NEWTON: Network Medicine, Epigenetics and Obesity
Study Details
Study Description
Brief Summary
The prevalence of obesity is increasing and affects more than 650 million people of all ages to become one of the foremost global health threats. Obesity is a complex syndrome that can seriously impair health through a broad range of complications such as cardiovascular disease, type 1 and 2 diabetes (T1D and T2D), cancer, musculoskeletal disorders, psychosocial imbalances, and reduced quality of life, and impacts the treatment of other conditions. Weight reduction has been shown to have a positive effect on these co-morbidities and may increase the effectiveness of treatments specific for other co-morbidities. Lifestyle modification is an integral part of the weight management journey, but is often insufficient on its own, and can be complimented by pharmacological and surgical add-on treatments to achieve greater and more sustainable weight loss, as appropriate. It is likely that there are subgroups of patients that are more suited to certain types of treatment and results risk dilution of perceived efficacy unless these groups are identified and treatment is personalised. The aim of this project is to identify pathophysiologically and clinically meaningful subgroups of obesity by performing Next Generation Sequencing (NGS) approaches and network based algorithm that will allow the optimisation of prevention and treatment of obesity and its complications.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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Detailed Description
Population study will be recruited at the Clinical Department of Internal Medicine and Specialistics, Department of Advanced Clinical and Surgical Sciences belonging to University of Campania "Luigi Vanvitelli". This study will be performed according to the principles outlined in the Helsinki Declaration. Subcutaneous adipose tissues located in the surgical incision will be withdrawn without the use of surgical devices in order to avoid the degradation of the biological sample from 50 obese subjects (25 obese subjects with Type 2 Diabetes vs 25 obese subjects without Type 2 Diabetes) undergoing bariatric surgery. As controls (n=50) we will recruit subcutaneous adipose tissues from patients without a clinical history of cardiovascular or dysmetabolic diseases undergoing to surgery for stress inguinal hernia. Clinical and demographic characteristics of the study population will be available from datasets generated by physicians.
From tissue samples the genomic DNA and the total RNA will be extracted. Genomic DNA will be extracted by using DNeasy Blood & Tissue kit (QIAGEN), according to manufacturer protocol. Pooled DNA samples consisting of equal quantities of DNA (2 µg) from cases and controls will be shipped to Genomix4Life Genomics and Bioinformatics Service, to perform a global DNA methylation analysis. For this aim, it will be used the Human Methylation 27K BeadChip platform by using Bisulfite conversion technology (BBRS-Seq). Total RNA will be extracted from tissues using RNeasy Mini Kit (QIAGEN) according to manufacturer protocol. The cDNA library preparation will be performed starting from 4 ug of total RNA by using Illumina TruSeq Libraries and then sequenced at high coverage on the Illumina HiSeq 2500 NGS platform. Nucleic acid concentrations and quality control from Genomix4Life will be assessed by using Nanodrop spectrophotometer (Thermo Fisher Scientific) and Qubit assay (Thermo Fisher Scientific) and TapeStation 4200 (Agilent). The weighted human DNA methylation PPI network (WMPN) will be construct to obtain a obesity interactome in both subgroups (obese and obese with T2D vs controls) based on differentially methylated genes in order to identify putative useful diagnostic biomarkers. The TargetScan algorithm , by searching the conserved seed pairing regions in the 3' untraslated regions (UTR) of genes based on whole genome alignment, will be used to robustly predict miRNA-target gene pairs from the same study population.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Obese subjects with and without T2D
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Other: DNA methylome and total RNA sequencing
Epigenomics tools combined with bioinformatic analysis to correlate putative useful clinical biomarkers with clinical features
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| control subjects
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Other: DNA methylome and total RNA sequencing
Epigenomics tools combined with bioinformatic analysis to correlate putative useful clinical biomarkers with clinical features
|
Outcome Measures
Primary Outcome Measures
- DNA methylation pattern in adipose tissues [3 months]
Using Methylation 27K BeadChip platform based on Bisulfite conversion technology, DNA methylation profile in adipose tissues of 25 preselected obese subject and 25 obese subjects respect with controls (n=50) will be performed.
- Bioinformatics analysis to predict putative novel candidate genes underlying obesity phenotype [6 months]
The network-based algorithm "Weighted Human DNA methylation PPI network (WMPN)" will be applied to methylome data in order to obtain a disease module containing the crucial differentially methylated genes both in obese patients and obese patients with T2D compared to controls.
- RNA sequencing analysisin adipose tissues [3 months]
RNA sequencing analysis by using Illumina HiSeq2000 Next Generatin Sequencing (NGS) platform will be performed for identifying differentially expressed micro-RNA and mRNA target both in obese patients and obese patients with T2D compared to controls.
Secondary Outcome Measures
- ROC curves analysis to evaluate putative DNA methylation/microRNA interactions [12 months]
ROC curves analysis to evaluate putative DNA methylation/microRNA interactions as diagnostic biomarkers to discriminate obesity subgroups.
- Linear regression analysis and BMI (Kg/m2). [12 months]
Linear regression to correlate epigenetic biomarkers with BMI (Kg/m2) in different soubgroups.
- Linear regression to correlate epigenetic biomarkers with proinflammatory cytokines [12 months]
Linear regression to correlate epigenetic biomarkers with proinflammatory cytokines (TNF-α,IL-6, PCR) levels in different soubgroups.
- Linear regression to correlate epigenetic biomarkers with HOMA index [12 months]
Linear regression to correlate epigenetic biomarkers with clinical variables, such as HOMA index glycemia(mmol/L) x insulinemia (mUI/L)/ 22.5, in different soubgroups.
Eligibility Criteria
Criteria
Inclusion Criteria:
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Meets the current American Diabetes Association guideline for diagnosis of diabetes (Diabetic group) and obesity
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Must be willing and able to comply with study requirements
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Must indicate their understanding of the study and willingness to participate by signing an appropriate informed consent form
Exclusion Criteria:
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History of cancer
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Malignancy disorders
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Active infections
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Chronic or immune-mediated diseases
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Primary disease requiring surgical intervention
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Unable to comply with the complication screening
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Less than 18 y of age
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Pregnant or are planning to become pregnant during the duration of the investigation
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Life expectancy <12 m
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Currently participating in any other clinical investigation
Contacts and Locations
Locations
No locations specified.Sponsors and Collaborators
- University of Campania "Luigi Vanvitelli"
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- PRIN2017F8ZB89