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Fitch AK, Malhotra S, Conroy R. Differentiating monogenic and syndromic obesities from polygenic obesity: Assessment, diagnosis, and management. OBESITY PILLARS 2024; 11:100110. [PMID: 38766314 PMCID: PMC11101890 DOI: 10.1016/j.obpill.2024.100110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/18/2024] [Accepted: 04/18/2024] [Indexed: 05/22/2024]
Abstract
Background Obesity is a multifactorial neurohormonal disease that results from dysfunction within energy regulation pathways and is associated with increased morbidity, mortality, and reduced quality of life. The most common form is polygenic obesity, which results from interactions between multiple gene variants and environmental factors. Highly penetrant monogenic and syndromic obesities result from rare genetic variants with minimal environmental influence and can be differentiated from polygenic obesity depending on key symptoms, including hyperphagia; early-onset, severe obesity; and suboptimal responses to nontargeted therapies. Timely diagnosis of monogenic or syndromic obesity is critical to inform management strategies and reduce disease burden. We outline the physiology of weight regulation, role of genetics in obesity, and differentiating characteristics between polygenic and rare genetic obesity to facilitate diagnosis and transition toward targeted therapies. Methods In this narrative review, we focused on case reports, case studies, and natural history studies of patients with monogenic and syndromic obesities and clinical trials examining the efficacy, safety, and quality of life impact of nontargeted and targeted therapies in these populations. We also provide comprehensive algorithms for diagnosis of patients with suspected rare genetic causes of obesity. Results Patients with monogenic and syndromic obesities commonly present with hyperphagia (ie, pathologic, insatiable hunger) and early-onset, severe obesity, and the presence of hallmark characteristics can inform genetic testing and diagnostic approach. Following diagnosis, specialized care teams can address complex symptoms, and hyperphagia is managed behaviorally. Various pharmacotherapies show promise in these patient populations, including setmelanotide and glucagon-like peptide-1 receptor agonists. Conclusion Understanding the pathophysiology and differentiating characteristics of monogenic and syndromic obesities can facilitate diagnosis and management and has led to development of targeted pharmacotherapies with demonstrated efficacy for reducing body weight and hunger in the affected populations.
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Affiliation(s)
| | - Sonali Malhotra
- Harvard Medical School, Boston, MA, USA
- Rhythm Pharmaceuticals, Inc., Boston, MA, USA
- Massachussetts General Hospital, Boston, MA, USA
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Ulusoy-Gezer HG, Rakıcıoğlu N. The Future of Obesity Management through Precision Nutrition: Putting the Individual at the Center. Curr Nutr Rep 2024; 13:455-477. [PMID: 38806863 PMCID: PMC11327204 DOI: 10.1007/s13668-024-00550-y] [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] [Accepted: 05/18/2024] [Indexed: 05/30/2024]
Abstract
PURPOSE OF REVIEW: The prevalence of obesity continues to rise steadily. While obesity management typically relies on dietary and lifestyle modifications, individual responses to these interventions vary widely. Clinical guidelines for overweight and obesity stress the importance of personalized approaches to care. This review aims to underscore the role of precision nutrition in delivering tailored interventions for obesity management. RECENT FINDINGS: Recent technological strides have expanded our ability to detect obesity-related genetic polymorphisms, with machine learning algorithms proving pivotal in analyzing intricate genomic data. Machine learning algorithms can also predict postprandial glucose, triglyceride, and insulin levels, facilitating customized dietary interventions and ultimately leading to successful weight loss. Additionally, given that adherence to dietary recommendations is one of the key predictors of weight loss success, employing more objective methods for dietary assessment and monitoring can enhance sustained long-term compliance. Biomarkers of food intake hold promise for a more objective dietary assessment. Acknowledging the multifaceted nature of obesity, precision nutrition stands poised to transform obesity management by tailoring dietary interventions to individuals' genetic backgrounds, gut microbiota, metabolic profiles, and behavioral patterns. However, there is insufficient evidence demonstrating the superiority of precision nutrition over traditional dietary recommendations. The integration of precision nutrition into routine clinical practice requires further validation through randomized controlled trials and the accumulation of a larger body of evidence to strengthen its foundation.
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Affiliation(s)
- Hande Gül Ulusoy-Gezer
- Department of Nutrition and Dietetics, Faculty of Health Sciences, Hacettepe University, 06100, Sıhhiye, Ankara, Türkiye
| | - Neslişah Rakıcıoğlu
- Department of Nutrition and Dietetics, Faculty of Health Sciences, Hacettepe University, 06100, Sıhhiye, Ankara, Türkiye.
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3
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Rao X, Shao Y, Wu H. Fishing for obesity-related gut microbiome enterotype. Cell Host Microbe 2024; 32:1209-1211. [PMID: 39146789 DOI: 10.1016/j.chom.2024.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Revised: 07/02/2024] [Accepted: 07/02/2024] [Indexed: 08/17/2024]
Abstract
Despite debate, the concept of enterotype-like clusters remains valuable for exploring the human gut microbiome and associated environmental factors. In this issue of Cell Host & Microbe, Wu et al. robustly identified an obesity-related enterotype-like cluster, Megamonas, and demonstrated its clinical relevance through cohort studies, mice, and cell experiments.
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Affiliation(s)
- Xuechen Rao
- Fudan Microbiome Center, State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, and Center for Obesity and Hernia Surgery, Department of General Surgery, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Yikai Shao
- Fudan Microbiome Center, State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, and Center for Obesity and Hernia Surgery, Department of General Surgery, Huashan Hospital, Fudan University, Shanghai 200040, China.
| | - Hao Wu
- Fudan Microbiome Center, State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, and Center for Obesity and Hernia Surgery, Department of General Surgery, Huashan Hospital, Fudan University, Shanghai 200040, China.
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4
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Wu C, Yang F, Zhong H, Hong J, Lin H, Zong M, Ren H, Zhao S, Chen Y, Shi Z, Wang X, Shen J, Wang Q, Ni M, Chen B, Cai Z, Zhang M, Cao Z, Wu K, Gao A, Li J, Liu C, Xiao M, Li Y, Shi J, Zhang Y, Xu X, Gu W, Bi Y, Ning G, Wang W, Wang J, Liu R. Obesity-enriched gut microbe degrades myo-inositol and promotes lipid absorption. Cell Host Microbe 2024; 32:1301-1314.e9. [PMID: 38996548 DOI: 10.1016/j.chom.2024.06.012] [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: 11/29/2023] [Revised: 04/29/2024] [Accepted: 06/14/2024] [Indexed: 07/14/2024]
Abstract
Numerous studies have reported critical roles for the gut microbiota in obesity. However, the specific microbes that causally contribute to obesity and the underlying mechanisms remain undetermined. Here, we conducted shotgun metagenomic sequencing in a Chinese cohort of 631 obese subjects and 374 normal-weight controls and identified a Megamonas-dominated, enterotype-like cluster enriched in obese subjects. Among this cohort, the presence of Megamonas and polygenic risk exhibited an additive impact on obesity. Megamonas rupellensis possessed genes for myo-inositol degradation, as demonstrated in vitro and in vivo, and the addition of myo-inositol effectively inhibited fatty acid absorption in intestinal organoids. Furthermore, mice colonized with M. rupellensis or E. coli heterologously expressing the myo-inositol-degrading iolG gene exhibited enhanced intestinal lipid absorption, thereby leading to obesity. Altogether, our findings uncover roles for M. rupellensis as a myo-inositol degrader that enhances lipid absorption and obesity, suggesting potential strategies for future obesity management.
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Affiliation(s)
- Chao Wu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fangming Yang
- BGI Research, Shenzhen 518083, China; Institute of Intelligent Medical Research (IIMR), BGI Genomics, Shenzhen 518083, China
| | - Huanzi Zhong
- BGI Research, Shenzhen 518083, China; Institute of Intelligent Medical Research (IIMR), BGI Genomics, Shenzhen 518083, China
| | - Jie Hong
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huibin Lin
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mingxi Zong
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huahui Ren
- BGI Research, Shenzhen 518083, China; Institute of Intelligent Medical Research (IIMR), BGI Genomics, Shenzhen 518083, China
| | - Shaoqian Zhao
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yufei Chen
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhun Shi
- BGI Research, Shenzhen 518083, China; Institute of Intelligent Medical Research (IIMR), BGI Genomics, Shenzhen 518083, China
| | - Xingyu Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Juan Shen
- BGI Research, Shenzhen 518083, China
| | - Qiaoling Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mengshan Ni
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Banru Chen
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhongle Cai
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Minchun Zhang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhiwen Cao
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kui Wu
- BGI Research, Shenzhen 518083, China; Institute of Intelligent Medical Research (IIMR), BGI Genomics, Shenzhen 518083, China; Guangdong Provincial Key Laboratory of Human Disease Genomics, Shenzhen Key Laboratory of Genomics, BGI Research, Shenzhen 518083, China
| | - Aibo Gao
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junhua Li
- BGI Research, Shenzhen 518083, China
| | - Cong Liu
- BGI Research, Shenzhen 518083, China
| | | | - Yan Li
- BGI Research, Shenzhen 518083, China
| | - Juan Shi
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yifei Zhang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xun Xu
- BGI Research, Shenzhen 518083, China
| | - Weiqiong Gu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yufang Bi
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guang Ning
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Weiqing Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Jiqiu Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Ruixin Liu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Jang MJ, Tan LJ, Park MY, Shin S, Kim JM. Identification of interactions between genetic risk scores and dietary patterns for personalized prevention of kidney dysfunction in a population-based cohort. Nutr Diabetes 2024; 14:62. [PMID: 39143076 PMCID: PMC11325018 DOI: 10.1038/s41387-024-00316-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 07/09/2024] [Accepted: 07/16/2024] [Indexed: 08/16/2024] Open
Abstract
BACKGROUND & AIM Chronic kidney disease (CKD) is a heterogeneous disorder that affects the kidney structure and function. This study investigated the effect of the interaction between genetic factors and dietary pattern on kidney dysfunction in Korean adults. METHODS Baseline data were obtained from the Ansan and Ansung Study of the Korean Genome and Epidemiology Study involving 8230 participants aged 40-69 years. Kidney dysfunction was defined as an estimated glomerular filtration rate < 90 mL/minute/1.73 m2. Genomic DNAs genotyped on the Affymetrix® Genome-Wide Human SNP array 5.0 were isolated from peripheral blood. A genome-wide association study using a generalized linear model was performed on 1,590,162 single-nucleotide polymorphisms (SNPs). To select significant SNPs, the threshold criterion was set at P-value < 5 × 10-8. Linkage disequilibrium clumping was performed based on the R2 value, and 94 SNPs had a significant effect. Participants were divided into two groups based on their generic risk score (GRS): the low-GR group had GRS > 0, while the high-GR group had GRS ≤ 0. RESULTS Three distinct dietary patterns were extracted, namely, the "prudent pattern," "flour-based and animal food pattern," and "white rice pattern," to analyze the effect of dietary pattern on kidney function. In the "flour-based and animal food pattern," higher pattern scores were associated with a higher prevalence of kidney dysfunction in both the low and high GR groups (P for trend < 0.0001 in the low-, high-GR groups of model 1; 0.0050 and 0.0065 in the low-, high-GR groups of model 2, respectively). CONCLUSIONS The results highlight a significant association between the 'flour-based and animal food pattern' and higher kidney dysfunction prevalence in individuals with both low and high GR. These findings suggest that personalized nutritional interventions based on GR profiles may become the basis for presenting GR-based individual dietary patterns for kidney dysfunction.
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Affiliation(s)
- Min-Jae Jang
- Department of Animal Science and Technology, Chung-Ang University, Gyeonggi-do, 17546, Korea
| | - Li-Juan Tan
- Department of Food and Nutrition, Chung-Ang University, Gyeonggi-do, 17546, Korea
| | - Min Young Park
- Department of Molecular Pathobiology, NYU College of Dentistry, New York, USA
| | - Sangah Shin
- Department of Food and Nutrition, Chung-Ang University, Gyeonggi-do, 17546, Korea.
| | - Jun-Mo Kim
- Department of Animal Science and Technology, Chung-Ang University, Gyeonggi-do, 17546, Korea.
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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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Welling MS, de Groot CJ, Mohseni M, Meeusen RE, Boon MR, van Haelst MM, van den Akker EL, van Rossum EF. Treatment with liraglutide or naltrexone-bupropion in patients with genetic obesity: a real-world study. EClinicalMedicine 2024; 74:102709. [PMID: 39050109 PMCID: PMC11268126 DOI: 10.1016/j.eclinm.2024.102709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 06/15/2024] [Accepted: 06/18/2024] [Indexed: 07/27/2024] Open
Abstract
Background Rare genetic obesity commonly features early-onset obesity, hyperphagia, and therapy-resistance to lifestyle interventions. Pharmacotherapy is often required to treat hyperphagia and induce weight loss. We describe clinical outcomes of glucagon-like peptide-1 analogue liraglutide or naltrexone-bupropion treatment in adults with molecularly confirmed genetic obesity (MCGO) or highly suspected for genetic obesity without definite diagnosis (HSGO). Methods We conducted a real-world cohort study at the Obesity Center CGG at Erasmus University Center, Rotterdam, Netherlands, between March 19, 2019, and August 14, 2023. All patients with MCGO and HSGO who were treated with either liraglutide or naltrexone-bupropion were included. Liraglutide 3 mg and naltrexone-bupropion were administered according to the manufacturer's protocol. Treatment evaluation occurred short-term, after 12 weeks on maximum or highest-tolerated dose, preceded by the 4-5 week dose escalation phase. Differences in anthropometrics, body composition, metabolic markers, self-reported appetite, eating behaviour, and quality of life (QoL) were evaluated. Findings Ninety-eight adults were included in the analysis: 23 patients with MCGO and 75 patients with HSGO, with median BMI of 42.0 kg/m2 (IQR 38.7-48.2) and 43.7 kg/m2 (IQR 38.0-48.7), respectively. After liraglutide treatment, median weight at evaluation significantly decreased compared to baseline in both groups: -4.7% (IQR -6.0 to -1.5) in patients with MCGO and -5.2% (IQR -8.1 to -3.5) in patients with HSGO. Additionally, improvements were observed in appetite, fat mass, fasting glucose, and HbA1c in both patients with MCGO and with HSGO. Patients with HSGO also reported significant improvements in several domains of QoL and eating behaviour. In patients with MCGO and HSGO treated with naltrexone-bupropion, mean weight at evaluation significantly differed from baseline: -5.2% ± 5.8 in patients with MCGO and -4.4% ± 4.7 in patients with HSGO. Appetite, fat mass, and waist circumference significantly decreased in both groups. Obesity-related comorbidities improved in significant proportions of patients treated with liraglutide or naltrexone-bupropion. Interpretation In conclusion, our short-term findings show potential of liraglutide and naltrexone-bupropion as treatment options for adults with (a clinical phenotype of) genetic obesity. Funding MB, EvdA, and EvR are supported by the Elisabeth Foundation, a non-profit foundation supporting academic obesity research.
