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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 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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2
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Perry AS, Piaggi P, Huang S, Nayor M, Freedman J, North K, Below J, Clish C, Murthy VL, Krakoff J, Shah RV. Human metabolic chambers reveal a coordinated metabolic-physiologic response to nutrition. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.08.24305087. [PMID: 38645000 PMCID: PMC11030300 DOI: 10.1101/2024.04.08.24305087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
The emerging field of precision nutrition is based on the notion that inter-individual responses across diets of different calorie-macronutrient content may contribute to inter-individual differences in metabolism, adiposity, and weight gain. Free-living diet studies have been traditionally challenged by difficulties in controlling adherence to prescribed calories and macronutrient content and rarely allow a period of metabolic stability prior to metabolic measures (to minimize influences of weight changes). In this context, key physiologic measures central to precision nutrition responses may be most precisely quantified via whole room indirect calorimetry over 24-h, in which precise control of activity and nutrition can be achieved. In addition, these studies represent unique "N of 1" human crossover metabolic-physiologic experiments during which specific molecular pathways central to nutrient metabolism may be discerned. Here, we quantified 263 circulating metabolites during a ≈40-day inpatient admission in which up to 94 participants underwent seven monitored 24-h nutritional interventions of differing macronutrient composition in a whole-room indirect calorimeter to capture precision metabolic responses. Broadly, we observed heterogenous responses in metabolites across dietary chambers, with the exception of carnitines which tracked with 24-h respiratory quotient. We identified excursions in shared metabolic species (e.g., carnitines, glycerophospholipids, amino acids) that mapped onto gold-standard calorimetric measures of substrate oxidation preference and lipid availability. These findings support a coordinated metabolic-physiologic response to nutrition, highlighting the relevance of these controlled settings to uncover biological pathways of energy utilization during precision nutrition studies.
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3
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Torkamani A, Chen SF, Lee SE, Sadaei H, Park JB, Khattab A, Henegar C, Wineinger N, Muse E. Meta-Prediction of Coronary Artery Disease Risk. RESEARCH SQUARE 2023:rs.3.rs-3694374. [PMID: 38196609 PMCID: PMC10775391 DOI: 10.21203/rs.3.rs-3694374/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Coronary artery disease (CAD) remains the leading cause of mortality and morbidity worldwide. Recent advances in large-scale genome-wide association studies have highlighted the potential of genetic risk, captured as polygenic risk scores (PRS), in clinical prevention. However, the current clinical utility of PRS models is limited to identifying high-risk populations based on the top percentiles of genetic susceptibility. While some studies have attempted integrative prediction using genetic and non-genetic factors, many of these studies have been cross-sectional and focused solely on risk stratification. Our primary objective in this study was to integrate unmodifiable (age / genetics) and modifiable (clinical / biometric) risk factors into a prospective prediction framework which also produces actionable and personalized risk estimates for the purpose of CAD prevention in a heterogenous adult population. Thus, we present an integrative, omnigenic, meta-prediction framework that effectively captures CAD risk subgroups, primarily distinguished by degree and nature of genetic risk, with distinct risk reduction profiles predicted from standard clinical interventions. Initial model development considered ~ 2,000 predictive features, including demographic data, lifestyle factors, physical measurements, laboratory tests, medication usage, diagnoses, and genetics. To power our meta-prediction approach, we stratified the UK Biobank into two primary cohorts: 1) a prevalent CAD cohort used to train baseline and prospective predictive models for contributing risk factors and diagnoses, and 2) an incident CAD cohort used to train the final CAD incident risk prediction model. The resultant 10-year incident CAD risk model is composed of 35 derived meta-features from models trained on the prevalent risk cohort, most of which are predicted baseline diagnoses with multiple embedded PRSs. This model achieved an AUC of 0.81 and macro-averaged F1-score of 0.65, outperforming standard clinical scores and prior integrative models. We further demonstrate that individualized risk reduction profiles can be derived from this model, with genetic risk mediating the degree of risk reduction achieved by standard clinical interventions.
