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| ORIGINAL ARTICLE |
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| Year : 2018 | Volume
: 15
| Issue : 1 | Page : 41-44 |
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Abdominometer: A novel instrument to determine the level of risk for cardiometabolic diseases
Anil I Sirisena1, Basil N Okeahialam2, E Emeka Ike3, D Stephen Pam1, J Linus Barki4
1 Department of Radiology, Jos University Teaching Hospital, Jos, Plateau State, Nigeria
2 Department of Medicine, Jos University Teaching Hospital, Jos, Plateau State, Nigeria
3 Department of Physics, University of Jos, Jos, Plateau State, Nigeria
4 University Health Centre, University of Jos, Jos, Plateau State, Nigeria
| Date of Web Publication | 7-May-2018 |
Correspondence Address:
Prof. Basil N Okeahialam
Department of Medicine, Jos University Teaching Hospital, Jos, Plateau State
Nigeria
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/njc.njc_20_17
Background: The standard measure for classifying obesity, the body mass index (BMI) has been found to be deficient in some populations, Sub-Saharan Africans inclusive. Until recently, waist-to-height ratio (WHtR) was considered an improvement in this regard. Abdominal height (AH) measured with a novel appliance was recently found to be a superior cardiac anthropometric measure in our population; hence, there is a need to correlate it mathematically with the older indices.
Objective: To determine a mathematical formula that permits computation of AH from BMI and WHtR.
Methodology: A total of 200 randomly selected consenting young adult Nigerians (100 males and 100 females) between the ages 16 and 44 years who were undergoing preadmission medical examinations in a higher educational institution participated in this study. Height and weight were measured to determine BMI; waist and hip circumferences were measured and waist-to-hip ratio and WHtR computed.
Results: Correlations between two anthropometric indices, BMI, and WHtR with AH were determined, and linear relationships were established using regression analysis to compute the AH using BMI and WHtR (P < 0.01). Reference levels of AH for low risk, increased risk, substantially increased risk, and severe risk were established. From this study, AH for severe risk level was found to be >32 cm and 30 cm by BMI and WHtR classifications, respectively.
Conclusion: Where there is no abdominometer to measure AH, it is possible from BMI and WHtR to determine AH; which has been shown to predict cardiometabolic diseases better in our population.
Keywords: Abdominal height, abdominometer, body mass index, cardiometabolic diseases, risk levels, waist-to-height ratio
How to cite this article:
Sirisena AI, Okeahialam BN, Ike E E, Pam D S, Barki J L. Abdominometer: A novel instrument to determine the level of risk for cardiometabolic diseases. Nig J Cardiol 2018;15:41-4 |
How to cite this URL:
Sirisena AI, Okeahialam BN, Ike E E, Pam D S, Barki J L. Abdominometer: A novel instrument to determine the level of risk for cardiometabolic diseases. Nig J Cardiol [serial online] 2018 [cited 2023 May 30];15:41-4. Available from: https://www.nigjcardiol.org/text.asp?2018/15/1/41/231966 |
| Introduction | |  |
Obesity is a determining factor in the development of cardiovascular diseases and is associated with an increased incidence of hypertension, diabetes, and metabolic syndrome.[1] Although body mass index (BMI) has served for long in classifying obesity, it has a deficiency of distinguishing the contribution of body frame size, muscle mass, and body fat to overall body mass.[2] Some studies have shown that measures of abdominal obesity such as waist circumference (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR) are best correlated with cardiovascular disease and mortality.[3] Moreover, BMI and WC are not equally applicable to all ethnic groups [4] and cutoff values may not be appropriate for Africans.[5] Another study shows that BMI, WC, WHR, and WHtR are all correlated with each other with incident cardiovascular diseases but with WHtR having the strongest gradient.[3] The measurement of abdominal height (AH), on the other hand, also known as sagittal abdominal diameter has shown better correlation with cardiovascular disease risk factors than BMI and WC [6] and stronger measure of abdominal fat than WC.[7] The abdominometer is an appliance conceptualized by Okeahialam and has been piloted locally,[7] showing good promise as a screening tool for cardiometabolic diseases.[8] It is thought to be better suited for our population. This study is designed to determine a regression equation that can determine AH from BMI and WHtR and come up with reference levels for low, increased, substantially increased, and severe risk for cardiometabolic diseases in the population.
| Methodology | | |
A total of 200 (100 males and 100 females) consecutively selected young adult Nigerians between the ages of 16–44 years undergoing preadmission medical examination in the medical centre of University of Jos, Nigeria participated in the study after giving informed verbal consent. The study protocol was approved by the medical centre. The anthropometric measurements such as weight, height, WC, hip circumference, and AH were recorded. Weight was measured in kilograms with a bathroom weighing scale calibrated every day to ensure there was no zero error. Participants wore only light clothing. Height was measured against a wall marked out in meters with the participant standing with feet together and back against the wall with no footwear or headgear. WC and hip circumference (HC) were measured using a measuring tape; the former in end expiration at a level midway between the lowest rib margin and the iliac crest. The latter was measured at the level of the trochanters of the femur along the line of the greatest posterior jutting of the buttocks. All measurements were in centimeters. The AH was measured with the novel locally made instrument, called “abdominometer”. Its mode of use has been described in an earlier study.[7] [Figure 1]a and [Figure 1]b show the abdominometer closed and in use, respectively. BMI was determined as quotient of weight in kilograms and square of height in meters, while WHtR and WHR for all the participants were computed from the measured data using Microsoft excel 2007 software package. | Figure 1: (a) – Left Panel: Abdominometer closed and not in use. The short arm of the “L” rests in the small of the back, and the longer arm has a graduation scale on which readings are taken (b) – Right Panel: Abdominometer in use with the swinging arm touching the anterior abdominal wall and distance read off on the graduated scale
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Statistics
IBM Statistical Package for Social Sciences Version 21(IBM Inc. New York, USA.) was used to carry out the statistical analysis. Pearson correlation and regression methods were used to determine the desired results.
