Loading

Journal of Cardiology and Clinical Research

Impact of Body Mass Index on Coronary Morphology

Research Article | Open Access Volume 10 | Issue 1 |

  • 1. Department of Anatomy, Yenepoya Medical College, India
  • 2. Department of Cardiology, Madras Medical Mission, India
  • 3. Department of Cardiology, KS Hegde Medical Academy and Hospital, India
  • 4. Department of Cardio Vascular Sciences, Sahakarana Hrudayalaya, Pariyaram Medical College, India
  • 5. Department of Community Medicine & Family Medicine, AIIMS Bhubaneswar, I
  • 6. Department of Cardiology, Care Hospital, India
+ Show More - Show Less
Corresponding Authors
Divia Paul A, Assistant Professor, Department of Anatomy, Yenepoya Medical College, Mangalore-5750, Karnataka, India, Tel: 91 8075789347; Email: divia_manoj@yahoo.com
Abstract

Introduction: Asians have lower mean BMI than that of non-Asian populations. There is a lack of sufficient data from Asian countries with WHO consultation to describe either there is an association of BMI with body fat or to CAD. The aim of the study was to correlate the BMI and coronary artery measurements to find out any association between them to be a precursor to CAD. The objective was focused to find the possible association of body mass index (BMI) with normal coronary vessel dimensions.

Materials and Methods: Four thousand angiograms from patients of Indian origin were studied prospectively after procuring the sanction for the same from the ethical committee of the pre-selected hospitals from four states of South India. Patient’s anthropometric measurements were done using the fore mentioned relevant equipments. BMI and BSA were calculated. Informed consents were obtained. Post CABG, post PCI patients and patient being diabetic for ≥5 years were excluded from the study.

Results: Among total sample population, normal coronary arteries were seen in 933 (23.3%), cases and 3,067 (76.7%), had diseased coronary arteries. The average weight was 63.19 ± 5.09 kg (range 90.00– 37.00 kg), height was 168.15 ± 4.60cm (range 190.00–135.00 cm). An overall significant negative correlation was observed among 933 cases of normal samples of indexed coronary artery measurements with BMI.

Conclusion: The present study concludes that with increase in BMI, there was a relative decrease in coronary artery diameter. The risk of CAD and associated multimorbidity is directly proportional to BMI.

Keywords

Body mass inde;Coronary artery;Body fat;Over weight

Citation

Divia Paul A, Ezhilan J, Subramanyam K, Ramakrishna A, Ashraf SM, et al. Impact of Body Mass Index on Coronary Morphology. J Cardiol Clin Res. 2022; 10(1): 1177.

Introduction

World health organisation (WHO), recommended that the classifications of bodyweight should be according to the degrees of underweight and gradations of overweight. These degress are associated with an increased risk of some non-communicable diseases [1,2]. A wide range of morbidity and mortality profiles based on social and economic determinants of health along with high risks profiles in certain cases which are observed among Asian population [3]. In contrast, June et al. (2002), reports that BMI cutoffs are associated with equivalent risk across ethnic groups even if it differs widely depending on the outcome and the risk estimate [4]. In general, Asians have lower mean BMI than that of non-Asian populations. Most studies assessing the effect of BMI on early clinical outcomes after CABG have compared hospital mortality and morbidity between obese and non-obese patients [5,6]. Many previous attempts to address this problem were limited by small sample sizes or a lack of data about potentially confounding factors [5-7]. There is a lack of sufficient data from Asian countries with WHO consultation to describe either there is an association of BMI with body fat or to CAD. Proper statistically correlated data can aid to predict an increaseor decrease in morbidity and mortality rates in populations of Asian countries, or in subgroups within countries.

Cardiovascular risk factors are higher in Asians, a lower BMI cut-off values are recommended for them compared to any other races or countries [3]. This indicates that cardiologists should pay more attention to find out the relationship between BMI and its effect on coronary artery measurements as it seems to have an effect on coronary artery morphology that can effect on patient outcome. Based on BMI, which is calculated as weight in kilograms divided by height in metres squared (kg/m2), the populations at health risks are classified. BMI cut-off points for overweight and obesity are mainly applicable for policy purposes, to inform and trigger policy action, to facilitate the prevention programmes, and to measure the effect of interventions. The associations between BMI and health outcomes within and across populations are helpful to ascertain the cause of diseases for epidemiological purposes. This can warrant a public health to inform policy, trigger action, facilitate prevention programmes, and to assess the effect of clinical interventions [3]. The relation between BMI and the percentage of body fat depends on age and sex, and it differs across ethnic groups [46,47]. The relative percentage of World health organisation (WHO), recommended that the classifications of bodyweight should be according to the degrees of underweight and gradations of overweight. These degress are associated with an increased risk of some non-communicable diseases [1,2]. A wide range of morbidity and mortality profiles based on social and economic determinants of health along with high risks profiles in certain cases which are observed among Asian population [3]. In contrast, June et al. (2002), reports that BMI cutoffs are associated with equivalent risk across ethnic groups even if it differs widely depending on the outcome and the risk estimate [4]. In general, Asians have lower mean BMI than that of non-Asian populations. Most studies assessing the effect of BMI on early clinical outcomes after CABG have compared hospital mortality and morbidity between obese and non-obese patients [5,6]. Many previous attempts to address this problem were limited by small sample sizes or a lack of data about potentially confounding factors [5-7]. There is a lack of sufficient data from Asian countries with WHO consultation to describe either there is an association of BMI with body fat or to CAD. Proper statistically correlated data can aid to predict an increaseor decrease in morbidity and mortality rates in populations of Asian countries, or in subgroups within countries.

