain. We decided to consider only CpG sites mapped to transcription start sites or promoter regions, as they theoretically might influence the regulation of their respective genes. Based on this consideration the exploration of methylation levels in isolated CpG sites identified 2072 differentially methylated probes (DMPs) mapping onto 1896 genes (Additional file 3) . The comparison between girls with anorexia (AN) and pubertal controls (CTP), reported a significant proportion of differentially methylated CpGs (FDR \u0026lt; 0.05 and |\u0026Delta;B| \u0026gt; 10%) located in intergenic regions (IGRs) and within the body of genes. Specifically, approximately 27% of these DMPs were located in promoter regions, including TSS1500 (10.2%), TSS200 (6.03%), 5\u0026apos;UTR (8.05%) and the first exon (2.47%) (Figure 1A). Interestingly, the global analysis of DMPs showed the same trend as previously reported in DMRs, with lower methylation levels in the AN group compared to the controls (Figure 1B). The comparison between the AN and control groups revealed several differentially methylated probes related to nutrition. In detail, genes involved in cholesterol and lipid processing (PRKAG2), glucose metabolism (IGF2R, RPTOR and SORBS1) and appetite regulation (GHRL, LEPR and CAMKK2). Others probes mapped onto genes relevant to immune function (such as NOD1, DSE, IRAK3, HHLA2, and NR1H3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong hypermethylated CpGs sites, there were some located near genes involved in the regulation of anxiety and depression as serotonin degradation signalling (ALDH4A1 and EXT2) (Figure 2). Additionally, we found a different methylation of CpGs sites in genes related to wound healing processes (TNXB) [19], skeletal homeostasis (LRP5)[20], and energy metabolism (FHL2)[21].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInterestingly, as shown in Table 3, 14 DMPs mapped onto\u0026nbsp;genes that have been previously associated with age of menarche: GHR, LEPR, NFAT5, GAB2, ARNTL, EIF4G1, RMI1, PTPRD, RORA, MKL2, MKRN3, CTBP2, WWP2, and DLK1 [22].\u003c/p\u003e\n\u003cp\u003eSubsequently, considering the list of genes with differentially methylated promoters, we performed a detailed bioinformatics analysis, to search putative transcription factors (TF) which are potentially regulating genes associated with our DMPs. In this way, we detected 180 TFs (FDR \u0026lt;0.05). The top 20 (full list shown in Additional file 2), include the receptor of sexual hormones as AR, ESR1, and PR that are crucial for reproductive processes such as menstrual cycle regulation and pregnancy. Additionally, the CEBPB factor which plays a role in the regulation of genes involved in immune response, metabolism, and differentiation of various cell types including adipocytes and hepatocytes, is also present in the list with a high score. Moreover, among genes with differentially methylated promoters there was an enrichment of genes containing Estrogen Responsive Elements (EREs) (hypergeometric test with p-value \u0026lt; 0.05), suggesting that some of the effects of estrogen signaling in puberty are modified through epigenetic mechanisms. However, we could not find a correlation between beta-values and estrogen levels, no CpGs were found directly correlated with them (data not shown). \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, to identify pathways that could be modulated by the differentially methylated genes, we performed Ingenuity Pathway Analysis (IPA). Interestingly, our results show differential methylation in signaling pathways that are easily correlated with reduced caloric intake, affecting metabolic processes and body mass. These include the white adipose tissue browning, sirtuin signaling, as well as insulin and leptin signaling. Additionally, IPA showed enrichment in neuronal signaling pathways such as the GnRH and semaphorin which have been previously reported to be associated with pubertal development [23, 24]. Other signaling pathways, as those involved in estrogen and ovarian cancer, present a different methylation status between the AN and control groups (Figure 3). The entire list of differentially methylated pathways can be found in the Additional file 4\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCG_ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP.Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eadj.P.Val\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edeltaBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeature\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecg17942553\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.71E-06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000133\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.10629\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eARNT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSS1500\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecg15205441\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.51E-08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.35E-06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.16401\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCTBP2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u0026apos;UTR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecg02795603\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.87E-08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.17E-06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.10834\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEIF4G1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u0026apos;UTR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecg24252966\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.97E-07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.29E-05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.12395\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEIF4G1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u0026apos;UTR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecg04116507\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.07E-08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.49E-06