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Anazco D, Acosta A. Precision medicine for obesity: current evidence and insights for personalization of obesity pharmacotherapy. Int J Obes (Lond) 2024:10.1038/s41366-024-01599-z. [PMID: 39127792 DOI: 10.1038/s41366-024-01599-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 06/17/2024] [Accepted: 07/29/2024] [Indexed: 08/12/2024]
Abstract
Obesity is a chronic and complex disease associated with increased morbidity, mortality, and financial burden. It is expected that by 2030 one of two people in the United States will have obesity. The backbone for obesity management continues to be lifestyle interventions, consisting of calorie deficit diets and increased physical activity levels, however, these interventions are often insufficient to achieve sufficient and maintained weight loss. As a result, multiple patients require additional interventions such as antiobesity medications or bariatric interventions in order to achieve clinically significant weight loss and improvement or resolution of obesity-associated comorbidities. Despite the recent advances in the field of obesity pharmacotherapy that have resulted in never-before-seen weight loss outcomes, comorbidity improvement, and even reduction in cardiovascular mortality, there is still a significant interindividual variability in terms of response to antiobesity medications, with a subset of patients not achieving a clinically significant weight loss. Currently, the trial-and-error paradigm for the selection of antiobesity medications results in increased costs and risks for developing side effects, while also reduces engagement in weight management programs for patients with obesity. The implementation of a precision medicine framework to the selection of antiobesity medications might help reduce heterogeneity and optimize weight loss outcomes by identifying unique subsets of patients, or phenotypes, that have a better response to a specific intervention. The detailed study of energy balance regulation holds promise, as actionable behavioral and physiologic traits could help guide antiobesity medication selection based on previous mechanistic studies. Moreover, the rapid advances in genotyping, multi-omics, and big data analysis might hold the key to discover additional signatures or phenotypes that might respond better to a certain intervention and might permit the widespread adoption of a precision medicine approach for obesity management.
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Affiliation(s)
- Diego Anazco
- Precision Medicine for Obesity Program, Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Andres Acosta
- Precision Medicine for Obesity Program, Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
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2
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Cho HW, Jin HS, Kim SS, Eom YB. Forensic height estimation using polygenic score in Korean population. Mol Genet Genomics 2024; 299:78. [PMID: 39120737 DOI: 10.1007/s00438-024-02172-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 07/30/2024] [Indexed: 08/10/2024]
Abstract
Height is known to be a classically heritable trait controlled by complex polygenic factors. Numerous height-associated genetic variants across the genome have been identified so far. It is also a representative of externally visible characteristics (EVC) for predicting appearance in forensic science. When biological evidence at a crime scene is deficient in identifying an individual, the examination of forensic DNA phenotyping using some genetic variants could be considered. In this study, we aimed to predict 'height', a representative forensic phenotype, by using a small number of genetic variants when short tandem repeat (STR) analysis is hard with insufficient biological samples. Our results not only replicated previous genetic signals but also indicated an upward trend in polygenic score (PGS) with increasing height in the validation and replication stages for both genders. These results demonstrate that the established SNP sets in this study could be used for height estimation in the Korean population. Specifically, since the PGS model constructed in this study targets only a small number of SNPs, it contributes to enabling forensic DNA phenotyping even at crime scenes with a minimal amount of biological evidence. To the best of our knowledge, this was the first study to evaluate a PGS model for height estimation in the Korean population using GWAS signals. Our study offers insight into the polygenic effect of height in East Asians, incorporating genetic variants from non-Asian populations.
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Affiliation(s)
- Hye-Won Cho
- Department of Medical Sciences, Graduate School, Soonchunhyang University, Asan, 31538, Chungnam, Republic of Korea
| | - Hyun-Seok Jin
- Department of Biomedical Laboratory Science, College of Life and Health Sciences, Hoseo University, Asan, 31499, Chungnam, Republic of Korea
| | - Sung-Soo Kim
- Department of Biomedical Laboratory Science, College of Life and Health Sciences, Hoseo University, Asan, 31499, Chungnam, Republic of Korea
| | - Yong-Bin Eom
- Department of Medical Sciences, Graduate School, Soonchunhyang University, Asan, 31538, Chungnam, Republic of Korea.