Cardiovascular risk factors are higher in Asians, a lower BMI cut-off values are recommended for them compared to any other races or countries [3]. This indicates that cardiologists should pay more attention to find out the relationship between BMI and its effect on coronary artery measurements as it seems to have an effect on coronary artery morphology that can effect on patient outcome. Based on BMI, which is calculated as weight in kilograms divided by height in metres squared (kg/m2), the populations at health risks are classified. BMI cut-off points for overweight and obesity are mainly applicable for policy purposes, to inform and trigger policy action, to facilitate the prevention programmes, and to measure the effect of interventions. The associations between BMI and health outcomes within and across populations are helpful to ascertain the cause of diseases for epidemiological purposes. This can warrant a public health to inform policy, trigger action, facilitate prevention programmes, and to assess the effect of clinical interventions [3]. The relation between BMI and the percentage of body fat depends on age and sex, and it differs across ethnic groups [46,47]. The relative percentage of body fat at different BMIs clearly varies within the populations. This in turn depends upon environmental factors and the amount of physical activity. There are differences in percentage of body fat between rural and urban populations in India. The relations between BMI and body fat implies that the higher percentage of body fat at lower BMIs can result in an increased risk of disease. The aim of the present study was to correlate the BMI and coronary artery measurements to find out any association between them to be a precursor to CAD. The objective was focused to find the possible association of body mass index (BMI) with normal coronary vessel dimensions.

METHODOLOGY

Study population

A cross sectional study was conducted in four cities of India. Hospitals were purposely selected according to the number of cardiac patients identified by them. The age of the study subjects was given a cut-off at 75 years due to marginal benefits marked during the follow-ups. Hence, a conservative approach is proven to be appropriate for the above-mentioned age which itself indicates a poor prognosis with an average yearly mortality rate of 33%–35% [10].The inclusion criteria were all patients who undergo percutaneous coronary angiographic procedure due to abnormalities in the normal cardiac parameters after obtaining their informed consent. Exclusion criteria were patients with a previous history of CABG and recanalized normal looking coronary arteries with or without in-stent restenosis coronary arteries as well as patients being diabetic for five or more than five years. The sample size was estimated by consulting a statistician and using the statistical software G* Power 3.0.10 and 4000 subjects were studied by convenience sampling. All ethical principles for human research were followed and Ethical approval was obtained from the Institutional Ethics Committee of all the hospitals from where data was collected.

Calculation of BMI

  1. BMI of a person was calculated as weight in kilograms divided by height in metres squared (kg/m2). As a measure of relative weight, BMI is easy to obtain. This is an acceptable proxy for thinness and fatness. It is directly related to health risks and death rates in many populations [11].
  2. The suggested categories of BMI for Asian populations are as follows

Less than 18·5 kg/m2 - underweight

18·5–23 kg/m2 - normal / increasing but acceptable risk 23–27·5 kg/m2 - overweight / increased risk

27·5 kg/m2 or more - obesity / higher high risk [12]

Weight of the patients was measured by digital weighing machine with crystal display by Health Genie digital weighing scale HD-221; silver pattern (Manufactured by Health Genie Company). Measured values were recorded as such with decimals without approximating it into highest or lowest decimal values. The height of the patient was measured by the height measuring scale with inches and centimetre calibrations by gadget Hero’s

height measuring scale/tape/stature meter (200 cm /78 inch), wall mounted type (Manufactured by Gadget Hero’s company).