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.12154\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGAB2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u0026apos;UTR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecg15466952\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.62E-07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.51E-05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.10346\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLEPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u0026apos;UTR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecg02761778\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.87E-07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.68E-05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.18258\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNFAT5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u0026apos;UTR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecg25172788\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.39E-06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000238\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.12531\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMI1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u0026apos;UTR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecg17181046\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.98E-07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.30E-05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.20816\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRORA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSS1500\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecg09789698\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.07E-09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.53E-06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.1106\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWWP2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u0026apos;UTR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecg16103098\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002119\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012164\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.172079\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDLK1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSS1500\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecg00215587\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000396\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003707\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.10076\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMKRN3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u0026apos;UTR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecg24779990\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000257\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002724\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.14077\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSS200\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecg11196813\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002353\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013115\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.124879\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePTPRD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSS1500\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecg20931294\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.58E-05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000372\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.11603\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMKL2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u0026apos;UTR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3: Genes previously associated ti the age of menarche [22].\u003c/p\u003e\n\u003cp\u003eFigure 3: Ingenuity canonical pathway analysis between AN and \u0026nbsp;CTP. In red are represented the pathways enriched with hypermethylated CpGs while, in green, the hypomethylated ones. The figure shows only part of the total pathways. The total list of pathways is available in additional file section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZNF Methylation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough the role of Zinc finger (ZNF) genes in pubertal process is well-established, there is, currently, no evidence of their involvement in eating disorders. Interestingly, our methylation profiling analysis identified numerous Zinc finger genes among those differentially methylated.\u003c/p\u003e\n\u003cp\u003eIn fact, 92 DMPs were mapped near ZNF genes. Some of these, such as ZNF148, which regulates the GABA transporter, and ZNF445 and ZNF365, which are involved in methylation processes and genomic stability, respectively, were previously reported by our group in another cohort of patients with pubertal disorders [17]. Additionally, other ZNF genes, such as ZNF574, ZNF30, ZMIZ2, and ZFAND2B, have been associated with the development of estrogen-dependent tumors, including breast and ovarian cancers. Notably, several differentially methylated ZNF genes are associated with metabolic syndrome, such as ZNF664, or with conditions like hypocholesterolemia and diabetes, such as ZNF860. Others, like ZFAT, are involved in adipocyte differentiation and body fat distribution. Furthermore, some genes, including ZBTB43 and ZNF331, are linked to changes induced by caloric restriction.