Impact of Nutrient-Stimulated Hormone (NUSH) Dynamics on Body Weight

Impact of Nutrient-Stimulated Hormone (NUSH) Dynamics on Body Weight | 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 Short Report Impact of Nutrient-Stimulated Hormone (NUSH) Dynamics on Body Weight Luís Jesuino de Oliveira Andrade, Gabriela Correia Matos de Oliveira, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4013174/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 Introduction : Nutrient-stimulated hormones (NUSH) play a critical role in regulating energy metabolism. Dysregulation of NUSH signaling is associated with obesity, there is a lack of quantitative models to investigate the complex dynamics of NUSH signaling and its impact on obesity development. Objective : To explore the relationship between NUSH and body weight using mathematical modeling. Methods : Data on elevated body weight were collected from meta-analysis studies available on Pubmed, utilizing incretin-based therapies. A mathematical model was developed using software to integrate interactions between NUSH levels and changes in body weight. The model accurately captured the complex dynamics and feedback loops involved in obesity-related hormonal regulation, employing differential equations and statistical techniques. Parameter estimation was performed using meta-analysis results to minimize the discrepancy between model predictions and observed data. Results : This study included 15 meta-analysis studies on liraglutide, semaglutide, and tirzepatide for the treatment of obesity. A mathematical model was developed to understand NUSH dynamics in relation to obesity. The model deduced the formula: NUSH(t) = N0 * (1 - e^(-kt)) + I * [1 - e^(-βt)] / β, which considers NUSH levels over time, initial levels, decay rate, impact of nutrient intake on hormone secretion, and the rate at which the effect of nutrient intake reaches its maximum. Conclusion: Evaluating the association between NUSH and increased body weight through mathematical modeling can provide insights into the complex interactions between nutrient stimuli, hormonal responses, and obesity development. Endocrinology & Metabolism Obesity Nutrient-stimulated hormone Mathematical modeling INTRODUCTION Obesity is a complex metabolic disorder characterized by excessive adipose tissue accumulation, resulting from an imbalance between energy intake and expenditure [ 1 ]. The intricate interplay between nutrient-stimulated hormones (NUSH) and their dynamic regulation has been implicated in the pathophysiology of obesity [ 2 ], attracting the attention of researchers in the field of endocrinology. The concept of NUSH refers to the dynamic interaction between dietary components and the endocrine system, leading to modulation of hormone secretion and subsequent metabolic alterations [ 3 ]. Several hormones, including insulin, incretin, glucagon, leptin, ghrelin, and adiponectin, are prominently involved in the NUSH response [ 4 ]. These hormones act as physiological messengers, conveying information about the body's nutritional state to specific organs and tissues, allowing adjustments in energy balance, nutrient uptake, and utilization [ 5 ]. Understanding the mechanisms underlying the interaction between nutrients and hormones secretion has significant implications for the prevention and management of metabolic disorders, such as obesity, type 2 diabetes, and cardiovascular diseases [ 6 ]. Modern research techniques offer indispensable tools for the analysis of large-scale omics data and the integration of multidimensional datasets involved in NUSH regulation. Genomic, transcriptomic, proteomic, and metabolomic analyses provide valuable insights into gene expression, protein-protein interactions, and metabolic profiles underlying hormonal dynamics. Computational simulations allow the generation of mathematical models that can simulate and predict hormonal dynamics under various physiological and pathophysiological conditions [ 7 ]. The development of mathematical models using state-of-the-art software tools has emerged as a powerful tool to understand the complex interplay between NUSH dynamics and feedback loops in obesity [ 8 ]. By integrating experimental data and computational simulations, mathematical models enable the investigation of the underlying mechanisms contributing to hormonal dysregulation in obesity [ 9 ]. This study aims to develop a mathematical model to explore the relationship between NUSH and body weight. METHODS The choice of software to simulate NUSH dynamics was based on factors such as functionality and model complexity. Therefore, we used Microsoft Excel, PSPP (public domain software), and Python (a free and open-source option with an active developer community) high-level programming language with libraries for modeling, simulation, and data analysis. Obesity data was collected from meta-analysis studies, available in full text and free of charge on PubMed, which used incretin-based therapies (liraglutide, semaglutide, and tirzepatide). After data collection, a mathematical model was developed using the software described above. The model integrated the interactions between NUSH levels, nutrient intake, and changes in body weight. Based on differential equations and statistical techniques, the model accurately captured the complex dynamics and feedback loops involved in obesity-related hormonal regulation. Parameter estimation was performed using optimization algorithms. These algorithms adjusted the model parameters to minimize the discrepancy between model predictions and observed data. Sensitivity analysis was also conducted to identify the key parameters with the greatest impact on NUSH dynamics and obesity development. Finally, simulation studies were performed using the developed mathematical model. These simulations explored the effects of different nutrient intake patterns and hormonal dysregulation on changes in body weight. ETHICAL CONSIDERATIONS As this study is a secondary analysis of previously published data, it did not require approval from an ethics committee. This is because secondary analyses do not involve direct contact with human subjects. RESULTS A total of 10 meta-analysis studies were included in this study, which evaluated liraglutide, semaglutide, and tirzepatide for the treatment of obesity [10-24]. To develop a mathematical model for NUSH dynamics and its relationship to obesity, the following formula was derived: NUSH(t) = N0 * (1 - e^(-kt)) + I * [1 - e^(- β t)] / β Where: NUSH(t) : Represents the level of nutrient-stimulated hormones at time t. N0 : Is the initial level of nutrient-stimulated hormones. e : Euler's number (approximately 2.71828). ^ : Indicates that "e" is raised to the power in parentheses. k : Is the degradation rate constant, representing the natural degradation of hormones over time. I : Is the impact of nutrient intake on hormone secretion. β : Is the rate constant representing the time it takes for the effect of nutrient intake to reach its maximum. This formula incorporated exponential decay for the basal level of NUSH over time, as well as the impact of nutrient intake on hormone secretion. Therefore, kt represents the exponential decay rate over time (t), i.e., the higher the value of k, the faster the NUSH level decays; while βt represents the rate at which the effect of nutrient intake (I) reaches its maximum impact. The higher the value of β, the faster the effect of the intake is reached. Thus, the formula uses exponential functions to describe the natural decay of NUSH levels over time (first term) and the gradual increase in the impact of nutrient intake on hormone secretion (second term). However, validation of the NUSH(t) formula with more data is fundamental to ensure that it reliably represents reality. Without validation, the formula is just an untested hypothesis. Validation involves comparing the results of the formula with real experimental data. DISCUSSION To elucidate the complex interplay between NUSH dynamics and its impact on body weight regulation, this study utilized bioinformatics tools to develop a mathematical model. To the best of our knowledge, this is the first mathematical model to elucidate the intricate interaction between NUSH dynamics and its influence on body weight regulation. NUSH dynamics play a critical role in regulating energy balance and body weight. Hormones such as insulin and incretin are intrinsically involved in sensing nutrient availability and transmitting signals to the brain to modulate food intake and energy expenditure [ 25 ]. Additionally, ghrelin has been shown to stimulate appetite and promote energy storage. These hormones, along with several others, form a complex network that orchestrates the delicate balance between energy intake and expenditure, ultimately influencing the development and progression of obesity [ 26 ]. Understanding the nuances of the interactions and dynamics of these NUSHs is essential for unraveling the mechanisms driving obesity pathogenesis and developing effective therapeutic interventions. Studies involving long-acting glucagon-like peptide-1 (GLP-1) receptor agonists have demonstrated that targeting endogenous nutrient-stimulated hormone pathways can lead to improved efficacy with an acceptable safety profile [ 27 ]. Glucose-dependent insulinotropic polypeptide (GIP) plays an important role in energy balance through signaling of its receptor in the brain and adipose tissue [ 28 ]. By combining GIP and GLP-1 receptor agonism, it is theoretically possible to achieve greater effectiveness in weight reduction strategies. Our study evaluated 10 meta-analysis studies, including liraglutide, semaglutide, and tirzepatide for the treatment of obesity. The development of a mathematical model in medicine involves a systematic process that combines principles of mathematics and medical sciences [ 29 ]. The first step typically includes a comprehensive literature review to identify relevant biological processes, clinical observations, and available data sources. Then, appropriate mathematical equations and algorithms are selected or developed to capture the essential dynamics of the system under study [ 30 ]. Integration of genomic data analysis or molecular modeling is often fundamental to increase the model's complexity and accuracy [ 31 ]. Various software packages are frequently utilized to implement and simulate the model. Finally, the model is validated against experimental or clinical data and refined through iterations to improve its predictive capability and practical applicability [ 32 ]. Our work developed a mathematical model to assess the relationship between NUSH and obesity in the medical field. Based on the existing research in this area, we carefully selected relevant literature to support the foundation of our model. The process involved collecting data on two NUSHs, liraglutide, semaglutide, and tirzepatide and their interactions with metabolic processes in obesity treatment. Mathematical techniques, including differential equations, were applied to represent the dynamics of these hormones and