Journal of
Diabetes and Endocrinology

  • Abbreviation: J. Diabetes Endocrinol.
  • Language: English
  • ISSN: 2141-2685
  • DOI: 10.5897/JDE
  • Start Year: 2010
  • Published Articles: 65

Full Length Research Paper

Prevalence of dyslipidemia and its correlation with anthropometric and blood pressure variables among type-2 diabetic patients

Temesgen Bizuayehu Wube
  • Temesgen Bizuayehu Wube
  • School of Medical Laboratory Sciences, College of Medicine and Health Sciences, Hawassa University, Awasa, Ethiopia.
  • Google Scholar
Tsegaye Alemayehu Begashaw
  • Tsegaye Alemayehu Begashaw
  • School of Medical Laboratory Sciences, College of Medicine and Health Sciences, Hawassa University, Awasa, Ethiopia.
  • Google Scholar
Agete Tadewos Hirigo
  • Agete Tadewos Hirigo
  • School of Medical Laboratory Sciences, College of Medicine and Health Sciences, Hawassa University, Awasa, Ethiopia.
  • Google Scholar

  •  Received: 27 June 2019
  •  Accepted: 09 August 2019
  •  Published: 29 February 2020


Co-morbidity of hypertension and dyslipidemia are found to be high in type-2 diabetes mellitus (DM) patients particularly with poor glycemic control. This study was aimed to assess the prevalence of dyslipidemia and its correlations with anthropometric and blood pressure variables among type-2 DM patients. The study was conducted on 314 type-2 DM patients at Hawassa University Comprehensive Specialized Hospital from February 28 to May 30, 2017. Socio-demographic and other data was collected using interview-directed structured questionnaire. In addition, serum biochemical parameters determined after overnight fasting and dyslipidemia was defined based on United State National Cholesterol Education program-III criteria. Statistical analysis such as Chi-square, student’s t-test/ Mann-Whitney U test, and Pearson correlation coefficient were conducted using Statistical Package for Social Sciences (SPSS) Version 20. The result showed that two hundred eight six (91.1%) participants had a minimum of one lipid parameter abnormal, which is well suited for the diagnosis of dyslipidemia. The prevalence of low HDL-cholesterol, high LDL-cholesterol, triglycerides and TC (total cholesterol) were 60.8, 14.3, 70.4 and 32.8%, respectively. The mean values of TC, LDL-cholesterol and uric acid (UA) were significantly higher among patients with body max index (BMI) ≥25 kg/m2 when compared to those with BMI <25 kg/m2 (p<0.01 for all). In addition, TC and TG were significantly higher among patients with BP ≥130/85 mmHg when compared to those patients with BP <130/85 mmHg (p<0.001 for both parameters). Triglycerides was significantly correlated with BMI, UA and hypertension (r=0.326, 0.553 and 0.22), respectively. TC showed significant correlation with BMI, UA and fasting blood sugar (FBS) (r=0.326, 0.298 and 0.132), respectively. Moreover, LDL-cholesterol was significantly correlated with waist circumference, BMI and FBS (r=0.16, 0.189 and 0.173), respectively. In conclusion, dyslipidemia is significantly higher in diabetes patients and it correlated with BMI, WC, blood pressure and UA. Therefore, lipid profiles should be performed periodically through treatment follow-up and proper management of correlated factors is vital in order to limit risks of cardiovascular diseases.

Key words: Anthropometric parameters, dyslipidemia, uric acid, hypertension, type-2 diabetes.


Abbreviation: BMI: body mass index, BP: blood pressure, DM: diabetes mellitus, FBS: fasting blood Sugar, HDL-c: High-density lipoprotein cholesterol, LDL-c: low-density lipoprotein cholesterol, NCEP: National Cholesterol Education Program adult treatment panel, TC: total cholesterol, TGs: triglycerides; SPSS: Statistical Package for Social Sciences, WC: waist circumference.  


