- Email: [email protected]
Predictors for diagnosing metabolic syndrome among hypertensive patients in a tertiary care centre
G Model
DSX-459; No. of Pages 3 Diabetes & Metabolic Syndrome: Clinical Research & Reviews xxx (2015) xxx–xxx
Contents lists available at ScienceDirect
Diabetes & Metabolic Syndrome: Clinical Research & Reviews journal homepage: www.elsevier.com/locate/dsx
Brief Communication
Predictors for diagnosing metabolic syndrome among hypertensive patients in a tertiary care centre Jaya Prakash Sahoo a,*, Savita Kumari b, Sanjay Jain b a b
Department of Endocrinology & Metabolism, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry, India Department of Medicine, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
A R T I C L E I N F O
A B S T R A C T
Keywords: Anthropometry Lipid profile Obesity
Metabolic syndrome (MS) was present in 71% of adult hypertensive subjects according to modified National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP-III) criteria in a tertiary care health centre. Female gender and higher body mass index (BMI) were predictors for MS among these patients. BMI cut off of 23 (overweight) had sensitivity of 94% and positive predictive value of 75% for diagnosing MS among them. ß 2015 Diabetes India. Published by Elsevier Ltd. All rights reserved.
1. Introduction Hypertension is seen in up to one third of adult population of India at present [1]. These patients are at risk for cardiovascular diseases (CVDs). Concomitant presence of metabolic syndrome further increases the risk for CVDs among them [2]. So, it is important to know the proportion of hypertensive subjects having metabolic syndrome (MS). The prevalence of MS varies from 29% to 68% among hypertensive patients in different studies [3–6]. As a resource poor country, it may not be possible to screen all hypertensive subjects for the presence of MS. Here comes the role of predictors for MS; so that only high risk hypertensive subjects can be targeted. Hence, this preliminary study was done to evaluate the predictors for the presence of MS among hypertensive subjects.
2. Subjects, materials and methods One hundred and seventeen hypertensive patients (PGIMER) (18 years) from the hypertension clinic of a tertiary care centre were included in this study over one year. The subjects having secondary hypertension (renal, endocrine, vascular & drug induced), chronic infection, malignancy and long term steroid therapy were excluded from this study. Additionally, pregnancy and post-partum (up to six weeks) ladies were also excluded. The
* Corresponding author at: House No-28, Lane-B, VVP Nagar, Puducherry 605009, India. E-mail address: [email protected] (J.P. Sahoo).
diagnosis and management of hypertension was based on JNC VII guidelines [7]. The study was approved by the ethical committee of the institute. All subjects gave their informed consent before participating in the study. A detailed history taking and physical examination including anthropometry (height, weight, & waist circumference (WC)) were performed in all patients [8]. Blood pressure (BP) was measured in right upper limb in sitting position [7]. Body mass index (BMI) was calculated by dividing the weight in kilograms by the height in metres squared. Overweight and obesity were defined as those having BMI 23–24.9 kg/m2 and BMI 25 kg/m2 respectively [8]. Blood chemistry included measurements of fasting plasma glucose (FPG) and lipid profile (total cholesterol (TC), triglyceride (TG) & high density lipoprotein (HDL) cholesterol). The serum low density lipoprotein (LDL) cholesterol was derived from Friedewald formula [9]. Non-HDL cholesterol was calculated by subtracting HDL cholesterol from total cholesterol. MS was diagnosed according to both modified National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP-III) and International Diabetes Federation (IDF) criteria [10,11]. 3. Statistical analysis Statistical analysis was done using SPSS software version 17. All continuous variables were presented as median with inter-quartile ranges (IQR) except age and LDL cholesterol, which were expressed as mean & standard deviation (SD). Mann–Whitney U test and unpaired t test were used to compare these variables between hypertensive subjects with and without MS. The categorical data were expressed as frequency (as percentages), which were compared using Chi Square test. Correlation between the variables
http://dx.doi.org/10.1016/j.dsx.2015.02.011 1871-4021/ß 2015 Diabetes India. Published by Elsevier Ltd. All rights reserved.