Four thousand QCA reports were collected and studied for the following parameters

  1. LMCA and RCA along with its main branches were assessed for the vessel morphology at the ostium and proximal segment among normal cases by stenosis analysis programme. This programme had incorporated an automated coronary analysis package of the Innova 2100 IQ Cath at an AW4.4 workstation or of the Siemens QCA – Scientific coronary analysis. The gender wise categorisation of the data was done to denote the mean differences in the artery measurements.
  2. Patient’s anthropometric measurements were done using the fore mentioned relevant equipments. BMI and BSA were calculated. BMI was calculated by the relevant formula weight in kilograms divided by height in metres squared (kg/m2). BSA was calculated from patient’s height and weight measurements using Mosteller’s formula. The diameters of the ten segments of coronary artery from angiogram study samples were indexed (adjusted) to BSA (mean diameter mm/m2BSA).
Results

Patient characteristics

Based on QCA analysis, categorisations of 4,000 samples were done. Among total sample population, there were 2,696 (67.4%) males and 1,304 (32.6%) females. Mean age of the patients was

54.50 ± 8.45 years (range 30–75 years). Among total sample population, normal coronary arteries were seen in 933 (23.3%) cases and 3,067 (76.7%) had diseased coronary arteries. Physical and demographic parameters were assessed. The average weight was 63.19 ± 5.09 kg (range 90.00– 37.00 kg), height was 168.15 ± 4.60cm (range 190.00–135.00 cm). BMI of 21 patients including 12 males and 9 females were not assessed due to technical or health issues.

BMI values were correlated with indexed and non-indexed CAM of normal sample population to study the association. An overall significant negative correlation was observed among 933 cases of normal samples of indexed CAM with BMI. Results indicated that as BMI increases; there was a relative decrease in coronary artery diameter. Statistical correlations with BMI among indexed CAM of gender wise categorised samples were noted down. There was a significant negative correlation between indexed CAM and BMI among 509 male samples. However, correlation was not significant among female sample population of 403 cases (Table 1).

Table 1: Correlation of BMI with indexed CAM in normal samples (n=933).

Indp. Variable

Correlation analysis

 

CAS included for correlation analysis

NSP

LMCA

LAD-o

LAD-p

DIAG

LCX-o

LCX-p

OM

RCA- o

RCA- p

RAM

 

 

 

 

 

 

BMI

r

T

-0.150**

-0.135**

-0.147**

-0.117**

-0.191**

-0.136**

-0.112**

-0.178**

-0.121**

-.135

p-value

 

<0.001***

<0.001***

<0.001***

<0.001***

<0.001***

<0.001***

<0.001***

<0.001***

<0.001***

0.177

r

M

-0.192**

-0.175**

-0.168**

-0.157**

-0.255**

-0.193**

-0.188**

-0.162**

-0.110*

-.107

p-value

 

<0.001***

<0.001***

<0.001***

<0.001***

<0.001***

<0.001***

<0.001***

<0.001***

<0.02*

.415

r

F

-0.122*

-0.106*

-0.134**

-0.084

-0.144**

-0.093

-0.055

-0.197**

-0.133**

-.191

p-value

 

<0.05*

<0.04*

<0.01*

0.106

<0.01*

0.071

0.295

<0.001***

<0.05*

0.232

Statistical test used: Pearson correlation test. p<0.001***indicates very highly significant difference, p<0.01**indicates highly significant difference, p<0.05*indicates significant difference, p>0.05 indicates non significant difference between BMI and indexed CAM of total, total male and total female samples.

Abbreviations: BMI-Body mass index, CAM- Coronary artery measurements,Indp.Variable- Independent variable, NSP- Normal Sample population,CAS- Coronary artery segments, r- correlation coefficient, T-Total normal samples, M- Male, F- Female, LMCA - Left main coronary artery, LAD (O, P) - Left anterior descending artery (Ostium, Proximal part),DIAG - Diagonal branch of LAD, LCx (O, P) - Left circumflex coronary artery (Ostium, Proximal

part),OM - Obtuse Marginal branch of LCx, RCA (O, P) - Right coronary artery (Ostium, Proximal part), RAM – Ramus branch of coronary artery.

Correlation of indexed CAM of normal samples with categorised BMI values shows that (Table 2),

Table 2: Correlation of indexed CAM of normal samples with categorised BMI values (n=933).

 

BMI Cs

 

Correlation analysis

CAS included for correlation analysis

LMCA

LAD-o

LAD-p

DIAG

LCX-o

LCX-p

OM

RCA- o

RCA- p

RAM

 

U.W

r

0.078

0.121

0.094

-0.049

-0.014

-0.021

0.104

0.057

0.083

0.643

p-value

0.662

0.489

0.597

0.780

0.937

0.903

0.554

0.746

0.635

0.242

 

N

r

-0.045

-0.020

-0.017

0.080

0.013

0.029

0.014

0.073

0.007

-0.374

p-value

0.552

0.791

0.819

0.291

0.864

0.693

0.849

0.332

0.927

0.153

 

OW

r

-0.143

-0.137

-0.086

-0.060

-0.142

-0.089

-0.91

-0.068

-0.047

-0.230

p-value

<0.01**

<0.01**

0.081

0.224

<0.01**

0.069

0.065

0.171

0.342

0.104

 

