Indexing adult obesity by waist-to-height and weight-to-height ratios

Yatsko, Andrew

doi:10.5430/jbei.v3n2p20

Cited by 3 publications

(11 citation statements)

References 34 publications

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“…[28] The waist circumference to height ratio (WCHR) is regarded to be a better measure in the same line as BMI. [28,30] Indeed, WCHR appears in Table 1 and BMI does not. Nevertheless, WCHR is outweighed by the sagittal abdominal diameter, also in Table 1.…”

Section: Discussionmentioning

confidence: 97%

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Adaptations of Relief for continuous domains of bioinformatics

Yatsko

2019

AIR

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Relief occupies a niche among feature selection methods for data classification. Filters are faster, wrappers are much slower. Relief is feature-set-aware, same as wrappers. However, it is thought being able to deselect only irrelevant, but not redundant features, same as filters. Iterative Reliefs seek to increase the separation margin between classes in the anisotropic space defined by weighted features. Reliefs for continuous domains are much less developed than for categorical domains. The paper discusses a number of adaptations for continuous spaces with Euclidean or Manhattan metric. The ability of Relief to detect redundant features is demonstrated. A dramatic reduction of the feature-set is achieved in a health diagnostics problem.

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Section: Discussionmentioning

confidence: 97%

“…The diameter is an emergent measure similar to waist circumference. [30] Neither waist circumference nor weight to height ratio, a simplified version of BMI [30] appear in Table 1. So, of the five BMI-like measures only two were retained.…”

Section: Discussionmentioning

confidence: 99%

Adaptations of Relief for continuous domains of bioinformatics

Yatsko

2019

AIR

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“…This is consistent with WCHR being more sensitive, and BMI more specific than the other measure in determining T2DM status in adults. [27] The classifications by either measure are expected to be close, and the little disagreement only affirms this conjecture. However, comparing frequencies is only able to provide a grand view.…”

Section: Figure 8 Wchr Category Guide Based On Who Bmi-for-age Overwmentioning

confidence: 92%

“…This is noteworthy because in adults the variation is instead decreasing with age, [27] which may be a result of the composite effect of obesity survival. [17] Despite the diversification, the uncompromising nature of height [28] imparts certain stability to both BMI and WCHR.…”

Section: Datamentioning

confidence: 94%

“…[37] Further, it can be expected that the overweight threshold is increasing with age past the adolescence. [27] 6.2 Population height increase Take BMI or WCHR -the taller, the healthier. In economics, the body height has been long known as a reliable estimator of the per-capita gross national product and, more generally, the standard of living.…”

Section: Overweight and Obese Wchrmentioning

confidence: 99%

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Correspondence between waist-to-height ratio and body mass index in juniors

Yatsko

2017

JBEI

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Despite the increase in body mass through childhood and adolescence is countered by the increase in height in the Body Mass Index (BMI), this measure is inadequate for judging the degree of excess weight among the young. Unlike using clearly defined cut points, same for any stage of adulthood, it is required to consult BMI-for-age charts, which can be a demanding exercise when data analysis is involved. The waist circumference to height ratio (WCHR) was hypothesised to be invariant to age change, and this is generally supported by the epidemiological evidence. This paper analyses a sample of NHANES data to find a connection between BMI, WCHR and Age. A strong linearity between the anthropometric measures is demonstrated, thus enabling estimation of WCHR for a given BMI and Age. The pattern of change of this parameter at BMI levels that indicate the transition to overweight state or obesity thus becomes unravelled. The results strongly support the feasibility of a universal WCHR threshold for the overweight state past early childhood and through adolescence, and the estimated one is similar to the WCHR levels found elsewhere in the literature.

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Personalised measures of obesity using waist to height ratios from an Australian health screening program

et al. 2019

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Objectives The aim of the current study is to generate waist circumference to height ratio cut-off values for obesity categories from a model of the relationship between body mass index and waist circumference to height ratio. We compare the waist circumference to height ratio discovered in this way with cut-off values currently prevalent in practice that were originally derived using pragmatic criteria. Method Personalized data including age, gender, height, weight, waist circumference and presence of diabetes, hypertension and cardiovascular disease for 847 participants over eight years were assembled from participants attending a rural Australian health review clinic (DiabHealth). Obesity was classified based on the conventional body mass index measure (weight/height 2 ) and compared to the waist circumference to height ratio. Correlations between the measures were evaluated on the screening data, and independently on data from the National Health and Nutrition Examination Survey that included age categories. Results This article recommends waist circumference to height ratio cut-off values based on an Australian rural sample and verified using the National Health and Nutrition Examination Survey database that facilitates the classification of obesity in clinical practice. Gender independent cut-off values are provided for waist circumference to height ratio that identify healthy (waist circumference to height ratio ≥0.45), overweight (0.53) and the three obese (0.60, 0.68, 0.75) categories verified on the National Health and Nutrition Examination Survey dataset. A strong linearity between the waist circumference to height ratio and the body mass index measure is demonstrated. Conclusion The recommended waist circumference to height ratio cut-off values provided a useful index for assessing stages of obesity and risk of chronic disease for improved healthcare in clinical practice.

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Indexing adult obesity by waist-to-height and weight-to-height ratios

Cited by 3 publications

References 34 publications

Adaptations of Relief for continuous domains of bioinformatics

Adaptations of Relief for continuous domains of bioinformatics

Correspondence between waist-to-height ratio and body mass index in juniors

Personalised measures of obesity using waist to height ratios from an Australian health screening program

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