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Affiliation(s)
- Mila S. Welling
- Obesity Center CGG, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Internal Medicine, Division of Endocrinology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Division of Endocrinology, Department of Pediatrics, Erasmus MC-Sophia Children's Hospital, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Cornelis J. de Groot
- Obesity Center CGG, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Internal Medicine, Division of Endocrinology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Division of Endocrinology, Department of Pediatrics, Erasmus MC-Sophia Children's Hospital, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, IJsselland Hospital, Capelle aan den IJssel, the Netherlands
| | - Mostafa Mohseni
- Obesity Center CGG, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Internal Medicine, Division of Endocrinology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Renate E.H. Meeusen
- Obesity Center CGG, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Internal Medicine, Division of Endocrinology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Division of Endocrinology, Department of Pediatrics, Erasmus MC-Sophia Children's Hospital, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Mariëtte R. Boon
- Obesity Center CGG, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Internal Medicine, Division of Endocrinology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Division of Endocrinology, Department of Pediatrics, Erasmus MC-Sophia Children's Hospital, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Mieke M. van Haelst
- Department of Human Genetics, Amsterdam Reproduction and Development Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
- Emma Center for Personalized Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Erica L.T. van den Akker
- Obesity Center CGG, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Division of Endocrinology, Department of Pediatrics, Erasmus MC-Sophia Children's Hospital, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Elisabeth F.C. van Rossum
- Obesity Center CGG, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Internal Medicine, Division of Endocrinology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Division of Endocrinology, Department of Pediatrics, Erasmus MC-Sophia Children's Hospital, University Medical Center Rotterdam, Rotterdam, the Netherlands
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Jansen PR, Vos N, van Uhm J, Dekkers IA, van der Meer R, Mannens MMAM, van Haelst MM. The utility of obesity polygenic risk scores from research to clinical practice: A review. Obes Rev 2024:e13810. [PMID: 39075585 DOI: 10.1111/obr.13810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 06/13/2024] [Accepted: 07/10/2024] [Indexed: 07/31/2024]
Abstract
Obesity represents a major public health emergency worldwide, and its etiology is shaped by a complex interplay of environmental and genetic factors. Over the last decade, polygenic risk scores (PRS) have emerged as a promising tool to quantify an individual's genetic risk of obesity. The field of PRS in obesity genetics is rapidly evolving, shedding new lights on obesity mechanisms and holding promise for contributing to personalized prevention and treatment. Challenges persist in terms of its clinical integration, including the need for further validation in large-scale prospective cohorts, ethical considerations, and implications for health disparities. In this review, we provide a comprehensive overview of PRS for studying the genetics of obesity, spanning from methodological nuances to clinical applications and challenges. We summarize the latest developments in the generation and refinement of PRS for obesity, including advances in methodologies for aggregating genome-wide association study data and improving PRS predictive accuracy, and discuss limitations that need to be overcome to fully realize its potential benefits of PRS in both medicine and public health.
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Affiliation(s)
- Philip R Jansen
- Amsterdam UMC, Department of Human Genetics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, VU University, Amsterdam, Netherlands
- Netherlands Institute for Neuroscience, Amsterdam, Netherlands
| | - Niels Vos
- Amsterdam UMC, Department of Human Genetics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
| | - Jorrit van Uhm
- Amsterdam UMC, Department of Human Genetics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
| | - Ilona A Dekkers
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Rieneke van der Meer
- Netherlands Obesity Clinic, Huis ter Heide, Netherlands
- Amsterdam UMC, Department of Endocrinology and Metabolism, University of Amsterdam, Amsterdam, Netherlands
| | - Marcel M A M Mannens
- Amsterdam UMC, Department of Human Genetics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
| | - Mieke M van Haelst
- Amsterdam UMC, Department of Human Genetics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
- Amsterdam UMC, Emma Center for Personalized Medicine, University of Amsterdam, Amsterdam, Netherlands
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9
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Wang L, Sun Y, Yang L, Wang S, Liu C, Wang Y, Niu Y, Huang Z, Zhang J, Wang C, Dong L. Engineering an energy-dissipating hybrid tissue in vivo for obesity treatment. Cell Rep 2024; 43:114425. [PMID: 38970789 DOI: 10.1016/j.celrep.2024.114425] [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: 11/14/2023] [Revised: 05/28/2024] [Accepted: 06/17/2024] [Indexed: 07/08/2024] Open
Abstract
Obesity is a global health challenge with limited therapeutic solutions. Here, we demonstrate the engineering of an energy-dissipating hybrid tissue (EDHT) in the body for weight control. EDHT is constructed by implanting a synthetic gel matrix comprising immunomodulatory signals and functional cells into the recipient mouse. The immunomodulatory signals induce the host stromal cells to create an immunosuppressive niche that protects the functional cells, which are overexpressing the uncoupling protein 1 (UCP1), from immune rejection. Consequently, these endogenous and exogenous cells co-develop a hybrid tissue that sustainedly produces UCP1 to accelerate the host's energy expenditure. Systematic experiments in high-fat diet (HFD) and transgenic (ob/ob) mice show that EDHT efficiently reduces body weight and relieves obesity-associated pathological conditions. Importantly, an 18-month observation for safety assessment excludes cell leakage from EDHT and reports no adverse physiological responses. Overall, EDHT demonstrates convincing efficacy and safety in controlling body weight.
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Affiliation(s)
- Lintao Wang
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, 163 Xianlin Avenue, Nanjing 210093, China; School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China
| | - Yajie Sun
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, 163 Xianlin Avenue, Nanjing 210093, China
| | - Lifang Yang
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, 163 Xianlin Avenue, Nanjing 210093, China
| | - Shaocong Wang
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, 163 Xianlin Avenue, Nanjing 210093, China
| | - Chunyan Liu
- Medical School, Nanjing University, Nanjing 210093, China
| | - Yulian Wang
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, 163 Xianlin Avenue, Nanjing 210093, China
| | - Yiming Niu
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
| | - Zhen Huang
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, 163 Xianlin Avenue, Nanjing 210093, China
| | - Junfeng Zhang
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, 163 Xianlin Avenue, Nanjing 210093, China; School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China; Medical School, Nanjing University, Nanjing 210093, China.
| | - Chunming Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China; Department of Pharmaceutical Sciences, Faculty of Health Science, University of Macau, Taipa, Macau SAR, China.
| | - Lei Dong
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, 163 Xianlin Avenue, Nanjing 210093, China; Chemistry and Biomedicine Innovation Center, Nanjing University, Nanjing 210093, China; National Resource Center for Mutant Mice, Nanjing, Jiangsu 210023, China.
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10
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Akiyama M, Tamiya G, Fujiwara K, Shiga Y, Yokoyama Y, Hashimoto K, Sato M, Sato K, Narita A, Hashimoto S, Ueda E, Furuta Y, Hata J, Miyake M, Ikeda HO, Suda K, Numa S, Mori Y, Morino K, Murakami Y, Shimokawa S, Nakamura S, Yawata N, Fujisawa K, Yamana S, Mori K, Ikeda Y, Miyata K, Mori K, Ogino K, Koyanagi Y, Kamatani Y, Ninomiya T, Sonoda KH, Nakazawa T. Genetic Risk Stratification of Primary Open-Angle Glaucoma in Japanese Individuals. Ophthalmology 2024:S0161-6420(24)00362-2. [PMID: 39023470 DOI: 10.1016/j.ophtha.2024.05.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 05/26/2024] [Accepted: 05/28/2024] [Indexed: 07/20/2024] Open
Abstract
PURPOSE To assess the impact of genetic risk estimation for primary open-angle glaucoma (POAG) in Japanese individuals. DESIGN Cross-sectional analysis. PARTICIPANTS Genetic risk scores (GRSs) were constructed based on a genome-wide association study (GWAS) of POAG in Japanese people. A total of 3625 Japanese individuals, including 1191 patients and 2434 controls (Japanese Tohoku), were used for the model selection. We also evaluated the discriminative accuracy of constructed GRSs in a dataset comprising 1034 patients and 1147 controls (the Japan Glaucoma Society Omics Group [JGS-OG] and the Genomic Research Committee of the Japanese Ophthalmological Society [GRC-JOS]) and 1900 participants from a population-based study (Hisayama Study). METHODS We evaluated 2 types of GRSs: polygenic risk scores using the pruning and thresholding procedure and a GRS using variants associated with POAG in the GWAS of the International Glaucoma Genetics Consortium (IGGC). We selected the model with the highest areas under the receiver operating characteristic curve (AUC). In the population-based study, we evaluated the correlations between GRS and ocular measurements. MAIN OUTCOME MEASURE Proportion of patients with POAG after stratification according to the GRS. RESULTS We found that a GRS using 98 variants, which showed genome-wide significance in the IGGC, showed the best discriminative accuracy (AUC, 0.65). In the Japanese Tohoku, the proportion of patients with POAG in the top 10% individuals was significantly higher than that in the lowest 10% (odds ratio [OR], 6.15; 95% confidence interval [CI], 4.35-8.71). In the JGS-OG and GRC-JOS, we confirmed similar impact of POAG GRS (AUC, 0.64; OR [top vs. bottom decile], 5.81; 95% CI, 3.79-9.01). In the population-based study, POAG prevalence was significantly higher in the top 20% individuals of the GRS compared with the bottom 20% (9.2% vs. 5.0%). However, the discriminative accuracy was low (AUC, 0.56). The POAG GRS was correlated positively with intraocular pressure (r = 0.08: P = 4.0 × 10-4) and vertical cup-to-disc ratio (r = 0.11; P = 4.0 × 10-6). CONCLUSIONS The GRS showed moderate discriminative accuracy for POAG in the Japanese population. However, risk stratification in the general population showed relatively weak discriminative performance. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Masato Akiyama
- Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
| | - Gen Tamiya
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Kohta Fujiwara
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yukihiro Shiga
- Department of Neuroscience, Université de Montréal, Montréal, Canada; Neuroscience Division, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montréal, Canada; Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Yu Yokoyama
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kazuki Hashimoto
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Masataka Sato
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kota Sato
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Advanced Ophthalmic Medicine, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Akira Narita
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Sawako Hashimoto
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Emi Ueda
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yoshihiko Furuta
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Jun Hata
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Masahiro Miyake
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Hanako O Ikeda
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Kenji Suda
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shogo Numa
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yuki Mori
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Kazuya Morino
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yusuke Murakami
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Sakurako Shimokawa
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Shun Nakamura
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Nobuyo Yawata
- Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kimihiko Fujisawa
- Department of Ophthalmology, Japan Community Healthcare Organization Kyushu Hospital, Fukuoka, Japan
| | - Satoshi Yamana
- Department of Ophthalmology, National Hospital Organization, Kyushu Medical Center, Fukuoka, Japan
| | - Kenichiro Mori
- Department of Ophthalmology, Aso Iizuka Hospital, Iizuka, Japan
| | - Yasuhiro Ikeda
- Department of Ophthalmology, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | | | - Keisuke Mori
- Department of Ophthalmology, International University of Health and Welfare, Nasu-shiobara, Tochigi, Japan
| | - Ken Ogino
- Department of Ophthalmology, Japanese Red Cross Wakayama Medical Center, Wakayama, Japan
| | - Yoshito Koyanagi
- Department of Ophthalmology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yoichiro Kamatani
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Toshiharu Ninomiya
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Koh-Hei Sonoda
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Toru Nakazawa
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Advanced Ophthalmic Medicine, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan; Department of Retinal Disease Control, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Ophthalmic Imaging and Information Analytics, Tohoku University Graduate School of Medicine, Sendai, Japan.
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11
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AlBaloul AH, Griffin J, Kopytek A, Elliott P, Frost G. Evidence of gene-nutrient interaction association with waist circumference, cross-sectional analysis. BMC Public Health 2024; 24:1842. [PMID: 38987751 PMCID: PMC11234640 DOI: 10.1186/s12889-024-19127-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: 06/15/2023] [Accepted: 06/13/2024] [Indexed: 07/12/2024] Open
Abstract
BACKGROUND AND AIMS Waist circumference (WC) is a significant indicator of body adiposity and is associated with increased mortality and morbidity of cardiovascular diseases. Although, single nutrient intake and candidate genes were previously associated with WC. Little is known about WC association with overall diet quality, genetic risk score and gene-nutrient interaction. This study aims to investigate the influence of overall diet quality and multiple WC-associated single nucleotide polymorphisms on WC. In addition to investigating gene-nutrient interaction association with WC. METHODS This study explored cross-sectional data from two large sample-size studies, to provide reproducible results. As a representation of the UK population, the Airwave Health Monitoring Study (n = 6,502) and the UK-Biobank Cohort Study (n = 171,129) were explored for factors associated with WC. Diet quality was evaluated based on the Mellen Index for Dietary Approaches to Stop Hypertension (Mellen-DASH). The genetic risk score for WC (GRS-Waist) was calculated by screening the population genotype for WC-associated single nucleotide polymorphisms. Multivariate linear regression models were built to explore WC association with diet quality and genetic risk score. Gene-nutrient interaction was explored by introducing the interaction term (GRS-Waist X Mellen-DASH score) to multivariate linear regression analysis. RESULTS The prevalence of high WC (Female > 80 cm, Male > 94 cm) was 46.5% and 51.7% in both populations. Diet quality and genetic risk score of WC were significantly associated with WC. There was no evidence of interaction between GRS-Waist, DASH diet scores and nutrient intake on WC. CONCLUSION This study's findings provided reproducible results on waist circumference association with diet and genetics and tested the possibility of gene-nutrient interaction. These reproducible results are successful in building the foundation for using diet and genetics for early identification of those at risk of having high WC and WC-associated diseases. In addition, evidence on gene-diet interactions on WC is limited and lacks replication, therefore our findings may guide future research in investigating this interaction and investigating its application in precision nutrition.
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Affiliation(s)
- Anwar H AlBaloul
- Department of Community Medicine and Behavioural Sciences, Faculty of Medicine, Kuwait University, Safat, Kuwait
- Section of Nutrition, Faculty of Medicine, Imperial College London, London, UK
| | - Jennifer Griffin
- Section of Nutrition, Faculty of Medicine, Imperial College London, London, UK
| | - Alexandra Kopytek
- Section of Nutrition, Faculty of Medicine, Imperial College London, London, UK
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Gary Frost
- Section of Nutrition, Faculty of Medicine, Imperial College London, London, UK.
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12
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Venkatesh SS, Ganjgahi H, Palmer DS, Coley K, Linchangco GV, Hui Q, Wilson P, Ho YL, Cho K, Arumäe K, Wittemans LBL, Nellåker C, Vainik U, Sun YV, Holmes C, Lindgren CM, Nicholson G. Characterising the genetic architecture of changes in adiposity during adulthood using electronic health records. Nat Commun 2024; 15:5801. [PMID: 38987242 PMCID: PMC11237142 DOI: 10.1038/s41467-024-49998-0] [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: 01/25/2023] [Accepted: 06/25/2024] [Indexed: 07/12/2024] Open
Abstract
Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 24.5 million primary-care health records in over 740,000 individuals in the UK Biobank, Million Veteran Program USA, and Estonian Biobank, to discover and validate the genetic architecture of adiposity trajectories. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI by 14%. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (APOE missense variant). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology of quantitative traits in adulthood.
