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Obesity and BMI Cut Points for Associated Comorbidities: Electronic Health Record Study.
Liu, Natalie; Birstler, Jen; Venkatesh, Manasa; Hanrahan, Lawrence; Chen, Guanhua; Funk, Luke.
Affiliation
  • Liu N; Department of Surgery, University of Wisconsin-Madison, Madison, WI, United States.
  • Birstler J; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States.
  • Venkatesh M; Department of Surgery, University of Wisconsin-Madison, Madison, WI, United States.
  • Hanrahan L; Department of Family Medicine and Community Health, University of Wisconsin-Madison, Madison, WI, United States.
  • Chen G; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States.
  • Funk L; Department of Surgery, University of Wisconsin-Madison, Madison, WI, United States.
J Med Internet Res ; 23(8): e24017, 2021 08 09.
Article in En | MEDLINE | ID: mdl-34383661
BACKGROUND: Studies have found associations between increasing BMIs and the development of various chronic health conditions. The BMI cut points, or thresholds beyond which comorbidity incidence can be accurately detected, are unknown. OBJECTIVE: The aim of this study is to identify whether BMI cut points exist for 11 obesity-related comorbidities. METHODS: US adults aged 18-75 years who had ≥3 health care visits at an academic medical center from 2008 to 2016 were identified from eHealth records. Pregnant patients, patients with cancer, and patients who had undergone bariatric surgery were excluded. Quantile regression, with BMI as the outcome, was used to evaluate the associations between BMI and disease incidence. A comorbidity was determined to have a cut point if the area under the receiver operating curve was >0.6. The cut point was defined as the BMI value that maximized the Youden index. RESULTS: We included 243,332 patients in the study cohort. The mean age and BMI were 46.8 (SD 15.3) years and 29.1 kg/m2, respectively. We found statistically significant associations between increasing BMIs and the incidence of all comorbidities except anxiety and cerebrovascular disease. Cut points were identified for hyperlipidemia (27.1 kg/m2), coronary artery disease (27.7 kg/m2), hypertension (28.4 kg/m2), osteoarthritis (28.7 kg/m2), obstructive sleep apnea (30.1 kg/m2), and type 2 diabetes (30.9 kg/m2). CONCLUSIONS: The BMI cut points that accurately predicted the risks of developing 6 obesity-related comorbidities occurred when patients were overweight or barely met the criteria for class 1 obesity. Further studies using national, longitudinal data are needed to determine whether screening guidelines for appropriate comorbidities may need to be revised.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Diabetes Mellitus, Type 2 Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Limits: Adult / Humans Language: En Journal: J Med Internet Res Journal subject: INFORMATICA MEDICA Year: 2021 Type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Diabetes Mellitus, Type 2 Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Limits: Adult / Humans Language: En Journal: J Med Internet Res Journal subject: INFORMATICA MEDICA Year: 2021 Type: Article Affiliation country: United States