High sensitivity C-reactive protein: A predictor of Metabolic syndrome in young medicos

of Kolkata

ABSTRACT

This study was done to find out the prevalence of Metabolic syndrome (MS) among young

medicos and to determine the relationship of highsensitivity C-reactive protein (hsCRP) with the

parameters of MS i.e. Body mass index, Waist circumference, Blood pressure, serum lipid

profile, Fasting plasma glucose and Insulin resistance (IR). 160 young medicos between 17-25

years of age of R.G.Kar medical college and hospital, Kolkata were selected as study population.

We found that the prevalence of MS is 13.13%. hsCRP is significantly elevated in medicos with

MS (p<0.001) in comparison to those without MS. Pearson’s correlation showed hsCRP is

significantly correlated with all the parameters of MS. Multivariate regression analysis showed

that the variable that appear to influence the changes in hsCRP most is IR. Receiver operating-

characteristic curve analysis shows that the area under the curve for the prediction of the MS is

0.969 for hsCRP with cut off value of >7.78 µg/ml with 100% sensitivity and 94% specificity.

Hence, it can be concluded that hsCRP can be used successfully as a predictor of MS in young

Indians with sedentary lifestyle.

KEY WORDS

Obesity, Metabolic syndrome, hsCRP, Insulin resistance and Young Medicos.

INTRODUCTION

Obesity is an increasingly important health problem worldwide including the developing

countries. Same observation holds good when urban India is concerned. Almost 30-65% of adult

urban Indians are either overweight or obese or have abdominal obesity (Misra et al., 2009).

Usual occurrence of obesity was found in aged population but now a days children and

adolescents also are seen to follow the same trend . The rising prevalence of obesity in India has

a direct correlation with increasing prevalence of obesity-related co morbidities, among which

the most important one is metabolic syndrome (MS) (Misra et al., 2009). Early identification of

MS prevents future development of cardiovascular disease (CVD) and type2 diabetes mellitus

(T2DM) (Sharma and Mishra, 2007). MS consists of a cardiovascular risk factors that are related

to insulin resistance (IR), elevated waist circumference (WC), hypertriglyceridemia, low HDL-

cholesterol (HDLC), hypertension, and fasting hyperglycemia. MS leads to a state of low grade

inflammation and functions as an independent predictor of long-term risk for CVD and T2DM

among both young adults and children. Inflammation as indicated by high sensitivity C-reactive

protein (hsCRP), is a key factor linking MS to CVD and T2DM (DeBoer et al., 2011). More

recently, the chronic low-grade inflammatory condition that often accompanies the metabolic

syndrome has been implicated as a major factor both in the installation of the metabolic

syndrome and its associated pathophysiological consequences (Sharma, 2011). CRP is one of the

most sensitive acute phase proteins and appears to be a nonspecific host defence against

inflammation. CRP is traditionally measured down to concentrations of 3-5 mg/L. But through

changes in methodology enables to measure hs-CRP down to concentrations around 0.3 mg/L.

This 10-fold improvement in sensitivity allows hs-CRP to be used to detect low levels of chronic

inflammation much earlier than before.High level of hsCRP is directly related to development of

obesity related complications such as CVD (Rifai et al., 2006, Ridker, 2001). MS is associated

with a prothrombotic and proinflammatory state. Visceral adipose tissue releases pro

inflammatory cytokines that drive CRP production. CRP correlates with generalized and

abdominal adiposity, and predicts future risk of CVD and T2DM (Vikram et al., 2006). Previous

cross-sectional studies have shown that elevated hs-CRP levels correlate significantly with

features of metabolic abnormality, including adiposity, hyperinsulinemia, IR, hyper

triglyceridemia and low HDLC (Frohlich et al., 2000, Hak et al., 1999, Chambers et al., 2001,

Mendall et al., 1996). Several studies have demonstrated that many people with MS have an

elevated hsCRP concentration (Saisho et al., 2010) which may predict their risk for future

adverse events (Tomiyama et al., 2005). Most of these studies was done on foreign population

and on aged people but very few studies were done on young especially in eastern region of

India so far. Due to increasing obesity in young India it is important to know the risk factors of

future development of CVD and T2DM in young.

AIMS AND OBJECTIVES

a) To find out the prevalence of MS in young.

b) To determine the relationship of hsCRP with different parameters of MS in them.

