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Year : 2017 | Volume : 5 | Issue : 4 | Page : 118 - 123  


Original Articles
Prevalence of metabolic syndrome amongst people with type 2 diabetes mellitus and its impacts on the occurrence of diabetic kidney disease

Gupta KS 1, Gupta SS 2*, Vali SA 3, Gathe S 4

1Dept of Dietetics, Sunils Diabetes Care n Research Centre Pvt Ltd, Nagpur, India, 2 Dept of Diabetology, Sunils Diabetes Care n Research Centre Pvt Ltd, Nagpur, India, 3 Dept of Home Science, Ex Prof and HOD, RTMNU Nagpur University, Nagpur, India, 4 Dept of Clinical Research and Epidemiology, Sunils Diabetes Care n Research Centre Pvt Ltd, Nagpur, India,

*Corresponding Author

Dr. Sunil Gupta

Email: drsgupta_ngp@rediffmail.com

Abstract:

Background: Prevalence of metabolic syndrome (MetS) amongst people with Type 2 diabetes mellitus (T2DM) is high. Though, diabetic kidney disease (DKD) is an increasingly important cause of morbidity and mortality worldwide, its association with MetS is not profoundly evaluated in Indian population.

Objectives: We aim to assess the prevalence of MetS and evaluate the impact of MetS on the occurrence of DKD amongst people with T2DM.

Methods: The demographic, anthropometry, blood pressure, lipids, CVD (based on ECG and history of CVD) and DKD (based on e-GFR < 60 ml/min/1.73 m2) data of 1037 T2DM was obtained. MetS was defined by NCEP ATP III guidelines. Association of DKD was evaluated with other variables including MetS and its components in terms of unadjusted OR. The Odds Ratios were adjusted by considering covariates like age, gender, duration of diabetes and HbA1c in the logistic regression model.

Results: The prevalence of MetS was 86.98% (female-91.3%, male-84.5%), DKD 6.85% and CVD 13.5%. Waist circumference contributed the most to MetS (91.69%) followed by HDL-C (71.73%) and TG (63.08%). Age and duration of diabetes showed significant positive association in occurrence of DKD. OR for people with MetS to develop DKD was high in univariate and multivariate analysis, though statistically insignificant. T2DM with hypertension are at higher risk of DKD with OR 2.1957 [95% CI: 1.1618, 4.1496] versus those without hypertension (p = 0.0154).

Conclusion: Indian people with T2DM have high prevalence of MetS, where waist and low HDL-cholesterol are major contributors. MetS insignificantly, while hypertension significantly increases the risk of DKD in people with T2DM having MetS.

Keywords: India, Type 2 Diabetes Mellitus, Metabolic Syndrome, Diabetic Kidney Disease

Introduction:

The prevalence of Type 2 Diabetes is increasing globally. IDF atlas 2017 has estimated that globally 424.9 millions people have diabetes, which is expected to increase to 628.6 millions in 2045. The maximum increase will be in South East Asia. Today, China tops in the list of countries with maximum number of diabetics and India ranks at second position. But, it is projected that by 2045, India will surpass all other countries including China and will be sheltering highest number of people with diabetes globally. 1 The metabolic syndrome is a condition characterized by a special constellation of reversible major risk factors for cardiovascular disease and type-2 diabetes. It is a cluster of the risk factors, which include glucose intolerance, abdominal obesity, high triglyceride, low HDL-C and high blood pressure. 2-4 Subject with three or more than three risk factors is diagnosed with metabolic syndrome. All of these components are related to weight gain, specifically intra-abdominal/ectopic fat accumulation and a large waist circumference. It is estimated that around 20-25 percent of the Worlds adult population have the metabolic syndrome. People with metabolic syndrome have a five-fold greater risk of developing type-2 diabetes. 5 Each year, 3.2 million people around the world die from complications associated with diabetes. Type 2 DM, has become one of the major causes of premature illness and death, mainly through the increased risk of CVD which is responsible for up to 80 per cent of these deaths. 6-7 Apart from the cardiovascular complications, diabetes is also a leading cause of blindness, amputation and kidney failure, account for much of the social and financial burden of the disease. 8 The underlying cause of the metabolic syndrome seems to be both insulin resistance and central obesity are considered significant factors. 9, 10 The prevalence of the metabolic syndrome and cardiovascular disease is expected to rise along with the global obesity and diabetes epidemic. 11 The International Obesity Task Force (IOTF) reports that 1.7 billion of the worlds population is already at a heightened risk of weight-related, non-communicable diseases such as type 2 DM and its complications like CVD and diabetic kidney diseases. 12 Diabetic kidney diseases is also becoming an increasingly important cause of morbidity and mortality worldwide owing to an increasing prevalence of type 2 DM associated with metabolic derangement and obesity. There is considerable evidence that obesity, hypertension and other elements of the metabolic syndrome (MetS) also contribute to the progression of renal disease independent of diabetes. Diabetic Kidney Disease (DKD) is preceded by an increase in glomerular filtration rate (GFR), microalbuminuria and glomerular hypertrophy. Poor glycemic control and elevated systolic blood pressure exacerbate the proteinuria and renal injury that may culminate in end-stage renal disease. 13 Though, metabolic syndrome has shown its clear positive relationship with CVD, there is scanty Indian data available towards the association of metabolic syndrome and diabetic kidney disease. 14 In this study, we aim to look at the prevalence of metabolic syndrome, and diabetic kidney disease (DKD) amongst people with known type 2 diabetes. We also intend to see the impact of MetS and contribution of various components of MetS on the occurrence of DKD.

Methodology:

After getting Institutional Ethics Committee approval, 1037 people with known T2DM attending a tertiary care center from central India during Jan 2015 to Dec 2015 were selected. The data on demographic, anthropometry, blood pressure, lipid profile, CVD (based on ECG and past history of CVD) and DKD (based on e-GFR less than 60 ml/min/1.73 m2) 15 was obtained. Anthropometric measurements, glycosylated hemoglobin A1c and lipids tests were carried out. Generalized obesity (BMI > 23 kg/m2) and abdominal obesity (WC > 90 cm in men and > 80 cm in women) were defined using WHO Asia Pacific guidelines. 16 Metabolic syndrome was defined as per the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) guidelines. 17 The association of DKD was evaluated with other variables including metabolic syndrome in terms of unadjusted OR. The Odds Ratios were adjusted by considering covariates like age, gender, duration of DM and HbA1c in the logistic regression model.

Statistical methods: The data on demographic, anthropometric and metabolic parameters were obtained and summarized in terms of frequencies and percentages. The parameters on continuous scale were expressed in terms of mean and standard deviation. The risk of diabetic nephropathy associated with different factors was obtained in terms of crude odds ratio. The adjusted odds ratios were obtained using multivariate logistic regression analysis. All the analyses were performed using SPSS version 20.0 (IBM Corp., Armonk USA) and statistical significance was evaluated at 5%.

Results:

Out of 1037 patients included in the study, 657 (63.36%) were males and 380 (36.64%) were females as shown in Table 1. The mean age of patients was 50.46+9.901 years and the mean duration of diabetes was 5.69+5.68 years. 78.8% of studied population had generalized obesity with BMI > 23 kg/m2. Overall, patients were obese as indicated by mean BMI of 26.56+4.43 kg/m2. Majority, i.e. 1001 (96.53%) cases had abnormal (high) WHR; while 83.67% had high waist circumference. 837 (80.71%) cases had HbA1c more than 7.0. and 973 (93.83%) cases had dyslipidemia. Metabolic syndrome (MetS) was found in 902 (86.98%) subjects. The prevalence of MetS in female (91.3%) was higher than in male subjects (84.5%). Waist circumference and low HDL-cholesterol were found to be the major contributors towards this high prevalence of metabolic syndrome in our population as shown in Table 2. The prevalence of DKD in studied group was 6.85% and of CAD was 13.5%

