Polygenic Risk Score to Predict Weight Loss Intervention in Children With Obesity

Obesity Clinical Trial

Official title:

Development of Polygenic Risk Score to Predict the Efficacy of Weight Loss Intervention in Children and Adolescents With Obesity

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05466097
• Other study ID #: 111078-F
• Status: Recruiting
• Start date: June 1, 2022
• Est. completion date: July 31, 2025

Study information

• Verified date: September 2023
• Source: Far Eastern Memorial Hospital
• Contact: Yu-Cheng Lin,, M.D., Ph.D.
• Phone: +886-89667000 Ext. 1723
• Email: q92421006[@]ntu.edu.tw
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial Summary

Children with obesity are prone to suffering from metabolic diseases, which undoubtedly increases the burden of public health. Since obesity is a multiple gene disease, a comprehensive approach using polygenic risk scores (PRS), rather than individual genetic variant, may be a more appropriate method. The aim of the study was to establish a polygenic risk score model to assess differences to assess differences in weight loss treatment outcomes.

Description:

The investigators hypothesize that obesity gene variants can predict the efficacy of weight loss intervention in obese children. The aim of the study was to establish a polygenic risk score model to assess differences to assess differences in weight loss treatment outcomes. The investigators will also analyze whether these gene variants have an effect on obesity comorbidities (hypertension, hyperlipidemia, non-alcoholic fatty liver disease, type 2 diabetes, obstructive sleep apnea, polycystic ovary syndrome, etc.). For participants with non-simple obesity, the investigators will collect their complete family history, and perform whole exome sequencing to identify possible rare disease-causing genes. The experimental design is as follows: Obese children and adolescent subjects will undergo a 6-month weight loss intervention program and be followed for 12-18 months. The investigators will analyze obesity and fatty liver-related genes in these adolescents using next-generation gene sequencing and/or gene chips, perform polygenic risk score analysis, and use an additive model to total the number of variant loci weighted by effect size. Whole exome gene sequencing refers to the human DNA map (hg19), and Sanger sequencing will be used to confirm the correctness of the variant site.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 300
• Est. completion date: July 31, 2025
• Est. primary completion date: July 31, 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A to 18 Years

Eligibility

Inclusion Criteria:

• Age <18 years old
• Obesity definition: BMI > 95% according to the age- and gender-specific standard by National Health Institute in Taiwan
• Willing to give written informed consent

Exclusion Criteria:

• Alcohol consumption
• Major systemic diseases, including cardiopulmonary disease, renal failure, cancer, and major psychotic disorder

Related Conditions & MeSH terms

• Children
• Obesity
• Weight Loss

Locations

Country	Name	City	State
Taiwan	Far Eastern Memorial Hospital	New Taipei City

Sponsors (2)

Lead Sponsor	Collaborator
Far Eastern Memorial Hospital	Ministry of Science and Technology, Taiwan

Country where clinical trial is conducted

Taiwan, 

References & Publications (13)

Bradfield JP, Taal HR, Timpson NJ, Scherag A, Lecoeur C, Warrington NM, Hypponen E, Holst C, Valcarcel B, Thiering E, Salem RM, Schumacher FR, Cousminer DL, Sleiman PM, Zhao J, Berkowitz RI, Vimaleswaran KS, Jarick I, Pennell CE, Evans DM, St Pourcain B, Berry DJ, Mook-Kanamori DO, Hofman A, Rivadeneira F, Uitterlinden AG, van Duijn CM, van der Valk RJ, de Jongste JC, Postma DS, Boomsma DI, Gauderman WJ, Hassanein MT, Lindgren CM, Magi R, Boreham CA, Neville CE, Moreno LA, Elliott P, Pouta A, Hartikainen AL, Li M, Raitakari O, Lehtimaki T, Eriksson JG, Palotie A, Dallongeville J, Das S, Deloukas P, McMahon G, Ring SM, Kemp JP, Buxton JL, Blakemore AI, Bustamante M, Guxens M, Hirschhorn JN, Gillman MW, Kreiner-Moller E, Bisgaard H, Gilliland FD, Heinrich J, Wheeler E, Barroso I, O'Rahilly S, Meirhaeghe A, Sorensen TI, Power C, Palmer LJ, Hinney A, Widen E, Farooqi IS, McCarthy MI, Froguel P, Meyre D, Hebebrand J, Jarvelin MR, Jaddoe VW, Smith GD, Hakonarson H, Grant SF; Early Growth Genetics Consortium. A genome-wide association meta-analysis identifies new childhood obesity loci. Nat Genet. 2012 May;44(5):526-31. doi: 10.1038/ng.2247. — View Citation

Cali AM, Caprio S. Obesity in children and adolescents. J Clin Endocrinol Metab. 2008 Nov;93(11 Suppl 1):S31-6. doi: 10.1210/jc.2008-1363. — View Citation

Eddowes PJ, Sasso M, Allison M, Tsochatzis E, Anstee QM, Sheridan D, Guha IN, Cobbold JF, Deeks JJ, Paradis V, Bedossa P, Newsome PN. Accuracy of FibroScan Controlled Attenuation Parameter and Liver Stiffness Measurement in Assessing Steatosis and Fibrosis in Patients With Nonalcoholic Fatty Liver Disease. Gastroenterology. 2019 May;156(6):1717-1730. doi: 10.1053/j.gastro.2019.01.042. Epub 2019 Jan 25. — View Citation