Please cite this article in press as: Sahoo JP, et al. Predictors for diagnosing metabolic syndrome among hypertensive patients in a tertiary care centre. Diab Met Syndr: Clin Res Rev (2015), http://dx.doi.org/10.1016/j.dsx.2015.02.011
G Model
DSX-459; No. of Pages 3 J.P. Sahoo et al. / Diabetes & Metabolic Syndrome: Clinical Research & Reviews xxx (2015) xxx–xxx
2
Table 1 Comparison between different parameters of hypertensive patients with and without metabolic syndrome. Parameters
MS (N = 83)
Non MS (N = 34)
Age (years)a Females Urban residents Duration of hypertension (years) No of antihypertensive medications Smokers Family history of hypertension Body mass index (kg/m2) Overweight/obesity Total cholesterol (mmol/l) LDL cholesterola (mmol/l) Non HDL cholesterol (mmol/l) Total/HDL cholesterol ratio LDL/HDL cholesterol ratio
48.08 (11.50) 49 (59%) 59 (71%) 03 (06)
44.97 (13.88) 10 (29.4%) 20 (59%) 2 (3)
2 (2)
2 (1)
7(8.4%) 39 (47%) 28 (06) 79 (95%) 5.19 (1.24) 3.19(0.97) 4.06 (1.13) 4.5 (1.8) 2.8 (1.5)
5 (14.7%) 14 (41.2%) 25 (5.2) 26 (75.8%) 4.86 (1.20) 3(0.87) 3.54 (1.34) 3.65 (1.29) 2.35 (1.14)
P value 0.25 0.004 0.20 0.57 0.40 0.31 0.56 0.001 0.002 0.12 0.28 0.007 <0.001 0.005
LDL: low density lipoprotein; HDL: high density lipoprotein. a Data are presented as mean (SD); others continuous variables are expressed as median (inter-quartile range).
was evaluated using Spearman’s correlation coefficients. The level of agreement in MS diagnosis between two criteria (NCEPATP-III & IDF) was assessed by pair wise comparisons using kappa-statistics (k). Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated in addition. P value <0.05 was considered as significant. 4. Results Modified NCEP ATP-III criteria diagnosed 83/117 (71%) hypertensive patients as MS in contrast to 77/117 (66%) by IDF criteria. The level of agreement between these two criteria was good (k = 0.88, P < 0.001). The proportion of each component among subjects with MS according to modified NCEP ATP-III criteria were as follows: central obesity (WC 80 cm in females & 90 cm in males) – 77/83 (93%), increased TG (1.69 mmol/l) – 54/83 (65%), decreased HDL (<1.29 mmol/l in females & <1.03 mmol/l in males) – 53/83 (64%), and increased FPG (5.56 mmol/l) – 37/83 (44.6%). Study subjects with MS had higher BMI, non-HDL cholesterol, total/HDL and LDL/HDL cholesterol ratios with more females (Table 1). MS had also a significant correlation with female gender (r = 0.27, P = 0.003) and BMI (r = 0.32, P < 0.001) (Table 2). BMI cut off of 23 (overweight) had sensitivity of 94% and PPV of 75% for diagnosing MS among these patients (Fig. 1). But it had low specificity (23.5%) and NPV (61.5%). 5. Discussion The prevalence of MS among hypertensive subjects in our study was similar to findings from other studies [3,4]. However, some Table 2 Correlations between different parameters and metabolic syndrome. Parameters
Age (years) Females Urban residents Duration of hypertension (years) No of antihypertensive medications Smokers Family history of hypertension Body mass index (kg/m2)
Spearman’s correlation coefficient (r) 0.07 0.27 0.12 0.05 0.08 0.09 0.05 0.32
P value
0.45 0.003 0.20 0.57 0.40 0.31 0.57 <0.001
Fig. 1. Receiver operating characteristic curve (ROC) showing body mass index (BMI) as a predictor of metabolic syndrome (AUC – 0.706, arrow shows BMI cut off of 23 corresponding to sensitivity of 94% & specificity of 23.5%).
