Some mathematical properties of weight-for-height indices used as measures of adiposity.

Benn, R T

doi:10.1136/jech.25.1.42

Cited by 204 publications

(202 citation statements)

References 8 publications

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“…If the measurements for a ratio are easy to obtain and it gives much better prediction than the single variable, then using the ratio is justi®ed. For example, many attempts have been made to derive new indices of adiposity from weight and height in order to better the body mass index (weight/height 2 ), 15,16 but most of these ratios are sex or population speci®c, as con®rmed in the present study. By consensus, weight/ height 2 is still the ratio of choice.…”

Section: Index Of Adiposity Ts Han Et Almentioning

confidence: 78%

“…15,16 Since body weight correlates signi®cantly with height, taller individuals are generally heavier than shorter ones. Thus in order to compare the differences in adiposity (the variable of interest) between individuals it is the relative weight that matters, so differences in height must be eliminated.…”

Section: Methodsmentioning

confidence: 99%

“…Practicality must be taken into account when deriving anthropometric indices, 23 to establish the advantage of ratios or other complex expressions over the single variable alone, ease of computation of the ratio, 16 the availability of the variables for measurement, and reliability of measurement in the ®eld. If the measurements for a ratio are easy to obtain and it gives much better prediction than the single variable, then using the ratio is justi®ed.…”

Section: Index Of Adiposity Ts Han Et Almentioning

confidence: 99%

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The influences of height and age on waist circumference as an index of adiposity in adults

Han

Seidell

Currall³

et al. 1997

Int J Obes

110

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OBJECTIVES: To assess the in¯uences of height and age on the differences in waist circumference between individuals of different stature. SUBJECTS: 3319 males and 4358 females from four studies in the UK and the Netherlands. MEASUREMENTS: Waist circumference, body weight, height, and age. RESULTS: Linear regression analysis of log 10 height as the independent variable on log 10 waist as the dependent variable was used to determine the optimal index powers (OIP) ( p ) to minimize the in¯uence of height in the relationships of waist/height p . Six out of eight samples of men and women had OIP of height not signi®cantly different from zero, with the remaining two groups had OIP between 0.15±0.58, indicating that height had very limited in¯uence on the differences in waist circumference measurement between individuals. Age adjustment increased the relationship between waist and height, with OIP of 0.19±0.89 in men and 0.02±0.58 in women. Without age adjustment, height explained 0.3±3.5% and 0.1±2.5% variance in waist in men and in women respectively, and the corresponding variances were 0.4±7.5% in men and 0.0±2.6% in women with age adjustment. A similar analysis of weight and height showed the OIP of height in weight/height p ratio ranged from 1.32±2.25 in men, and 0.87±1.74 in women without age adjustment, and from 1.47±2.24 in men and 1.25±1.96 in women with age adjustment. CONCLUSION: Height and age had limited in¯uences on the differences in waist between Caucasian subjects of different stature. Waist alone may be used to indicate adiposity or to re¯ect metabolic risk factors. In contrast, the in¯uence of height on body weight is important.

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Section: Index Of Adiposity Ts Han Et Almentioning

confidence: 78%

Section: Methodsmentioning

confidence: 99%

Section: Index Of Adiposity Ts Han Et Almentioning

confidence: 99%

See 1 more Smart Citation

The influences of height and age on waist circumference as an index of adiposity in adults

Han

Seidell

Currall³

et al. 1997

Int J Obes

110

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“…Therefore dividing indices by height to create ratios would violate the principle for deriving indices of adiposity. 18 There may be separate in¯uence of height on health risks, independent of body fat, but height does not contribute in measurement of intra-abdominal fat or fatness.…”

Section: Discussionmentioning

confidence: 99%

Predicting intra-abdominal fatness from anthropometric measures: the influence of stature

