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Assessing cardiometabolic risk in middle-aged adults using body mass index and waist-height ratio: are two indices better than one? A cross-sectional study.

Overview of attention for article published in Diabetology & Metabolic Syndrome, January 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (74th percentile)

Mentioned by

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4 tweeters
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1 Facebook page

Citations

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21 Dimensions

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40 Mendeley
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Title
Assessing cardiometabolic risk in middle-aged adults using body mass index and waist-height ratio: are two indices better than one? A cross-sectional study.
Published in
Diabetology & Metabolic Syndrome, January 2015
DOI 10.1186/s13098-015-0069-5
Pubmed ID
Authors

Millar, Seán R, Perry, Ivan J, Phillips, Catherine M

Abstract

A novel obesity classification method has been proposed using body mass index (BMI) and waist-height ratio (WHtR) together. However, the utility of this approach is unclear. In this study we compare the metabolic profiles in subjects defined as overweight or obese by both measures. We examine a range of metabolic syndrome features, pro-inflammatory cytokines, acute-phase response proteins, coagulation factors and white blood cell counts to determine whether a combination of both indices more accurately identifies individuals at increased obesity-related cardiometabolic risk. This was a cross-sectional study involving a random sample of 1856 men and women aged 46-73 years. Metabolic and anthropometric profiles were assessed. Linear and logistic regression analyses were used to compare lipid, lipoprotein, blood pressure, glycaemic and inflammatory biomarker levels between BMI and WHtR tertiles. Multinomial logistic regression was performed to determine cardiometabolic risk feature associations with BMI and WHtR groupings. Receiver operating characteristic curve analysis was used to evaluate index discriminatory ability. The combination of BMI and WHtR tertiles identified consistent metabolic variable differences relative to those characterised on the basis of one index. Similarly, odds ratios of having cardiometabolic risk features were noticeably increased in subjects classified as overweight or obese by both measures when compared to study participants categorised by either BMI or WHtR separately. Significant discriminatory improvement was observed for detecting individual cardiometabolic risk features and adverse biomarker levels. In a fully adjusted model, only individuals within the highest tertile for both indices displayed a significant and positive association with pre-diabetes, OR: 3.4 (95 % CI: 1.9, 6.0), P < 0.001. These data provide evidence that the use of BMI and WHtR together may improve body fat classification. Risk stratification using a composite index may provide a more accurate method for identifying high and low-risk subjects.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 9 23%
Researcher 6 15%
Student > Postgraduate 5 13%
Student > Ph. D. Student 4 10%
Student > Master 3 8%
Other 6 15%
Unknown 7 18%
Readers by discipline Count As %
Medicine and Dentistry 13 33%
Nursing and Health Professions 4 10%
Agricultural and Biological Sciences 3 8%
Social Sciences 2 5%
Sports and Recreations 1 3%
Other 6 15%
Unknown 11 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 July 2016.
All research outputs
#1,806,761
of 8,091,619 outputs
Outputs from Diabetology & Metabolic Syndrome
#63
of 288 outputs
Outputs of similar age
#59,496
of 233,303 outputs
Outputs of similar age from Diabetology & Metabolic Syndrome
#1
of 4 outputs
Altmetric has tracked 8,091,619 research outputs across all sources so far. Compared to these this one has done well and is in the 77th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 288 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 233,303 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them