Skip to main content

Advertisement

SpringerLink
Log in

The Influence of Single Nucleotide Polymorphisms On Body Weight Trajectory After Bariatric Surgery: A Systematic Review

  • REVIEW
  • Published:
Current Obesity Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

To conduct a systematic review to summarize the results of studies on this subject and to identify whether single nucleotide polymorphisms (SNPs) are good prognostic markers for body weight trajectory after bariatric surgery.

Recent Findings

A considerable number of events can influence the body weight trajectory after bariatric surgery, and in the post-genomic era, genetic factors have been explored.

Summary

This study is registered with PROSPERO (CRD42021240903). SNPs positively associated with poor weight loss after bariatric surgery were rs17702901, rs9939609, rs1360780, rs1126535, rs1137101, rs17782313, rs490683, and rs659366. Alternatively, SNPs rs2229616, rs5282087, rs490683, rs9819506, rs4771122, rs9939609, rs4846567, rs9930506, rs3813929, rs738409, rs696217, rs660339, rs659366, rs6265, rs1801260, and rs2419621 predicted a higher weight loss after bariatric surgery. Six studies performed with a genetic risk score (GRS) model presented significant associations between GRS and outcomes following bariatric surgery. This systematic review shows that, different SNPs and genetic models could be good predictors for body weight trajectory after bariatric surgery. Based on the results of the selected studies for this Systematic Review is possible to select SNPs and metabolic pathways of interest for the GRS construction to predict the outcome of bariatric surgery to be applied in future studies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Data Availability

The data supporting our findings is available in the supplementary materials.

Abbreviations

BMI:

Body mass index

BMIL:

Body mass index lost

EBMI:

Excess of body mass index

EBMIL:

Excess of body mass index lost

EWL:

Excess weight loss

GRS:

Genetic risk score

MAF:

Minor allele frequency

PRS:

Polygenic risk score

RYGB:

Roux en Y gastric bypass

SG:

Sleeve gastrectomy

SNP:

Single nucleotide polymorphism

TWL:

Total weight lost

WL:

Weight lost

5HTR2C :

5-Hydroxytryptamine receptor 2C

ABP1 :

Endoplasmic reticulum auxin binding protein 1

ACHE :

Acetylcholinesterase

ACSL5 :

Acyl-CoA synthetase long chain family member 5

ADIPOQ :

Adiponectin, C1Q and collagen domain containing

ADRB2 :

Adrenoceptor beta 2

AGBL4 :

AGBL carboxypeptidase 4

AGRP :

Agouti related neuropeptide

AGT :

Angiotensinogen

APOB :

Apolipoprotein B

BCDIN3D :

BCDIN3 domain containing RNA methyltransferase

BDNF :

Brain derived neurotrophic factor

C6ORF106 :

C6orf106 homolog

CD40L :

CD40 ligand

CENPF :

Centromere protein F

CETP :

Cholesteryl ester transfer protein

CHAT :

Choline acetyltransferase

CITED2 :

Cbp/p300 interacting transactivator with Glu/Asp rich carboxy-terminal domain 2

CLOCK :

Circadian locomotor output cycles kaput

CNR1 :

Cannabinoid receptor 1

CYP1A2 :

Cytochrome P450 family 1 subfamily A member 2

CYP3A4 :

Cytochrome P450 family 3 subfamily A member 4

CYP3A5 :

Cytochrome P450 family 3 subfamily A member 5

DIO2 :

Deiodinase, iodothyronine, type II

DNM3 :

Dynamin 3

DRD1IP (CALY) :

Calcyon neuron specific vesicular protein

ELOVL6 :

ELOVL fatty acid elongase 6

ENTPD6 :

Ectonucleoside triphosphate diphosphohydrolase 6

ESR1 :

Estrogen receptor 1

FABP2 :

Fatty acid binding protein 2

FKBP5 :

FKBP prolyl isomerase 5

FTO :

Alpha-ketoglutarate dependent dioxygenase

FUT2 :

Fucosyltransferase 2

GBE1 :

1,4-Alpha-glucan branching enzyme 1

GHRL :

Ghrelin and obestatin prepropeptide

GHSR :

Growth hormone secretagogue receptor

GNB :

Gastrointestinal nematode burden

GNB3 :

G protein subunit beta 3

GUCY1A2 :

Guanylate cyclase 1 soluble subunit alpha 2

HIF1A :

Hypoxia inducible factor 1 subunit alpha

HIP1R :

Huntingtin-interacting protein 1-related protein

HOXC13 :

Homeobox C13

HTR1A :

5-Hydroxytryptamine receptor 1A

IFI30 :

IFI30 lysosomal thiol reductase

IGF1R :

Insulin like growth factor 1 receptor

IL-6 :

Interleukin 6

INSIG2 :

Insulin induced gene 2

IPO11 :

Importin 11

IRS1 :

Insulin receptor substrate 1

KCNK2 :