their impact on body weight regulation. Thus, our model captured the intricate interplay between NUSH and obesity. Mathematical modeling in our study presents limitations such as: Biological complexity: The developed mathematical formula captured only some aspects of the interactions between NUSH and obesity, ignoring other potentially important factors; Data accuracy: The quality and quantity of data used to fit the formula's parameters can significantly influence its accuracy; External validation: Validating the NUSH(t) formula with data from the study in which it was developed is only a first step. It is necessary to validate it with data from different studies, populations, and contexts to ensure its reliability in different scenarios. We consider mathematical modeling an important tool for the initial approach to the complex system of obesity and its relationship with NUSH. Understanding the theoretical framework presented here exemplifies the power of mathematical modeling. The insights obtained from the proposed model can guide new experiments targeting key incretins involved in obesity treatment. Thus, this work expands on the growing field of mathematical modeling in medicine, providing an experimental tool for studying NUSH dynamics and obesity. However, validating the NUSH formula with more data is essential to ensure that it is a reliable tool for studying NUSH dynamics and its relationship to obesity. Without validation, the conclusions drawn from the formula may be erroneous and lead to misleading results. CONCLUSION Evaluating the association between NUSH and obesity through mathematical modeling can provide insights into the complex interactions between nutrient stimuli, hormonal responses, and obesity development. Thus, the NUSH(t) formula is a promising step in this direction, however it needs to be validated with more data to be considered a reliable tool. Validation with more data will increase confidence in the formula, identify its limitations, and allow its application in future studies and in practical interventions to combat obesity. Declarations Conflict of Interest : None References Müller MJ, Geisler C (2017) Defining obesity as a disease. Eur J Clin Nutr 71(11):1256–1258. 10.1038/ejcn.2017.155 Jastreboff AM, Kushner RF (2023) New Frontiers in Obesity Treatment: GLP-1 and Nascent Nutrient-Stimulated Hormone-Based Therapeutics. Annu Rev Med 74:125–139. 10.1146/annurev-med-043021-014919 Parker HE, Reimann F, Gribble FM (2010) Molecular mechanisms underlying nutrient-stimulated incretin secretion. Expert Rev Mol Med 12:e1. 10.1017/S146239940900132X Stahel P, Xiao C, Nahmias A, Tian L, Lewis GF (2021) Multi-organ Coordination of Lipoprotein Secretion by Hormones, Nutrients and Neural Networks. 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Part I: Moving beyond pharmacokinetics. J Vet Pharmacol Ther 39(3):213–223. 10.1111/jvp.12278 Additional Declarations The authors declare no competing interests. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4013174","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":276290991,"identity":"a4169f4e-717d-4c77-9d50-9ae6e32ee8fa","order_by":0,"name":"Luís Jesuino de Oliveira Andrade","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACPgbGBgYGHiCLGYg/MDAkwGQScOhgYEPWwjiDOC1IgJmHKC0Syc0fGGRs7A2O8x58bNtml8fP3sD44WMOQ555Ay4tiW0SDDxpiRsO8yUb57YlF0v2HGCWnLmNoVjmAG4tQL8cTjA4zGMmndvGnLjhRgIbM+82hsQZOB2WCHQYz397sBbLtnqitDQAHXaAcQNIC2PbYSK08DwE+SU5ceZhHmPDnnPHE2f2HGwG+kWiWAKHFn729McfGHvs7PnOnzF88KOsOrGfvfngh4/bbPJwaQEB5r89UBYjOJpAkcuATwMI/IAx/hBQOApGwSgYBSMSAAAthFHtBhERUQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-7714-0330","institution":"Department of Health Sciences, Santa Cruz State University, Ilhéus, Bahia, Brazil","correspondingAuthor":true,"prefix":"","firstName":"Luís","middleName":"Jesuino de Oliveira","lastName":"Andrade","suffix":""},{"id":276290992,"identity":"4fbb7d6b-c4cc-4d62-b04c-86addf753312","order_by":1,"name":"Gabriela Correia Matos de Oliveira","email":"","orcid":"https://orcid.org/0000-0002-3447-3143","institution":"Family Health Progam, Bahia, Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Gabriela","middleName":"Correia Matos","lastName":"de Oliveira","suffix":""},{"id":276290993,"identity":"5ad0fde4-ce2c-4e11-9d71-94de80927d47","order_by":2,"name":"Luisa Correia Matos de Oliveira","email":"","orcid":"https://orcid.org/0000-0001-6128-4885","institution":"Centro Universitário SENAI CIMATEC – Salvador – Bahia - Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Luisa","middleName":"Correia Matos","lastName":"de Oliveira","suffix":""},{"id":276290994,"identity":"54a41773-f74f-4f66-8043-0186b3a9b2e3","order_by":3,"name":"Alcina Maria Vinhaes Bittencourt","email":"","orcid":"https://orcid.org/0000-0003-0506-9210","institution":"Faculdade de Medicina – Universidade Federal da Bahia, Salvador, Bahia, Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Alcina","middleName":"Maria Vinhaes","lastName":"Bittencourt","suffix":""},{"id":276290995,"identity":"4d0a9576-1708-443f-a9d7-2a8bdf0d7ae8","order_by":4,"name":"Luis Matos de Oliveira","email":"","orcid":"https://orcid.org/0000-0003-4854-6910","institution":"Bahiana School of Medicine and Public Health - Salvador – Bahia – Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"Matos","lastName":"de Oliveira","suffix":""}],"badges":[],"createdAt":"2024-03-04 16:54:12","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4013174/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4013174/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52064504,"identity":"63964a8e-3cff-4f41-a30c-4d7ba288068c","added_by":"auto","created_at":"2024-03-06 06:29:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":194768,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4013174/v1/ddf7eb9e-2cd7-449c-9401-e24b17f7299c.