Diabetes mellitus (DM) is a progressive non-communicable chronic disease and insulin hormone absolute deficiency/resistance caused it. Disturbances in insulin hormone leads to metabolic disturbances like hyperglycemia, lipids, carbohydrates and proteins (Habib, 2006; Mooradian, 2009). Coexistent of hypertension and diabetes is a common risk factor for disease complications, for instance coronary artery disease, cerebrovascular disease, peripheral vascular disease, and  DM  related  kidney  disease (Rawshani et al., 2017; Gregg et al., 2014). Type 2 DM patients are at increased risk of accelerated atherosclerosis and premature death (American diabetic association, 2002). Dyslipidemias may have a contribution to accelerated atherosclerosis and it is a risk factor for developing coronary artery disease (CAD) (American diabetic association, 2000). Different epidemiological studies have pointed out the relationships between the complications of diabetes and blood pressure (BP), glycaemia and LDL-cholesterol (LDL-c) (Sarwar et al., 2011; Stratton et al., 2000). Co-morbidity with hypertension (HTN) and dyslipidemia are found to be high in type-2 DM patients particularly with poor glycemic control. The relationship of these dysregulations proposes that these patients may be at a higher risk of developing cardiovascular diseases (CVDs) (Prabodh et al., 2012). In addition, these combined metabolic disorders like hypertension and hyperlipidemia may lead to cardio-cerebrovascular diseases and stroke, which may eventually lead to death (Sun et al., 2014). One study revealed that Total cholesterol (TC), raised triglycerides (TGs) and low HDL-cholesterol (HDL-c) were increased significantly in type-2 DM patients (Bhowmik et al., 2018). HDL-c showed an inverse association with body mass index (BMI); but LDL-c, TC and TGs were positively correlated with BMI (Omotoye et al., 2016). High concentration of free fatty acids prevents insulin mediated glucose uptake in the body and it predisposes to overproduction of very low-density lipoprotein cholesterol (VLDL-c (Mithal et al., 2014; Shradha and Sisodia, 2010).

In addition, TC was higher in hypertensive type-2 DM female patients when compared to hypertensive type-2 DM male patients (Gordon et al., 2010). Mulugeta et al. (2012) reported a significant correlation between glycemic control and dyslipidemia, particularly with serum TG and TC. Moreover, high BP, high BMI, aging, and experiences of prolonged duration with DM were significantly associated with the prevalence of dyslipidemia in DM patients (Bekele et al., 2017).

However, the possible interactions of BP and anthropometric variables with dyslipidemia in diabetic patients have not been studied in detail. Therefore, this study was designed to assess the prevalence of dyslipidemia and its correlations with anthropometric and BP variables among type-2 DM patients.



Study settings and study population

This cross-sectional study  was  conducted  at  Hawassa  University comprehensive specialized Hospital, Hawassa, Sidama zone from February 28 to May 30, 2017.  Hawassa is an administrative city of Southern Nation, Nationalities and People Regional (SNNPR) State and located 275 km from Addis Ababa, the capital city of Ethiopia. This administrative city encompasses about 133,097 populations (Population of Cities in Ethiopia, 2019). In addition, it has two governmental hospitals and nine public health centers.

Data collection procedures

A written consent was obtained from each study individual before collection of data. Interview-directed pre-structured questionnaire was used to collect socio-demographic and clinical related data. All type 2 DM patients who had a regular follow-up in the hospital were eligible in the study. However, patients on lipid level affecting drugs, pregnant women, patients with known cardiac disorders and renal failure were not included in the study. Anthropometric related data (weight and height) were collected based on WHO guideline (WHO, 2010). BMI was calculated as weight (in kilogram) divided by the height square (in m2). In addition, Systolic BP (SBP) and diastolic BP (DBP) were measured by mercury-based sphygmomanometer after the subjects had rested for at least 10 min. Repeated BP measurement was done within 3 min differences and average was taken to ensure accuracy.

Moreover, BMI was classified according to international conventions of expert panel (1998): underweight (<18.5 Kg/m2), normal body weight (18.5 to 24.9 Kg/m2), overweight (25 to 29.9 kg/m2), and obesity (≥30 kg/m2). Waist circumference (WC) was measured at the midpoint between the lower border of the rib cage and the iliac crest by using a flexible non-stretching tape and it expressed in centimeter (cm). The abnormality of WC was rated as >102 cm in men and >88 cm in women (NCEP-III, 2002).

Furthermore, dyslipidemia was defined based on National Cholesterol Education Program Adult Treatment Panel III guideline (NCEP- ATP III, 2002).  The following criteria’s was used for the diagnosis of overall dyslipidemia: TC ≥200 mg/dl, HDL-c (<40 mg/dl for men and <50mg/dl for women), LDL-c ≥ 130 mg/dl and TG ≥150mg/dl. Hyperuricemia was defined by raised uric acid level (>7.2 mg/dl in males and >6.0 mg/dl in females (Sui et al., 2008). 

