Path Analysis of the Effect of Waist-Pelvic Circumference, Body Mass Index, and Abdominal Circumference on the Occurrence of Prediabetes
Abstract
Background: Diabetes mellitus is an important health problem in the world. Pre-diabetes is a state of blood sugar levels above normal but below the criteria for diabetes. American Diabetes Association (ADA) uses criteria for hemoglobin A1C (HbA1C) levels of 5.7% to 6.4% to define pre-diabetes. The prevalence of pre-diabetes was the highest in overweight individuals. In many studies, body fat levels were assessed by indicators of waist-pelvic circumference, abdominal circumference, and BMI. Among the three, it is still a debate which is more influential on the condition of pre-diabetes. The purpose of this study was to determine the effects of waist-pelvic circumference, BMI, and abdominal circumference in pre-diabetes.
Subjects and Method: A cross-sectional study was conducted at Prodia Clinic, Surakarta, Central Java, from January to March 2019. A sample of 200 study subjects was selected by fixed disease sampling. The dependent variable was pre-diabetes. The independent variables were the waist-pelvic circumference, BMI, and abdominal circumference. The data on HbA1C was measured by NGSP standardized ion-exchange HPLC method. The data were analyzed by path analysis.
Results: Abdominal circumference >90 cm in men and >80 cm in women (b= 0.87; 95% CI= 0.23 to 1.51; p= 0.008) and age ≥45 years old (b = 1.70; 95% CI = 0.93 to 2.46; p <0.001) were directly increased pre-diabetes. Pre-diabetes was indirectly affected by waist–pelvic circumference, gender, and obesity.
Conclusions: Abdominal circumference >90 cm in men and >80 cm in women and age ≥45 years old are directly increased pre-diabetes. Pre-diabetes is indirectly affected by waist–pelvic circumference, gender, and obesity.
Keywords: prediabetes, abdominal circumference, waist-pelvic circumference, body mass index
Correspondence: Cindy Lestyani Loekito. Masters Program in Nutrition, Universitas Sebelas Maret. Jl. Ir. Sutami 36A, Surakarta 57126, Central Java. Email: cindy.l.l@student.uns.ac.id. Mobile:082134424950.
Indonesian Journal of Medicine (2019), 4(3): 252-258
https://doi.org/10.26911/theijmed.2019.04.03.08
References
Abtahi F, Naghshzan A, Zibaeenezhad M, Heydari S, Khosropanah S, Zamirian M, Aghasadeghi K, Moaref A (2010). The relationship between body mass index and pre-diabetes in teachers residing in Shiraz-Iran 2009. Global journal of health science 8(4). https://doi.org/10.5539/gjhs.v8n4p95
Åkesson K, Hanberger L, Samuelsson U (2015). The influence of age, gender, insulin dose, IMT, and blood pressure on metabolic control in young patients with type 1 diabetes. Pediatr. Diabetes 16: 581-586. https://doi.org/10.1111/pedi.12219.
American Diabetes Association (2019). Classification and diagnosis of diabetes: Standards of Medical Care in Diabêtes 2019. Diabetes Care. https://doi.org.10.2337/dc19-S002
Beydoun MA, Wang Y (2009). Gender–ethnic disparity in IMT and waist circumference distribution shifts in US adults. Obes. Silver Spring Md 17: 169-176. https://doi.org/10.1038/oby.2008.492
Chen F, Guo Z, Wu M, Zhou Z, Luo W (2015). Impact of dynamic changes of waist circumference and body mass index on type 2 diabetes mellitus risk. Zhonghua Yu Fang Yi Xue Za Zhi 49: 1092-1097. https://doi.org/10.3760/cma.j.issn.02539624.2015.12.014
Compeán-Ortiz LG, Trujillo-Olivera LE, Valles-Medina AM, Reséndiz-González E, García-Solano B, Pérez BDA (2018). Obesity, physical activity and pre-diabetes in adult children of people with diabetes. Rev. Lat. Am. Enfermagem25. https://doi.org/10.1590/15188345.b2102.2981
Czernichow S, Kengne AP, Huxley RR, Batty GD, de Galan B, Grobbee D, Pillai A, Zoungas S, Marre M, Wood ward M, Neal B, Chalmers J (2011). Comparison of waist-to-hip ratio and other obesity indices as predictors of cardiovascular disease risk in people with type 2 diabetes: a prospective cohort study from ADVANCE. Eur. J. Cardiovasc. Prev. Rehabil. Off. J. Eur. Soc. Cardiol. Work. Groups Epidemiol. Prev. Card. Rehabil. Exerc. Physiol. 18: 312-319. htts://doi.org/10.1097/HJR.0b013e32833c1aa3
Díaz-Redondo A, Giráldez-García C, Carrillo L, Serrano R, García-Soidán FJ, Artola S, Franch J, Díez J, Ezkurra P, Millaruelo JM, Seguí M, Sangrós J, Martínez-Candela J, Muñoz P, Goday A, Regidor E (2015). Modifiable risk factors associated with pre-diabetes