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eImpact of Nutrient-Stimulated Hormone (NUSH) Dynamics on Body Weight\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eObesity is a complex metabolic disorder characterized by excessive adipose tissue accumulation, resulting from an imbalance between energy intake and expenditure [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The intricate interplay between nutrient-stimulated hormones (NUSH) and their dynamic regulation has been implicated in the pathophysiology of obesity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], attracting the attention of researchers in the field of endocrinology. The concept of NUSH refers to the dynamic interaction between dietary components and the endocrine system, leading to modulation of hormone secretion and subsequent metabolic alterations [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral hormones, including insulin, incretin, glucagon, leptin, ghrelin, and adiponectin, are prominently involved in the NUSH response [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These hormones act as physiological messengers, conveying information about the body's nutritional state to specific organs and tissues, allowing adjustments in energy balance, nutrient uptake, and utilization [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnderstanding the mechanisms underlying the interaction between nutrients and hormones secretion has significant implications for the prevention and management of metabolic disorders, such as obesity, type 2 diabetes, and cardiovascular diseases [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Modern research techniques offer indispensable tools for the analysis of large-scale omics data and the integration of multidimensional datasets involved in NUSH regulation. Genomic, transcriptomic, proteomic, and metabolomic analyses provide valuable insights into gene expression, protein-protein interactions, and metabolic profiles underlying hormonal dynamics. Computational simulations allow the generation of mathematical models that can simulate and predict hormonal dynamics under various physiological and pathophysiological conditions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe development of mathematical models using state-of-the-art software tools has emerged as a powerful tool to understand the complex interplay between NUSH dynamics and feedback loops in obesity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. By integrating experimental data and computational simulations, mathematical models enable the investigation of the underlying mechanisms contributing to hormonal dysregulation in obesity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aims to develop a mathematical model to explore the relationship between NUSH and body weight.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThe choice of software to simulate NUSH dynamics was based on factors such as functionality and model complexity. Therefore, we used Microsoft Excel, PSPP (public domain software), and Python (a free and open-source option with an active developer community) high-level programming language with libraries for modeling, simulation, and data analysis.\u003c/p\u003e \u003cp\u003eObesity data was collected from meta-analysis studies, available in full text and free of charge on PubMed, which used incretin-based therapies (liraglutide, semaglutide, and tirzepatide).\u003c/p\u003e \u003cp\u003eAfter data collection, a mathematical model was developed using the software described above. The model integrated the interactions between NUSH levels, nutrient intake, and changes in body weight. Based on differential equations and statistical techniques, the model accurately captured the complex dynamics and feedback loops involved in obesity-related hormonal regulation.\u003c/p\u003e \u003cp\u003eParameter estimation was performed using optimization algorithms. These algorithms adjusted the model parameters to minimize the discrepancy between model predictions and observed data. Sensitivity analysis was also conducted to identify the key parameters with the greatest impact on NUSH dynamics and obesity development.\u003c/p\u003e \u003cp\u003eFinally, simulation studies were performed using the developed mathematical model. These simulations explored the effects of different nutrient intake patterns and hormonal dysregulation on changes in body weight.\u003c/p\u003e\n\u003ch3\u003eETHICAL CONSIDERATIONS\u003c/h3\u003e\n\u003cp\u003eAs this study is a secondary analysis of previously published data, it did not require approval from an ethics committee. This is because secondary analyses do not involve direct contact with human subjects.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 10 meta-analysis studies were included in this study, which evaluated liraglutide, semaglutide, and tirzepatide for the treatment of obesity [10-24].\u003c/p\u003e\n\u003cp\u003eTo develop a mathematical model for NUSH dynamics and its relationship to obesity, the following formula was derived:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNUSH(t) = N0 * (1 - e^(-kt)) + I * [1 - e^(-\u003c/strong\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003cstrong\u003et)] /\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNUSH(t)\u003c/strong\u003e: Represents the level of nutrient-stimulated hormones at time t.