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Affiliation(s)
- Ali Torkamani
- Scripps Research & Scripps Research Translational Institute
| | - Shang-Fu Chen
- Scripps Research & Scripps Research Translational Institute
| | - Sang Eun Lee
- Asan Medical Center, University of Ulsan College of Medicine
| | - Hossein Sadaei
- Scripps Research & Scripps Research Translational Institute
| | | | - Ahmed Khattab
- Scripps Research & Scripps Research Translational Institute
| | | | | | - Evan Muse
- Scripps Translational Science Institute, The Scripps Research Institute, Scripps Health
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4
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Muntané G, Vázquez-Bourgon J, Sada E, Martorell L, Papiol S, Bosch E, Navarro A, Crespo-Facorro B, Vilella E. Polygenic risk scores enhance prediction of body mass index increase in individuals with a first episode of psychosis. Eur Psychiatry 2023; 66:e28. [PMID: 36852609 PMCID: PMC10044301 DOI: 10.1192/j.eurpsy.2023.9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND Individuals with a first episode of psychosis (FEP) show rapid weight gain during the first months of treatment, which is associated with a reduction in general physical health. Although genetics is assumed to be a significant contributor to weight gain, its exact role is unknown. METHODS We assembled a population-based FEP cohort of 381 individuals that was split into a Training (n = 224) set and a Validation (n = 157) set to calculate the polygenic risk score (PRS) in a two-step process. In parallel, we obtained reference genome-wide association studies for body mass index (BMI) and schizophrenia (SCZ) to examine the pleiotropic landscape between the two traits. BMI PRSs were added to linear models that included sociodemographic and clinical variables to predict BMI increase (∆BMI) in the Validation set. RESULTS The results confirmed considerable shared genetic susceptibility for the two traits involving 449 near-independent genomic loci. The inclusion of BMI PRSs significantly improved the prediction of ∆BMI at 12 months after the onset of antipsychotic treatment by 49.4% compared to a clinical model. In addition, we demonstrated that the PRS containing pleiotropic information between BMI and SCZ predicted ∆BMI better at 3 (12.2%) and 12 months (53.2%). CONCLUSIONS We prove for the first time that genetic factors play a key role in determining ∆BMI during the FEP. This finding has important clinical implications for the early identification of individuals most vulnerable to weight gain and highlights the importance of examining genetic pleiotropy in the context of medically important comorbidities for predicting future outcomes.
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Affiliation(s)
- Gerard Muntané
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira i Virgili, Reus, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Institut de Biologia Evolutiva (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain
| | - Javier Vázquez-Bourgon
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Department of Psychiatry, University Hospital Marqués de Valdecilla, Instituto de Investigación Marqués de Valdecilla (IDIVAL), Santander, Spain.,Departamento de Medicina y Psiquiatría, Facultad de Medicina, Universidad de Cantabria, Santander, Spain
| | - Ester Sada
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira i Virgili, Reus, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | - Lourdes Martorell
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira i Virgili, Reus, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | - Sergi Papiol
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Department of Psychiatry, Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University, Munich, Germany
| | - Elena Bosch
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Institut de Biologia Evolutiva (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain
| | - Arcadi Navarro
- Institut de Biologia Evolutiva (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain.,Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.,Barcelonaβeta Brain Research Center, Fundació Pasqual Maragall, Barcelona, Spain
| | - Benedicto Crespo-Facorro
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Department of Psychiatry, Instituto de Biomedicina de Sevilla (IBiS), University Hospital Virgen del Rocío, Seville, Spain
| | - Elisabet Vilella
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira i Virgili, Reus, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
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5
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Shah RV, Steffen LM, Nayor M, Reis JP, Jacobs DR, Allen NB, Lloyd-Jones D, Meyer K, Cole J, Piaggi P, Vasan RS, Clish CB, Murthy VL. Dietary metabolic signatures and cardiometabolic risk. Eur Heart J 2023; 44:557-569. [PMID: 36424694 PMCID: PMC10169425 DOI: 10.1093/eurheartj/ehac446] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 06/23/2022] [Accepted: 07/28/2022] [Indexed: 11/27/2022] Open
Abstract
AIMS Observational studies of diet in cardiometabolic-cardiovascular disease (CM-CVD) focus on self-reported consumption of food or dietary pattern, with limited information on individual metabolic responses to dietary intake linked to CM-CVD. Here, machine learning approaches were used to identify individual metabolic patterns related to diet and relation to long-term CM-CVD in early adulthood. METHODS AND RESULTS In 2259 White and Black adults (age 32.1 ± 3.6 years, 45% women, 44% Black) in the Coronary Artery Risk Development in Young Adults (CARDIA) study, multivariate models were employed to identify metabolite signatures of food group and composite dietary intake across 17 food groups, 2 nutrient groups, and healthy eating index-2015 (HEI2015) diet quality score. A broad array of metabolites associated with diet were uncovered, reflecting food-related components/catabolites (e.g. fish and long-chain unsaturated triacylglycerols), interactions with host features (microbiome), or pathways broadly implicated in CM-CVD (e.g. ceramide/sphingomyelin lipid metabolism). To integrate diet with metabolism, penalized machine learning models were used to define a metabolite signature linked to a putative CM-CVD-adverse diet (e.g. high in red/processed meat, refined grains), which was subsequently associated with long-term diabetes and CVD risk numerically more strongly than HEI2015 in CARDIA [e.g. diabetes: standardized hazard ratio (HR): 1.62, 95% confidence interval (CI): 1.32-1.97, P < 0.0001; CVD: HR: 1.55, 95% CI: 1.12-2.14, P = 0.008], with associations replicated for diabetes (P < 0.0001) in the Framingham Heart Study. CONCLUSION Metabolic signatures of diet are associated with long-term CM-CVD independent of lifestyle and traditional risk factors. Metabolomics improves precision to identify adverse consequences and pathways of diet-related CM-CVD.
