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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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M JN, Bharadwaj D. The complex web of obesity: from genetics to precision medicine. Expert Rev Endocrinol Metab 2024:1-16. [PMID: 38869356 DOI: 10.1080/17446651.2024.2365785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 06/05/2024] [Indexed: 06/14/2024]
Abstract
INTRODUCTION Obesity is a growing public health concern affecting both children and adults. Since it involves both genetic and environmental components, the management of obesity requires both, an understanding of the underlying genetics and changes in lifestyle. The knowledge of obesity genetics will enable the possibility of precision medicine in anti-obesity medications. AREAS COVERED Here, we explore health complications and the prevalence of obesity. We discuss disruptions in energy balance as a symptom of obesity, examining evolutionary theories, its multi-factorial origins, and heritability. Additionally, we discuss monogenic and polygenic obesity, the converging biological pathways, potential pharmacogenomics applications, and existing anti-obesity medications - specifically focussing on the leptin-melanocortin and incretin pathways. Comparisons between childhood and adult obesity genetics are made, along with insights into structural variants, epigenetic changes, and environmental influences on epigenetic signatures. EXPERT OPINION With recent advancements in anti-obesity drugs, genetic studies pinpoint new targets and allow for repurposing existing drugs. This creates opportunities for genotype-informed treatment options. Also, lifestyle interventions can help in the prevention and treatment of obesity by altering the epigenetic signatures. The comparison of genetic architecture in adults and children revealed a significant overlap. However, more robust studies with diverse ethnic representation is required in childhood obesity.
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Affiliation(s)
- Janaki Nair M
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, India
| | - Dwaipayan Bharadwaj
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, India
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Mehlig K, Foraita R, Nagrani R, Wright MN, De Henauw S, Molnár D, Moreno LA, Russo P, Tornaritis M, Veidebaum T, Lissner L, Kaprio J, Pigeot I. Genetic associations vary across the spectrum of fasting serum insulin: results from the European IDEFICS/I.Family children's cohort. Diabetologia 2023; 66:1914-1924. [PMID: 37420130 PMCID: PMC10473990 DOI: 10.1007/s00125-023-05957-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 04/27/2023] [Indexed: 07/09/2023]
Abstract
AIMS/HYPOTHESIS There is increasing evidence for the existence of shared genetic predictors of metabolic traits and neurodegenerative disease. We previously observed a U-shaped association between fasting insulin in middle-aged women and dementia up to 34 years later. In the present study, we performed genome-wide association (GWA) analyses for fasting serum insulin in European children with a focus on variants associated with the tails of the insulin distribution. METHODS Genotyping was successful in 2825 children aged 2-14 years at the time of insulin measurement. Because insulin levels vary during childhood, GWA analyses were based on age- and sex-specific z scores. Five percentile ranks of z-insulin were selected and modelled using logistic regression, i.e. the 15th, 25th, 50th, 75th and 85th percentile ranks (P15-P85). Additive genetic models were adjusted for age, sex, BMI, survey year, survey country and principal components derived from genetic data to account for ethnic heterogeneity. Quantile regression was used to determine whether associations with variants identified by GWA analyses differed across quantiles of log-insulin. RESULTS A variant in the SLC28A1 gene (rs2122859) was associated with the 85th percentile rank of the insulin z score (P85, p value=3×10-8). Two variants associated with low z-insulin (P15, p value <5×10-6) were located on the RBFOX1 and SH3RF3 genes. These genes have previously been associated with both metabolic traits and dementia phenotypes. While variants associated with P50 showed stable associations across the insulin spectrum, we found that associations with variants identified through GWA analyses of P15 and P85 varied across quantiles of log-insulin. CONCLUSIONS/INTERPRETATION The above results support the notion of a shared genetic architecture for dementia and metabolic traits. Our approach identified genetic variants that were associated with the tails of the insulin spectrum only. Because traditional heritability estimates assume that genetic effects are constant throughout the phenotype distribution, the new findings may have implications for understanding the discrepancy in heritability estimates from GWA and family studies and for the study of U-shaped biomarker-disease associations.