- Department of Biomedical Laboratory Science, College of Medical Sciences, Soonchunhyang University, 22 Soonchunhyang-ro, Sinchang-myeon, Asan-si, 31538, Chungcheongnam-do, Republic of Korea.
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Qu HQ, Connolly JJ, Kraft P, Long J, Pereira A, Flatley C, Turman C, Prins B, Mentch F, Lotufo PA, Magnus P, Stampfer MJ, Tamimi R, Eliassen AH, Zheng W, Knudsen GPS, Helgeland O, Butterworth AS, Hakonarson H, Sleiman PM. Trans-ethnic polygenic risk scores for body mass index: An international hundred K+ cohorts consortium study. Clin Transl Med 2023; 13:e1291. [PMID: 37337639 DOI: 10.1002/ctm2.1291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/16/2023] [Accepted: 05/27/2023] [Indexed: 06/21/2023] Open
Abstract
BACKGROUND While polygenic risk scores hold significant promise in estimating an individual's risk of developing a complex trait such as obesity, their application in the clinic has, to date, been limited by a lack of data from non-European populations. As a collaboration model of the International Hundred K+ Cohorts Consortium (IHCC), we endeavored to develop a globally applicable trans-ethnic PRS for body mass index (BMI) through this relatively new international effort. METHODS The polygenic risk score (PRS) model was developed, trained and tested at the Center for Applied Genomics (CAG) of The Children's Hospital of Philadelphia (CHOP) based on a BMI meta-analysis from the GIANT consortium. The validated PRS models were subsequently disseminated to the participating sites. Scores were generated by each site locally on their cohorts and summary statistics returned to CAG for final analysis. RESULTS We show that in the absence of a well powered trans-ethnic GWAS from which to derive marker SNPs and effect estimates for PRS, trans-ethnic scores can be generated from European ancestry GWAS using Bayesian approaches such as LDpred, by adjusting the summary statistics using trans-ethnic linkage disequilibrium reference panels. The ported trans-ethnic scores outperform population specific-PRS across all non-European ancestry populations investigated including East Asians and three-way admixed Brazilian cohort. CONCLUSIONS Here we show that for a truly polygenic trait such as BMI adjusting the summary statistics of a well powered European ancestry study using trans-ethnic LD reference results in a score that is predictive across a range of ancestries including East Asians and three-way admixed Brazilians.
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Affiliation(s)
- Hui-Qi Qu
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - John J Connolly
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Alexandre Pereira
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Christopher Flatley
- Division of Health Data and Digitalization, Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway
| | - Constance Turman
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Bram Prins
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Frank Mentch
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Paulo A Lotufo
- Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
- Centro de Pesquisas Clínicas e Epidemiológicas, Hospital Universitário, Universidade de São Paulo, São Paulo, Brazil
| | - Per Magnus
- University of Oslo, Oslo, Norway
- Center for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Meir J Stampfer
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Nutrition, Harvard T.H., Chan School of Public Health, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Rulla Tamimi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - A Heather Eliassen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Gun Peggy Stromstad Knudsen
- Division of Health Data and Digitalization, Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway
| | - Oyvind Helgeland
- Division of Health Data and Digitalization, Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- The National Institute for Health Research Blood and Transplant Research Unit (NIHR BTRU) in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Hakon Hakonarson
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Division of Pulmonary Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Patrick M Sleiman
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
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Muntané G, Vázquez-Bourgon J, Sada E, Martorell L, Papiol S, Bosch E, Navarro A, Crespo-Facorro B, Vilella E. Polygenic risk scores enhance prediction of body mass index increase in individuals with a first episode of psychosis. Eur Psychiatry 2023; 66:e28. [PMID: 36852609 PMCID: PMC10044301 DOI: 10.1192/j.eurpsy.2023.9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND Individuals with a first episode of psychosis (FEP) show rapid weight gain during the first months of treatment, which is associated with a reduction in general physical health. Although genetics is assumed to be a significant contributor to weight gain, its exact role is unknown. METHODS We assembled a population-based FEP cohort of 381 individuals that was split into a Training (n = 224) set and a Validation (n = 157) set to calculate the polygenic risk score (PRS) in a two-step process. In parallel, we obtained reference genome-wide association studies for body mass index (BMI) and schizophrenia (SCZ) to examine the pleiotropic landscape between the two traits. BMI PRSs were added to linear models that included sociodemographic and clinical variables to predict BMI increase (∆BMI) in the Validation set. RESULTS The results confirmed considerable shared genetic susceptibility for the two traits involving 449 near-independent genomic loci. The inclusion of BMI PRSs significantly improved the prediction of ∆BMI at 12 months after the onset of antipsychotic treatment by 49.4% compared to a clinical model. In addition, we demonstrated that the PRS containing pleiotropic information between BMI and SCZ predicted ∆BMI better at 3 (12.2%) and 12 months (53.2%). CONCLUSIONS We prove for the first time that genetic factors play a key role in determining ∆BMI during the FEP. This finding has important clinical implications for the early identification of individuals most vulnerable to weight gain and highlights the importance of examining genetic pleiotropy in the context of medically important comorbidities for predicting future outcomes.
