Journal of Personalized Medicine Article A Clinical-Genetic Score for Predicting Weight Loss after Bariatric Surgery: The OBEGEN Study Andreea Ciudin 1,2,3,† , Enzamaría Fidilio 1,2,† , Liliana Gutiérrez-Carrasquilla 4 , Assumpta Caixàs 5,6 , Núria Vilarrasa 3,7 , Silvia Pellitero 8 , Andreu Simó-Servat 7 , Ramon Vilallonga 9 , Amador Ruiz 9 , Maricruz de la Fuente 10 , Alexis Luna 11 , Enric Sánchez 4 , Mercedes Rigla 5,6 , Cristina Hernández 1,2,3 , Eduardo Salas 12 , Rafael Simó 1,2,3, * and Albert Lecube 3,4, * 1 Endocrinology and Nutrition Department, Hospital Universitari Vall d’Hebron, 08035 Barcelona, Spain; aciudin@vhebron.net (A.C.); enzamaria.fidilio@vhir.org (E.F.); cristina.hernandez@vhir.org (C.H.) 2 Diabetes and Metabolism Research Unit, Vall d’Hebron Institut de Recerca (VHIR), Universitat Autònoma de Barcelona, 08025 Barcelona, Spain 3 Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; nuriag@bellvitgehospital.cat 4 Endocrinology and Nutrition Department, Hospital Universitari Arnau de Vilanova, Obesity, Diabetes and Metabolism Research Group (ODIM), Institut de Recerca Biomèdica de Lleida (IRBLleida), Universitat de Lleida, 25198 Lleida, Spain; liligutierrezc@gmail.com (L.G.-C.); kikke03@gmail.com (E.S.) 5 Endocrinology and Nutrition Department, Hospital Universitari Parc Tauli, Medicine Department,  Universitat Autònoma de Barcelona, Institut d’Investigació i Innovació Parc Taulí, 08208 Sabadell, Spain;  acaixas@tauli.cat (A.C.); mrigla@tauli.cat (M.R.) Citation: Ciudin, A.; Fidilio, E.; 6 Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Gutiérrez-Carrasquilla, L.; Caixàs, A.; Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain 7 Department of Endocrinology, Diabetes and Nutrition, Bellvitge University Hospital-IDIBELL, L’Hospitalet Vilarrasa, N.; Pellitero, S.; Simó-Servat, A.; Vilallonga, R.; Ruiz, de Llobregat, 08907 Barcelona, Spain; andreusimoservat@gmail.com 8 Endocrine and Nutrition Department, Germans Trias i Pujol University Hospital, Germans Trias i Pujol A.; de la Fuente, M.; et al. A Research Institute (IGTP), 08916 Badalona, Spain; spellitero.germanstrias@gencat.cat Clinical-Genetic Score for Predicting 9 Endocrine, Metabolic and Bariatric Unit, Department of General and Digestive Surgery, Vall d’Hebron Weight Loss after Bariatric Surgery: University Hospital, Universitat Autònoma de Barcelona, Center of Excellence for the EAC-BS, The OBEGEN Study. J. Pers. Med. 08035 Barcelona, Spain; vilallongapuy@gmail.com (R.V.); gordeju@gmail.com (A.R.) 2021, 11, 1040. https://doi.org/ 10 General and Digestive Surgery Department, Hospital Universitari Arnau de Vilanova, Institute de Recerca 10.3390/jpm11101040 Biomèdica de Lleida, 25198 Lleida, Spain; mcruzdlfuente@hotmail.com 11 Surgery Department, Esofago-Gastric Surgery Section, Hospital Universitari Parc Taulí, Institut d’Investigació Academic Editors: Kalliopi Kotsa, i Innovació Parc Taulí, 08208 Sabadell, Spain; aluna@tauli.cat 12 Scientific Department, Gendiag, 08028 Barcelona, Spain; eduardo.santos.salas@gmail.com Theocharis Koufakis and Maria Grammatiki * Correspondence: rafael.simo@vhir.org (R.S.); alecube@gmail.com (A.L.) † A.C. and E.F. have contributed equally to this work. Received: 13 September 2021 Accepted: 12 October 2021 Abstract: Around 30% of the patients that undergo bariatric surgery (BS) do not reach an appropriate Published: 17 October 2021 weight loss. The OBEGEN study aimed to assess the added value of genetic testing to clinical variables in predicting weight loss after BS. A multicenter, retrospective, longitudinal, and observational Publisher’s Note: MDPI stays neutral study including 416 patients who underwent BS was conducted (Clinical.Trials.gov- NCT02405949). with regard to jurisdictional claims in 50 single nucleotide polymorphisms (SNPs) from 39 genes were examined. Receiver Operating published maps and institutional affil- Characteristic (ROC) curve analysis were used to calculate sensitivity and specificity. Satisfactory iations. response to BS was defined as at nadir excess weight loss >50%. A good predictive model of response [area under ROC of 0.845 (95% CI 0.805–0.880), p < 0.001; sensitivity 90.1%, specificity 65.5%] was obtained by combining three clinical variables (age, type of surgery, presence diabetes) and nine SNPs located in ADIPOQ, MC4R, IL6, PPARG, INSIG2, CNR1, ELOVL6, PLIN1 and BDNF genes. Copyright: © 2021 by the authors. This predictive model showed a significant higher area under ROC than the clinical score (p = 0.0186). Licensee MDPI, Basel, Switzerland. The OBEGEN study shows the key role of combining clinical variables with genetic testing to increase This article is an open access article the predictability of the weight loss response after BS. This finding will permit us to implement a distributed under the terms and personalized medicine which will be associated with a more cost-effective clinical practice. conditions of the Creative Commons Attribution (CC BY) license (https:// Keywords: obesity; bariatric surgery; weight loss; genetics; polygenic risk creativecommons.org/licenses/by/ 4.0/). J. Pers. Med. 2021, 11, 1040. https://doi.org/10.3390/jpm11101040 https://www.mdpi.com/journal/jpm J. Pers. Med. 2021, 11, 1040 2 of 11 1. Introduction Obesity is a multifactorial and complex disease, caused by the contribution and inter- action of environmental and genetic factors [1,2]. Its prevalence has increased dramatically in recent decades, and up to one-fifth of the whole world’s population is expected to live with obesity by 2025 [3]. The discouraging results provided by the conventional treat- ment have led to the progressive use of the bariatric surgery (BS) as a main treatment for morbidly obesity in Western countries [4]. However, around 30% of the patients that undergo BS do not reach an appropriate weight loss and/or do not resolve the comorbidities associated with obesity [5–7]. This fail is associated with a decline in health-related