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Affiliation(s)
- Kirsten Mehlig
- School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.
| | - Ronja Foraita
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Rajini Nagrani
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Marvin N Wright
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
- Department of Mathematics and Computer Science, University of Bremen, Bremen, Germany
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Stefaan De Henauw
- Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Dénes Molnár
- Department of Paediatrics, Medical School, University of Pécs, Pécs, Hungary
| | - Luis A Moreno
- GENUD (Growth, Exercise, Nutrition and Development) Research Group, University of Zaragoza, Zaragoza, Spain
- Instituto Agroalimentario de Aragón (IA2), Zaragoza, Spain
- Instituto de Investigación Sanitaria de Aragón (IIS Aragón), Zaragoza, Spain
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
| | - Paola Russo
- Institute of Food Sciences, National Research Council, Avellino, Italy
| | | | | | - Lauren Lissner
- School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Iris Pigeot
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
- Department of Mathematics and Computer Science, University of Bremen, Bremen, Germany
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Smith DM, Loughnan R, Friedman NP, Parekh P, Frei O, Thompson WK, Andreassen OA, Neale M, Jernigan TL, Dale AM. Heritability Estimation of Cognitive Phenotypes in the ABCD Study ® Using Mixed Models. Behav Genet 2023; 53:169-188. [PMID: 37024669 PMCID: PMC10154273 DOI: 10.1007/s10519-023-10141-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 03/15/2023] [Indexed: 04/08/2023]
Abstract
Twin and family studies have historically aimed to partition phenotypic variance into components corresponding to additive genetic effects (A), common environment (C), and unique environment (E). Here we present the ACE Model and several extensions in the Adolescent Brain Cognitive Development℠ Study (ABCD Study®), employed using the new Fast Efficient Mixed Effects Analysis (FEMA) package. In the twin sub-sample (n = 924; 462 twin pairs), heritability estimates were similar to those reported by prior studies for height (twin heritability = 0.86) and cognition (twin heritability between 0.00 and 0.61), respectively. Incorporating SNP-derived genetic relatedness and using the full ABCD Study® sample (n = 9,742) led to narrower confidence intervals for all parameter estimates. By leveraging the sparse clustering method used by FEMA to handle genetic relatedness only for participants within families, we were able to take advantage of the diverse distribution of genetic relatedness within the ABCD Study® sample.
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Affiliation(s)
- Diana M Smith
- Neurosciences Graduate Program, University of California San Diego, La Jolla, CA, USA.
- Center for Human Development, University of California, San Diego, La Jolla, CA, USA.
- Center for Multimodal Imaging and Genetics, San Diego School of Medicine, University of California, La Jolla, CA, USA.
| | - Robert Loughnan
- Population Neuroscience and Genetics Lab, University of California, San Diego, La Jolla, CA, USA
| | - Naomi P Friedman
- Institute for Behavioral Genetics, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Pravesh Parekh
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Wesley K Thompson
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Michael Neale
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Terry L Jernigan
- Center for Human Development, University of California, San Diego, La Jolla, CA, USA
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
- Department of Radiology, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, San Diego School of Medicine, University of California, La Jolla, CA, USA
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
- Department of Radiology, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Department of Neuroscience, University of California, San Diego School of Medicine, La Jolla, CA, USA