OB

r

-0.034

-0.021

-0.041

-0.080

-0.089

-0.015

0.039

-0.118

-0.062

-0.150

p-value

0.616

0.759

0.550

0.246

0.193

0.829

0.573

0.084

0.362

0.439

The normal saples were categorized based on‘WHO Expert Consultation; Appropriate-BMI values of Asian populations for BMI’, 2004 [41] Statistical test used: Pearson correlation test. p<0.001***indicates very highly significant difference, p<0.01**indicates highly significant difference, p<0.05*indicates significant difference, p>0.05 indicates non significant difference between categorised normal BMI samples and indexed CAM of total samples.

Abbreviations: CAM- Coronary artery measurements,BMI Cs-Body mass index categories, CAS- Coronary artery segments,U.W- under weight, N-Normal, OW- over weight, OB- Obese, r- correlation coefficient, LMCA - Left main coronary artery, LAD (O, P) - Left anterior descending artery (Ostium, Proximal part), DIAG - Diagonal branch of LAD, LCx (O, P) - Left circumflex coronary artery (Ostium, Proximal part),OM - Obtuse Marginal

branch of LCx, RCA (O, P) - Right coronary artery (Ostium, Proximal part), RAM – Ramus branch of coronary artery.

indexed normal CAM of the study samples were correlated with BMI cut-off levels for Asian populations [3]. A highly significant correlation was observed in overweight (OW), category of BMI with LMCA, LAD-o, LCx-o. No significant correlations were observed in other segments of indexed CAM with BMI categories. Correlation of BMI with non- indexed CAM in normal samples (n=933), indicated that (Table 3),

Table 3: Correlation of BMI with non-indexed CAM in normal samples (n=933).

Indp. Variable

Correlation

CAS included for correlation analysis

 

analysis

NSP

LMCA

LAD-o

LAD- p

DIAG

LCX- o

LCX- p

OM

RCA- o

RCA- p

RAM

 

 

 

 

 

BMI

r

T

0.013

0.001

0.001

-0.010

-0.055

0.012

0.022

-0.050

0.006

0.047

p-value

 

0.687

0.972

0.979

0.761

0.100

0.722

0.516

0.131

0.849

0.643

r

M

-0.004

-0.005

0.018

-0.014

-0.094*

-0.013

-0.035

0.009

0.055

0.026

p-value

 

0.936

0.906

0.685

0.759

<0.05*

0.763

0.430

0.846

0.220

0.841

r

F

0.035

0.014

-0.005

0.001

-0.019

0.041

0.076

-0.197**

-0.095

0.104

p-value

 

0.484

0.776

0.923

0.981

0.703

0.415

0.133

<0.001***

0.059

0.519

Statistical test used: Pearson correlation test. p<0.001***indicates very highly significant difference, p<0.01**indicates highly significant difference, p<0.05*indicates significant difference, p>0.05 indicates non significant difference between BMI and non-indexed CAM of total, total male and total female samples.

Abbreviations: BMI-Body mass index, CAM- Coronary artery measurements, Indp.Variable- Independent variable, CAS- Coronary artery segments, NSP- Normal Sample population, r- correlation coefficient, T-Total normal samples, M- Male, F- Female, LMCA - Left main coronary artery, LAD (O, P) - Left anterior descending artery (Ostium, Proximal part),DIAG - Diagonal branch of LAD, LCx (O, P) - Left circumflex coronary artery (Ostium, Proximal

part),OM - Obtuse Marginal branch of LCx, RCA (O, P) - Right coronary artery (Ostium, Proximal part), RAM – Ramus branch of coronary artery.

no significant correlations were observed between non-indexed CAM of normal samples and BMI among total as well as in gender wise categorised samples. Hence, the correlation analyses of non-indexed CAM of normal samples with categorised BMI values for for Asian populations [3], were not done.

DISCUSSION

The association of BMI and comorbidities are perhaps not consistent within populations over time. In the present study, it was observed that when BMI increases there wasa relative decrease in coronary artery diameter. The main ethnic groups of India comprise of three categories, i.e., Indo-Aryan (North Indian), Mongoloid (North East Indian), and Dravidian (South Indian) populations. The series of body composition analyses using a standard format confirmed that there are obvious differences in the relation between BMI, BSA and the percentage of body fat across ethnic groups [13]. Ethnicity, an overlooked perspective of the obesity epidemic could be related to different

socioeconomic trajectories, thus representing one of the axes of social adversity associated with the weight change [14].