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOver the past two decades, numerous studies have highlighted the role of a complex interplay between genetic, biological, psychological and environmental factors (such as stress, diet, trauma, or environmental exposures) in the development of anorexia nervosa (AN) [25]. In addition, epigenetics has emerged as a critical mechanism, regulating gene expression in response to environmental stimuli without altering the DNA sequence [10, 26]. Despite the complexity and the high comorbidity of AN with other psychiatric disorders, the underlying risk factors and specific biomarkers remain poorly understood. This knowledge gap has become even more pressing due to the recent surge in eating disorder diagnoses, hospitalizations, and worsening of symptoms, phenomena exacerbated by the Covid-19 pandemic and associated lockdown measures [27, 28]. In this research landscape, our study aimed to analyze DNA methylation profiles in peripheral blood leukocytes from adolescent girls diagnosed with AN and amenorrhea, comparing them to those of healthy pubertal controls. Specifically, our study identified 87 regions and 2,072 differentially methylated CpG sites, with a higher percentage of reduced methylation in girls with AN. This finding aligns with existing literature that associates DNA hypomethylation with energy deprivation and chronic stress [29, 30] , both of which are key features of AN. Nutritional deficiencies, including a lack of methyl donors such as folates [31, 32], due to prolonged fasting in AN may contribute to the hypomethylation observed in these patients. This potential link underlines how severe caloric restriction can lead to molecular alterations that can affect gene expression and hormonal balance.\u003c/p\u003e\n\u003cp\u003eEchoing previous studies [11, 16], we have reported significant epigenetic changes in genes central to metabolic, nutritional and immune functions. Longitudinal studies by Steiger [16] highlighted the dynamic nature of DNA methylation in subjects with active AN and those in remission. Indeed, nutritional intake and body mass index (BMI) appear to directly influence methylation with potentially reversible changes and with the remission that often leads to normalization of methylation levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNevertheless, data reported in the literature about global methylation in anorexic patients are often conflicting, likely due to the dynamic nature of the methylation process. For easier and more accurate comparison, it would be necessary to know the stage of the disease and the duration of malnutrition. Steiger himself reported that the severity of malnutrition (reflected by a low BMI) or the duration of malnutrition exposure (indicated by the chronicity of the disease) is generally associated with lower methylation levels [15]. However, differences between our cohort and Steiger's, as well as variations in the methodologies used, probably contribute to the difficulty of comparing data. The two studies differ significantly in their experimental design (longitudinal versus cross-sectional) and in the composition of the cohorts, which primarily varies in terms of age, pharmacological treatments, and other environmental factors (as smoke). Additionally, genomic methylation profiles were assessed using different methods: while Steiger combined the analysis of two distinct arrays, the Infinium Human Methylation 450 BeadChips and the Infinium MethylationEPIC BeadChips, considering only the probes in common between the two methods, we used exclusively the MethylationEPIC BeadChips (850K) kit.\u003c/p\u003e\n\u003cp\u003eDespite the differences, there is therefore considerable overlap between our data and Steiger's findings, particularly with regard to differentially methylated genes associated with nutritional regulation and metabolism, such as RPTOR, SORBS1 and IGF2R (involved in glucose and insulin metabolism), CAMKK2 and GHRL (related to appetite control) or PRKAG2 (related to body mass and lipid metabolism). In particular, a lower methylation of PRKAG2 has been associated to a higher BMI in a community sample [33]. In addition, we identified genes linked to inflammation and immune response, such as NOD1, DSE, IRAK3 and HHLA2, as well energy metabolism, such as FHL2. We also confirmed that genes such as TNXB and NR1H3, which have previously been associated with anorexia nervosa [11, 14], included differentially methylated sites. The TNXB gene (Tenascin XB) encodes an extracellular matrix glycoprotein with anti-adhesive properties, essential for maintaining tissue integrity and functionality [34]. However, no direct correlation between TNXB and processes such as body weight regulation, malnutrition, or anorexia nervosa has been reported to date. Nonetheless, the extracellular matrix plays a pivotal role in tissue metabolism, particularly in adipose tissue [35], and could indirectly influence processes relevant to anorexia nervosa . Investigating the epigenetic profile of TNXB in patients with anorexia nervosa may provide valuable insights into potential mechanisms of metabolic adaptation or tissue vulnerability associated with this condition.\u003c/p\u003e\n\u003cp\u003eAdditionally, an intriguing finding involves the hypermethylation of the NR1H3 gene, first described by Booij in 2015 [11]. This gene, besides being a key regulator of macrophage function, is critically involved in transcriptional programs governing lipid homeostasis and inflammatory processes [36]. Knockout mouse models have also highlighted its central role in regulating cholesterol homeostasis [37] suggesting that NR1H3 may be relevant to understanding the molecular mechanisms underlying hunger, systemic inflammation, and lipid balance, which are potentially implicated in the pathophysiology of anorexia nervosa.\u003c/p\u003e\n\u003cp\u003eIn addition to methylation differences in genes associated with anorexia, we also investigated methylation status of genes associated to pubertal development. Amenorrhea, and the consequent delay in puberty, is a hallmark symptom of AN in adolescent girls [38] and is closely tied to disruptions in the endocrine signals that regulate normal pubertal progression [39, 40]. One of the most striking findings of this study was the different methylation of the MKRN3 gene, a crucial regulator of puberty [41, 42]. MKRN3 is known to inhibit the secretion of gonadotropin-releasing hormone (GnRH), thereby delaying the onset of puberty. The hypomethylation observed in the MKRN3 gene in the AN (anorexia nervosa) group suggest a potential loss of epigenetic repression, which might contribute to the functional hypothalamic hypogonadism seen in these patients [43]. This observation aligns with the clinical manifestation of primary or secondary amenorrhea in