Cardiometabolic risk assessment in Eastern Slovak young adults using anthropometric indicators

Authors

DOI:

https://doi.org/10.18778/1898-6773.86.4.07

Keywords:

Anthropometry, Cardiometabolic complications, Asymptomatic individual, Primary prevention, Young adulthood

Abstract

Introduction: Selected anthropometric indicators, such as anthropometric measurements, indices, or ratios could be reliable predictors of future cardiometabolic risk in primary prevention, especially in young adults.

Aim: This study aimed to establish cardiometabolic risk status in young Eastern Slovak adults according to anthropometric indicators.

Material and methods: Indicators used in this study, such as heart rate, blood pressure, five anthropometric measurements, as well as a total of 23 anthropometric indices and ratios were selected based on the available literature. These indicators were analyzed in 162 young adult participants of both sexes with a mean age of 20.78±2.22 years. The analyzed indices and ratios were calculated by routine anthropometry and were correlated with blood pressure and heart rate in the whole research group as well as among subgroups divided according to sex, obesity and hypertension status.

Results: Our results showed frequently higher values of input characteristics in males (71.88%), and statistically significant differences between sexes in 81.25% of the characteristics. The values of systolic blood pressure were above the norm in all males, and they also dominated in the obesity group. Correlation analyses conducted on all participants and in subgroups indicated a positive statistical significance in several indicators. The vast majority of the anthropometric indicators were significantly correlated with physiological indicators in almost all subgroups. Only A body shape index (ABSI) correlation coefficients did not show a significant correlation with physiological indicators in all analyzed subgroups. The correlations tended to be stronger among subgroup exhibiting potential to obesity. All analyzed indices and ratios were significantly correlated (p ≤ 0.05), predominantly with blood pressure components rather than heart rate, especially in participants with the potential for disease complications than in participants without them.

Conclusion: The analyzed indicators are noninvasive and useful although they may be at different levels of association and clinical significance for various conditions. Thus some of the indicators may be standardly used in the early diagnostic process for monitoring cardiovascular health and risk stratification of patients.

Downloads

Download data is not yet available.

References

Abolnezhadian F, Hosseini SA, Alipour M, Zakerkish M, Cheraghian B, Ghandil P, et al. 2020. Association Metabolic Obesity Phenotypes with Cardiometabolic Index, Atherogenic Index of Plasma and Novel Anthropometric Indices: A Link of FTO-rs9939609 Polymorphism. Vasc Health Risk Manag. 16:249–256. https://doi.org/10.2147/VHRM.S251927
View in Google Scholar

Amirabdollahian F, Haghighatdoost F. 2018 Anthropometric Indicators of Adiposity Related to Body Weight and Body Shape as Cardiometabolic Risk Predictors in British Young Adults: Superiority of Waistto-Height Ratio. J Obes 2018:8370304. https://doi.org/10.1155/2018/8370304
View in Google Scholar

Antonini-Canterin F, Di Nora C, Poli S, Sparacino L, Cosei I, Ravasel A, et al. 2018. Obesity, cardiac remodeling, and metabolic profile: Validation of a new simple index beyond body mass index. J Cardiovasc Echography 28:18–25. https://doi.org/10.4103/jcecho.jcecho_63_17
View in Google Scholar

Ashwell M, Gunn P, Gibson S. 2012. Waistto-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev 13:275–286. https://doi.org/10.1111/j.1467-789X.2011.00952.x
View in Google Scholar

Barden AE, Huang R-Ch, Beilin LJ, Rauschert S, Tsai I-J, Oddy WH, et al. 2022. Identifying young adults at high risk of cardiometabolic disease using cluster analysis and the Framingham 30-yr risk score. Nutr Metab Cardiovasc Dis 32(2):429–35. https://doi.org/10.1016/j.numecd.2021.10.006
View in Google Scholar

Bergman RN, Stefanovski D, Buchanan TA, Sumner AE, Reynolds JC, Sebring NG, et al. 2011. A better index of body adiposity. Obesity (Silver Spring) 19(5):1083–9. https://doi.org/10.1038/oby.2011.38
View in Google Scholar

Brugada J, Katritsis DG, Arbelo E, Arribas F, Bax JJ, Blomström-Lundqvist C, et al. 2020. 2019 ESC Guidelines for the management of patients with supraventricular tachycardia. The Task Force for the management of patients with supraventricular tachycardia of the European Society of Cardiology (ESC). Eur Heart J 41(5):655–720. https://doi.org/10.1093/eurheartj/ehz467
View in Google Scholar

Casadei K, Kiel J. 2022. Anthropometric Measurement. [e-book]. Treasure Island (FL): StatPearls Publishing. Available through: https://www.ncbi.nlm.nih.gov/books/NBK537315/
View in Google Scholar

