Background: Diabetic is associated with the risk of having lipid peroxidation causing macro and micro vascular diseases. Malondialdehyde (MDA) is a mutagen product of lipid peroxidation.Objective: This research was to identify some factors associated to plasma MDA (P-MDA) level in type 2 diabetes mellitus.Method: Eighty two subjects were randomly recruited. Subjets were members of Persadia, physical training club for diabetic at Mardjoeki Mahdi Hospital in Bogor. Data of fruits, vegetables, and nuts-legumes-seeds consumption were collected by interviewing using semi quantitative food frequency questionnaires. Fasting blood glucose and plasma MDA were assayed using Wills method, based on the reaction of thiobarbituric acid. Other data such as physical training, smoking status, and diseases history were collected by interviewing using structured questionnaires. Nutritional status data was determined as body mass index. All data was analyzed using Odds Ratio (OR) and logistic regression test to identify the dominant factors contributed to P-MDA level.Results: Factors that contribute to P-MDA level is vegetables, nuts-legumes-seeds consumption and physical training. Consuming vegetables less than 300 g/d and nuts-legumes-seeds less than 2.5 servings/d are associated significantly with having high level of P-MDA (≥0.8 nmol/L), respectively OR=5 (95%CI:1.154-22.02) and OR=4.8 (95%CI:1.26-18.57). Not having physical activity routinely is risk for having high level P-MDA (OR=1.3; 95%CI:1.12-1.45). Those factors are associated to P-MDA level independently.Conclusion: Vegetables and nuts-legumes-seeds consumption have a significant contribution to P-MDA level, it is important to know further which type and chemical content in those food that can prevent lipid peroxidation.

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).