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Affiliation(s)
- Samvida S Venkatesh
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
| | - Habib Ganjgahi
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Duncan S Palmer
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Kayesha Coley
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Gregorio V Linchangco
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Qin Hui
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Peter Wilson
- Atlanta VA Health Care System, Decatur, GA, USA
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs Boston Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kadri Arumäe
- Institute of Psychology, Faculty of Social Sciences, University of Tartu, Tartu, Estonia
| | - Laura B L Wittemans
- Novo Nordisk Research Centre Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Christoffer Nellåker
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Uku Vainik
- Institute of Psychology, Faculty of Social Sciences, University of Tartu, Tartu, Estonia
- Estonian Genome Centre, Institute of Genomics, Faculty of Science and Technology, University of Tartu, Tartu, Estonia
- Department of Neurology and Neurosurgery, Faculty of Medicine and Health Sciences, University of McGill, Montreal, Canada
| | - Yan V Sun
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Chris Holmes
- Department of Statistics, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, Medical Sciences Division, University of Oxford, Oxford, UK
- The Alan Turing Institute, London, UK
| | - Cecilia M Lindgren
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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13
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Eunjin B, Ji Y, Jo J, Kim Y, Lee JP, Won S, Lee J. Effects of polygenic risk score and sodium and potassium intake on hypertension in Asians: A nationwide prospective cohort study. Hypertens Res 2024:10.1038/s41440-024-01784-7. [PMID: 38982292 DOI: 10.1038/s41440-024-01784-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 06/11/2024] [Accepted: 06/15/2024] [Indexed: 07/11/2024]
Abstract
Genetic factors, lifestyle, and diet have been shown to play important roles in the development of hypertension. Increased salt intake is an important risk factor for hypertension. However, research on the involvement of genetic factors in the relationship between salt intake and hypertension in Asians is lacking. We aimed to investigate the risk of hypertension in relation to sodium and potassium intake and the effects of genetic factors on their interactions. We used Korean Genome and Epidemiology Study data and calculated the polygenic risk score (PRS) for the effect of systolic and diastolic blood pressure (SBP and DBP). We also conducted multivariable logistic modeling to evaluate associations among incident hypertension, PRSSBP, PRSDBP, and sodium and potassium intake. In total, 41,351 subjects were included in the test set. The top 10% PRSSBP group was the youngest of the three groups (bottom 10%, middle, top 10%), had the highest proportion of women, and had the highest body mass index, baseline BP, red meat intake, and alcohol consumption. The multivariable logistic regression model revealed the risk of hypertension was significantly associated with higher PRSSBP, higher sodium intake, and lower potassium intake. There was significant interaction between sodium intake and PRSSBP for incident hypertension especially in sodium intake ≥2.0 g/day and PRSSBP top 10% group (OR 1.27 (1.07-1.51), P = 0.007). Among patients at a high risk of incident hypertension due to sodium intake, lifestyle modifications and sodium restriction were especially important to prevent hypertension.
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Affiliation(s)
- Bae Eunjin
- Department of Internal Medicine, Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea
- Department of Internal Medicine, College of Medicine, Gyeongsang National University, Jinju, Republic of Korea
- Institute of Medical Science, College of Medicine, Gyeongsang National University, Jinju, Republic of Korea
| | - Yunmi Ji
- College of Natural Sciences, Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Jinyeon Jo
- Department of Public Health Sciences, Institute of Health & Environment, Seoul National University, Seoul, Republic of Korea
| | - Yaerim Kim
- Department of Internal Medicine, College of Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Jung Pyo Lee
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sungho Won
- Department of Public Health Sciences, Institute of Health & Environment, Seoul National University, Seoul, Republic of Korea.
- Interdisciplinary Program for Bioinformatics, College of Natural Science, Seoul National University, Seoul, Republic of Korea.
- RexSoft Corps, Seoul, Republic of Korea.
| | - Jeonghwan Lee
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea.
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
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14
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Chen Y, Huang B, Liang H, Ji H, Wang Z, Song X, Zhu H, Song S, Yuan W, Wu Q, Miao M. Gestational organophosphate esters (OPEs) exposure in association with placental DNA methylation levels of peroxisome proliferator-activated receptors (PPARs) signaling pathway-related genes. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 947:174569. [PMID: 38977092 DOI: 10.1016/j.scitotenv.2024.174569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Revised: 07/01/2024] [Accepted: 07/04/2024] [Indexed: 07/10/2024]
Abstract
BACKGROUND Organophosphate esters (OPEs) exposure could affect offspring health. However, the underlying mechanisms are not well documented. OBJECTIVES Based on a birth cohort study, we aimed to investigate the associations among gestational OPEs exposure, placental DNA methylation levels of peroxisome proliferator-activated receptor (PPAR) signaling pathway-related genes, and fetal growth. METHODS We measured the concentrations of eight OPE metabolites in maternal urine samples and neonatal anthropometric measurements in 733 mother-child pairs. In 327 placental samples, we assessed the DNA methylation levels of 14 genes which were involved in the PPARs signaling pathway and expressed in placenta. Multiple linear regression models were used to examine the associations of OPEs exposure with placental DNA methylation, and of OPEs and placental DNA methylation with neonatal anthropometric measurements. Causal mediation analyses were conducted to examine the potential mediating role of placental DNA methylation in the pathway between OPEs exposure and fetal growth. RESULTS We observed a general pattern of OPEs exposure being associated with hypermethylation of candidate genes, with statistically significant associations identified for several OPEs with RXRA, ACAA1, ACADL, ACADM, PLTP, and NR1H3 methylation. Further, gestational exposure to BCIPP, DPP, BBOEP, ∑NCl-OPEs, and ∑OPEs tended to be associated with lower anthropometric measurements, with more significant associations observed on arm circumference, and abdominal and back skinfold thickness. Notably, RXRA, ACAA1, ACOX1, CPT2, ACADM, and NR1H3 methylation tended to be associated with lower neonatal anthropometric measurements, especially for abdominal and back skinfold thickness. Moreover, mediation analyses showed that 19.42 % of the total effect of DPP on the back skinfold thickness was mediated by changes in RXRA methylation, and there was a significant indirect effect of RXRA methylation. CONCLUSIONS Gestational OPEs exposure could disrupt the placental DNA methylation levels of PPAR signaling pathway-related genes, which might contribute to the effect of OPEs on fetal growth.
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Affiliation(s)
- Yafei Chen
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, NHC Key Lab of Reproduction Regulation, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Baoqin Huang
- The Third Affiliated Hospital, SUN YAT-SEN University, Guangzhou 510631, China
| | - Hong Liang
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, NHC Key Lab of Reproduction Regulation, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Honglei Ji
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, NHC Key Lab of Reproduction Regulation, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Ziliang Wang
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, NHC Key Lab of Reproduction Regulation, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Xiuxia Song
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, NHC Key Lab of Reproduction Regulation, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Haijun Zhu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, NHC Key Lab of Reproduction Regulation, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Shujuan Song
- Hangzhou Center for Disease Control and Prevention, Hangzhou 310021, Zhejiang Province, China
| | - Wei Yuan
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, NHC Key Lab of Reproduction Regulation, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Qihan Wu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, NHC Key Lab of Reproduction Regulation, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China.
| | - Maohua Miao
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, NHC Key Lab of Reproduction Regulation, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China.
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15
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Friedman MI, Sørensen TIA, Taubes G, Lund J, Ludwig DS. Trapped fat: Obesity pathogenesis as an intrinsic disorder in metabolic fuel partitioning. Obes Rev 2024. [PMID: 38961319 DOI: 10.1111/obr.13795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 05/24/2024] [Accepted: 06/13/2024] [Indexed: 07/05/2024]
Abstract
Our understanding of the pathophysiology of obesity remains at best incomplete despite a century of research. During this time, two alternative perspectives have helped shape thinking about the etiology of the disorder. The currently prevailing view holds that excessive fat accumulation results because energy intake exceeds energy expenditure, with excessive food consumption being the primary cause of the imbalance. The other perspective attributes the initiating cause of obesity to intrinsic metabolic defects that shift fuel partitioning from pathways for mobilization and oxidation to those for synthesis and storage. The resulting reduction in fuel oxidation and trapping of energy in adipose tissue drives a compensatory increase in energy intake and, under some conditions, a decrease in expenditure. This theory of obesity pathogenesis has historically garnered relatively less attention despite its pedigree. Here, we present an updated comprehensive formulation of the fuel partitioning theory, focused on evidence gathered over the last 80 years from major animal models of obesity showing a redirection of fuel fluxes from oxidation to storage and accumulation of excess body fat with energy intake equal to or even less than that of lean animals. The aim is to inform current discussions about the etiology of obesity and by so doing, help lay new foundations for the design of more efficacious approaches to obesity research, treatment and prevention.
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Affiliation(s)
| | - Thorkild I A Sørensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
- Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
- Center for Childhood Health, Copenhagen, Denmark
| | | | - Jens Lund
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - David S Ludwig
- New Balance Foundation Obesity Prevention Center, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Exercise and Sports, University of Copenhagen, Denmark
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16
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Lankester J, Guarischi-Sousa R, Hilliard AT, Shere L, Husary M, Crowe S, Tsao PS, Rehkopf DH, Assimes TL. Increased BMI associated with decreased breastfeeding initiation in Million Veteran Program participants. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.02.24309047. [PMID: 39006437 PMCID: PMC11245076 DOI: 10.1101/2024.07.02.24309047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Background Breastfeeding has been associated with maternal and infant health benefits but has been inversely associated with body mass index (BMI) prepartum. Breastfeeding and BMI are both linked to socioeconomic factors. Methods Data from parous female participants with available breastfeeding information from the Million Veteran Program cohort was included. BMI at enrollment and earliest BMI available were extracted, and polygenic scores (PGS) for BMI were calculated. We modeled breastfeeding for one month or more as a function of BMI at enrollment; earliest BMI where available pre-pregnancy; and PGS for BMI. We conducted Mendelian randomization for breastfeeding initiation using PGS as an instrumental variable. Results A higher BMI predicted a lower likelihood of breastfeeding for one month or more in all analyses. A +5 kg/m 2 BMI pre-pregnancy was associated with a 24% reduced odds of breastfeeding, and a +5 kg/m 2 genetically predicted BMI was associated with a 17% reduced odds of breastfeeding. Conclusions BMI predicts a lower likelihood of breastfeeding for one month or longer. Given the high success of breastfeeding initiation regardless of BMI in supportive environments as well as potential health benefits, patients with elevated BMI may benefit from additional postpartum breastfeeding support.
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17
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Kim MS, Shim I, Fahed AC, Do R, Park WY, Natarajan P, Khera AV, Won HH. Association of genetic risk, lifestyle, and their interaction with obesity and obesity-related morbidities. Cell Metab 2024; 36:1494-1503.e3. [PMID: 38959863 DOI: 10.1016/j.cmet.2024.06.004] [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] [Received: 08/18/2023] [Revised: 02/26/2024] [Accepted: 06/06/2024] [Indexed: 07/05/2024]
Abstract
The extent to which modifiable lifestyle factors offset the determined genetic risk of obesity and obesity-related morbidities remains unknown. We explored how the interaction between genetic and lifestyle factors influences the risk of obesity and obesity-related morbidities. The polygenic score for body mass index was calculated to quantify inherited susceptibility to obesity in 338,645 UK Biobank European participants, and a composite lifestyle score was derived from five obesogenic factors (physical activity, diet, sedentary behavior, alcohol consumption, and sleep duration). We observed significant interaction between high genetic risk and poor lifestyles (pinteraction < 0.001). Absolute differences in obesity risk between those who adhere to healthy lifestyles and those who do not had gradually expanded with an increase in polygenic score. Despite a high genetic risk for obesity, individuals can prevent obesity-related morbidities by adhering to a healthy lifestyle and maintaining a normal body weight. Healthy lifestyles should be promoted irrespective of genetic background.
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Affiliation(s)
- Min Seo Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul 06355, Republic of Korea; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Injeong Shim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul 06355, Republic of Korea; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Akl C Fahed
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Seoul 06351, Republic of Korea
| | - Pradeep Natarajan
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Amit V Khera
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Verve Therapeutics, Boston, MA 02215, USA.
| | - Hong-Hee Won
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul 06355, Republic of Korea; Samsung Genome Institute, Samsung Medical Center, Seoul 06351, Republic of Korea.
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18
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Hochner H, Butterman R, Margaliot I, Friedlander Y, Linial M. Obesity risk in young adults from the Jerusalem Perinatal Study (JPS): the contribution of polygenic risk and early life exposure. Int J Obes (Lond) 2024; 48:954-963. [PMID: 38472354 PMCID: PMC11216986 DOI: 10.1038/s41366-024-01505-7] [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] [Received: 10/11/2023] [Revised: 02/12/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024]
Abstract
BACKGROUND/OBJECTIVES The effects of early life exposures on offspring life-course health are well established. This study assessed whether adding early socio-demographic and perinatal variables to a model based on polygenic risk score (PRS) improves prediction of obesity risk. METHODS We used the Jerusalem Perinatal study (JPS) with data at birth and body mass index (BMI) and waist circumference (WC) measured at age 32. The PRS was constructed using over 2.1M common SNPs identified in genome-wide association study (GWAS) for BMI. Linear and logistic models were applied in a stepwise approach. We first examined the associations between genetic variables and obesity-related phenotypes (e.g., BMI and WC). Secondly, socio-demographic variables were added and finally perinatal exposures, such as maternal pre-pregnancy BMI (mppBMI) and gestational weight gain (GWG) were added to the model. Improvement in prediction of each step was assessed using measures of model discrimination (area under the curve, AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI). RESULTS One standard deviation (SD) change in PRS was associated with a significant increase in BMI (β = 1.40) and WC (β = 2.45). These associations were slightly attenuated (13.7-14.2%) with the addition of early life exposures to the model. Also, higher mppBMI was associated with increased offspring BMI (β = 0.39) and WC (β = 0.79) (p < 0.001). For obesity (BMI ≥ 30) prediction, the addition of early socio-demographic and perinatal exposures to the PRS model significantly increased AUC from 0.69 to 0.73. At an obesity risk threshold of 15%, the addition of early socio-demographic and perinatal exposures to the PRS model provided a significant improvement in reclassification of obesity (NRI, 0.147; 95% CI 0.068-0.225). CONCLUSIONS Inclusion of early life exposures, such as mppBMI and maternal smoking, to a model based on PRS improves obesity risk prediction in an Israeli population-sample.
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Affiliation(s)
- Hagit Hochner
- Braun School of Public Health, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Rachely Butterman
- Braun School of Public Health, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ido Margaliot
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel
| | - Yechiel Friedlander
- Braun School of Public Health, Hebrew University of Jerusalem, Jerusalem, Israel.
| | - Michal Linial
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel
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19
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Arsenault BJ, Carpentier AC, Poirier P, Després JP. Adiposity, type 2 diabetes and atherosclerotic cardiovascular disease risk: Use and abuse of the body mass index. Atherosclerosis 2024; 394:117546. [PMID: 38692978 DOI: 10.1016/j.atherosclerosis.2024.117546] [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] [Received: 12/18/2023] [Revised: 03/29/2024] [Accepted: 04/10/2024] [Indexed: 05/03/2024]
Abstract
The worldwide prevalence of individuals with an elevated body weight has increased steadily over the past five decades. Billions of research dollars have been invested to improve our understanding of the causes and consequences of having an elevated body weight. All this knowledge has, however, failed to influence populational body weight trajectories of most countries around the world. Research on the definition of "obesity" has also evolved. Body mass index (BMI), the most commonly used tool to make its diagnosis, has major limitations. In this review article, we will highlight evidence from observational studies, genetic association studies and randomized clinical trials that have shown the remarkable inter-individual differences in the way humans store energy as body fat. Increasing evidence also suggests that, as opposed to weight inclusive, lifestyle-based approaches, weight-centric approaches advising people to simply eat less and move more are not sustainable for most people for long-term weight loss and maintenance. It is time to recognize that this outdated approach may have produced more harm than good. On the basis of pathophysiological, genetic and clinical evidence presented in this review, we propose that it may be time to shift away from the traditional clinical approach, which is BMI-centric. Rather, emphasis should be placed on actionable lifestyle-related risk factors aiming at improving overall diet quality and increasing physical activity level in the general population.
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Affiliation(s)
- Benoit J Arsenault
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Québec (QC), Canada; Department of Medicine, Faculty of Medicine, Université Laval, Québec (QC), Canada
| | - André C Carpentier
- Division of Endocrinology, Department of Medicine, Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Université de Sherbrooke, Sherbrooke (QC), Canada
| | - Paul Poirier
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Québec (QC), Canada; Faculté de pharmacie, Université Laval, Québec (QC), Canada
| | - Jean-Pierre Després
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Québec (QC), Canada; VITAM - Centre de recherche en santé durable, CIUSSS de la Capitale-Nationale, Québec (QC), Canada; Department of Kinesiology, Faculty of Medicine, Université Laval, Québec (QC), Canada.
