Impact of Obesity on Hematological Parameters and Iron Indices

Impact of Obesity on Hematological Parameters and Iron Indices | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of Obesity on Hematological Parameters and Iron Indices Safa Matbouly Sayed, Mohammed Mostafa Al-Tawil, Dina Abdelmoneim Abdelhakam, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6926044/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Obesity in adolescents can be accompanied by dyslipidemia, insulin resistance (IR) and chronic low-grade inflammation, with increased levels of proinflammatory cytokines. Therefore, hematological parameters should be evaluated in obese children and adolescents. Objectives: Assessment of the obesity impact on hematological parameters and iron indices. Patients and methods: This cross sectional study included 60 obese children or adolescents recruited from Pediatric Obesity Clinic, Children’s Hospital, Ain Shams University, Cairo, Egypt and compared to 30 children and adolescent with normal BMI during the period from October 2023 to April 2024. Data regarding medical history, general examination, vital signs and laboratory investigation were collected. Inflammatory markers related to hematological indices were calculated and hepcidin was evaluated among cases and control using enzyme-linked immunosorbent assay (ELISA). Results: The age of cases ranged from 6 to 15 years and the mean was 10.22 ± 2.29 years with a predominance of male gender (60%). Inflammatory markers including neutrophil platelet score, immature to total neutrophil (I:T), neutrophil lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), neutrophil to albumin ratio, white blood cell count to mean platelet volume (WBC/MPV), systemic immune-inflammation index (SII), prognostic nutritional index (PNI) and C-reactive protein (CRP) to albumin ratio were all significantly higher among obese cases compared to the controls. Hepcidin was significantly increased among obese cases 654.6 (450.88-1608.75) pg/ml compared to the controls 102.55 (60.05-182.98) pg/ml, p-value = 0.001. Conclusion: This study illustrated the association of each of hematological indices and hepcidin with childhood obesity as hepcidin was significantly elevated among cases compared to controls, displaying a sensitivity of 98.3% and a specificity of 83.3% at a cutoff point of 302.8 pg/ml. This was linked more to inflammation and metabolic alterations as inflammatory and metabolic were also elevated among obese cases. Childhood obesity hematological indices hepcidin INTRODUCTION Obesity is a complex and pervasive disease that is defined as having a Body Mass Index (BMI) greater than or equal to the 95th percentile for age and gender in children aged two years and older. However, the Centers for Disease Prevention and Control (CDC) recommend using the World Health Organization weight-for-length age and gender-specific charts rather than the BMI [1]. There is a rapid increase in the prevalence of overweight during the last decades in Eastern Mediterranean Region (EMR) due to modern lifestyles [2]. The total leukocyte count (TLC) is an inflammatory marker that is elevated in obesity and metabolic syndrome (MetS) [3]. Increased platelet count and activation also occur as part of chronic inflammation in obesity, along with elevated red blood cell count (RBC) parameters, such as hemoglobin (Hb) and hematocrit (Hct) levels [4]. On the other hand, hepcidin has a crucial role in innate immunity and it is also induced by inflammation as increased erythropoietic activity suppresses hepcidin [5]. The inflammation produced by adipocyte hypertrophy and consequent hypoxia and death results in the secretion of cytokines by the adipocytes themselves in larger amounts from the macrophages surrounding them [6]. Compared with subcutaneous fat, visceral adipose tissue is more densely infiltrated by macrophages (crown structures) secreting inflammatory cytokines, which include IL-6, which stimulates hepatic hepcidin synthesis when central obesity is present [7]. PATIENTS AND METHODS This cross-sectional study included 60 obese children or adolescents recruited from Pediatric Obesity Clinic, Children’s Hospital, Ain Shams University, Cairo, Egypt and compared to 30 children and adolescent with normal BMI from October 2023 to March 2024. Obese patients where BMI-standard deviation score (SDS) greater than or equal to +2.0) from obesity clinic, while the control group were age and sex matched healthy normal children controls with normal weight (BMI-SDS between -2.0 and +1.0). Inclusion criteria: Obese children 6-18 years old attending at Pediatric Obesity Clinic, Children’s Hospital, Ain Shams University. Exclusion criteria: Patients with chronic diseases, patients with secondary obesity as genetic syndromes like (Turner syndrome or Down syndrome) or endocrinal causes like (growth hormone deficiency, hypothyroidism or Cushing’s syndrome), patients receiving drugs that could affect weight (systemic steroids, psychiatric medications like antipsychotic or antidepressants), history of acute infection during time of sampling, children with leukopenia (total leucocyte count [TLC] ˂4x10 3 /mL), children with leukocytosis (TLC ˃13.0x10 3 /mL), thrombocytopenia (platelet count [PLT] ˂150x10 3 /mL) and patients on iron therapy. Ethical considerations: This study was conducted after approval of the Research Ethics Committee of Ain Shams University Hospitals and an informed consent was obtained from the parents or caregivers of each participant. Patient evaluation included sociodemographic characteristics, medical history and diet history. General examination included Tanner score according to Tanner classification [8], weight, height and BMI standard deviation scores (SDS) according to age and sex specific reference values [9 & 10] , waist circumference and its SDS calculated and compared to normal references for age and sex [11] , Hip