Potassium two pore domain channel subfamily K member 2

KCNK3 :

Potassium two pore domain channel subfamily K member 3

KSR2 :

Kinase suppressor of RAS 2

LEP :

Leptin

LEPR :

Leptin receptor

LIPC :

Lipase C, hepatic type

LYPLAL1 :

Lysophospholipase like 1

MAP2K5:

Mitogen-activated protein kinase kinase 5

MBOAT7 :

Membrane bound O-acyltransferase domain containing 7

MC4R :

Melanocortin 4 receptor

MTCH2 :

Mitochondrial carrier 2

MTHFR :

Methylenetetrahydrofolate reductase

MTIF3 :

Mitochondrial translational initiation factor 3

NLRC3 :

NLR family CARD domain containing 3

NMBR :

Neuromedin B receptor

NPC1 :

NPC intracellular cholesterol transporter 1

NR3C1 :

Nuclear receptor subfamily 3 group C member 1

NUDT3 :

Nudix hydrolase 3

NUP54 :

Nucleoporin 54

PCSK1 :

Proprotein convertase subtilisin/kexin type 1PGC1α

PGC1α (PPARGC1A) :

PPARG coactivator 1 alpha

PIGC :

Phosphatidylinositol glycan anchor biosynthesis class C

PIK3C2 :

Phosphatidylinositol 3-kinase Pik3

PIK3R1 :

Phosphoinositide-3-kinase regulatory subunit 1

PKHD1 :

PKHD1 ciliary IPT domain containing fibrocystin/polyductin

PCSK1 :

Proprotein convertase, subtilisin/kexin-type, 1

PNPLA :

PNPLA domain-containing protein

POMC :

Proopiomelanocortin

PPARG :

Peroxisome proliferator activated receptor gamma

PRKAG1 :

Protein kinase, amp-activated, noncatalytic, gamma-1

PRKD1 :

Protein kinase D1

PTBP2 :

Polypyrimidine tract binding protein 2

RAB21 :

RAB-associated protein RAB21

RAPGEF3 :

RAP guanine nucleotide exchange factor 3

SCARB1 :

Scavenger receptor class B member 1

SCARB2 :

Scavenger receptor class B member 2

SH2B1 :

SH2B adaptor protein 1

SIM1 :

SIM bHLH transcription factor 1

SLC39A8 :

Solute carrier family 39 member 8

SRC1 :

Steroid receptor coactivator 1

ST8SIA2 :

ST8 alpha-N-acetyl-neuraminide alpha-2,8-sialyltransferase 2

TAS1R2 :

Taste 1 receptor member 2

TCF7L2 :

Transcription factor 7 like 2

TFAP2B :

Transcription factor AP-2 beta

TM6SF2 :

Transmembrane 6 superfamily member 2

TMEM160 :

Transmembrane protein 160

TNF :

Tumor necrosis factor

UCP2 :

Uncoupling protein 2

UCP3 :

Uncoupling protein 3

USP37 :

Ubiquitin specific peptidase 37

VEGFA :

Vascular endothelial growth factor A

VKORC1 :

Vitamin K epoxide reductase complex subunit 1

ZBTB7B :

Zinc finger- and btb domain-containing protein 7B

ZFHX3 :

Zinc finger homeobox 3

ZFR2 :

Zinc finger RNA-binding protein 2

ZNF169 :

Zinc finger protein 169

ZNF608 :

Zinc finger protein 608

ZNRF3 :

Zinc and ring finger 3

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. WHO Consultation on Obesity (1999: Geneva, Switzerland) & World Health Organization. Obesity: Preventing and managing the global epidemic. 2000. https://apps.who.int/iris/handle/10665/42330.

  2. World Health Organization WHO. Prevalence of overweight among adults, BMI ≥ 25, crude Estimates by country. 2017. https://apps.who.int/gho/data/node.main.BMI25C?lang=en.

  3. Finucane MM, Stevens GA, Cowan M, et al. National, regional, and global trends in body mass index since 1980: Systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants. Lancet. 2011;377:557–67. https://doi.org/10.1016/S0140-6736(10)620.

  4. Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metabolism. 2019;92:6–10. https://doi.org/10.1016/j.metabol.2018.09.00.

    Article  CAS  PubMed  Google Scholar 

  5. World Health Organization WHO. Prevalence of overweight among adults, BMI ≥ 25, crude Estimates by country. https://apps.who.int/gho/data/node.main.BMI25C?lang=en.

  6. Ministério da Saúde. Vigitel Brasil 2020: Vigilância de fatores de risco e proteção para doenças crônicas por inquerito telefônico. Brasília: Editora MS, 2021.

