Journal of Medicine and Applied Clinical Sciences | Volume 2 Issue 2 | Pages: 1-7 | Doi : 10.67914/jmacs/rsa/2.2/1-7

Research Article

OPEN ACCESS | Published on : 25-Oct-2026

Cross-sectional study of the association between abdominal volume index and some anthropometric and cardiometabolic variables in perimenopausal Nigerian women

Folakemi Priscilla Olawepo
• Department of Anatomy, Faculty of Basic Medical Sciences, College of Health Sciences, University of Ilorin, Ilorin, Nigeria.

Sekinat Titilayo Abdulhameed
• Department of Anatomy, Faculty of Basic Medical Sciences, College of Health Sciences, University of Ilorin, Ilorin, Nigeria.


• Oluwole Busayo Akinola
• Department of Anatomy, Faculty of Basic Medical Sciences, College of Health Sciences, University of Ilorin, Ilorin, Nigeria.

Abstract

Background: The abdominal volume index (AVI) is a marker of visceral obesity and is known to correlate with glucose intolerance and type 2 diabetes mellitus. In a population of 46 perimenopausal Nigerian women (45-55 years) resident in a suburb of the Ilorin metropolis, we studied the association between AVI and related anthropometric measures of central and general obesity; as well as how AVI correlates with some cardiovascular risk factors.

Methods: In this population, we measured standing height, body weight, waist circumference (WC), and hip circumference (HC) using standard protocols. From these, the AVI, body mass index (BMI), waist to hip ratio (WHR), and waist to height ratio (WHtR) were calculated. Further, we measured blood pressure, blood glucose and fasting serum lipids.

Results: Using Pearson’s correlation coefficient, AVI shows the strongest and significant (p<0.05) association with WC (r=0.99), followed by WHtR (r=0.94), HC (r=0.88), and BMI (r=0.73). Moreover, AVI has positive though weak association with blood pressure, blood glucose and serum lipids, except HDL-c, where the association is negative (r=-0.04).

Conclusion: In the perimenopausal Nigerian women, AVI has the strongest correlation with WC, and both anthropometric indices are valuable predictors of metabolic and cardiovascular risks. Similar future studies that enlist larger numbers of perimenopausal women are however recommended. Such studies are especially helpful in sub-Saharan Africa, where, due to resource limitation, large percentages of individuals are unable to afford regular cardiometabolic assessments at the health centres.

Keywords

perimenopausal, abdominal volume index, metabolic syndrome, anthropometry, Nigerian women

References

• Banack, H. R., Bea, J. W., Chen, Z., Blew, R. M., Nicholas, S., Stefanick, M., Wild, R. A., Manson, J. A. E., & Odegaard, A. O. (2023). Longitudinal patterns of abdominal visceral and subcutaneous adipose tissue, total body composition, and anthropometric measures in postmenopausal women: Results from the Women’s Health Initiative. International Journal of Obesity, 47(4), 288–296. https://doi.org/10.1038/s41366-023-01266-9

  Chen, G.-C., Arthur, R., Kamensky, V., Chai, J. C., Yu, B., Shadyab, A. H., Allison, M., Sun, Y., Saquib, N., Wild, R. A., Bao, W., Dannenberg, A. J., Rohan, T. E., Kaplan, R. C., Wassertheil-Smoller, S., & Qi, Q. (2022). Body fat distribution, cardiometabolic traits, and risk of major lower-extremity arterial disease in postmenopausal women. Diabetes Care, 45(1), 222–231. https://doi.org/10.2337/dc21-1565

  Choi, S. (2011). Anthropometric measures and lipid coronary heart disease risk factors in Korean immigrants with type 2 diabetes. Journal of Cardiovascular Nursing, 26(5), 414–422. https://doi.org/10.1097/JCN.0b013e3182017c1f

  Ehrampoush, E., Arasteh, P., Homayounfar, R., Cheraghpour, M., Alipour, M., Naghizadeh, M. M., Hadibarhaghtalab, M., Davoodi, S. H., Askari, A., & Razaz, J. M. (2017). New anthropometric indices or old ones: Which is the better predictor of body fat? Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 11(4), 257–263. https://doi.org/10.1016/j.dsx.2016.08.027

  Goedecke, J. H., Kufe, C. N., Masemola, M., Lichaba, M., Seipone, I. D., Mendham, A. E., Gibson, H., Hawley, J. M., Selva, D. M., Magodoro, I. M., Kengne, A. P., Chikowore, T., Crowther, N. J., Norris, S. A., Karpe, F., Olsson, T., Storbeck, K.-H., & Micklesfield, L. K. (2025). SHBG, testosterone, and type 2 diabetes risk in middle-aged African women: Exploring the effect of HIV and menopause. The Journal of Clinical Endocrinology & Metabolism, 110(12), 3380–3390. https://doi.org/10.1210/clinem/dgaf256

  Hammer, M. D., Andersen, A. J., Larsen, S. C., Simonsen, M. K., & Heitmann, B. L. (2022). The association between general and central obesity and the risks of coronary heart disease in women with and without a familial predisposition to obesity: Findings from the Danish Nurse Cohort. International Journal of Obesity, 46, 433–436. https://doi.org/10.1038/s41366-021-00990-4