circumference and waist-hip ratio compared to normal age and sex reference ranges [12] and vital signs. Laboratory investigations included complete blood count (CBC) using Beckman Coulter HmX Hematology Analyzer; C-reactive protein (CRP) by Roche Cobas C311 autoanalyzer, serum albumin by AU680 auto-analyzer; fasting lipid profile using guidelines of the National Cholesterol Education Program for children and adolescents [13]; calculation of inflammatory scores and indices as immature to total neutrophil (I:T) ratio, neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), neutrophil platelet score, neutrophil percentage-to-albumin ratio, MPV/platelets, WBCs/MPV, systemic immune-inflammation index (SII) (Platelet count × neutrophil count/lymphocyte count), prognostic index (PI) which depends on CRP and WBCs values, prognostic nutritional index (PNI), CRP/albumin ratio, modified Glasgow Prognostic Score (mGPS) [14]. Fasting blood glucose (FBG) hemoglobin A1c (HbA1c) were evaluated using American Diabetes Association (ADA) Standards of Care for Diabetes (2019) [15]. Insulin resistance was assessed by the homeostasis model assessment for insulin resistance (HOMA-IR) index. A cutoff value of >2.7 was used as an index of insulin resistance in children and adolescents [16 & 17]. Iron profile was assessed as serum ferritin, iron and total iron binding capacity (TIBC) [18], while hepcidin level was measured using Hepicidin enzyme-immunosorbent assay (ELISA) kit, [19]. Catalogue. No: E1019Hu, Bioassay Technology Laboratory, Shanghai, China. Statistical analysis: Data were analyzed using IBM statistical package for the social sciences (SPSS) Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp. significance of the obtained results was judged at the (0.05) level. Kolmogorov-Smirnov test was used to test for normality of quantitative data. Quantitative data was described using median and inter quartile range (IQR) for non-parametric data and Mann Whitney test was used to compare the 2 groups. While mean, standard deviation (SD) for parametric data and Independent Student t test was used to compare the 2 groups. Qualitative data was described using number and percent where Chi-Square test was used for comparison of 2 or more groups and Monte Carlo test was used for correction for Chi-Square test when more than 25% of cells have count less than 5 in tables (>2*2). The diagnostic performance of a test was evaluated using Receiver Operating Characteristic (ROC) curve analysis. Binary stepwise logistic regression was used for prediction of independent variables of binary outcome. RESULTS Table (1) shows that there was no significant difference between the two groups regarding sociodemographic characteristics. Table (2) shows that neutrophil platelet score, I:T ratio, NLR, PLR, neutrophil to albumin ratio, WBC/MPV, SII, PNI and CAR were significantly elevated among cases compared to controls, yet albumin was significantly decreased among cases. Table (3) shows that serum ferritin, TC, TG and LDL were significantly higher among cases compared to controls, yet HDL was significantly lower among cases. Fasting insulin, FBG, HOMA IR and HbA1c were significantly higher among cases compared to controls, yet ISI was significantly lower among cases. Hepcidin was also significantly elevated among cases compared to controls. Table (4) shows that the area under ROC curve for hepcidin in differentiation of obese children from normal subjects is 0.957 with the best detected cut off point is 302.8 pg/ml yielding total accuracy of 93.3%. Table (5) shows that there was a significant positive correlation between hepcidin and each of duration of AF, SBP, DBP, SBP and DBP percentiles for age, weight, BMI, WC, HC, neutrophil to albumin ratio, WBC/MPV, CAR, TC, TG, LDL, fasting insulin, FBG, HOMA IR and HbA1c, while there was a significant negative correlation between hepcidin and each of WHR, PLR, PNI, albumin, HDL and ISI. All study data is availiable when requested DISCUSSION The etiology of obesity is multifactorial, nevertheless the genetic factors play a significant role in development of obesity and DM later in life. This was in line with Corica et al. (2018) who performed a study that included 260 children and reported that family history of obesity and cardiometabolic diseases including DM and central obesity are important risk factors of obesity in childhood [ 20 ]. Our results also showed a statistically significant increase among obese cases as regards lipid profile. Almost half of patients had borderline levels of TC (51.7%) and LDL (56.7%), while elevated levels of TC were found in (3.3%) and elevated LDL were found in (8.3%). Interestingly, (95%) had low level of HDL implying that these patients are at risk of cardiometabolic disease. Similar results were previously obtained by Abd Al-Ghani et al. (2022) as serum TG, TC, LDL and HDL cholesterol were significantly higher among Egyptian obese group compared to the control group [ 21 ]. As for the glycemic profile, we also found a statistically significant increase among obese cases with regard to FBG, HbA1c, and insulin profile including fasting insulin, HOMA IR and ISI. The insulin resistance was reported in 33 patients (55%) and the prevalence of prediabetes was reported in 21 patients (35%). Similar to our results, Rodríguez-Mortera et al. (2021) conducted a cross-sectional study with 29 obese and 30 lean adolescents from Mexico where HbA1c was significantly higher among cases (p = 0.05). Insulin and HOMA-IR were 2-fold higher in obese adolescents (p < 0.001) [ 7 ]. This could be explained by the fact that insulin resistance positively correlated with the BMI and proportion of body fat. Children with overweight or obesity have lower insulin sensitivity than children with average body weight [ 22 ]. Our findings showed that all inflammatory markers including neutrophil platelet score, I:T ratio, NLR, PLR, neutrophil to albumin