Assessing Traditional and Novel Indices of Adiposity as Risk Pred
Journal of Nutrition & Food Sciences

Journal of Nutrition & Food Sciences
Open Access

ISSN: 2155-9600

Research Article - (2018) Volume 8, Issue 1

Assessing Traditional and Novel Indices of Adiposity as Risk Predictors for Metabolic Syndrome in British Young Adults

Farzad Amirabdollahian1* and Fahimeh Haghighatdoost2
1School of Health Sciences, Liverpool Hope University, Hope Park, Liverpool, UK
2Department of Community Nutrition, School of Nutrition and Food Science, Isfahan University of Medical Sciences, Isfahan, Iran
*Corresponding Author: Farzad Amirabdollahian, Associate Professor, School of Health Sciences, Liverpool Hope University, Hope Park, Liverpool, L16 9JD, UK, Tel: +44 0151 291 3799


Emerging adulthood has been characterized with poor dietary habits [1,2]. These poor dietary habits have been associated with the transition to independence, stress, academic and peer pressure and taking responsibility for food choice when starting to study at university [3-5]. Several studies have reported that university students fail to meet the dietary guidelines [6-8] and gain weight in the university years [9- 12], which can have adverse health consequences leading to an increased risk of obesity, type 2 diabetes and cardiovascular diseases in later life [13,14]. The existence of metabolic syndrome (MetS) in young adults can be a predictor of these chronic conditions in older adults [15,16].

MetS is defined as a cluster of metabolic conditions associated with abdominal obesity including: elevated blood pressure, impaired glucose tolerance, insulin resistance, elevated triglycerides, and low level of high-density lipoprotein cholesterol concentrations [17]. Similarly, the term ‘Cardiometabolic risk’ is characterized by the existence of the elements of MetS, namely: central obesity, impaired glucose metabolism, hypertension and dyslipidaemia [18,19]. Within the UK, several studies have investigated the prevalence and correlates of MetS in ethnic minority groups [20-23] and/or in patients with particular clinical conditions [24-28]; however, such research in emerging adulthood in the UK is scarce [29].

Amongst metabolic conditions of MetS, abdominal adiposity is of particular importance as it independently predicts the risk of other comorbidities and metabolic conditions [30]. Several anthropometric measures have been used as proxy indicators of central or whole-body adiposity:

Body Mass Index (BMI) developed by Adolphe Quetelet in 1832 [31] has been extensively used as a traditional proxy measure of adiposity [32]. However, BMI is a weight-for-height measure and by nature unable to distinguish between fat mass and muscle mass and to establish regional fat distribution [33]. These two substantial limitations question the discriminatory power of BMI in practice as it can potentially produce false diagnosis of adiposity, overestimate fat accumulation in tall and underestimate it in short people [32,34,35]. Furthermore, the limitation in estimating central obesity matters, as abdominal fat is a more important cardiometabolic (CM) risk predictor compared with overall body fatness [33].

Waist circumference (WC) has been recommended with the advantage of assessing central adiposity [36,37]; however, its application in practice has been questioned due to different cutoff points for men and women and emerging evidence showing a variation in diagnostic thresholds between ethnic groups [33,37-40].

Similarly, the proposed ratio of waist circumference to hip circumference (WHR) as a measure of relative fat distribution requires specific gender and ethnic group cutoff points [41,42]. Further, throughout weight loss with reduction of circumferences of both waist and hip, the ratio of waist to hip circumferences may not change substantially and therefore limits the practical utility of the index for the CM risk management [43].

To eliminate the confounding impact of height on the association between anthropometry and CM risk [44,45], Waist to Height Ratio (WHtR) was proposed as a simple, non-invasive and effective screening tool [46-52] benefiting from extensive literature to support its use in relation with CM risk [53-59] and cross validation with a widely used universal cutoff point measure for identification of the abdominal obesity in different ethnic groups [60-67]. Despite this, not only has the superiority of WHtR to other anthropometric measures, as a better predictor of central adiposity and chronic diseases been questioned [68- 71]; but also the use of its universal yardstick for establishing central obesity in different ethnic groups has been challenged [51,72-78].