Present study did not have any statistically significant correlations among total samples of non-indexed CAM with BMI cut-off levels suggested for Asian populations. Leung et al. (1991), [15], also reported no significant correlation between coronary artery dimensions with the anthropometric measurements or with BSA. Similar results were observed by Samet et al. (2015), with no significant correlation between cross-sectional area of the coronary artery and BMI of the patients in their studies. But cross-sectional area of the coronary artery and height of the patients had a weak positive correlation [16]. Even though these studies have not mentioned whether they used indexed or non- indexed data, it was assumed as non-indexed data. However, for indexed CAM an overall significant negative correlation was 

found with BMI for both total and male samples. Other studies utilizing indexed CAM data have not found any such correlation in total samples between BMI and CAM. But similar to the present study Yasmin et al. (2013), reported a gender specific and age dependant significant correlation between RCA diameter and BMI of the male samples [17]. This is plausible because higher the BMI more will be fat deposit and smaller will be the coronary dimensions. Smaller coronaries would theoretically require a lower atheroma burden to develop stenosis thereby leading to premature CAD. The risk of cardio metabolic multimorbidity increases as BMI increases. Risk severity was observed more in obese and moderately to severe overweight people compared with individuals with a healthy BMI [18]. The same was observed in the present study

Asian populations have higher cardiovascular risk factors than western populations at any given levels of BMI. Zeina et al. (2007), [19] also reported significant correlation between LMCA cross-sectional area of male samples with height, weight, and BSA [20]. This indicates that cardiologists should pay more attention to BMI among male patients, as it seems to have effect on coronary artery morphology and this in turn will have effect on patient outcome. Yet, there are some who believe that BMI alone is not such an important factor for CAM. George et al. (2014), has propounded that environmental factors are the major predictors for obesity and degree or distribution of body fat cannot be represented accurately by BMI [21]. Considering the epidemic of obesity induced diseases it is understandable that there could be multiple factors affecting its occurrence like social, economic, environmental and genetic.

CONCLUSION

The present study concludes that with increase in BMI, there was a relative decrease in coronary artery diameter. The risk of CAD and associated multimorbidity is directly proportional to BMI. Risk severity was observed more in obese (higher high risk) and moderately to severe overweight (increased risk) people compared with individuals with a healthy BMI.

HIGHLIGHTS OF THE STUDY

Present study reports the largest multi-centric data of coronary artery dimensions among Indian population. The study included patients with different clinical presentations from 4 states of south India, thereby having a diverse representation of the south Indian population

LIMITATIONS OF THE STUDY

We could correlate the diet habits, life style, physical activity and age, adjusted data with BMI to coronary artery measurements. This would have enhanced the study results.

CONFLICT OF INTEREST

All authors have none to declare. We declare there is no conflict of interest and no financial supports or grants were received for conduction of the study.

AUTHOR CONTRIBUTIONS

All authors hereby declare that their contribution was equal towards the formation of the manuscript.

ACKNOWLEDGEMENTS

All authors appreciate the great effort of chief cardiac technicians of the cardiac catheterization laboratories, K.S Hegde Medical Academy and Hospital, Karnataka, Madras Medical Mission (MMM), Chennai, India, Pariyaram Medical college, Kannur, Kerala, India, Care Hospital, Banjara Hills, Hyderabad, India for their timely help and assistance in the conduction of this study. All authors appreciate the great effort of Mr. Jessil Jose, FIA, (FELLOW OF INSTITUTE OF ACTUARIES), Head of Reserving

company: Advent Underwriting Limited, United Kingdom for the analysis and verification of the data of this study.