ratio, WBC/MPV, SII, PNI and CAR were significantly elevated among cases compared to controls. This was in compliance with Aydin et al. (2015) who found that NLR was significantly higher among the Turkish obese group (p = 0.03) compared to healthy controls, which was directly related to the degree of obesity [32]. Additionally, Mărginean et al. (2019) found a significant increase among Romanian obese children compared to healthy controls regarding leukocyte count (p = 0.0379), lymphocyte count (p = 0.0002), platelet count (p = 0.0006) and CRP level (p < 0.0001) with no statistically significant difference regarding PLR, likely, due to the significant increase in both platelet and lymphocyte counts in the overweight/obese group [ 24 ]. In another study by Yazaki et al. (2022) , PLR showed an association with BMI and WHR in Turkish children and adolescents. This association reflects visceral adiposity and is related to insulin resistance [ 25 ]. Likewise, Ding et al. (2021) studied 1240 Chinese patients with T2DM and MPV was significantly higher in patients with MetS ( P < .001). The mechanism underlying higher MPV in MetS patients may be explained by adipose tissue, which secretes various adipokines and cytokines, including leptin, adiponectin, interleukin, and nitric oxide that cause megakaryocytes to produce larger platelets [ 26 ]. Besides, Zhou et al. (2024) recruited 20,011 Chinese adults, of whom (39.32%) were obese and found a significant relationship between obesity as SII levels demonstrating that elevated SII levels are linked to metabolic syndrome [ 27 ]. In explanation, the proliferation of adipocytes triggers the fostering of a proinflammatory adipose tissue microenvironment, and consequently disrupts organ or tissue homeostasis [ 28 ]. Serum ferritin was also significantly higher among cases compared to controls. As for hepcidin, the levels of hepcidin were significantly increased among cases versus controls with a sensitivity of 98.3% and a specificity of 83.3% to differentiate obese cases at a cutoff of 302.8 pg/ml. In line with Rodríguez-Mortera et al. (2021) , (70%) of the obese group have significantly higher levels of ferritin versus the lean group. These also reported that the increased IL-6 levels were more suggestive of chronic inflammation rather than iron overload and to a certain extent, a surrogate marker of MetS [ 7 ]. This could be due to several factors, including greater chronic inflammation in adolescents with obesity as evidenced by higher levels of inflammatory biomarkers, greater visceral adipose tissue mass, and greater insulin resistance that all aggravate chronic inflammation, atherogenic dyslipidemia and visceral adipose tissue hypertrophy [ 7 ]. On the other hand, several studies have suggested that ferritin level in adolescents is as an important early indicator for the risk of developing metabolic disorders [29 & 30]. This study has shown significant positive correlation between hepcidin and BMI (r = 0.592, p = 0.001). it also shows a significant positive correlation between insulin resistance expressed as HOMA-IR and hepcidin (r = 0.583, p = 0.001). Hepcidin also correlated with lipid metabolic parameters in obese adolescents, TC (r = 0.589, p = 0.001), TG (r = 0.361, p = 0.001), LDL (r = 0.643, p = 0.001) and HDL (r = 0.637, p = 0.001). In line with Kumar et al. (2019) studied 131 Indian children and reported a significant correlation between hepcidin and ferritin levels and hepcidin and BMI (p = 0.019) as it plays a vital role in hepcidin dynamics as high BMI is associated with a proinflammatory status that results in higher hepcidin levels via IL-6 [ 31 ]. Moreover, Rodríguez-Mortera et al. (2021) , Hepcidin was correlated with HOMA-IR (r = 0.29). In obese adolescents, hepcidin correlated with TG (r = 0.47) and LDL (r = 0.39), suggesting a link between atherogenic dyslipoproteinemia and hepcidin levels [ 7 ]. There are some limitations of this study that need to be acknowledged. The relatively small sample size and lack of generalization. The lack of correlation with dietary habits and the assessment of related complications. There was also no assessment of visceral fat. Conversely, this study will make a significant contribution to the existing literature as it involved pediatric patients and was able to identify the risk factors associated with the early phases of obesity-related inflammatory process. CONCLUSION This study illustrated the association of hepcidin and childhood obesity as it was significantly elevated among cases compared to controls, displaying a sensitivity of 98.3% and a specificity of 83.3% at a cutoff point of 302.8 pg/ml. This was linked more to inflammation and metabolic alterations as inflammatory and metabolic parameters were also elevated among obese cases. Declarations Ethics Approval and consent to participate. Ain Shams University Faculty of Medicine Research Ethics committee (REC) FWA000017585 [email protected] tel:{202}2685–7539. Author Contribution Safa M. Mohammed performed data analysis and interpretation, managed the literature searches and wrote the first draft of the manuscript. Mohammed M. Al-Tawil provided the idea of the study and study design, and contributed to study supervision and revision of manuscript. Dina A. Abdelhakam shared in analysis of data and study supervision. Rana A. mahmoud collected the data and contributed to analysis of the results and literature searches. Marwa F. Elkefl contributed to study concent, design, supervision and critical revision of the manuscript for important intellectual content. all authors reviewed the manuscript Data Availability all study data is available when requested Funding Research funding NO FUND WITH GIVEN TO THIS Research References Tiwari A, Daley SF, Balasundaram P. Obesity in Pediatric Patients. [Updated 2023 Mar 8]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK570626/ Abdelkarim O, Ammar A, Trabelsi K, Cthourou H, Jekauc D, Irandoust K, et al. Prevalence of Underweight and Overweight and Its Association with Physical Fitness in Egyptian Schoolchildren. 