anorexic girls and arise the possibility that disrupted pubertal progression in AN could involve epigenetic mechanisms. However, although these findings are consistent with previous studies that have highlighted how delayed puberty and amenorrhea are consequences of profound metabolic and endocrine disruptions induced by severe caloric restriction and weight loss, further studies are needed to verify whether the altered methylation of MKRN3 translates into changes in its expression and to clarify its role in the pathophysiology of delayed puberty associated with AN. Similarly, we have identified differential methylation in several other genes related to pubertal timing and reproductive health, including GHR, LEPR, GAB2 and DLK1, suggesting a potential role in delayed menarche in anorexia [22] . In particular, the DLK1 (Delta-Like 1 Homolog) gene, part of the non-canonical Notch signalling pathway, is a key regulator of pubertal development, metabolism and adipocyte differentiation [44-46]. Although there is no direct evidence associating DLK1 to anorexia, it is possible its indirect implication in metabolic and eating disorders affecting energy balance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, our study revealed that differentially methylated CpGs were enriched in genes involved in estrogen and GnRH signaling, further supporting the notion that anorexia disrupts the delicate hormonal balance necessary for normal pubertal development [47]. These pathways are known to play a crucial role in reproductive health, and their epigenetic deregulation in AN could provide insights into the observed delay in menarche and ongoing reproductive dysfunction in affected individuals. In fact, the search for transcription factors targeting DMPs revealed higher scores for hormone receptors such as progesterone, androgens and estrogens, suggesting that epigenetics may modulate hormone action and response. As reported by Tremolizzo et al. [12], steroid hormone levels in AN patients were inversely correlated with overall DNA methylation in the blood. The correlation analysis between estrogen levels and DMPs in our cohort did not yield significant results, probably due to the cross-sectional nature of the study and the limited number of the samples. However, a significant number of DMPs were identified within or in close proximity to genes containing high-affinity estrogen-responsive elements. Such genes could be involved in pubertal timing and endocrine development, confirming the findings of previous longitudinal studies on normal pubertal transition. [48, 49].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn innovative aspect of our study is the identification of an epigenetic signature associated with zinc finger proteins (ZNFs), known for their crucial role in transcriptional regulation and chromatin remodeling, fundamental for genomic stability and key processes such as pubertal development and metabolism [50]. Specifically, we identified 92 ZNF genes with differential methylation in patients with anorexia nervosa (AN). The protein products of many of the identified ZNF genes appear to be involved in critical biological pathways. For instance, ZNF664 is implicated in the development of metabolic syndrome [51, 52], ZFAT is involved in adipocyte differentiation and fat distribution [53], while ZBTB43 and ZNF331 play a role in the cellular response to caloric restriction [54, 55]. Among these, ZNF148 is particularly noteworthy as it participates in neurotransmission processes underlying pubertal timing by regulating the GABA transporter (SLC6A1) and in metabolism by modulating the insulin receptor (IRS2) [56]. Similarly, ZNF445 and ZFP57 are involved in genomic imprinting and metabolic pathways, as evidenced by ZNF445’s role in Temple syndrome and the regulation of imprinting regions such as the MEG3/DLK1 locus [57-59]. Furthermore, genome-wide association studies (GWAS) have identified SNPs near ZNF genes associated with age at menarche, reinforcing their role in pubertal regulation [50], which may be disrupted in pathological conditions such as anorexia nervosa. Although there is no direct evidence linking ZNF genes to eating disorders, the differential methylation observed here suggests potential short- and/or long-term consequences of these epigenetic modifications in the disorder.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, the analysis of canonical pathways revealed an enrichment of differentially methylated genes involved in key developmental processes related to anorexia and pubertal development, including neuronal signaling, energy metabolism, and the browning of white adipocytes. \u0026nbsp;These pathways include the sirtuin signaling pathway, which has been shown to play a crucial role not only in neuronal migration and the metabolic regulation of pubertal development, but especially in energy balance and energy sensing at the central level [60, 61]. Other noteworthy differentially methylated pathways involve semaphorins and GnRH. Notably, intercellular communication within the GnRH neural network is partially mediated by semaphorin signaling, which is essential for the development of hypothalamic circuits and the regulation of GnRH release by circulating sex steroids [62]. Insufficient semaphorin signaling contributes to certain forms of reproductive disorders in humans, such as hypogonadotropic hypogonadism [63] , defective neuroendocrine control of the ovarian cycle in adults [64], and obesity [65].\u003c/p\u003e\n\u003cp\u003eFurthermore, we reported that the G protein-coupled receptor (GPCR) signaling pathway exhibits differential methylation between individuals with anorexia nervosa (AN) and healthy pubertal controls. GPCRs play crucial roles in neurosensory and endocrine systems, influencing processes like insulin secretion, β-cell expansion, and glucose homeostasis [66], and are potential therapeutic targets for metabolic disorders such as type 2 diabetes. GPCRs mediate actions of major neurotransmitters, including serotonin and noradrenaline, which are implicated in psychiatric disorders like the depression associated with AN [67].