A Genetic Risk Score Is Associated with Weight Loss Following Roux-en Y Gastric Bypass Surgery.

Bandstein M, Voisin S, Nilsson EK, Schultes B, Ernst B, Thurnheer M, Benedict C, Mwinyi J, Schiöth HB

Obes Surg 26 (9) 2183-2189 [2016-09-00; online 2016-02-03]

Currently, Roux-en Y gastric bypass (RYGB) is the most efficient therapy for severe obesity. Weight loss after surgery is, however, highly variable and genetically influenced. Genome-wide association studies have identified several single nucleotide polymorphisms (SNP) associated with body mass index (BMI) and waist-hip ratio (WHR). We aimed to identify two genetic risk scores (GRS) composed of weighted BMI and WHR-associated SNPs to estimate their impact on excess BMI loss (EBMIL) after RYGB surgery. Two hundred and thirty-eight obese patients (BMI 45.1 ± 6.2 kg/m(2), 74 % women), who underwent RYGB, were genotyped for 35 BMI and WHR-associated SNPs and were followed up after 2 years. SNPs with high impact on post-surgical weight loss were filtered out using a random forest model. The filtered SNPs were combined into a GRS and analyzed in a linear regression model. An up to 11 % lower EBMIL with higher risk score was estimated for two GRS models (P = 0.026 resp. P = 0.021) composed of seven BMI-associated SNPs (closest genes: MC4R, TMEM160, PTBP2, NUDT3, TFAP2B, ZNF608, MAP2K5, GNPDA2, and MTCH2) and of three WHR-associated SNPs (closest genes: HOXC13, LYPLAL1, and DNM3-PIGC). Patients within the lowest GRS quartile had higher EBMIL compared to patients within the other three quartiles in both models. We identified two GRSs composed of BMI and WHR-associated SNPs with significant impact on weight loss after RYGB surgery using random forest analysis as a SNP selection tool. The GRS may be useful to pre-surgically evaluate the risks for patients undergoing RYGB surgery.

Bioinformatics Compute and Storage [Service]

NGI Uppsala (SNP&SEQ Technology Platform) [Service]

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PubMed 26832135

DOI 10.1007/s11695-016-2072-9

Crossref 10.1007/s11695-016-2072-9

10.1007/s11695-016-2072-9

pmc PMC4985537