authors reported it as low as 29–35% [5,6]. This variation in prevalence of MS can be explained by different factors like ethnicity, age, gender distribution, duration of hypertension, sample size, type of study and criteria used. The prevalence of MS among the study participants was approximately twice as compared to the general population [12]. Similar finding has been reported by Lee et al. from Korea (60% vs. 27%) [13]. Additionally, NCEP-ATP-III criteria picked up more MS cases compared to IDF definition in studies from India including our subjects [3]. However, IDF definition diagnosed more MS cases compared to NCEP-ATP-III criteria in studies from Africa and Europe [5,14]. Among different components of NCEP-ATP-III criteria, central obesity was the most common and impaired fasting glucose was the least common among hypertensive subjects with MS. This reaffirms the fact that central obesity is an important health problem among adult population in India [3,8]. Another important observation from our study is that non-HDL cholesterol, total/HDL and LDL/HDL cholesterol ratios but not LDL cholesterol were higher in patients with MS. This thing happens when LDL cholesterol is derived by using Friedewald formula for subjects having high serum TG levels like patients with MS [15]. There were more females compared to males among MS group. This is related to the differential relationship between central obesity and gender among hypertensive population [16]. Other than female gender, higher BMI was also associated with presence of MS in our subjects similar to findings from a Korean study [13]. This relationship even exists in non-hypertensive subjects [12]. BMI cut off of 23(overweight) had high sensitivity (94%) with low specificity (23.5%) for diagnosing MS among study participants (Fig. 1). So, this BMI cut off can be used for screening of MS among hypertensive subjects. But we should not forget that the study was done at a tertiary care centre with a small sample size. The target organ damage is more in hypertensive subjects with MS as compared to those without it [2,13]. So, all hypertensive subjects should be screened for presence of MS during the initial diagnosis itself. However, doing FPG & lipid profile may not be possible in all hypertensive patients in a country like India. Therefore, MS should be ruled out at least in overweight (BMI 23) hypertensive subjects so that early intervention can be initiated to take care additional cardio-metabolic risk factors in them.
Please cite this article in press as: Sahoo JP, et al. Predictors for diagnosing metabolic syndrome among hypertensive patients in a tertiary care centre. Diab Met Syndr: Clin Res Rev (2015), http://dx.doi.org/10.1016/j.dsx.2015.02.011
G Model
DSX-459; No. of Pages 3 J.P. Sahoo et al. / Diabetes & Metabolic Syndrome: Clinical Research & Reviews xxx (2015) xxx–xxx
Source of funding None.
Conflict of interest None declared. References [1] Neupane D, McLachlan CS, Sharma R, Gyawali B, Khanal V, Mishra SR, et al. Prevalence of hypertension in member countries of South Asian Association for Regional Cooperation (SAARC): systematic review and meta-analysis. Medicine (Baltimore) 2014;93(13):e74. [2] Redon J, Cı´fkova´ R. The metabolic syndrome in hypertension: diagnostic and therapeutic implications. Curr Hypertens Rep 2007;9(4):305–13. [3] Thakur S, Raina S, Thakur S, Negi PC, Verma BS. Prevalence of metabolic syndrome among newly diagnosed hypertensive patients in the hills of Himachal Pradesh, India. Indian J Endocrinol Metab 2013;17(4):723–6. [4] Farsang C, Naditch-Brule L, Perlini S, Zidek W, Kjeldsen SE, GOOD investigators. Inter-regional comparisons of the prevalence of cardiometabolic risk factors in patients with hypertension in Europe: the GOOD survey. J Hum Hypertens 2009;23:316–24. [5] Akintunde AA, Ayodele OE, Akinwusi PO, Opadijo GO. Metabolic syndrome: comparison of occurrence using three definitions in hypertensive subjects. Clin Med Res 2011;9(1):26–31. [6] Tsioufis C, Kasiakogias A, Tsiachris D, Kordalis A, Thomopoulos C, Giakoumis M, et al. Metabolic syndrome and exaggerated blood pressure response to exercise in newly diagnosed hypertensive patients. Eur J Prev Cardiol 2012;19: 467–73.