\u003c/p\u003e\n\u003cp\u003eTwo other key pathways here reported are leptin and insulin. It has been shown that, in underweight anorexic patients, plasma levels of leptin are strongly reduced and correlated with decreased body mass index (BMI) and body fat content, as are those of insulin [68]. In addition, pathways that modulate energy metabolism, such as AMP-activated protein kinase (AMPK) and white adipose tissue browning, also showed different methylation status. In fact, in the study by Bredella et al. [69], the link between brown adipose tissue (BAT), bone mineral density (BMD), and Pref-1 (DLK1) levels in young women, including subjects with anorexia nervosa (AN), remitted anorexics, and those with normal weight, was analyzed. Women with active BAT showed higher BMD and lower Pref-1 levels than those without BAT. These results suggest that BAT may positively influence bone health, with implications for conditions such as AN.\u003c/p\u003e\n\u003cp\u003eAlthough the present analysis of methylation patterns in anorexia nervosa (AN) provides valuable insights, we acknowledge several limitations as the small cohort size, which may affect the statistical significance of the results, and the absence of RNA samples that prevented the validation of gene expression changes associated with the detected methylation variations. Secondly, the use of peripheral tissues such as blood instead of disease-relevant tissues (e.g. brain, adipose tissue or gonads) introduces another limitation although ethical and logistical constraints make it difficult to study such tissues in humans. However, studies on blood or saliva have been shown to reflect epigenetic trends observed in other tissues, thus supporting their utility as surrogates in many contexts [70]. \u0026nbsp;However, future efforts would be needed to integrate multi-omics datasets, such as ChIP-Seq or RNA-Seq, and broader and more defined patient cohort selection criteria to minimize confounding factors, paving the way for more reproducible and comparable results.\u003c/p\u003e\n\u003cp\u003eDespite these limitations, epigenetic studies in anorexia nervosa remain a crucial avenue to advance our understanding of this disorder. The identification of specific epigenetic signatures not only sheds light on the complex interplay between genetic predispositions and environmental influences, but also promises practical applications in the clinical setting. These signatures could serve as reliable biomarkers to diagnose more accurately anorexia, as well as for other eating disorders, follow its progression and assess the risks of remission or relapse. Additionally, epigenetic profiles may provide valuable insights into developmental alterations, such as those affecting pubertal maturation, which are closely associated with anorexia and its long-term health consequences. This capability is particularly valuable in addressing the dynamic and multifaceted nature of the disease. Moreover, the reversibility of some epigenetic changes with nutritional and therapeutic interventions underlines their potential as indicators of treatment response. Beyond clinical applications, these studies also offer an unprecedented opportunity to uncover new mechanisms underlying the disorder, paving the way for innovative therapeutic approaches targeting both metabolic and pubertal \u0026nbsp;pathways. By harnessing this knowledge, clinicians could adopt a more personalized approach to the management of AN, tailoring treatments to patients' unique epigenetic profiles and paving the way for the development of innovative therapeutic strategies. Future efforts should focus on refining these biomarkers and expanding their application through longitudinal studies and the integration of multi-omics, thus maximizing their potential to transform both research and clinical practice in the field of eating disorders.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total \u0026nbsp;of 10 girls (mean age 13.625± 1.79) with an active diagnosis of Anorexia Nervosa (AN) according to DSM-IV-TR and functional hypogonadotropic hypogonadism were enrolled at the Division of Child and Adolescent Psychiatry (eating disorder service for developmental age) of the University of Campania Luigi Vanvitelli, Naples. \u0026nbsp;All patients presented with AN restrictive subtype (AN-R).\u0026nbsp;Functional hypogonadotropic hypogonadism is defined as \u0026nbsp;reduced \u0026nbsp; levels of FSH, LH and 17-besta-estradiol or inappropriately low levels compared to clinical pubertal stage in absence of organic lesion and thyroid disfunction.\u0026nbsp;Primary amenorrhea was defined as the absence of menarche five years after initial breast development, Secondary amenorrhea has been defined \u0026nbsp;as the cessation of previously regular menses for three consecutive months or previously irregular menses for six months.\u003c/p\u003e\n\u003cp\u003eAll the patients were not under medications when DNA methylation analysis \"was performed, except for one patient who had started aripiprazole six months earlier. Eleven age-comparable (mean age 13.49 ± 1.48), pubertal, drugfree control (CR) females without history of psychiatric or neurological disorders were also recruited. Pubertal controls were selected among \u0026nbsp;patients who \u0026nbsp;were referred to Paediatric Endocrinology Units of the University of Campania “Luigi Vanvitelli,” Naples on suspicion of poor growth and were therefore considered healthy. Inclusion criteria for healthy controls were: BMI between 10-85th according to WHO growth chart 2006, pubertal stage, no history of menses irregularity \u0026nbsp;chronical drug assumption, and cycles irregularity if menarche already occurred.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Exclusion criteria for both AN and CR controls were: \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- haematological or hepatic disorders,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- cancer\u003c/p\u003e\n\u003cp\u003e-recent infections or surgery\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Alcohol abuse was denied by all recruited participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFollowing ethics committee approval of the study, parents have written informed consent, as well as all recruited subjects gave informed written consent to participate, before undergoing the study procedures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWeight, height and BMI standard deviations (SDs) were calculated using WHO growth chart 2006 for sex and age [71]\u0026nbsp;. Clinical examinations were performed in all patients and controls to assess pubertal stage according to Tanner classification.