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20
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Trinder M, Cermakova L, Ruel I, Baass A, Paquette M, Wang J, Kennedy BA, Hegele RA, Genest J, Brunham LR. Influence of Polygenic Background on the Clinical Presentation of Familial Hypercholesterolemia. Arterioscler Thromb Vasc Biol 2024; 44:1683-1693. [PMID: 38779854 PMCID: PMC11208056 DOI: 10.1161/atvbaha.123.320287] [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: 10/17/2023] [Accepted: 05/08/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND Heterozygous familial hypercholesterolemia (FH) is among the most common genetic conditions worldwide that affects ≈ 1 in 300 individuals. FH is characterized by increased levels of low-density lipoprotein cholesterol (LDL-C) and increased risk of coronary artery disease (CAD), but there is a wide spectrum of severity within the FH population. This variability in expression is incompletely explained by known risk factors. We hypothesized that genome-wide genetic influences, as represented by polygenic risk scores (PRSs) for cardiometabolic traits, would influence the phenotypic severity of FH. METHODS We studied individuals with clinically diagnosed FH (n=1123) from the FH Canada National Registry, as well as individuals with genetically identified FH from the UK Biobank (n=723). For all individuals, we used genome-wide gene array data to calculate PRSs for CAD, LDL-C, lipoprotein(a), and other cardiometabolic traits. We compared the distribution of PRSs in individuals with clinically diagnosed FH, genetically diagnosed FH, and non-FH controls and examined the association of the PRSs with the risk of atherosclerotic cardiovascular disease. RESULTS Individuals with clinically diagnosed FH had higher levels of LDL-C, and the incidence of atherosclerotic cardiovascular disease was higher in individuals with clinically diagnosed compared with genetically identified FH. Individuals with clinically diagnosed FH displayed enrichment for higher PRSs for CAD, LDL-C, and lipoprotein(a) but not for other cardiometabolic risk factors. The CAD PRS was associated with a risk of atherosclerotic cardiovascular disease among individuals with an FH-causing genetic variant. CONCLUSIONS Genetic background, as expressed by genome-wide PRSs for CAD, LDL-C, and lipoprotein(a), influences the phenotypic severity of FH, expanding our understanding of the determinants that contribute to the variable expressivity of FH. A PRS for CAD may aid in risk prediction among individuals with FH.
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Affiliation(s)
- Mark Trinder
- Centre for Heart Lung Innovation, University of British Columbia and St. Paul’s Hospital, Vancouver, Canada (M.T., L.C., L.R.B.)
| | - Lubomira Cermakova
- Centre for Heart Lung Innovation, University of British Columbia and St. Paul’s Hospital, Vancouver, Canada (M.T., L.C., L.R.B.)
| | - Isabelle Ruel
- Research Institute of the McGill University Health Centre, Montreal, Canada (I.R., J.G.)
| | - Alexis Baass
- Montreal Clinical Research Institute, Canada (A.B., M.P.)
| | | | - Jian Wang
- Departments of Medicine and Biochemistry, Schulich School of Medicine and Robarts Research Institute, Western University, London, Canada (J.W., B.A.K., R.A.H.)
| | - Brooke A. Kennedy
- Departments of Medicine and Biochemistry, Schulich School of Medicine and Robarts Research Institute, Western University, London, Canada (J.W., B.A.K., R.A.H.)
| | - Robert A. Hegele
- Departments of Medicine and Biochemistry, Schulich School of Medicine and Robarts Research Institute, Western University, London, Canada (J.W., B.A.K., R.A.H.)
| | - Jacques Genest
- Research Institute of the McGill University Health Centre, Montreal, Canada (I.R., J.G.)
| | - Liam R. Brunham
- Centre for Heart Lung Innovation, University of British Columbia and St. Paul’s Hospital, Vancouver, Canada (M.T., L.C., L.R.B.)
- Departments of Medicine and Medical Genetics, University of British Columbia, Vancouver, Canada (L.R.B.)
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21
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Sycamnias L, Kerr JA, Lange K, Saffery R, Wang Y, Wake M, Olds T, Dwyer T, Burgner D, Grobler AC. Polygenic Risk Scores and the Risk of Childhood Overweight/Obesity in Association With the Consumption of Sweetened Beverages: A Population-Based Cohort Study. Child Obes 2024; 20:354-365. [PMID: 37851993 DOI: 10.1089/chi.2023.0012] [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] [Indexed: 10/20/2023]
Abstract
Background: Sugar-sweetened beverage (SSB) and non-nutritive sweetened beverage (NNSB) consumption is associated with obesity and are targets for population-level dietary interventions. In children (<16 years), we evaluate whether SSB or NNSB consumption is associated with subsequent (2 years later) overweight and/or obesity, and the effect of consumption on subsequent overweight/obesity differs by BMI polygenic risk score (BMI-PRS). Methods: The nationally representative Longitudinal-Study-of-Australian-Children had biennial data collection from birth (n = 5107) until age 14/15 years (n = 3127). At age 11/12 years, a comprehensive biomedical assessment, including PRS assessment, was undertaken (n = 1422). Parent- or self-reported beverage consumption (SSBs: soft drinks, energy drinks, and/or juice; NNSBs: diet drinks) was measured as any/none over previous 24 hours. BMI-PRS was derived using published results (high PRS ≥75th percentile). At ages 4/5-14/15 children were classified as having obesity, overweight/obesity, or not having overweight/obesity using BMI z-score (CDC cut points). Results: SSB consumption had limited association with subsequent overweight/obesity. NNSB consumption was associated with ∼8% more children with subsequent overweight/obesity at most ages. In older children with high BMI-PRS, associations between NNSB consumption and subsequent overweight/obesity strengthened with age [at age 14-15 for high BMI-PRS, difference in proportion with overweight/obesity among NNSB consumers vs. nonconsumers = 0.38 (95% confidence interval: 0.22 to 0.55, p ≤ 0.001)]. There was limited association between SSB consumption and BMI-PRS. Conclusion: NNSB consumption was associated with increased risk of overweight/obesity for children with greater genetic risk at older ages (12-15 years). Focused intervention among children with high genetic risk could target NNSB consumption; however, reverse causality (children with genetic risk and/or high BMI consume more NNSBs) cannot be excluded.
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Affiliation(s)
- Lachlan Sycamnias
- Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
| | - Jessica A Kerr
- Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
- Department of Psychological Medicine, University of Otago Christchurch, Christchurch, New Zealand
| | - Katherine Lange
- Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
| | - Richard Saffery
- Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
| | - Yichao Wang
- Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
- Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health, Deakin University, Geelong, Victoria, Australia
| | - Melissa Wake
- Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Tim Olds
- Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Alliance for Research in Exercise, Nutrition and Activity, University of South Australia, Adelaide, South Australia, Australia
| | - Terry Dwyer
- Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - David Burgner
- Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
- Department of General Medicine, Royal Children's Hospital, Parkville, Victoria, Australia
| | - Anneke C Grobler
- Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
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22
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Houtz C. Weighty matters: Considering the ethics of genetic risk scores for obesity. Genet Med 2024; 26:101196. [PMID: 38907622 DOI: 10.1016/j.gim.2024.101196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 06/14/2024] [Accepted: 06/15/2024] [Indexed: 06/24/2024] Open
Affiliation(s)
- Cassie Houtz
- University of Pennsylvania Perelman School of Medicine, Department of Medical Ethics and Health Policy, Philadelphia, PA.
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23
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Ekberg KM, Michelini G, Schneider KL, Docherty AR, Shabalin AA, Perlman G, Kotov R, Klein DN, Waszczuk MA. Associations between polygenic risk scores for cardiometabolic phenotypes and adolescent depression and body dissatisfaction. Pediatr Res 2024:10.1038/s41390-024-03323-z. [PMID: 38879627 DOI: 10.1038/s41390-024-03323-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: 03/13/2024] [Revised: 05/22/2024] [Accepted: 05/27/2024] [Indexed: 08/07/2024]
Abstract
BACKGROUND Adolescents with elevated body mass index (BMI) are at an increased risk for depression and body dissatisfaction. Type 2 diabetes (T2D) is an established risk factor for depression. However, shared genetic risk between cardiometabolic conditions and mental health outcomes remains understudied in youth. METHODS The current study examined associations between polygenic risk scores (PRS) for BMI and T2D, and symptoms of depression and body dissatisfaction, in a sample of 827 community adolescents (Mage = 13.63, SDage = 1.01; 76% girls). BMI, depressive symptoms, and body dissatisfaction were assessed using validated self-report questionnaires. RESULTS BMI-PRS was associated with phenotypic BMI (β = 0.24, p < 0.001) and body dissatisfaction (β = 0.17, p < 0.001), but not with depressive symptoms. The association between BMI-PRS and body dissatisfaction was significantly mediated by BMI (indirect effect = 0.10, CI [0.07-0.13]). T2D-PRS was not associated with depression or body dissatisfaction. CONCLUSIONS The results suggest phenotypic BMI may largely explain the association between genetic risk for elevated BMI and body dissatisfaction in adolescents. Further research on age-specific genetic effects is needed, as summary statistics from adult discovery samples may have limited utility in youth. IMPACT The association between genetic risk for elevated BMI and body dissatisfaction in adolescents may be largely explained by phenotypic BMI, indicating a potential pathway through which genetic predisposition influences body image perception. Furthermore, age-specific genetic research is needed to understand the unique influences on health outcomes during adolescence. By identifying BMI as a potential mediator in the association between genetic risk for elevated BMI and body dissatisfaction, the current findings offer insights that could inform interventions targeting body image concerns and mental health in this population.
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Affiliation(s)
- Krista M Ekberg
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA.
| | - Giorgia Michelini
- Department of Biological and Experimental Psychology, Queen Mary University of London, London, UK
| | - Kristin L Schneider
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA
| | - Anna R Docherty
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
| | - Andrey A Shabalin
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
| | - Greg Perlman
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
| | - Roman Kotov
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
| | - Daniel N Klein
- Department of Psychology, Stony Brook University, Stony Brook, NY, USA
| | - Monika A Waszczuk
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA
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24
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Law PP, Mikheeva LA, Rodriguez-Algarra F, Asenius F, Gregori M, Seaborne RAE, Yildizoglu S, Miller JRC, Tummala H, Mesnage R, Antoniou MN, Li W, Tan Q, Hillman SL, Rakyan VK, Williams DJ, Holland ML. Ribosomal DNA copy number is associated with body mass in humans and other mammals. Nat Commun 2024; 15:5006. [PMID: 38866738 PMCID: PMC11169392 DOI: 10.1038/s41467-024-49397-5] [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: 07/21/2023] [Accepted: 06/03/2024] [Indexed: 06/14/2024] Open
Abstract
Body mass results from a complex interplay between genetics and environment. Previous studies of the genetic contribution to body mass have excluded repetitive regions due to the technical limitations of platforms used for population scale studies. Here we apply genome-wide approaches, identifying an association between adult body mass and the copy number (CN) of 47S-ribosomal DNA (rDNA). rDNA codes for the 18 S, 5.8 S and 28 S ribosomal RNA (rRNA) components of the ribosome. In mammals, there are hundreds of copies of these genes. Inter-individual variation in the rDNA CN has not previously been associated with a mammalian phenotype. Here, we show that rDNA CN variation associates with post-pubertal growth rate in rats and body mass index in adult humans. rDNA CN is not associated with rRNA transcription rates in adult tissues, suggesting the mechanistic link occurs earlier in development. This aligns with the observation that the association emerges by early adulthood.
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Affiliation(s)
- Pui Pik Law
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, UK
- The Blizard Institute, School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Liudmila A Mikheeva
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, UK
| | | | - Fredrika Asenius
- UCL EGA Institute for Women's Health, University College London, London, UK
| | - Maria Gregori
- UCL EGA Institute for Women's Health, University College London, London, UK
| | - Robert A E Seaborne
- The Blizard Institute, School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Centre for Human and Applied Physiological Studies, King's College London, London, UK
| | - Selin Yildizoglu
- The Blizard Institute, School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - James R C Miller
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, UK
| | - Hemanth Tummala
- The Blizard Institute, School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Robin Mesnage
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, UK
| | - Michael N Antoniou
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, UK
| | - Weilong Li
- Population Research Unit, University of Helsinki, Helsinki, Finland
| | - Qihua Tan
- Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Copenhagen, Denmark
| | - Sara L Hillman
- UCL EGA Institute for Women's Health, University College London, London, UK
| | - Vardhman K Rakyan
- The Blizard Institute, School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - David J Williams
- UCL EGA Institute for Women's Health, University College London, London, UK
| | - Michelle L Holland
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, UK.
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Cho YH, Sakong H, Oh MJ, Seo TB. Assessing the Risk of Normal Weight Obesity in Korean Women across Generations: A Study on Body Composition and Physical Fitness. Healthcare (Basel) 2024; 12:1142. [PMID: 38891217 PMCID: PMC11171998 DOI: 10.3390/healthcare12111142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 05/27/2024] [Accepted: 06/01/2024] [Indexed: 06/21/2024] Open
Abstract
Normal weight obesity (NWO) refers to a condition in which the body mass index falls within the normal range, but the percent of body fat is excessive. Although there are reports of a high prevalence of cardiovascular and metabolic diseases in NWO, analyses regarding physical fitness have been lacking. Therefore, the purpose of this study was to analyze the age-related prevalence of NWO and to examine physical fitness across generations. Our study utilized a dataset comprising 119,835 participants for analysis. The prevalence of NWO across ages was examined using cross-tabulation analysis. For body composition and physical fitness, medians and group differences were assessed by generation through Kruskal-Wallis and Bonferroni post hoc tests. Additionally, univariate logistic regression was adopted to analyze the odds ratio. The prevalence of NWO in Korean women was 18.3%. The fat-free mass of the NWO group was consistently lower than that of both the group with normal body mass indexes (Normal) and obese body mass indexes (Obesity) across all generations. Additionally, the waist circumference and blood pressure were greater in the now group than in the Normal group. When considering maximal strength, muscle endurance, power, balance, and coordination, the NWO group exhibited lower levels compared to the Normal group. The NWO group showed lower muscle mass than both the Normal and Obesity groups, resulting in significantly reduced physical fitness compared to that of the Normal group, similar to the Obesity group. This condition may increase not only the risk of posing a potentially more serious health concern than obesity but also the risk of falls in elderly people. Therefore, based on this study, it is crucial to not only define obesity using BMI criteria but also to diagnose NWO. Public health policies and preventive measures must be implemented accordingly.