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Handley D, Rafey MF, Almansoori S, Brazil JF, McCarthy A, Amin HA, O’Donnell M, Blakemore AI, Finucane FM. Higher Waist Hip Ratio Genetic Risk Score Is Associated with Reduced Weight Loss in Patients with Severe Obesity Completing a Meal Replacement Programme. J Pers Med 2022; 12:jpm12111881. [PMID: 36579607 PMCID: PMC9695448 DOI: 10.3390/jpm12111881] [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: 09/27/2022] [Revised: 10/24/2022] [Accepted: 11/03/2022] [Indexed: 11/12/2022] Open
Abstract
Background: A better understanding of the influence of genetic factors on the response to lifestyle interventions in people with obesity may allow the development of more personalised, effective and efficient therapeutic strategies. We sought to determine the influence of six obesity-related genetic risk scores on the magnitude of weight lost by patients with severe obesity who completed a dietary intervention. Methods: In this single-centre prospective cohort study, participants with severe and complicated obesity who completed a 24-week, milk-based meal replacement programme were genotyped to detect the frequency of common risk alleles for obesity and type 2 diabetes-related traits. Genetic risk scores (GRS) for six of these traits were derived. Participants with a potentially deleterious monogenic gene variant were excluded from the analysis. Results: In 93 patients completing the programme who were not carrying a known obesity-related gene mutation, 35.5% had diabetes, 53.8% were female, mean age was 51.4 ± 11 years, mean body mass index was 51.5 ± 8.7 and mean total weight loss percent at 24 weeks was 16 ± 6.3%. The waist-hip ratio (WHR) GRS was inversely associated with percentage total weight loss at 24 weeks (adjusted β for one standard deviation increase in WHR GRS -11.6 [-23.0, -0.3], p = 0.045), and patients in the lowest tertile of WHR GRS lost more weight. Conclusions: Patients with severe and complicated obesity with a genetic predisposition to central fat accumulation had less weight loss in a 24-week milk-based meal replacement programme, but there was no evidence for influence from the five other obesity-related genetic risk scores on the response to dietary restriction.
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Affiliation(s)
- Dale Handley
- College of Health, Medical and Life Sciences, Brunel University London, London UB8 3PH, UK
| | - Mohammed Faraz Rafey
- Bariatric Medicine Service, Centre for Diabetes, Endocrinology and Metabolism, Galway University Hospitals, H91 YR71 Galway, Ireland
- HRB Clinical Research Facility, University of Galway, H91 CF50 Galway, Ireland
- Department of Medicine, University of Galway, H91 CF50 Galway, Ireland
| | - Sumaya Almansoori
- College of Health, Medical and Life Sciences, Brunel University London, London UB8 3PH, UK
- Faculty of Medicine, Imperial College London, London SW7 2BX, UK
- International Centre for Forensic Science, General Department of Forensic Science and Criminology, Dubai Police, Dubai 00000, United Arab Emirates
| | - John F. Brazil
- Bariatric Medicine Service, Centre for Diabetes, Endocrinology and Metabolism, Galway University Hospitals, H91 YR71 Galway, Ireland
- HRB Clinical Research Facility, University of Galway, H91 CF50 Galway, Ireland
- Department of Medicine, University of Galway, H91 CF50 Galway, Ireland
| | - Aisling McCarthy
- Bariatric Medicine Service, Centre for Diabetes, Endocrinology and Metabolism, Galway University Hospitals, H91 YR71 Galway, Ireland
| | - Hasnat A. Amin
- College of Health, Medical and Life Sciences, Brunel University London, London UB8 3PH, UK
| | - Martin O’Donnell
- HRB Clinical Research Facility, University of Galway, H91 CF50 Galway, Ireland
- Department of Medicine, University of Galway, H91 CF50 Galway, Ireland
| | - Alexandra I. Blakemore
- College of Health, Medical and Life Sciences, Brunel University London, London UB8 3PH, UK
- Department of Medicine, University of Galway, H91 CF50 Galway, Ireland
- Faculty of Medicine, Imperial College London, London SW7 2BX, UK
| | - Francis M. Finucane
- College of Health, Medical and Life Sciences, Brunel University London, London UB8 3PH, UK
- Bariatric Medicine Service, Centre for Diabetes, Endocrinology and Metabolism, Galway University Hospitals, H91 YR71 Galway, Ireland
- HRB Clinical Research Facility, University of Galway, H91 CF50 Galway, Ireland
- Department of Medicine, University of Galway, H91 CF50 Galway, Ireland
- Correspondence: ; Tel.: +353-(39)-1893803
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