In recent years, several novel and/or contemporary measures of anthropometric adiposity were proposed to address the limitations of the aforementioned traditional and established measures:

A correction to the equation of BMI was offered to produce a better predictor of the postoperative complications amongst colorectal cancer patients [79]; but its validity and discriminatory power in relation with CM risk has yet to be tested in large samples.

Another measure is Bergman et al. proposed Body Adiposity Index (BAI), calculated from hip circumference and height (Table 1) as a predictor of percentage body fat, which was validated against dual-energy X-ray absorptiometry (DXA) measurements in a large sample of Mexican adults [80]. Several studies confirmed validity and practical use of BAI [81-93]; nonetheless, an extensive body of knowledge from studies in range of ethnic groups and patient populations questioned its validity in comparison with reference methods and/or in association with the CM risk [94-125].

Measure Author (year) Equation
Body Mass Index (BMI) Quetelet (1832) BMI = body weight [kg] / (height [m]2)
Waist Circumference (WC) WHO (1997) Circumference of the waist measured in standardized position as advised by the WHO
Waist to Hip Ratio (WHR) WHO (1997) WHR = waist circumference [cm] / hip circumference [cm]
Waist to Height Ratio (WHtR) Hsieh et al. (1995) WHtR = waist circumference [cm]/ height [cm]
New Body Mass Index (New BMI) Van vugt et al. (2015) New BMI = 1.3 x (weight [kg] / height [m]2)
Body Adiposity Index (BAI) Bergman et al. (2011) BAI (percentage body fat, BF%) = (hip circumference [cm]/height [m]1.5) − 18.
A Body Shape Index (ABSI) Krakauer & Krakauer (2012) ABSI = waist circumference [cm] / (BMI [kg/m2]0.66 x height [m]0.5)
Clinica Universidad de- Navarra – Body Adiposity Estimator (CUN- BAE) Gomez-Ambrosi et al. (2012) BF%= -44.988 + (0.503 x age [years]) + (10.689
x sex) + (3.172 x BMI [kg/m2])-(0.026 x BMI2[kg/m2]) + (0.181 x BMI [kg/m2] x sex) - (0.02 x BMI [kg/m2] x age)- (0.005 x BMI2 [kg/m2] x sex) + (0.00021 x BMI2 [kg/m2] x age) where male = 0 and female = 1

Table 1: Proxy indices of anthropometric adiposity used in this study, reference and equation for calculation.

The Clinica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) has been proposed to estimate percentage body fat from BMI, gender and age (Table 1) [126] however, preliminary promising findings [127] and clinical usefulness was debated in some other studies [128-130].

A Body Shape Index (ABSI) was developed taking into consideration WC as a proxy measure of abdominal obesity, but adjusting for weight and height [131]. Several studies confirmed the practical validity of ABSI [132-139] however others questioned its clinical use because of the limited association with measures of body fat [140], mortality [141,142] and CM risk [143-149].

The inconsistencies, limitations and discrepancies on reported validity of traditional and novel indices of adiposity demonstrates a gap in knowledge and need to conduct further studies in the field. Table 1 shows the traditional and novel proxy indicators of adiposity with reference to the predictive equation model used to calculate each index.

Studies assessing the practical use of the anthropometric proxy indicators of adiposity either investigate their validity against a notional reference method or study their associations with chronic disease. The rationale for this approach is that in principle, a valid anthropometric proxy index of adiposity must have a strong correlation with measured percentage body fat [150] and/or strong association with comorbidities and indeed a discriminatory power to predict their risk [151].

In the current study, to assess the predictive discriminatory power of different anthropometric indices of adiposity, we not only compared them against an objective index measured, but also compared their associations with CM risk markers taking into consideration known confounding factors such as the level of physical activity. Therefore, the aim of this study was to investigate which proxy measure of anthropometric adiposity has the strongest association a) with measured percentage body fat and b) with CM risk indices in healthy young adults in North West England.