References
  1. Status WP. The use and interpretation of anthropometry. Geneva CH: WHO 1995, technical report 1995: 854.
  2. Obesity-Preventing W. managing the global epidemic. Report of a WHO Consultation on Obesity. Geneva: WHO. 1997: 7-17.
  3. WHO EC. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet (London, England). 2004; 363:157-163.
  4. Stevens J, Cai J, Jones DW. The effect of decision rules on the choice of a body mass index cutoff for obesity: examples from African American and white women. Am J Clin Nutr. 2002; 75: 986-992.
  5. Moulton MJ, Creswell LL, Mackey ME, Cox JL, Rosenbloom M. Obesity is not a risk factor for significant adverse outcomes after cardiac surgery. Circulation. 1996; 94: 87-92.
  6. Koshal A, Hendry P, Raman SV, Keon WJ. Should obese patients not undergo coronary artery surgery? Can J Surg. 1985; 28: 331-334.
  7. Schwann TA, Habib RH, Zacharias A, Parenteau GL, Riordan CJ, Durham SJ, et al. Effects of body size on operative, intermediate and long-term outcome after coronary artery bypass operation. Ann Thorac Surg. 2001; 71: 521-531.
  8. Deurenberg-Yap M, Schmidt G, Staveren van WA, Deurenberg P. The paradox of low body mass index and high body fat percent among Chinese, Malays and Indians in Singapore. Int J Obes. 2000; 24:1011- 1017.
  9. He M, Tan KCB, Li ETS, Kung AWC. Body fat determination by dual energy X-ray absorptiometry and its relation to body mass index and waist circumference in Hong Kong Chinese. Int J Obes. 2001; 25: 748- 752.
  10. von Kodolitsch Y, Franzen O, Lund GK, Koschyk DH, Ito WD, MeinertzT. Coronary artery anomalies Part II: recent insights from clinical investigations. Z Kardiol. 2005; 94: 1-13.
  11. Low S, Chin MC, Ma S, Heng D, Deurenberg-Yap M. Rationale for redefining obesity in Asians. Ann Acad Med Singapore. 2009; 38: 66- 69.
  12. Shekharappa KR, Smilee Johncy   S,   Mallikarjuna   PT,   Vedavathi KJ, Jayarajan MP. Correlation between body mass index andcardiovascular parameters in obese and non-obese in different age groups. Int J Biol Med Res. 2011; 2: 551-555.
  13. Mungreiphy N, Dhall M, Tyagi R, Saluja K, Kumar A, Tungdim MG, et al. Ethnicity, obesity and health pattern among Indian population. J Nat Sc Biol Med. 2012; 3: 52-59.
  14. Chor D, Faerstein E, Kaplan GA, Lynch JW, Lopes CS. Association of weight change with ethnicity and life course socioeconomic position among Brazilian civil servants. Int J Epidemiol. 2004; 33: 100-106.
  15. Leung W-H, Stadius ML, Alderman EL. Determinants of normal coronary artery dimensions in humans. Circulation. 1991; 84: 2294- 2306.
  16. Verim S, Öztürk E, Küçük U, Kara K, Saglam M, Karde?o?lu E. Cross-sectional area   measurement   of   the   coronary   arteries using CT angiography at the level of the bifurcation: is there a relationship? Diagn Interv Radiol. 2015; 21: 454-458.
  17. Hamirani YS, Nasir K, Avanes E, Kadakia J, Budoff MJ. Coronary artery diameter related to calcium scores and coronary risk factors as measured with multidetector computed tomography: a substudy of the ACCURACY trial. Tex Heart Inst J. 2013; 40: 261.
  18. Kivimäki M, Kuosma E, Ferrie JE, Luukkonen R, Nyberg ST, Alfredsson L, et al. Overweight, obesity, and risk of cardiometabolic multimorbidity: pooled analysis of individual-level data for 120 813 adults from 16 cohort studies from the USA and Europe. Lancet Public Health. 2017; 2: 277-285.
  19. Zeina AR, Odeh M, Blinder J, Rosenschein U, Barmeir E. Myocardial bridge: Evaluation on MDCT. Am J Roentgenol. 2007; 188: 1069-1073.
  20. Kurotobi S, Nagai T, Kawakami N, Sano T. Coronary diameter in normal infants, children and patients with Kawasaki disease. Pediatr Int. 2002; 44: 1-4.
  21. Bray GA. Handbook of Obesity--Volume 1: Epidemiology, Etiology, and Physiopathology: CRC Press; 2014

Divia Paul A, Ezhilan J, Subramanyam K, Ramakrishna A, Ashraf SM, et al. Impact of Body Mass Index on Coronary Morphology. J Cardiol Clin Res. 2022; 10(1): 1177.