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Affiliation(s)
- Ravi V Shah
- Vanderbilt University Medical Center, Vanderbilt Clinical and Translational Research Center (VTRACC), Nashville, TN, USA
| | - Lyn M Steffen
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN, USA
| | - Matthew Nayor
- Cardiology Division, Boston University School of Medicine, Boston, MA, USA
| | - Jared P Reis
- Epidemiology Branch, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - David R Jacobs
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN, USA
| | - Norrina B Allen
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Donald Lloyd-Jones
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Katie Meyer
- Nutrition Department, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Joanne Cole
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Paolo Piaggi
- Department of Information Engineering, University of Pisa, Pisa, Italy
| | - Ramachandran S Vasan
- Sections of Preventive Medicine and Epidemiology and Cardiovascular Medicine, Department of Medicine, and Department of Epidemiology, Boston University Schools of Medicine and Public Health, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Clary B Clish
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Venkatesh L Murthy
- Department of Medicine and Radiology, University of Michigan, 1338 Cardiovascular Center, Ann Arbor, MI 48109-5873, USA
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6
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Kafyra M, Kalafati IP, Dimitriou M, Grigoriou E, Kokkinos A, Rallidis L, Kolovou G, Trovas G, Marouli E, Deloukas P, Moulos P, Dedoussis GV. Robust Bioinformatics Approaches Result in the First Polygenic Risk Score for BMI in Greek Adults. J Pers Med 2023; 13:jpm13020327. [PMID: 36836561 PMCID: PMC9960517 DOI: 10.3390/jpm13020327] [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: 12/23/2022] [Revised: 01/29/2023] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
Quantifying the role of genetics via construction of polygenic risk scores (PRSs) is deemed a resourceful tool to enable and promote effective obesity prevention strategies. The present paper proposes a novel methodology for PRS extraction and presents the first PRS for body mass index (BMI) in a Greek population. A novel pipeline for PRS derivation was used to analyze genetic data from a unified database of three cohorts of Greek adults. The pipeline spans various steps of the process, from iterative dataset splitting to training and test partitions, calculation of summary statistics and PRS extraction, up to PRS aggregation and stabilization, achieving higher evaluation metrics. Using data from 2185 participants, implementation of the pipeline enabled consecutive repetitions in splitting training and testing samples and resulted in a 343-single nucleotide polymorphism PRS yielding an R2 = 0.3241 (beta = 1.011, p-value = 4 × 10-193) for BMI. PRS-included variants displayed a variety of associations with known traits (i.e., blood cell count, gut microbiome, lifestyle parameters). The proposed methodology led to creation of the first-ever PRS for BMI in Greek adults and aims at promoting a facilitating approach to reliable PRS development and integration in healthcare practice.