Sample size calculation and sampling technique

Sample size was calculated based on single population proportion formula with 65.6% prevalence of dyslipidemia among DM patients (Bekele et al., 2017). Using the formula, the final sample size was calculated to be 346. To select participants from the study population, direct patients flow was checked for one week in the diabetic clinic. It showed that the average weekly DM patients flow was about 80 to 90. Finally, every fourth DM patient was nominated using systematic random sampling technique.

Blood sample collection and laboratory diagnosis

About 4 to 5 mL of venous whole blood was taken from each participant after 8 to 12 h fasting. The serum was obtained through centrifugation after a proper clot formation. In addition, serum samples were analyzed for uric acid, fasting blood sugar (FBS) andlipid  profile  (TC,   HDL-c   and   TGs)  using  Random  access  A25 BioSystem chemistry analyzer [BioSystems S.A. Costa Brava 30, Barcelona (Spain) (BioSystems™, Spain)]. Serum uric acid, TC, TGs and FBS were measured by enzymatic colorimetric assay techniques (Linear chemicals, Montgat, Spain). While HDL-c and LDL-c were analyzed using direct homogeneous enzymatic colorimetric assay techniques (Linear chemicals, Montgat, Spain).

Ethical approval and consent to participate

Ethical clearance was obtained from the ethical review committee of Institute of Health Sciences, Jimma University (Ref no IHRPGC/283/017). Clearance letter was submitted to Institutional

Review Board of Hawassa University, College of Medicine and Health Sciences and Hospital Clinical Director office for the necessary review and research permission, respectively. Moreover, all the study participants were well informed about the procedure of the study and written informed consent was obtained from all the study participants before data collection. In addition, the confidentiality of private information was strictly conserved.

Data quality control management

Before actual data collection, the quality of data was checked by pre-testing of 10% questionnaires in Sidama Zone “Adare Hospital”, which is about 5 km away from the study area and then all amendments was done for structuring questionnaires. Trained nurses who were working in the medical outpatient department collected all relevant data of DM patients including primary and secondary data. Quality control samples run were strictly performed before running patients’ samples as well as along with patients’ samples to assess the reliability of the whole performances status. In addition, standard operating procedure (SOP) was followed from pre-analytical phase to result releasing.

Data analysis

Collected data were coded, entered in to and analyzed using SPSS (Version 22.0). Frequencies and percentages were used for categorical variables, while continuous variables were presented as mean and standard deviation (±SD). Skewed or abnormally distributed data were presented as median and interquartile range (IQR). Chi-square and or fisher’s exact test was used for categorical variables In addition the comparison of quantitative variables was done by student t test or Mann-Whitney U test based on the nature of continuous data distribution. Finally, Pearson correlation coefficient was used to assess the correlations between each lipid profiles and independent variables.



Socio-demographic and lipid patterns

A total of 314 study participants were enrolled in the study with 90.7% response rate. Majority, 67.0% (211/314) of the study participants were males and the rest were females. The mean age of the study participants was 49.8 ± 9.8 years with the age range of 30 to 80 years. The mean duration of participants with diabetes  was  5  years  and  33  (10.5%) were   cigarette  smokers (Table 1).



The mean values of WC (cm), TC (mg/dl), LDL-c (mg/dl), uric acid (mg/dl) and FBS(mg/dl) were significantly higher among DM patients having BMI greater than or equals to 25 Kg/m2 when compared to those who have BMI lesser than 25 Kg/m2 (p<0.01 for all). In addition, median values of SBP, DBP and TGs were significantly higher in overweight to obese patients when compared to those patients who have BMI <25 Kg/m2 (p<0.0001 for all, Table 2). 



The mean of BMI, WC, TC, LDL-c, uric acid and FBS were significantly higher among DM patients having BP ≥ 130/85 mmHg when compared to those who have BP < 130/85 mmHg (p<0.05 for all, Table 3). 



Prevalence of dyslipidemia

Two hundred eighty six (91.1%) of the participants had a minimum of one lipid parameter abnormal that is compatible with the diagnosis of dyslipidemia according to NCEP-ATP III guideline. Dyslipidemia is significantly higher in males when compared to females (59.5% vs. 31.5%, p=0.03). In addition, BMI showed significant difference in dyslipidemia (Table 4). Furthermore, hypertriglyceridemia was the most prevalent lipid profile (Figure 1).