3
[7] Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo Jr JL, et al. The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure. JAMA 2003;289:2560–72. [8] Misra A, Chowbey P, Makkar BM, Vikram NK, Wasir JS, Chadha D, et al. Consensus statement for diagnosis of obesity, abdominal obesity and the metabolic syndrome for Asian Indians and recommendations for physical activity, medical and surgical management. JAPI 2009;57:163–70. [9] Rifai N, Warnick GR, McNamara JR, Belcher JD, Grinstead GF, Frantz Jr ID. Measurement of low-density-lipoprotein cholesterol in serum: a status report. Clin Chem 1992;38:150–60. [10] Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the 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). JAMA 2001;285:2486–97. [11] Alberti KG, Zimmet P, Shaw J. Metabolic syndrome – a new world-wide definition. A consensus statement from the International Diabetes Federation. Diabetes Med 2006;23:469–80. [12] Deedwania PC, Gupta R, Sharma KK, Achari V, Gupta B, Maheshwari A, et al. High prevalence of metabolic syndrome among urban subjects in India: a multisite study. Diabetes Metab Syndr 2014;8(3):156–61. [13] Lee SR, Cha MJ, Kang DY, Oh KC, Shin DH, Lee HY. Increased prevalence of metabolic syndrome among hypertensive population: ten years’ trend of the Korean National Health and Nutrition Examination Survey. Int J Cardiol 2013;166(3):633–9. [14] de la Sierra A, Romero R, Bonet J, Pe´rez M, Lo´pez JS, Ravella R, et al. Prevalence and general features of the metabolic syndrome in the Spanish hypertensive population [Article in Spanish]. Med Clin (Barc) 2006;126(11):406–9. [15] Lemieux I, Lamarche B, Couillard C, Pascot A, Cantin B, Bergeron J, et al. Total cholesterol/HDL cholesterol ratio vs LDL cholesterol/HDL cholesterol ratio as indices of ischemic heart disease risk in men: the Quebec Cardiovascular Study. Arch Intern Med 2001;161(22):2685–92. [16] Gus M, Fuchs SC, Moreira LB, Moraes RS, Wiehe M, Silva AF, et al. Association between different measurements of obesity and incidence of hypertension. Am J Hypertens 2004;17:50–3.
Please cite this article in press as: Sahoo JP, et al. Predictors for diagnosing metabolic syndrome among hypertensive patients in a tertiary care centre. Diab Met Syndr: Clin Res Rev (2015), http://dx.doi.org/10.1016/j.dsx.2015.02.011
DSX-459; No. of Pages 3 Diabetes & Metabolic Syndrome: Clinical Research & Reviews xxx (2015) xxx–xxx
Contents lists available at ScienceDirect
Diabetes & Metabolic Syndrome: Clinical Research & Reviews journal homepage: www.elsevier.com/locate/dsx
Brief Communication
Predictors for diagnosing metabolic syndrome among hypertensive patients in a tertiary care centre Jaya Prakash Sahoo a,*, Savita Kumari b, Sanjay Jain b a b
Department of Endocrinology & Metabolism, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry, India Department of Medicine, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
A R T I C L E I N F O
A B S T R A C T
Keywords: Anthropometry Lipid profile Obesity
Metabolic syndrome (MS) was present in 71% of adult hypertensive subjects according to modified National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP-III) criteria in a tertiary care health centre. Female gender and higher body mass index (BMI) were predictors for MS among these patients. BMI cut off of 23 (overweight) had sensitivity of 94% and positive predictive value of 75% for diagnosing MS among them. ß 2015 Diabetes India. Published by Elsevier Ltd. All rights reserved.
1. Introduction Hypertension is seen in up to one third of adult population of India at present [1]. These patients are at risk for cardiovascular diseases (CVDs). Concomitant presence of metabolic syndrome further increases the risk for CVDs among them [2]. So, it is important to know the proportion of hypertensive subjects having metabolic syndrome (MS). The prevalence of MS varies from 29% to 68% among hypertensive patients in different studies [3–6]. As a resource poor country, it may not be possible to screen all hypertensive subjects for the presence of MS. Here comes the role of predictors for MS; so that only high risk hypertensive subjects can be targeted. Hence, this preliminary study was done to evaluate the predictors for the presence of MS among hypertensive subjects.
