Combination of Single-Nucleotide Polymorphisms and Preoperative Body Mass Index to Predict Weight Loss After Laproscopic Sleeve Gastrectomy in Chinese Patients with Body Mass Index ≥ 32.5 kg/m2.
Obes Surg
; 32(12): 3951-3960, 2022 12.
Article
em En
| MEDLINE
| ID: mdl-36279045
BACKGROUND: Single-nucleotide polymorphisms (SNPs) associated with obesity predict laparoscopic Roux-en-Y gastric bypass (LRYGB) and biliopancreatic diversion with duodenal switch (BPD/DS) for weight loss with good efficiency. However, prediction of weight loss after laparoscopic sleeve gastrectomy using SNPs has not been well investigated. OBJECTIVES: To predict weight loss after laparoscopic sleeve gastrectomy using obesity-related SNPs and clinical variants in Chinese patients with body mass index (BMI) ≥ 32.5 kg/m2. METHODS: We detected 29 SNPs. Binary logistic regression was used to screen SNPs and clinical variables with predictive value. Receiver operating characteristic (ROC) curves were plotted for clinical variables, SNPs, and their combination, and areas under the ROC curve (AUC) were compared. Internal and external validation tests were performed. RESULTS: rs12535708, rs651821, and rs5082 were constructed as the genetic risk score (GRS). Preoperative BMI was constructed as the clinical risk score (CRS). Preoperative BMI and SNPs were constructed as the cumulative genetic risk score (CGRS). ROC curves of GRS, CRS, and CGRS showed that the optimal cutoffs were 0.831 (AUC = 0.840; sensitivity, 92.96%; specificity, 64.29%), 43.46 kg/m2 (AUC = 0.830; sensitivity, 76.06%; specificity, 85.71%), and 0.921 (AUC = 0.931; sensitivity, 77.46%; specificity, 92.86%), respectively. The AUC of CGRS was significantly greater than that of CRS (P < 0.05) and greater than GRS without statistical significance. CONCLUSION: In Chinese patients with BMI ≥ 32.5 kg/m2, GRS and CRS could predict weight loss success. However, CGRS was superior to GRS or CRS alone.
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Base de dados:
MEDLINE
Assunto principal:
Obesidade Mórbida
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Derivação Gástrica
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Laparoscopia
Tipo de estudo:
Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Humans
País como assunto:
Asia
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article