Identifying cut-points in anthropometric indexes for predicting previously undiagnosed diabetes and cardiovascular risk factors in the Tongan population

Craig, Pippa; Colagiuri, Stephen; Hussain, Zafirul; Palu, Taniela

doi:10.1016/j.orcp.2006.08.001

Cited by 21 publications

(14 citation statements)

References 32 publications

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“…The age limit in the inclusion criteria was 18 years or older; however, we included two studies in which participants’ ages were >15 years22 or 15 to 74 years 27. Furthermore, four of the included studies did not determine range of age but rather provided mean age with standard deviation 25,28,29,34…”

Section: Resultsmentioning

confidence: 99%

Predicting cardiometabolic risk: waist-to-height ratio or BMI. A meta-analysis

Savva¹,

Lamnisos²,

Kafatos³

2013

DMSO

215

169

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Background and objectivesThe identification of increased cardiometabolic risk among asymptomatic individuals remains a huge challenge. The aim of this meta-analysis was to compare the association of body mass index (BMI), which is an index of general obesity, and waist-to-height ratio (WHtR), an index of abdominal obesity, with cardiometabolic risk in cross-sectional and prospective studies.MethodsPubMed and Embase databases were searched for cross-sectional or prospective studies that evaluated the association of both BMI and WHtR with several cardiometabolic outcomes. The strength of relative risk (RR) with 95% confidence interval (CI) was calculated using the optimal cutoffs of BMI and WHtR in cross-sectional studies, while any available cutoff was used in prospective studies. The pooled estimate of the ratio of RRs (rRR [=RRBMI/RRWHtR]) with 95% CIs was used to compare the association of WHtR and BMI with cardiometabolic risk. Meta-regression was used to identify possible sources of heterogeneity between the studies.ResultsTwenty-four cross-sectional studies and ten prospective studies with a total number of 512,809 participants were identified as suitable for the purpose of this meta-analysis. WHtR was found to have a stronger association than BMI with diabetes mellitus (rRR: 0.71, 95% CI: 0.59–0.84) and metabolic syndrome (rRR: 0.92, 95% CI: 0.89–0.96) in cross-sectional studies. Also in prospective studies, WHtR appears to be superior to BMI in detecting several outcomes, including incident cardiovascular disease, cardiovascular disease mortality, and all-cause mortality. The usefulness of WHtR appears to be better in Asian than in non-Asian populations. BMI was not superior to WHtR in any of the outcomes that were evaluated. However, the results of the utilized approach should be interpreted cautiously because of a substantial heterogeneity between the results of the studies. Meta-regression analysis was performed to explain this heterogeneity, but none of the evaluated factors, ie, sex, origin (Asians, non-Asians), and optimal BMI or WHtR cutoffs were significantly related with rRR.ConclusionThe results of this meta-analysis support the use of WHtR in identifying adults at increased cardiometabolic risk. However, further evidence is warranted because of a substantial heterogeneity between the studies.

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Section: Resultsmentioning

confidence: 99%

Predicting cardiometabolic risk: waist-to-height ratio or BMI. A meta-analysis

Savva¹,

Lamnisos²,

Kafatos³

2013

DMSO

215

169

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“…Approaches based on ROC curve analyses have been used almost exclusively in studies aiming to choose waist circumference cut-points in different ethnic groups (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(27)(28)(29)(30)(31)(32)(33)(34), with few studies using other methods (46). Stevens et al have explored other methods for the choice of cut-points in relation to the BMI (37).…”

Section: Should the Youden Index/roc Curves Be Used To Determine Waismentioning

confidence: 99%

“…Specific cut‐points for other ethnic groups have been published by some organizations (12), but not the World Health Organization. A large number of studies have attempted to define waist circumference cut‐points related to ethnicity (13,14,15,16,17,18–19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34), mostly using approaches based on receiver operating characteristic (ROC) curves. Little evidence exists from longitudinal studies with incident outcomes such as type 2 diabetes and cardiovascular disease.…”

Section: Introductionmentioning

confidence: 99%

Cut‐points for Waist Circumference in Europids and South Asians

Cameron¹,

Sicree²,

Zimmet³

et al. 2010

Obesity

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There is little strong evidence that currently recommended higher waist circumference cut‐points for Europids compared with South Asians are associated with similar risk for type 2 diabetes. This study was designed to provide such evidence. Longitudinal studies over 5 years were conducted among 5,515 Europid and 2,214 ethnically South Asian participants. Age‐standardized diabetes incidence at different levels of waist circumference and incidence difference relative to a reference value were calculated. The Youden Index was used to determine waist circumference cut‐points. At currently recommended cut‐points, estimated annual diabetes incidence for a 50‐year‐old Europid was <0.6% for both sexes, and for a 50‐year‐old South Asian, 5.8% for men and 2.1% for women. Annual diabetes incidence of 1% was observed for a 50 year old at a waist circumference 35–40 cm greater in Europid compared to South Asian men and women. Incidence difference between recommended cut‐points and a reference value (80 cm in men, 70 cm in women) was 0.3 and 4.4% per year for Europid and South Asian men, and 0.2 and 0.8% per year for Europid and South Asian women, respectively. Waist circumference cut‐points chosen using the Youden Index were shown to be dependent on obesity levels in the population. The much higher observed risk of diabetes in South Asians compared to Europids at the respective recommended waist circumference cut‐points suggests that differences between them should be greater. Approaches that use the Youden Index to select waist circumference cut‐points are inappropriate and should not be used for this purpose.

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“…The phenomenon of higher waist circumference in women has also been reported in some other populations in Tonga (249) and Oman (139). They showed females had higher WC estimates or cutoff points than males but the differences between the genders within studies did not appear to vary significantly.…”

Section: Discussionsupporting

confidence: 63%

Waist circumference and risk of cardiovascular disease, type 2 diabetes and mortality among Aboriginal adults in an Australian community

Adegbija¹

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Identifying cut-points in anthropometric indexes for predicting previously undiagnosed diabetes and cardiovascular risk factors in the Tongan population

Cited by 21 publications

References 32 publications

Predicting cardiometabolic risk: waist-to-height ratio or BMI. A meta-analysis

Predicting cardiometabolic risk: waist-to-height ratio or BMI. A meta-analysis

Cut‐points for Waist Circumference in Europids and South Asians

Waist circumference and risk of cardiovascular disease, type 2 diabetes and mortality among Aboriginal adults in an Australian community

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