\u003c/p\u003e\n\u003cp\u003eThe Italian version of Eating Attitudes Test (\u003cstrong\u003eEAT-26) was administered\u003c/strong\u003eto all AN subject as a standardized self-report measure of symptoms and concerns characteristic of eating disorders and the\u0026nbsp;\u0026nbsp;CDI 2 for comprehensive multi-rater assessment of depressive symptoms [72].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Italian version of the Child Behavior Checklist (CBCL) for \u0026nbsp;children and adolescents aged 6 to 18 years \u0026nbsp;has been administred to all AN subjects to assess behavioral and emotional problems [73].\u0026nbsp;The Child depression Inventory (CDI) test for in children and adolescents between the ages of 7 and 17 was performed to measure the cognitive, affective, and behavioral signs of depression\u0026nbsp;[74].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLaboratory studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll blood samples were drawn between 8:00 and 8:30 a.m. from an antecubital vein, clotted, centrifuged, and serum was stored at −20 °C until analyses were performed. FSH and LH concentrations were determined by immunochemiluminometric assay (ICMA) with inter-assay coefficient of variation less than 5%. Serum 17-β-estradiol was measured by using competitive chemiluminescent immunoassay using coated magnetic particles with inter-assay coefficient of variation less than 5%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSamples preparation and genome-wide DNA methylation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic DNA was extracted from peripheral blood leukocytes using standard procedures. DNA quality and quantity were assessed by NanoDrop (Thermo Fisher Scientific) and electrophoresis on 1% agarose gel. Microarray analyses were performed by Genomix4life S.R.L. (Baronissi, Salerno, Italy). DNA concentration in each sample was assayed with a Qubit fluorometer. Bisulfite converted DNA (250 ng) was used for analysis of whole-genome methylation using the MethylationEPIC BeadChip (Illumina, San Diego, CA, USA). In brief, bisulfite converted DNA was whole-genome amplified for 20 h followed by end-point fragmentation. Fragmented DNA was precipitated, denatured and hybridized to the BeadChips for 20 h at 48 °C. The BeadChips were washed and the hybridized primers were extended and labeled before scanning the BeadChips using the Illumina iScan system. Data analysis was performed as described in Casarotto et al \u0026nbsp;[75]. CpGs beta values were obtained using ChAMP on R (v. 4.2.0). Values of blood cell composition were obtained from the patients and missing values were imputed with a random forest model. Singular Value Decomposition was not able to detect the blood cell composition as factor that account for a substantial fraction of variation, for this reason no corrections were performed. Only CpGs with a detection p-value \u0026lt; 0.01 have been considered for further analysis. CpGs were considered differentially methylated if the beta difference between the groups (Δβ) was more than 10% (|Δβ| \u0026gt; 0.1) and FDR values \u0026lt; 0.05. Further analyses were performed on the CpGs located within promoter regions, which are defined as CpGs annotated in the TSS1500, TSS200, 1 Exone, and 5’UTR. DMR analysis was performed with ChAMP using the BumpHunter model with default parameters. DMR with FDR values \u0026gt; 0.05 and |Δβ| \u0026lt; 0.05 were excluded. The canonical pathways were generated through the use of QIAGEN IPA (QIAGEN Inc., https://digitalinsights.qiagen.com/IPA) and only the ones with -log(p-value) \u0026gt; 1.3 were considered significant. List of regions with high affinity EREs in the human genome were downloaded from the “MOUSE AND HUMAN ERE DATABASES” (http://mapageweb.umontreal.ca/maders/eredatabase) and intersected with the genomic coordinates of the Differentially Methylated Positions (DMPs). Hypergeometric test with p-value \u0026lt; 0.05 was used to assess the enrichment of ERE in our DMPs. Transcription factors were found using “epigenetic Landscape In Silico deletion Analysis” (LISA - http://lisa.cistrome.org/) with default parameters. All the statistics, pie charts, and heatmaps were obtained using R.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eALDH4A1\u003c/strong\u003e – Aldehyde Dehydrogenase 4 Family Member A1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAMPK\u003c/strong\u003e – AMP-Activated Protein Kinase\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAN\u003c/strong\u003e – Anorexia Nervosa\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eARNTL\u003c/strong\u003e – Aryl Hydrocarbon Receptor Nuclear Translocator Like\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBAT\u003c/strong\u003e – Brown Adipose Tissue\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMD\u003c/strong\u003e – Bone Mineral Density\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e – Body Mass Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMR\u003c/strong\u003e – Basal Metabolic Rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCAMKK2\u003c/strong\u003e – Calcium/Calmodulin Dependent Protein Kinase Kinase 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCBCL\u003c/strong\u003e – Child Behavior Checklist\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCDH7\u003c/strong\u003e – Cadherin 7\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCDI\u003c/strong\u003e – Child Depression Inventory\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCLU\u003c/strong\u003e – Clusterin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCpG\u003c/strong\u003e – Cytosine-phosphate-Guanine\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCTBP2\u003c/strong\u003e – C-Terminal Binding Protein 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDMP\u003c/strong\u003e – Differentially Methylated Position\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDMR\u003c/strong\u003e – Differentially Methylated Region\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNMT1\u003c/strong\u003e – DNA Methyltransferase 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNMT3B\u003c/strong\u003e – DNA Methyltransferase 3 Beta\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDSE\u003c/strong\u003e – Dermatan Sulfate Epimerase\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDLK1\u003c/strong\u003e – Delta Like Non-Canonical Notch Ligand 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEAT-26\u003c/strong\u003e – Eating Attitudes Test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEIF4G1\u003c/strong\u003e – Eukaryotic Translation Initiation Factor 4 Gamma 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eERE\u003c/strong\u003e – Estrogen Responsive Element\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eESR1\u003c/strong\u003e – Estrogen Receptor 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEXT2\u003c/strong\u003e – Exostosin Glycosyltransferase 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFDR\u003c/strong\u003e – False Discovery Rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFHL2\u003c/strong\u003e – Four And A Half LIM Domains 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFSH\u003c/strong\u003e – Follicle Stimulating Hormone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGAB2\u003c/strong\u003e – GRB2 Associated Binding Protein 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGHR\u003c/strong\u003e – Growth Hormone Receptor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGHRL\u003c/strong\u003e – Ghrelin And Obestatin Prepropeptide\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGnRH\u003c/strong\u003e – Gonadotropin-Releasing Hormone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGPCR\u003c/strong\u003e – G Protein-Coupled Receptor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGWAS\u003c/strong\u003e – Genome-Wide Association Study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHHLA2\u003c/strong\u003e – HERV-H LTR-Associating 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eICMA\u003c/strong\u003e – Immunochemiluminometric Assay\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIGF2R\u003c/strong\u003e – Insulin Like Growth Factor 2 Receptor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIPA\u003c/strong\u003e – Ingenuity Pathway Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIRAK3\u003c/strong\u003e – Interleukin 1 Receptor Associated Kinase 3\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLEPR\u003c/strong\u003e – Leptin Receptor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLH\u003c/strong\u003e – Luteinizing Hormone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLTA\u003c/strong\u003e – Lymphotoxin Alpha\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLRP5\u003c/strong\u003e – LDL Receptor Related Protein 5\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMKL2\u003c/strong\u003e – Megakaryoblastic Leukemia Translocation 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMKRN3\u003c/strong\u003e – Makorin Ring Finger Protein 3\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNFAT5\u003c/strong\u003e – Nuclear Factor Of Activated T-Cells 5\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOD1\u003c/strong\u003e – Nucleotide Binding Oligomerization Domain Containing 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNR1H3\u003c/strong\u003e – Nuclear Receptor Subfamily 1 Group H Member 3\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePR\u003c/strong\u003e – Progesterone Receptor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePRKAG2\u003c/strong\u003e – Protein Kinase AMP-Activated Non-Catalytic Subunit Gamma 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePTPRD\u003c/strong\u003e – Protein Tyrosine Phosphatase Receptor Type D\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRMI1\u003c/strong\u003e – RecQ Mediated Genome Instability 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRORA\u003c/strong\u003e – RAR Related Orphan Receptor A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRPTOR\u003c/strong\u003e – Regulatory Associated Protein Of MTOR Complex 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSFMBT1\u003c/strong\u003e – Scm Like With Four MBT Domains 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSORBS1\u003c/strong\u003e – Sorbin And SH3 Domain Containing 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTSS\u003c/strong\u003e – Transcription Start Site\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTNXB\u003c/strong\u003e – Tenascin XB\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUTR\u003c/strong\u003e – Untranslated Region\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWWP2\u003c/strong\u003e – WW Domain Containing E3 Ubiquitin Protein Ligase 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZNF30\u003c/strong\u003e – Zinc Finger Protein 30\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZNF148\u003c/strong\u003e – Zinc Finger Protein 148\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZNF331\u003c/strong\u003e – Zinc Finger Protein 331\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZNF365\u003c/strong\u003e – Zinc Finger Protein 365\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZNF445\u003c/strong\u003e – Zinc Finger Protein 445\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZNF574\u003c/strong\u003e – Zinc Finger Protein 574\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZNF664\u003c/strong\u003e – Zinc Finger Protein 664\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZNF860\u003c/strong\u003e – Zinc Finger Protein 860\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZFAND2B\u003c/strong\u003e – Zinc Finger AN1-Type Containing 2B\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZFAT\u003c/strong\u003e – Zinc Finger And AT-Hook Domain Containing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZFP57\u003c/strong\u003e – Zinc Finger Protein 57\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZBTB43\u003c/strong\u003e – Zinc Finger And BTB Domain Containing 43\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZMIZ2\u003c/strong\u003e – Zinc Finger MIZ-Type Containing 2\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involving Human Participants was approved by the ethics committee of “Luigi Vanvitelli” University (Protocol number 23.26-20200008943) and was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw methylation data and the normalized beta-values are available on ArrayExpress (E-MTAB-14823).