Materials and Methods

Study design and participants

Five hundred and fifty (236 male and 314 female) participants aged 18–25 years were recruited in a cross-sectional study. The study was conducted within the framework of the Collaborative Investigation on Nutritional Status of Young Adults (CINSYA) in the city of Liverpool, UK. Participants were recruited by convenience sampling from universities across the Northwest of England between 2014 and 2016 and attended two clinical visits (Figure 1). All participants gave their written, informed consent for inclusion in the study, prior to participation. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Liverpool Hope University.


Figure 1: Schematic demonstrating the design of study.

Demographic data was collected by questionnaire using questions extracted from a validated questionnaire from The UK National Diet and Nutrition Survey (NDNS) Figure 1 [152].

Physical measurements

Body composition, fat and fat-free mass, total body water and the overall percentage body fat were assessed by Tanita MC-180MA which is a multi-frequency Bioelectrical Impedance Body Composition analyser (Tanita Ltd, Tokyo, Japan). For this assessment, participants were in light closing (i.e. commonly 0.5 kg estimated weight of the light clothing automatically deducted by the equipment) while they removed their shoes and socks before stepping on the equipment. Tanita MC- 180MA also measured body mass to the nearest 0.1 kg; which was used for calculation of BMI and other anthropometric indices.

Height was determined in Frankfort Plane position using a SECA201 stadiometer (SECA GMBH & Co, Hamburg, Germany). Systolic and diastolic blood pressure (SBP and DBP respectively) was measured in a seated position by Omron 907 Professional Blood Pressure Monitor (Omron Corporation, Kyoto, Japan) on the average mode, where the equipment produces an average of two consecutive automatic measurements. The blood pressure was measured twice in the start and toward the end of the first visit and the SBP and DBP were recorded as the mean of the two measurements. The circumferences of waist and hip were measured using non-stretchable tape measure over light clothing as advised within the literature [37].

Traditional BMI calculated as body weight divided by squared height (kg/m2) was classified into two categories as normal (18.5 - 24.9 kg/m2) and overweight or obese (≥ 25 kg/m2). The new BMI, which was calculated as 1.3* weight/height2 (kg/m2), was also classified into two categories using the same cut-off points: normal (18.5 - 24.9 kg/m2), overweight or obese (≥ 25 kg/m2) [79]. ABSI [131], BAI [80] and CUN-BAE [126] were calculated based on earlier suggested formulas (Table 1). Since there are no population specific defined cut-off points for these three indices and also for WHR, sex-specified medians for each one was used to categorise participants into two groups (equal or more than median or lower than median). Abdominal obesity was assessed using WC and WHtR. Based on WC, participants were divided into abdominal obese, where the WC was ≥ 102 cm in male, and ≥ 88 cm in female or non-abdominally obese, where WC was <102 cm in male and <88 cm in female [153]. WHtR was calculated by dividing WC by height, which was classified as abdominal obese or non-abdominally obese using cut-off point 0.50 [47]. With regard to the cut-off points for Body fat measured by Bioelectrical Impedance Body fat analyser, as per manufacturer’s guidelines, ≥ 20% of total body fat in males and ≥ 33% in females were considered as excessive body fat, whilst lower amounts were defined as normal values.

Diet and physical activity

A three-day integrated diet and physical activity diary was used to assess energy and nutrient intake and to estimate energy expenditure. The diet diary was extracted from the validated questionnaires of the UK’s National Diet and Nutrition Survey (NDNS) [152] with minimal adjustments. To improve compliance and enhance accuracy, standardised guidelines used in NDNS, a completed example and food portion pictures were supplied and prompts on time, place and portion sizes were shown in the diet diary. The diaries were analysed for energy, macronutrients and micronutrients using Microdiet dietary analysis software (Microdiet v3, Downlee systems Ltd, Salford, UK).

A validated 3-day physical activity diary produced by Bouchard et al. was used to assess physical activity. The analysed output of the diary produced total energy expenditure as kcal/kg/day and min/day spent in light/moderate/vigorous activity [154].