et al. 1997

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OBJECTIVE: To investigate the in¯uence of height on the relationships between the intra-abdominal fat and anthropometric measures. SUBJECTS: Twenty healthy female volunteers aged 20±51 y from Aberdeen, and 71 men and 34 women aged 19±85 y from Nijmegen, The Netherlands. OUTCOME MEASURES: Intra-abdominal fat volumes by magnetic resonance imaging (MRI) in Aberdeen and crosssectional areas at L4-L5 level by computerised tomography (CT) in Nijmegen, height, body mass index (BMI), waist circumference, waist sagittal and transverse diameters, waist to hip ratio, and skinfolds. RESULTS: In the MRI study the women with BMI 20±33 kg/m 2 , waist circumference 62±97 cm, height 148±172 cm, and intra-abdominal fat volume 0.07±2.66 kg, waist circumference gave the highest correlation of simple indices with intraabdominal fat volume, explaining 77.8% of variance. Single cross-sectional MRI cuts predicted volume with r 0.94± 0.99. Height in various levels of index power was not related to waist circumference, waist diameters, BMI, or skinfolds and did not improve prediction of intra-abdominal fat volume or of cross-sectional intra-abdominal fat area at any level. The CT study of men and women with BMI 18±32 kg/m 2 and 19±38 kg/m 2 , waist circumference 71±112 cm and 74±125 cm, height 158±197 cm and 151±182 cm, and intra-abdominal fat area 13±274 cm 2 and 19±221 cm 2 respectively, height also had little in¯uence on the relationships of intra-abdominal fat area with waist circumference or with any other indices of adiposity in linear or quadratic models. Compared to younger subjects, intra-abdominal fat area was higher in older subjects for a given waist circumference. CONCLUSIONS: Height does not importantly in¯uence the differences in measures of adiposity or intra-abdominal fat volume in women, or intra-abdominal fat area in both sexes. Age does in¯uence the prediction of intra-abdominal fat from waist circumference, but waist circumference alone has a predictable simple relationship with intra-abdominal fat volume or area, which is likely to relate to the prediction of health risk for health promotion.

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“…Body mass index (BMI) was computed according to Quetelet's index (weight height -2 , kg m -2 ), which is essentially an indicator of weight unrelated to height (Benn, 1971). Odds ratios (OR) with their corresponding 95% confidence intervals (CI) were derived using unconditional multiple logistic regression analysis, fitted by the method of maximum likelihood (Breslow and Day, 1980).…”

Section: Discussionmentioning

confidence: 99%

Body weight and risk of soft-tissue sarcoma

et al. 1999

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A population-based case-control study (Zahm et al, 1989) conducted in Kansas, USA and based on 133 cases showed that the risk of soft-tissue sarcoma (STS) increased with adult body weight, the odds ratio (OR) being 2.0 for people weighing more than 200 pounds. To provide further information on this issue, we analysed data from a large case-control study conducted in Italy. SUBJECTS AND METHODSData were derived from a case-control study of STS, conducted between 1983 and 1998 in the greater Milan area and the province of Pordenone, northern Italy (Serraino et al, 1991;Tavani et al, 1997).Cases were 217 patients (113 men, median age 52 years, range 21-79; and 104 women, median age 54 years, range 16-79) with histologically confirmed incident (i.e. diagnosed within the year preceding the interview) STS (International Classification of Diseases IX Edition ICD-171) admitted to the cancer institutes and major teaching and general hospitals of the areas under surveillance.Controls were 1297 patients (792 men, median age 55 years, range 17-79; and 505 women, median age 57 years, range 17-79) admitted to the same network of hospitals for acute, nonneoplastic and non-immune-related conditions. Of the comparison group, 15% were admitted for traumas, 27% for other orthopaedic disorders (such as low back pain and disc diseases), 29% for acute surgical conditions (such as acute appendicitis or strangulated hernia), and 28% for other miscellaneous illnesses (such as ear, nose and throat, eye, dental or skin disorders). On average, fewer than 4% of the eligible cases and controls refused the interview.Trained interviewers used a structured questionnaire to collect data on socio-demographic characteristics, anthropometric measures (self-reported weight and height), personal and family history of selected medical conditions, a few selected occupational and environmental exposures, smoking status and consumption of alcohol, coffee and selected dietary items. Data analysisBody mass index (BMI) was computed according to Quetelet's index (weight height -2 , kg m -2 ), which is essentially an indicator of weight unrelated to height (Benn, 1971). Odds ratios (OR) with their corresponding 95% confidence intervals (CI) were derived using unconditional multiple logistic regression analysis, fitted by the method of maximum likelihood (Breslow and Day, 1980). The regression models included terms for study centre, year of recruitment, age (8 levels plus a continuous term) and education. Table 1 gives the distribution of STS cases and controls by sex according to age and BMI, and the OR of STS according to BMI. The risk of STS rose with BMI, and the OR was 3.28 in subjects with a BMI > 30 compared to those with BMI ≤ 20. The OR was 3.49 for men and 3.26 for women. The risk estimates did not change after further adjustment for employment in agriculture and radiation therapy: the OR became 3.41 and 3.53 respectively for men and women with BMI > 30 compared to those with BMI ≤ 20. RESULTSA separate analysis for broad categories of anatomical sit...

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Some mathematical properties of weight-for-height indices used as measures of adiposity.

Cited by 204 publications

References 8 publications

The influences of height and age on waist circumference as an index of adiposity in adults

The influences of height and age on waist circumference as an index of adiposity in adults

Predicting intra-abdominal fatness from anthropometric measures: the influence of stature

Body weight and risk of soft-tissue sarcoma

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