The association between diabetic kidney disease (DKD) with the levels of different risk factors were evaluated by using unadjusted (univariate analysis) and adjusted (multivariate analysis) odds ratio and shown in Table 3. DKD was treated as dependent variable and age, gender, duration of DM, BMI, diet, and family history of DM, waist hip ratio, HbA1C, hemoglobin, hypothyroidism and metabolic syndrome as independent variables. Independent variable age showed highly significant association with the disease as indicated by p-value < 0.0001, where the odds of having DKD was 4.1446 [95% CI: 2.1235, 8.0894] times higher in group with age > 50 years, as compared to those with < 50 years of age. As compared to patients having duration of type 2 diabetes (T2DM) < 10 years, the odds ratio was 3.2212 [95% CI: 1.7075, 6.0765] times higher in the patients whose duration of T2DM was > 10 years. The difference was statistically significant with p value of 0.0003. Further, the odds of DKD in subjects with low hemoglobin was 2.2078 [95% CI: 1.1909, 4.0930] times significantly higher as compared to patients with normal hemoglobin (p-value = 0.0119)

In multivariate logistic regression analysis, only those independent variables (risk factors) which were found significant in univariate analysis were included in the model. The adjusted odds ratio associated with subjects having age > 50 years was 3.2894 [95% CI: 1.6468, 6.5698] times higher as compared to subjects with < 50 years of age; which was statistically significant as indicated by p-value of 0.0007. Subjects suffering with T2DM for more than 10 years showed adjusted odds ratio of 2.1058 [95% CI: 1.0804, 4.1041] times higher as compared to those having < 10 years duration of DM (p-value=0.0287). Further, the adjusted odds ratio for patient group with low hemoglobin level was 1.654 [0.8720, 3.1372] times higher as compared to those having normal hemoglobin level, however the effect was statistically insignificant (p-value = 0.1234). The OR associated with metabolic syndrome to develop diabetic nephropathy was high in univariate as well as multivariate analysis, though it was statistically insignificant. Although, metabolic syndrome was insignificant in univariate model due to higher OR, it was retained in the multivariate model to understand its effect on diabetic nephropathy. The effect in multivariate model was found statistically insignificant (p-value = 0.1372) with adjusted odds ratio of 2.4831 [95% CI: 0.7485, 8.2372] as compared to patients without metabolic syndrome. High waist circumference was found to be the highest contributing factor to MetS with 91.69%, followed by HDL-C with 71.73% and TG with 63.08% (Table 1, 2). Table 4 shows the unadjusted and adjusted odds ratio associated with four factors of metabolic syndrome with diabetic kidney disease (DKD) as dependent variable. The crude odds ratio associated with abnormal waist circumference was 1.4018 [95% CI: 0.5423, 3.6234] times higher in subjects as compared to those having it in normal range although insignificant (p-value = 0.4857). Similarly, subjects with abnormal triglycerides and HDL-C levels were found at higher risk of DKD, although statistically insignificant. However, hypertension was strongly associated with DKD as indicated by crude odds ratio of 2.4146 [95% CI: 1.2906, 4.5176] and the effect was statistically significant with p value of 0.0058. In the adjusted analysis, the factors found significant in univariate analysis were included in the multivariate analysis model. The adjusted odds ratio associated with abnormal waist circumference was 1.1404 [95% CI: 0.4352, 2.9883] times higher as compare to normal waist circumference. The effect was statistically insignificant with p-value of 0.7892. The risk of DKD associated among patients having hypertension was 2.1957 [95% CI: 1.1618, 4.1496] times higher as compared to patients without hypertension and the effect was also statistically significant as indicated by p-value of 0.0154.