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a grant (390) funded by “VALERE: VAnviteLli pEr la RicErca” program of University of Campania “L. Vanvitelli.”\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.G. conceptualized the idea of the manuscript and also coordinated the research process and reviewed drafts. S.P. and D.P. performed genetic analysis. S.P. analyzed and interpreted the data and prepared the draft manuscript. D.P. performed methylation and bioinformatic analysis. M.C., F.S. and M.G.G. enrolled the patients, performed the clinical examinations\u0026nbsp;and supervised the paper; M.G.G. \u0026nbsp;E.M.d.G. supervised the whole work and administered the project. F.A. and G.R.U. enrolled the controls and took care of the clinical data and data analysis.\u0026nbsp;G.C contributed to part of the project design and data analysis process. All authors read and approved the final manuscript and take full responsibility for its content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully thank all the patients, family members and staff from all the units that participated in the study. Preliminary but partial data of this paper have been presented during the 62th annual meeting of the European Society of Paediatric Endocrinology as free oral communication. The extended results of the preliminary research are contained in this paper, they are original and not published anywhere else.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHerpertz-Dahlmann B. Adolescent eating disorders: update on definitions, symptomatology, epidemiology, and comorbidity. Child and adolescent psychiatric clinics of North America. 2015;24(1):177-96.\u003c/li\u003e\n\u003cli\u003eJuli R, Juli MR, Juli G, Juli L. 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Clinical epigenetics. 2022;14(1):171.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Anorexia, epigenetics, methylation, amenorrhea, puberty","lastPublishedDoi":"10.21203/rs.3.rs-6352266/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6352266/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Anorexia nervosa (AN) is a severe eating disorder characterized by extreme calorie restriction and high morbidity strikes especially at young age. While genome-wide and twin studies suggest a strong genetic component, recent findings emphasize the significant role of environmental factors, including stress and social pressures, in its development. Epigenetic mechanisms, particularly DNA methylation, may mediate these interactions, influencing metabolism and reproductive health. Despite these advances, research on cohorts of young adolescents remains limited. Therefore, this study aims to investigate the epigenetic alterations associated with AN in adolescent females with functional hypogonadotropic hypogonadism, compared to normal-weight pubertal controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Examination of methylome changes associated with AN in 10 girls revealed 87 differentially methylated regions (DMRs) compared to 11 healthy girls. The majority of these DMRs exhibited hypomethylation in the AN group (69%) and harbored genes, such as Clusterin (CLU), regulating hypothalamic feeding pathways, and MKRN3, involved in pubertal timing regulation. Moreover, we showed that methylation levels in members of AN group differed from those in the control group for 2072 CpG sites (62.40% hypomethylated) located within genes previously associated with metabolic processes (e.g., LEPR and NR1H3) and age of menarche (e.g., GHR, MKL2, GAB2, CTBP2, and DLK1). Additionally, some CpGs located in genes such as CDH7, associated with hypogonadotropic hypogonadism. Interestingly, a total of 92 differentially methylated genes encoding zinc-finger (ZNF) proteins, including MKRN3, exhibited distinct methylation profiles between the two groups. Ingenuity Pathway Analysis of all CpGs highlighted pathways related to metabolism and body mass (e.g. leptin, and sirtuin signaling), neuronal signaling (e.g., semaphorin), and several pathways potentially associated with puberty (e.g., estrogen and GNRH signaling).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Most of the observed changes concerned hypomethylation of DMR and CpG sites in the AN group. These epigenetic changes mainly affected transcriptional repressors, such as ZNFs, and genes associated with energy deprivation, metabolism, inflammation and pubertal development with consequent changes in the methylation status of key signalling pathways such as GnRH, leptin and insulin. In conclusion, distinct methylation profiles were observed in the AN and control groups suggesting that epigenetic modifications may serve as potential biomarkers of metabolic dysfunction and pubertal arrest, underlining their interconnected role in the pathophysiology of the disease.\u003c/p\u003e","manuscriptTitle":"DNA Methylation profiles in girls with anorexia nervosa and functional hypothalamic amenorrhea: a pilot study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-05 17:53:47","doi":"10.21203/rs.3.rs-6352266/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4a25b515-acb2-4e0b-a14b-33e7c329770f","owner":[],"postedDate":"June 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-27T07:08:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-05 17:53:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6352266","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6352266","identity":"rs-6352266","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}