  7. Khan SS, Ning H, Wilkins JT, et al. Association of body mass index with lifetime risk of cardiovascular disease and compression of morbidity. JAMA Cardiol. 2018;3:280–7. https://doi.org/10.1001/jamacardio.2018.0.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Blüher M. Obesity: global epidemiology and pathogenesis. Nat Rev Endocrinol. 2019;15:288–98. https://doi.org/10.1038/s41574-019-0176-8.

    Article  PubMed  Google Scholar 

  9. Berthoud HR, Klein S. Advances in Obesity: Causes, Consequences, and Therapy. Gastroenterology. 2017;152:1635–7. https://doi.org/10.1053/j.gastro.2017.0.

    Article  PubMed  Google Scholar 

  10. Nicoletti CF, Cortes-Oliveira C, Pinhel MAS, et al. Bariatric surgery and precision nutrition. Nutrients. 2017;9:974. https://doi.org/10.3390/nu9090974.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Locke AE, Kahali B, Berndt SI, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518:197–206. https://doi.org/10.1038/nature14177.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Page MJ, Moher D, Bossuyt PM, et al. Research Methods and Reporting Prisma 2020 explanation and elaboration : updated guidance and exemplars for reporting systematic reviews review findings . The Preferred. BMJ (Clinical Res. 2021;372:n160. https://doi.org/10.1136/bmj.n160.

  13. Mcgowan J, Sampson M, Salzwedel DM, et al. PRESS Peer Review of Electronic Search Strategies : 2015 Guideline Statement. J Clin Epidemiol. 2016;75:40–6. https://doi.org/10.1016/j.jclinepi.2016.0.

    Article  PubMed  Google Scholar 

  14. Ouzzani M, Hammady H, Fedorowicz Z, et al. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016;5:1–10. https://doi.org/10.1186/s13643-016-0384-4.

    Article  Google Scholar 

  15. The Joanna Briggs Institute. Checklist for Cohort Studies: Critical Appraisal tools for use in JBI Systematic Reviews. North Adelaide, Australia, 2020.

  16. The Joanna Briggs Institute. Checklist for Case Control Studies: Critical Appraisal tools for use in JBI Systematic Reviews. North Adelaide, Australia, 2017.

  17. Velázquez-fernández D, Mercado-celis G, Flores-morales J, 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;27:1481–92. https://doi.org/10.1007/s11695-016-2501.

    Article  Google Scholar 

  18. Novais PFS, Weber TK, Lemke N, et al. Gene polymorphisms as a predictor of body weight loss after Roux-en-Y gastric bypass surgery among obese women. Obes Res Clin Pract. 2016;10:724–7. https://doi.org/10.1016/j.orcp.2016.07.00.

    Article  PubMed  Google Scholar 

  19. Sarzynski MA, Jacobson P, Rankinen T, et al. Associations of markers in 11 obesity candidate genes with maximal weight loss and weight regain in the SOS bariatric surgery cases. Int J Obes. 2011;35:676–83. https://doi.org/10.1038/ijo.2010.166.

    Article  CAS  Google Scholar 

  20. Benenati N, Bufano A, Cantara S, et al. Type 2 deiodinase p.Thr92Ala polymorphism does not affect the severity of obesity and weight loss after bariatric surgery. Sci Rep. 2022;12:10643. https://doi.org/10.1038/s41598-022-14863-x.

  21. Figueroa-Vega N, Jordán B, Pérez-Luque EL, et al. Effects of sleeve gastrectomy and rs9930506 FTO variants on angiopoietin / Tie-2 system in fat expansion and M1 macrophages recruitment in morbidly obese subjects. Endocrine. 2016;54:700–13. https://doi.org/10.1007/s12020-016-1070-y.

    Article  CAS  PubMed  Google Scholar 

  22. •• Campos A, Cifuentes L, Hashem A, et al. Effects of Heterozygous Variants in the Leptin-Melanocortin Pathway on Roux-en-Y Gastric Bypass Outcomes: a 15-Year Case-Control Study. Obes Surg. 2022;32:2632–40. https://doi.org/10.1007/s11695-022-0612. Weight regain was positively associated with the presence of a heterozygous variant in the leptin-melanocortin pathway in the mid- and long-term after Roux-en-Y Gastric Bypass.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Valette M, Poitou C, Beyec J Le, et al. Melanocortin-4 Receptor Mutations and Polymorphisms Do Not Affect Weight Loss after Bariatric Surgery. PLoS One. 2012;7:e48221. https://doi.org/10.1371/journal.pone.00482.

  24. Bandstein M, Schultes B, Ernst B, et al. The Role of FTO and Vitamin D for the Weight Loss Effect of Roux-en-Y Gastric Bypass Surgery in Obese Patients. Obes Surg. 2015;25:2071–7. https://doi.org/10.1007/s11695-015-1644.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Bandstein M, Voisin S, Nilsson EK, et al. A Genetic Risk Score Is Associated with Weight Loss Following Roux-en Y Gastric Bypass Surgery. Obes Surg. 2016;26:2183–9. https://doi.org/10.1007/s11695-016-2072.