Discussion:

The prevalence of obesity and metabolic syndrome amongst people with type 2 diabetes is high. M Deepa et al 18 have shown in CURES: 47 study that, in Asian Indians, the age standardized prevalence of generalized obesity was 45.9% [95% CI: 43.9–47.9%], (women: 47.4%; men: 43.2%, p = 0.210), while that of abdominal obesity was 46.6% [95% CI: 44.6–48.6%], (women: 56.2%4 men: 35.1%, P<0.001). Our study has shown the higher prevalence of generalized obesity including overweight (78.8%), central obesity (83.7%) and metabolic syndrome (86.98%). This is because all our studied subjects have known type 2 diabetes, with mean age of 50.457+9.901 years and the mean duration of diabetes of 5.69+5.69 years. Waist circumference and low HDL-cholesterol were found to be the major contributors towards this high prevalence of metabolic syndrome in our population. Pooled data from 54 countries shows that at least 80% of cases of end-stage renal disease (ESRD) are caused by diabetes, hypertension or a combination of the two. The incidence of ESRD is up to 10 times as high in adults with diabetes as those without. 19 In CURE 45 study 20, the prevalence of overt nephropathy was found to be 2.2% in urban citizen with diabetes. We considered e-GFR of less than 60 ml/min/1.73 m2 to define renal impairment as cut off point for DKD. Our data has shown the prevalence of DKD as 6.85%. We observed that people with long duration of diabetes and higher age, are significantly at higher risk to have diabetic nephropathy. There are scanty Indian papers available for the association of diabetic nephropathy with metabolic syndrome. Mohan Vinoth et al 14 in his study on 241 diabetic from Goa has shown that 17.4% of people had diabetic nephropathy. He also observed that DKD was higher in people with MetS (21.22%) versus people without MetS (6.45%). Our data of 1037 T2DM subjects have also observed that people with MetS have higher prevalence of DKD (6.4%) compared to those without MetS (2.52%). Thus, people with metabolic syndrome are 2.5 times higher risk to develop diabetic nephropathy versus those without MetS, though statistically insignificant. CURE 45 study 20 has shown that hypertension is positively associated with overt diabetic nephropathy. We have evaluated the impact of various components of MetS on DKD. High waist circumference, low HDL-C and high triglyceride have shown to insignificantly increase risk of DKD, while hypertension has shown highly significant association with DKD in people with T2DM having MetS.

Conclusion:

Prevalence of metabolic syndrome is high amongst people with type 2 diabetes mellitus. Central obesity and low HDL-cholesterol were observed to be the major contributors towards this high prevalence. Though, metabolic syndrome is traditionally known for its association with macro-angiopathy, the risk of diabetic nephropathy was found to be higher in our studied population of T2 DM having metabolic syndrome. Amongst all components of metabolic syndrome, hypertension plays the most significant role towards occurrence of diabetic kidney disease in people with type 2 diabetes mellitus with metabolic syndrome.

References:

  1. International Diabetes Federation (IDF) Diabetes Atlas, Eighth edition, 2017
  2. Alberti KG, Zimmet P, Shaw J; IDF Epidemiology Task Force Consensus Group. The metabolic syndrome new worldwide definition. Lancet 2005;366:1059-62.
  3. Alberti KG, Zimmet P, Shaw J. Metabolic syndrome-a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med 2006;23(5):469-80.
  4. Mohan V, Deepa M. The metabolic syndrome in developing countries, Diabetes Voice May 2006 Volume 51 Special Issue
  5. Stern M, Williams K, Gonzalez-Villalpando C et al. Does the metabolic syndrome improve identification of individuals at risk of type-2 diabetes and/or cardiovascular disease? Diabetes Care 2004;27(11):2676-81
  6. Diabetes Atlas, third edition, International Diabetes Federation, 2006
  7. UKPDS Group. UK Prospective Diabetes Study 17: A nine-year update of a randomized, controlled trial on the effect of improved metabolic control on complications in non-insulin-dependent diabetes mellitus. Ann Intern Med 1996;124:136–45
  8. World Health Organization. Prevention of diabetes mellitus. Tech Rep Ser 844. WHO, Geneva, 1994
  9. Hu G, Qiao Q, Tuomilehto J et al. Plasma insulin and cardiovascular mortality in non-diabetic European men and women: a meta-analysis of data from eleven prospective studies. The DECODE Insulin Study Group. Diabetologia 2004;47:1245–56
  10. Carr DB, Utzschneider KM, Hull RL et al. Intra-abdominal fat is a major determinant of the National Cholesterol Education Program Adult Treatment Panel III criteria for the metabolic syndrome. Diabetes 2004;53(8):2087-94
  11. Han TS, Mike EJ Lean. A clinical perspective of obesity, metabolic syndrome and cardiovascular disease. J Royal Society Med Cardiovasc Dis 2005;5(0):1–13.
  12. Cole TJ, Lobstein T. Extended international (IOTF) body mass index cut-offs for thinness, overweight and obesity. Pediatr Obes 2012;7(4):284-94.
  13. Maric C, Hall JE. Obesity, metabolic syndrome and diabetic nephropathy. Contributions to nephrology. 2011;170:28-35.
  14. Vinoth M, Pinto NR, Ferreira A et al. Metabolic syndrome and hypertension in diabetic nephropathy patients in rural Goa, India. Int J Contemp Med Res 2016;3(4):1174-6.
  15. Lin CH, Chang YC, Chuang LM. Early detection of diabetic kidney disease: Present limitations and future perspectives. World J Diabetes. 2016;7(14):290-301.
  16. Misra A, Chowbey P, Makkar BM et al. Consensus Group, API. 2009;57.
  17. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III) final report. Circulation 2002; 106: 3143–3421.
  18. Deepa M, Farooq S, Deepa R et al. Prevalence and significance of generalized and central body obesity in an urban Asian Indian population in Chennai, India (CURES: 47) Eur J Clin Nutr 2009;63:259–67
  19. United States Renal Data System. International Comparisons. In United States Renal Data System. 2014 USRDS annual data report: Epidemiology of kidney disease in the United States. Bethesda (MD): National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases; 2014:188–210.
  20. Unnikrishnan RI, Rema M, Pradeepa R et al. Prevalence and risk factors of diabetic nephropathy in an urban South Indian population: the Chennai Urban Rural Epidemiology Study (CURES 45). Diabetes Care 2007;30(8):2019-24.

Table 1: Demographic profile of studied people with type 2 DM

Characteristics

Levels

No.

%

Mean+SD

Age (years)

< 50

531

51.21

50.457+9.901

> 50

506

48.79

Gender

Female

380

36.64

Male

657

63.36

Duration of DM (years)

< 10

854

82.35

5.692+5.688

> 10

183

17.65

BMI (kg/m2)

< 23

220

21.22

26.565+4.431

> 23

817

78.78

Diet

Veg

458

44.17

Mixed

579

55.83

Family history of DM

No

369

35.58

Yes

668

64.42

Waist hip ratio

Normal

36

3.47

0.993+0.064

Abnormal

1001

96.53

HbA1C (%)

≤ 7

200

19.29

8.969+2.192

> 7

837

80.71

HB (g/dL)

Normal

803

77.43

13.261+1.766

Low

234

22.57

Dyslipidemia

No

64

6.17

Yes

973

93.83

Hypertension

No

500

48.22

Yes

537

51.78

Hypothyroidism

No

911

87.85

Yes

126

12.15

Metabolic syndrome

No

135

13.02

Yes

902

86.98

Table 2: Contribution of various components towards metabolic syndrome

Characteristics

Levels

Metabolic Syndrome present [No. (%)]

Waist circumference

Normal

73 (8.09)

