Polygenic risk score for predicting weight loss after bariatric surgery
Juan de Toro-Martín, Frédéric Guénard, André Tchernof, Louis Pérusse, Simon Marceau, Marie-Claude Vohl
Juan de Toro-Martín, Frédéric Guénard, André Tchernof, Louis Pérusse, Simon Marceau, Marie-Claude Vohl
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Clinical Research and Public Health Genetics

Polygenic risk score for predicting weight loss after bariatric surgery

Abstract

BACKGROUND. The extent of weight loss among patients undergoing bariatric surgery is highly variable. Herein, we tested the contribution of genetic background to such interindividual variability after biliopancreatic diversion with duodenal switch. METHODS. Percentage of excess body weight loss (%EBWL) was monitored in 865 patients over a period of 48 months after bariatric surgery, and two polygenic risk scores were constructed with 186 and 11 (PRS186 and PRS11) single nucleotide polymorphisms previously associated with body mass index (BMI). RESULTS. The accuracy of the %EBWL logistic prediction model — including initial BMI, age, sex, and surgery modality, and assessed as the area under the receiver operating characteristics (ROC) curve adjusted for optimism (AUCadj = 0.867) — significantly increased after the inclusion of PRS186 (ΔAUCadj = 0.021; 95% CI of the difference [95% CIdiff] = 0.005–0.038) but not PRS11 (ΔAUCadj= 0.008; 95% CIdiff= –0.003–0.019). The overall fit of the longitudinal linear mixed model for %EBWL showed a significant increase after addition of PRS186 (–2 log-likelihood = 12.3; P = 0.002) and PRS11 (–2 log-likelihood = 9.9; P = 0.007). A significant interaction with postsurgery time was found for PRS186 (β = –0.003; P = 0.008) and PRS11 (β = –0.008; P = 0.03). The inclusion of PRS186 and PRS11 in the model improved the cost-effectiveness of bariatric surgery by reducing the percentage of false negatives from 20.4% to 10.9% and 10.2%, respectively. CONCLUSION. These results revealed that genetic background has a significant impact on weight loss after biliopancreatic diversion with duodenal switch. Likewise, the improvement in weight loss prediction after addition of polygenic risk scores is cost-effective, suggesting that genetic testing could potentially be used in the presurgical assessment of patients with severe obesity. FUNDING. Heart and Stroke Foundation of Canada (G-17-0016627) and Canada Research Chair in Genomics Applied to Nutrition and Metabolic Health (no. 950-231-580).

Authors

Juan de Toro-Martín, Frédéric Guénard, André Tchernof, Louis Pérusse, Simon Marceau, Marie-Claude Vohl

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Figure 5

Cost-effectiveness analysis of polygenic risk scores.

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Cost-effectiveness analysis of polygenic risk scores. (A) Cost curves of...
(A) Cost curves of the final logistic prediction model including all the demographic and clinical predictors (Final Model: sex, age, type of surgery, and initial BMI), Model + PRS186, and Model + PRS11 are shown. Minimal misclassification cost was obtained by estimating a false negative (FN) decision (actual LWL patients assigned to HWL+NWL group) to be 5 times as costly as a false positive (FP) decision (actual HWL+NWL assigned to LWL group). (B) Confusion matrix of each model showing the different distribution of correctly (true positives, TP, and true negatives, TN) and incorrectly (FN and FP) assigned observations. Blue lines indicate the probability cutoff with minimal misclassification cost for each model (final model = 0.21, model + PRS186 = 0.12, and model + PRS11 = 0.14). Red and blue points represent TN and FN; green and yellow points, TP and FP, respectively. Gray shaded areas are violin plots representing the density of observations across probability cutoffs. HWL, NWL, and LWL: high-, normal-, and low-weight-loss groups.