2. Subjects, materials and methods One hundred and seventeen hypertensive patients (PGIMER) (18 years) from the hypertension clinic of a tertiary care centre were included in this study over one year. The subjects having secondary hypertension (renal, endocrine, vascular & drug induced), chronic infection, malignancy and long term steroid therapy were excluded from this study. Additionally, pregnancy and post-partum (up to six weeks) ladies were also excluded. The
* Corresponding author at: House No-28, Lane-B, VVP Nagar, Puducherry 605009, India. E-mail address: [email protected] (J.P. Sahoo).
diagnosis and management of hypertension was based on JNC VII guidelines [7]. The study was approved by the ethical committee of the institute. All subjects gave their informed consent before participating in the study. A detailed history taking and physical examination including anthropometry (height, weight, & waist circumference (WC)) were performed in all patients [8]. Blood pressure (BP) was measured in right upper limb in sitting position [7]. Body mass index (BMI) was calculated by dividing the weight in kilograms by the height in metres squared. Overweight and obesity were defined as those having BMI 23–24.9 kg/m2 and BMI 25 kg/m2 respectively [8]. Blood chemistry included measurements of fasting plasma glucose (FPG) and lipid profile (total cholesterol (TC), triglyceride (TG) & high density lipoprotein (HDL) cholesterol). The serum low density lipoprotein (LDL) cholesterol was derived from Friedewald formula [9]. Non-HDL cholesterol was calculated by subtracting HDL cholesterol from total cholesterol. MS was diagnosed according to both modified National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP-III) and International Diabetes Federation (IDF) criteria [10,11]. 3. Statistical analysis Statistical analysis was done using SPSS software version 17. All continuous variables were presented as median with inter-quartile ranges (IQR) except age and LDL cholesterol, which were expressed as mean & standard deviation (SD). Mann–Whitney U test and unpaired t test were used to compare these variables between hypertensive subjects with and without MS. The categorical data were expressed as frequency (as percentages), which were compared using Chi Square test. Correlation between the variables
http://dx.doi.org/10.1016/j.dsx.2015.02.011 1871-4021/ß 2015 Diabetes India. Published by Elsevier Ltd. All rights reserved.
Please cite this article in press as: Sahoo JP, et al. Predictors for diagnosing metabolic syndrome among hypertensive patients in a tertiary care centre. Diab Met Syndr: Clin Res Rev (2015), http://dx.doi.org/10.1016/j.dsx.2015.02.011
G Model
DSX-459; No. of Pages 3 J.P. Sahoo et al. / Diabetes & Metabolic Syndrome: Clinical Research & Reviews xxx (2015) xxx–xxx
2
Table 1 Comparison between different parameters of hypertensive patients with and without metabolic syndrome. Parameters
MS (N = 83)
Non MS (N = 34)
Age (years)a Females Urban residents Duration of hypertension (years) No of antihypertensive medications Smokers Family history of hypertension Body mass index (kg/m2) Overweight/obesity Total cholesterol (mmol/l) LDL cholesterola (mmol/l) Non HDL cholesterol (mmol/l) Total/HDL cholesterol ratio LDL/HDL cholesterol ratio
48.08 (11.50) 49 (59%) 59 (71%) 03 (06)
44.97 (13.88) 10 (29.4%) 20 (59%) 2 (3)
2 (2)
2 (1)
7(8.4%) 39 (47%) 28 (06) 79 (95%) 5.19 (1.24) 3.19(0.97) 4.06 (1.13) 4.5 (1.8) 2.8 (1.5)
5 (14.7%) 14 (41.2%) 25 (5.2) 26 (75.8%) 4.86 (1.20) 3(0.87) 3.54 (1.34) 3.65 (1.29) 2.35 (1.14)
P value 0.25 0.004 0.20 0.57 0.40 0.31 0.56 0.001 0.002 0.12 0.28 0.007 <0.001 0.005
LDL: low density lipoprotein; HDL: high density lipoprotein. a Data are presented as mean (SD); others continuous variables are expressed as median (inter-quartile range).