Correlations of anthropometric, blood pressure and other variables with dyslipidemia

TGs indicated a significant positive correlation with BMI (r = 0.326; P<0.0001) and uric acid (r = 0.553; P<0.0001). TC showed a significant correlation with BMI (r = 0.326; P <0.0001), uric acid (r = 0.298; P<0.0001) and FBS (r=0.132; p=0.02). In addition, a significant positive correlation of LDL-c was observed with WC, DBP, and FBS. Furthermore, age and duration since the diagnosis of DM did not show any significant correlations with lipid profiles (Table 5).










This cross-sectional study carried out in a resource poor East African setting was to evaluate the correlations of anthropometric and BP with dyslipidemia (lipid derangements) among diabetic patients in Hawassa. Majority (91.1%) of the participants had a minimum of one lipid parameter abnormal that is compatible with the diagnosis of dyslipidemia. Dyslipidemia is significantly higher in males when compared to females. The present study indicated that the prevalence of dyslipidemia was 91.1%. The finding was comparable with the prevalence reported by Abdel-Aal et al. (2008), which was 90%. However, the rate is  much  higher  when  compared  with the study conducted in other part of Ethiopia (Bekele et al., 2017) where the prevalence of dyslipidemia was 65.6%. We found that the prevalence of abnormal lipid profiles of low HDL-c, high LDL-c, TG and TC were 60.8, 14.3, 70.4 and 32.8%, respectively. The rate of low  HDL-c, raised LDL-c, hypertriglyceridemia and hypercholesterolemia was lower than the finding reported from Jordan (Abdel-Aal et al., 2008) where 83.9, 91.5, 77.2, and 83.1% were the prevalence of low HDL-c, raised LDL-c, TC and TG, respectively. Conversely, lower rate of abnormal lipid profiles was reported from other similar region of South  Ethiopia except LDL-c (Bekele et al., 2017). The variability may be attributed to the variations in individual’s life style, geographical location, the rate of physical exercise performance.

We found that TC, LDL-c, uric acid and FBS were significantly higher among patients with BMI ≥25 kg/m2 when compared to BMI <25 kg/m2. SBP, DBP and TGs were significantly higher among patients with BMI ≥25 kg/m2 when compared to those patients with BMI <25 kg/m2. In addition, TGs indicated a significant positive correlation with BMI and uric acid. Moreover, TC showed a significant correlation with BMI, uric acid and FBS. Furthermore, a significant positive correlation of LDL-c was observed with WC, DBP, FBS and hypertension.  Our study indicated that median TGs, mean TC and mean LDL-c were significantly higher among patients who had BMI ≥25 kg/m2 (215, 196.5, and 97.5) compared to patients with BMI<25 kgm2 (159, 167.9 and 86.1), respectively. Similarly, most  studies  have  revealed  that overweight to obese patients had high hyperlipidemia (Omotoye et al., 2016; Rao et al., 2016; Ranganathan et al., 2015) and this may increase the risks of cardiovascular related complications in DM patients. In addition, we found that TC, LDL-c and TGs levels were significantly higher in patients who had BP ≥130/85 mmHg when compared to patients with normal BP. This finding is in line with the report of Forhad et al. (2015), Rahman et al. (2016), and Choudhury et al. (2014). This revealed that hypertension has close association with dyslipidemia in DM patients. Conversely, one study revealed that dyslipidemia could not be worsening by the presence of hypertension in DM patients (Arshad et al., 2016). This non-correlation may be due to ethnic, geographical, life style and other factors.

The present study revealed statistically significant positive correlations between BMI and LDL-c, BMI and TC, and BMI and TGs in DM patients. This is in line with the study reported by Humaera et al. (2017) from Jatinangor population in Indonesia. Furthermore, different studies reported that a negative correlation between BMI and HDL-c (Humaera et al., 2017; Omotoye et al., 2016; Shamai et al., 2011; Babikr et al., 2016) however, we found that insignificant correlation between HDL-c and BMI and this is in line with the study reported from Tamil Nadu (Ranganathan et al., 2015).

The present study showed that a positive correlation between SBP and DBP with TGs. This is in support with the study of Lani et al. (2015) who revealed that there was a statistically significant correlation between TG and DBP; however, TC correlated with SBP.

We   found   that   serum   LDL-c,  TC  and   TGs   were significantly associated with uric acid. This in line with the study reported from US (Peng et al., 2015). Moreover, the level of uric acid increased in patients with coronary heart diseases (Sathiya et al., 2014). While, Kim et al. (2016) reported the correlation of uric acid with TGs. Furthermore, it was observed that FBS correlated with TC and LDL-c. Conversely, study report of Iranian adults indicated no correlation/association between FBS and lipid profile (Chehrei et al., 2007). The differences may be attributed to variation  in  glycemic  control status  of   DM patients.