The full procedure of capillary whole blood lipid and glucose analysis was detailed previously [155]. In brief, participants fasted overnight for least 8 hours before capillary puncture of whole blood sample was obtained. After cleaning the site with alcohol and drying it, a capillary sample of 35 μl was collected using a lancet and capillary tube/plunger with heparin anticoagulant. The sample was injected into the equipment cassette; which was inserted to the analyser. The Alere LDX (Alere, San Diego, CA) was used as a point of care capillary whole blood glucose and lipid analyser to assess total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG). These variables were then used by the analyser to calculate low-density lipoprotein cholesterol (LDL-C) using Friedewald equation [155] and the ratio of TC/HDL. The fasting blood glucose concentration was also measured by the analyser.

Statistical analysis

Data analysis was conducted using SPSS version 23 for Windows (IBM SPSS, Inc., Armonk, NY, USA). All data are expressed as mean ± standard error of mean (SE). The required sample size (n=543) was estimated with 95% confidence interval and prediction of the prevalence MetS to be around 8% (based on the midpoint of the values reported in comparable age groups [143,156], and with setting a margin of error and a potential for dropout. Because of the relatively large sample size of the study (n>500), we did not perform Kolmogorov- Smirnov or Shapiro-Wilk test to assess the normal distribution of the data; however, the data was scanned to remove SPSS identified outliers based on 95% confidence interval for the mean as well as participants with improbable energy intake (i.e. reported average calorie intake <800 kcal or >4200 kcal in line with previous literature [157]), and/or participants who did not have their anthropometric data completed. Out of 565 potential participants who contributed to study, fifteen participants were excluded as obvious outliers of normal distributions or missing key anthropometric information. Data from 550 participants (236 males and 314 females) with fully productive profile were used in the final analytical dataset.

Descriptive statistics were performed to establish the demographic profile of the study population. Inferential statistics were used to address the main research questions. To investigate the strengths of the association between the proxy indices of anthropometric adiposity and measured body fat, regression analysis was used.

To investigate the relationship between CM risk factors and BMI, new BMI, ABSI, CUNBAE, BAI, WC, WHR and WHtR the odds ratio was calculated with 95% confidence interval from linear and logistic models in crude and adjusted models respectively, controlling for the effect of smoking, age and physical activity in the adjusted model. To investigate the discriminatory ability of each proxy index of anthropometric adiposity over the possible values to detect CM risk, area under the receiver-operating characteristic (ROC) curve (AUC) was quantified and tested. Further details of the statistical analysis procedure have been described previously [143].


Participants characteristics

A total of 550 young adults participated in the study and the participants’ mean age and BMI were 21.2 years and 24.2 kg/m2, respectively. Of these participants 57.1% were female and 96.2% were British. Most of participants were single (80.1%) and 14.0% of them were current smoker. Means of all serum lipids, fasting blood sugar, and diastolic blood pressure were in normal range. Mean systolic blood pressure of participants was 123.1 mmHg (Table 2).

Index Mean  ± SE or %
Age (years) 21.19 ± 0.10
BMI (kg/m2) 24.18 ± 0.18
Body fat (%) 24.60 ± 0.39
WC (cm) 80.51 ± 0.50
CUN-BAE 26.03 ± 0.36
BAI 45.08 ± 0.22
ABSI 0.00030 ± 0.0000035
New BMI(kg/m2) 24.11 ± 0.18
WHR 0.80 ± 0.003
WHtR 0.47 ± 0.003
Total cholesterol (mg/dL) 158.70 ± 1.49
LDL-C (mg/dL) 104.83 ± 16.56
HDL-C (mg/dL) 59.30 ± 5.59
Triglyceride (mg/dL) 115.44 ± 12.07
Fasting blood sugar (mg/dL) 89.82 ± 0.50
Systolic blood pressure (mmHg) 123.14 ± 0.61
Diastolic blood pressure (mmHg) 75.59 ± 0.45
Female (%) 57.1
British (home students) (%) 96.2
Single (%) 80.1
Smoker (%) 14

Table 2: Characteristics of the study population.