  Kabat, G. C., Heo, M., van Horn, L. V., Kazlauskaite, R., Getaneh, A., Ard, J., Vitolins, M. Z., Waring, M. E., Zaslavsky, O., Wassertheil-Smoller, S., et al. (2014). Longitudinal association of anthropometric measures of adiposity with cardiometabolic risk factors in postmenopausal women. Annals of Epidemiology, 24(12), 896–902. https://doi.org/10.1016/j.annepidem.2014.10.007

  Li, Q., Wang, P., Li, G., Chang, Y., Guo, X., Sun, Y., & Zhang, X. (2022). Using the changes of several simple anthropometric indices to predict the occurrence of metabolic syndrome: Findings from medically under-resourced communities in rural China. Frontiers in Endocrinology, 13, 1014541. https://doi.org/10.3389/fendo.2022.1014541

  Marlatt, K. L., Pitynski-Miller, D. R., Gavin, K. M., et al. (2022). Body composition and cardiometabolic health across the menopause transition. Obesity, 30, 14–27. https://doi.org/10.1002/oby.23289

  Mina, T., Xie, W., Low, D. Y. W., Wang, X. Y., Lam, B. C. C., Sadhu, N., Ng, H. K., Aziz, N.-A., Tong, T. Y. Y., Kerk, S. K., Choo, W. L., Low, G. L., Ibrahim, H., Lim, L., Tai, E. S., Wansaicheong, G., Dalan, R., Yew, Y. W., Elliott, P., Riboli, E., Loh, M. C. S., Ngeow, J. Y. Y., Lee, E. S., Lee, J. C. K., Best, J., & Chambers, J. C. (2024). Adiposity and metabolic health in Asian populations: An epidemiological study using dual-energy x-ray absorptiometry in Singapore. The Lancet Diabetes & Endocrinology, 12(10), 704–715. https://doi.org/10.1016/S2213-8587(24)00195-5

  Moreira, M. A., da Câmara, S. M. A., Fernandes, S. G. G., Azevedo, I. G., & Maciel, Á. C. C. (2022). Metabolic syndrome in middle-aged and older women: A cross-sectional study. Women's Health, 18, 17455065211070673. https://doi.org/10.1177/17455065211070673

  Nagayama, D., Watanabe, Y., Yamaguchi, T., Suzuki, K., Saiki, A., Fujishiro, K., & Shirai, K. (2022). Issue of waist circumference for the diagnosis of metabolic syndrome regarding arterial stiffness: Possible utility of a body shape index in middle-aged nonobese Japanese urban residents receiving health screening. Obesity Facts, 15(2), 160–169. https://doi.org/10.1159/000520418

  Neufeld, L. M., Jones-Smith, J. C., Garcia, R., & Fernald, L. C. (2008). Anthropometric predictors for the risk of chronic disease in non-diabetic, non-hypertensive young Mexican women. Public Health Nutrition, 11(2), 159–167.

  Sadeghi, E., Khodadadiyan, A., Hosseini, S. A., et al. (2024). Novel anthropometric indices for predicting type 2 diabetes mellitus. BMC Public Health, 24, 1033. https://doi.org/10.1186/s12889-024-18541-7

  Sekgala, M. D., Sewpaul, R., Kengne, A. P., Mchiza, Z., & Peer, N. (2024). Clinical utility of novel anthropometric indices in identifying type 2 diabetes mellitus among South African adult females. BMC Public Health, 24, 2676. https://doi.org/10.1186/s12889-024-20168-7

  Singh, Z., Verma, V., & Yadav, N. (2024). Body indices based receiver operating characteristics curve models are important risk assessing tools for metabolic diseases among Asian women. Human Nutrition & Metabolism, 35, 200243. https://doi.org/10.1016/j.hnm.2024.200243

  Sirisena, A., Okeahialam, B., & Ogah, O. S. (2022). Association of obesity anthropometric indices with hypertension, diabetes mellitus and hypertriglyceridemia in apparently healthy adult Nigerian population. World Journal of Cardiology, 14(6), 363–371. https://doi.org/10.4330/wjc.v14.i6.363

  Wang, H., Liu, A., Zhao, T., Gong, X., Pang, T., Zhou, Y., Xiao, Y., Yan, Y., Fan, C., Teng, W., et al. (2017). Comparison of anthropometric indices for predicting the risk of metabolic syndrome and its components in Chinese adults: A prospective, longitudinal study. BMJ Open, 7(9), e016062.

  Wang, X., Wang, L., & Xiang, W. (2023). Mechanisms of ovarian aging in women: A review. Journal of Ovarian Research, 16, 67. https://doi.org/10.1186/s13048-023-01151-z

  Yamada, Y., Murakami, H., Kawakami, R., Gando, Y., Nanri, H., Nakagata, T., et al. (2022). Association between skeletal muscle mass or percent body fat and metabolic syndrome development in Japanese women: A 7-year prospective study. PLOS ONE, 17(10), e0263213. https://doi.org/10.1371/journal.pone.0263213

  Zhou, P., Liu, W., Sun, K., Zhao, Z., Zhu, W., Zhang, J., & Wang, W. (2025). Comparison of the predictive value of 17 anthropometric indices for the prevalence of metabolic syndrome among Chinese residents: A cross-sectional study. Diabetology & Metabolic Syndrome, 17, 118. https://doi.org/10.1186/s13098-025-01679-8