quality of life and patients report feelings of frustration, anger, and even depression [8,9]. Therefore, the identification of new predictive factors of response to BS seems mandatory [10–13]. This strategy will permit us to identify the best candidates to BS and even to select the type of BS technique, thus optimizing the health care resources. Inheritance is responsible for 40–75% of all the causes of obesity, a percentage modu- lated by the epigenetic influence [2,14]. Through genome-wide association studies (GWAS), a series of gene variants and single nucleotide polymorphisms (SNPs) in more than 120 genes have been linked with eating behavior, energy expenditure, response to diet, or lifestyle interventions [15,16]. As each variant alone has little effect on body weight, genetic predisposition is conditioned by the simultaneous presence of SNPs in multiple genes [11]. The possibility of elucidating the best combination of SNPs responsible for the variability of the response to BS in terms of weight loss offers the opportunity to design individualized therapy strategies. Our group has recently showed that an algorithm based on the selection of SNPs associated with predisposition to obesity, appetite regulation, and weight loss was able to predict the percentage of excess body weight loss (%EWL) after BS with high sensitivity and specificity [17]. It should be noted that this was a pilot study including only women who undergo a Roux-en-Y gastric bypass (RYGB), precluding the extrapolation of the results to the whole population. The objective of the OBEGEN study is to confirm and extend our previous data regarding the added value of genetic testing to clinical variables in predicting the weight loss after BS. For this purpose, a total of 416 subjects (women and men) were evaluated, both RYGB and sleeve gastrectomy (SG) were included, and 50 SNPs were assessed. 2. Materials and Methods 2.1. Statement on Ethics The OBEGEN study was approved by the human ethics committee of the University Arnau de Vilanova Hospital of Lleida (CEIC-1743). All potential participants gave writ- ten informed consent to join the study, which was conducted according to the Helsinki Declaration and the Good Clinical Practice Guidelines. The study was registered in Clinical.Trials.gov- NCT02405949 (accessed on 16 October 2021). 2.2. Study Design and Description of the Study Population The OBEGEN project was a multicenter, retrospective, longitudinal, and observational study investigating the role of some genetic variants added to clinical variables to predict weight loss after BS. A total of 416 patients who underwent BS between January 2017 and August 2018 at the Obesity Units of four University Hospitals in Catalonia (Spain) were included. Eligible patients were men and women ≥18 years old, who underwent BS at least 18 months prior to the study. Among the 449 patients who met these criteria, 33 were excluded because of the following reasons: current pregnancy (n = 2), development of drug or alcohol abuse or eating disorders after bariatric surgery (n = 5), markedly mobility problems (n = 4), a different bariatric surgery technique apart from RYGB or SG (n = 9), use of weight lowering pharmacotherapy (n = 7) or a second surgical intervention required during follow-up (n = 6). The study flow chart is displayed in Figure 1. drug or alcohol abuse or eating disorders after bariatric surgery (n = 5), markedly m problems (n = 4), a different bariatric surgery technique apart from RYGB or SG use of weight lowering pharmacotherapy (n = 7) or a second surgical intervention r J. Pers. Med. 2021, 11, 1040 during follow-up (n = 6). The study flow chart is displayed in Figure 1.3 of 11 Figure 1. Flow chart of the OBEGEN study population. Figure 1. Flow chart of the OBEGEN study population. All the patients agreed to participate in the study and signed the informed consent underwent completeagreed All the patients medical to history, anthropometric participate in themeasurement, study and physical signedexamination, the informed and DNA sampling. underwent complete medical history, anthropometric measurement, p 2.3. Outcome examination, andWeight DNA Measures sampling. The primary endpoint was the %EWL at nadir. Excess body weight (EBW) was defined as the amount of weight that was in excess from the ideal body weight (IBW). IBW was 2.3. Outcome Weight Measures estimated according to the 1983 Metropolitan Life Insurance Tables (use the midpoint for medium The primary frame) [18]. Thewas endpoint %EWL thewas calculated %EWL accordingExcess at nadir. to the formula: [total body weight (EB preoperative weight (kg) − weight after bariatric surgery (kg)/EBW (kg)] × 100. Those defined as the amount of weight that was in excess from the ideal body weigh with a reduction in their %EWL >50% at nadir were considered “good responders” [19]. IBW was estimated according to the 1983 Metropolitan Life Insurance Tables 2.4. Genotyping midpoint for medium frame) [18]. The %EWL was calculated according to the f DNA was extracted from saliva samples and processed by GoldenGate® Genotyping [total Assay preoperative weight (kg) − weight after bariatric surgery (kg)/EBW (kg) for VeraCode. The genetic predisposition was assessed using the 50 SNPS in 39 Those genes withincluded a reduction in their %EWL in a commercial >50% nutrigenomic at nadir product, the were considered Nutri inCode “good resp (NiC) (Ferrer [19]. inCode, Barcelona, Spain). This product includes SNPs that had previously been associated with susceptibility to weight loss, both in response to lifestyle intervention and BS [8,20,21]. In addition, Nutri inCode also includes selected variants of published GWAS studies or 2.4. Genotyping replication studies related to genetic susceptibility to regulate appetite and develop type 2 diabetes DNA was andextracted obesity [22,23]. from Finally, salivain the present study, samples and the panel has been processed Gen enriched with ® by GoldenGate new 11 SNPs compared to our pilot study (Table 1) [17]. Assay for VeraCode. The genetic predisposition was assessed using the 50 SNP genes included in a commercial nutrigenomic product, the Nutri inCode (NiC) inCode, Barcelona, Spain). This product includes SNPs that had previous associated with susceptibility to weight loss, both in response to lifestyle interven BS [8,20,21]. In addition, Nutri inCode also includes selected variants of published studies or replication studies related to genetic susceptibility to regulate appe develop type 2 diabetes and obesity [22,23]. Finally, in the present study, the pa J. Pers. Med. 2021, 11, 1040 4 of 11 Table 1. Selected genes and single nucleotide polymorphisms evaluated in the OBEGEN study. Chromosome Allocation Gene Name SNP of Human Ortholog ACE angiotensin I converting enzyme 17q23.3 rs4343 rs16861209 ADIPOQ adiponectin, C1Q and collagen domain containing 3q27.3. rs2241766 rs4994 ADRB3 adrenoceptor beta 3 8p11.23. rs9693898 AGRP Agouti related neuropeptide 16q22.1 rs11575892 * AGTR1 angiotensin II receptor type 1 3q24. rs5186 APOA2 apolipoprotein A2 11q23.3. rs5082 APOA5 apolipoprotein A5 11q23.3. rs651821 APOC3 apolipoprotein C3 11q23.3. CD010 rs6265 BDNF brain derived neurotrophic factor 11p14.1. rs925946 CCDC93 coiled-coil domain containing 93 2q14.1 rs10490628 * CDKAL1 CDK5 regulatory subunit associated protein 1 like 1 6p22.3. rs7754840 CDKN2B cyclin dependent kinase inhibitor 2B 9p21.3. rs10811661 rs4580704 CLOCK clock circadian regulator 4q12. rs4864548 CNR1 cannabinoid receptor 1 6q15 rs6454674 * ELOVL6 ELOVL fatty acid elongase 6 4q25. rs682447 ESR1 estrogen receptor 1 6q25.1 rs3778099 * FTO fat mass and obesity associated 16q12.2. rs9939609 GHRL ghrelin and obestatin prepropeptide 3p25.3. rs696217 IGF2 insulin like growth factor 2 11p15.5 rs680 * rs7566605 INSIG2 insulin induced gene 2 2q14.1. rs3771942 * IL-1B interleukin 1 beta 2q14.1 rs1143643 * IL6 interleukin 6 7p15.3. rs1800795 LEP leptin 7q32.1. rs12535708 LEPR leptin receptor 1p31.3. rs1137100 LPL lipoprotein lipase 8p.22. rs328 rs12970134 rs52820871 MC4R melanocortin 4 receptor 18q21.32. rs17700633 rs2229616 rs17782313 MTCH2 mitochondrial carrier 2 11p11.2. rs10838738 NEGR1 neuronal growth regulator 1 1p31.1 rs2568958 * rs1052700 PLIN1 perilipin 1 15q26.1. rs894160 PPARA peroxisome proliferator activated receptor alpha 22q13.31. rs1800206 PPARG peroxisome proliferator activated receptor gamma 3p25.2. rs1801282 PCSK1 proprotein convertase subtilisin/kexin type 1 5q15. rs6235 PON1 paraoxonase 1 7q21.3. CD014 SIRT1 sirtuin 1 10q21.3 rs7069102 * TCF7L2 transcription factor 7 like 2 10q25.2. rs7903146 TMEM18 transmembrane protein 18 2p25.3 rs2867125 * UCP1 uncoupling protein 1 4q31.2 rs45539933 * UCP2 uncoupling protein 2 11q13.4. rs659366 UCP3 uncoupling protein 3 11q13.4. rs1800849 WFS1 wolframin ER transmembrane glycoprotein 4p16.1. rs10010131 SNP: single nucleotide polymorphism. *: Genetic variants that have been added from the pilot study [17]. 2.5. Statistical Methods A normal distribution of the variables was established using the Kolmogorov–Smirnov test, and data are expressed as the mean ± SD, median (interquartile range), or as a percentage. Comparisons between groups were made using the Student’s t-test and J. Pers. Med. 2021, 11, 1040 5 of 11 the Mann–Whitney U test for quantitative variables, and the Pearson’s chi-squared for categorical variables. The relationship between continuous variables was examined by the Pearson linear correlation test. To assess the best predictive clinical–genetic risk score, patients were distributed into two groups according to the BS response: (i) %EWL ≤ 50% (n = 113); (ii) %EWL > 50% n = 301). On one side, the different SNPs were coded as 0, 1, or 2 according to the number of risk alleles associated with a favorable weight response. Clinical variables analyzed included gender, age at surgery, preoperative weight and BMI, type of surgery (RYGB or SG), EBW, and presence of type 2 diabetes. Univariate and multivariate logistic regressions were used to establish associations between the genetic and/or the clinical variables and the loss of weight. In this way, we generated three risk scores: (i) a clinical risk score, (ii) a genetic risk score, and (iii) the OBEGEN clinical-genetic risk score (OBEGEN- CGRS), which includes both the selected clinical and genetic variants in the multivariate logistic regression. Akaike Information Criterion (AIC)-based backward selection was used to remove not significant variables, from an initial model containing all the candidate predictors. The calibration of the logistic model’s adequacy was determined using the test of fit by the Hosmer–Lemeshow. The accuracy of different scores/models in discriminating those who obtained the objective weight loss (%EWL > 50%) from those who did not achieve the objective weight loss (for evaluating the prediction performance of the models) was evaluated using a Receiver Operating Characteristic (ROC) curve analysis. The cut-offs to calculate the sensitivity and specificity of the developed algorithms were selected as the point which maximizes the Youden index. An odds ratio with its 95% confidence interval was finally calculated. The total area under the ROC (AUROC) curve was interpreted following guidelines: 0.9–1.0 excellent, 0.8–0.9, good; 0.7–0.8, fair; 0.6–0.7, poor; and 0.5–0.6, not useful. Comparisons between the obtained AUROC were compared using the method of Hanley and McNeil. All the contrasts were bilateral with a significance level of 0.05. The data were ana- lyzed with the Statistical Package for the Social Sciences software (IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY, USA). 