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Affiliation(s)
- Yeong-Hyun Cho
- Department of Sport Science, College of Natural Science, Jeju National University, 102 Jejudaehak-ro, Jeju 63243, Republic of Korea;
| | - Hyuk Sakong
- Korea Institute of Sport Science, 727 Hwarang-ro, Nowongu, Seoul 01794, Republic of Korea;
| | - Myung-Jin Oh
- Division of Sports Science, Baekseok University, 1 Baekseokdaehak-ro, Dongnam-gu, Cheonan-si 31065, Republic of Korea;
| | - Tae-Beom Seo
- Department of Sport Science, College of Natural Science, Jeju National University, 102 Jejudaehak-ro, Jeju 63243, Republic of Korea;
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Hansen Edwards C, Håkon Bjørngaard J, Minet Kinge J, Åberge Vie G, Halsteinli V, Ødegård R, Kulseng B, Waaler Bjørnelv G. The healthcare costs of increased body mass index-evidence from The Trøndelag Health Study. HEALTH ECONOMICS REVIEW 2024; 14:36. [PMID: 38822866 PMCID: PMC11143647 DOI: 10.1186/s13561-024-00512-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/13/2024] [Indexed: 06/03/2024]
Abstract
BACKGROUND Earlier studies have estimated the impact of increased body mass index (BMI) on healthcare costs. Various methods have been used to avoid potential biases and inconsistencies. Each of these methods measure different local effects and have different strengths and weaknesses. METHODS In the current study we estimate the impact of increased BMI on healthcare costs using nine common methods from the literature: multivariable regression analyses (ordinary least squares, generalized linear models, and two-part models), and instrumental variable models (using previously measured BMI, offspring BMI, and three different weighted genetic risk scores as instruments for BMI). We stratified by sex, investigated the implications of confounder adjustment, and modelled both linear and non-linear associations. RESULTS There was a positive effect of increased BMI in both males and females in each approach. The cost of elevated BMI was higher in models that, to a greater extent, account for endogenous relations. CONCLUSION The study provides solid evidence that there is an association between BMI and healthcare costs, and demonstrates the importance of triangulation.
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Affiliation(s)
- Christina Hansen Edwards
- Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Johan Håkon Bjørngaard
- Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- Faculty of Nursing and Health Sciences, Nord University, Levanger, Norway
| | - Jonas Minet Kinge
- Norwegian Institute of Public Health, Oslo, Norway
- Department of Health Management and Health Economics, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Gunnhild Åberge Vie
- Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Vidar Halsteinli
- Regional Center for Healthcare Improvement, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Rønnaug Ødegård
- Regional Center for Obesity Research and Innovation, Department of Surgery, St. Olavs Hospital, Trondheim, Norway
- Department of Clinical Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Bård Kulseng
- Regional Center for Obesity Research and Innovation, Department of Surgery, St. Olavs Hospital, Trondheim, Norway
- Department of Clinical Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Gudrun Waaler Bjørnelv
- Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Health Management and Health Economics, Institute of Health and Society, University of Oslo, Oslo, Norway
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27
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Li YY, Madduri SS, Rezeli ET, Santos C, Freeman III H, Peng J, McRitchie SL, Pathmasiri W, Hursting SD, Sumner SJ, Stewart DA. Macronutrient-differential dietary pattern impacts on body weight, hepatic inflammation, and metabolism. Front Nutr 2024; 11:1356038. [PMID: 38868554 PMCID: PMC11168494 DOI: 10.3389/fnut.2024.1356038] [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: 12/14/2023] [Accepted: 04/24/2024] [Indexed: 06/14/2024] Open
Abstract
Introduction Obesity is a multi-factorial disease frequently associated with poor nutritional habits and linked to many detrimental health outcomes. Individuals with obesity are more likely to have increased levels of persistent inflammatory and metabolic dysregulation. The goal of this study was to compare four dietary patterns differentiated by macronutrient content in a postmenopausal model. Dietary patterns were high carbohydrate (HC), high fat (HF), high carbohydrate plus high fat (HCHF), and high protein (HP) with higher fiber. Methods Changes in body weight and glucose levels were measured in female, ovariectomized C57BL/6 mice after 15 weeks of feeding. One group of five mice fed the HCHF diet was crossed over to the HP diet on day 84, modeling a 21-day intervention. In a follow-up study comparing the HCHF versus HP dietary patterns, systemic changes in inflammation, using an 80-cytokine array and metabolism, by untargeted liquid chromatography-mass spectrometry (LCMS)-based metabolomics were evaluated. Results Only the HF and HCHF diets resulted in obesity, shown by significant differences in body weights compared to the HP diet. Body weight gains during the two-diet follow-up study were consistent with the four-diet study. On Day 105 of the 4-diet study, glucose levels were significantly lower for mice fed the HP diet than for those fed the HC and HF diets. Mice switched from the HCHF to the HP diet lost an average of 3.7 grams by the end of the 21-day intervention, but this corresponded with decreased food consumption. The HCHF pattern resulted in dramatic inflammatory dysregulation, as all 80 cytokines were elevated significantly in the livers of these mice after 15 weeks of HCHF diet exposure. Comparatively, only 32 markers changed significantly on the HP diet (24 up, 8 down). Metabolic perturbations in several endogenous biological pathways were also observed based on macronutrient differences and revealed dysfunction in several nutritionally relevant biosynthetic pathways. Conclusion Overall, the HCHF diet promoted detrimental impacts and changes linked to several diseases, including arthritis or breast neoplasms. Identification of dietary pattern-specific impacts in this model provides a means to monitor the effects of disease risk and test interventions to prevent poor health outcomes through nutritional modification.
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Affiliation(s)
- Yuan-yuan Li
- Metabolomics and Exposome Laboratory, Nutrition Research Institute, Department of Nutrition, University of North Carolina at Chapel Hill, Kannapolis, NC, United States
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Supradeep S. Madduri
- Metabolomics and Exposome Laboratory, Nutrition Research Institute, Department of Nutrition, University of North Carolina at Chapel Hill, Kannapolis, NC, United States
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Erika T. Rezeli
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Charlene Santos
- Animal Studies Core Lab, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Herman Freeman III
- Metabolomics and Exposome Laboratory, Nutrition Research Institute, Department of Nutrition, University of North Carolina at Chapel Hill, Kannapolis, NC, United States
| | - Jing Peng
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Susan L. McRitchie
- Metabolomics and Exposome Laboratory, Nutrition Research Institute, Department of Nutrition, University of North Carolina at Chapel Hill, Kannapolis, NC, United States
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Wimal Pathmasiri
- Metabolomics and Exposome Laboratory, Nutrition Research Institute, Department of Nutrition, University of North Carolina at Chapel Hill, Kannapolis, NC, United States
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Stephen D. Hursting
- Metabolomics and Exposome Laboratory, Nutrition Research Institute, Department of Nutrition, University of North Carolina at Chapel Hill, Kannapolis, NC, United States
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Susan J. Sumner
- Metabolomics and Exposome Laboratory, Nutrition Research Institute, Department of Nutrition, University of North Carolina at Chapel Hill, Kannapolis, NC, United States
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Delisha A. Stewart
- Metabolomics and Exposome Laboratory, Nutrition Research Institute, Department of Nutrition, University of North Carolina at Chapel Hill, Kannapolis, NC, United States
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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28
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Son JE. Genetics, pharmacotherapy, and dietary interventions in childhood obesity. JOURNAL OF PHARMACY & PHARMACEUTICAL SCIENCES : A PUBLICATION OF THE CANADIAN SOCIETY FOR PHARMACEUTICAL SCIENCES, SOCIETE CANADIENNE DES SCIENCES PHARMACEUTIQUES 2024; 27:12861. [PMID: 38863827 PMCID: PMC11165095 DOI: 10.3389/jpps.2024.12861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 05/16/2024] [Indexed: 06/13/2024]
Abstract
Childhood obesity has emerged as a major global health issue, contributing to the increased prevalence of chronic conditions and adversely affecting the quality of life and future prospects of affected individuals, thereby presenting a substantial societal challenge. This complex condition, influenced by the interplay of genetic predispositions and environmental factors, is characterized by excessive energy intake due to uncontrolled appetite regulation and a Westernized diet. Managing obesity in childhood requires specific considerations compared with adulthood, given the vulnerability of the critical juvenile-adolescent period to toxicity and developmental defects. Consequently, common treatment options for adult obesity may not directly apply to younger populations. Therefore, research on childhood obesity has focused on genetic defects in regulating energy intake, alongside pharmacotherapy and dietary interventions as management approaches, with an emphasis on safety concerns. This review aims to summarize canonical knowledge and recent findings on genetic factors contributing to childhood obesity. Additionally, it assesses the efficacy and safety of existing pharmacotherapies and dietary interventions and suggests future research directions. By providing a comprehensive understanding of the complex dynamics of childhood obesity, this review aims to offer insights into more targeted and effective strategies for addressing this condition, including personalized healthcare solutions.
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Affiliation(s)
- Joe Eun Son
- School of Food Science and Biotechnology, Research Institute of Tailored Food Technology, Kyungpook National University, Daegu, Republic of Korea
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29
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Murthy VL, Mosley JD, Perry AS, Jacobs DR, Tanriverdi K, Zhao S, Sawicki KT, Carnethon M, Wilkins JT, Nayor M, Das S, Abel ED, Freedman JE, Clish CB, Shah RV. Metabolic liability for weight gain in early adulthood. Cell Rep Med 2024; 5:101548. [PMID: 38703763 PMCID: PMC11148768 DOI: 10.1016/j.xcrm.2024.101548] [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: 11/16/2022] [Revised: 03/27/2023] [Accepted: 04/10/2024] [Indexed: 05/06/2024]
Abstract
While weight gain is associated with a host of chronic illnesses, efforts in obesity have relied on single "snapshots" of body mass index (BMI) to guide genetic and molecular discovery. Here, we study >2,000 young adults with metabolomics and proteomics to identify a metabolic liability to weight gain in early adulthood. Using longitudinal regression and penalized regression, we identify a metabolic signature for weight liability, associated with a 2.6% (2.0%-3.2%, p = 7.5 × 10-19) gain in BMI over ≈20 years per SD higher score, after comprehensive adjustment. Identified molecules specified mechanisms of weight gain, including hunger and appetite regulation, energy expenditure, gut microbial metabolism, and host interaction with external exposure. Integration of longitudinal and concurrent measures in regression with Mendelian randomization highlights the complexity of metabolic regulation of weight gain, suggesting caution in interpretation of epidemiologic or genetic effect estimates traditionally used in metabolic research.
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Affiliation(s)
- Venkatesh L Murthy
- Division of Cardiovascular Medicine, Department of Medicine, University of Michigan, Ann Arbor, MI, USA.
| | - Jonathan D Mosley
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Andrew S Perry
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - David R Jacobs
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Kahraman Tanriverdi
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Shilin Zhao
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | | | | | | | - Matthew Nayor
- Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Saumya Das
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
| | - E Dale Abel
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jane E Freedman
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Clary B Clish
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Ravi V Shah
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA.
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30
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Moll M, Hecker J, Platig J, Zhang J, Ghosh AJ, Pratte KA, Wang RS, Hill D, Konigsberg IR, Chiles JW, Hersh CP, Castaldi PJ, Glass K, Dy JG, Sin DD, Tal-Singer R, Mouded M, Rennard SI, Anderson GP, Kinney GL, Bowler RP, Curtis JL, McDonald ML, Silverman EK, Hobbs BD, Cho MH. Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.20.24307621. [PMID: 38826461 PMCID: PMC11142287 DOI: 10.1101/2024.05.20.24307621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Rationale Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear. Objectives Define high-risk COPD subtypes using both genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics. Methods We defined high-risk groups based on PRS and TRS quantiles by maximizing differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets. We tested multivariable associations of subgroups with clinical outcomes and compared protein-protein interaction networks and drug repurposing analyses between high-risk groups. Measurements and Main Results We examined two high-risk omics-defined groups in non-overlapping test sets (n=1,133 NHW COPDGene, n=299 African American (AA) COPDGene, n=468 ECLIPSE). We defined "High activity" (low PRS/high TRS) and "severe risk" (high PRS/high TRS) subgroups. Participants in both subgroups had lower body-mass index (BMI), lower lung function, and alterations in metabolic, growth, and immune signaling processes compared to a low-risk (low PRS, low TRS) reference subgroup. "High activity" but not "severe risk" participants had greater prospective FEV 1 decline (COPDGene: -51 mL/year; ECLIPSE: - 40 mL/year) and their proteomic profiles were enriched in gene sets perturbed by treatment with 5-lipoxygenase inhibitors and angiotensin-converting enzyme (ACE) inhibitors. Conclusions Concomitant use of polygenic and transcriptional risk scores identified clinical and molecular heterogeneity amongst high-risk individuals. Proteomic and drug repurposing analysis identified subtype-specific enrichment for therapies and suggest prior drug repurposing failures may be explained by patient selection.
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31
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MacCarthy G, Pazoki R. Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank. J Clin Med 2024; 13:2955. [PMID: 38792496 PMCID: PMC11122671 DOI: 10.3390/jcm13102955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/01/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
Background and Objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension classification model and investigate the potential influence of genetic liability for multiple risk factors linked to CVD on hypertension risk using the random forest and the neural network. Materials and Methods: The study involved 244,718 European participants, who were divided into training and testing sets. Genetic liabilities were constructed using genetic variants associated with CVD risk factors obtained from genome-wide association studies (GWAS). Various combinations of machine learning models before and after feature selection were tested to develop the best classification model. The models were evaluated using area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using the random forest and the neural network methods, respectively. Adding genetic liabilities improved the AUC for the random forest but not for the neural network. The best classification model was achieved when feature selection and classification were performed using random forest (AUC = 0.71, Spiegelhalter z score = 0.10, p-value = 0.92, calibration slope = 0.99). This model included genetic liabilities for total cholesterol and low-density lipoprotein (LDL). Conclusions: The study highlighted that incorporating genetic liabilities for lipids in a machine learning model may provide incremental value for hypertension classification beyond baseline characteristics.
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Affiliation(s)
- Gideon MacCarthy
- Cardiovascular and Metabolic Research Group, Division of Biomedical Sciences, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, London UB8 3PH, UK
| | - Raha Pazoki
- Cardiovascular and Metabolic Research Group, Division of Biomedical Sciences, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, London UB8 3PH, UK
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, St Mary’s Campus, Norfolk Place, Imperial College London, London W2 1PG, UK
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Passero K, Noll JG, Verma SS, Selin C, Hall MA. Longitudinal method comparison: modeling polygenic risk for post-traumatic stress disorder over time in individuals of African and European ancestry. Front Genet 2024; 15:1203577. [PMID: 38818035 PMCID: PMC11137250 DOI: 10.3389/fgene.2024.1203577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 04/15/2024] [Indexed: 06/01/2024] Open
Abstract
Cross-sectional data allow the investigation of how genetics influence health at a single time point, but to understand how the genome impacts phenotype development, one must use repeated measures data. Ignoring the dependency inherent in repeated measures can exacerbate false positives and requires the utilization of methods other than general or generalized linear models. Many methods can accommodate longitudinal data, including the commonly used linear mixed model and generalized estimating equation, as well as the less popular fixed-effects model, cluster-robust standard error adjustment, and aggregate regression. We simulated longitudinal data and applied these five methods alongside naïve linear regression, which ignored the dependency and served as a baseline, to compare their power, false positive rate, estimation accuracy, and precision. The results showed that the naïve linear regression and fixed-effects models incurred high false positive rates when analyzing a predictor that is fixed over time, making them unviable for studying time-invariant genetic effects. The linear mixed models maintained low false positive rates and unbiased estimation. The generalized estimating equation was similar to the former in terms of power and estimation, but it had increased false positives when the sample size was low, as did cluster-robust standard error adjustment. Aggregate regression produced biased estimates when predictor effects varied over time. To show how the method choice affects downstream results, we performed longitudinal analyses in an adolescent cohort of African and European ancestry. We examined how developing post-traumatic stress symptoms were predicted by polygenic risk, traumatic events, exposure to sexual abuse, and income using four approaches-linear mixed models, generalized estimating equations, cluster-robust standard error adjustment, and aggregate regression. While the directions of effect were generally consistent, coefficient magnitudes and statistical significance differed across methods. Our in-depth comparison of longitudinal methods showed that linear mixed models and generalized estimating equations were applicable in most scenarios requiring longitudinal modeling, but no approach produced identical results even if fit to the same data. Since result discrepancies can result from methodological choices, it is crucial that researchers determine their model a priori, refrain from testing multiple approaches to obtain favorable results, and utilize as similar as possible methods when seeking to replicate results.
