Limits...
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.

Millar SR, Perry IJ, Phillips CM - Diabetol Metab Syndr (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Public Health, HRB Centre for Health and Diet Research, University College Cork, 4th Floor, Western Gateway Building, Western Road, Cork, Ireland.

ABSTRACT

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

No MeSH data available.


Related in: MedlinePlus

Overlap of normal weight, overweight and obese defined by BMI and WHtR. The figure shows Venn diagrams illustrating overlap of BMI and WHtR tertiles for (a) normal weight, (b) overweight and (c) obese
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4562186&req=5

Fig1: Overlap of normal weight, overweight and obese defined by BMI and WHtR. The figure shows Venn diagrams illustrating overlap of BMI and WHtR tertiles for (a) normal weight, (b) overweight and (c) obese

Mentions: The levels of agreement between normal weight, overweight and obese tertiles are shown in Fig. 1. Kappa statistics were similar for normal and obese classifications (Kappa: 0.66, SE: 0.02 for normal weight vs. Kappa: 0.68, SE: 0.02 for obese) with marginal overlap between subjects defined as overweight (Kappa: 0.38, SE: 0.02). In both overweight and obese groups (Table 2), the combination of BMI and WHtR tertiles identified consistent and significant metabolic variable differences relative to those characterised on the basis of one index. Subjects that were classified as overweight or obese by both indices displayed higher mean BMI, WC and median triglyceride levels, reduced HDL-C and adiponectin concentrations, and a higher percentage had adverse biomarker levels, insulin resistance, metabolic feature clustering and pre-diabetes.Fig. 1


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.

Millar SR, Perry IJ, Phillips CM - Diabetol Metab Syndr (2015)

Overlap of normal weight, overweight and obese defined by BMI and WHtR. The figure shows Venn diagrams illustrating overlap of BMI and WHtR tertiles for (a) normal weight, (b) overweight and (c) obese
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4562186&req=5

Fig1: Overlap of normal weight, overweight and obese defined by BMI and WHtR. The figure shows Venn diagrams illustrating overlap of BMI and WHtR tertiles for (a) normal weight, (b) overweight and (c) obese
Mentions: The levels of agreement between normal weight, overweight and obese tertiles are shown in Fig. 1. Kappa statistics were similar for normal and obese classifications (Kappa: 0.66, SE: 0.02 for normal weight vs. Kappa: 0.68, SE: 0.02 for obese) with marginal overlap between subjects defined as overweight (Kappa: 0.38, SE: 0.02). In both overweight and obese groups (Table 2), the combination of BMI and WHtR tertiles identified consistent and significant metabolic variable differences relative to those characterised on the basis of one index. Subjects that were classified as overweight or obese by both indices displayed higher mean BMI, WC and median triglyceride levels, reduced HDL-C and adiponectin concentrations, and a higher percentage had adverse biomarker levels, insulin resistance, metabolic feature clustering and pre-diabetes.Fig. 1

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Public Health, HRB Centre for Health and Diet Research, University College Cork, 4th Floor, Western Gateway Building, Western Road, Cork, Ireland.

ABSTRACT

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

No MeSH data available.


Related in: MedlinePlus