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Affiliation(s)
- Maria Kafyra
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, 17671 Athens, Greece
| | - Ioanna Panagiota Kalafati
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, 17671 Athens, Greece
- Department of Nutrition and Dietetics, School of Physical Education, Sport Science and Dietetics, University of Thessaly, 42132 Trikala, Greece
| | - Maria Dimitriou
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, 17671 Athens, Greece
- Department of Nutritional Science and Dietetics, School of Health Science, University of the Peloponnese, Antikalamos, 24100 Kalamata, Greece
| | - Effimia Grigoriou
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, 17671 Athens, Greece
| | - Alexandros Kokkinos
- First Department of Propaedeutic and Internal Medicine, Laiko General Hospital, Athens University Medical School, 11527 Athens, Greece
| | - Loukianos Rallidis
- Second Department of Cardiology, Medical School, National and Kapodistrian University of Athens, Attikon Hospital, 12462 Athens, Greece
| | - Genovefa Kolovou
- Cardiometabolic Center, Metropolitan Hospital, 18547 Piraeus, Greece
| | - Georgios Trovas
- Laboratory for the Research of Musculoskeletal System “Th. Garofalidis”, School of Medicine, National and Kapodistrian University of Athens, KAT General Hospital, Athinas 10th Str., 14561 Athens, Greece
| | - Eirini Marouli
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Panos Deloukas
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Panagiotis Moulos
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center ‘Alexander Fleming’, 16672 Vari, Greece
- Correspondence:
| | - George V. Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, 17671 Athens, Greece
- Genome Analysis, 17671 Athens, Greece
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7
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O'Sullivan JW, Ashley EA, Elliott PM. Polygenic risk scores for the prediction of cardiometabolic disease. Eur Heart J 2023; 44:89-99. [PMID: 36478054 DOI: 10.1093/eurheartj/ehac648] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 08/28/2022] [Accepted: 10/27/2022] [Indexed: 12/12/2022] Open
Abstract
Cardiometabolic diseases contribute more to global morbidity and mortality than any other group of disorders. Polygenic risk scores (PRSs), the weighted summation of individually small-effect genetic variants, represent an advance in our ability to predict the development and complications of cardiometabolic diseases. This article reviews the evidence supporting the use of PRS in seven common cardiometabolic diseases: coronary artery disease (CAD), stroke, hypertension, heart failure and cardiomyopathies, obesity, atrial fibrillation (AF), and type 2 diabetes mellitus (T2DM). Data suggest that PRS for CAD, AF, and T2DM consistently improves prediction when incorporated into existing clinical risk tools. In other areas such as ischaemic stroke and hypertension, clinical application appears premature but emerging evidence suggests that the study of larger and more diverse populations coupled with more granular phenotyping will propel the translation of PRS into practical clinical prediction tools.
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Affiliation(s)
- Jack W O'Sullivan
- Stanford Center for Inherited Cardiovascular Disease, Stanford University School of Medicine, Stanford, CA, USA
- Division of Cardiology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Euan A Ashley
- Stanford Center for Inherited Cardiovascular Disease, Stanford University School of Medicine, Stanford, CA, USA
- Division of Cardiology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Perry M Elliott
- UCL Institute of Cardiovascular Science, Gower Street, London WC1E 6BT, UK
- St. Bartholomew's Hospital, W Smithfield, London EC1A 7BE, UK
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8
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Perry AS, Tanriverdi K, Risitano A, Hwang SJ, Murthy VL, Nayor M, Zhao S, Levy D, Shah RV, Freedman JE. The inflammatory proteome, obesity, and medical weight loss and regain in humans. Obesity (Silver Spring) 2023; 31:150-158. [PMID: 36334095 PMCID: PMC9923277 DOI: 10.1002/oby.23587] [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: 06/12/2022] [Revised: 08/12/2022] [Accepted: 08/16/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Weight regain occurs after medical weight loss via mechanisms of post-weight-loss "metabolic adaptation." The relationship of inflammatory proteins with weight loss/regain was studied to determine a role for inflammation in metabolic adaptation. METHODS Seventy-four proteins central to inflammation and immune regulation (Olink) were analyzed in plasma from up to 490 participants in a trial of medical weight-loss maintenance. Cross-sectional and longitudinal associations of proteins with weight were measured using linear and mixed effects regression models and t testing, with replication in the Framingham Heart Study. RESULTS Broad changes in the inflammatory proteome were observed among the study cohort (60% women, 35% African American) with initial weight loss of ≈8 kg from a median 94 kg at study entry (33/74 proteins; 7 increased; 26 decreased), many of which tracked with weight regain of median ≈2 kg over the next 30 months. Ten proteins were associated with different rates of weight regain, some specifying pathways of chemotaxis and innate immune responses. Several of the observed protein associations were also linked to prevalent obesity in the Framingham Heart Study. CONCLUSIONS Broad changes in the inflammatory proteome track with changes in weight and may identify specific pathways that modify patterns of weight regain.