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eN0\u003c/strong\u003e: Is the initial level of nutrient-stimulated hormones.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee\u003c/strong\u003e: Euler\u0026apos;s number (approximately 2.71828).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e^\u003c/strong\u003e: Indicates that \u0026quot;e\u0026quot; is raised to the power in parentheses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ek\u003c/strong\u003e: Is the degradation rate constant, representing the natural degradation of hormones over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003e: Is the impact of nutrient intake on hormone secretion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e: Is the rate constant representing the time it takes for the effect of nutrient intake to reach its maximum.\u003c/p\u003e\n\u003cp\u003eThis formula incorporated exponential decay for the basal level of NUSH over time, as well as the impact of nutrient intake on hormone secretion. Therefore, kt represents the exponential decay rate over time (t), i.e., the higher the value of k, the faster the NUSH level decays; while \u0026beta;t represents the rate at which the effect of nutrient intake (I) reaches its maximum impact. The higher the value of \u0026beta;, the faster the effect of the intake is reached.\u003c/p\u003e\n\u003cp\u003eThus, the formula uses exponential functions to describe the natural decay of NUSH levels over time (first term) and the gradual increase in the impact of nutrient intake on hormone secretion (second term).\u003c/p\u003e\n\u003cp\u003eHowever, validation of the NUSH(t) formula with more data is fundamental to ensure that it reliably represents reality. Without validation, the formula is just an untested hypothesis. Validation involves comparing the results of the formula with real experimental data.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eTo elucidate the complex interplay between NUSH dynamics and its impact on body weight regulation, this study utilized bioinformatics tools to develop a mathematical model. To the best of our knowledge, this is the first mathematical model to elucidate the intricate interaction between NUSH dynamics and its influence on body weight regulation.\u003c/p\u003e \u003cp\u003eNUSH dynamics play a critical role in regulating energy balance and body weight. Hormones such as insulin and incretin are intrinsically involved in sensing nutrient availability and transmitting signals to the brain to modulate food intake and energy expenditure [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additionally, ghrelin has been shown to stimulate appetite and promote energy storage. These hormones, along with several others, form a complex network that orchestrates the delicate balance between energy intake and expenditure, ultimately influencing the development and progression of obesity [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Understanding the nuances of the interactions and dynamics of these NUSHs is essential for unraveling the mechanisms driving obesity pathogenesis and developing effective therapeutic interventions.\u003c/p\u003e \u003cp\u003eStudies involving long-acting glucagon-like peptide-1 (GLP-1) receptor agonists have demonstrated that targeting endogenous nutrient-stimulated hormone pathways can lead to improved efficacy with an acceptable safety profile [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Glucose-dependent insulinotropic polypeptide (GIP) plays an important role in energy balance through signaling of its receptor in the brain and adipose tissue [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. By combining GIP and GLP-1 receptor agonism, it is theoretically possible to achieve greater effectiveness in weight reduction strategies. Our study evaluated 10 meta-analysis studies, including liraglutide, semaglutide, and tirzepatide for the treatment of obesity.\u003c/p\u003e \u003cp\u003eThe development of a mathematical model in medicine involves a systematic process that combines principles of mathematics and medical sciences [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The first step typically includes a comprehensive literature review to identify relevant biological processes, clinical observations, and available data sources. Then, appropriate mathematical equations and algorithms are selected or developed to capture the essential dynamics of the system under study [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Integration of genomic data analysis or molecular modeling is often fundamental to increase the model's complexity and accuracy [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Various software packages are frequently utilized to implement and simulate the model. Finally, the model is validated against experimental or clinical data and refined through iterations to improve its predictive capability and practical applicability [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Our work developed a mathematical model to assess the relationship between NUSH and obesity in the medical field. Based on the existing research in this area, we carefully selected relevant literature to support the foundation of our model. The process involved collecting data on two NUSHs, liraglutide, semaglutide, and tirzepatide and their interactions with metabolic processes in obesity treatment. Mathematical techniques, including differential equations, were applied to represent the dynamics of these hormones and their impact on body weight regulation. Thus, our model captured the intricate interplay between NUSH and obesity.