2019. Int. J Environ Res Public Health ; 17(1):75-85. Seo IH, Lee YJ. Usefulness of Complete Blood Count (CBC) to Assess Cardiovascular and Metabolic Diseases in Clinical Settings: A Comprehensive Literature Review. 2022; Biomedicines; 10(11):2697. 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Front Endocrinol (Lausanne). 2018; 9:187. Abd Al-Ghani Saad Kaka M, Mohammed Ghanem S, Abd Al-Aziz Ahmed S, Abd Al-Kreem Al-Dahshan T. Lipids Profile Among Egyptian School Age Obese Children. Al-Azhar Med J. 2022; 51(1): 761-770. Al-Beltagi M, Bediwy AS, Saeed NK. Insulin-resistance in paediatric age: Its magnitude and implications. World J Diabetes. 2022; 13(4):282–307. Aydin M, Yilmaz A, Donma MM, Tulubas F, Demirkol M, Erdogan M, et al. Neutrophil/lymphocyte ratio in obese adolescents. North Clin Istanb. 2015;2(2):87-91. Mărginean CO, Meliţ LE, Ghiga DV, Mărginean MO. Early Inflammatory Status Related to Pediatric Obesity. Front Pediatr. 2019; 7:241. Yazaki LG, Faria JCP, Souza FIS, Sarni ROS. Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios of overweight children and adolescents. Rev Assoc Med Bras (1992). 2022; 68(8):1006-1010. Ding Q, Wang F, Guo X, Liang M. The relationship between mean platelet volume and metabolic syndrome in patients with type 2 diabetes mellitus: A retrospective study. Medicine (Baltimore). 2021; 100(13): e25303. Zhou Y, Wang Y, Wu T, Zhang A, Li Y. Association between obesity and systemic immune inflammation index, systemic inflammation response index among US adults: a population-based analysis. Lipids Health Dis. 2024; 23(1):245. Shim YS, Kang MJ, Oh YJ, Baek JW, Yang S, Hwang IT. Association of serum ferritin with insulin resistance, abdominal obesity, and metabolic syndrome in Korean adolescent and adults: The Korean National Health and Nutrition Examination Survey, 2008 to 2011. Medicine (Baltimore). 2017; 96(8):e6179. Hitha H, Gowda D, Mirajkar A. Serum ferritin level as an early indicator of metabolic dysregulation in young obese adults - a cross-sectional study. Can J Physiol Pharmacol. 2018; 96(12):1255-1260. Shattnawi KK, Alomari MA, Al-Sheyab N, Bani Salameh A. The relationship between plasma ferritin levels and body mass index among adolescents. Sci Rep. 2018; 8(1):15307. Kumar S, Bhatia P, Jain R, Bharti B. Plasma Hepcidin Levels in Healthy Children: Review of Current Literature Highlights Limited Studies. J Pediatr Hematol Oncol. 2019 41(3):238-242. Tables Tables 1 to 5 are available in the Supplementary Files section Additional Declarations No competing interests reported. Supplementary Files Tables.docx 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6926044","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":487047084,"identity":"e5c25563-af14-4e0e-adfe-074181ff90f6","order_by":0,"name":"Safa Matbouly Sayed","email":"","orcid":"","institution":"Ain Shams University","correspondingAuthor":false,"prefix":"","firstName":"Safa","middleName":"Matbouly","lastName":"Sayed","suffix":""},{"id":487047085,"identity":"50f200ca-b5fd-4483-99db-6aaba7dfbcc7","order_by":1,"name":"Mohammed Mostafa Al-Tawil","email":"","orcid":"","institution":"Ain Shams University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"Mostafa","lastName":"Al-Tawil","suffix":""},{"id":487047086,"identity":"dba4345b-e5d0-43bd-8883-89eb0e9d1375","order_by":2,"name":"Dina Abdelmoneim Abdelhakam","email":"","orcid":"","institution":"Ain Shams University","correspondingAuthor":false,"prefix":"","firstName":"Dina","middleName":"Abdelmoneim","lastName":"Abdelhakam","suffix":""},{"id":487047087,"identity":"56ac581a-076a-49eb-baad-2be7430501cb","order_by":3,"name":"marwa fathy Elkefl","email":"data:image/png;base64,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","orcid":"","institution":"Ain Shams University","correspondingAuthor":true,"prefix":"","firstName":"marwa","middleName":"fathy","lastName":"Elkefl","suffix":""},{"id":487047088,"identity":"0cadf789-fb00-4b3c-9fde-9050a436dba9","order_by":4,"name":"Rana abdelhakim Mahmoud","email":"","orcid":"","institution":"Ain Shams University","correspondingAuthor":false,"prefix":"","firstName":"Rana","middleName":"abdelhakim","lastName":"Mahmoud","suffix":""}],"badges":[],"createdAt":"2025-06-18 22:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6926044/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6926044/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92061387,"identity":"e6aa202d-f148-45da-96f1-31a5422ff687","added_by":"auto","created_at":"2025-09-24 08:17:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":990887,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6926044/v1/830748ba-556c-4b22-b4b4-557bc196c886.pdf"},{"id":87006819,"identity":"21a6fe15-3474-476e-800e-a8ce8d598e69","added_by":"auto","created_at":"2025-07-18 08:29:27","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":42589,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6926044/v1/4dfb718791dcf35791c34d2e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Obesity on Hematological Parameters and Iron Indices","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eObesity is a\u0026nbsp;complex and pervasive disease that is\u0026nbsp;defined as having a Body Mass Index (BMI) greater than or equal to the 95th percentile for age and gender in children aged two\u0026nbsp;years and older. However, the\u0026nbsp;Centers for Disease Prevention and Control (CDC) recommend using the World Health Organization weight-for-length\u0026nbsp;age and gender-specific charts rather than the BMI [1].\u003c/p\u003e\n\u003cp\u003eThere is a rapid increase in the prevalence of overweight during the last decades in Eastern Mediterranean Region (EMR) due to modern lifestyles [2].