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Affiliation(s)
- Kristin Passero
- Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
| | - Jennie G. Noll
- Department of Psychology, Mount Hope Family Center, University of Rochester, Rochester, NY, United States
| | - Shefali Setia Verma
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Claire Selin
- Center for Childhood Deafness, Language, and Learning, Boys Town National Research Hospital, Omaha, NE, United States
| | - Molly A. Hall
- Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
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Chen J, Martingano AJ, Ravuri S, Foor K, Fortney C, Carnell S, Batheja S, Persky S. Teaching gene-environment interaction concepts with narrative vignettes: Effects on knowledge, stigma, and behavior motivation. PLoS One 2024; 19:e0300452. [PMID: 38722839 PMCID: PMC11081345 DOI: 10.1371/journal.pone.0300452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 02/28/2024] [Indexed: 05/13/2024] Open
Abstract
Gene-environment interaction (GxE) concepts underlie a proper understanding of complex disease risk and risk-reducing behavior. Communicating GxE concepts is a challenge. This study designed an educational intervention that communicated GxE concepts in the context of eating behavior and its impact on weight, and tested its efficacy in changing knowledge, stigma, and behavior motivation. The study also explored whether different framings of GxE education and matching frames with individual eating tendencies would result in stronger intervention impact. The experiment included four GxE education conditions and a control condition unrelated to GxE concepts. In the education conditions, participants watched a video introducing GxE concepts then one of four narrative vignettes depicting how a character's experience with eating hyperpalatable or bitter tasting food (reward-based eating drive vs. bitter taste perception scenario) is influenced by genetic or environmental variations (genetic vs. environmental framings). The education intervention increased GxE knowledge, genetic causal attributions, and empathetic concern. Mediation analyses suggest that causal attributions, particularly to genetics and willpower, are key factors that drive downstream stigma and eating behavior outcomes and could be targeted in future interventions. Tailoring GxE education frames to individual traits may lead to more meaningful outcomes. For example, genetic (vs. environmental) framed GxE education may reduce stigma toward individuals with certain eating tendencies among individuals without such tendencies. GxE education interventions would be most likely to achieve desired outcomes such as reducing stigma if they target certain causal beliefs and are strategically tailored to individual attributes.
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Affiliation(s)
- Junhan Chen
- Social and Behavioral Research Branch, National Human Genome Research Institute, Bethesda, MD, United States of America
| | | | - Siri Ravuri
- Social and Behavioral Research Branch, National Human Genome Research Institute, Bethesda, MD, United States of America
| | - Kaylee Foor
- Social and Behavioral Research Branch, National Human Genome Research Institute, Bethesda, MD, United States of America
| | - Christopher Fortney
- Social and Behavioral Research Branch, National Human Genome Research Institute, Bethesda, MD, United States of America
| | - Susan Carnell
- School of Medicine, Johns Hopkins University, Baltimore, MD, United States of America
| | - Sapna Batheja
- College of Public Health, George Mason University, Fairfax, VA, United States of America
| | - Susan Persky
- Social and Behavioral Research Branch, National Human Genome Research Institute, Bethesda, MD, United States of America
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Dashti HS, Scheer FAJL, Saxena R, Garaulet M. Impact of polygenic score for BMI on weight loss effectiveness and genome-wide association analysis. Int J Obes (Lond) 2024; 48:694-701. [PMID: 38267484 DOI: 10.1038/s41366-024-01470-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 01/05/2024] [Accepted: 01/12/2024] [Indexed: 01/26/2024]
Abstract
BACKGROUND While environmental factors play an important role in weight loss effectiveness, genetics may also influence its success. We examined whether a genome-wide polygenic score for BMI was associated with weight loss effectiveness and aimed to identify common genetic variants associated with weight loss. METHODS Participants in the ONTIME study (n = 1210) followed a uniform, multimodal behavioral weight-loss intervention. We first tested associations between a genome-wide polygenic score for higher BMI and weight loss effectiveness (total weight loss, rate of weight loss, and attrition). We then conducted a genome-wide association study (GWAS) for weight loss in the ONTIME study and performed the largest weight loss meta-analysis with earlier studies (n = 3056). Lastly, we ran exploratory GWAS in the ONTIME study for other weight loss outcomes and related factors. RESULTS We found that each standard deviation increment in the polygenic score was associated with a decrease in the rate of weight loss (Beta (95% CI) = -0.04 kg per week (-0.06, -0.01); P = 3.7 × 10-03) and with higher attrition after adjusting by treatment duration. No associations reached genome-wide significance in meta-analysis with previous GWAS studies for weight loss. However, associations in the ONTIME study showed effects consistent with published studies for rs545936 (MIR486/NKX6.3/ANK1), a previously noted weight loss locus. In the meta-analysis, each copy of the minor A allele was associated with 0.12 (0.03) kg/m2 higher BMI at week five of treatment (P = 3.9 × 10-06). In the ONTIME study, we also identified two genome-wide significant (P < 5×10-08) loci for the rate of weight loss near genes implicated in lipolysis, body weight, and metabolic regulation: rs146905606 near NFIP1/SPRY4/FGF1; and rs151313458 near LSAMP. CONCLUSION Our findings are expected to help in developing personalized weight loss approaches based on genetics. CLINICAL TRIAL REGISTRATION Obesity, Nutrigenetics, Timing, and Mediterranean (ONTIME; clinicaltrials.gov: NCT02829619) study.
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Affiliation(s)
- Hassan S Dashti
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Broad Institute, Cambridge, MA, USA.
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.
| | - Frank A J L Scheer
- Broad Institute, Cambridge, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Richa Saxena
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Marta Garaulet
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA.
- Department of Physiology, Regional Campus of International Excellence, University of Murcia, 30100, Murcia, Spain.
- Biomedical Research Institute of Murcia, IMIB-Arrixaca-UMU, University Clinical Hospital, 30120, Murcia, Spain.
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van der Ark-Vonk EM, Puijk MV, Pasterkamp G, van der Laan SW. The Effects of FABP4 on Cardiovascular Disease in the Aging Population. Curr Atheroscler Rep 2024; 26:163-175. [PMID: 38698167 PMCID: PMC11087245 DOI: 10.1007/s11883-024-01196-5] [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] [Accepted: 03/05/2024] [Indexed: 05/05/2024]
Abstract
PURPOSE OF REVIEW Fatty acid-binding protein 4 (FABP4) plays a role in lipid metabolism and cardiovascular health. In this paper, we cover FABP4 biology, its implications in atherosclerosis from observational studies, genetic factors affecting FABP4 serum levels, and ongoing drug development to target FABP4 and offer insights into future FABP4 research. RECENT FINDINGS FABP4 impacts cells through JAK2/STAT2 and c-kit pathways, increasing inflammatory and adhesion-related proteins. In addition, FABP4 induces angiogenesis and vascular smooth muscle cell proliferation and migration. FABP4 is established as a reliable predictive biomarker for cardiovascular disease in specific at-risk groups. Genetic studies robustly link PPARG and FABP4 variants to FABP4 serum levels. Considering the potential effects on atherosclerotic lesion development, drug discovery programs have been initiated in search for potent inhibitors of FABP4. Elevated FABP4 levels indicate an increased cardiovascular risk and is causally related to acceleration of atherosclerotic disease, However, clinical trials for FABP4 inhibition are lacking, possibly due to concerns about available compounds' side effects. Further research on FABP4 genetics and its putative causal role in cardiovascular disease is needed, particularly in aging subgroups.
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Affiliation(s)
- Ellen M van der Ark-Vonk
- Central Diagnostics Laboratory, Division Laboratory, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
| | - Mike V Puijk
- Central Diagnostics Laboratory, Division Laboratory, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
| | - Gerard Pasterkamp
- Central Diagnostics Laboratory, Division Laboratory, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
| | - Sander W van der Laan
- Central Diagnostics Laboratory, Division Laboratory, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands.
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Wei A, Border R, Fu B, Cullina S, Brandes N, Jang SK, Sankararaman S, Kenny E, Udler MS, Ntranos V, Zaitlen N, Arboleda V. Investigating the sources of variable impact of pathogenic variants in monogenic metabolic conditions. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.09.14.23295564. [PMID: 37745486 PMCID: PMC10516069 DOI: 10.1101/2023.09.14.23295564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Over three percent of people carry a dominant pathogenic variant, yet only a fraction of carriers develop disease. Disease phenotypes from carriers of variants in the same gene range from mild to severe. Here, we investigate underlying mechanisms for this heterogeneity: variable variant effect sizes, carrier polygenic backgrounds, and modulation of carrier effect by genetic background (marginal epistasis). We leveraged exomes and clinical phenotypes from the UK Biobank and the Mt. Sinai BioMe Biobank to identify carriers of pathogenic variants affecting cardiometabolic traits. We employed recently developed methods to study these cohorts, observing strong statistical support and clinical translational potential for all three mechanisms of variable carrier penetrance and disease severity. For example, scores from our recent model of variant pathogenicity were tightly correlated with phenotype amongst clinical variant carriers, they predicted effects of variants of unknown significance, and they distinguished gain- from loss-of-function variants. We also found that polygenic scores predicted phenotypes amongst pathogenic carriers and that epistatic effects can exceed main carrier effects by an order of magnitude.
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Lariviere D, Craig SJC, Paul IM, Hohman EE, Savage JS, Wright RO, Chiaromonte F, Makova KD, Reimherr ML. Methylation profiles at birth linked to early childhood obesity. J Dev Orig Health Dis 2024; 15:e7. [PMID: 38660759 PMCID: PMC11268442 DOI: 10.1017/s2040174424000060] [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] [Indexed: 04/26/2024]
Abstract
Childhood obesity represents a significant global health concern and identifying its risk factors is crucial for developing intervention programs. Many "omics" factors associated with the risk of developing obesity have been identified, including genomic, microbiomic, and epigenomic factors. Here, using a sample of 48 infants, we investigated how the methylation profiles in cord blood and placenta at birth were associated with weight outcomes (specifically, conditional weight gain, body mass index, and weight-for-length ratio) at age six months. We characterized genome-wide DNA methylation profiles using the Illumina Infinium MethylationEpic chip, and incorporated information on child and maternal health, and various environmental factors into the analysis. We used regression analysis to identify genes with methylation profiles most predictive of infant weight outcomes, finding a total of 23 relevant genes in cord blood and 10 in placenta. Notably, in cord blood, the methylation profiles of three genes (PLIN4, UBE2F, and PPP1R16B) were associated with all three weight outcomes, which are also associated with weight outcomes in an independent cohort suggesting a strong relationship with weight trajectories in the first six months after birth. Additionally, we developed a Methylation Risk Score (MRS) that could be used to identify children most at risk for developing childhood obesity. While many of the genes identified by our analysis have been associated with weight-related traits (e.g., glucose metabolism, BMI, or hip-to-waist ratio) in previous genome-wide association and variant studies, our analysis implicated several others, whose involvement in the obesity phenotype should be evaluated in future functional investigations.
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Affiliation(s)
- Delphine Lariviere
- Department of Biochemistry and Molecular Biology, Penn State University, University Park, PA, USA
| | - Sarah J C Craig
- Department of Biology, Penn State University, University Park, PA, USA
- Center for Medical Genomics, Penn State University, University Park, PA, USA
| | - Ian M Paul
- Center for Medical Genomics, Penn State University, University Park, PA, USA
- Department of Pediatrics, Penn State College of Medicine, Hershey, PA, USA
| | - Emily E Hohman
- Center for Childhood Obesity Research, Penn State University, University Park, PA, USA
| | - Jennifer S Savage
- Center for Childhood Obesity Research, Penn State University, University Park, PA, USA
- Nutrition Department, Penn State University, University Park, PA, USA
| | | | - Francesca Chiaromonte
- Center for Medical Genomics, Penn State University, University Park, PA, USA
- Department of Statistics, Penn State University, University Park, PA, USA
- L'EMbeDS, Sant'Anna School of Advanced Studies, Piazza Martiri della Libertà, Pisa, Italy
| | - Kateryna D Makova
- Department of Biology, Penn State University, University Park, PA, USA
- Center for Medical Genomics, Penn State University, University Park, PA, USA
| | - Matthew L Reimherr
- Center for Medical Genomics, Penn State University, University Park, PA, USA
- Department of Statistics, Penn State University, University Park, PA, USA
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Muse ED, Topol EJ. Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management. Cell Metab 2024; 36:670-683. [PMID: 38428435 PMCID: PMC10990799 DOI: 10.1016/j.cmet.2024.02.002] [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: 10/26/2023] [Revised: 01/25/2024] [Accepted: 02/06/2024] [Indexed: 03/03/2024]
Abstract
The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.
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Affiliation(s)
- Evan D Muse
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA
| | - Eric J Topol
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA.
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Fansa S, Acosta A. The melanocortin-4 receptor pathway and the emergence of precision medicine in obesity management. Diabetes Obes Metab 2024; 26 Suppl 2:46-63. [PMID: 38504134 DOI: 10.1111/dom.15555] [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: 12/21/2023] [Revised: 02/15/2024] [Accepted: 02/29/2024] [Indexed: 03/21/2024]
Abstract
Over the past few decades, there has been a global surge in the prevalence of obesity, rendering it a globally recognized epidemic. Contrary to simply being a medical condition, obesity is an intricate disease with a multifactorial aetiology. Understanding the precise cause of obesity remains a challenge; nevertheless, there seems to be a complex interplay among biological, psychosocial and behavioural factors. Studies on the genetic factors of obesity have revealed several pathways in the brain that play a crucial role in food intake regulation. The best characterized pathway, thus far, is the leptin-melanocortin pathway, from which disruptions are responsible for the majority of monogenic obesity disorders. The effectiveness of conservative lifestyle interventions in addressing monogenic obesity has been limited. Therefore, it is crucial to complement the management strategy with pharmacological and surgical options. Emphasis has been placed on developing drugs aimed at replacing the absent signals, with the goal of restoring the pathway. In both monogenic and polygenic forms of obesity, outcomes differ across various interventions, likely due to the multifaceted nature of the disease. This underscores the need to explore alternative therapeutic strategies that can mitigate this heterogeneity. Precision medicine can be regarded as a powerful tool that can address this concern, as it values the understanding of the underlying abnormality triggering the disease and provides a tailored treatment accordingly. This would assist in optimizing outcomes of the current therapeutic approaches and even aid in the development of novel treatments capable of more effectively managing the global obesity epidemic.