    Article  PubMed  PubMed Central  Google Scholar 

  26. •• de Luis D, Izaola O, Primo D, et al. The gene variant rs2419621 of ACYL-CoA synthetase long-chain 5 gene is associated with weight loss and metabolic changes in response to a robotic sleeve gastrectomy in morbid obese subjects. Eur Rev Med Pharmacol Sci. 2021;25:7037–43. https://doi.org/10.26355/eurrev_202111_. After 12 months of bariatric surgery T alleles carriers for the ACSL5-SNP (rs2419621) lost more the excess of wheight than CC genotype carriers.

  27. Geloneze SR, Geloneze B, Morari J, et al. PGC1 a gene Gly482Ser polymorphism predicts improved metabolic, inflammatory and vascular outcomes following bariatric surgery. Int J Obes. 2012;36:363–8. https://doi.org/10.1038/ijo.2011.176.

    Article  CAS  Google Scholar 

  28. Hartmann IB, Fries GR, Bücker J, et al. The FKBP5 polymorphism rs1360780 is a ssociated with lower weight loss after bariatric surgery : 26 months of follow-up. Surg Obes Relat Dis. 2016;12:1554–60. https://doi.org/10.1016/j.soard.2016.04.

    Article  PubMed  Google Scholar 

  29. Hatoum IJ, Greenawalt DM, Cotsapas C, et al. Weight Loss after Gastric Bypass Is Associated with a Variant at 15q26 . 1. Am J Hum Genet. 2013;92:827–34. https://doi.org/10.1016/j.ajhg.2013.04.00.

  30. •• Katsareli EA, Amerikanou C, Rouskas K, et al. A Genetic Risk Score for the Estimation of Weight Loss After Bariatric Surgery. Obes Surg. 2020;30:1482–90. https://doi.org/10.1007/s11695-019-0432. GRS was significantly associated with a higher % of excess weight loss at 12 and 24 months after bariatric surgery, indicating that it can be a good outcome predictor

    Article  CAS  PubMed  Google Scholar 

  31. Kops NL, Vivan MA, Horvath JDC, et al. FABP2, LEPR223, LEP656, and FTO Polymorphisms: Effect on Weight Loss 2 Years After Bariatric Surgery. Obes Surg. 2018;28:2705–11. https://doi.org/10.1007/s11695-018-3213.

    Article  PubMed  Google Scholar 

  32. Krawczyk M, Jiménez-agüero R, Alustiza JM, et al. PNPLA3 p . I148M variant is associated with greater reduction of liver fat content after bariatric surgery. Surg Obes Relat Dis. 2016;12:1838–46. https://doi.org/10.1016/j.soard.2016.06.

  33. Komorniak N, Kley AM-V, Nalian A, et al. Can the fut 2 gene variant have an effect on the body weight of patients undergoing bariatric surgery?—preliminary, exploratory study. Nutrients. 2020;12:2621. https://doi.org/10.3390/nu12092621.

  34. Matzko ME, Argyropoulos G, Wood GC, et al. Association of Ghrelin Receptor Promoter Polymorphisms with Weight Loss Following Roux-en-Y Gastric Bypass Surgery. Obes Surg. 2012;22:783–90. https://doi.org/10.1007/s11695-012-0631-2.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Mirshahi UL, Still CD, Masker KK, et al. The MC4R(I251L) Allele Is Associated with Better Metabolic Status and More Weight Loss after Gastric Bypass Surgery. J Clin Endocrinol Metab. 2011;96:E2088–96. https://doi.org/10.1210/jc.2011-1549.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Nicoletti CF, de Oliveira APRP, Brochado MJF, et al. The Ala55Val and -866G > A polymorphisms of the UCP2 gene could be biomarkers for weight loss in patients who had Roux-en-Y gastric bypass. Nutrition. 2017;33:326–30. https://doi.org/10.1016/j.nut.2016.07.020.

    Article  CAS  PubMed  Google Scholar 

  37. Novais PFS, Crisp AH, Leandro-Merhi VA, et al. Lack of association between 11 gene polymorphisms on weight loss 1 year after Roux-en-y gastric bypass surgery in woman. J Hum Nutr Diet. 2022;35:731–8. https://doi.org/10.1111/jhn.13000.

    Article  PubMed  Google Scholar 

  38. •• Peña E, Caixás A, Arenas C, et al. Influence of the BDNF Val66Met polymorphism on weight loss after bariatric surgery : a 24-month follow-up. Surg Obes Relat Dis. 2021;17:185–92. https://doi.org/10.1016/j.soard.2020.08.0. The Met variant (T allele) for the BDNF-SNP rs6265 was associated with greater weight loss 24 months after bariatric surgery in patients without type 2 diabetes.

