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Zafirovska M, Zafirovski A, Režen T, Pintar T. The Outcome of Metabolic and Bariatric Surgery in Morbidly Obese Patients with Different Genetic Variants Associated with Obesity: A Systematic Review. Nutrients 2024; 16:2510. [PMID: 39125390 PMCID: PMC11313945 DOI: 10.3390/nu16152510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Revised: 07/22/2024] [Accepted: 07/27/2024] [Indexed: 08/12/2024] Open
Abstract
Metabolic and bariatric surgery (MBS) effectively treats obesity and related comorbidities, though individual responses vary. This systematic review examines how genetic variants influence MBS outcomes in morbidly obese patients. A comprehensive search in PubMed, Embase, Medline, and the Cochrane Library identified 1572 studies, with 52 meeting the inclusion criteria. Two reviewers independently filtered and selected studies, including relevant cross-references. Research focused on polymorphisms in genes such as UCP2, UCP3, 5-HT2C, MC4R, FKBP5, FTO, CAT haplotypes, LYPAL-1, PTEN, FABP-2, CNR1, LEP656, LEP223, GLP-1R, APOA-1, APOE, ADIPOQ, IL-6, PGC1a, TM6SF2, MBOAT7, PNPLA3, TCF7L2, ESR1, GHSR, GHRL, CD40L, DIO2, ACSL5, CG, TAS2R38, CD36, OBPIIa, NPY, BDNF, CLOCK, and CAMKK2. Most studies explored associations with post-surgery weight loss, while some examined metabolic, cardiovascular, taste, and eating behavior effects as well. Understanding the role of genetic factors in weight loss and metabolic outcomes post-MBS can help tailor personalized treatment plans for improved efficacy and long-term success. Further research with larger sample sizes and extended follow-up is needed to clarify the effects of many genetic variants on MBS outcomes in morbidly obese patients.
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Affiliation(s)
- Marija Zafirovska
- Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia; (M.Z.); (A.Z.)
- Association of General Practice/Family Medicine of South-East Europe (AGP/FM SEE), St. Vladimir Komarov No. 40/6, 1000 Skopje, North Macedonia
| | - Aleksandar Zafirovski
- Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia; (M.Z.); (A.Z.)
- General Hospital Jesenice, Cesta maršala Tita 112, 4270 Jesenice, Slovenia
- Clinical Institute of Radiology, University Medical Centre Ljubljana, Zaloška cesta 7, 1000 Ljubljana, Slovenia
| | - Tadeja Režen
- Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia; (M.Z.); (A.Z.)
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Zaloška cesta 4, 1000 Ljubljana, Slovenia
| | - Tadeja Pintar
- Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia; (M.Z.); (A.Z.)
- Department of Abdominal Surgery, University Medical Centre Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
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Duarte ACS, da Silva NR, Santos Gonçalves VS, Corgosinho FC, de Carvalho KMB, Horst MA. The Influence of Single Nucleotide Polymorphisms On Body Weight Trajectory After Bariatric Surgery: A Systematic Review. Curr Obes Rep 2023; 12:280-307. [PMID: 37389759 DOI: 10.1007/s13679-023-00514-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/26/2023] [Indexed: 07/01/2023]
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. 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.
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Affiliation(s)
- Amélia Cristina Stival Duarte
- Nutritional Genomics Research Group, Faculty of Nutrition, Federal University of Goiás (UFG), Goiânia, 74690-900, Brazil.