\u003c/p\u003e\n\u003cp\u003eThe total leukocyte count (TLC) is an inflammatory marker that is elevated in obesity and metabolic syndrome (MetS) [3]. Increased platelet count and activation also occur as part of chronic inflammation in obesity, along with elevated red blood cell count (RBC) parameters, such as hemoglobin (Hb) and hematocrit (Hct) levels [4].\u003c/p\u003e\n\u003cp\u003eOn the other hand, hepcidin has a crucial role in innate immunity and it is also induced by inflammation as increased erythropoietic activity suppresses hepcidin [5]. The inflammation produced by adipocyte hypertrophy and consequent hypoxia and death results in the secretion of cytokines by the adipocytes themselves in larger amounts from the macrophages surrounding them [6].\u003c/p\u003e\n\u003cp\u003eCompared with subcutaneous fat, visceral adipose tissue is more densely infiltrated by macrophages (crown structures) secreting inflammatory cytokines, which include IL-6, which stimulates hepatic hepcidin synthesis when central obesity is present [7].\u003c/p\u003e"},{"header":"PATIENTS AND METHODS","content":"\u003cp\u003eThis cross-sectional study included 60 obese children or adolescents recruited from Pediatric Obesity Clinic, Children\u0026rsquo;s Hospital, Ain Shams University, Cairo, Egypt and compared to 30 children and adolescent with normal BMI from October 2023 to March 2024. Obese patients where BMI-standard deviation score (SDS) greater than or equal to +2.0) from obesity clinic, while the control group were age and sex matched healthy normal children controls with normal weight (BMI-SDS between -2.0 and +1.0).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion criteria: \u003c/strong\u003eObese children 6-18 years old attending at Pediatric Obesity Clinic, Children\u0026rsquo;s Hospital, Ain Shams University. \u003cstrong\u003eExclusion criteria: \u003c/strong\u003ePatients with chronic diseases, patients with secondary obesity as genetic syndromes like (Turner syndrome or Down syndrome) or endocrinal causes like (growth hormone deficiency, hypothyroidism or Cushing\u0026rsquo;s syndrome), patients receiving drugs that could affect weight (systemic steroids, psychiatric medications like antipsychotic or antidepressants), history of acute infection during time of sampling, children with leukopenia (total leucocyte count [TLC] ˂4x10\u003csup\u003e3\u003c/sup\u003e /mL), children with leukocytosis (TLC ˃13.0x10\u003csup\u003e3\u003c/sup\u003e /mL), thrombocytopenia (platelet count [PLT] ˂150x10\u003csup\u003e3\u003c/sup\u003e /mL) and patients on iron therapy.\u003cstrong\u003eEthical considerations:\u003c/strong\u003e This study was conducted after approval of the Research Ethics Committee of Ain Shams University Hospitals and an informed consent was obtained from the parents or caregivers of each participant.\u003c/p\u003e\n\u003cp\u003ePatient evaluation included sociodemographic characteristics, medical history and diet history. General examination included Tanner score according to Tanner classification [8], weight, height and BMI standard deviation scores (SDS) according to age and sex specific reference values [9 \u0026amp; 10]\u003cstrong\u003e, \u003c/strong\u003ewaist circumference and its SDS calculated and compared to normal references for age and sex [11]\u003cstrong\u003e, \u003c/strong\u003eHip circumference and waist-hip ratio compared to normal age and sex reference ranges [12] and vital signs.\u003c/p\u003e\n\u003cp\u003eLaboratory investigations included complete blood count (CBC) using Beckman Coulter HmX Hematology Analyzer; C-reactive protein (CRP) by Roche Cobas C311 autoanalyzer, serum albumin by AU680 auto-analyzer; fasting lipid profile using guidelines of the National Cholesterol Education Program for children and adolescents [13]; calculation of inflammatory scores and indices as immature to total neutrophil (I:T) ratio, neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), neutrophil platelet score, neutrophil percentage-to-albumin ratio, MPV/platelets, WBCs/MPV, systemic immune-inflammation index (SII) (Platelet count \u0026times; neutrophil count/lymphocyte count), prognostic index (PI) which depends on CRP and WBCs values, prognostic nutritional index (PNI), CRP/albumin ratio, modified Glasgow Prognostic Score (mGPS) [14].\u003c/p\u003e\n\u003cp\u003eFasting blood glucose (FBG) hemoglobin A1c (HbA1c) were evaluated using American Diabetes Association (ADA) Standards of Care for Diabetes (2019) [15]. Insulin resistance was assessed by the homeostasis model assessment for insulin resistance (HOMA-IR) index. A cutoff value of \u0026gt;2.7 was used as an index of insulin resistance in children and adolescents [16 \u0026amp; 17]. Iron profile was assessed as serum ferritin, iron and total iron binding capacity (TIBC) [18], while hepcidin level was measured using Hepicidin enzyme-immunosorbent assay (ELISA) kit, [19]. Catalogue. No: E1019Hu, Bioassay Technology Laboratory, Shanghai, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis:\u003c/strong\u003e Data were analyzed using IBM statistical package for\u0026nbsp;the social sciences (SPSS) Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp. significance of the obtained results was judged at the (0.05) level. Kolmogorov-Smirnov test was used to test for normality of quantitative data. Quantitative data was described using median and inter quartile range (IQR) for non-parametric data and Mann Whitney test was used to compare the 2 groups. While mean, standard deviation (SD) for parametric data and Independent Student t test was used to compare the 2 groups. Qualitative data was described using number and percent where Chi-Square test was used for comparison of 2 or more groups and Monte Carlo test was used for correction for Chi-Square test when more than 25% of cells have count less than 5 in tables (\u0026gt;2*2). The diagnostic performance of a test was evaluated using Receiver Operating Characteristic (ROC) curve analysis. Binary stepwise logistic regression was used for prediction of independent variables of binary outcome.