MATERIALS AND METHODS

This descriptive, observational and cross sectional study was conducted in a tertiary

referral care teaching Institution, R.G.Kar Medical college and hospital, Kolkata in the

department of Biochemistry and Community medicine from July 2012 to June 2013, after taking

the required ethical clearance from the institute. The young medicos between 17-25 years of age

were selected as study population. After taking written informed consent the medicos were

interviewed, detailed history was taken and the case record form was filled. After an initial

general survey systemic examinations were done. Subjects with history of diabetes mellitus,

endocrinal disorders, smoking, hypertension, impaired lipid profile, cardiac diseases were

excluded from the study. The total study population became 160.

Definition of MS

Modified 2005 NCEP ATP III 2 (National cholesterol education programme, adult

treatment panel III) criteria and consensus guidelines of IDF (International diabetes federation)

were considered for diagnosis of MS. According to the guideline, presence of at least three of the

following components: (a) abdominal obesity (waist circumference > 90 cm for Asian men and >

80 cm for Asian women) (b) triglycerides ≥ 150mg/dl (c) HDLC ≤ 40mg/dl for men or 50mg/dl

for women (d) systolic/diastolic blood pressure ≥ 130/85mmHg or receiving drug treatment and

(e) fasting plasma glucose ≥ 100mg/dl was required for diagnosis of MS (Dhanaraj et al., 2009).

Height, weight, Body mass index(BMI), WC and Systolic and diastolic blood

pressure(SBP/DBP) in mm Hg of the participants were recorded, followed by laboratory analysis

of Fasting plasma glucose(FPG), Fasting plasma insulin(FPI), IR, Serum Lipid profile and

Serum hs-CRP.

Anthropometric measurements

Anthropometric measurements (height, weight, BMI and WC) were taken with the

subject dressed in light clothing with shoes removed. Height was recorded to the nearest 1 cm.

Weight was recorded using a portable beam balance weighing machine to the nearest of 100 gm.

Then individual student’s BMI was then calculated by dividing their weight (kg.) with their

height’s square in meters(kg/m2). Measurement of WC in cm. was taken midway between the

inferior margin of the last rib and the crest of the ileum in a horizontal plane.

LABORATORY ANALYSIS

For analysis of biochemical parameters fasting venous blood was collected taking all

precaution in fluoride and clotted vials as per norm required for different test parameters. 10 ml

of fasting venous blood was collected from each participant, 8 ml dispensed in clotted vial and 2

ml in fluoride vial. Blood was allowed to clot and centrifuged to separate the serum from cells.

Samples were then stored in (-200 C) in separate aliquots before analysis. FPG was estimated by

Glucose oxidase / peroxidase method (Trinder, 1969a, Trinder, 1969b). Serum total cholesterol

(Chol) was measured by cholesterol oxidase phenylperoxidase aminophenozonphenol (CHOD-

PAP) method (Allain et al., 1974) serum Triglyceride(TG) by glycerol phosphate oxidase

peroxidase (GPO-PAP) method (Fossati and Prencipe, 1982, Bucolo and David, 1973) serum

HDLC by Direct HDL cholesterol enzymatic method (Assmann et al., 1983, 1992) serum Low

density lipoprotein (LDLC) & Very low density lipoprotein (VLDLC) cholesterol was calculated

using Friedewald et al. formula (Friedewald et al., 1972). The quantitative determination of

serum hs-CRP was done by Microplate Immuno enzymometric assay. Kits were commercially

available namely Accubind (Monobind Inc.USA). hsCRP was measured by sandwich ELISA

method in µg/ml (Kimberly et al., 2003) with 6.9% within assay and 6.7% between assay

precision. Serum insulin was also measured by sandwich ELISA method in µIU/ml (Turkington

et al., 1982, Kahn and Rosenthal, 1979) with 7.2% within assay and 6.5% between assay

precision. Kits were commercially available namely Accubind (Monobind Inc.USA). Estimation

of serum IR was done by Homeostatic model of assessment(HOMA-IR) method (Matthews et

al., 1985) first proposed by David R. Matthews et al in 1985 where IR was assessed from basal

(fasting) glucose and insulin with the help of computer based software.

STATISTICAL ANALYSIS

Statistical analysis was done by software based computer programme (SPSS version 17

and MedCalc version 11.3). Values were expressed in Mean ± SD. p value <0.05 was considered

significant. For comparison between the two groups (with MS and without MS) independent

sample ‘t’ test was performed. For determination of relationship of hsCRP with the other

parameters of MS, Pearson’s bivariate correlation and multiple regression analysis were done.

For estimation of prediction value of hsCRP for MS receiver-operating characteristic (ROC)

curve analysis was done.