    Article  PubMed  Google Scholar 

  39. Peña E, Caixàs A, Arenas C, et al. Role of the FKBP5 polymorphism rs1360780, age, sex, and type of surgery in weight loss after bariatric surgery : a follow-up study. Surg Obes Relat Dis. 2020;16:581–9. https://doi.org/10.1016/j.soard.2019.12.0.

    Article  PubMed  Google Scholar 

  40. Rasmussen-torvik L, Baldridge AS, Pacheco JA, et al. rs4771122 predicts multiple measures of long-term weight loss after bariatric surgery. Obes Surg. 2016;25:2225–9. https://doi.org/10.1007/s11695-015-1872.

    Article  Google Scholar 

  41. Resende CMM, Durso DF, Borges KBG, et al. The polymorphism rs17782313 near MC4R gene is related with anthropometric changes of bariatric surgery over 60 months of follow up in women. Clin Nutr. 2018;37:1286–92. https://doi.org/10.1016/j.clnu.2017.05.

    Article  CAS  PubMed  Google Scholar 

  42. Rinella ES, Still C, Shao Y, et al. Genome-wide association of single-nucleotide polymorphisms with weight loss outcomes after Roux-en-Y gastric bypass surgery. J Clin Endocrinol Metab. 2013;98:E1131–6. https://doi.org/10.1210/jc.2012-3421.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Rodrigues GKD, Durso DF, Resende CMM, et al. A single FTO gene variant rs9939609 is associated with body weight evolution in a multiethnic extremely obese population who underwent bariatric surgery. Nutrition. 2015;31:1344–50. https://doi.org/10.1016/j.nut.2015.05.0.

    Article  CAS  PubMed  Google Scholar 

  44. Rouskas K, Cauchi S, Raverdy V, et al. Weight loss independent association of TCF7 L2 gene polymorphism with fasting blood glucose after Roux-en-Y gastric bypass in type 2 diabetic patients. Surg Obes Relat Dis. 2014;10:679–83. https://doi.org/10.1016/j.soard.2013.12.016.

    Article  PubMed  Google Scholar 

  45. Seip RL, Papasavas P, Stone A, et al. Comparative physiogenomic analyses of weight loss in response to 2 modes of bariatric surgery : demonstration with candidate neuropsychiatric and cardiometabolic genes. Surg Obes Relat Dis. 2016;12:369–77. https://doi.org/10.1016/j.soard.2015.09.0.

    Article  PubMed  Google Scholar 

  46. Still CD, Wood GC, Chu X, et al. High Allelic Burden of Four Obesity SNPs Is Associated With Poorer Weight Loss Outcomes Following Gastric Bypass Surgery. Obesity. 2011;19:1676–83. https://doi.org/10.1038/oby.2011.3.

    Article  CAS  PubMed  Google Scholar 

  47. • Torrego-Ellacuría M, Barabash A, Matía-Martín P, et al. Influence of CLOCK Gene Variants on Weight Response after Bariatric Surgery. Nutrients. 2022;14:3472. https://doi.org/10.3390/nu14173472. The A allele carriers (AA+AG) for CLOCK-SNP rs1801260 presented a minor percentage of total weight loss after 6 years of bariatric surgery when compared with GG carriers (minor allele). The weight regain was higher in the AA+AG group after the same period than that in GG carriers.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Vitolo E, Santini E, Seghieri M, et al. Heterozygosity for the rs696217 SNP in the Preproghrelin Gene Predicts Weight Loss After Bariatric Surgery in Severely Obese Individuals. Obes Surg. 2017;27:961–7. https://doi.org/10.1007/s11695-016-2387-6.

    Article  PubMed  Google Scholar 

  49. Aasbrenn M, Schnurr TM, Have CT, et al. Genetic Determinants of Weight Loss After Bariatric Surgery. Obes Surg. 2019;29:2554–61. https://doi.org/10.1007/s11695-019-0387.

    Article  PubMed  Google Scholar 

  50. •• Aasbrenn M, Svendstrup M, Schnurr TM, et al. PLOS ONE Genetic markers of abdominal obesity and weight loss after gastric bypass surgery. PLoS One. 2021;16:e0252525. https://doi.org/10.1371/journal.pone.025. Patients with values of GRS to 77 BMI-related genes (GRS-BMI77) over 82.1 had an EBMIL of 77%, whereas individuals with scores of GRS-BMI77 below 64.4 lost 68.8% of EBMI.

  51. •• Antoine D, Guéant-Rodriguez RM, Chèvre JC, et al. Low-frequency Coding Variants Associated with Body Mass Index Affect the Success of Bariatric Surgery. J Clin Endocrinol Metab. 2022;107:E1074–84. https://doi.org/10.1210/clinem/dgab77. The protective genetic score (based on 5 BMI-decreasing low-frequency coding variants) was associated with a greater BMI decrease, and lower risk of BMI regain after bariatric surgery.

