Batasan indeks massa tubuh dan lingkar perut diabetesi di Indonesia untuk prediksi abnormalitas kadar HDL-kolesterol dan tekanan darah

Nazarina Nazarina; Sri Prihartini; Rika Rachmawati

doi:10.22146/ijcn.18993

Batasan indeks massa tubuh dan lingkar perut diabetesi di Indonesia untuk prediksi abnormalitas kadar HDL-kolesterol dan tekanan darah

https://doi.org/10.22146/ijcn.18993

Nazarina Nazarina^(1*), Sri Prihartini⁽²⁾, Rika Rachmawati⁽³⁾

(1) Pusat Teknologi Terapan Kesehatan dan Epidemiologi Klinik, Badan Penelitian dan Pengembangan Kesehatan, Kementerian Kesehatan, Bogor
(2) Pusat Teknologi Terapan Kesehatan dan Epidemiologi Klinik, Badan Penelitian dan Pengembangan Kesehatan, Kementerian Kesehatan, Bogor
(3) Pusat Teknologi Terapan Kesehatan dan Epidemiologi Klinik, Badan Penelitian dan Pengembangan Kesehatan, Kementerian Kesehatan, Bogor
(*) Corresponding Author

Abstract

Background: According to National Basic Health Survey (Riskesdas) 2007 and 2013 in Indonesia, diabetes prevalence had been increasing from 1,1% to 1,5%. Diabetic tends to have obesity related to abnormal blood lipid level and high blood pressure which lead to some complications such as cardiovascular diseases and hypertension. Therefore early prevention of complications is needed.

Objective: This study was to identify body mass index (BMI) and waist circumference (WC) cut-off point in Indonesian diabetic as the predictor of lipid profile and high blood pressure abnormality.

Method: The Crossectional study using secondary data, Riskesdas 2007. Subjects in this study were 615 diabetics who admitted been diagnosed as diabetes by physicians and/or had oral glucose test result ≥ 200 mg%. Data that had been analyzed were lipid profile (total cholesterol, LDL-chol, HDL-chol) and systolic-diastolic blood pressure, BMI (kg/cm2), WC (cm), lifestyle, and subject’s characteristic. Receiver Operating Characteristic (ROC) is used to identify BMI and WC cut-off point for predicting lipid profile and blood pressure abnormality.

Results: On the average, subjects have high blood pressure and dyslipidemia. Both IMT and LP are able to predict high blood pressure and low HDL-chol significantly (AUC ≥ 59; all p>0,05). BMI=23 kg/cm2 can predict low HDl-chol (Se=63,3%; Sp=54,0%; p=0,04), high systolic (Se=68,3%; Sp=60,6%; p=0,000) and diastolic (Se=68,3%; Sp=60,6%; p=0,000) blood pressure in men, whereas in women can predict only low HDL-chol (Se=72,3%; Sp=47,8%; p=0,000). LP=80 cm can screen high systolic (Se=73,8%; Sp=63,6%; p=0,000) and diastolic (Se=72,4%; Sp=55,3%; p=0,000) blood pressure in men and high systolic blood pressure in women (Se=71,5%; Sp=52,6%; p=0,000). However, to predict low HDL-chol in women, cut-off point of LP is 78 cm (Se=74,2%; Sp=41,5%; p=0,003).

Conclusion: Although BMI and LP can be used to predict high blood pressure and low HDL-chol, however, both measures have the different function when they are applied to both gender. To predict low HDL-chol in men and women, BMI=23 kg/cm2 can be used, and LP=80 cm can be applied to screen high systolic blood pressure in both genders. Nevertheless, more research is needed to show the consistency of these results, such as using better study design and considering for confounding variables (ethnic, diabetes duration, lifestyle, hypertension, and diabetes medicine).

Keywords

diabetes; body mass index; waist circumference; HDL-cholesterol; blood pressure

Full Text:

PDF

References

Kariadi SHKS. Kecenderungan global dalam pengendalian diabetes. Progress in Diabetology and Related Disorders: from Bench to Clinical Pratice; Forum Diabetes Nasional III. Bandung: PERKENI; 2005.

Kemenkes RI. Riset kesehatan dasar (Riskesdas) 2013. Jakarta: Kemenkes RI; 2013.

Dagogo. Neuroendocrine regulation of food intake. Nutrition and diabetes; pathophysiology and management. Opara E, editor. Boca Raton: CRC Press, Taylor and Francis Group; 2005.