This study showed a high prevalence of dyslipidemia and it was significantly higher in males when compared to females. BP, BMI, uric acid, FBS and WC appear to be correlated with the progression of dyslipidemia in type 2 diabetes and may assist as extrapolative factor for the progress of significant root causes of cardiovascular related diseases and deaths in developing countries. Therefore, lipid profiles should be checked periodically through treatment follow-up including anthropometric variables and BP management in order to limit risks of cardiovascular diseases in DM patients.



The nature of the study design  was  cross-sectional  and the findings of the study described only about a single point in time. We did not present the frequency of individual lipid profile based on National Cholesterol Education Program Adult Treatment Panel (NCEP-ATP) III criteria rather than the overall dyslipidemia, because the aim was assessing correlations.



The authors declared that they have no conflict of interests.



Abdel-Aal NM, Ahmad AT, Froelicher ES, Batieha AM, Hamza MM, Ajlouni KM (2008). Prevalence of dyslipidemia in patients with type 2 diabetes in Jordan. Saudi Medical Journal 29(10):1423-1428.


American Diabetes Association (2000). Annual Meeting, 1999: More on cardiovascular disease. Diabetes Care 23:845-852.


American Diabetes Association (2002). Diabetes and cardiovascular disease review. Issue 3. Alexandria (VA): American College of Cardiology; 2002. 



Arshad AR, Tipu HN, Paracha AI (2016). The impact of hypertension on lipid parameters in type 2 diabetes. JPMA 66:1262-1266.


Babikr WG, Alshahrani ASA , Hamid HGM , Abdelraheem HMK (2016). The correlation of HbA1c with body mass index and HDL-cholesterol in type 2 diabetic patients. Biomedical Research 27:1280-1283.


Bekele S, Yohannes T, Mohammed AE (2017). Dyslipidemia and associated factors among diabetic patients attending Durame General Hospital in Southern Nations, Nationalities, and People's Region. Diabetes, Metabolic Syndrome and Obesity 22(10):265-271.


Bhowmik B, Siddiquee T, Mujumder A, Afsana F, Ahmed T, Ibrahimu A et al (2018). Lipid Profile and Its Association with Diabetes and Prediabetes in a Rural Bangladeshi Population. Int. J. Environ. Res. Public Health 15:1944.


Chehrei A, Sadrnia S, Keshteli AH, Daneshmand MA, Rezaei J(2007). Correlation of dyslipidemia with waist to height ratio, waist circumference, and body mass index in Iranian adults. Asia Pacific Journal of Clinical Nutrition 16(2):248-253.


Choudhury KN, Mainuddin AKM, Wahiduzzaman M, Islam SMS(2014). Serum lipid profile and its association with hypertension in Bangladesh. Vascular Health and Risk Management 10 327-332.


Expert panel on the Identification (1998). Evaluation, and treatment of overweight and obesity in adults: executive summary of the clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults. Archives of Internal Medicine 158:1855-1867.


Forhad CRQ, Kabir A, Biswas T, Choudhury KN, Rahman MZ., Hussain DA, Ghosh SK (2014). Status of Lipid Profile among the Hypertensive Patients in Bangladesh. University Heart Journal 10(2):73-77.


Gordon L, Ragoobirsingh D, Morrison EY, Choo-Kang E, McGrowder D, Martorell E (2010). Lipid Profile of Type 2 Diabetic and Hypertensive Patients in the Jamaican Population. Journal of Laboratory Physicians 2:25-30.


Gregg EW, Li Y, Wang J, Rios Burrows N, Ali MK, Rolka D, Williams DE, Geiss L (2014). Changes in diabetes-related complications in the United States, 1990-2010. New England Journal of Medicine 370(16):1514-1523.


Habib S (2006). Frequency distribution of atherogenic dyslipidemia in Saudi type 2 diabetic patients. Pakistan Journal of Physiology 2(2):20-23.


Humaera Z, Sukandar H, Rachmayati S, Sofiatin Y, Roesli RMA (2017). Body Mass Index Correlates with Lipid Profile in Jatinangor Population. Journal of Hypertension 35:e-Supplement 3.