    Article  PubMed  Google Scholar 

  52. Balasar Ö, Çakır T, Erkal Ö, et al. The effect of rs9939609 FTO gene polymorphism on weight loss after laparoscopic sleeve gastrectomy. Surg Endosc. 2016;30:121–5. https://doi.org/10.1007/s00464-015-4169-y.

    Article  PubMed  Google Scholar 

  53. Bandstein M, Mwinyi J, Ernst B, et al. A genetic variant in proximity to the gene LYPLAL1 is associated with lower hunger feelings and increased weight loss following Roux-en-Y gastric bypass surgery. Scand J Gastroenterol. 2016;51:1050–5. https://doi.org/10.3109/00365521.2016.1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Ciudin A, Fidilio E, Gutiérrez-Carrasquilla L, et al. A Clinical-Genetic Score for Predicting Weight Loss after Bariatric Surgery : The OBEGEN Study. J Pers Med. 2021;11:1040. https://doi.org/10.3390/jpm11101040.

    Article  PubMed  PubMed Central  Google Scholar 

  55. •• De OMS, Rodrigues M, Rossoni EA, et al. 2 866G / A and Ins / Del polymorphisms in UCP2 gene are associated with reduced short-term weight loss in patients who underwent Roux-en-Y gastric bypass. Surg Obes Relat Dis. 2021;17:1263–70. https://doi.org/10.1016/j.soard.2021.03. Excess weight loss was higher in GG carriers for UCP2-SNP (rs659366) at 6, 12 and 18 months after bariatric surgery, when compared with AA carriers.

    Article  Google Scholar 

  56. Ciudin A, Fidilio E, Ortiz A, et al. 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. https://doi.org/10.3390/jcm8070964.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Gupta SR, Zhou Y, Wadden TA, et al. A Systematic Review of Genetic Correlates of Weight Loss After Bariatric Surgery. Obes Surg. 2021;31:4612–23. https://doi.org/10.1007/s11695-021-0558.

    Article  PubMed  Google Scholar 

  58. Khera AV, Chaffin M, Wade KH, et al. Polygenic prediction of weight and obesity trajectories from birth to adulthood. Physiol Behav. 2017;176:139–48. https://doi.org/10.1016/j.cell.2019.03.028.

    Article  CAS  Google Scholar 

  59. Pulit SL, Stoneman C, Morris AP, et al. Meta-Analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum Mol Genet. 2019;28:166–74. https://doi.org/10.1093/hmg/ddy327.

    Article  CAS  PubMed  Google Scholar 

  60. de Toro-Martín J, Guénard F, Tchernof A, et al. Polygenic risk score for predicting weight loss after bariatric surgery. JCI insight. 2018;3:1–12. https://doi.org/10.1172/jci.insight.122011.

    Article  Google Scholar 

  61. Abdalla MMI. Central and peripheral control of food intake. Endocr Regul. 2017;51:52–70. https://doi.org/10.1515/enr-2017-0006.

  62. Obradovic M, Sudar-Milovanovic E, Soskic S, et al. Leptin and Obesity: Role and Clinical Implication. Front Endocrinol (Lausanne). 2021;12:1–14.

    Article  Google Scholar 

  63. Tamez M, Ramos-Barragan V, Mendoza-Lorenzo P, et al. Adipocyte Size and Leptin Receptor Expression in Human Subcutaneous Adipose Tissue After Roux-en-Y Gastric Bypass. Obes Surg. 2017;27:3330–2.

    PubMed  Google Scholar 

  64. Baker M, Gaukrodger N, Mayosi BM, et al. Association between common polymorphisms of the proopiomelanocortin gene and body fat distribution: A family study. Diabetes. 2005;54:2492–6. https://doi.org/10.2337/diabetes.54.8.2.

    Article  CAS  PubMed  Google Scholar 

  65. Yang LK, Tao YX. Biased signaling at neural melanocortin receptors in regulation of energy homeostasis. Biochim Biophys Acta - Mol Basis Dis. 2017;1863:2486–95.

    Article  CAS  PubMed  Google Scholar 

  66. Yu K, Li L, Zhang L, et al. Association between MC4R rs17782313 genotype and obesity: A meta-analysis. Gene. 2020;733:144372. https://doi.org/10.1016/j.gene.2020.144372.

  67. van Galen KA, ter Horst KW, Serlie MJ. Serotonin, food intake, and obesity. Obes Rev. 2021;22:1–13. https://doi.org/10.1111/obr.13210wileyonlin.

    Article  Google Scholar 

  68. Nonogaki K. The Regulatory Role of the Central and Peripheral Serotonin Network on Feeding Signals in Metabolic Diseases. Int J Mol Sci. 2022;23:1600. https://doi.org/10.3390/ijms23031600.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Chen Y, Wang Y, Fang X, et al. Association of the HTR2C-759C/T polymorphism and antipsychotic-induced weight gain: A meta-analysis. Gen Psychiatry. 2020;33:e100192. https://doi.org/10.1136/gpsych-2020-100192.