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Affiliation(s)
- Sima Fansa
- Precision Medicine for Obesity Program, Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Andres Acosta
- Precision Medicine for Obesity Program, Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
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Alkis T, Luo X, Wall K, Brody J, Bartz T, Chang PP, Norby FL, Hoogeveen RC, Morrison AC, Ballantyne CM, Coresh J, Boerwinkle E, Psaty BM, Shah AM, Yu B. A polygenic risk score of atrial fibrillation improves prediction of lifetime risk for heart failure. ESC Heart Fail 2024; 11:1086-1096. [PMID: 38258344 PMCID: PMC10966276 DOI: 10.1002/ehf2.14665] [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: 07/28/2023] [Revised: 11/01/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024] Open
Abstract
AIMS Heart failure (HF) has shared genetic architecture with its risk factors: atrial fibrillation (AF), body mass index (BMI), coronary heart disease (CHD), systolic blood pressure (SBP), and type 2 diabetes (T2D). We aim to assess the association and risk prediction performance of risk-factor polygenic risk scores (PRSs) for incident HF and its subtypes in bi-racial populations. METHODS AND RESULTS Five PRSs were constructed for AF, BMI, CHD, SBP, and T2D in White participants of the Atherosclerosis Risk in Communities (ARIC) study. The associations between PRSs and incident HF and its subtypes were assessed using Cox models, and the risk prediction performance of PRSs was assessed using C statistics. Replication was performed in the ARIC study Black and Cardiovascular Health Study (CHS) White participants. In 8624 ARIC study Whites, 1922 (31% cumulative incidence) HF cases developed over 30 years of follow-up. PRSs of AF, BMI, and CHD were associated with incident HF (P < 0.001), where PRSAF showed the strongest association [hazard ratio (HR): 1.47, 95% confidence interval (CI): 1.41-1.53]. Only the addition of PRSAF to the ARIC study HF risk equation improved C statistics for 10 year risk prediction from 0.812 to 0.829 (∆C: 0.017, 95% CI: 0.009-0.026). The PRSAF was associated with both incident HF with reduced ejection fraction (HR: 1.43, 95% CI: 1.27-1.60) and incident HF with preserved ejection fraction (HR: 1.46, 95% CI: 1.33-1.62). The associations between PRSAF and incident HF and its subtypes, as well as the improved risk prediction, were replicated in the ARIC study Blacks and the CHS Whites (P < 0.050). Protein analyses revealed that N-terminal pro-brain natriuretic peptide and other 98 proteins were associated with PRSAF. CONCLUSIONS The PRSAF was associated with incident HF and its subtypes and had significant incremental value over an established HF risk prediction equation.
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Affiliation(s)
- Taryn Alkis
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public HealthUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Xi Luo
- Department of Biostatistics and Data Science, School of Public HealthUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Katherine Wall
- Department of Biostatistics and Data Science, School of Public HealthUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Jennifer Brody
- Cardiovascular Health Research UnitUniversity of WashingtonSeattleWAUSA
- Department of MedicineUniversity of WashingtonSeattleWAUSA
| | - Traci Bartz
- Cardiovascular Health Research Unit, Departments of Medicine and BiostatisticsUniversity of WashingtonSeattleWAUSA
| | - Patricia P. Chang
- Division of CardiologyUniversity of North Carolina School of MedicineChapel HillNCUSA
| | - Faye L. Norby
- Division of Epidemiology and Community HealthUniversity of MinnesotaMinneapolisMNUSA
| | | | - Alanna C. Morrison
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public HealthUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | | | - Josef Coresh
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMDUSA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public HealthUniversity of Texas Health Science Center at HoustonHoustonTXUSA
- Human Genome Sequencing CenterBaylor College of MedicineHoustonTXUSA
| | - Bruce M. Psaty
- Cardiovascular Health Research UnitUniversity of WashingtonSeattleWAUSA
- Department of MedicineUniversity of WashingtonSeattleWAUSA
- Department of EpidemiologyUniversity of WashingtonSeattleWAUSA
- Department of Health Systems and Population HealthUniversity of WashingtonSeattleWAUSA
| | - Amil M. Shah
- Department of Internal MedicineUT Southwestern Medical CenterDallasTXUSA
| | - Bing Yu
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public HealthUniversity of Texas Health Science Center at HoustonHoustonTXUSA
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Aagaard KM, Barkin SL, Burant CF, Carnell S, Demerath E, Donovan SM, Eneli I, Francis LA, Gilbert-Diamond D, Hivert MF, LeBourgeois MK, Loos RJF, Lumeng JC, Miller AL, Okely AD, Osganian SK, Ramirez AG, Trasande L, Van Horn LV, Wake M, Wright RJ, Yanovski SZ. Understanding risk and causal mechanisms for developing obesity in infants and young children: A National Institutes of Health workshop. Obes Rev 2024; 25:e13690. [PMID: 38204366 DOI: 10.1111/obr.13690] [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: 11/10/2022] [Revised: 10/02/2023] [Accepted: 11/21/2023] [Indexed: 01/12/2024]
Abstract
Obesity in children remains a major public health problem, with the current prevalence in youth ages 2-19 years estimated to be 19.7%. Despite progress in identifying risk factors, current models do not accurately predict development of obesity in early childhood. There is also substantial individual variability in response to a given intervention that is not well understood. On April 29-30, 2021, the National Institutes of Health convened a virtual workshop on "Understanding Risk and Causal Mechanisms for Developing Obesity in Infants and Young Children." The workshop brought together scientists from diverse disciplines to discuss (1) what is known regarding epidemiology and underlying biological and behavioral mechanisms for rapid weight gain and development of obesity and (2) what new approaches can improve risk prediction and gain novel insights into causes of obesity in early life. Participants identified gaps and opportunities for future research to advance understanding of risk and underlying mechanisms for development of obesity in early life. It was emphasized that future studies will require multi-disciplinary efforts across basic, behavioral, and clinical sciences. An exposome framework is needed to elucidate how behavioral, biological, and environmental risk factors interact. Use of novel statistical methods may provide greater insights into causal mechanisms.
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Affiliation(s)
- Kjersti M Aagaard
- Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, Baylor College of Medicine, Houston, Texas, USA
- Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, Texas, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Shari L Barkin
- Department of Pediatrics, Children's Hospital of Richmond, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Charles F Burant
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Susan Carnell
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Ellen Demerath
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Sharon M Donovan
- Division of Nutritional Sciences, University of Illinois, Urbana-Champaign, Illinois, USA
- Department of Food Science and Human Nutrition, University of Illinois, Urbana-Champaign, Illinois, USA
| | - Ihuoma Eneli
- Center for Healthy Weight and Nutrition, Department of Pediatrics, Nationwide Children's Hospital, Columbus, Ohio, USA
- Center of Nutrition, Department of Pediatrics, University of Colorado, Aurora, Colorado, USA
| | - Lori A Francis
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Diane Gilbert-Diamond
- Department of Epidemiology, Medicine and Pediatrics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
| | - Marie-France Hivert
- Division of Chronic Disease Research Across the Lifecourse (CoRAL), Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Monique K LeBourgeois
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, Colorado, USA
| | - Ruth J F Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Julie C Lumeng
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Alison L Miller
- Department of Health Behavior and Health Education, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | - Anthony D Okely
- School of Health and Society, Faculty of Arts, Social Sciences and Humanities, University of Wollongong, Wollongong, New South Wales, Australia
- llawarra Health and Medical Research Institute, Wollongong, New South Wales, Australia
- Department of Sport, Food, and Natural Sciences, Western Norway University of Applied Sciences, Sogndal, Norway
| | - Stavroula K Osganian
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Amelie G Ramirez
- Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Leonardo Trasande
- Department of Pediatrics, New York University (NYU) School of Medicine, New York, New York, USA
- Department of Environmental Medicine, New York University (NYU) School of Medicine, New York, New York, USA
- Department of Population Health, New York University (NYU) School of Medicine, New York, New York, USA
| | - Linda V Van Horn
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois, USA
| | - Melissa Wake
- Population Health, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, The University of Melbourne, Parkville, Victoria, Australia
| | - Rosalind J Wright
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, Kravis Children's Hospital, New York, New York, USA
| | - Susan Z Yanovski
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA
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Singh RK, Zhao Y, Elze T, Fingert J, Gordon M, Kass MA, Luo Y, Pasquale LR, Scheetz T, Segrè AV, Wiggs JL, Zebardast N. Polygenic Risk Scores for Glaucoma Onset in the Ocular Hypertension Treatment Study. JAMA Ophthalmol 2024; 142:356-363. [PMID: 38483402 PMCID: PMC10941023 DOI: 10.1001/jamaophthalmol.2024.0151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 01/14/2024] [Indexed: 03/17/2024]
Abstract
Importance Primary open-angle glaucoma (POAG) is a highly heritable disease, with 127 identified risk loci to date. Polygenic risk score (PRS) may provide a clinically useful measure of aggregate genetic burden and improve patient risk stratification. Objective To assess whether a PRS improves prediction of POAG onset in patients with ocular hypertension. Design, Setting, and Participants This was a post hoc analysis of the Ocular Hypertension Treatment Study. Data were collected from 22 US sites with a mean (SD) follow-up of 14.0 (6.9) years. A total of 1636 participants were followed up from February 1994 to December 2008; 1077 participants were enrolled in an ancillary genetics study, of which 1009 met criteria for this analysis. PRS was calculated using summary statistics from the largest cross-ancestry POAG meta-analysis, with weights trained using 8 813 496 variants from 449 186 cross-ancestry participants in the UK Biobank. Data were analyzed from July 2022 to December 2023. Exposures From February 1994 to June 2002, participants were randomized to either topical intraocular pressure-lowering medication or close observation. After June 2002, both groups received medication. Main Outcomes and Measures Outcome measures were hazard ratios for POAG onset. Concordance index and time-dependent areas under the receiver operating characteristic curve were used to compare the predictive performance of multivariable Cox proportional hazards models. Results Of 1009 included participants, 562 (55.7%) were female, and the mean (SD) age was 55.9 (9.3) years. The mean (SD) PRS was significantly higher for 350 POAG converters (0.24 [0.95]) compared with 659 nonconverters (-0.12 [1.00]) (P < .001). POAG risk increased 1.36% (95% CI, 1.08-1.64) with each higher PRS decile, with conversion ranging from 9.52% (95% CI, 7.09-11.95) in the lowest PRS decile to 21.81% (95% CI, 19.37-24.25) in the highest decile. Comparison of low-risk and high-risk PRS tertiles showed a 2.0-fold increase in 20-year POAG risk for participants of European and African ancestries. In the subgroup randomized to delayed treatment, each increase in PRS decile was associated with a 0.52-year (95% CI, 0.01-1.03) decrease in age at diagnosis (P = .047). No significant linear association between PRS and age at POAG diagnosis was present in the early treatment group. Prediction models significantly improved with the addition of PRS as a covariate (C index = 0.77) compared with the Ocular Hypertension Treatment Study baseline model (C index = 0.75) (P < .001). Each 1-SD higher PRS conferred a mean hazard ratio of 1.25 (95% CI, 1.13-1.44) for POAG onset. Conclusions and Relevance Higher PRS was associated with increased risk for POAG in patients with ocular hypertension. The inclusion of a PRS improved the prediction of POAG onset. Trial Registration ClinicalTrials.gov Identifier: NCT00000125.
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Affiliation(s)
- Rishabh K. Singh
- Department of Ophthalmology, Columbia University Medical Center, New York, New York
- Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts
| | - Yan Zhao
- Massachusetts Eye and Ear, Harvard Medical School, Boston
| | - Tobias Elze
- Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts
| | - John Fingert
- Carver College of Medicine, University of Iowa, Iowa City
| | - Mae Gordon
- Washington University School of Medicine, St Louis, Missouri
| | - Michael A. Kass
- Washington University School of Medicine, St Louis, Missouri
| | - Yuyang Luo
- Massachusetts Eye and Ear, Harvard Medical School, Boston
| | | | - Todd Scheetz
- Carver College of Medicine, University of Iowa, Iowa City
| | - Ayellet V. Segrè
- Massachusetts Eye and Ear, Harvard Medical School, Boston
- Ocular Genomics Institute, Massachusetts Eye and Ear, Boston
| | - Janey L. Wiggs
- Massachusetts Eye and Ear, Harvard Medical School, Boston
- Ocular Genomics Institute, Massachusetts Eye and Ear, Boston
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Prunell-Castañé A, Beyer F, Witte V, Sánchez Garre C, Hernán I, Caldú X, Jurado MÁ, Garolera M. From the reward network to whole-brain metrics: structural connectivity in adolescents and young adults according to body mass index and genetic risk of obesity. Int J Obes (Lond) 2024; 48:567-574. [PMID: 38145996 DOI: 10.1038/s41366-023-01451-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 12/05/2023] [Accepted: 12/12/2023] [Indexed: 12/27/2023]
Abstract
BACKGROUND Obesity is a multifactorial condition. Genetic variants, such as the fat mass and obesity related gene (FTO) polymorphism, may increase the vulnerability of developing obesity by disrupting dopamine signaling within the reward network. Yet, the association of obesity, genetic risk of obesity, and structural connectivity of the reward network in adolescents and young adults remains unexplored. We investigate, in adolescents and young adults, the structural connectivity differences in the reward network and at the whole-brain level according to body mass index (BMI) and the FTO rs9939609 polymorphism. METHODS One hundred thirty-two adolescents and young adults (age range: [10, 21] years, BMI z-score range: [-1.76, 2.69]) were included. Genetic risk of obesity was determined by the presence of the FTO A allele. Whole-brain and reward network structural connectivity were analyzed using graph metrics. Hierarchical linear regression was applied to test the association between BMI-z, genetic risk of obesity, and structural connectivity. RESULTS Higher BMI-z was associated with higher (B = 0.76, 95% CI = [0.30, 1.21], P = 0.0015) and lower (B = -0.003, 95% CI = [-0.006, -0.00005], P = 0.048) connectivity strength for fractional anisotropy at the whole-brain level and of the reward network, respectively. The FTO polymorphism was not associated with structural connectivity nor with BMI-z. CONCLUSIONS We provide evidence that, in healthy adolescents and young adults, higher BMI-z is associated with higher connectivity at the whole-brain level and lower connectivity of the reward network. We did not find the FTO polymorphism to correlate with structural connectivity. Future longitudinal studies with larger sample sizes are needed to assess how genetic determinants of obesity change brain structural connectivity and behavior.
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Affiliation(s)
- Anna Prunell-Castañé
- Departament de Psicologia Clínica i Psicobiologia, Facultat de Psicologia, Universitat de Barcelona, Passeig de la Vall d'Hebron, 171, 08035, Barcelona, Spain
- Institut de Neurociències, Universitat de Barcelona, Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | - Frauke Beyer
- Clinic for Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Veronica Witte
- Clinic for Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Consuelo Sánchez Garre
- Pediatric Endocrinology Unit, Hospital de Terrassa, Consorci Sanitari de Terrassa, Terrassa, Barcelona, Spain
| | - Imma Hernán
- Molecular Genetics Unit, Consorci Sanitari de Terrassa, Terrassa, Spain
| | - Xavier Caldú
- Departament de Psicologia Clínica i Psicobiologia, Facultat de Psicologia, Universitat de Barcelona, Passeig de la Vall d'Hebron, 171, 08035, Barcelona, Spain
- Institut de Neurociències, Universitat de Barcelona, Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | - María Ángeles Jurado
- Departament de Psicologia Clínica i Psicobiologia, Facultat de Psicologia, Universitat de Barcelona, Passeig de la Vall d'Hebron, 171, 08035, Barcelona, Spain.
- Institut de Neurociències, Universitat de Barcelona, Barcelona, Spain.
- Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain.
| | - Maite Garolera
- Brain, Cognition and Behavior: Clinical Research, Consorci Sanitari de Terrassa, Terrassa, Barcelona, Spain
- Neuropsychology Unit, Hospital de Terrassa, Consorci Sanitari de Terrassa, Terrassa, Barcelona, Spain
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Sun C, Cheng X, Xu J, Chen H, Tao J, Dong Y, Wei S, Chen R, Meng X, Ma Y, Tian H, Guo X, Bi S, Zhang C, Kang J, Zhang M, Lv H, Shang Z, Lv W, Zhang R, Jiang Y. A review of disease risk prediction methods and applications in the omics era. Proteomics 2024:e2300359. [PMID: 38522029 DOI: 10.1002/pmic.202300359] [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: 09/15/2023] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 03/25/2024]
Abstract
Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.