The prevalence of CM risk factors is shown in Figure 2. The most prevalent risk factor among anthropometric measurements was excess body fat (32.7%), which was near to the prevalence of overweight or obesity determined by traditional BMI formula (32.5%) (Figure 2A). Overall, 6.8% of participants were affected by MetS, 57.6% of them had at least one risk factor and 18.1% at least two risk factors for cardiometabolic diseases. The most prevalent CM risk factor among biochemical markers was low serum HDL-C levels (30.7%), whilst the lowest prevalent one was elevated LDL-C levels (8.1%) (Figure 2B).


Figure 2: Prevalence of cardiometabolic risk in the study population. 2A) Indicates the prevalence of risk factors related to anthropometric measures (Traditional/ new BMI ≥ 25 kg/m2, abdominal obese: WC ≥ 102 cm in males and ≥ 88 cm in females, elevated WHtR: ≥ 0.5, and excess body fat: body fat ≥ 20% in male and ≥ 33% in female). 2B) Indicates the prevalence of risk factors related to biochemical test: total cholesterol ≥ 200 mg/dL, LDL ≥ 130 mg/dL, HDL-C<40 mg/ dL in male and <50 mg/dL in female, Triglyceride ≥ 150 mg/dL Fasting blood sugar ≥ 100 mg/dl, hypertension: SBP ≥ 130.0 and/or DBP ≥ 85.0 mmHg). Metabolic syndrome was defined as the presence of three or more of the following components: (1) abdominal adiposity (waist circumference 488 cm); (2) low serum HDL-C (o50 mg/dl); (3) high serum triacylglycerol levels (≥150 mg/dl); (4) elevated blood pressure (≥130/852 mm Hg); (5) abnormal glucose homeostasis (fasting plasma glucose level ≥110 mg/dl).

Association with percentage body fat

Table 3 indicates the correlation of various anthropometric measures with body fat percent. Overall, novel indices of adiposity, except for ABSI, had stronger correlation with measured body fat percentage compared with traditional indices. The highest correlation with body fat was observed for CUNBAE (r=0.828, P<0.0001).

  Traditional indices of adiposity
Measured body fat (%) BMI WC WHR WHtR
0.546 0.307 -0.012 0.479
  Novel indices of adiposity
Measured body fat (%) New- BMI CUN- BAE BAI ABSI
0.589 0.828 0.681 -0.426

1Using linear regression: P for all<0.0001.

Table 3: Pearson correlation coefficient for linear regression of proxy anthropometric measures of adiposity with measured percentage body fat1.

Association with cardiometabolic risk

The correlation between different anthropometric measurements and serum lipids and fasting blood sugar are presented in Table 4. Fasting blood sugar was positively correlated to traditional BMI, WC, WHR and new BMI. Total cholesterol was also directly correlated with body fat percent, traditional BMI, WHtR, new BMI, CUNBAE and BAI. Serum TG was weakly correlated to WHR. Other biochemical risk factors were not significantly correlated with proxy anthropometric measures of adiposity.

  Blood sugar Total
Body fat
0.027 0.173§ -0.004 -0.016 0.026
BMI 0.131§ 0.103§ -0.006 -0.049 0.052
WC 0.236§ 0.069 0.025 -0.052 0.055
WHR 0.208§ 0.032 0.013 0.022 0.087§
WHtR 0.195 0.126§ 0.040 -0.031 0.061
New-BMI 0.106§ 0.129§ -0.004 -0.033 0.054
CUN-BAE 0.001 0.177§ 0.024 -0.052 0.037
BAI 0.028 0.160§ 0.050 -0.036 0.005
ABSI -0.070 -0.078 0.028 0.048 -0.040

1Using linear regression. §P<0.05

Table 4: Linear regression of proxy anthropometric measures of adiposity with cardiometabolic risk1.