RESULTS

It was found that among 160 young medicos 21(13.13%) were diagnosed to have MS

who fulfilled the definition criteria of MS (Table 1). Independent samples ‘t’ test showed serum

hsCRP level was significantly elevated in those with MS (9.89±0.72) µg/ml in comparison to

those who did not have MS(3.14±2.08) µg/ml; p<0.01.(Table 2). Pearson’s bivariate correlation

analysis showed hsCRP was significantly correlated with all the parameters of MS, p value was

<0.01 in all cases.(Table 3) and correlation coefficient (‘r’) was highest for HOMA-IR(r=0.877)

(Table 3). When multiple regression analysis was done by stepwise method, where hsCRP was

taken as dependent variable it was found that among all the parameters of MS, hsCRP was

significantly dependent on BMI, FPI, HOMA-IR, CHOL and SBP (p<0.01) and among them

hsCRP can be most strongly predicted by HOMA-IR, where R-square value was 0.8265 and

coefficient was 6.4868 for HOMA-IR(Table 4). ROC curve analysis was done, it showed that the

area under ROC curve (AUC) for the prediction of the MS was 0.969 for hsCRP (Figure 1, Table

5), with p value <0.01 and the value of hsCRP >7.78µg/ml indicated the cutoff value of MS

with100% sensitivity and 94.2% specificity.(Table 5 and Figure 1).

DISCUSSION

Obesity increases the production of proinflammatory mediators from adipose tissue T cells and

contributes to insulin resistance in animal models (Yang et al., 2010). Statistically significant

associations of inflammation (hsCRP) with obesity were also reported in human. In the year

2006, N.K.Vikram et al found that among 324 young (14-25 years) urban North Indian male

fasting insulin and hs-CRP levels correlated significantly with BMI and WC. (Vikram et al.,

2006). Other research data done in different part of the world also suggests that there is a strong

link between overweight/adiposity with elevated CRP (Brooks et al., 2010, McDade et al., 2008,

Moran et al., 2007). Trayhurn et al studied this obesity related inflammation and marked it as

low grade chronic inflammation (Trayhurn and Wood, 2004). There is also increasing evidence

to show that this chronic subclinical inflammation is associated with the metabolic dysfunction

associated with obesity, which links to IR and MS (Sharma, 2011, Hotamisligil, 2006). Even in

children and adolescent it is also well established (Winer et al., 2006).High levels of CRP have

been shown to be an independent predictor of cardiovascular risk for all degrees of severity of

the MS. hs-CRP has been developed and used as a marker to predict coronary vascular diseases

in metabolic syndrome (Sharma, 2011). Furthermore, several studies demonstrate that CRP can

be used to predict the development of type 2 diabetes mellitus also (Pradhan et al., 2001, Pradhan

et al., 2003, Freeman et al., 2002). Tarkun et al found that endothelium-dependent vasodilation

was also correlated with hsCRP concentrations (Tarkun et al., 2004). But most of these studies

were done on western population. For example. Ridker et al conducted a study on American

women, where they suggested that measurement of CRP adds clinically important prognostic

information to the metabolic syndrome (Ridker et al., 2003). Western population differs

completely from Eastern population in respect to food habit, socio economic status, hours of

physical activity and genetic profile. Now a days obesity and its complications rapidly increases

in India also especially among urban population (Goyal et al., 2010). Medical students are

mostly habituated in sedentary lifestyle and involved in very little physical activities. There are

only very few studies have been done on young to know the role of hsCRP in MS specially in

eastern region of India till date. In this background it is important to find out the prevalence of

MS in young population where obesity plays an important role, as well as the role of hsCRP in

MS among young.

The reasons behind choosing young medicos as the study population in the present study

were manifold. They represent the at risk young urban generation, with sedentary lifestyle, at the

same time in future they will be in contact with the community. Aim of the present study is to

spread awareness among medicos at young age, so that they can help spreading the message

among young at risk people which may benefit them in modification of lifestyle earlier.

Magnitude of the problem among these study subjects can be used as a proxy for the rest young

population.

The present study followed the definition MS according to NCEP ATP III and IDF

guidelines and found out the prevalence of MS is 13.13% among 160 young medicos. A study

conducted by Apurva Sawant et al reported similar type of finding, where the prevalence of MS

was 19.52% among 548 study subjects of urban India in Mumbai (Sawant et al., 2011). In a

prospective study, Han TS et al (Han et al., 2002) found that at baseline, CRP correlated

significantly (P < 0.001) with all metabolic indexes and after 6 years, 14.2% of men and 16.0%

of women developed the MS. Our study corroborates with the other studies. We found that

serum hsCRP level was significantly elevated in MS group (9.89±0.72) µg/ml in comparison to

normal (3.14±2.08) µg/ml and hsCRP was significantly correlated with MS parameters (p<0.01).