Cohen J. 1988. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Routledge.
View in Google Scholar

Corbatón-Anchuelo A, Krakauer JC, Serrano- García I, Krakauer NY, Martínez-Larrad MT, Serrano-Ríos M. 2021. A Body Shape Index (ABSI) and Hip Index (HI) Adjust Waist and Hip Circumferences for Body Mass Index, But Only ABSI Predicts High Cardiovascular Risk in the Spanish Caucasian Population. Metab Syndr Relat Disord 19(6):352–357. https://doi.org/10.1089/met.2020.0129
View in Google Scholar

Dominguez LJ, Sayón-Orea C, Gea A, Toledo E, Barbagallo M, Martínez-González MA. 2021. Increased Adiposity Appraised with CUN-BAE Is Highly Predictive of Incident Hypertension. The SUN Project. Nutrients 13(10):3309. https://doi.org/10.3390/nu13103309
View in Google Scholar

Egan BM, Stevens-Fabry S. 2015. Prehypertensio–prevalence, health risks, and management strategies. Nat Rev Cardiol. 12(5):289–300. https://doi.org/10.1038/nrcardio.2015.17
View in Google Scholar

Falhammar H, Filipsson Nyström H, Wedell A, Thorén M. 2011. Cardiovascular risk, metabolic profile, and body composition in adult males with congenital adrenal hyperplasia due to 21-hydroxylase deficiency. Eur J Endocrinol 164(2):285–93. https://doi.org/10.1530/EJE-10-0877
View in Google Scholar

Fu S, Luo L, Ye P, Liu Y, Zhu B, Bai Y, et al. 2014. The abilities of new anthropometric indices in identifying cardiometabolic abnormalities, and influence of residence area and lifestyle on these anthropometric indices in a Chinese community-dwelling population. Clin Interv Aging. 9:179–189. https://doi.org/10.2147/CIA.S54240
View in Google Scholar

Gómez-Ambrosi J, Silva C, Catalán V, Rodríguez A, Galofré JC, Escalada J, et al. 2012. Clinical usefulness of a new equation for estimating body fat. Diabetes Care. 35(2):383–388. https://doi.org/10.2337/dc11-1334
View in Google Scholar

Gutema BT, Chuka A, Ayele G, Megersa ND, Bekele M, Baharu A, et al. 2020. Predictive capacity of obesity indices for high blood pressure among southern Ethiopian adult population: a WHO STEPS survey. BMC Cardiovasc Disord 20(1):421. https://doi.org/10.1186/s12872-020-01686-9
View in Google Scholar

Hingorjo MR, Qureshi MA, Mehdi A. 2012. Neck circumference as a useful marker of obesity: a comparison with body mass index and waist circumference. J Pak Med Assoc 62(1):36–40.
View in Google Scholar

Chaudhary S, Alam M, Singh S, Deuja S, Karmacharya P, Mondal M. 2019. Correlation of Blood Pressure with Body Mass Index, Waist Circumference and Waist by Hip Ratio. J Nepal Health Res Counc 16(41):410–413.
View in Google Scholar

Christakoudi S, Riboli E, Evangelou E, Tsilidis KK. 2022. Associations of body shape index (ABSI) and hip index with liver, metabolic, and inflammatory biomarkers in the UK Biobank cohort. Sci Rep. 2022;12(1):8812. https://doi.org/10.1038/s41598-022-12284-4
View in Google Scholar

Jelena J, Baltic ZM, Milica Z, Ivanovic J, Boskovic M, Popovic M, et al. 2016. Relationship between Body Mass Index and Body Fat Percentage among Adolescents from Serbian Republic. J child Obes 1:10. https://doi.org/10.21767/2572-5394.100010
View in Google Scholar

Kang NL. 2021. Association Between Obesity and Blood Pressure in Common Korean People. Vasc Health Risk Manag 17:371–377. https://doi.org/10.2147/VHRM.S316108
View in Google Scholar

Lahole S, Rawekar R, Kumar S, Acharya S, Wanjari A, Gaidhane S, et al. 2022. Anthropometric indices and its association with hypertension among young medical students: A 2 year cross-sectional study. J Family Med Prim Care11(1):281– 286. https://doi.org/10.4103/jfmpc.jfmpc_1231_21
View in Google Scholar

Mangalavalli SM, Kaliyaperumal SS, Deepika V, Teli SS, Soundariya K. 2021. Association of neck circumference with prehypertension and obesity in young paramedical students. Biomedicine 41(1):99–103. https://doi.org/10.51248/.v41i1.542
View in Google Scholar

Minetto MA, Pietrobelli A, Busso C, Bennett JP, Ferraris A, Shepherd JA, et al. 2022. Digital Anthropometry for Body Circumference Measurements: European Phenotypic Variations throughout the Decades. J Pers Med 12(6):906. https://doi.org/10.3390/jpm12060906
View in Google Scholar