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eTable (1) shows that there was no significant difference between the two groups regarding sociodemographic characteristics.\u003c/p\u003e\n\u003cp\u003eTable (2) shows that neutrophil platelet score, I:T ratio, NLR, PLR, neutrophil to albumin ratio, WBC/MPV, SII, PNI and CAR were significantly elevated among cases compared to controls, yet albumin was significantly decreased among cases.\u003c/p\u003e\n\u003cp\u003eTable (3) shows that serum ferritin, TC, TG and LDL were significantly higher among cases compared to controls, yet HDL was significantly lower among cases. Fasting insulin, FBG, HOMA IR and HbA1c were significantly higher among cases compared to controls, yet ISI was significantly lower among cases. Hepcidin was also significantly elevated among cases compared to controls.\u003c/p\u003e\n\u003cp\u003eTable (4) shows that the area under ROC curve for hepcidin in differentiation of obese children from normal subjects is 0.957 with the best detected cut off point is 302.8 pg/ml yielding total accuracy of 93.3%.\u003c/p\u003e\n\u003cp\u003eTable (5) shows that there was a significant positive correlation between hepcidin and each of duration of AF, SBP, DBP, SBP and DBP percentiles for age, weight, BMI, WC, HC, neutrophil to albumin ratio, WBC/MPV, CAR, TC, TG, LDL, fasting insulin, FBG, HOMA IR and HbA1c, while there was a significant negative correlation between hepcidin and each of WHR, PLR, PNI, albumin, HDL and ISI.\u003c/p\u003e\n\u003cp\u003eAll study data is availiable when requested\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe etiology of obesity is multifactorial, nevertheless the genetic factors play a significant role in development of obesity and DM later in life.\u003c/p\u003e\u003cp\u003eThis was in line with \u003cb\u003eCorica et al. (2018)\u003c/b\u003e who performed a study that included 260 children and reported that family history of obesity and cardiometabolic diseases including DM and central obesity are important risk factors of obesity in childhood [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur results also showed a statistically significant increase among obese cases as regards lipid profile. Almost half of patients had borderline levels of TC (51.7%) and LDL (56.7%), while elevated levels of TC were found in (3.3%) and elevated LDL were found in (8.3%). Interestingly, (95%) had low level of HDL implying that these patients are at risk of cardiometabolic disease.\u003c/p\u003e\u003cp\u003eSimilar results were previously obtained by \u003cb\u003eAbd Al-Ghani et al. (2022)\u003c/b\u003e as serum TG, TC, LDL and HDL cholesterol were significantly higher among Egyptian obese group compared to the control group [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAs for the glycemic profile, we also found a statistically significant increase among obese cases with regard to FBG, HbA1c, and insulin profile including fasting insulin, HOMA IR and ISI. The insulin resistance was reported in 33 patients (55%) and the prevalence of prediabetes was reported in 21 patients (35%).\u003c/p\u003e\u003cp\u003eSimilar to our results, \u003cb\u003eRodr\u0026iacute;guez-Mortera et al. (2021)\u003c/b\u003e conducted a cross-sectional study with 29 obese and 30 lean adolescents from Mexico where HbA1c was significantly higher among cases (p\u0026thinsp;=\u0026thinsp;0.05). Insulin and HOMA-IR were 2-fold higher in obese adolescents (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis could be explained by the fact that insulin resistance positively correlated with the BMI and proportion of body fat. Children with overweight or obesity have lower insulin sensitivity than children with average body weight [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur findings showed that all inflammatory markers including neutrophil platelet score, I:T ratio, NLR, PLR, neutrophil to albumin ratio, WBC/MPV, SII, PNI and CAR were significantly elevated among cases compared to controls.\u003c/p\u003e\u003cp\u003eThis was in compliance with \u003cb\u003eAydin et al. (2015)\u003c/b\u003e who found that NLR was significantly higher among the Turkish obese group (p\u0026thinsp;=\u0026thinsp;0.03) compared to healthy controls, which was directly related to the degree of obesity [32].\u003c/p\u003e\u003cp\u003eAdditionally, \u003cb\u003eMărginean et al. (2019)\u003c/b\u003e found a significant increase among Romanian obese children compared to healthy controls regarding leukocyte count (p\u0026thinsp;=\u0026thinsp;0.0379), lymphocyte count (p\u0026thinsp;=\u0026thinsp;0.0002), platelet count (p\u0026thinsp;=\u0026thinsp;0.0006) and CRP level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) with no statistically significant difference regarding PLR, likely, due to the significant increase in both platelet and lymphocyte counts in the overweight/obese group [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn another study by \u003cb\u003eYazaki et al. (2022)\u003c/b\u003e, PLR showed an association with BMI and WHR in Turkish children and adolescents. This association reflects visceral adiposity and is related to insulin resistance [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLikewise, \u003cb\u003eDing et al. (2021)\u003c/b\u003e studied 1240 Chinese patients with T2DM and MPV was significantly higher in patients with MetS (\u003cem\u003eP\u003c/em\u003e \u0026lt; .001). The mechanism underlying higher MPV in MetS patients may be explained by adipose tissue, which secretes various adipokines and cytokines, including leptin, adiponectin, interleukin, and nitric oxide that cause megakaryocytes to produce larger platelets [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBesides, \u003cb\u003eZhou et al. (2024)\u003c/b\u003e recruited 20,011 Chinese adults, of whom (39.32%) were obese and found a significant relationship between obesity as SII levels demonstrating that elevated SII levels are linked to metabolic syndrome [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn explanation, the proliferation of adipocytes triggers the fostering of a proinflammatory adipose tissue microenvironment, and consequently disrupts organ or tissue homeostasis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSerum ferritin was also significantly higher among cases compared to controls. As for hepcidin, the levels of hepcidin were significantly increased among cases versus controls with a sensitivity of 98.3% and a specificity of 83.3% to differentiate obese cases at a cutoff of 302.8 pg/ml.