Obesity Causation Analysis of Disease-Induced Obesity
InquiryOverview
Disease-induced obesity causal analyses aim to reveal the complex mechanisms of how specific diseases cause or exacerbate obesity. Such analyses involve in-depth studies of the effects of various diseases and their treatments on body weight, to identify disease-related obesity mechanisms and propose effective intervention strategies. Disease-induced obesity is often a multifactorial process, which includes biological mechanisms of the disease itself, medication side effects, metabolic abnormalities, and lifestyle changes. In this analysis, researchers delve into the impact of disease on body weight through the integrated use of advanced technological tools such as genomics, metabolomics, and biomarker testing. These techniques not only helped identify how diseases alter metabolic pathways and hormone levels but also revealed potential biomarkers and mechanisms associated with obesity. In addition, data analysis and statistical modeling are used to resolve the complex relationship between disease and obesity, providing a scientific basis to support clinical decision-making.
Unraveling Disease-Induced Obesity: Precision Insights for Targeted Solutions
Protheragen has been working for years on the research on Targets for Developing Anti-Obesity Therapeutics, Anti-Obesity Therapy Development, and Preclinical Studies of Anti-Obesity Therapeutics, and has accumulated rich research experience in the field of obesity research.
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Our experts apply their professional knowledge and vast experience to provide disease-induced obesity causation analysis services. The diseases covered in our analysis of disease-induced obesity causation services include:
- Cushing's syndrome
- Thyroid Underactivity
- Polycystic ovary syndrome
- Hypothyroidism
- Prader-Willi syndrome
- Cohen syndrome
- Alstrom syndrome
- Fragile X syndrome (FXS)
- Down syndrome
- Bardet-Biedl syndrome
- WAGR syndrome
- Smith-Magenis syndrome
- Kallmann syndrome
Biomarker Analysis
Firstly, we investigate the disease to select biomarkers relevant to the study objectives, such as metabolites (fatty acids, glucose, etc.) or inflammatory markers (C-reactive protein, interleukin-6 (IL-6), etc.). Our experts then measure the biomarkers using appropriate experimental techniques and collate the measured biomarker data to form a dataset. We use statistical methods to assess the relationship between biomarker levels and disease and obesity. For example, to analyze differences in marker levels between patients with disease and healthy controls and to explore the correlation between these markers and body weight. Among others, biomarker measurement techniques include, but are not limited to immunoassays and mass spectrometry. Enzyme-linked immunosorbent assay (ELISA) is to determine protein levels and mass spectrum for the detection of metabolites and small molecules.
Metabolic Pathway Analysis
Our experts identify the specific disease and type of obesity under study and then determine the associated metabolic pathways including energy metabolism, fat metabolism, and glucose metabolism. Metabolomics technology is utilized to analyze metabolites in samples, generate profiles, and collect data on concentrations of key metabolites (e.g. fatty acids, glucose, amino acids) related to targeted metabolic pathways. Subsequently, we assess significantly affected metabolic pathways in the disease state by comparing differences in levels of key metabolites between disease and control groups and using bioinformatics tools for metabolic pathway enrichment analyses. We then examine how affected metabolic pathways are linked to obesity. For example, we investigate how abnormal fatty acid metabolism leads to fat accumulation. Additionally, we explore how key enzymes or transcription factors in metabolic pathways regulate metabolite levels in disease states that impact body weight.
Workflow
Applications
- Certain drug treatments can lead to weight gain, and analyzing these side effects can help develop new drugs to reduce the risk of obesity.
- Disease-induced obesity causation analysis can help personalize obesity management strategies based on specific diseases and their impact on metabolism.
- Disease-induced obesity causation analysis could help in the development of novel drugs designed to enhance the drug effect while reducing or avoiding obesity.
Advantages
- We have advanced data analysis platforms and technologies to handle complex biological data such as genomic data, and metabolomic data.
- We offer highly accurate biomarker assays that identify disease mechanisms associated with obesity, thus providing in-depth causal analyses.
- We have an interdisciplinary team of experts including biologists, data scientists, and pharmacologists. These experts can analyze the relationship between disease and obesity from multiple perspectives, providing comprehensive insights.
Publication Data
Technologies: Genomic association studies (GWAS), Next generation sequencing (NGS)
Journal: International Journal of Molecular Sciences
Published: 2022
IF: 6.208
Results: This article discusses the genetic studies of obesity in humans. The article mentions that treatments for obesity include lifestyle changes, medications, and bariatric surgery. Genetic factors are not only risk factors for obesity but also influence the response to weight loss treatments. The article also mentions some of the genetic mutations associated with obesity, such as defects in POMC, PCSK1, and LEPR, as well as rare genetic disorders associated with obesity, such as Bardet-Biedl syndrome and Alstrom syndrome. The article also discusses the relationship between some diseases and obesity, such as the association between Smith-Magenis syndrome and Cohen syndrome and obesity. In Smith-Magenis syndrome, more than 90% of the patients develop overweight or obesity after the age of 10. In contrast, patients with Cohen syndrome are usually unable to gain weight in infancy and early childhood, but rapidly become significantly overweight in adolescence, mainly with accumulation of trunk fat. Functional studies of Cohen syndrome have shown that preadipocytes lacking the VPS13B protein in patients are more likely to differentiate into fat-storing cells, leading to increased fat accumulation.
Frequently Asked Questions
Why do diseases trigger obesity?
Diseases can trigger obesity through several mechanisms, including:
- Altered metabolism: Some diseases, such as hypothyroidism, decrease the rate of metabolism, leading to weight gain.
- Hormonal imbalances: Some diseases, such as Cushing's syndrome, increase body fat storage.
- Medication side effects: Medications used to treat certain chronic conditions, such as antipsychotics and glucocorticoids, can lead to weight gain.
How do the results of analyses affect patient management?
- Optimise treatment regimens: Adaptation of the treatment program to reduce the risk of obesity based on the results of the analysis.
- Develop personalized interventions: The results of the analysis provide patients with personalized dietary and lifestyle advice.
- Improve medication choices: Selection of medications with less impact on body weight or development of a comprehensive management plan to deal with medication side effects based on the results of the analyses.
Protheragen's disease-induced obesity causality analysis service uncovers the mechanisms of how specific diseases contribute to obesity through advanced biomarker measurements and metabolic pathway analysis techniques. Our team of experts provides detailed mechanistic insights through comprehensive data analysis and bioinformatics tools to help improve patient health outcomes. If you are interested in our service, please feel free to contact us!
Reference
- Mahmoud, R.; et al. Genetics of obesity in humans: a clinical review. International journal of molecular sciences. 2022, 23(19): 11005.
All of our services and products are intended for preclinical research use only and cannot be used to diagnose, treat or manage patients.