Hossain P, Kawar B, Nahas MEI. Obesity and diabetes in developing world - a growing challenge. N Engl J Med 2007;356:213-5.

Leibson CL, Williamson DF, Melton III LJ, Palumbo PJ, Smith SA, Ransom JE, Schilling PL, Narayan KMV. Temporal trends in BMI among adults with diabetes. Diabetes Care 2001;24(9):1584-9.

Adi S. The importance of tight blood glucose control in cardiovascular complications. Progress in Diabetology and Related Disorders: from Bench to Clinical Pratice; Forum Diabetes Nasional III. Bandung: PERKENI; 2005.

Wang Y, Rimm EB, Stampfer MJ, Willet WC, Hu FB. Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men. Am J Clin Nutr 2005;81(3):555-63.

Snijder MB, van Dam RM, Visser M, Seidell JC. What aspects of body fat are particularly hazardous and how do we measure them? Int J Epidemiol 2006;35:83-92.

Klein LC, Corwin EJ, Ceballos RM. Leptin, hunger, and body weight: influence gender, tobacco, smoking, and smoking abstinence. Addictive Behaviour 2004;29(5):921-7.

WHO expert consultation. Appropiate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363:157-63.

Guricci S, Hartriyani Y, Hautvast JG, Deurenberg P. Prediction extracellular water and total body water by multifrequency bio-electrical impedance in Southeast Asian population. Asia Pac J Clin Nutr 1999;8(2):155-9.

Almatsier S, Instalasi Gizi RSCM. Penuntun diet. Jakarta: Gramedia; 2003.

PERKENI. Konsensus pengelolaan dan pencegahan diabetes mellitus tipe 2 di Indonesia. Jakarta: PERKENI; 2011.

Zou KH, Liu A, Bandos AI, Ohno-Machado L, Rockette HE. Statistical evaluation of diagnostic performance topicx in ROC analysis. Boca Raton: CRC Press; 2012.

Shah SZA, Devrajani BR, Devrajani T, Bibi I. Frequency of dyslipidemia in obese versus non-obese in relation to body mass index (BMI), waist hip ratio (WHR) and waist circumference (WC). Pakistan Journal of Science 2010;62(1).

Kayode JA, Sola AO, Matthew AS, Adesola BO, Ademola I, Adedeji AT, Adelani AS. Lipid profile of type 2 diabetic patients at rural tertiary hospital in Nigeria. J Diabetes Endicronol 2010;1(4):46-51.

Pietrobelli A, Lee RC, Capristo E, Deckelbaum RJ, Heymsfield SB. An independent, inverse association of high-density-lipoprotein cholesterol concentration with nonadipose body mass. Am J Clin Nutr 1999;69(4):614-20.

Schroder H, Marrugat J, Elosua R, Covas MI. Relationship between body mass index, serum cholesterol, leisure-time physical activity, and diet in a Mediterranean Southern-Europe population. British Journal Nurtrition 2003;90:431-9.

Li WC, Chen IC, Chang YC, Loke SS, Wang SH, Hsiao KY. Waist-to height ratio, waist circumference, and body mass index as indices of cardiometabolic risk among 36,642 Taiwanese adults. Eur J Nutr 2013;52(1):57-65.

Mancia G. The association of hypertension and diabetes: prevalence, cardiovascular risk and protection by blood pressure reduction. Acta Diabetol 2005;42:S17-S25.

Baratta R, Amato S, Degano C, Farina MG, Patanè G, Vigneri R, Frittitta L. Adinopectin relationship with lipid metabolism is independent of body fat mass: evidence from both cross-sectional and intervention studies. J Clin Endocrinol Metab 2004;89(6):2665-71.

Benetou V, Bamia C, Trichopoulos D, Mountokalakis T, Psaltopoulou T, Trichopoulou A. The association of body mass index and waist circumference with blood pressure depends on age and gender: a study of 10,928 non-smoking adults in the Greek EPIC cohort. Eur J Epidemiol 2004;19(8):803-9.

Sakurai M, Miura K, Takamura T, Ota T, Ishizaki M, Morikawa Y, Kido T, Naruse Y, Nakagawa H. Gender difference in the association between anthropometric indices of obesity and blood pressure in Japanese. Hypertens Res 2006;29(2):75-80.

Pi-Sunyer FX. The obesity epidemic: pathophysiology and consequences of obesity. Obes Res 2002;10(2):97S-104S.

DOI: https://doi.org/10.22146/ijcn.18993