El-Sayed Moustafa JS, Froguel P. From obesity genetics to the future of personalized obesity therapy. Nat Rev Endocrinol. 2013 Jul;9(7):402-13. doi: 10.1038/nrendo.2013.57. Epub 2013 Mar 26. Erratum In: Nat Rev Endocrinol. 2014 Jan;10(1):4. — View Citation

Ford AL, Hunt LP, Cooper A, Shield JP. What reduction in BMI SDS is required in obese adolescents to improve body composition and cardiometabolic health? Arch Dis Child. 2010 Apr;95(4):256-61. doi: 10.1136/adc.2009.165340. Epub 2009 Dec 4. — View Citation

Gurdasani D, Barroso I, Zeggini E, Sandhu MS. Genomics of disease risk in globally diverse populations. Nat Rev Genet. 2019 Sep;20(9):520-535. doi: 10.1038/s41576-019-0144-0. Epub 2019 Jun 24. Erratum In: Nat Rev Genet. 2019 Jul 3;: — View Citation

Heitkamp M, Siegrist M, Molnos S, Brandmaier S, Wahl S, Langhof H, Grallert H, Halle M. Obesity Genes and Weight Loss During Lifestyle Intervention in Children With Obesity. JAMA Pediatr. 2021 Jan 1;175(1):e205142. doi: 10.1001/jamapediatrics.2020.5142. Epub 2021 Jan 4. — View Citation

Herrera BM, Lindgren CM. The genetics of obesity. Curr Diab Rep. 2010 Dec;10(6):498-505. doi: 10.1007/s11892-010-0153-z. — View Citation

Holzapfel C, Sag S, Graf-Schindler J, Fischer M, Drabsch T, Illig T, Grallert H, Stecher L, Strack C, Caterson ID, Jebb SA, Hauner H, Baessler A. Association between Single Nucleotide Polymorphisms and Weight Reduction in Behavioural Interventions-A Pooled Analysis. Nutrients. 2021 Mar 2;13(3):819. doi: 10.3390/nu13030819. — View Citation

Khera AV, Chaffin M, Wade KH, Zahid S, Brancale J, Xia R, Distefano M, Senol-Cosar O, Haas ME, Bick A, Aragam KG, Lander ES, Smith GD, Mason-Suares H, Fornage M, Lebo M, Timpson NJ, Kaplan LM, Kathiresan S. Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood. Cell. 2019 Apr 18;177(3):587-596.e9. doi: 10.1016/j.cell.2019.03.028. — View Citation

Sun C, Kovacs P, Guiu-Jurado E. Genetics of Obesity in East Asians. Front Genet. 2020 Oct 20;11:575049. doi: 10.3389/fgene.2020.575049. eCollection 2020. — View Citation

Walley AJ, Asher JE, Froguel P. The genetic contribution to non-syndromic human obesity. Nat Rev Genet. 2009 Jul;10(7):431-42. doi: 10.1038/nrg2594. — View Citation

Wand H, Lambert SA, Tamburro C, Iacocca MA, O'Sullivan JW, Sillari C, Kullo IJ, Rowley R, Dron JS, Brockman D, Venner E, McCarthy MI, Antoniou AC, Easton DF, Hegele RA, Khera AV, Chatterjee N, Kooperberg C, Edwards K, Vlessis K, Kinnear K, Danesh JN, Parkinson H, Ramos EM, Roberts MC, Ormond KE, Khoury MJ, Janssens ACJW, Goddard KAB, Kraft P, MacArthur JAL, Inouye M, Wojcik GL. Improving reporting standards for polygenic scores in risk prediction studies. Nature. 2021 Mar;591(7849):211-219. doi: 10.1038/s41586-021-03243-6. Epub 2021 Mar 10. — View Citation

* Note: There are 13 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	weight loss	changes of weight and/or BMI z score	6 month
Primary	obesity severity	BMI z score/BMI percentile	1 month
Primary	fatty liver	quantification by liver ultrasound/Fibroscan	1 month
Secondary	hyperlipidemia	including triglyceride, HDL cholesterol, total cholesterol	1 month
Secondary	hypertension	systolic and diastolic blood pressure	1 month
Secondary	fasting glucose	hyperglycemia	1 month
Secondary	HbA1c	hyperglycemia	1 month
Secondary	2 hours glucose tolerance test	hyperglycemia	1 month

External Links

•   A genome-wide association meta-analysis identifies new childhood obesity loci
•   Accuracy of FibroScan Controlled Attenuation Parameter and Liver Stiffness Measurement in Assessing Steatosis and Fibrosis in Patients With Nonalcoholic Fatty Liver Disease. Gastroenterology
•   Association between Single Nucleotide Polymorphisms and Weight Reduction in Behavioural Interventions-A Pooled Analysis
•   From obesity genetics to the future of personalized obesity therapy
•   Genetics of Obesity in East Asians
•   Genomics of disease risk in globally diverse populations
•   Improving reporting standards for polygenic scores in risk prediction studies
•   Obesity Genes and Weight Loss During Lifestyle Intervention in Children With Obesity
•   Obesity in children and adolescents
•   Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood
•   The genetic contribution to non-syndromic human obesity
•   The genetics of obesity
•   What reduction in BMI SDS is required in obese adolescents to improve body composition and cardiometabolic health?