UNLABELLED: To evaluate the receiver operating characteristics (ROC) to determine the cutoffs of waist circumference as a potential population directed screening tool for hypercholesterolaemia (> or = 6.5 mmol/L), low high density lipoprotein cholesterol (< 0.9 mmol/L), and hypertension (treated and/or systolic > or = 160 and/or diastolic blood pressure > or = 95 mmHg), in 2183 men and 2698 women aged 20 to 59 years selected at random from Dutch civil registries. MAIN OUTCOME MEASURES: Height, weight, body mass index (BMI), waist circumference, total plasma cholesterol and high density lipoprotein cholesterol concentrations, and blood pressure. RESULTS: ROC curves showed that sensitivity equalled specificity at waist circumferences between 93-95 cm in men and 81-84 cm in women for identifying individual risk factors, and 92 cm in men and 81 cm in women for identifying those with at least one risk factor. Sensitivity and specificity were equal at levels between 61% to 69% for identifying individual risk factors, with positive predictions (56.8% in men and 37.8% in women) within 2% of those using previously defined 'Action Level 1' of waist circumference 94 cm in men and 80 cm in women (58.8% in men and 37.4% in women). Risk prediction by anthropometric methods was relatively low: ROC areas for identifying each risk factor by waist varied from 55% to 60%, and reached about 65% for identifying at least one risk factor. Height accounted for less than 0.3% of variance in waist circumference. Using BMI at 25 kg/m2 gave similar prediction to waist, but its combination with waist did not improve predictive values. CONCLUSIONS: Measurement of waist circumference 'Action Level 1' at 94 cm (37 inches) in men and 80 cm (32 inches) in women could be adopted as a simpler valid alternative to BMI for health promotion, to alert those at risk of cardiovascular disease, and as a guide to risk avoidance by self-weight management.
UNLABELLED: To evaluate the receiver operating characteristics (ROC) to determine the cutoffs of waist circumference as a potential population directed screening tool for hypercholesterolaemia (> or = 6.5 mmol/L), low high density lipoprotein cholesterol (< 0.9 mmol/L), and hypertension (treated and/or systolic > or = 160 and/or diastolic blood pressure > or = 95 mmHg), in 2183 men and 2698 women aged 20 to 59 years selected at random from Dutch civil registries. MAIN OUTCOME MEASURES: Height, weight, body mass index (BMI), waist circumference, total plasma cholesterol and high density lipoprotein cholesterol concentrations, and blood pressure. RESULTS: ROC curves showed that sensitivity equalled specificity at waist circumferences between 93-95 cm in men and 81-84 cm in women for identifying individual risk factors, and 92 cm in men and 81 cm in women for identifying those with at least one risk factor. Sensitivity and specificity were equal at levels between 61% to 69% for identifying individual risk factors, with positive predictions (56.8% in men and 37.8% in women) within 2% of those using previously defined 'Action Level 1' of waist circumference 94 cm in men and 80 cm in women (58.8% in men and 37.4% in women). Risk prediction by anthropometric methods was relatively low: ROC areas for identifying each risk factor by waist varied from 55% to 60%, and reached about 65% for identifying at least one risk factor. Height accounted for less than 0.3% of variance in waist circumference. Using BMI at 25 kg/m2 gave similar prediction to waist, but its combination with waist did not improve predictive values. CONCLUSIONS: Measurement of waist circumference 'Action Level 1' at 94 cm (37 inches) in men and 80 cm (32 inches) in women could be adopted as a simpler valid alternative to BMI for health promotion, to alert those at risk of cardiovascular disease, and as a guide to risk avoidance by self-weight management.
Authors: Deok Yun Ju; Young Gil Choe; Yong Kyun Cho; Dong Suk Shin; Su Hyeon Yoo; Seo Hyoung Yim; Ji Yong Lee; Jung Ho Park; Hong Joo Kim; Dong Il Park; Chong Il Sohn; Woo Kyu Jeon; Byung Ik Kim Journal: Clin Mol Hepatol Date: 2013-06-27
Authors: K Ekoru; G A V Murphy; E H Young; H Delisle; C S Jerome; F Assah; B Longo-Mbenza; J P D Nzambi; J B K On'Kin; F Buntix; M C Muyer; D L Christensen; C S Wesseh; A Sabir; C Okafor; I D Gezawa; F Puepet; O Enang; T Raimi; E Ohwovoriole; O O Oladapo; P Bovet; W Mollentze; N Unwin; W K Gray; R Walker; K Agoudavi; S Siziya; J Chifamba; M Njelekela; C M Fourie; S Kruger; A E Schutte; C Walsh; D Gareta; A Kamali; J Seeley; S A Norris; N J Crowther; D Pillay; P Kaleebu; A A Motala; M S Sandhu Journal: Int J Obes (Lond) Date: 2017-10-03 Impact factor: 5.095
Authors: Hans Hauner; Peter Bramlage; Christian Lösch; Elisabeth Steinhagen-Thiessen; Heribert Schunkert; Jürgen Wasem; Karl-Heinz Jöckel; Susanne Moebus Journal: BMC Public Health Date: 2008-08-11 Impact factor: 3.295