3. Results 3.1. Baseline Characteristics of Patients The main baseline characteristics of patients included in the OBEGEN study are shown in Table 2. After a follow-up period of 14.6 ± 0.8 months, 301 (72.3%) patients achieved a %EWL higher than 50%. Patients with a favorable weight response were younger, mainly women, and underwent RYGB. 3.2. Construction of a Clinical Risk Score When only available clinical data were evaluated, the multivariable logistic regression model showed than age at BS, type of BS and presence of type 2 diabetes were indepen- dent risk factors for predicting a favorable weight loss in the entire population (Table 3). Therefore, a clinical risk score was developed including these three variables that showed an AUROC for predicting a good response to BS of 0.775 [95% confidence interval (CI) 0.731 to 0.814, p < 0.0001], with a sensitivity of 93.0% and a specificity of 50.4%. The calibra- tion of the adequacy of the model determined by the Hosmer–Lemeshow test was 0.522. J. Pers. Med. 2021, 11, 1040 6 of 11 Table 2. Main clinical characteristics, metabolic, and anthropometry data of patients included in the study and according to the weight response to bariatric surgery. Total %EWL > 50% %EWL ≤ 50% p Patients, n (%) 416 301 (72.3) 115 (27.6) <0.001 Female, n (%) 348 (83.6) 260 (86.3) 88 (76.5) <0.001 Age (yrs) 48.3 ± 10.3 49.0 ± 10.4 51.5 ± 9.2 0.003 SG, n (%) 137 (32.9) 105 (34.8) 32 (27.8) <0.001 RYGB, n (%) 280 (67.3) 218 (72.4) 62 (53.9) <0.001 Initial BMI (Kg/m2 ) 44.3 ± 7.9 44.5 ± 9.6 44.2 ± 7.3 0.554 Initial weight (Kg) 113.0 ± 18.4 112.3 ± 20.3 113.4 ± 13.8 0.361 Excess weight (Kg) 49.3 ± 17.3 48.7 ± 19.2 51.3 ± 12.2 0.020 Nadir BMI (Kg/m2 ) 29.9 ± 5.8 28.5 ± 4.5 37.0 ± 6.8 <0.001 Type 2 diabetes, n (%) 173 (41.5) 128 (42.5) 45 (39.1) <0.001 Hypertension (%) 286 (68.7) 204 (67.7) 82 (71.3) <0.001 Dyslipidemia (%) 306 (73.5) 212 (70.4) 94 (81.7) 0.010 Sleep apnea (%) 116 (27.8) 88 (29.2) 28 (24.3) <0.001 Data are mean ± SD, median (range) or n (percentage). SG: sleeve gastrectomy; RYGB: Roux-en-Y gastric bypass; BMI: body mass index. EWL: excess of weight loss. Hypertension was defined by increased systolic (≥140 mmHg) or increased diastolic (≥90 mmHg) blood pressure or using antihypertensive drugs, according to current guidelines. Dyslipidemia was defined using lipid-lowering drugs, decreased values of HDL cholesterol (men< 0.9 mmol/L, women <1.0 mmol/L) or by at least one increased value of total cholesterol (>5.2 mmol/L), LDL cholesterol or triglycerides (>1.7 mmol/L). Table 3. Clinical and genetic variables that significantly predicts favorable weight loss after BS in the entire population of the OBEGEN project. Clinical Variables Genetic Variants All selected Variants Coefficient Coefficient † Coefficient † Age −0.03458 rs16861209 −0.30388 CRS 1.13897 Type of surgery 0.69588 rs17782313 0.32234 GRS 1.30048 Type 2 Diabetes 3.05077 rs1800795 −0.33407 Constant −1.34401 Constant −3.75577 rs1801282 0.33407 rs3771942 −0.17997 rs6454674 0.24788 rs682447 0.41113 rs894160 0.28848 rs925946 0.28604 Constant −0.30768 †: Coefficients in multiple logistic regression model. CRS: clinical risk score; GRS: genetic risk score. 3.3. Construction of a Genetic Risk Score Additionally, when genetic data were analyzed alone, the multivariate logistic regres- sion equation for predicting a favorable weight loss response after the BS included nine insSNPs located in ADIPOQ, MC4R, IL-6, PPARG, INSIG2, CNR1, ELOVL6, PLIN1, and BDNF (Table 3). This genetic risk score showed an AUROC of 0.648 (95% CI 0.597 to 0.696, p < 0.0001), with a sensitivity of 48.7% and a specificity of 75.0%. The calibration of the adequacy of the model determined by the Hosmer–Lemeshow test was 0.922. 3.4. Construction of the OBEGEN Clinical-Genetic Risk Score Based on the clinical and genetic data from our population, we created the OBEGEN- CGRS, including age at surgery, type of surgery, presence of type 2 diabetes, and the nine SNPs associated with weight loss in response to BS (Table 3). The OBEGEN-CGRS score ranges from −4 to +4 points, with a cut-off point to define a good responder of 0.662. This predictive model showed an AUROC of 0.845 (95% CI 0.805 to 0.880, p < 0.001), with a sensitivity of 90.1% and a specificity of 65.5%. The calibration of the model’s adequacy determined by the Hosmer–Lemeshow test was 0.927. An internal validation of this clinical- Based on the clinical and genetic data from our population, we created the OBEGEN- CGRS, including age at surgery, type of surgery, presence of type 2 diabetes, and the nine SNPs associated with weight loss in response to BS (Table 3). The OBEGEN-CGRS score ranges from −4 to +4 points, with a cut-off point to define a good responder of 0.662. This J. Pers. Med. 2021, 11, 1040 predictive model showed an AUROC of 0.845 (95% CI 0.805 to 0.880, p < 0.001), with a 7 of 11 sensitivity of 90.1% and a specificity of 65.5%. The calibration of the model’s adequacy determined by the Hosmer–Lemeshow test was 0.927. An internal validation of this clinical-genetic algorithm was performed using a Bootstrap method to quantify the genetic algorithm uncertainty waswith associated performed using aThe the AUROC. Bootstrap method result was to quantify an AUC of 0.845the uncertainty (95% CI: 0.800 associated to 0.888). with the AUROC. The result was an AUC of 0.845 (95% CI: 0.800 to 0.888). The OBEGEN-CGRS The OBEGEN-CGRSscore scoreshowed showed a significant higher a significant AUROC higher than either AUROC than the clinical either the score (p = 0.0186) or the genetic score (p < 0.0001) (Figure 2). clinical score (p = 0.0186) or the genetic score (p < 0.0001) (Figure 2). Figure Figure 2. 2. The The predictive predictive capacity capacity of of favorable favorable weight weight loss loss (%EWL (%EWL >> 50%) 50%) obtained obtained byby the the clinical clinical risk riskscore score(CRS), (CRS),the thegenetic geneticrisk riskscore score (GRS), (GRS),and and the the clinical clinical plus plus genetic genetic risk risk score score (CGRS) (CGRS) inin our our study population in the OBEGEN project. study population in the OBEGEN project. 4. 