was evaluated using Spearman’s correlation coefficients. The level of agreement in MS diagnosis between two criteria (NCEPATP-III & IDF) was assessed by pair wise comparisons using kappa-statistics (k). Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated in addition. P value <0.05 was considered as significant. 4. Results Modified NCEP ATP-III criteria diagnosed 83/117 (71%) hypertensive patients as MS in contrast to 77/117 (66%) by IDF criteria. The level of agreement between these two criteria was good (k = 0.88, P < 0.001). The proportion of each component among subjects with MS according to modified NCEP ATP-III criteria were as follows: central obesity (WC 80 cm in females & 90 cm in males) – 77/83 (93%), increased TG (1.69 mmol/l) – 54/83 (65%), decreased HDL (<1.29 mmol/l in females & <1.03 mmol/l in males) – 53/83 (64%), and increased FPG (5.56 mmol/l) – 37/83 (44.6%). Study subjects with MS had higher BMI, non-HDL cholesterol, total/HDL and LDL/HDL cholesterol ratios with more females (Table 1). MS had also a significant correlation with female gender (r = 0.27, P = 0.003) and BMI (r = 0.32, P < 0.001) (Table 2). BMI cut off of 23 (overweight) had sensitivity of 94% and PPV of 75% for diagnosing MS among these patients (Fig. 1). But it had low specificity (23.5%) and NPV (61.5%). 5. Discussion The prevalence of MS among hypertensive subjects in our study was similar to findings from other studies [3,4]. However, some Table 2 Correlations between different parameters and metabolic syndrome. Parameters
Age (years) Females Urban residents Duration of hypertension (years) No of antihypertensive medications Smokers Family history of hypertension Body mass index (kg/m2)
Spearman’s correlation coefficient (r) 0.07 0.27 0.12 0.05 0.08 0.09 0.05 0.32
P value
0.45 0.003 0.20 0.57 0.40 0.31 0.57 <0.001
Fig. 1. Receiver operating characteristic curve (ROC) showing body mass index (BMI) as a predictor of metabolic syndrome (AUC – 0.706, arrow shows BMI cut off of 23 corresponding to sensitivity of 94% & specificity of 23.5%).
authors reported it as low as 29–35% [5,6]. This variation in prevalence of MS can be explained by different factors like ethnicity, age, gender distribution, duration of hypertension, sample size, type of study and criteria used. The prevalence of MS among the study participants was approximately twice as compared to the general population [12]. Similar finding has been reported by Lee et al. from Korea (60% vs. 27%) [13]. Additionally, NCEP-ATP-III criteria picked up more MS cases compared to IDF definition in studies from India including our subjects [3]. However, IDF definition diagnosed more MS cases compared to NCEP-ATP-III criteria in studies from Africa and Europe [5,14]. Among different components of NCEP-ATP-III criteria, central obesity was the most common and impaired fasting glucose was the least common among hypertensive subjects with MS. This reaffirms the fact that central obesity is an important health problem among adult population in India [3,8]. Another important observation from our study is that non-HDL cholesterol, total/HDL and LDL/HDL cholesterol ratios but not LDL cholesterol were higher in patients with MS. This thing happens when LDL cholesterol is derived by using Friedewald formula for subjects having high serum TG levels like patients with MS [15]. There were more females compared to males among MS group. This is related to the differential relationship between central obesity and gender among hypertensive population [16]. Other than female gender, higher BMI was also associated with presence of MS in our subjects similar to findings from a Korean study [13]. This relationship even exists in non-hypertensive subjects [12]. BMI cut off of 23(overweight) had high sensitivity (94%) with low specificity (23.5%) for diagnosing MS among study participants (Fig. 1). So, this BMI cut off can be used for screening of MS among hypertensive subjects. But we should not forget that the study was done at a tertiary care centre with a small sample size. The target organ damage is more in hypertensive subjects with MS as compared to those without it [2,13]. So, all hypertensive subjects should be screened for presence of MS during the initial diagnosis itself. However, doing FPG & lipid profile may not be possible in all hypertensive patients in a country like India. Therefore, MS should be ruled out at least in overweight (BMI 23) hypertensive subjects so that early intervention can be initiated to take care additional cardio-metabolic risk factors in them.
Please cite this article in press as: Sahoo JP, et al. Predictors for diagnosing metabolic syndrome among hypertensive patients in a tertiary care centre. Diab Met Syndr: Clin Res Rev (2015), http://dx.doi.org/10.1016/j.dsx.2015.02.011
G Model
DSX-459; No. of Pages 3 J.P. Sahoo et al. / Diabetes & Metabolic Syndrome: Clinical Research & Reviews xxx (2015) xxx–xxx
Source of funding None.