Crude and multivariable- adjusted odds ratio (ORs) and 95% CI for the presence of at least one risk factor, at least two risk factors for CM diseases and MetS (i.e. at least three risk factor for CM diseases) are shown in Table 5. All traditional and novel indices of adiposity were associated with increased risk of having MetS, at least one risk factor or at least two risk factors of CM diseases except for ABSI. We observed that higher ABSI decreased the risk of MetS by 75% in the crude model; however, controlling for various potential confounders disappeared this association. ABSI was not also related to the risk of at least one risk factor or at least two risk factors of CM diseases. Overall, the direct link between traditional indices of adiposity and CM risks were stronger rather than novel indices. Amongst the traditional indices, abdominal adiposity, particularly elevated WC, was the best predictor of CM risks. However, amongst the novel indices the best predictor for MetS was CUN-BAE, whereas the best predictor for at least one risk factor or at least two risk factors of CM diseases was new BMI.

  MetS P value At least two risk factors P value At least one risk factor P value
Crude model            
Body fat percent 7.47 (3.44, 16.22) <0.0001 2.25 (1.42, 3.54) <0.0001 1.96 (1.33, 2.89) 0.001
BMI 10.50 (4.51, 24.43) <0.0001 2.62 (1.66, 4.14) <0.0001 2.36 (1.59, 3.51) <0.0001
WC 32.40 (14.62, 71.80) <0.0001 2.85 (1.60, 5.08) <0.0001 3.21 (1.69, 6.07) <0.0001
WHR 16.49 (3.92, 69.31) <0.0001 2.05 (1.28, 3.30) 0.003 1.43 (1.00, 2.04) 0.048
WHtR 26.32 (9.14, 75.78) <0.0001 2.98 (1.87, 4.73) <0.0001 2.92 (1.90, 4.49) <0.0001
New-BMI 9.23 (4.12, 20.66) <0.0001 2.87 (1.82, 4.54) <0.0001 2.71 (1.81, 4.07) <0.0001
CUN-BAE 12.81 (3.88, 42.24) <0.0001 1.82 (1.15, 2.87) 0.010 1.92 (1.34, 2.74) <0.0001
BAI 9.32 (3.26, 26.71) <0.0001 1.80 (1.14, 2.85) 0.011 1.79 (1.26, 2.56) 0.001
ABSI 0.25 (0.11, 0.56) 0.001 0.71 (0.45, 1.11) 0.137 0.72 (0.50, 1.02) 0.066
Adjusted model            
Body fat percent 5.33 (2.36, 12.07) <0.0001 1.90 (1.16, 3.14) 0.011 1.89 (1.24, 2.89) 0.003
BMI 7.99 (3.15, 20.32) <0.0001 2.19 (1.27, 3.78) 0.005 2.54 (1.58, 4.07) <0.0001
WC 58.04 (18.30, 184.10) <0.0001 2.65 (1.38, 5.09) 0.003 2.96 (1.50, 5.86) 0.002
WHR 16.26 (3.77, 70.12) <0.0001 1.83 (1.09, 3.06) 0.021 1.45 (0.98, 2.15) 0.061
WHtR 20.88 (7.00, 62.29) <0.0001 2.55 (1.53, 4.24) <0.0001 3.07 (1.91, 4.91) <0.0001
New-BMI 6.60 (2.73, 15.95) <0.0001 2.37 (1.39, 4.05) 0.002 2.96 (1.84, 4.75) <0.0001
CUN-BAE 9.02 (2.57, 31.73) 0.001 1.37 (0.79, 2.35) 0.258 1.88 (1.22, 2.90) 0.004
BAI 6.91 (2.37, 20.16) <0.0001 1.62 (1.00, 2.63) 0.050 1.77 (1.22, 2.58) 0.003
ABSI 0.41 (0.17, 1.01) 0.052 0.99 (0.58, 1.67) 0.961 0.83 (0.55, 1.25) 0.374

1Using Logistic regression.
2Derived from a Mantel-Haenszel extension chi- square test.

Table 5: Multivariate adjusted odds ratio (and 95% Confidence intervals) for cardiometabolic risk associated with proxy measures of anthropometric adiposity1.