4.Discussion Discussion In this In this study study we provide evidence evidence that thatthe thecombination combinationofofclinicalclinicalplus plusgenetic geneticdata datais isa areliable reliable method method toto predict predictthe weight the weight response response after BS.BS. after The TheOBEGEN-CGRS OBEGEN-CGRS permits permitsus to progress us to progress towards the personalization towards in theinmanagement the personalization the management of patients with severe of patients with obesity, severe seeking seeking obesity, maximum efficiency maximum with thewith efficiency leastthe surgical damage.damage. least surgical AlthoughBS Although BSprovides provides successful successful weight weight loss loss inin most most of of the the cases, cases, 25–30% 25–30% ofof patients patients who undergo BS who BS may maynotnotachieve achievethethedesired desired weight weight reduction reduction [5–7]. ThisThis [5–7]. failure is con- failure is sidered multifactorial, with some preoperative factors associated considered multifactorial, with some preoperative factors associated with the hospital with the hospital center (i.e., surgeons’ center experience, (i.e., surgeons’ bariatric procedure, experience, preoperativepreoperative bariatric procedure, education, and recommended education, and weight loss before recommended BS), the weight losspatient before(age, BS), gender, ethnicity, the patient (age,preoperative BMI, and gender, ethnicity, comorbidi- preoperative ties associated BMI, with obesity), and comorbidities and psychosocial associated with obesity), features (economic resources, and psychosocial features household (economic type, and personality disorders) [11–13,24–26]. The real factors that resources, household type, and personality disorders) [11–13,24–26]. The real factors that predict weight loss following BS are still far to be determined. This is due to the predict weight loss following BS are still far to be determined. This is due to the inconsistency in reporting and the methodological inconsistency in reporting weaknesses in analysis, which and the methodological include ainlittle weaknesses if anywhich analysis, consideration include of genetic factors [21,27]. a little if any consideration of genetic factors [21,27]. The development of new predictive tools for BS, based on some of the previous pre- dictive factors, but at the same time fueled by new components, is a real need for clinicians who treat obesity worldwide. These instruments should help physicians to identify the best candidates who must undergo BS and to surgeons to optimize the surgical procedure. Few studies have addressed the influence of genetics on long-term dynamic changes in body weight with ambiguous results [16,28]. The Swedish Obesity Study analyzed the impact of various SNPs from 11 genes in 1443 BS cases [29]. After the evaluation of 20 gene SNPs in 249 morbidly obese subjects undergoing RYGB, Velázquez-Fernández et al. showed that POMC rs1042571 was the only associated with favorable weight loss [30]. J. Pers. Med. 2021, 11, 1040 8 of 11 In another group of 1011 subjects, an increasing number of SNPs alleles in or near the FTO, INSIG2, MC4R, and PCSK1 genes negatively influenced weight loss trajectories after RYGB in those with an initial BMI >50 kg/m2 [10]. More recently, in a group of 146 individuals, carriers of another variant of the FTO gene (rs9939609) showed a lower success rate, as well as a greater and faster weight recovery beyond 2 years after BS [31]. Nevertheless, the same SNP was not associated with different weight loss after 6 months of SG in 74 morbidly obese patients [32]. Regarding the MC4R gene, among 1433 subjects with a follow-up period of 12 months after RYGB, carriers of the I251L variant lost 9% more weight compared with the noncarriers [20]. Finally, a prospective observational study with 105 patients evaluated SNPs in the leptin receptor, FTO and FABP2 genes [33]. This study showed that carriers of the LEP223 (rs1137101) experienced close to 25% lower excess weight at 12 and 24 months after bariatric surgery. Beyond the isolated study of one or the other gene, genetic risk scores composed by adiposity-related SNPs have been related with weight loss after RYGB or SG in Swiss, Danish, and Greek populations [34–36]. It should be noted that only three of the nine genes included in the OBEGEN-CGRS appeared previously reported in association with the weight loss response after BS [10,20,36]. This fact highlights the complexity of the genetic basis associated with the development of obesity, but also that the genes related to the therapeutic response may be different from those proposed so far. In the OBEGEN study, including both genders and different bariatric procedures, the combination of clinic and genetic data enhanced the predictive capacity of the genetic risk score, with improved sensitivity and specificity. Altogether, our findings support the use of genetic testing in clinical practice. Among the multiple variants of genes that were evaluated, nine of them interact with clinical variables to modify the weight response to BS in the OBEGEN study. While low serum adiponectin levels have been associated with central obesity, insulin resistance, metabolic syndrome, and type 2 diabetes, ADIPOQ (rs16861209) has been significantly associated with elevated fasting serum adiponectin levels [37,38]. Similarly, although IL-6 significantly increases the risk of obesity, in the PREDIMED trial carriers of the rs1800795 showed greater weight loss with the Mediterranean diet with supplements of olive oil compared to a Mediterranean diet low-fat diet than heterozygous and non-carrier carriers after 3 years of intervention [39,40]. In addition, PPARG (rs1801282) was associated not only with short-term (6-month) and long-term (2-year) weight loss but also with weight regain in the Diabetes Prevention Program [41]. PLIN1, a circadian lipid stabilizing protein in the adipocyte, has been associated with body weight regulation and PLIN1 (rs894160) with variability in weight loss [42,43]. Elovl6, a microsomal enzyme involved in the elongation of saturated and monounsaturated fatty acids with 12, 14, and 16 carbons, regulates mitochondrial function and thermogenic capacity in brown adipose tissue [44]. The BDNF (rs925946), INSIG2 (rs3771942) and CNR1 (rs6454674) variants are well stablished genetic determinants of obesity [45–47]. Similarly, MC4R (rs17782313) is associated with high dietary intake and different obesity-related phenotypic traits, such as insulin resistance, type 2 diabetes, and hypertriglyceridemia [48]. However, so far there are no data on the response to weight loss treatments regarding these last four variants. The main differences between our CGRS and previous studies is located in the method- ology assessment. Previous studies have been interested in analyzing the association be- tween the number of genetic risk variables and the greater or lower weight loss following BS. However, we have focused our interest on the elaboration of predictive equations mixing clinical and genetic variables and the identification of predictive cut-off in those predictive equations. We have also calculated the prognostic capability of our score in identifying the “good” and “bad” responders to BS. We believe that this is a concept of great interest and more reliable in daily clinical practice. In this way, a patient who requires BS is exposed to an intervention that is difficult to reverse, so it is vital to be able to predict success or failure with a high degree of certainty. Currently, the choice of surgical technique is based on the baseline BMI and the presence of comorbidities. The introduction of a J. Pers. Med. 2021, 11, 1040 9 of 11 genetic predisposition score in the decision-making algorithms will bring us closer to the best selection of the patient, to choose the most convenient surgery, and to improve current health outcomes in our population. There are some potential limitations that need to be considered when contemplating the results of our study. First, we evaluated a selected population of patients who under- went bariatric surgery, excluding those who underwent to other surgical techniques other than RYGB and SG, as well as those who had used weight lowering pharmacotherapy or had required a second surgical intervention. However, we believe that the inclusion of these more extreme cases might increase rather than reduce the reliability of our score. Second, the OBEGEN project is a retrospective one, meaning that no irrefutable clinical consequences can still be inferred to general population. Prospective long-term studies testing the genetic basis of patients with severe obesity before the recommendation of bariatric procedures are needed. Third, the characterization of favorable weight loss after BS is controversial [19]. In the OBEGEN study we have used a classic definition (%EWL>50) to better confirm our previous pilot study, so the results could be different if the chosen definition had been another. Finally, our model needs to be validated in an untrained dataset to fully demonstrate its applicability in the real clinical practice. 5. Conclusions In conclusion, the OBEGEN project shows how the addition of genetic testing to the currently used clinical variables significantly improve our capability to identify patients with obesity who will be “good” or “bad” responders to BS in terms of weight loss. This information should help us both to personalize the therapeutic approach in severe obesity (e.g., select beforehand surgical techniques with a higher degree of malabsorption in those patients identified as “bad” responders), thus optimizing the limited health resources. Author Contributions: Conceptualization, A.C. (Andreea Ciudin), C.H., E.S. (Eduardo Salas), R.S., and A.L. (Albert Lecube); methodology A.C. (Andreea Ciudin), R.S., and A.L. (Albert Lecube); validation, E.F., L.G.-C., A.C. (Andreea Ciudin), A.C. (Assumpta Caixàs), N.V., S.P., and A.S.-S.; formal analysis, A.C. (Andreea Ciudin), E.S. (Eduardo Salas), R.S., and A.L. (Albert Lecube); investigation, E.F., L.G.-C., A.C. (Andreea Ciudin), A.C. (Assumpta Caixàs), N.V., S.P., A.S.-S., A.L. (Alexis Luna), E.S. (Enric Sánchez), M.R., C.H., R.V., A.R., M.d.l.F., and A.L. (Albert Lecube); resources, A.L. (Albert Lecube); data curation, E.F., L.G.-C., A.C. (Andreea Ciudin), A.C. (Assumpta Caixàs), N.V., S.P., A.S.-S., E.S. (Enric Sánchez), and M.R.; writing—original draft preparation, A.C. (Andreea Ciudin) and A.L. (Albert Lecube); writing—review and editing, A.C. (Andreea Ciudin), E.S. (Eduardo Salas), R.S., and A.L. (Albert Lecube); supervision, A.C. (Andreea Ciudin) and A.L. (Albert Lecube); funding acquisition, A.L. (Albert Lecube). All authors have read and agreed to the published version of the manuscript. Funding: This research was supported by grants from the “Pla Estratègic de Recerca i Innovació en Salut” (PERIS) 2016–2020 (SLT002/16/00497), the Instituto de Salud Carlos III (PI PI18/00964), Fondos FEDER “Una manera de hacer Europa”), and Menarini España. CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM) is an initiative of the Instituto Carlos III. Institutional Review Board Statement: The OBEGEN study was approved by the human ethics committee of the University Arnau de Vilanova Hospital of Lleida (CEIC-1743). Written informed consent was obtained from all subjects involved in the study, which was conducted according to the Helsinki Declaration and the Good Clinical Practice Guidelines. The study was registered in Clinical.Trials.gov- NCT02405949. Informed Consent Statement: Written informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions. Conflicts of Interest: The authors declare no conflict of interest. J. Pers. Med. 2021, 11, 1040 10 of 11 References 1. Swinburn, B.A.; Sacks, G.; Hall, K.D.; McPherson, K.; Finegood, D.T.; Moodie, M.; Gortmaker, S.L. The global obesity pandemic: Shaped by global drivers and local