\u003c/p\u003e\u003cp\u003eIn line with \u003cb\u003eRodr\u0026iacute;guez-Mortera et al. (2021)\u003c/b\u003e, (70%) of the obese group have significantly higher levels of ferritin versus the lean group. These also reported that the increased IL-6 levels were more suggestive of chronic inflammation rather than iron overload and to a certain extent, a surrogate marker of MetS [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis could be due to several factors, including greater chronic inflammation in adolescents with obesity as evidenced by higher levels of inflammatory biomarkers, greater visceral adipose tissue mass, and greater insulin resistance that all aggravate chronic inflammation, atherogenic dyslipidemia and visceral adipose tissue hypertrophy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOn the other hand, several studies have suggested that ferritin level in adolescents is as an important early indicator for the risk of developing metabolic disorders [29 \u0026amp; 30].\u003c/p\u003e\u003cp\u003eThis study has shown significant positive correlation between hepcidin and BMI (r\u0026thinsp;=\u0026thinsp;0.592, p\u0026thinsp;=\u0026thinsp;0.001). it also shows a significant positive correlation between insulin resistance expressed as HOMA-IR and hepcidin (r\u0026thinsp;=\u0026thinsp;0.583, p\u0026thinsp;=\u0026thinsp;0.001). Hepcidin also correlated with lipid metabolic parameters in obese adolescents, TC (r\u0026thinsp;=\u0026thinsp;0.589, p\u0026thinsp;=\u0026thinsp;0.001), TG (r\u0026thinsp;=\u0026thinsp;0.361, p\u0026thinsp;=\u0026thinsp;0.001), LDL (r\u0026thinsp;=\u0026thinsp;0.643, p\u0026thinsp;=\u0026thinsp;0.001) and HDL (r\u0026thinsp;=\u0026thinsp;0.637, p\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eIn line with \u003cb\u003eKumar et al. (2019)\u003c/b\u003e studied 131 Indian children and reported a significant correlation between hepcidin and ferritin levels and hepcidin and BMI (p\u0026thinsp;=\u0026thinsp;0.019) as it plays a vital role in hepcidin dynamics as high BMI is associated with a proinflammatory status that results in higher hepcidin levels via IL-6 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMoreover, \u003cb\u003eRodr\u0026iacute;guez-Mortera et al. (2021)\u003c/b\u003e, Hepcidin was correlated with HOMA-IR (r\u0026thinsp;=\u0026thinsp;0.29). In obese adolescents, hepcidin correlated with TG (r\u0026thinsp;=\u0026thinsp;0.47) and LDL (r\u0026thinsp;=\u0026thinsp;0.39), suggesting a link between atherogenic dyslipoproteinemia and hepcidin levels [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThere are some limitations of this study that need to be acknowledged. The relatively small sample size and lack of generalization. The lack of correlation with dietary habits and the assessment of related complications. There was also no assessment of visceral fat. Conversely, this study will make a significant contribution to the existing literature as it involved pediatric patients and was able to identify the risk factors associated with the early phases of obesity-related inflammatory process.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study illustrated the association of hepcidin and childhood obesity as it was significantly elevated among cases compared to controls, displaying a sensitivity of 98.3% and a specificity of 83.3% at a cutoff point of 302.8 pg/ml. This was linked more to inflammation and metabolic alterations as inflammatory and metabolic parameters were also elevated among obese cases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics Approval and consent to participate.\u003c/h2\u003e\n\u003cp\u003eAin Shams University Faculty of Medicine Research Ethics committee (REC) FWA000017585 [email protected] tel:{202}2685\u0026ndash;7539.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eSafa M. Mohammed performed data analysis and interpretation, managed the literature searches and wrote the first draft of the manuscript. Mohammed M. Al-Tawil provided the idea of the study and study design, and contributed to study supervision and revision of manuscript. Dina A. Abdelhakam shared in analysis of data and study supervision. Rana A. mahmoud collected the data and contributed to analysis of the results and literature searches. Marwa F. Elkefl contributed to study concent, design, supervision and critical revision of the manuscript for important intellectual content. all authors reviewed the manuscript\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eall study data is available when requested\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eResearch funding\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"434\"\u003e\n\u003cp\u003e\u003cstrong\u003eNO FUND WITH GIVEN TO THIS Research\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cstrong\u003eTiwari A, Daley SF, Balasundaram P.\u003c/strong\u003e Obesity in Pediatric Patients. [Updated 2023 Mar 8]. In: StatPearls [Internet]. 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J Egypt Public Health Assoc. 2014; 89(2): 53-59.\u0026rlm;\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePanichsillaphakit E, Suteerojntrakool O, Pancharoen C, Nuchprayoon I, Chomtho S. \u003c/strong\u003eThe Association between hepcidin and iron status in children and adolescents with obesity. J Nutr Metab. 