Received : 21 Jan 2022
Accepted : 08 Feb 2022
Published : 10 Feb 2022
Journals
Annals of Otolaryngology and Rhinology
ISSN : 2379-948X
Launched : 2014
JSM Schizophrenia
Launched : 2016
Journal of Nausea
Launched : 2020
JSM Internal Medicine
Launched : 2016
JSM Hepatitis
Launched : 2016
JSM Oro Facial Surgeries
ISSN : 2578-3211
Launched : 2016
Journal of Human Nutrition and Food Science
ISSN : 2333-6706
Launched : 2013
JSM Regenerative Medicine and Bioengineering
ISSN : 2379-0490
Launched : 2013
JSM Spine
ISSN : 2578-3181
Launched : 2016
Archives of Palliative Care
ISSN : 2573-1165
Launched : 2016
JSM Nutritional Disorders
ISSN : 2578-3203
Launched : 2017
Annals of Neurodegenerative Disorders
ISSN : 2476-2032
Launched : 2016
Journal of Fever
ISSN : 2641-7782
Launched : 2017
JSM Bone Marrow Research
ISSN : 2578-3351
Launched : 2016
JSM Mathematics and Statistics
ISSN : 2578-3173
Launched : 2014
Journal of Autoimmunity and Research
ISSN : 2573-1173
Launched : 2014
JSM Arthritis
ISSN : 2475-9155
Launched : 2016
JSM Head and Neck Cancer-Cases and Reviews
ISSN : 2573-1610
Launched : 2016
JSM General Surgery Cases and Images
ISSN : 2573-1564
Launched : 2016
JSM Anatomy and Physiology
ISSN : 2573-1262
Launched : 2016
JSM Dental Surgery
ISSN : 2573-1548
Launched : 2016
Annals of Emergency Surgery
ISSN : 2573-1017
Launched : 2016
Annals of Mens Health and Wellness
ISSN : 2641-7707
Launched : 2017
Journal of Preventive Medicine and Health Care
ISSN : 2576-0084
Launched : 2018
Journal of Chronic Diseases and Management
ISSN : 2573-1300
Launched : 2016
Annals of Vaccines and Immunization
ISSN : 2378-9379
Launched : 2014
JSM Heart Surgery Cases and Images
ISSN : 2578-3157
Launched : 2016
Annals of Reproductive Medicine and Treatment
ISSN : 2573-1092
Launched : 2016
JSM Brain Science
ISSN : 2573-1289
Launched : 2016
JSM Biomarkers
ISSN : 2578-3815
Launched : 2014
JSM Biology
ISSN : 2475-9392
Launched : 2016
Archives of Stem Cell and Research
ISSN : 2578-3580
Launched : 2014
Annals of Clinical and Medical Microbiology
ISSN : 2578-3629
Launched : 2014
JSM Pediatric Surgery
ISSN : 2578-3149
Launched : 2017
Journal of Memory Disorder and Rehabilitation
ISSN : 2578-319X
Launched : 2016
JSM Tropical Medicine and Research
ISSN : 2578-3165
Launched : 2016
JSM Head and Face Medicine
ISSN : 2578-3793
Launched : 2016
JSM Cardiothoracic Surgery
ISSN : 2573-1297
Launched : 2016
JSM Bone and Joint Diseases
ISSN : 2578-3351
Launched : 2017
JSM Bioavailability and Bioequivalence
ISSN : 2641-7812
Launched : 2017
JSM Atherosclerosis
ISSN : 2573-1270
Launched : 2016
Journal of Genitourinary Disorders
ISSN : 2641-7790
Launched : 2017
Journal of Fractures and Sprains
ISSN : 2578-3831
Launched : 2016
Journal of Autism and Epilepsy
ISSN : 2641-7774
Launched : 2016
Annals of Marine Biology and Research
ISSN : 2573-105X
Launched : 2014
JSM Health Education & Primary Health Care
ISSN : 2578-3777
Launched : 2016
JSM Communication Disorders
ISSN : 2578-3807
Launched : 2016
Annals of Musculoskeletal Disorders
ISSN : 2578-3599
Launched : 2016
Annals of Virology and Research
ISSN : 2573-1122
Launched : 2014
JSM Renal Medicine
ISSN : 2573-1637
Launched : 2016
Journal of Muscle Health
ISSN : 2578-3823
Launched : 2016
JSM Genetics and Genomics
ISSN : 2334-1823
Launched : 2013
JSM Anxiety and Depression
ISSN : 2475-9139
Launched : 2016
Clinical Journal of Heart Diseases
ISSN : 2641-7766
Launched : 2016
Annals of Medicinal Chemistry and Research
ISSN : 2378-9336
Launched : 2014
JSM Pain and Management
ISSN : 2578-3378
Launched : 2016
JSM Women's Health
ISSN : 2578-3696
Launched : 2016
Clinical Research in HIV or AIDS
ISSN : 2374-0094
Launched : 2013
Journal of Endocrinology, Diabetes and Obesity
ISSN : 2333-6692
Launched : 2013
Journal of Substance Abuse and Alcoholism
ISSN : 2373-9363
Launched : 2013
JSM Neurosurgery and Spine
ISSN : 2373-9479
Launched : 2013
Journal of Liver and Clinical Research
ISSN : 2379-0830
Launched : 2014
Journal of Drug Design and Research
ISSN : 2379-089X
Launched : 2014
JSM Clinical Oncology and Research
ISSN : 2373-938X
Launched : 2013
JSM Bioinformatics, Genomics and Proteomics