The ROC Curve analysis examining the AUCs (and 95% CIs) of anthropometric measures in the prediction of MetS and cardiometabolic risks are shown in Table 6. The lowest AUC for all three MetS, at least one risk factor or at least two risk factors of CM belonged to ABSI. Consistent with results of logistic regression, the highest AUC for MetS was related to WC and WHtR. The greatest AUC for at least two risk factors of CM was related to WHtR, which was not statistically different from body fat, BMI, WC and new-BMI. Although the highest AUC for at least one risk factor of CM belonged to WHtR, they were not statistically different from all other indices (except for ABSI).

  MetS At least two risk factors At least one risk factor
Body fat percent 0.771 (0.694, 0.847) 0.605 (0.544, 0.665) 0.595 (0.545, 0.644)
BMI 0.827 (0.747, 0.906) 0.614 (0.548, 0.680) 0.615 (0.566, 0.663)
WC 0.889 (0.831, 0.947) 0.640 (0.575, 0.705) 0.612 (0.563, 0.660)
WHR 0.782 (0.723, 0.841) 0.638 (0.575, 0.701) 0.585 (0.536, 0.634)
WHtR 0.892 (0.831, 0.953) 0.663 (0.600, 0.727) 0.631 (0.583, 0.680)
New-BMI 0.826 (0.746, 0.907) 0.637 (0.572, 0.702) 0.630 (0.582, 0.678)
CUN-BAE 0.768 (0.680, 0.856) 0.596 (0.530, 0.661) 0.600 (0.551, 0.650)
BAI 0.776 (0.692, 0.860) 0.581 (0.517, 0.646) 0.599 (0.549, 0.648)
ABSI 0.233 (0.140, 0.327) 0.427 (0.358, 0.495) 0.415 (0.365, 0.464)

Table 6: Area under curve analysis for cardiometabolic risk associated with traditional and contemporary measures of adiposity.


To the best of our knowledge, this is the first study reporting the prevalence of CM risk and MetS in young adults in Northwest of the UK, and the first that compared the association between variety of proxy indicators of adiposity with measured body fat and CM risk in this population. The study addressed its question about the clinical usefulness of different anthropometric indices of adiposity and contribute to our understanding of broad picture of nutritional status of young adults in the UK:

The current study demonstrated a very strong correlation between CUN-BAE and measured body fat and, also a strong association between WC and WHtR and CM risk. Apart from ABSI, all other indices of adiposity were associated with CM risk when tested using multivariate adjusted OR. The ROC examination also confirmed the superiority of WC and WHtR based on their greatest AUC and their diagnostic power in detection of MetS.

The strength of the correlation between CUN-BAE and measured body fat, which was in line with the previous literature [126], suggests CUN-BAE to be a potentially useful measure of adiposity for our population; however, this strength was not replicated to the same extent when CUN-BAE was associated with CM risk in testing through multivariate OR and in particular when the effect of potential confounding factors were taken into consideration in our multivariate adjusted OR analysis. Furthermore, the prediction equation formula of CUN-BAE is rather complicated and this limits the clinical usefulness of this measure in practice.

In addition to CUN-BAE, other novel measures of anthropometric adiposity also showed statistically significant strong association with measured body fat, with the strength of the association declining gradually from BAI, New-BMI and ABSI respectively; broadly producing superior correlates with adiposity compared with more traditional measures such as WC and WHR. On the other hand, as seen with CUN-BAE, the novel measures of adiposity did not produce superior association with CM risk factors limiting their clinical usefulness for the current population. The strong association of the novel measures of adiposity with measured body fat was not surprising as these measures are often generated or validated based on the linear regression prediction equations against measured adiposity in cross-sectional studies [83,91,100,110,112,122-126,128,129,140] and they typically require further validation to establish their association with chronic non-communicable diseases.