| | - Nara Rubia da Silva
- Nutritional Genomics Research Group, Faculty of Nutrition, Federal University of Goiás (UFG), Goiânia, 74690-900, Brazil
| | | | - Flávia Campos Corgosinho
- Graduate Program in Nutrition and Health. School of Nutrition, Federal University of Goiás (UFG), Goiânia, 74690-900, Brazil
- Graduate Program in Health Science. School of Medicine, Federal University of Goiás (UFG), Goiânia, 74690-900, Brazil
| | - Kênia Mara Baiocchi de Carvalho
- Graduate Program in Public Health, University of Brasilia (UnB), Brasilia, 70910-900, Brazil
- Graduate Program in Human Nutrition, University of Brasilia (UnB), Brasilia, 70910-900, Brasil
| | - Maria Aderuza Horst
- Nutritional Genomics Research Group, Faculty of Nutrition, Federal University of Goiás (UFG), Goiânia, 74690-900, Brazil
- Graduate Program in Nutrition and Health. School of Nutrition, Federal University of Goiás (UFG), Goiânia, 74690-900, Brazil
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Richter LR, Albert BI, Zhang L, Ostropolets A, Zitsman JL, Fennoy I, Albers DJ, Hripcsak G. Data assimilation on mechanistic models of glucose metabolism predicts glycemic states in adolescents following bariatric surgery. Front Physiol 2022; 13:923704. [PMID: 36518108 PMCID: PMC9744230 DOI: 10.3389/fphys.2022.923704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 10/11/2022] [Indexed: 11/29/2022] Open
Abstract
Type 2 diabetes mellitus is a complex and under-treated disorder closely intertwined with obesity. Adolescents with severe obesity and type 2 diabetes have a more aggressive disease compared to adults, with a rapid decline in pancreatic β cell function and increased incidence of comorbidities. Given the relative paucity of pharmacotherapies, bariatric surgery has become increasingly used as a therapeutic option. However, subsets of this population have sub-optimal outcomes with either inadequate weight loss or little improvement in disease. Predicting which patients will benefit from surgery is a difficult task and detailed physiological characteristics of patients who do not respond to treatment are generally unknown. Identifying physiological predictors of surgical response therefore has the potential to reveal both novel phenotypes of disease as well as therapeutic targets. We leverage data assimilation paired with mechanistic models of glucose metabolism to estimate pre-operative physiological states of bariatric surgery patients, thereby identifying latent phenotypes of impaired glucose metabolism. Specifically, maximal insulin secretion capacity, σ, and insulin sensitivity, SI, differentiate aberrations in glucose metabolism underlying an individual's disease. Using multivariable logistic regression, we combine clinical data with data assimilation to predict post-operative glycemic outcomes at 12 months. Models using data assimilation sans insulin had comparable performance to models using oral glucose tolerance test glucose and insulin. Our best performing models used data assimilation and had an area under the receiver operating characteristic curve of 0.77 (95% confidence interval 0.7665, 0.7734) and mean average precision of 0.6258 (0.6206, 0.6311). We show that data assimilation extracts knowledge from mechanistic models of glucose metabolism to infer future glycemic states from limited clinical data. This method can provide a pathway to predict long-term, post-surgical glycemic states by estimating the contributions of insulin resistance and limitations of insulin secretion to pre-operative glucose metabolism.
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Affiliation(s)
- Lauren R. Richter
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, United States
| | - Benjamin I. Albert
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, United States
| | - Linying Zhang
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, United States
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, United States
| | - Jeffrey L. Zitsman
- Division of Pediatric Surgery, Department of Surgery, Columbia University Irving Medical Center, New York, NY, United States
| | - Ilene Fennoy
- Division of Pediatric Endocrinology, Metabolism, and Diabetes, Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, United States
| | - David J. Albers
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, United States,Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, CO, United States,Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, United States,*Correspondence: George Hripcsak,
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Brito MDF, Torre C, Silva-Lima B. Scientific Advances in Diabetes: The Impact of the Innovative Medicines Initiative. Front Med (Lausanne) 2021; 8:688438. [PMID: 34295913 PMCID: PMC8290522 DOI: 10.3389/fmed.2021.688438] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/02/2021] [Indexed: 12/16/2022] Open
Abstract
Diabetes Mellitus is one of the World Health Organization's priority diseases under research by the first and second programmes of Innovative Medicines Initiative, with the acronyms IMI1 and IMI2, respectively. Up to October of 2019, 13 projects were funded by IMI for Diabetes & Metabolic disorders, namely SUMMIT, IMIDIA, DIRECT, StemBANCC, EMIF, EBiSC, INNODIA, RHAPSODY, BEAT-DKD, LITMUS, Hypo-RESOLVE, IM2PACT, and CARDIATEAM. In general, a total of €447 249 438 was spent by IMI in the area of Diabetes. In order to prompt a better integration of achievements between the different projects, we perform a literature review and used three data sources, namely the official project's websites, the contact with the project's coordinators and co-coordinator, and the CORDIS database. From the 662 citations identified, 185 were included. The data collected were integrated into the objectives proposed for the four IMI2 program research axes: (1) target and biomarker identification, (2) innovative clinical trials paradigms, (3) innovative medicines, and (4) patient-tailored adherence programmes. The IMI funded projects identified new biomarkers, medical and research tools, determinants of inter-individual variability, relevant pathways, clinical trial designs, clinical endpoints, therapeutic targets and concepts, pharmacologic agents, large-scale production strategies, and patient-centered predictive models for diabetes and its complications. Taking into account the scientific data produced, we provided a joint vision with strategies for integrating personalized medicine into healthcare practice. The major limitations of this article were the large gap of data in the libraries on the official project websites and even the Cordis database was not complete and up to date.