Mladenova S. 2019. Prevalence of anthropometric and cardiovascular risk factors among Bulgarian university students. Journal of the Anthropological Society of Serbia Niš. 54 (1-2):1–13. https://doi.org/10.5937/gads54-20049
View in Google Scholar

Nişancı Kılınç F, Çakır B, Eşer Durmaz S, Özenir Çiler, Ekici EM. 2019. Evaluation of obesity in university students with neck circumference and determination of emotional appetite. Progr Nutr. 21(2):339–46. https://doi.org/10.23751/pn.v21i2.7094
View in Google Scholar

Padilla CJ, Ferreyro FA, Arnold WD. 2021. Anthropometry as a readily accessible health assessment of older adults. Exp Gerontol 153:111464. https://doi.org/10.1016/j.exger.2021.111464
View in Google Scholar

Peterson CM, Su H, Thomas DM, Heo M, Golnabi AH, Pietrobelli A, et al. 2017. Tri-Ponderal Mass Index vs Body Mass Index in Estimating Body Fat During Adolescence. JAMA Pediatr. 171(7):629–636. https://doi.org/10.1001/jamapediatrics.2017.0460
View in Google Scholar

Pina A, Castelletti S. 2021. COVID-19 and Cardiovascular Disease: a Global Perspective. Curr Cardiol Rep 23(10):135. https://doi.org/10.1007/s11886-021-01566-4
View in Google Scholar

Piqueras P, Ballester A, Durá-Gil JV, Martinez- Hervas S, Redón J, Real JT. 2021. Anthropometric Indicators as a Tool for Diagnosis of Obesity and Other Health Risk Factors: A Literature Review. Front Psychol 12:631179. https://doi.org/10.3389/fpsyg.2021.631179
View in Google Scholar

Roriz AKC, Passos LCS, Oliveira CCD, Eickemberg M, Moreira PDA, Ramos, LB. 2016. Anthropometric clinical indicators in the assessment of visceral obesity: An update. Nutr. clín. diet. hosp 36(2):168–179. https://doi.org/10.12873/362carneirororiz
View in Google Scholar

Stewart A, Marfell-Jones M, Olds T, De Ridder H. 2011. International Society for Advancement of Kinanthropometry International standards for anthropometric assessment. 3rd ed. Lower Hutt, New Zealand: International Society for the Advancement of Kinanthropometry.
View in Google Scholar

Tanrikulu MA, Agirbasli M, Berenson G. 2017. Primordial Prevention of Cardiometabolic Risk in Childhood. Adv Exp Med Biol. 956:489–496. https://doi.org/10.1007/5584_2016_172
View in Google Scholar

Tran NTT, Blizzard CL, Luong KN, Truong NLV, Tran BQ, Otahal P, et al. 2018. The importance of waist circumference and body mass index in cross-sectional relationships with risk of cardiovascular disease in Vietnam. PLoS One 13(5):e0198202. https://doi.org/10.1371/journal.pone.0198202
View in Google Scholar

Van Haute M, Rondilla E 2nd, Vitug JL, Batin KD, Abrugar RE, Quitoriano F, et al. 2020. Assessment of a proposed BMI formula in predicting body fat percentage among Filipino young adults. Sci Rep 10(1):21988. https://doi.org/10.1038/s41598-020-79041-3
View in Google Scholar

World Health Organization. 2000. Obesity: Preventing and Man-aging the Global Epidemic. WHO Obesity Technical Report Series 894. Geneva, Switzerland: World Health Organization.
View in Google Scholar

World Health Organization. 2008. Waist circumference and waist-hip ratio. Report of a WHO Expert Consultation Geneva.
View in Google Scholar

Wu Y, Li H, Tao X, Fan Y, Gao Q, Yang J. 2021. Optimised anthropometric indices as predictive screening tools for metabolic syndrome in adults: a cross-sectional study. BMJ Open 11(1):e043952. https://doi.org/10.1136/bmjopen-2020-043952
View in Google Scholar

Downloads

Published

2023-12-27 — Updated on 2024-02-12

Versions

How to Cite

Zigová, M., Petrejčíková, E., Mydlárová Blaščáková, M., Gaľová, J., Hedviga Vašková, Kalafutová, S., & Šlebodová, M. (2024). Cardiometabolic risk assessment in Eastern Slovak young adults using anthropometric indicators. Anthropological Review, 86(4), 81–97. https://doi.org/10.18778/1898-6773.86.4.07 (Original work published December 27, 2023)

Issue

Section

Articles

Similar Articles

<< < 8 9 10 11 12 13 14 15 16 17 > >> 

You may also start an advanced similarity search for this article.