Abnormal

827 (91.69)

Triglycerides (mg/dL)

< 150

329 (36.47)

> 150

569 (63.08)

HDL-C (mg/dL)

Normal

248 (27.49)

Abnormal

647 (71.73)

Hypertension

No

376 (41.69)

Yes

526 (58.31)

Table 3: Unadjusted and adjusted risk of diabetic kidney disease (DKD) associated with the levels of different risk factors

Characteristics

Levels

Diabetic Nephropathy patients/Total (%)

Prevalence odds ratio [95% CI]; P-value

Unadjusted

Adjusted

Age (years)

< 50

12/461 (2.6)

1

1

> 50

36/361 (9.97)

4.1446 [2.1235 , 8.0894]; < 0.0001

3.2894 [1.6468 , 6.5698]; 0.0007

Gender

Female

21/291 (7.22)

1

Male

27/531 (5.08)

0.6888 [0.3821 , 1.2415]; 0.2148

Duration of DM (years)

< 10

32/702 (4.56)

1

1

> 10

16/120 (13.33)

3.2212 [1.7075 , 6.0765]; 0.0003

2.1058 [1.0804 , 4.1041]; 0.0287

BMI (kg/m2)

< 23

7/170 (4.12)

1

> 23

41/652 (6.29)

1.5625 [0.6882 , 3.5477];� 0.2860

Diet

Veg

22/457 (4.81)

1

Mixed

26/365 (7.12)

1.5165 [0.8446 , 2.7228]; 0.1632

Family history of DM

No

17/291 (5.84)

1

Yes

31/531(5.84)

1.0007 [0.5440 , 1.8410]; 0.9982

Waist-Hip-Ratio

Normal

4/33 (12.12)

1

Abnormal

44/789 (5.58)

0.4282 [0.1441 , 1.2719]; 0.1268

HbA1C (%)

≤ 7

6/173 (3.47)

1

> 7

42/649 (6.47)

1.9259 [0.8049 , 4.6079]; 0.1409

HB (g/dL)

Normal

31/651 (4.76)

1

1

Abnormal

17/171 (9.94)

2.2078 [1.1909 , 4.0930]; 0.0119

1.654 [0.8720 , 3.1372]; 0.1234

Thyroid

No

39/725 (5.38)

1

Yes

9/97 (9.28)

1.7990 [0.8430 , 3.8391]; 0.1289

Metabolic syndrome

No

3/119 (2.52)

1

1

Yes

45/703 (6.4)

2.6444 [0.8083 , 8.6512]; 0.1078

2.4831 [0.7485 , 8.2372]; 0.1372

Table 4: Effect of various components of Metabolic Syndrome on Diabetic Kidney Disease

Characteristics

Levels

* *DKD Total (%)

Odds ratio [ 95% CI ]; P-value

Unadjusted

Adjusted

Waist circumference

Normal

5/118 (4.24)

1.00*

1.00*

Abnormal

41/702 (5.84)

1.4018 [0.5423, 3.6234]; 0.4857

1.1404 [0.4352, 2.9883]; 0.7892

Triglycerides (mg/dl)

< 150

18/356 (5.06)

1.00*

1.00*

> 150

30/454 (6.61)

1.3286 [0.7280, 2.4247]; 0.3546

1.4551 [0.7712, 2.7460]; 0.2469

HDL-C (mg/dl)

Normal

15/275 (5.45)

1.00*

1.00*

Abnormal

33/528 (6.25)

1.1556 [0.6163, 2.1665]; 0.6521

1.1367 [0.5881, 2.1970]; 0.7033

Hypertension

No

15/420 (3.57)

1.00*

1.00*

Yes

33/402 (8.21)

2.4146 [1.2906, 4.5176]; 0.0058

2.1957 [1.1618, 4.1496]; 0.0154

*Reference level, **DKD: e-GFR cut off: 60 ml/min/1.73 m2





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