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Affiliation(s)
- Andrew S Perry
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Kahraman Tanriverdi
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Antonina Risitano
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Shih-Jen Hwang
- Framingham Heart Study, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Venkatesh L Murthy
- Department of Medicine and Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Matthew Nayor
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Shilin Zhao
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Daniel Levy
- Framingham Heart Study, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Ravi V Shah
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Jane E Freedman
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
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9
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Kitchen C, Chang HY, Weiner JP, Kharrazi H. Assessing the Added Value of Vital Signs Extracted from Electronic Health Records in Healthcare Risk Adjustment Models. Healthc Policy 2022; 15:1671-1682. [PMID: 36092549 PMCID: PMC9462838 DOI: 10.2147/rmhp.s356080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 03/26/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose Patient vital signs are related to specific health risks and outcomes but are underutilized in the prediction of health-care utilization and cost. To measure the added value of electronic health record (EHR) extracted Body Mass Index (BMI) and blood pressure (BP) values in improving healthcare risk and utilization predictions. Patients and Methods A sample of 12,820 adult outpatients from the Johns Hopkins Health System (JHHS) were identified between 2016 and 2017, having high data quality and recorded values for BMI and BP. We evaluated the added value of BMI and BP in predicting health-care utilization and cost through a retrospective cohort design. BMI, mean arterial pressure (MAP), systolic and diastolic BPs were summarized as annual aggregated values. Concurrent annual BMI and MAP changes were quantified as the difference between maximum and minimum recorded values. Model performance estimates consisted of repeated 10-fold cross validation, compared to base model point estimates for demographic and diagnostic, coded events: (1) patient age and sex, (2) age, sex, and the Charlson weighted index, (3) age, sex and the Johns Hopkins ACG system’s DxPM risk score. Results Both categorical BMI and BP were progressively indicative of disease comorbidity, but not uniformly related to health-care utilization or cost. Annual change in BMI and MAP improved predictions for most concurrent year outcomes when compared to base models. Conclusion When a healthcare system lacks relevant diagnostic or risk assessment information for a patient, vital signs may be useful for a simple estimation of disease risk, cost and utilization.
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Affiliation(s)
- Christopher Kitchen
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Hsien-Yen Chang
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jonathan P Weiner
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Hadi Kharrazi
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.,Division of Health Sciences Informatics, Johns Hopkins School of Medicine, Baltimore, MD, USA
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10
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Shi M, Chen W, Sun X, Bazzano LA, He J, Razavi AC, Li C, Qi L, Khera AV, Kelly TN. Association of Genome-Wide Polygenic Risk Score for Body Mass Index With Cardiometabolic Health From Childhood Through Midlife. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2022; 15:e003375. [PMID: 35675159 DOI: 10.1161/circgen.121.003375] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 04/15/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Genetic information may help to identify individuals in childhood who are at increased risk for cardiometabolic disease. METHODS We included 1201 BHS (Bogalusa Heart Study) participants (832 White participants and 369 Black participants) who were followed up to 42.3 years, starting at a mean age of 9.8 years. A validated genome-wide polygenic risk score (PRS) was tested for association with midlife body mass index (BMI), fasting plasma glucose, and systolic blood pressure using multiple linear regression models. Cox proportional hazards models tested associations of the PRS with incident obesity, diabetes, and hypertension. All analyses were conducted according to race and adjusted for baseline age, sex, ancestry, and BMI. RESULTS The constructed PRS was significantly and modestly correlated with midlife BMI in both White and Black participants, with correlation coefficients of 0.27 (P=1.94×10-8) and 0.16 (P=5.50×10-3), respectively. In White participants, per SD increase of PRS was associated with an average 1.29 kg/m2 higher BMI (P=4.44×10-9), 2.82 mg/dL higher fasting plasma glucose (P=1.17×10-3), and 1.09 mm Hg higher systolic blood pressure (P=3.57×10-2) at midlife. The PRS also conferred a 26% higher increased risk of obesity (P=3.50×10-6) in White participants. In addition, the variance in midlife BMI explained increased from 0.1973 to 0.2293 when PRS was added to the model including age, sex, principal components, and baseline BMI (P<0.0001). No associations were observed in Black participants. CONCLUSIONS Adiposity-related genetic information independently predicted cardiometabolic health in White BHS participants. Null associations observed in Black BHS participants highlight the urgent need for PRS development in multi-ancestry populations.
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Affiliation(s)
- Mengyao Shi
- Department of Epidemiology, School of Public Health, and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases Medical College of Soochow University, Suzhou, China (M.S.)