in men and women: a cross-sectional analysis of the cohort study in primary health care on the evolution of patients with pre-diabetes (PREDAPS Study). BMC Fam. Pract. 16. https://doi.org/10.1186/s1287501402163
Han C, Liu Y, Sun X, Luo X, Zhang L, Wang B, Ren Y, Zhou J, Zhao Y, Zhang D, Liu X, Zhang M, Hu D (2017). Prediction of a new body shape index and body adiposity estimator for development of type 2 diabetes mellitus: The Rural Chinese Cohort Study. Br. J. Nutr. 118: 771–776.https://doi.org/ 10.1017/S0007114517002859
Kashiwagi R, Iwahashi H, Yamada Y, Sakaue T, Okita T, Kawachi Y, Iwamoto R, Saisho K, Tamba S, Yamamoto K, Watanabe T, Fujimoto T, Matsuzawa Y (2017). Effective waist circumference reduction rate necessary to avoid the development of type 2 diabetes in Japanese men with abdominal obesity. Endocr. J. 64: 881-894. https://doi.org/ 10.1507/endocrj.EJ17-0113
Luo W, Guo Z, Hu X, Zhou Z, Wu M, Zhang L, Liu J (2013). 2 years change of waist circumference and body mass index and associations with type 2 diabetes mellitus in cohort populations. Obes. Res. Clin. Pract. 7: e290-296. https://doi.org/10.1016/j.orcp.2012.02.005
Mansour AA, Al-Jazairi MI (2007).Cutoff values for anthropometric variables that confer increased risk of type 2 diabetes mellitus and hyper tension in Iraq. Arch. Med.Res.38: 253–258. https://doi.org/10.1016/j.arcmed.2006.09.014
Pandey U, Midha T, Rao YK, Katiyar P, Wal P, Kaur S, Martolia DS (2017). Anthropometric indicators as predictor of pre-diabetes in Indian adolescents. Indian Heart J. 69: 474-479. https://doi.org/10.1016/j.ihj.2017.06.006
Pani LN, Korenda L, Meigs JB, Driver C, Chamany S, Fox CS, Sullivan L, D, Agostino RB, Nathan DM (2008) Effect of aging on A1C levels in individuals without diabetes: evidence from the Framingham Off spring Study and the National Health and Nutrition Examination Survey 2001-2004. Diabetes Care. 31: 1991-1996. https://doi.org/10.2337/dc080577
Soewondo P, Pramono LA (2011). Prevalence, characteristics, and predictors of pre-diabetes in Indonesia. Med. J. Indonesia. 20: 283-294. https://doi.org/10.13181/mji.v20i4.465
Tabák AG, Herder C, Rathmann W, Brunner EJ, Kivimäki M (2012). Pre-diabetes: a highrisk state for diabetes development. The Lancet 379: 2279-2290. https://doi.org/10.1016/S01406736(12)602839
Vijayakumar P, Nelson RG, Hanson RL, Knowler WC, Sinha M (2017). HbA1C and the Prediction of Type 2 Diabetes in Children and Adults. Diabetes Care 40: 16-21.https://doi.org/10.2337/dc16-13-58.
Wahyuni N, Murbawani EA (2016). Hubungan lingkar pinggang dan rasio lingkar pinggang panggul dengan kadar serum high sensitivitycreactive protein pada remaja yang mengalami obesitas. https://doi.org/10.14710/jnc.v5i4.1644.
Wang D, Xing XH, Wu XB (2013). Healthy lifestyles of university students in China and Influential Factors. Sci. World J. https://doi.org/10.1155/2013/412950
Wang F, Chen Y, Chang Y, Sun G, Sun Y (2018). New anthropometric indices or old ones: which perform better in estimating cardiovascular risks in Chinese adults. BMC Cardiovasc. Disord. 18. https://doi.org/10.1186/s12872-018-0754z
Wang J, Wang H, Liu J, Ma J. (2013). The association between body mass index, waist circumference with body fat percent, and abdominal fat rate in over weight and obese pupils. Zhonghua Yu Fang Yi Xue Za Zhi 47: 603–607. https:// doi.org/10.3760/cma.j.issn.02539624.2013.07.006
Yanai H, Adachi H, Masui Y, Katsuyama H, Kawaguchi A, Hakoshima M, Waragai Y, Harigae T, Hamasaki H, Sako A (2018). Exercise therapy for patients with type 2 diabetes: A narrative review. J. Clin. Med. Res. 10: 365-369. https://doi.org/10.14740/jocmr3382w
Yang XY, Zhang M, Luo XP, Wang JJ, Yin L, Pang C, Wang GA, Shen YX, et al. (2016). Body mass index, waist circumference and waist-to-height ratio associated with the incidence of type 2 diabetes mellitus: a cohort study. Zhong hua Yu Fang Yi Xue Za Zhi 50: 328-333 https://doi.or/10.3760/cma.j.issn.02539624.2016.04.009
Yuan SP, Huang CN, Liao HC, Lin YT, Wang Y (2014). Glycemic control outcomes by gender in the pay for performance system: A retrospective database analysis in patients with type 2 diabetes mellitus. Int. J. Endocrinol. https://doi.org/10.1155/2014/575124.
