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Association of triglyceride to HDL cholesterol ratio with cardiometabolic outcomes
  1. May Yang1,
  2. Joseph Rigdon2,
  3. Sandra A Tsai3
  1. 1 Stanford University School of Medicine, Stanford, California, USA
  2. 2 Quantitative Sciences Unit, Stanford University School of Medicine, Stanford, California, USA
  3. 3 Stanford University Department of Medicine, Stanford, California, USA
  1. Correspondence to May Yang, Stanford University School of Medicine, Stanford CA 94305, USA; mayyang{at}stanford.edu

Abstract

Electronic medical records (EMRs) offer a potential opportunity to identify patients at high risk for cardiometabolic disease, which encompasses type 2 diabetes and cardiovascular disease (CVD). The objective of this retrospective cohort study is to use information gathered from EMR to investigate the association between triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) and cardiometabolic outcomes in a general population of subjects over 50 years of age during a follow-up period of 8–9 years. TG/HDL-C was recorded for each of 1428 subjects in 2008, and diagnoses of type 2 diabetes and CVD were recorded through chart review until 2017. Cox proportional hazards models controlling for demographic characteristics and other risk factors demonstrated that high TG/HDL-C (>2.5 in women or >3.5 in men) was significantly associated with increased incidence of type 2 diabetes (HR 1.66; 95% CI 1.07 to 2.57; p=0.0230). There was also a suggested association between high TG/HDL-C and incidence of CVD (HR 1.51; 95% CI 0.98 to 2.35; p=0.0628). These findings suggest that using TG/HDL-C, which can be easily calculated from data in an EMR, should be another tool used in identifying patients at high cardiometabolic risk.

  • cardiovascular disease

http://dx.doi.org/10.1136/jim-2018-000869

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    Footnotes

    • Contributors MY and SAT designed the project. MY conducted the data collection and wrote the manuscript. JR completed the statistical analysis and designed the figures and tables. SAT oversaw the project.

    • Funding Funding provided by the Stanford Medical Scholars Fellowship Program. STRIDE was supported by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health (NIH), through grant UL1 TR001085.

    • Disclaimer The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

    • Competing interests None declared.

    • Patient consent Not required.

    • Ethics approval Stanford School of Medicine, IRB-8.

    • Provenance and peer review Not commissioned; externally peer reviewed.