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Affiliation(s)
- Chen Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Xiangshu Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Jing Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Haiyan Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Junxian Tao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Yu Dong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Siyu Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Rui Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xin Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yingnan Ma
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Hongsheng Tian
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xuying Guo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuo Bi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chen Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jingxuan Kang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Mingming Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongchao Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhenwei Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wenhua Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ruijie Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
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45
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Almuwaqqat Z, Hui Q, Liu C, Zhou JJ, Voight BF, Ho YL, Posner DC, Vassy JL, Gaziano JM, Cho K, Wilson PWF, Sun YV. Long-Term Body Mass Index Variability and Adverse Cardiovascular Outcomes. JAMA Netw Open 2024; 7:e243062. [PMID: 38512255 PMCID: PMC10958234 DOI: 10.1001/jamanetworkopen.2024.3062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 01/23/2024] [Indexed: 03/22/2024] Open
Abstract
Importance Body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) is a commonly used estimate of obesity, which is a complex trait affected by genetic and lifestyle factors. Marked weight gain and loss could be associated with adverse biological processes. Objective To evaluate the association between BMI variability and incident cardiovascular disease (CVD) events in 2 distinct cohorts. Design, Setting, and Participants This cohort study used data from the Million Veteran Program (MVP) between 2011 and 2018 and participants in the UK Biobank (UKB) enrolled between 2006 and 2010. Participants were followed up for a median of 3.8 (5th-95th percentile, 3.5) years. Participants with baseline CVD or cancer were excluded. Data were analyzed from September 2022 and September 2023. Exposure BMI variability was calculated by the retrospective SD and coefficient of variation (CV) using multiple clinical BMI measurements up to the baseline. Main Outcomes and Measures The main outcome was incident composite CVD events (incident nonfatal myocardial infarction, acute ischemic stroke, and cardiovascular death), assessed using Cox proportional hazards modeling after adjustment for CVD risk factors, including age, sex, mean BMI, systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, smoking status, diabetes status, and statin use. Secondary analysis assessed whether associations were dependent on the polygenic score of BMI. Results Among 92 363 US veterans in the MVP cohort (81 675 [88%] male; mean [SD] age, 56.7 [14.1] years), there were 9695 Hispanic participants, 22 488 non-Hispanic Black participants, and 60 180 non-Hispanic White participants. A total of 4811 composite CVD events were observed from 2011 to 2018. The CV of BMI was associated with 16% higher risk for composite CVD across all groups (hazard ratio [HR], 1.16; 95% CI, 1.13-1.19). These associations were unchanged among subgroups and after adjustment for the polygenic score of BMI. The UKB cohort included 65 047 individuals (mean [SD] age, 57.30 (7.77) years; 38 065 [59%] female) and had 6934 composite CVD events. Each 1-SD increase in BMI variability in the UKB cohort was associated with 8% increased risk of cardiovascular death (HR, 1.08; 95% CI, 1.04-1.11). Conclusions and Relevance This cohort study found that among US veterans, higher BMI variability was a significant risk marker associated with adverse cardiovascular events independent of mean BMI across major racial and ethnic groups. Results were consistent in the UKB for the cardiovascular death end point. Further studies should investigate the phenotype of high BMI variability.
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Affiliation(s)
- Zakaria Almuwaqqat
- Veterans Affairs Atlanta Healthcare System, Decatur, Georgia
- Division of Cardiology, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia
| | - Qin Hui
- Veterans Affairs Atlanta Healthcare System, Decatur, Georgia
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, Georgia
| | - Chang Liu
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, Georgia
| | - Jin J. Zhou
- Department of Medicine and Biostatistics, University of California, Los Angeles
- Veterans Affairs Phoenix Healthcare System, Phoenix, Arizona
| | - Benjamin F. Voight
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
- Department of Systems Pharmacology and Translational Therapeutics, Department of Genetics, University of Pennsylvania, Philadelphia\
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston
| | - Daniel C. Posner
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston
| | - Jason L. Vassy
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - J. Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston
- Division of Aging, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Peter W. F. Wilson
- Veterans Affairs Atlanta Healthcare System, Decatur, Georgia
- Division of Cardiology, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia
| | - Yan V. Sun
- Veterans Affairs Atlanta Healthcare System, Decatur, Georgia
- Division of Cardiology, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, Georgia
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Skowronski AA, Leibel RL, LeDuc CA. Neurodevelopmental Programming of Adiposity: Contributions to Obesity Risk. Endocr Rev 2024; 45:253-280. [PMID: 37971140 PMCID: PMC10911958 DOI: 10.1210/endrev/bnad031] [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: 06/07/2023] [Revised: 09/29/2023] [Accepted: 10/19/2023] [Indexed: 11/19/2023]
Abstract
This review analyzes the published evidence regarding maternal factors that influence the developmental programming of long-term adiposity in humans and animals via the central nervous system (CNS). We describe the physiological outcomes of perinatal underfeeding and overfeeding and explore potential mechanisms that may mediate the impact of such exposures on the development of feeding circuits within the CNS-including the influences of metabolic hormones and epigenetic changes. The perinatal environment, reflective of maternal nutritional status, contributes to the programming of offspring adiposity. The in utero and early postnatal periods represent critically sensitive developmental windows during which the hormonal and metabolic milieu affects the maturation of the hypothalamus. Maternal hyperglycemia is associated with increased transfer of glucose to the fetus driving fetal hyperinsulinemia. Elevated fetal insulin causes increased adiposity and consequently higher fetal circulating leptin concentration. Mechanistic studies in animal models indicate important roles of leptin and insulin in central and peripheral programming of adiposity, and suggest that optimal concentrations of these hormones are critical during early life. Additionally, the environmental milieu during development may be conveyed to progeny through epigenetic marks and these can potentially be vertically transmitted to subsequent generations. Thus, nutritional and metabolic/endocrine signals during perinatal development can have lifelong (and possibly multigenerational) impacts on offspring body weight regulation.
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Affiliation(s)
- Alicja A Skowronski
- Division of Molecular Genetics, Department of Pediatrics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10032, USA
- Naomi Berrie Diabetes Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Rudolph L Leibel
- Division of Molecular Genetics, Department of Pediatrics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10032, USA
- Naomi Berrie Diabetes Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Charles A LeDuc
- Division of Molecular Genetics, Department of Pediatrics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10032, USA
- Naomi Berrie Diabetes Center, Columbia University Irving Medical Center, New York, NY 10032, USA
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Arnouk L, Chantereau H, Courbage S, Tounian P, Clément K, Poitou C, Dubern B. Hyperphagia and impulsivity: use of self-administered Dykens' and in-house impulsivity questionnaires to characterize eating behaviors in children with severe and early-onset obesity. Orphanet J Rare Dis 2024; 19:84. [PMID: 38395939 PMCID: PMC10893692 DOI: 10.1186/s13023-024-03085-1] [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: 08/05/2023] [Accepted: 02/19/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND The determinants of early-onset obesity (< 6 years) are not completely elucidated, however eating behavior has a central role. To date no study has explored eating behavior in children with severe, early-onset obesity. Self-administered questionnaire data from these children were examined to evaluate eating behavior and the etiology of early-onset obesity. METHODS Children with severe, early-onset obesity (body mass index [BMI] > International Obesity Task Force [IOTF] 30) of different etiologies (hypothalamic obesity [HO], intellectual disability with obesity [IDO], common polygenic obesity [CO]) were prospectively included. BMI history and responses from the Dykens' Hyperphagia Questionnaire and an in-house Impulsivity Questionnaire at first visit were compared between groups. RESULTS This cohort of 75 children (39 girls; mean age ± standard deviation [SD] 10.8 ± 4.4 years) had severe, early-onset obesity at an age of 3.8 ± 2.7 years, with a BMI Z-score of 4.9 ± 1.5. BMI history varied between the 3 groups, with earlier severe obesity in the HO group versus 2 other groups (BMI > IOTF40 at 3.4 ± 1.6 vs. 4.6 ± 1.6 and 8.4 ± 4.1 years for the IDO and CO groups, respectively [P < 0.01]). Absence of adiposity rebound was more prevalent in the HO group (87% vs. 63% and 33% for the IDO and CO groups, respectively [P < 0.01]). The Dykens' mean total score for the cohort was 22.1 ± 7.2 with no significant between-group differences. Hyperphagia (Dykens' score > 19) and impulsivity (score > 7) were found in 50 (67%) and 11 children (15%), respectively, with no difference between the HO, IDO and CO groups regarding the number of patients with hyperphagia (10 [67%], 14 [74%], and 26 [63%] children, respectively) or impulsivity (2 [13%], 1 [7%], and 8 [19%] children, respectively). Children with food impulsivity had significantly higher total and severity scores on the Dykens' Questionnaire versus those without impulsivity. CONCLUSION The Dykens' and Impulsivity questionnaires can help diagnose severe hyperphagia with/without food impulsivity in children with early-onset obesity, regardless of disease origin. Their systematic use can allow more targeted management of food access control in clinical practice and monitor the evolution of eating behavior in the case of innovative therapeutic targeting hyperphagia.
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Affiliation(s)
- Lara Arnouk
- Pediatric Nutrition and Gastroenterology Department, French Reference Center for Prader-Willi Syndrome and Other Rare Obesities (PRADORT), Assistance Publique Hôpitaux de Paris, Trousseau Hospital, 26 Avenue du Dr Netter, 75012, Paris, France
| | - Hélène Chantereau
- Pediatric Nutrition and Gastroenterology Department, French Reference Center for Prader-Willi Syndrome and Other Rare Obesities (PRADORT), Assistance Publique Hôpitaux de Paris, Trousseau Hospital, 26 Avenue du Dr Netter, 75012, Paris, France
| | - Sophie Courbage
- Pediatric Nutrition and Gastroenterology Department, French Reference Center for Prader-Willi Syndrome and Other Rare Obesities (PRADORT), Assistance Publique Hôpitaux de Paris, Trousseau Hospital, 26 Avenue du Dr Netter, 75012, Paris, France
| | - Patrick Tounian
- Pediatric Nutrition and Gastroenterology Department, French Reference Center for Prader-Willi Syndrome and Other Rare Obesities (PRADORT), Assistance Publique Hôpitaux de Paris, Trousseau Hospital, 26 Avenue du Dr Netter, 75012, Paris, France
- INSERM, Nutrition and Obesity: Systemic Approaches, NutriOmics, Research Unit, Sorbonne Université, Paris, France
| | - Karine Clément
- Nutrition Department, French Reference Center for Prader-Willi Syndrome and Other Rare Obesities (PRADORT), Assistance Publique Hôpitaux de ParisPitié-Salpêtrière Hospital, Paris, France
- INSERM, Nutrition and Obesity: Systemic Approaches, NutriOmics, Research Unit, Sorbonne Université, Paris, France
| | - Christine Poitou
- Nutrition Department, French Reference Center for Prader-Willi Syndrome and Other Rare Obesities (PRADORT), Assistance Publique Hôpitaux de ParisPitié-Salpêtrière Hospital, Paris, France
- INSERM, Nutrition and Obesity: Systemic Approaches, NutriOmics, Research Unit, Sorbonne Université, Paris, France
| | - Béatrice Dubern
- Pediatric Nutrition and Gastroenterology Department, French Reference Center for Prader-Willi Syndrome and Other Rare Obesities (PRADORT), Assistance Publique Hôpitaux de Paris, Trousseau Hospital, 26 Avenue du Dr Netter, 75012, Paris, France.
- INSERM, Nutrition and Obesity: Systemic Approaches, NutriOmics, Research Unit, Sorbonne Université, Paris, France.
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48
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Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Barone Gibbs B, Beaton AZ, Boehme AK, Commodore-Mensah Y, Currie ME, Elkind MSV, Evenson KR, Generoso G, Heard DG, Hiremath S, Johansen MC, Kalani R, Kazi DS, Ko D, Liu J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Perman SM, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Tsao CW, Urbut SM, Van Spall HGC, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Palaniappan LP. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation 2024; 149:e347-e913. [PMID: 38264914 DOI: 10.1161/cir.0000000000001209] [Citation(s) in RCA: 124] [Impact Index Per Article: 124.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
BACKGROUND The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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49
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Jung H, Jung HU, Baek EJ, Kwon SY, Kang JO, Lim JE, Oh B. Integration of risk factor polygenic risk score with disease polygenic risk score for disease prediction. Commun Biol 2024; 7:180. [PMID: 38351177 PMCID: PMC10864389 DOI: 10.1038/s42003-024-05874-7] [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: 09/19/2023] [Accepted: 01/30/2024] [Indexed: 02/16/2024] Open
Abstract
Polygenic risk score (PRS) is useful for capturing an individual's genetic susceptibility. However, previous studies have not fully exploited the potential of the risk factor PRS (RFPRS) for disease prediction. We explored the potential of integrating disease-related RFPRSs with disease PRS to enhance disease prediction performance. We constructed 112 RFPRSs and analyzed the association of RFPRSs with diseases to identify disease-related RFPRSs in 700 diseases, using the UK Biobank dataset. We uncovered 6157 statistically significant associations between 247 diseases and 109 RFPRSs. We estimated the disease PRSs of 70 diseases that exhibited statistically significant heritability, to generate RFDiseasemetaPRS-a combined PRS integrating RFPRSs and disease PRS-and compare the prediction performance metrics between RFDiseasemetaPRS and disease PRS. RFDiseasemetaPRS showed better performance for Nagelkerke's pseudo-R2, odds ratio (OR) per 1 SD, net reclassification improvement (NRI) values and difference of R2 considered by variance of R2 in 31 out of 70 diseases. Additionally, we assessed risk classification between two models by examining OR between the top 10% and remaining 90% individuals for the 31 diseases; RFDiseasemetaPRS exhibited better R2, NRI and OR than disease PRS. These findings highlight the importance of utilizing RFDiseasemetaPRS, which can provide personalized healthcare and tailored prevention strategies.
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Affiliation(s)
- Hyein Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | - Hae-Un Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | | | - Shin Young Kwon
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | - Ji-One Kang
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Ji Eun Lim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea.
| | - Bermseok Oh
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea.
- Mendel Inc, Seoul, Republic of Korea.
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea.
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50
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Agius R, Pace NP, Fava S. Phenotyping obesity: A focus on metabolically healthy obesity and metabolically unhealthy normal weight. Diabetes Metab Res Rev 2024; 40:e3725. [PMID: 37792999 DOI: 10.1002/dmrr.3725] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 07/23/2023] [Accepted: 08/11/2023] [Indexed: 10/06/2023]
Abstract
Over the past 4 decades, research has shown that having a normal body weight does not automatically imply preserved metabolic health and a considerable number of lean individuals harbour metabolic abnormalities typically associated with obesity. Conversely, excess adiposity does not always equate with an abnormal metabolic profile. In fact, evidence exists for the presence of a metabolically unhealthy normal weight (MUHNW) and a metabolically healthy obese (MHO) phenotype. It has become increasingly recognised that different fat depots exert different effects on the metabolic profile of each individual by virtue of their location, structure and function, giving rise to these different body composition phenotypes. Furthermore, other factors have been implicated in the aetiopathogenesis of the body composition phenotypes, including genetics, ethnicity, age and lifestyle/behavioural factors. Even though to date both MHO and MUHNW have been widely investigated and documented in the literature, studies report different outcomes on long-term cardiometabolic morbidity and mortality. Future large-scale, observational and population-based studies are required for better profiling of these phenotypes as well as to further elucidate the pathophysiological role of the adipocyte in the onset of metabolic disorders to allow for better risk stratification and a personalised treatment paradigm.
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Affiliation(s)
- Rachel Agius
- University of Malta Medical School, Msida, Malta
- Mater Dei Hospital, Msida, Malta
| | | | - Stephen Fava
- University of Malta Medical School, Msida, Malta
- Mater Dei Hospital, Msida, Malta
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