Amongst all traditional and novel measures of adiposity investigated, ABSI produced the weakest association with CM risk and negative association with percentage body fat. The current finding proposes that ABSI has substantial limitations for using in this population as the measure had no statistical association with CM risk factors and consequently insignificant association in multivariate adjusted OR and the smallest AUC in the ROC curves amongst all traditional and novel measures of anthropometric adiposity. This finding was in contrast with some previous studies [132,134,135,137,139]; whereas, confirming some other studies [143-147]. This is difficult to explain these contrasting findings particularly in view of the different endpoint outcome variables used in different studies. For instance, the study of Kraukaeur and Kraukaur [131] proposed ABSI as a predictor of premature mortality; whereas, our study investigated the discriminatory power of this measure in distinguishing CM risk. Despite this, we thought a potential explanation for the findings may be based on the nature and relationship between the variables used in ABSI’s calculation. Conceptually, while ABSI was proposed to associate body shape to health outcomes independently of the variables defining body size (i.e. height, WC and BMI) [131,158]; we argue that in principle the interrelationship between these defining variables may restrict its clinical usefulness. In particular, we support the previously proposed hypotheses that ABSI’s dependency to body height may confound its capacity to distinguish CM risk in study populations [44,45,143,158].

The ROC analysis demonstrates that the highest AUC belongs to WHtR confirming the discriminatory power of this measure for our target population; however, WC and new-BMI also produced comparable AUC for detection of MetS as potential alternatives. Similarly, the largest AUC in relation with one or two CM risk factor belonged to WHtR overall confirming the previously reported superiority of WHtR compared with other indices of adiposity [49]. The acceptable statistically significant association of the WHtR against measured body fat and strong association with CM risk in logistic regression, and also the great AUC when testing ROC curve of the WHtR, together with simplicity and clarity of the public health message in communication of the use of the measure (i.e. keep your waist size less than half of your height) are amongst the reasons to propose WHtR as a clinically superior measure of anthropometric adiposity for our population.

The current study has some limitations and strengths to be considered in the interpretation of the results and in the design and conduct of the future similar investigations. The current study examined participants recruited through non-randomized convenient sampling, who might not be fully representative sample of young adults residing in the NW of England. This may limit the generalizability of our findings and the results should be interpreted carefully and in view of the above limitation.

In the current study and in consideration of our sample size, use of a lab based objective reference method of assessment of body composition such as magnetic resonance imaging (MRI), dual-energy X-ray absorptiometry (DXA) and/or hydro-densitometry [159] was not plausible and hence we used a multi-frequency bioelectrical impedance body composition analyzer with an advanced functionality to measure percentage body fat for which, the validity and clinical usefulness was demonstrated in previous studies [160,161].

Although we did not stratify our analysis by gender, its confounding effect was controlled. In addition, due to differences in fat and lean mass tissue between men and women, we categorized participants based on gender-specific cut-off points for anthropometric measures, which do not have predefined threshold that minimize its confounding effect.

The current investigation used measured basic anthropometric indices and this was an advantage compared with some previous investigations using self-reported anthropometry. The assessors of anthropometry in this study were graduate nutritionists; who were trained by a qualified registered nutritionist with experience of assessment of anthropometry. The traditional and novel measures used in the analysis were computed electronically and this restricted the potential impact of the human error.

Although we considered various potential confounding factors in our analysis, the etiology of the MetS and occurrence of the CM risk in the population is heterogonous and yet to be fully understood. Future studies, should therefore control for wider range of potential confounders including dietary, socioeconomic, and biochemical measures to elucidate the associations between anthropometric measures and CM risk with elimination of potential mediating and moderating factors.


Overall, most of the traditional and novel measures of anthropometric adiposity showed statistically significant association with measured body fat and with CM risk factors. Novel measures such as CUN-BAE, New-BMI and BAI demonstrated strong association with measured adiposity, but weaker discriminatory power in identifying CM risk. On the other hand, more traditional measures such BMI, WC and WHtR showed mediocre association with measured percentage body fat and superior discriminatory power in identifying CM risk.

We recommend the use of WHtR based on its greater association with CM risk in comparison with other measures, and also based on its simplicity, efficacy and clarity of its public health message. Our findings proposed that ABSI cannot be used to distinguish participants with or without CM risk.


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