| Results | | |
The age of the cohort ranged from 16 to 42 years with a mean (standard deviation) of 21.37 (4.5) years. For BMI, the mean was 24.13 (5.5) kg per square meter. The other anthropometric measures are shown in [Table 1].
The correlation between AH with four different common anthropometric indices (WC, BMI, WHtR, and WHR) is given in [Table 2]. The correlation coefficients attained statistical significance in all situations suggesting that the new index, AH, is a useful measure of cardiometabolic risk. | Table 2: Correlations between abdominal height and common anthropometric indices
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From the correlations, it was found that AH has strong correlations with all the anthropometric indices: BMI (r = 0.915, P < 0.01), WHtR (r = 0.942, P < 0.01), WC (r = 0.942, P < 0.01), and WHR (r = 0.465, P < 0.01). However, in this study, we studied the relationship between the AH with BMI and WHtR. The linear relationships between AH with BMI and WHtR, respectively, were determined by regression analysis. [Table 3] and [Figure 2] show the regression coefficients and the lines of best fit. | Table 3: Regression coefficients for abdominal height versus body mass index and waist-to-height ratio
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From the regression coefficients, the following linear equations are obtained:
- AH = 0.744 (BMI) + 1.993
- AH = 46.991 (WHtR) − 2.741
A subanalysis was done along the line of gender for gender-specific regression equations, and the following emerged:
AH = 0.582 (BMI) +5.032 and AH = 44.757 (WHtR) −1.537 (males) and AH = 0.737 (BMI) +2.706 and AH = 49.322 (WHtR) −4.184 (females).
From these equations and given the differences in gender-specific regression equations, the gender-specific corresponding risk values of AH were computed as shown in [Table 4]a and [Table 4]b.
| Discussion | | |
As shown in recent published works, at least for Africans, AH measurements using the abdominometer predict cardiometabolic diseases better than other anthropometric indices which had withstood the test of time.[8] However, these anthropometric indices though still having their value albeit weak among Africans are still in common use. The need for a mathematically derived formula to convert one to the other becomes necessary. In this study, mathematical formulae derived from regression coefficients have been arrived at for this purpose according to gender. It is therefore possible to determine AH in both sexes from BMI and WHtR, even in situ ations where AH has not been directly measured due to unavailability of the abdominometer; yet to be mass produced. Following from these formulae and established levels of risk from the other anthropometric indices, cutoff values determining risk of cardiometabolic diseases using AH have been determined as shown in [Table 4]a and [Table 4]b. This permits translating AH value ranges to varying degrees of risk. It is therefore possible to ascribe risk values to AH measurements during population field studies or in clinics and by so initiating preventive and curative steps as indicated.
From these results, there appears to be no significant difference observed in all risk levels between the different sexes in AH by WHtR. For AH by BMI, however, females appear to require higher values for the different risk levels. Therefore, severe risk for cardiometabolic diseases cutoff value for AH is found to be above 30.0 cm, and 25 cm for increased risk. This would require wide application for verification and confirmation.
| Conclusion | | |
AH values above 30 cm should call for investigation and necessary curative action, while values above 25 cm should call for preventive actions such as lifestyle intervention. Given its better suitability for Africans, it is recommended for more widespread use in Sub-Saharan Africa.
Declaration of patient consent
The authors certify that they have obtained all appropriate patient consent forms. In the form the patient(s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
| References | | |
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| 3. | Litwin SE. Which measure of obesity best predict cardiovascular risk? J Am Coll Cardiol 2008;52:616-9. [ PUBMED] |
| 4. | Wimalawansa SJ. Thermogenesis based interventions for obesity and type 2 diabetes mellitus. Expert Rev Endocrinol Metab 2013;8:275-80. |
| 5. | Murphy GA, Asiki G, Nsuhuga RN, Young EH, Seeley J, Sandhu MS, et al. The use of anthropometric measures for cardiometabolic identification in a renal African population. Diabetic Care 2014;37:64-5. |
| 6. | de Souza NC, de Oliveira EP. Sagittal abdominal diameter shows better correlation with cardiovascular risk factors than waist circumference and BMI. J Diabetes Metab Disord 2013;12:41. [ PUBMED] |
| 7. | Okeahialam BN, Diala UM, Uwakwe J, Ejeh I, Ozilo U. Utility of the abdominometer: A novel contribution to cardiovascular anthropometry. Food Nutr Sci 2015;6:1202-7. |
| 8. | Okeahialam BN, Diala UM, Uwakwe J, Ejeh I, Ozoilo U. Abdominal height measures cardiometabolic risk better than body mass index: Result of a preliminary study. JMR 2016;2:149-51. |
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4]
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