2021; 2021: 9944035.\u0026rlm;\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCorica D, Aversa T, Valenzise M, Messina MF, Alibrandi A, De Luca F, et al. \u003c/strong\u003eDoes Family History of Obesity, Cardiovascular, and Metabolic Diseases Influence Onset and Severity of Childhood Obesity? Front Endocrinol (Lausanne). 2018; 9:187.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eAbd Al-Ghani Saad Kaka \u003c/strong\u003e\u003cstrong\u003eM, Mohammed Ghanem S, Abd Al-Aziz Ahmed S, Abd Al-Kreem Al-Dahshan T. \u003c/strong\u003eLipids Profile Among Egyptian School Age Obese Children. 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Rev Assoc Med Bras (1992). 2022; 68(8):1006-1010.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDing \u003c/strong\u003e\u003cstrong\u003eQ, Wang F, Guo X, Liang M.\u003c/strong\u003e The relationship between mean platelet volume and metabolic syndrome in patients with type 2 diabetes mellitus: A retrospective study. Medicine (Baltimore). 2021; 100(13): e25303.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eZhou Y, Wang Y, Wu T, Zhang A, Li Y.\u003c/strong\u003e Association between obesity and systemic immune inflammation index, systemic inflammation response index among US adults: a population-based analysis. Lipids Health Dis. 2024; 23(1):245.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eShim YS, Kang MJ, Oh YJ, Baek JW, Yang S, Hwang IT. \u003c/strong\u003eAssociation of serum ferritin with insulin resistance, abdominal obesity, and metabolic syndrome in Korean adolescent and adults: The Korean National Health and Nutrition Examination Survey, 2008 to 2011. Medicine (Baltimore). 2017; 96(8):e6179.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHitha \u003c/strong\u003e\u003cstrong\u003eH, Gowda D, Mirajkar A.\u003c/strong\u003e Serum ferritin level as an early indicator of metabolic dysregulation in young obese adults - a cross-sectional study. Can J Physiol Pharmacol. 2018; 96(12):1255-1260.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eShattnawi \u003c/strong\u003e\u003cstrong\u003eKK, Alomari MA, Al-Sheyab N, Bani Salameh A.\u003c/strong\u003e The relationship between plasma ferritin levels and body mass index among adolescents. Sci Rep. 2018; 8(1):15307.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eKumar\u003c/strong\u003e\u003cstrong\u003e S, Bhatia P, Jain R, Bharti B. \u003c/strong\u003ePlasma Hepcidin Levels in Healthy Children: Review of Current Literature Highlights Limited Studies. J Pediatr Hematol Oncol. 2019 41(3):238-242.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 5 are available in the Supplementary Files section\u003c/p\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":"Childhood obesity, hematological indices, hepcidin","lastPublishedDoi":"10.21203/rs.3.rs-6926044/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6926044/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eObesity in adolescents can be accompanied by dyslipidemia, insulin resistance (IR) and chronic low-grade inflammation, with increased levels of proinflammatory cytokines. Therefore, hematological parameters should be evaluated in obese children and adolescents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAssessment of the obesity impact on hematological parameters and iron indices.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatients and methods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross sectional study included 60 obese children or adolescents recruited from Pediatric Obesity Clinic, Children’s Hospital, Ain Shams University, Cairo, Egypt and compared to 30 children and adolescent with normal BMI during the period from October 2023 to April 2024. Data regarding medical history, general examination, vital signs and laboratory investigation were collected. Inflammatory markers related to hematological indices were calculated and hepcidin was evaluated among cases and control using enzyme-linked immunosorbent assay (ELISA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe age of cases ranged from 6 to 15 years and the mean was 10.22 ± 2.29 years with a predominance of male gender (60%). Inflammatory markers including neutrophil platelet score, immature to total neutrophil (I:T), neutrophil lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), neutrophil to albumin ratio, white blood cell count to mean platelet volume (WBC/MPV), systemic immune-inflammation index (SII), prognostic nutritional index (PNI) and C-reactive protein (CRP) to albumin ratio were all significantly higher among obese cases compared to the controls. Hepcidin was significantly increased among obese cases 654.6 (450.88-1608.75) pg/ml compared to the controls 102.55 (60.05-182.98) pg/ml, p-value = 0.001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study illustrated the association of each of hematological indices and hepcidin with childhood obesity as hepcidin was significantly elevated among cases compared to controls, displaying a sensitivity of 98.3% and a specificity of 83.3% at a cutoff point of 302.8 pg/ml. This was linked more to inflammation and metabolic alterations as inflammatory and metabolic were also elevated among obese cases.\u003c/p\u003e","manuscriptTitle":"Impact of Obesity on Hematological Parameters and Iron Indices","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 08:29:23","doi":"10.21203/rs.3.rs-6926044/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":"06ab9178-c94b-4bd3-8622-f95f37f17679","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-24T08:09:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-18 08:29:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6926044","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6926044","identity":"rs-6926044","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}