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Quantifying the phenome-wide disease burden of obesity using electronic health records and genomics.
Robinson, Jamie R; Carroll, Robert J; Bastarache, Lisa; Chen, Qingxia; Pirruccello, James; Mou, Zongyang; Wei, Wei-Qi; Connolly, John; Mentch, Frank; Crane, Paul K; Hebbring, Scott J; Crosslin, David R; Gordon, Adam S; Rosenthal, Elisabeth A; Stanaway, Ian B; Hayes, M Geoffrey; Wei, Wei; Petukhova, Lynn; Namjou-Khales, Bahram; Zhang, Ge; Safarova, Mayya S; Walton, Nephi A; Still, Christopher; Bottinger, Erwin P; Loos, Ruth J F; Murphy, Shawn N; Jackson, Gretchen P; Abumrad, Naji; Kullo, Iftikhar J; Jarvik, Gail P; Larson, Eric B; Weng, Chunhua; Roden, Dan; Khera, Amit V; Denny, Joshua C.
Afiliación
  • Robinson JR; Department of Biomedical Informatics, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, USA.
  • Carroll RJ; Department of Surgery, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, USA.
  • Bastarache L; Department of Biomedical Informatics, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, USA.
  • Chen Q; Department of Biomedical Informatics, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, USA.
  • Pirruccello J; Department of Biomedical Informatics, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, USA.
  • Mou Z; Department of Biostatistics, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, USA.
  • Wei WQ; Center for Genomics Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Connolly J; Department of Surgery, University of California, San Diego, California, USA.
  • Mentch F; Department of Biomedical Informatics, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, USA.
  • Crane PK; The Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
  • Hebbring SJ; The Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
  • Crosslin DR; Department of Medicine, University of Washington, Seattle, Washington, USA.
  • Gordon AS; Center for Human Genetics, Marshfield Clinic Research Institute, Marshfield, Wisconsin, USA.
  • Rosenthal EA; Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA.
  • Stanaway IB; Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
  • Hayes MG; Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington Medical Center, University of Washington, Seattle, Washington, USA.
  • Wei W; Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA.
  • Petukhova L; Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
  • Namjou-Khales B; University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA.
  • Zhang G; Department of Epidemiology, Columbia University, New York, New York, USA.
  • Safarova MS; Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
  • Walton NA; Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
  • Still C; Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota, USA.
  • Bottinger EP; Department of Biomedical and Translational Informatics, Geisinger Health System, Danville, Pennsylvania, USA.
  • Loos RJF; Department of Biomedical and Translational Informatics, Geisinger Health System, Danville, Pennsylvania, USA.
  • Murphy SN; The Charles Bronfman Institute for Personalized Medicine at Mount Sinai, The Mindich Child Health and Development Institute, New York, New York, USA.
  • Jackson GP; The Charles Bronfman Institute for Personalized Medicine at Mount Sinai, The Mindich Child Health and Development Institute, New York, New York, USA.
  • Abumrad N; Department of Neurology, Partners Healthcare, Boston, Massachusetts, USA.
  • Kullo IJ; Department of Biomedical Informatics, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, USA.
  • Jarvik GP; Department of Surgery, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, USA.
  • Larson EB; Department of Surgery, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, USA.
  • Weng C; Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota, USA.
  • Roden D; Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington Medical Center, University of Washington, Seattle, Washington, USA.
  • Khera AV; Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA.
  • Denny JC; Department of Biomedical Informatics, Columbia University, New York, New York, USA.
Obesity (Silver Spring) ; 30(12): 2477-2488, 2022 12.
Article en En | MEDLINE | ID: mdl-36372681
OBJECTIVE: High BMI is associated with many comorbidities and mortality. This study aimed to elucidate the overall clinical risk of obesity using a genome- and phenome-wide approach. METHODS: This study performed a phenome-wide association study of BMI using a clinical cohort of 736,726 adults. This was followed by genetic association studies using two separate cohorts: one consisting of 65,174 adults in the Electronic Medical Records and Genomics (eMERGE) Network and another with 405,432 participants in the UK Biobank. RESULTS: Class 3 obesity was associated with 433 phenotypes, representing 59.3% of all billing codes in individuals with severe obesity. A genome-wide polygenic risk score for BMI, accounting for 7.5% of variance in BMI, was associated with 296 clinical diseases, including strong associations with type 2 diabetes, sleep apnea, hypertension, and chronic liver disease. In all three cohorts, 199 phenotypes were associated with class 3 obesity and polygenic risk for obesity, including novel associations such as increased risk of renal failure, venous insufficiency, and gastroesophageal reflux. CONCLUSIONS: This combined genomic and phenomic systematic approach demonstrated that obesity has a strong genetic predisposition and is associated with a considerable burden of disease across all disease classes.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Diabetes Mellitus Tipo 2 / Fenómica Límite: Humans Idioma: En Revista: Obesity (Silver Spring) Asunto de la revista: CIENCIAS DA NUTRICAO / FISIOLOGIA / METABOLISMO Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Diabetes Mellitus Tipo 2 / Fenómica Límite: Humans Idioma: En Revista: Obesity (Silver Spring) Asunto de la revista: CIENCIAS DA NUTRICAO / FISIOLOGIA / METABOLISMO Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos