Skip to main content

Main menu

  • Home
  • Content
    • Current issue
    • Past issues
    • Collections
  • About
    • General information
    • Staff
    • Editorial board
    • Open access
    • Contact
  • CMAJ JOURNALS
    • CMAJ
    • CJS
    • JAMC
    • JPN

User menu

Search

  • Advanced search
CMAJ Open
  • CMAJ JOURNALS
    • CMAJ
    • CJS
    • JAMC
    • JPN
CMAJ Open

Advanced Search

  • Home
  • Content
    • Current issue
    • Past issues
    • Collections
  • About
    • General information
    • Staff
    • Editorial board
    • Open access
    • Contact
  • RSS feeds
Research

Feasibility of identifying and describing the burden of early-onset metabolic syndrome in primary care electronic medical record data: a cross-sectional analysis

Jamie J. Boisvenue, Carlo U. Oliva, Donna P. Manca, Jeffrey A. Johnson and Roseanne O. Yeung
November 24, 2020 8 (4) E779-E787; DOI: https://doi.org/10.9778/cmajo.20200007
Jamie J. Boisvenue
School of Public Health (Boisvenue, Johnson, Yeung), and Department of Computing Science (Oliva), Faculty of Science, and Department of Family Medicine (Manca), Faculty of Medicine & Dentistry, University of Alberta; Northern Alberta Primary Care Research Network (Manca); Division of Endocrinology and Metabolism (Yeung), Department of Medicine, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alta.
MSc
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Carlo U. Oliva
School of Public Health (Boisvenue, Johnson, Yeung), and Department of Computing Science (Oliva), Faculty of Science, and Department of Family Medicine (Manca), Faculty of Medicine & Dentistry, University of Alberta; Northern Alberta Primary Care Research Network (Manca); Division of Endocrinology and Metabolism (Yeung), Department of Medicine, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alta.
BSc
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Donna P. Manca
School of Public Health (Boisvenue, Johnson, Yeung), and Department of Computing Science (Oliva), Faculty of Science, and Department of Family Medicine (Manca), Faculty of Medicine & Dentistry, University of Alberta; Northern Alberta Primary Care Research Network (Manca); Division of Endocrinology and Metabolism (Yeung), Department of Medicine, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alta.
MD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jeffrey A. Johnson
School of Public Health (Boisvenue, Johnson, Yeung), and Department of Computing Science (Oliva), Faculty of Science, and Department of Family Medicine (Manca), Faculty of Medicine & Dentistry, University of Alberta; Northern Alberta Primary Care Research Network (Manca); Division of Endocrinology and Metabolism (Yeung), Department of Medicine, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alta.
PhD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Roseanne O. Yeung
School of Public Health (Boisvenue, Johnson, Yeung), and Department of Computing Science (Oliva), Faculty of Science, and Department of Family Medicine (Manca), Faculty of Medicine & Dentistry, University of Alberta; Northern Alberta Primary Care Research Network (Manca); Division of Endocrinology and Metabolism (Yeung), Department of Medicine, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alta.
MD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Tables
  • Related Content
  • Responses
  • Metrics
  • PDF
Loading

Article Figures & Tables

Figures

  • Tables
  • Figure 1:
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 1:

    Case-finding algorithm for detecting cases of metabolic syndrome (MetS). All patients within this data set are assessed using the 10 possible combinations based on the case definition outlined in Table 1. Each combination consists of 3 factors. An individual is counted as having MetS only once despite the possibility for meeting the criteria in multiple combinations.

  • Figure 2:
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 2:

    Flow of data extraction and cleaning from the NAPCReN-CPCSSN data repository. Note: BMI = body mass index, CPCSSN = Canadian Primary Care Sentinel Surveillance Network, dBP = diastolic blood pressure, NAPCReN = Northern Alberta Primary Care Research Network, sBP = systolic blood pressure.

  • Figure 3:
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 3:

    Percentages of participants with and without metabolic syndrome (MetS) who have specific risk factors and diseases. The proportion of participants achieving MetS component cut-off points with and without MetS is numerically represented in parenthesis. Error bars indicate 95% confidence interval. Note: BP = blood pressure, HDL-C = high-density lipoprotein cholesterol.

  • Figure 4:
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 4:

    Distributions of missing data in (A) those with metabolic syndrome and (B) those with a BMI ≥ 25. Panel A shows the percent missing of MetS factors among those with MetS. Panel B shows the percent missing of MetS factors among those who are overweight or obese (BMI ≥ 25) regardless of MetS status. The proportion of missing data among those with MetS (panel A) or those who are overweight or obese (panel B) is numerically presented in parentheses. Error bars indicate 95% confidence interval. Note: BMI = body mass index, FBG = fasting blood glucose, HbA1c = hemoglobin A1c, HDL = high-density lipoprotein, MetS = metabolic syndrome, sBP/dBP = systolic/diastolic blood pressure, TG = triglycerides.

  • Figure 5:
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 5:

    Degree of missing data on physical examination and laboratory investigations among 0–5 factors for metabolic syndrome. Participants are numerically represented in parenthesis on the x-axis. Error bars indicate 95% confidence intervals. Note: BMI = body mass index, BP = blood pressure, FBG = fasting blood glucose, HbA1c = hemoglobin A1c, HDL = high-density lipoprotein, TG = triglycerides.

Tables

  • Figures
    • View popup
    • Download powerpoint
    Table 1:

    Harmonized criteria for defining metabolic syndrome: 3 or more factors to make a diagnosis

    Metabolic syndrome criteriaCut-off point*
    Overweight and obeseBMI ≥ 25†
    Elevated BP‡CPCSSN diagnosis of hypertension
    or
    systolic BP ≥ 130 mm Hg
    or
    diastolic BP ≥ 85 mm Hg
    DysglycemiaCPCSSN diagnosis of diabetes
    or
    HbA1c ≥ 6.0%
    or
    FBG ≥ 5.6 mmol/L
    HypertriglyceridemiaTriglycerides ≥ 1.7 mmol/L
    Low HDL cholesterolHDL cholesterol ≥ 1.0 mmol/L in men, ≥ 1.3 mmol/L in women
    • Note: BMI = body mass index, BP = blood pressure, CPCSSN = Canadian Primary Care Sentinel Surveillance Network, FBG = fasting blood glucose, HbA1c = hemoglobin A1c, HDL = high-density lipoprotein.

    • ↵* Cut-off points are based on previously established formal criteria for metabolic syndrome, BMI, (25) elevated BP, HbA1c and FBG, (21) HDL cholesterol and triglycerides, (15), (18) and CPCSSN disease diagnosis. (26)

    • ↵† BMI cut-off points for outliers at ≥ 15 and ≥ 50; if BMI is ≥ 30, central obesity can be assumed.

    • ↵‡ BP cut-off points for outliers at 60–300 mm Hg systolic, 30–200 mm Hg diastolic.

    • View popup
    • Download powerpoint
    Table 2:

    Baseline characteristics of study sample stratified by presence of metabolic syndrome*

    CharacteristicTotal
    n = 15 766
    Metabolic syndrome
    n = 700
    No metabolic syndrome
    n = 15 066
    No. (%)†Mean ± SD‡No. (%)Mean ± SD‡No. (%)Mean ± SD‡
    Age, yr15 766 (100.0)30.9 ± 5.9700 (100.0)34.3 ± 4.815 066 (100.0)30.8 ± 5.9
    Sex, female10 002 (63.4)–346 (49.4)–9656 (64.1)–
    BMI11 835 (75.0)27.9 ± 7.5657 (93.8)35.8 ± 10.611 178 (74.2)27.4 ± 7.0
    Systolic BP, mm Hg14 185 (90.0)119.2 ± 13.0678 (96.8)130.1 ± 13.313 507 (89.6)118.7 ± 12.7
    Diastolic BP, mm Hg14 185 (90.0)75.9 ± 9.9678 (96.8)84.1 ± 9.313 507 (89.6)75.5 ± 9.7
    FBG, mmol/L2007 (12.7)5.1 ± 1.3343 (49.0)5.7 ± 1.71664 (11.0)5.0 ± 1.1
    HbA1c, %3846 (24.4)5.4 ± 0.9548 (78.3)5.9 ± 1.43298 (21.9)5.3 ± 0.7
    Triglycerides, mmol/L, median (IQR)2568 (16.3)1.3 (0.2–2.4)633 (90.4)2.1 (1.0–3.2)1935 (12.8)1.1 (0.4–1.8)
    HDL cholesterol, mmol/L2403 (15.2)1.4 ± 0.4608 (86.8)1.1 ± 0.21795 (11.9)1.4 ± 0.4
    • Note: BMI = body mass index, BP = blood pressure, FBG = fasting blood glucose, HbA1c = hemoglobin A1c, HDL = high-density lipoprotein, IQR = interquartile range, SD = standard deviation.

    • ↵* For metabolic syndrome cases: overweight includes BMI ≥ 25; systolic BP ≥ 130 mm Hg and diastolic BP ≥ 85 mm Hg; FBG ≥ 5.6 mmol/L; HbA1c ≥ 6.0%; triglycerides ≥ 1.7 mmol/L; reduced HDL cholesterol < 1.0 (men), < 1.3 (women). Exclusion of outliers was based on expert clinical judgment for BMI < 15 and > 55, and BP outside the range of 60–300 mm Hg for systolic BP and 30–200 mm Hg for diastolic BP.

    • ↵† Represents no. (%) of participants for whom these data were available, except for sex, which represents proportion of female participants.

    • ↵‡ Unless stated otherwise.

    • View popup
    • Download powerpoint
    Table 3:

    Prevalence of combinations meeting the minimum 3 factors for metabolic syndrome

    Combination of metabolic syndrome factors*No. (%) of patients
    Metabolic syndrome
    n = 700
    Overweight + elevated BP + hypertriglyceridemia291 (41.6)
    Overweight + reduced HDL cholesterol + hypertriglyceridemia247 (35.3)
    Overweight + elevated BP + reduced HDL cholesterol226 (32.3)
    Overweight + elevated BP + dysglycemia171 (24.4)
    Elevated BP + reduced HDL cholesterol + hypertriglyceridemia159 (22.7)
    Overweight + dysglycemia + hypertriglyceridemia115 (16.4)
    Overweight + dysglycemia + reduced HDL cholesterol100 (14.3)
    Elevated BP + dysglycemia + hypertriglyceridemia79 (11.3)
    Dysglycemia + reduced HDL cholesterol + hypertriglyceridemia73 (10.4)
    Elevated BP + dysglycemia + reduced HDL cholesterol67 (9.5)
    • Note: BP = blood pressure, HDL = high-density lipoprotein.

    • ↵* Elevated BP includes a Canadian Primary Care Sentinel Surveillance Network (CPCSSN) diagnosis of hypertension or BP ≥ 130/85 mm Hg. Dysglycemia includes validated CPCSSN diagnosis of diabetes or fasting blood glucose (FBG) ≥ 5.6 mmol/L or hemoglobin A1c (HbA1c) ≥ 6.0%. Overweight includes body mass index (BMI) ≥ 25. Hypertriglyceridemia includes triglycerides ≥ 1.7 mmol/L and reduced HDL cholesterol < 1.0 mmol/L (men), < 1.3 mmol/L (women).

PreviousNext
Back to top

In this issue

CMAJ Open: 8 (4)
Vol. 8, Issue 4
1 Oct 2020
  • Table of Contents
  • Index by author

Article tools

Respond to this article
Print
Download PDF
Article Alerts
To sign up for email alerts or to access your current email alerts, enter your email address below:
Email Article

Thank you for your interest in spreading the word on CMAJ Open.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Feasibility of identifying and describing the burden of early-onset metabolic syndrome in primary care electronic medical record data: a cross-sectional analysis
(Your Name) has sent you a message from CMAJ Open
(Your Name) thought you would like to see the CMAJ Open web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Feasibility of identifying and describing the burden of early-onset metabolic syndrome in primary care electronic medical record data: a cross-sectional analysis
Jamie J. Boisvenue, Carlo U. Oliva, Donna P. Manca, Jeffrey A. Johnson, Roseanne O. Yeung
Oct 2020, 8 (4) E779-E787; DOI: 10.9778/cmajo.20200007

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Feasibility of identifying and describing the burden of early-onset metabolic syndrome in primary care electronic medical record data: a cross-sectional analysis
Jamie J. Boisvenue, Carlo U. Oliva, Donna P. Manca, Jeffrey A. Johnson, Roseanne O. Yeung
Oct 2020, 8 (4) E779-E787; DOI: 10.9778/cmajo.20200007
Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like

Related Articles

  • Correction: Feasibility of identifying and describing the burden of early-onset metabolic syndrome in primary care electronic medical record data: a cross-sectional analysis
  • PubMed
  • Google Scholar

Cited By...

  • Correction: Feasibility of identifying and describing the burden of early-onset metabolic syndrome in primary care electronic medical record data: a cross-sectional analysis
  • Google Scholar

Similar Articles

Collections

  • Clinical
    • Family Medicine, General Practice, Primary Care
      • Clinical research
    • Endocrinology & Metabolism
      • Diabetes
    • Cardiovascular Medicine
      • Hypertension
    • Nutrition & Metabolism
      • Obesity

Content

  • Current issue
  • Past issues
  • Collections

About

  • General Information
  • Staff
  • Editorial Board
  • Advisory Panel
  • Contact Us
  • Reprints
  • Copyright and Permissions
CMAJ Group

Copyright 2025, CMA Impact Inc. or its licensors. All rights reserved. ISSN 2291-0026

All editorial matter in CMAJ OPEN represents the opinions of the authors and not necessarily those of the Canadian Medical Association or its subsidiaries.

To receive any of these resources in an accessible format, please contact us at CMAJ Group, 500-1410 Blair Towers Place, Ottawa ON, K1J 9B9; p: 1-888-855-2555; e: [email protected].

CMA Civility, Accessibility, Privacy

 

 

Powered by HighWire