By using multiple regression analysis we found that among all the parameters of MS, hsCRP was

significantly dependent (p<0.01) on HOMA-IR, BMI, SBP and CHOL with highest level of

dependence being on HOMA-IR. So it is in accordance with Han Ts et al. The ROC curve

analysis shows that the predictive power of hsCRP was very high (AUC=0.969, p<0.01) for MS.

Hence it can be concluded that hsCRP can be used successfully as a predictor of MS in young

Indian population specially who are involved in sedentary activities. If marked early, the young

population can successfully be counseled to modify their lifestyle in a manner that would

significantly reduce morbidity and mortality of T2DM and CVD. So our motto is to catch them

young. Early prediction in them may lead to a healthier India.

Acknowledgement

We are heartily obliged to the young medicos of R. G. Kar Medical College & Hospital,

Kolkata; whose co-operation and participation was the main pillars for the completion of this

work.

TABLES

Table 1

Distribution of medicos according to the MS

With MS Without MSNumber of

Medicos(N=160) 21 (13.13)% 139 (86.87)%

Male (N=84) 11 (13.09)% 73 (86.91)%Female (N=76) 10 (13.16)% 66 (86.84)%

Table 2

Values of BMI, WC, hsCRP, FPG, FPI, HOMA-IR, Lipid profile and Blood pressure in medicos with MS and without MS

Parameters Without MS (N=139) With MS (N=21)BMI (kg/m2) 22.64±1.67 30.92±2.77 **

WC (cm.)Male (N=84)Female (N=76)

88.33±7.5280.06±5.68

102.45±3.23**92.50±4.14 **

hsCRP (µg/ml) 3.14±2.08 9.89±0.72 **FPG (mg/dl) 85.55±9.09 110.29±5.67 **FPI (µIU/ml) 9.27±3.88 23.78±3.19 **HOMA-IR 1.19±0.51 3.14±0.42 **

Chol (mg/dl) 133.81±15.42 204.00±11.37 **TG (mg/dl) 100.71±14.11 159.43±6.03 **

HDLC (mg/dl) Male (N=84)Female (N=76)

41.96±3.1250.92±3.15

37.45±1.86 **44.10±4.07 **

LDLC (mg/dl) 67.44±16.78 131.49±12.05 **VLDLC (mg/dl) 20.14±2.82 31.89±1.21 **SBP (mm/Hg) 120.39±8.18 135.00±5.64 **DBP (mm/Hg) 77.56±5.14 86.67±3.72 **

Values are expressed in Mean±SD, ** indicates significant difference (p<0.01) in medicos with MS in comparison to without MS.

Table 3

Correlation between hsCRP and other parameters of MS

Parameters hsCRPPearson’s correlation coefficient (r)

BMI 0.856 **WC 0.611 **FPG 0.777 **FPI 0.864 **

HOMA-IR 0.877 **Chol 0.809 **TG 0.727 **

HDLC (-) 0.341 **LDLC 0.781 **

VLDLC 0.727 **SBP 0.594 **DBP 0.552 **

** indicates significant correlation between hsCRP with the other parameter.

Table 4

Multiple regression analysis between hsCRP with the other parameters

Dependent variable Y= hsCRP

Independent variables Coefficient Std. Error‘t’

value‘p’

value

(Constant) -9.7971

BMI 0.2188 0.06868 3.186 0.0017

FPI -0.6343 0.2071 -3.063 0.0026

HOMA_IR 6.4868 1.5969 4.062 0.0001

CHOL 0.01653 0.007036 2.349 0.0201

SBP 0.03240 0.01353 2.395 0.0178

Analysis of Variance F p<0.001

Coefficient of determination R2

ratio=146.6810

= 0.8265

Stepwise method was applied for regression analysis, variables were entered when p<0.05 and removed when p>0.1, variables not included in the model were WC, FPG, TG,HDLC,VLDLC, LDLC and DBP.

FIGURE

Figure 1

Area under ROC curve for the prediction of MS for hsCRP

Table 5

ROC curve analysis for the prediction of MS for hsCRP

AreaSignificance

level

Asymptotic 95% Confidence Interval

Lower Bound Upper Bound

0.969 0.001 0.929 0.990

Criterion values and coordinates of the ROC curve

Criterion(µg/ml) Sensitivity Specificity>4.88 100.00 91.37>5.11 100.00 92.09>6.97 100.00 92.81>7.11 100.00 93.53>7.78 100.00 94.24>8.54 95.24 94.24>8.69 90.48 94.24>8.81 85.71 94.24>8.86 80.95 94.24

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