environments. Lancet 2011, 378, 804–814. [CrossRef] 2. Locke, A.; Kahali, B.; Berndt, S.I.; Justice, A.E.; Pers, T.; Day, F.; Powell, C.; Vedantam, S.; Buchkovich, M.L.; Yang, J.; et al. Genetic studies of body mass index yield new insights for obesity biology. Nat. Cell Biol. 2015, 518, 197–206. [CrossRef] 3. Null, N. Trends in adult body-mass index in 200 countries from 1975 to 2014: A pooled analysis of 1698 population-based measurement studies with 19·2 million participants. Lancet 2016, 387, 1377–1396. [CrossRef] 4. Angrisani, L.; Santonicola, A.; Iovino, P.; Vitiello, A.; Higa, K.; Himpens, J.; Buchwald, H.; Scopinaro, N. IFSO Worldwide Survey 2016: Primary, Endoluminal, and Revisional Procedures. Obes. Surg. 2018, 28, 3783–3794. [CrossRef] [PubMed] 5. Sarabu, N. Weight and Metabolic Outcomes 12 Years after Gastric Bypass. N. Engl. J. Med. 2018, 378, 93–96. [CrossRef] [PubMed] 6. Kowalewski, P.K.; Olszewski, R.; Wal˛edziak, M.; Janik, M.; Kwiatkowski, A.; Gałazka- ˛ Świderek, N.; Cichoń, K.; Bragoszewski, ˛ J.; Paśnik, K. Long-Term Outcomes of Laparoscopic Sleeve Gastrectomy—A Single-Center, Retrospective Study. Obes. Surg. 2017, 28, 130–134. [CrossRef] 7. Roth, A.E.; Thornley, C.J.; Blackstone, R.P. Outcomes in Bariatric and Metabolic Surgery: An Updated 5-Year Review. Curr. Obes. Rep. 2020, 9, 380–389. [CrossRef] 8. Karlsson, J.; Taft, C.; Rydén, A.; Sjöström, L.; Sullivan, M. Ten-year trends in health-related quality of life after surgical and conventional treatment for severe obesity: The SOS intervention study. Int. J. Obes. 2007, 31, 1248–1261. [CrossRef] 9. Kubik, J.F.; Gill, R.S.; Laffin, M.; Karmali, S. The Impact of Bariatric Surgery on Psychological Health. J. Obes. 2013, 2013, 1–5. [CrossRef] 10. Still, C.D.; Wood, G.C.; Chu, X.; Erdman, R.; Manney, C.H.; Benotti, P.N.; Petrick, A.T.; Strodel, W.E.; Mirshahi, U.L.; Mirshahi, T.; et al. High Allelic Burden of Four Obesity SNPs Is Associated With Poorer Weight Loss Outcomes Following Gastric Bypass Surgery. Obesity 2011, 19, 1676–1683. [CrossRef] 11. Livhits, M.; Mercado, C.; Yermilov, I.; Parikh, J.A.; Dutson, E.; Mehran, A.; Ko, C.Y.; Gibbons, M.M. Preoperative Predictors of Weight Loss Following Bariatric Surgery: Systematic Review. Obes. Surg. 2011, 22, 70–89. [CrossRef] 12. Aliakbarian, H.; Bhutta, H.Y.; Heshmati, K.; Kunju, S.U.; Sheu, E.G.; Tavakkoli, A. Pre-operative Predictors of Weight Loss and Weight Regain Following Roux-en-Y Gastric Bypass Surgery: A Prospective Human Study. Obes. Surg. 2020, 30, 4852–4859. [CrossRef] 13. Kitamura, R.; Chen, R.; Trickey, A.; Eisenberg, D. Positive and Negative Independent Predictive Factors of Weight Loss After Bariatric Surgery in a Veteran Population. Obes. Surg. 2020, 30, 2124–2130. [CrossRef] 14. Moustafa, J.S.E.-S.; Froguel, P. From obesity genetics to the future of personalized obesity therapy. Nat. Rev. Endocrinol. 2013, 9, 402–413. [CrossRef] 15. Pigeyre, M.; Yazdi, F.T.; Kaur, Y.; Meyre, D. Recent progress in genetics, epigenetics and metagenomics unveils the pathophysiology of human obesity. Clin. Sci. 2016, 130, 943–986. [CrossRef] 16. Bray, M.S.; Loos, R.; McCaffery, J.; Ling, C.; Franks, P.; Weinstock, G.; Snyder, M.P.; Vassy, J.; Agurs-Collins, T.; The Conference Working Group. NIH working group report—Using genomic information to guide weight management: From universal to precision treatment. Obesity 2015, 24, 14–22. [CrossRef] [PubMed] 17. Ciudin, A.; Fidilio, E.; Ortiz, A.; Pich, S.; Salas, E.; Mesa, J.; Hernández, C.; Simó-Servat, O.; Lecube, A.; Simó, R. Genetic Testing to Predict Weight Loss and Diabetes Remission and Long-Term Sustainability after Bariatric Surgery: A Pilot Study. J. Clin. Med. 2019, 8, 964. [CrossRef] [PubMed] 18. Listed, N. 1983 metropolitan height and weight tables. Stat. Bull. Metrop. Life Found. 1983, 64, 3–9. 19. Grover, B.T.; Morell, M.C.; Kothari, S.N.; Borgert, A.J.; Kallies, K.J.; Baker, M.T. Defining Weight Loss After Bariatric Surgery: A Call for Standardization. Obes. Surg. 2019, 29, 3493–3499. [CrossRef] 20. Mirshahi, U.L.; Still, C.D.; Masker, K.K.; Gerhard, G.S.; Carey, D.J.; Mirshahi, T. TheMC4R(I251L) Allele Is Associated with Better Metabolic Status and More Weight Loss after Gastric Bypass Surgery. J. Clin. Endocrinol. Metab. 2011, 96, E2088–E2096. [CrossRef] 21. Moleres, A.; Rendo-Urteaga, T.; Zulet, M.A.; Marcos, A.; Campoy, C.; Garagorri, J.M.; Martínez, J.A.; Azcona-Sanjulián, M.C.; Marti, A. Obesity Susceptibility Loci on Body Mass Index and Weight Loss in Spanish Adolescents after a Lifestyle Intervention. J. Pediatr. 2012, 161, 466–470.e2. [CrossRef] 22. Blakemore, A.I.F.; Froguel, P. Investigation of Mendelian forms of obesity holds out the prospect of personalized medicine. Ann. N. Y. Acad. Sci. 2010, 1214, 180–189. [CrossRef] 23. Welter, D.; MacArthur, J.; Morales, J.; Burdett, A.; Hall, P.; Junkins, H.; Klemm, A.; Flicek, P.; Manolio, T.; Hindorff, L.; et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2013, 42, D1001–D1006. [CrossRef] 24. Parri, A.; Benaiges, D.; Schröder, H.; Izquierdo-Pulido, M.; Ramón, J.; Villatoro, M.; Roux, J.F.-L.; Goday, A. Preoperative Predictors of Weight Loss at 4 Years Following Bariatric Surgery. Nutr. Clin. Pr. 2015, 30, 420–424. [CrossRef] 25. Gerken, A.L.H.; Rohr-Kräutle, K.-K.; Weiss, C.; Seyfried, S.; Reissfelder, C.; Vassilev, G.; Otto, M. Handgrip Strength and Phase Angle Predict Outcome After Bariatric Surgery. Obes. Surg. 2020, 31, 200–206. [CrossRef] [PubMed] 26. Nielsen, M.S.; Christensen, B.J.; Schmidt, J.B.; Tækker, L.; Holm, L.; Lunn, S.; Ritz, C.; Albrechtsen, N.J.W.; Holst, J.J.; Schnurr, T.M.; et al. Predictors of weight loss after bariatric surgery—A cross-disciplinary approach combining physiological, social, and psychological measures. Int. J. Obes. 2020, 44, 2291–2302. [CrossRef] [PubMed] J. Pers. Med. 2021, 11, 1040 11 of 11 27. Cottam, S.; Cottam, D.; Cottam, A. Sleeve Gastrectomy Weight Loss and the Preoperative and Postoperative Predictors: A Systematic Review. Obes. Surg. 2019, 29, 1388–1396. [CrossRef] 28. McCaffery, J.M. Precision behavioral medicine: Implications of genetic and genomic discoveries for behavioral weight loss treatment. Am. Psychol. 