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (M.S., W.C., X.S., L.A.B., J.H., A.C.R., C.L., L.Q., T.N.K.)
| | - Wei Chen
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (M.S., W.C., X.S., L.A.B., J.H., A.C.R., C.L., L.Q., T.N.K.)
| | - Xiao Sun
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (M.S., W.C., X.S., L.A.B., J.H., A.C.R., C.L., L.Q., T.N.K.)
| | - Lydia A Bazzano
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (M.S., W.C., X.S., L.A.B., J.H., A.C.R., C.L., L.Q., T.N.K.)
| | - Jiang He
- Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.H., A.C.R.)
| | - Alexander C Razavi
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (M.S., W.C., X.S., L.A.B., J.H., A.C.R., C.L., L.Q., T.N.K.)
- Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.H., A.C.R.)
| | - Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (M.S., W.C., X.S., L.A.B., J.H., A.C.R., C.L., L.Q., T.N.K.)
| | - Lu Qi
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (M.S., W.C., X.S., L.A.B., J.H., A.C.R., C.L., L.Q., T.N.K.)
| | - Amit V Khera
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA (A.V.K.)
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (A.V.K.)
| | - Tanika N Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (M.S., W.C., X.S., L.A.B., J.H., A.C.R., C.L., L.Q., T.N.K.)
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11
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Wang Z, Choi SW, Chami N, Boerwinkle E, Fornage M, Redline S, Bis JC, Brody JA, Psaty BM, Kim W, McDonald MLN, Regan EA, Silverman EK, Liu CT, Vasan RS, Kalyani RR, Mathias RA, Yanek LR, Arnett DK, Justice AE, North KE, Kaplan R, Heckbert S, de Andrade M, Guo X, Lange LA, Rich S, Rotter JI, Ellinor PT, Lubitz SA, Blangero J, Shoemaker MB, Darbar D, Gladwin MT, Albert CM, Chasman DI, Jackson RD, Kooperberg C, Reiner AP, O’Reilly PF, Loos RJF. The Value of Rare Genetic Variation in the Prediction of Common Obesity in European Ancestry Populations. Front Endocrinol (Lausanne) 2022; 13:863893. [PMID: 35592775 PMCID: PMC9110787 DOI: 10.3389/fendo.2022.863893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 03/11/2022] [Indexed: 01/05/2023] Open
Abstract
Polygenic risk scores (PRSs) aggregate the effects of genetic variants across the genome and are used to predict risk of complex diseases, such as obesity. Current PRSs only include common variants (minor allele frequency (MAF) ≥1%), whereas the contribution of rare variants in PRSs to predict disease remains unknown. Here, we examine whether augmenting the standard common variant PRS (PRScommon) with a rare variant PRS (PRSrare) improves prediction of obesity. We used genome-wide genotyped and imputed data on 451,145 European-ancestry participants of the UK Biobank, as well as whole exome sequencing (WES) data on 184,385 participants. We performed single variant analyses (for both common and rare variants) and gene-based analyses (for rare variants) for association with BMI (kg/m2), obesity (BMI ≥ 30 kg/m2), and extreme obesity (BMI ≥ 40 kg/m2). We built PRSscommon and PRSsrare using a range of methods (Clumping+Thresholding [C+T], PRS-CS, lassosum, gene-burden test). We selected the best-performing PRSs and assessed their performance in 36,757 European-ancestry unrelated participants with whole genome sequencing (WGS) data from the Trans-Omics for Precision Medicine (TOPMed) program. The best-performing PRScommon explained 10.1% of variation in BMI, and 18.3% and 22.5% of the susceptibility to obesity and extreme obesity, respectively, whereas the best-performing PRSrare explained 1.49%, and 2.97% and 3.68%, respectively. The PRSrare was associated with an increased risk of obesity and extreme obesity (ORobesity = 1.37 per SDPRS, Pobesity = 1.7x10-85; ORextremeobesity = 1.55 per SDPRS, Pextremeobesity = 3.8x10-40), which was attenuated, after adjusting for PRScommon (ORobesity = 1.08 per SDPRS, Pobesity = 9.8x10-6; ORextremeobesity= 1.09 per SDPRS, Pextremeobesity = 0.02). When PRSrare and PRScommon are combined, the increase in explained variance attributed to PRSrare was small (incremental Nagelkerke R2 = 0.24% for obesity and 0.51% for extreme obesity). Consistently, combining PRSrare to PRScommon provided little improvement to the prediction of obesity (PRSrare AUC = 0.591; PRScommon AUC = 0.708; PRScombined AUC = 0.710). In summary, while rare variants show convincing association with BMI, obesity and extreme obesity, the PRSrare provides limited improvement over PRScommon in the prediction of obesity risk, based on these large populations.