Kim EH, Bae KH, Jeon JH, Choi YK, Kim MK, Kim HS, Park KG, Seo HA (2016). Relation between uric acid and lipid profiles in patients with type 2 diabetes. In: 18th European Congress of Endocrinology (Vol. 41). BioScientifica.


Lani AU, Yusra P, Arfi S (2015). Correlation between serum lipid profile and blood pressure in NTB general hospital. Journal of Hypertension 33:e32.


Mithal A, Majhi D, Shunmugavelu M, Talwarkar PG, Vasnawala H, Raza AS (2014). Prevalence of dyslipidemia in adult Indian diabetic patients: a cross-sectional study (SOLID). Indian Journal of Endocrinology and Metabolism 18(5):642-647.


Mooradian A (2009). Dyslipidemia in type 2 diabetes mellitus. Nature Clinical Practice Endocrinology and Metabolism 5(3):150-159.


Mulugeta Y Chawla R, Kebede T, Worku Y (2012). Dyslipidemia associated with poor glycemic control in type 2 diabetes mellitus and the protective effect of metformin supplementation. Indian Journal of Clinical Biochemistry 27(4):363-369.


Omotoye FE, Fadupin GT (2016). Effect of Body Mass Index on Lipid Profile of Type 2 Diabetic Patients at An Urban Tertiary Hospital in Nigeria. IOSR Journal of Dental and Medical Sciences 15:65-70.


Peng TC, Wang CC, Kao TW, Chan JYH, Yang YH, Chang YW, Chen WL (2015). Relationship between hyperuricemia and lipid profiles in US adults. BioMed Research International, 2015.


Population of Cities in Ethiopia (2019). 



Prabodh S, Deva P, Godi S, Yeruva R, Nadella C, Sripada D (2012). Hypertension and Dyslipidemia in Type 2 Diabetes Mellitus patients of Guntur and Krishna districts in Andhra Pradesh, India. National Journal of Laboratory Medicine 1(1):7-10.


Rahman H, Khatun F, Amin R (2016). Comparison of Lipid Profile Status between Hypertensive and Normotensive People of Bangladesh. Dinajpur Medical College Journal 9:202-208.


Ranganathan S, Krishnan TU, Radhakrishnan S (2015). Comparison of dyslipidemia among the normal-BMI and high-BMI group of people of rural Tamil Nadu. Medical Journal of Dr. D.Y. Patil University 8:149-152.


Rao W, Su Y, Yang G, Ma Y, Liu R, Zhang S, et al (2016). Cross-Sectional Associations between Body Mass Index and Hyperlipidemia among Adults in Northeastern China. International Journal of Environmental Research and Public Health 13:516.


Rawshani A, Rawshani A, Franze' n S, Eliasson B, Svensson AM, Miftaraj M, et al (2017). Mortality and cardiovascular disease in type 1 and type 2 diabetes. The New England Journal of Medicine 376:1407-1418.


Sarwar N, Gao P, Rao Kondapally Seshasai S, Gobin R, Kaptoge S, Di Angelantonio E, et al (2011). Diabetes mellitus, fasting glucose, and risk of cause-specific death. The New England Journal of Medicine 364:829-834.


Sathiya R, Velu VK, Niranjan G, Srinivasan AR, Amirtha GB, Ramesh R, et al (2014). A Comparative Study of Serum Uric Acid levels and Lipid Ratios in Coronary Artery Disease Patients. International Journal of Biomedical Science 10:124-128.


Shamai L, Lurix E, Shen M, Novaro GM, Szomstein S, Rosenthal R, et al (2011). Association of body mass index and lipid profiles: evaluation of a broad spectrum of body mass index patients including the morbidly obese. Obesity Surgery 21:42-47.


Shradha B, Sisodia SS ((2010)). Diabetes, dyslipidemia, antioxidant and status of oxidative stress. International Journal of Research in Ayurveda and Pharmacy 1(1):33-42.


Stratton IM, Adler AI, Neil HA, Matthews DR, Manley SE, Cull CA, et al (2000). Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. British Medical Journal 321:405-412.


Sui X, Church TS, Meriwether RA, Lobelo F, Blair SN (2008). Uric acid and the development of the MetS in women and men. Metabolism. 57(6):845-52.


Sun B, Cheng X, Lc M, Tian H, Cl L (2014). Relationship between metabolic diseases and all-cause and cardiovascular disease death in elderly male diabetics during a 10-year follow-up. National Medical Journal of China 94:591-595.


World Health Organization (WHO) (2010). Chronic diseases and health promotion: stepwise approach to surveillance (STEPS). 2010.