  70. Schnor NPP, Verlengia R, Novais PFS, et al. Association of 5-HT2C (Rs3813929) and UCP3 (rs1800849) gene polymorphisms with type 2 diabetes in obese women candidates for bariatric surgery. Arch Endocrinol Metab. 2017;61:326–31.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Lan N, Lu Y, Zhang Y, et al. FTO – A Common Genetic Basis for Obesity and Cancer. Front Genet. 2020;11:1–12. https://doi.org/10.3389/fgene.2020.559138.

    Article  CAS  Google Scholar 

  72. Frayling TM, Timpson NJ, Weedon MN, et al. A Common Variant in the FTO Gene Is Associated with Body Mass Index and Predisposes to Childhood and Adult Obesity. Science (80- ). 2007;316:889–94. https://doi.org/10.1126/science.1141634.

  73. Fredriksson R, Hägglund M, Olszewski PK, et al. The obesity gene, FTO, is of ancient origin, up-regulated during food deprivation and expressed in neurons of feeding-related nuclei of the brain. Endocrinology. 2008;149:2062–71. https://doi.org/10.1210/en.2007-1457.

    Article  CAS  PubMed  Google Scholar 

  74. Xiang L, Wu H, Pan A, et al. FTO genotype and weight loss in diet and lifestyle interventions : a systematic review and meta-analysis. Am J Clin Nutr. 2016;103:1162–70. https://doi.org/10.3945/ajcn.115.123448.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Yeo GSH. The role of the FTO (Fat Mass and Obesity Related) locus in regulating body size and composition. Mol Cell Endocrinol. 2014;397:34–41. https://doi.org/10.1016/j.mce.2014.09.012.

    Article  CAS  PubMed  Google Scholar 

  76. Zhao X, Yang Y, Sun BF, et al. FTO and obesity: Mechanisms of association. Curr Diab Rep. 2014;14:486. https://doi.org/10.1007/s11892-014-0486-0.

    Article  CAS  PubMed  Google Scholar 

  77. Donadelli M, Dando I, Fiorini C, et al. UCP2, a mitochondrial protein regulated at multiple levels. Cell Mol Life Sci. 2014;71:1171–90. https://doi.org/10.1007/s00018-013-1407.

    Article  CAS  PubMed  Google Scholar 

  78. Li J, Jiang R, Cong X, et al. UCP2 gene polymorphisms in obesity and diabetes, and the role of UCP2 in cancer. FEBS Lett. 2019;593:2525–34. https://doi.org/10.1002/1873-3468.13546.

    Article  CAS  PubMed  Google Scholar 

  79. Gamble KL, Berry R, Frank SJ, et al. Circadian clock control of endocrine factors. Nat Rev Endocrinol. 2014;10:466–75. https://doi.org/10.1038/nrendo.2014.78.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Lekkas D, Paschos GK. The circadian clock control of adipose tissue physiology and metabolism. Auton Neurosci Basic Clin. 2019;219:66–70. https://doi.org/10.1016/j.autneu.2019.05.00.

    Article  CAS  Google Scholar 

  81. Papandonatos GD, Pan Q, Pajewski NM, et al. Genetic predisposition to weight loss and regain with lifestyle intervention: Analyses from the diabetes prevention program and the look AHEAD randomized controlled trials. Diabetes. 2015;64:4312–21. https://doi.org/10.2337/db15-0441.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Lee S, Park D, Lim C, et al. mtIF3 is locally translated in axons and regulates mitochondrial translation for axonal growth. BMC Biol. 2022;20:1–16. https://doi.org/10.1186/s12915-021-01215-w.

  83. Flores YN, Velázquez-Cruz R, Ramírez P, et al. Association between PNPLA3 (rs738409), LYPLAL1 (rs12137855), PPP1R3B (rs4240624), GCKR (rs780094), and elevated transaminase levels in overweight/obese Mexican adults. Mol Biol Rep. 2016;43:1359–69. https://doi.org/10.1007/s11033-016-4058.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Dimeglio C, Becouarn G, Topart P, et al. Weight Loss Trajectories After Bariatric Surgery for Obesity : Mathematical Model and Proof-of-Concept Study. JMIR Med Informatics. 2020;8:e13672. https://doi.org/10.2196/13672.

  85. Belligoli A, Bettini S, Segato G, et al. Predicting Responses to Bariatric and Metabolic Surgery. Curr Obes Rep. 2020;9:373–9. https://doi.org/10.1007/s13679-020-00390-.

    Article  PubMed  Google Scholar 

  86. Heinberg LJ, Bond DS, Carroll I, et al. Identifying Mechanisms that Predict Weight Trajectory after Bariatric Surgery: Rationale and Design of the BioBehavioral Trial. Surg Obes Relat Dis. 2021;16:1816–26. https://doi.org/10.1016/j.soard.2020.06.