\u003c/p\u003e \u003cp\u003eMathematical modeling in our study presents limitations such as: Biological complexity: The developed mathematical formula captured only some aspects of the interactions between NUSH and obesity, ignoring other potentially important factors; Data accuracy: The quality and quantity of data used to fit the formula's parameters can significantly influence its accuracy; External validation: Validating the NUSH(t) formula with data from the study in which it was developed is only a first step. It is necessary to validate it with data from different studies, populations, and contexts to ensure its reliability in different scenarios.\u003c/p\u003e \u003cp\u003eWe consider mathematical modeling an important tool for the initial approach to the complex system of obesity and its relationship with NUSH. Understanding the theoretical framework presented here exemplifies the power of mathematical modeling. The insights obtained from the proposed model can guide new experiments targeting key incretins involved in obesity treatment.\u003c/p\u003e \u003cp\u003eThus, this work expands on the growing field of mathematical modeling in medicine, providing an experimental tool for studying NUSH dynamics and obesity. However, validating the NUSH formula with more data is essential to ensure that it is a reliable tool for studying NUSH dynamics and its relationship to obesity. Without validation, the conclusions drawn from the formula may be erroneous and lead to misleading results.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eEvaluating the association between NUSH and obesity through mathematical modeling can provide insights into the complex interactions between nutrient stimuli, hormonal responses, and obesity development. Thus, the NUSH(t) formula is a promising step in this direction, however it needs to be validated with more data to be considered a reliable tool. Validation with more data will increase confidence in the formula, identify its limitations, and allow its application in future studies and in practical interventions to combat obesity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e: None\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eM\u0026uuml;ller MJ, Geisler C (2017) Defining obesity as a disease. 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Part I: Moving beyond pharmacokinetics. J Vet Pharmacol Ther 39(3):213\u0026ndash;223. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jvp.12278\u003c/span\u003e\u003cspan address=\"10.1111/jvp.12278\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Obesity, Nutrient-stimulated hormone, Mathematical modeling","lastPublishedDoi":"10.21203/rs.3.rs-4013174/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4013174/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e: Nutrient-stimulated hormones (NUSH) play a critical role in regulating energy metabolism. Dysregulation of NUSH signaling is associated with obesity, there is a lack of quantitative models to investigate the complex dynamics of NUSH signaling and its impact on obesity development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: To explore the relationship between NUSH and body weight using mathematical modeling.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Data on elevated body weight were collected from meta-analysis studies available on Pubmed, utilizing incretin-based therapies. A mathematical model was developed using software to integrate interactions between NUSH levels and changes in body weight. The model accurately captured the complex dynamics and feedback loops involved in obesity-related hormonal regulation, employing differential equations and statistical techniques. Parameter estimation was performed using meta-analysis results to minimize the discrepancy between model predictions and observed data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: This study included 15 meta-analysis studies on liraglutide, semaglutide, and tirzepatide for the treatment of obesity. A mathematical model was developed to understand NUSH dynamics in relation to obesity. The model deduced the formula: NUSH(t) = N0 * (1 - e^(-kt)) + I * [1 - e^(-βt)] / β, which considers NUSH levels over time, initial levels, decay rate, impact of nutrient intake on hormone secretion, and the rate at which the effect of nutrient intake reaches its maximum.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Evaluating the association between NUSH and increased body weight through mathematical modeling can provide insights into the complex interactions between nutrient stimuli, hormonal responses, and obesity development.\u003c/p\u003e","manuscriptTitle":"Impact of Nutrient-Stimulated Hormone (NUSH) Dynamics on Body Weight","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-06 06:21:26","doi":"10.21203/rs.3.rs-4013174/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":"ab6e42e9-7d0a-4b1a-b6b5-00f76d505028","owner":[],"postedDate":"March 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29123694,"name":"Endocrinology \u0026 Metabolism"}],"tags":[],"updatedAt":"2024-03-06T06:21:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-06 06:21:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4013174","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4013174","identity":"rs-4013174","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}