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Affiliation(s)
- Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Shing Wan Choi
- Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, New York, NY, United States
| | - Nathalie Chami
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, United States
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Susan Redline
- Division of Sleep Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States
| | - Jennifer A. Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States
- Department of Epidemiology, University of Washington, Seattle, WA, United States
| | - Wonji Kim
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Merry-Lynn N. McDonald
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Elizabeth A. Regan
- Division of Rheumatology, Department of Medicine, National Jewish Health, Denver, CO, United States
| | - Edwin K. Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Ramachandran S. Vasan
- National Heart, Lung and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, MA, United States
- Section of Preventive Medicine and Epidemiology, Evans Department of Medicine, Boston University School of Medicine, Boston, MA, United States
- Whitaker Cardiovascular Institute and Cardiology Section, Evans Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| | - Rita R. Kalyani
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Rasika A. Mathias
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Lisa R. Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Donna K. Arnett
- College of Public Health, University of Kentucky, Lexington, KY, United States
| | - Anne E. Justice
- Department of Population Health Services, Geisinger Health, Danville, PA, United States
| | - Kari E. North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Robert Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Susan R. Heckbert
- Department of Epidemiology, University of Washington, Seattle, WA, United States
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States
| | - Mariza de Andrade
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States
| | - Leslie A. Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anchutz Medical Camus, Aurora, CA, United States
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States
| | - Patrick T. Ellinor
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, United States
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, United States
| | - Steven A. Lubitz
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, United States
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, United States
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, United States
| | - M. Benjamin Shoemaker
- Departments of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Dawood Darbar
- Division of Cardiology, University of Illinois at Chicago, Chicago, IL, United States
| | - Mark T. Gladwin
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Christine M. Albert
- Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Daniel I. Chasman
- Department of Medicine, Harvard Medical School, Boston, MA, United States
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Rebecca D. Jackson
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, The Ohio State University, Columbus, OH, United States
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Alexander P. Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, United States
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Paul F. O’Reilly
- Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, New York, NY, United States
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- *Correspondence: Ruth J. F. Loos,
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12
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Workalemahu T, Rahman ML, Ouidir M, Wu J, Zhang C, Tekola-Ayele F. Associations of maternal blood pressure-raising polygenic risk scores with fetal weight. J Hum Hypertens 2022; 36:69-76. [PMID: 33536548 PMCID: PMC8329099 DOI: 10.1038/s41371-021-00483-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 12/12/2020] [Accepted: 01/13/2021] [Indexed: 01/31/2023]
Abstract
Maternal blood pressure (BP) is associated with variations in fetal weight, an important determinant of neonatal and adult health. However, the association of BP-raising genetic risk with fetal weight is unknown. We tested the associations of maternal BP-raising polygenic risk scores (PRS) with estimated fetal weights (EFWs) at 13, 20, 27, and 40 weeks of gestation. This study included 622 White, 637 Black, 568 Hispanic, and 238 Asian pregnant women with genotype data from the NICHD Fetal Growth Studies. PRS of systolic (SBP) and diastolic BP (DBP) were calculated for each participant based on summary statistics from a recent genome-wide association study. Linear regression models were used to compare mean EFW differences between the highest versus lowest tertile of PRS, adjusting for maternal age, education, parity, genetic principal components and fetal sex. Hispanics in the highest DBP PRS tertile, compared to those in the lowest, had 8.1 g (95% CI: -15.1, -1.1), 32.4 g (-58.4, -6.4) and 119.4 g (-218.1, -20.7) lower EFW at 20, 27 and 40 weeks, respectively. Similarly, Asians in the highest DBP PRS tertile had 137.2 g (-263.5, -10.8) lower EFW at week 40, and those in the highest tertile of SBP PRS had 3.2 g (-5.8, -0.7), 12.9 g (-23.5, -2.4), and 39.8 g (-76.9, -2.7) lower EFWs at 13, 20, and 27 weeks. The findings showed that pregnant women's genetic susceptibility to high BP contributes to reduced fetal growth, suggesting a potential future clinical application in perinatal health.