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Affiliation(s)
- Gerard Muntané
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira i Virgili, Reus, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Institut de Biologia Evolutiva (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain
| | - Javier Vázquez-Bourgon
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Department of Psychiatry, University Hospital Marqués de Valdecilla, Instituto de Investigación Marqués de Valdecilla (IDIVAL), Santander, Spain.,Departamento de Medicina y Psiquiatría, Facultad de Medicina, Universidad de Cantabria, Santander, Spain
| | - Ester Sada
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira i Virgili, Reus, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | - Lourdes Martorell
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira i Virgili, Reus, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | - Sergi Papiol
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Department of Psychiatry, Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University, Munich, Germany
| | - Elena Bosch
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Institut de Biologia Evolutiva (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain
| | - Arcadi Navarro
- Institut de Biologia Evolutiva (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain.,Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.,Barcelonaβeta Brain Research Center, Fundació Pasqual Maragall, Barcelona, Spain
| | - Benedicto Crespo-Facorro
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Department of Psychiatry, Instituto de Biomedicina de Sevilla (IBiS), University Hospital Virgen del Rocío, Seville, Spain
| | - Elisabet Vilella
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira i Virgili, Reus, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
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Borisevich D, Schnurr TM, Engelbrechtsen L, Rakitko A, Ängquist L, Ilinsky V, Aadahl M, Grarup N, Pedersen O, Sørensen TIA, Hansen T. Non-linear interaction between physical activity and polygenic risk score of body mass index in Danish and Russian populations. PLoS One 2021; 16:e0258748. [PMID: 34662357 PMCID: PMC8523041 DOI: 10.1371/journal.pone.0258748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 10/04/2021] [Indexed: 11/19/2022] Open
Abstract
Body mass index (BMI) is a highly heritable polygenic trait. It is also affected by various environmental and behavioral risk factors. We used a BMI polygenic risk score (PRS) to study the interplay between the genetic and environmental factors defining BMI. First, we generated a BMI PRS that explained more variance than a BMI genetic risk score (GRS), which was using only genome-wide significant BMI-associated variants (R2 = 13.1% compared to 6.1%). Second, we analyzed interactions between BMI PRS and seven environmental factors. We found a significant interaction between physical activity and BMI PRS, even when the well-known effect of the FTO region was excluded from the PRS, using a small dataset of 6,179 samples. Third, we stratified the study population into two risk groups using BMI PRS. The top 22% of the studied populations were included in a high PRS risk group. Engagement in self-reported physical activity was associated with a 1.66 kg/m2 decrease in BMI in this group, compared to a 0.84 kg/m2 decrease in BMI in the rest of the population. Our results (i) confirm that genetic background strongly affects adult BMI in the general population, (ii) show a non-linear interaction between BMI genetics and physical activity, and (iii) provide a standardized framework for future gene-environment interaction analyses.
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Affiliation(s)
- Dmitrii Borisevich
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- * E-mail:
| | - Theresia M. Schnurr
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Line Engelbrechtsen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Gynecology and Obstetrics, Herlev Hospital, Herlev, Denmark
| | | | - Lars Ängquist
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Mette Aadahl
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Thorkild I. A. Sørensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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