2018, 73, 1045–1055. [CrossRef] 29. Sarzynski, M.A.; Jacobson, P.; Rankinen, T.; Carlsson, B.; Sjöström, L.; Bouchard, C.; Carlsson, L.M.S. Associations of markers in 11 obesity candidate genes with maximal weight loss and weight regain in the SOS bariatric surgery cases. Int. J. Obes. 2010, 35, 676–683. [CrossRef] 30. Velázquez-Fernández, D.; Mercado-Celis, G.; Flores-Morales, J.; Clavellina-Gaytán, D.; Vidrio, R.; Vidrio, E.; Mosti, M.; Sánchez- Aguilar, H.; Rodriguez, D.; León, P.; et al. Analysis of Gene Candidate SNP and Ancestral Origin Associated to Obesity and Postoperative Weight Loss in a Cohort of Obese Patients Undergoing RYGB. Obes. Surg. 2016, 27, 1481–1492. [CrossRef] [PubMed] 31. Rodrigues, G.K.; Resende, C.M.; Durso, D.F.; Rodrigues, L.A.; Silva, J.L.P.; Reis, R.C.; Pereira, S.S.; Ferreira, D.C.; Franco, G.R.; Alvarez-Leite, J. A single FTO gene variant rs9939609 is associated with body weight evolution in a multiethnic extremely obese population that underwent bariatric surgery. Nutrition 2015, 31, 1344–1350. [CrossRef] 32. Balasar, Ö.; Çakır, T.; Erkal, Ö.; Aslaner, A.; Cekic, B.; Uyar, M.; Bülbüller, N.; Oruç, M.T. The effect of rs9939609 FTO gene polymorphism on weight loss after laparoscopic sleeve gastrectomy. Surg. Endosc. 2015, 30, 121–125. [CrossRef] 33. Kops, N.L.; Vivan, M.A.; Horvath, J.D.C.; de Castro, M.L.D.; Friedman, R. FABP2, LEPR223, LEP656, and FTO Polymorphisms: Effect on Weight Loss 2 Years After Bariatric Surgery. Obes. Surg. 2018, 28, 2705–2711. [CrossRef] 34. Bandstein, M.; Voisin, S.; Nilsson, E.K.; Schultes, B.; Ernst, B.; Thurnheer, M.; Benedict, C.; Mwinyi, J.; Schiöth, H.B. A Genetic Risk Score is Associated with Weight Loss Following Roux-en Y Gastric Bypass Surgery. Obes. Surg. 2016, 26, 2183–2189. [CrossRef] 35. Aasbrenn, M.; Schnurr, T.; Have, C.T.; Svendstrup, M.; Hansen, D.L.; Worm, D.; Balslev-Harder, M.; Hollensted, M.; Grarup, N.; Burgdorf, K.S.; et al. Genetic Determinants of Weight Loss after Bariatric Surgery. Obes. Surg. 2019, 29, 2554–2561. [CrossRef] 36. Katsareli, E.A.; Amerikanou, C.; Rouskas, K.; Dimopoulos, A.; Diamantis, T.; Alexandrou, A.; Griniatsos, J.; Bourgeois, S.; Dermitzakis, E.; Ragoussis, J.; et al. A Genetic Risk Score for the Estimation of Weight Loss After Bariatric Surgery. Obes. Surg. 2020, 30, 1482–1490. [CrossRef] 37. Peters, K.; Beilby, J.; Cadby, G.; Warrington, N.M.; Bruce, D.G.; Davis, W.A.; Davis, T.M.; Wiltshire, S.; Knuiman, M.; McQuillan, B.M.; et al. A comprehensive investigation of variants in genes encoding adiponectin (ADIPOQ) and its receptors (ADIPOR1/R2), and their association with serum adiponectin, type 2 diabetes, insulin resistance and the metabolic syndrome. BMC Med. Genet. 2013, 14, 15. [CrossRef] [PubMed] 38. Kyriakou, T.; Collins, L.J.; Spencer-Jones, N.J.; Malcolm, C.; Wang, X.; Snieder, H.; Swaminathan, R.; Burling, K.A.; Hart, D.J.; Spector, T.D.; et al. Adiponectin gene ADIPOQ SNP associations with serum adiponectin in two female populations and effects of SNPs on promoter activity. J. Hum. Genet. 2008, 53, 718–727. [CrossRef] [PubMed] 39. Gholami, M.; Sharifi, F.; Shahriari, S.; Khoshnevisan, K.; Larijani, B.; Amoli, M.M. Association of interleukin-6 polymorphisms with obesity: A systematic review and meta-analysis. Cytokine 2019, 123, 154769. [CrossRef] 40. Razquin, C.; Martinez, J.A.; Martinez-Gonzalez, M.A.; Fernández-Crehuet, J.; Santos, J.M.; Marti, A. A Mediterranean diet rich in virgin olive oil may reverse the effects of the −174G/C IL6 gene variant on 3-year body weight change. Mol. Nutr. Food Res. 2010, 54, S75–S82. [CrossRef] [PubMed] 41. Delahanty, L.M.; Pan, Q.; Jablonski, K.A.; Watson, K.E.; McCaffery, J.; Shuldiner, A.; Kahn, S.E.; Knowler, W.C.; Florez, J.C.; Franks, P.W.; et al. Genetic Predictors of Weight Loss and Weight Regain After Intensive Lifestyle Modification, Metformin Treatment, or Standard Care in the Diabetes Prevention Program. Diabetes Care 2011, 35, 363–366. [CrossRef] [PubMed] 42. Soenen, S.; Mariman, E.C.; Vogels, N.; Bouwman, F.G.; Hoed, M.D.; Brown, L.; Westerterp-Plantenga, M.S. Relationship between perilipin gene polymorphisms and body weight and body composition during weight loss and weight maintenance. Physiol. Behav. 2009, 96, 723–728. [CrossRef] 43. Garaulet, M.; Vera, B.; Bonnet-Rubio, G.; Gómez-Abellán, P.; Lee, Y.-C.; Ordovás, J.M. Lunch eating predicts weight-loss effectiveness in carriers of the common allele at PERILIPIN1: The ONTIME (Obesity, Nutrigenetics, Timing, Mediterranean) study. Am. J. Clin. Nutr. 2016, 104, 1160–1166. [CrossRef] 44. Tan, C.Y.; Virtue, S.; Bidault, G.; Dale, M.; Hagen, R.; Griffin, J.L.; Vidal-Puig, A. Brown Adipose Tissue Thermogenic Capacity Is Regulated by Elovl6. Cell Rep. 2015, 13, 2039–2047. [CrossRef] 45. Akbarian, S.-A.; Salehi-Abargouei, A.; Pourmasoumi, M.; Kelishadi, R.; Nikpour, P.; Heidari-Beni, M. Association of Brain-derived neurotrophic factor gene polymorphisms with body mass index: A systematic review and meta-analysis. Adv. Med. Sci. 2018, 63, 43–56. [CrossRef] 46. Fox, C.S.; Heard-Costa, N.; Cupples, L.A.; Dupuis, J.; Vasan, R.S.; Atwood, L.D. Genome-wide association to body mass index and waist circumference: The Framingham Heart Study 100K project. BMC Med. Genet. 2007, 8, S18. [CrossRef] [PubMed] 47. Benzinou, M.; Chèvre, J.-C.; Ward, K.J.; Lecoeur, C.; Dina, C.; Lobbens, S.; Durand, E.; Delplanque, J.; Horber, F.F.; Heude, B.; et al. Endocannabinoid receptor 1 gene variations increase risk for obesity and modulate body mass index in European populations. Hum. Mol. Genet. 2008, 17, 1916–1921. [CrossRef] 48. Yu, K.; Li, L.; Zhang, L.; Guo, L.; Wang, C. Association between MC4R rs17782313 genotype and obesity: A meta-analysis. Gene 2020, 733, 144372. [CrossRef] [PubMed]