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Affiliation(s)
- Tsegaselassie Workalemahu
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Mohammad L. Rahman
- Harvard Medical School, Department of Population Medicine and Harvard Pilgrim Healthcare Institute, Boston, MA, USA
| | - Marion Ouidir
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Jing Wu
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Cuilin Zhang
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Fasil Tekola-Ayele
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
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13
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Lloyd-Jones DM, Lewis CE, Schreiner PJ, Shikany JM, Sidney S, Reis JP. The Coronary Artery Risk Development In Young Adults (CARDIA) Study: JACC Focus Seminar 8/8. J Am Coll Cardiol 2021; 78:260-277. [PMID: 34266580 PMCID: PMC8285563 DOI: 10.1016/j.jacc.2021.05.022] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 05/23/2021] [Accepted: 05/25/2021] [Indexed: 12/15/2022]
Abstract
The CARDIA (Coronary Artery Risk Development in Young Adults) study began in 1985 to 1986 with enrollment of 5,115 Black or White men and women ages 18 to 30 years from 4 US communities. Over 35 years, CARDIA has contributed fundamentally to our understanding of the contemporary epidemiology and life course of cardiovascular health and disease, as well as pulmonary, renal, neurological, and other manifestations of aging. CARDIA has established associations between the neighborhood environment and the evolution of lifestyle behaviors with biological risk factors, subclinical disease, and early clinical events. CARDIA has also identified the nature and major determinants of Black-White differences in the development of cardiovascular risk. CARDIA will continue to be a unique resource for understanding determinants, mechanisms, and outcomes of cardiovascular health and disease across the life course, leveraging ongoing pan-omics work from genomics to metabolomics that will define mechanistic pathways involved in cardiometabolic aging.
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Affiliation(s)
- Donald M Lloyd-Jones
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
| | - Cora E Lewis
- Department of Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, Alabama, USA
| | - Pamela J Schreiner
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, Minnesota, USA
| | - James M Shikany
- Division of Preventive Medicine, University of Alabama at Birmingham School of Medicine, Birmingham, Alabama, USA
| | - Stephen Sidney
- Kaiser Permanente Northern California Division of Research, Oakland, California, USA
| | - Jared P Reis
- National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA
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14
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Bunch TJ. Reflections from the Book of the Dead: Weighing the impact of epicardial fat on atrial fibrillation vulnerability. J Cardiovasc Electrophysiol 2021; 32:900-902. [PMID: 33600058 DOI: 10.1111/jce.14958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 02/10/2021] [Accepted: 02/13/2021] [Indexed: 10/22/2022]
Affiliation(s)
- T Jared Bunch
- Department of Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, USA
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15
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Sorigue M, Cañamero E, Sancho JM. Precision medicine in follicular lymphoma: Focus on predictive biomarkers. Hematol Oncol 2020; 38:625-639. [PMID: 32700331 DOI: 10.1002/hon.2781] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 07/16/2020] [Accepted: 07/16/2020] [Indexed: 02/06/2023]
Abstract
Current care for patients with follicular lymphoma (FL) offers most of them long-term survival. Improving it further will require careful patient selection. This review focuses on predictive biomarkers (ie, those whose outcome correlations depend on the treatment strategy) in FL, because awareness of what patient subsets benefit most or least from each therapy will help in this task. The first part of this review aims to summarize what biomarkers are predictive in FL, the magnitude of the effect and the quality of the evidence. We find predictive biomarkers in the setting of (a) indication of active treatment, (b) front-line induction (use of anthracyline-based regimens, CHOP vs bendamustine, addition of rituximab), (c) post-(front-line)induction (rituximab maintenance, radioimmunotherapy), and (d) relapse (hematopoietic stem cell transplant) and targeted agents. The second part of this review discusses the challenges of precision medicine in FL, including (a) cost, (b) clinical relevance considerations, and (c) difficulties over the broad implementation of biomarkers. We then provide our view on what biomarkers may become used in the next few years. We conclude by underscoring the importance of assessing the potential predictiveness of available biomarkers to improve patient care but also that there is a long road ahead before reaching their broad implementation due to remaining scientific, technological, and economic hurdles.
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Affiliation(s)
- Marc Sorigue
- Department of Hematology, ICO-Hospital Germans Trias i Pujol, Institut de Recerca Josep Carreras, Universitat Autònoma de Barcelona, Badalona, Spain
| | - Eloi Cañamero
- Department of Hematology, ICO-Hospital Germans Trias i Pujol, Institut de Recerca Josep Carreras, Universitat Autònoma de Barcelona, Badalona, Spain
| | - Juan-Manuel Sancho
- Department of Hematology, ICO-Hospital Germans Trias i Pujol, Institut de Recerca Josep Carreras, Universitat Autònoma de Barcelona, Badalona, Spain
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