Conflict of interest None declared. References [1] Neupane D, McLachlan CS, Sharma R, Gyawali B, Khanal V, Mishra SR, et al. Prevalence of hypertension in member countries of South Asian Association for Regional Cooperation (SAARC): systematic review and meta-analysis. Medicine (Baltimore) 2014;93(13):e74. [2] Redon J, Cı´fkova´ R. The metabolic syndrome in hypertension: diagnostic and therapeutic implications. Curr Hypertens Rep 2007;9(4):305–13. [3] Thakur S, Raina S, Thakur S, Negi PC, Verma BS. Prevalence of metabolic syndrome among newly diagnosed hypertensive patients in the hills of Himachal Pradesh, India. Indian J Endocrinol Metab 2013;17(4):723–6. [4] Farsang C, Naditch-Brule L, Perlini S, Zidek W, Kjeldsen SE, GOOD investigators. Inter-regional comparisons of the prevalence of cardiometabolic risk factors in patients with hypertension in Europe: the GOOD survey. J Hum Hypertens 2009;23:316–24. [5] Akintunde AA, Ayodele OE, Akinwusi PO, Opadijo GO. Metabolic syndrome: comparison of occurrence using three definitions in hypertensive subjects. Clin Med Res 2011;9(1):26–31. [6] Tsioufis C, Kasiakogias A, Tsiachris D, Kordalis A, Thomopoulos C, Giakoumis M, et al. Metabolic syndrome and exaggerated blood pressure response to exercise in newly diagnosed hypertensive patients. Eur J Prev Cardiol 2012;19: 467–73.
3
[7] Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo Jr JL, et al. The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure. JAMA 2003;289:2560–72. [8] Misra A, Chowbey P, Makkar BM, Vikram NK, Wasir JS, Chadha D, et al. Consensus statement for diagnosis of obesity, abdominal obesity and the metabolic syndrome for Asian Indians and recommendations for physical activity, medical and surgical management. JAPI 2009;57:163–70. [9] Rifai N, Warnick GR, McNamara JR, Belcher JD, Grinstead GF, Frantz Jr ID. Measurement of low-density-lipoprotein cholesterol in serum: a status report. Clin Chem 1992;38:150–60. [10] Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the 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). JAMA 2001;285:2486–97. [11] Alberti KG, Zimmet P, Shaw J. Metabolic syndrome – a new world-wide definition. A consensus statement from the International Diabetes Federation. Diabetes Med 2006;23:469–80. [12] Deedwania PC, Gupta R, Sharma KK, Achari V, Gupta B, Maheshwari A, et al. High prevalence of metabolic syndrome among urban subjects in India: a multisite study. Diabetes Metab Syndr 2014;8(3):156–61. [13] Lee SR, Cha MJ, Kang DY, Oh KC, Shin DH, Lee HY. Increased prevalence of metabolic syndrome among hypertensive population: ten years’ trend of the Korean National Health and Nutrition Examination Survey. Int J Cardiol 2013;166(3):633–9. [14] de la Sierra A, Romero R, Bonet J, Pe´rez M, Lo´pez JS, Ravella R, et al. Prevalence and general features of the metabolic syndrome in the Spanish hypertensive population [Article in Spanish]. Med Clin (Barc) 2006;126(11):406–9. [15] Lemieux I, Lamarche B, Couillard C, Pascot A, Cantin B, Bergeron J, et al. Total cholesterol/HDL cholesterol ratio vs LDL cholesterol/HDL cholesterol ratio as indices of ischemic heart disease risk in men: the Quebec Cardiovascular Study. Arch Intern Med 2001;161(22):2685–92. [16] Gus M, Fuchs SC, Moreira LB, Moraes RS, Wiehe M, Silva AF, et al. Association between different measurements of obesity and incidence of hypertension. Am J Hypertens 2004;17:50–3.
Please cite this article in press as: Sahoo JP, et al. Predictors for diagnosing metabolic syndrome among hypertensive patients in a tertiary care centre. Diab Met Syndr: Clin Res Rev (2015), http://dx.doi.org/10.1016/j.dsx.2015.02.011