  87. Deeks J, Higgins J, Altman D, et al. Chapter 10: Analysing data and undertaking meta-analyses. In: Cochrane Handbook for Systematic Reviews of Interventions version 6.3 (updated February 2022). 2022, p. Available from https://www.training.cochrane.org/handbook.

  88. Courcoulas AP, King WC, Belle SH, et al. Seven-year weight trajectories and health outcomes in the Longitudinal Assessment of Bariatric Surgery (LABS) study. JAMA Surg. 2018;153:427–34. https://doi.org/10.1001/jamasurg.2017.502.

    Article  PubMed  Google Scholar 

  89. King WC, Hinerman AS, Courcoulas AP. Weight regain following bariatric surgery: a systematic literature review and comparison across studies using a large reference sample. Surg Obes Relat Dis. 2020;16:1133–44. https://doi.org/10.1016/j.soard.2020.03.

  90. Justice AE, Winkler TW, Feitosa MF, et al. Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits. Nat Commun. 2017;8:1–19.

    Article  Google Scholar 

  91. Bruschetta G, Kim JD, Diano S, et al. Overexpression of melanocortin 2 receptor accessory protein 2 (MRAP2) in adult paraventricular MC4R neurons regulates energy intake and expenditure. Mol Metab. 2018;18:79–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Asai M, Ramachandrappa S, Joachim M, et al. Loss of Function of the Melanocortin 2 Receptor. Science (80- ). 2013;341:275–8.

Download references

Acknowledgements

The authors would like to thank Marina Maintinguer Norde (University of São Paulo – USP) and Patrícia Borges Botelho (University of Brasilia – UnB) for their contribution to the PRESS protocol.

Funding

This study was funded by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (Process nº 422620/2021-1 and n°434159/2018-2). Partial financial support was received from the “Pró Reitoria de Pesquisa e Inovação” of the Federal University of Goiás, that financed the grammatical review of the manuscript in English. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Author information

Authors and Affiliations

  1. Nutritional Genomics Research Group, Faculty of Nutrition, Federal University of Goiás (UFG), Goiânia, 74690-900, Brazil

    Amélia Cristina Stival Duarte, Nara Rubia da Silva & Maria Aderuza Horst

  2. Graduate Program in Public Health, University of Brasilia (UnB), Brasilia, 70910-900, Brazil

    Vivian Siqueira Santos Gonçalves & Kênia Mara Baiocchi de Carvalho

  3. Graduate Program in Nutrition and Health. School of Nutrition, Federal University of Goiás (UFG), Goiânia, 74690-900, Brazil

    Flávia Campos Corgosinho & Maria Aderuza Horst

  4. Graduate Program in Health Science. School of Medicine, Federal University of Goiás (UFG), Goiânia, 74690-900, Brazil

    Flávia Campos Corgosinho

  5. Graduate Program in Human Nutrition, University of Brasilia (UnB), Brasilia, 70910-900, Brasil

    Kênia Mara Baiocchi de Carvalho

Authors
  1. Amélia Cristina Stival Duarte
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nara Rubia da Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vivian Siqueira Santos Gonçalves
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Flávia Campos Corgosinho
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kênia Mara Baiocchi de Carvalho
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Maria Aderuza Horst
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: Amélia Cristina Stival Duarte, Maria Aderuza Horst, and Kênia Mara Baiocchi de Carvalho. Literature search: Amélia Cristina Stival Duarte, and Vivian Siqueira Santos Gonçalves. Selection of studies: Amélia Cristina Stival Duarte, Nara Rubia da Silva, and Maria Aderuza Horst. Data extraction: Amélia Cristina Stival Duarte, and Nara Rubia da Silva. Data curation: Amélia Cristina Stival Duarte, and Vivian Siqueira Santos Gonçalves. Writing – original draft: Amélia Cristina Stival Duarte.Writing – review & editing: Maria Aderuza Horst, Flávia Campos Corgosinho, Kênia Mara Baiocchi de Carvalho, and Vivian Siqueira Santos Gonçalves. Validation: Maria Aderuza Horst, and Kênia Mara Baiocchi de Carvalho. Supervision: Maria Aderuza Horst, Flávia Campos Corgosinho, and Kênia Mara Baiocchi de Carvalho. Project Administration: Amélia Cristina Stival Duarte, Maria Aderuza Horst, and Kênia Mara Baiocchi de Carvalho. Funding Acquisition: Maria Aderuza Horst.

Corresponding author

Correspondence to Amélia Cristina Stival Duarte.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 20 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Duarte, A.C.S., da Silva, N.R., Santos Gonçalves, V.S. et al. The Influence of Single Nucleotide Polymorphisms On Body Weight Trajectory After Bariatric Surgery: A Systematic Review. Curr Obes Rep 12, 280–307 (2023). https://doi.org/10.1007/s13679-023-00514-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13679-023-00514-3

Keywords

Search

Navigation