ISSN : 2576-1102
Launched : 2014
JSM Chemistry
ISSN : 2334-1831
Launched : 2013
Journal of Trauma and Care
ISSN : 2573-1246
Launched : 2014
JSM Surgical Oncology and Research
ISSN : 2578-3688
Launched : 2016
Annals of Food Processing and Preservation
ISSN : 2573-1033
Launched : 2016
Journal of Radiology and Radiation Therapy
ISSN : 2333-7095
Launched : 2013
JSM Physical Medicine and Rehabilitation
ISSN : 2578-3572
Launched : 2016
Annals of Clinical Pathology
ISSN : 2373-9282
Launched : 2013
Annals of Cardiovascular Diseases
ISSN : 2641-7731
Launched : 2016
Journal of Behavior
ISSN : 2576-0076
Launched : 2016
Annals of Clinical and Experimental Metabolism
ISSN : 2572-2492
Launched : 2016
Clinical Research in Infectious Diseases
ISSN : 2379-0636
Launched : 2013
JSM Microbiology
ISSN : 2333-6455
Launched : 2013
Journal of Urology and Research
ISSN : 2379-951X
Launched : 2014
Journal of Family Medicine and Community Health
ISSN : 2379-0547
Launched : 2013
Annals of Pregnancy and Care
ISSN : 2578-336X
Launched : 2017
JSM Cell and Developmental Biology
ISSN : 2379-061X
Launched : 2013
Annals of Aquaculture and Research
ISSN : 2379-0881
Launched : 2014
Clinical Research in Pulmonology
ISSN : 2333-6625
Launched : 2013
Journal of Immunology and Clinical Research
ISSN : 2333-6714
Launched : 2013
Annals of Forensic Research and Analysis
ISSN : 2378-9476
Launched : 2014
JSM Biochemistry and Molecular Biology
ISSN : 2333-7109
Launched : 2013
Annals of Breast Cancer Research
ISSN : 2641-7685
Launched : 2016
Annals of Gerontology and Geriatric Research
ISSN : 2378-9409
Launched : 2014
Journal of Sleep Medicine and Disorders
ISSN : 2379-0822
Launched : 2014
JSM Burns and Trauma
ISSN : 2475-9406
Launched : 2016
Chemical Engineering and Process Techniques
ISSN : 2333-6633
Launched : 2013
Annals of Clinical Cytology and Pathology
ISSN : 2475-9430
Launched : 2014
JSM Allergy and Asthma
ISSN : 2573-1254
Launched : 2016
Journal of Neurological Disorders and Stroke
ISSN : 2334-2307
Launched : 2013
Annals of Sports Medicine and Research
ISSN : 2379-0571
Launched : 2014
JSM Sexual Medicine
ISSN : 2578-3718
Launched : 2016
Annals of Vascular Medicine and Research
ISSN : 2378-9344
Launched : 2014
JSM Biotechnology and Biomedical Engineering
ISSN : 2333-7117
Launched : 2013
Journal of Hematology and Transfusion
ISSN : 2333-6684
Launched : 2013
JSM Environmental Science and Ecology
ISSN : 2333-7141
Launched : 2013
JSM Nanotechnology and Nanomedicine
ISSN : 2334-1815
Launched : 2013
Journal of Ear, Nose and Throat Disorders
ISSN : 2475-9473
Launched : 2016
JSM Ophthalmology
ISSN : 2333-6447
Launched : 2013
Journal of Pharmacology and Clinical Toxicology
ISSN : 2333-7079
Launched : 2013
Annals of Psychiatry and Mental Health
ISSN : 2374-0124
Launched : 2013
Medical Journal of Obstetrics and Gynecology
ISSN : 2333-6439
Launched : 2013
Annals of Pediatrics and Child Health
ISSN : 2373-9312
Launched : 2013
JSM Clinical Pharmaceutics
ISSN : 2379-9498
Launched : 2014
JSM Foot and Ankle
ISSN : 2475-9112
Launched : 2016
JSM Alzheimer's Disease and Related Dementia
ISSN : 2378-9565
Launched : 2014
Journal of Addiction Medicine and Therapy
ISSN : 2333-665X
Launched : 2013
Journal of Veterinary Medicine and Research
ISSN : 2378-931X
Launched : 2013
Annals of Public Health and Research
ISSN : 2378-9328
Launched : 2014
Annals of Orthopedics and Rheumatology
ISSN : 2373-9290
Launched : 2013
Journal of Clinical Nephrology and Research
ISSN : 2379-0652
Launched : 2014
Annals of Community Medicine and Practice
ISSN : 2475-9465
Launched : 2014
Annals of Biometrics and Biostatistics
ISSN : 2374-0116
Launched : 2013
JSM Clinical Case Reports
ISSN : 2373-9819
Launched : 2013
Journal of Cancer Biology and Research
ISSN : 2373-9436
Launched : 2013
Journal of Surgery and Transplantation Science
ISSN : 2379-0911
Launched : 2013
Journal of Dermatology and Clinical Research
ISSN : 2373-9371
Launched : 2013
JSM Gastroenterology and Hepatology
ISSN : 2373-9487
Launched : 2013
Annals of Nursing and Practice
ISSN : 2379-9501
Launched : 2014
JSM Dentistry
ISSN : 2333-7133
Launched : 2013
Author Information X