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Affiliation(s)
| | - Carla Torre
- Faculty of Pharmacy, University of Lisbon, Lisbon, Portugal.,Laboratory of Systems Integration Pharmacology, Clinical & Regulatory Science-Research Institute for Medicines (iMED.ULisboa), Lisbon, Portugal
| | - Beatriz Silva-Lima
- Faculty of Pharmacy, University of Lisbon, Lisbon, Portugal.,Laboratory of Systems Integration Pharmacology, Clinical & Regulatory Science-Research Institute for Medicines (iMED.ULisboa), Lisbon, Portugal
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Pedersen HK, Gudmundsdottir V, Pedersen MK, Brorsson C, Brunak S, Gupta R. Ranking factors involved in diabetes remission after bariatric surgery using machine-learning integrating clinical and genomic biomarkers. NPJ Genom Med 2016; 1:16035. [PMID: 29263820 PMCID: PMC5685313 DOI: 10.1038/npjgenmed.2016.35] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2015] [Revised: 08/22/2016] [Accepted: 08/25/2016] [Indexed: 01/07/2023] Open
Abstract
As weight-loss surgery is an effective treatment for the glycaemic control of type 2 diabetes in obese patients, yet not all patients benefit, it is valuable to find predictive factors for this diabetic remission. This will help elucidating possible mechanistic insights and form the basis for prioritising obese patients with dysregulated diabetes for surgery where diabetes remission is of interest. In this study, we combine both clinical and genomic factors using heuristic methods, informed by prior biological knowledge in order to rank factors that would have a role in predicting diabetes remission, and indeed in identifying patients who may have low likelihood in responding to bariatric surgery for improved glycaemic control. Genetic variants from the Illumina CardioMetaboChip were prioritised through single-association tests and then seeded a larger selection from protein-protein interaction networks. Artificial neural networks allowing nonlinear correlations were trained to discriminate patients with and without surgery-induced diabetes remission, and the importance of each clinical and genetic parameter was evaluated. The approach highlighted insulin treatment, baseline HbA1c levels, use of insulin-sensitising agents and baseline serum insulin levels, as the most informative variables with a decent internal validation performance (74% accuracy, area under the curve (AUC) 0.81). Adding information for the eight top-ranked single nucleotide polymorphisms (SNPs) significantly boosted classification performance to 84% accuracy (AUC 0.92). The eight SNPs mapped to eight genes - ABCA1, ARHGEF12, CTNNBL1, GLI3, PROK2, RYBP, SMUG1 and STXBP5 - three of which are known to have a role in insulin secretion, insulin sensitivity or obesity, but have not been indicated for diabetes remission after bariatric surgery before.
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Affiliation(s)
- Helle Krogh Pedersen
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Valborg Gudmundsdottir
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Mette Krogh Pedersen
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
- Department of Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Caroline Brorsson
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Søren Brunak
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
- Department of Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ramneek Gupta
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
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