Predictors of Serum Levels of High Sensitivity C-Reactive Protein and Systolic Blood Pressure in Overweight and Obese Nondiabetic Women in Tehran: A Cross-Sectional Study

Abstract

Objective:

Extra fat mass is usually accompanied by metabolic as well as clinical derangements, including systemic inflammation and high blood pressure. This study aimed to evaluate the associations among anthropometric indicators, blood levels of high-sensitivity C-reactive protein (hsCRP), lipid profile, blood glucose, insulin resistance, and blood pressure and determine the actual predictors of hsCRP and blood pressure in overweight/obese nondiabetic women in Tehran.

Subjects and Methods:

A total of 200 women with body mass index (BMI) of ≥25 kg/m² were enrolled in a cross-sectional study. Dietary intake and anthropometric as well as laboratory evaluations, including fasting plasma glucose (FPG), lipid profile, serum insulin, and hsCRP, were performed for all the subjects. Pearson (r) and Spearman (r _s) correlation coefficients and multivariate linear regression analysis were used to establish a model to predict hsCRP and systolic blood pressure (SBP) variations.

Results:

Although serum hsCRP directly correlated with levels of FPG, triglycerides (TG), total cholesterol, BMI, and waist circumference (WC), its strongest association was found with percent of body fat mass (FM) (r _s = 0.326, p < 0.001). Also, SBP directly correlated with FPG, TG, and FM, but it was more strongly correlated with BMI (r = 0.343, p < 0.001) and WC (r _s = 0.350, p < 0.001). No association was found between blood or anthropometric variables and dietary data. In different regression models, WC and FM were the predictors of hsCRP, but BMI was the significant predictor of SBP.

Conclusion:

Adiposity in Iranian middle-aged women can affect both inflammatory biomarkers and SBP, thus predisposing for metabolic syndrome and further morbidities. We identified FM and WC as the predictors of serum hsCRP levels and BMI as the predictor of SBP in our population.

Introduction

O besity is now considered as a very serious health problem affecting a significant proportion of people at all ages.^1
–3 The prevalence of obesity has steadily increased over the last several decades in industrialized countries as well as in the developing world.² Results of the Third National Surveillance of Risk Factors of Non-Communicable Diseases (SuRFNCD-2007) showed that prevalence of obesity and overweight in Iranian adults was 22.3% and 36.3%, respectively.⁴ The obesity epidemic is closely associated with a prominent rise in the incidence of hypertension,⁵ both of which result in a major increase in cardiovascular risk.⁶ Evidence regarding the importance of obesity in relation to hypertension (HTN) is available from trials of weight reduction and its effect on blood pressure as a clinical outcome.⁷ Although a great part of the cardiovascular risk attributable to obesity may be mediated through effects on blood pressure, lipids, and/or glucose tolerance, some of this risk may be interceded by inflammatory pathways.⁸ Obesity is a major inducer of systemic inflammation and this likely contributes to the obesity-linked cardiovascular disease (CVD).⁹ Raised serum levels of C-reactive protein (CRP), an inflammatory marker that is an independent predictor of CVD,^10
–12 have been reported in obese individuals,^13,14 and recently it has been reported to be positively associated with body mass index (BMI).¹⁵ One of the important and strong contributors to the increased serum levels of CRP is visceral adiposity,^9,16,17 because adipokines have been shown to stimulate hepatic synthesis of CRP.^18
–20 Numerous studies have demonstrated that CRP is positively associated with such measures of truncal adiposity as waist circumference (WC)²¹ and waist-to-hip ratio (WHR).^22
–24 However, the associations among dietary intake (fat, carbohydrate, and protein), lipid profile, blood glucose, insulin resistance indicators, and serum levels of CRP have been investigated less in nondiabetic subjects. The purpose of this study was, first, to evaluate the associations among anthropometric indicators, blood levels of high-sensitivity CRP (hsCRP), lipid profile, blood glucose, insulin resistance, and blood pressure and, second, to determine the actual predictors of hsCRP and blood pressure in middle-aged overweight/obese nondiabetic women in Tehran.

Subjects and Methods

Study design

This was a cross-sectional study performed during the fall and winter of 2009–2010. In the first visit, all subjects were provided with full information about the study before they signed a written informed consent. A general questionnaire for demographic data was completed for the participants and then they were instructed to fast for at least 12 h for blood sampling in the next day. Percent of body fat mass (FM) was also estimated for all the participants. Data on dietary intake were collected in different days. The scientific and ethical issues of this study were approved by the Research Council and the Ethical Committee of the National Nutrition and Food Technology Research Institute (NNFTRI), respectively.

Subjects

Two hundred women were enrolled in the study. All participants were recruited from clients attending health centers for routine checkup and from schools staff in Tehran by simple sampling. The inclusion criteria comprised: (1) Willingness to participate; (2) age 30–50 years; (3) being nonmenopausal; (4) BMI ≥25; (5) absence of any clinical disease; and (6) having taken no vitamin supplement for the period 3 months preceding the study. This latter criterion was necessary because many subjects might take supplements irregularly so that estimation of their nutrient intakes could be very problematic. Exclusion criteria were: (1) Pregnancy or lactation and (2) current clinical disease, especially kidney, liver, cardiovascular, respiratory, or inflammatory.

Dietary assessment

To assess dietary intake, we employed a semiquantitative Food Frequency Questionnaire (FFQ) and 24-h dietary recall for 2 days (including a weekend day). The validity of the questionnaires has been shown previously.²⁵

Anthropometry and blood pressure

Weight was measured to the nearest of 0.1 kg using a digital scale (Seca 840, Germany). Height was measured to the nearest of 0.1 cm without shoes with a measuring tape. BMI was calculated using the equation BMI = weight (kg)/height (m²). WC was measured at the midpoint between the lowest rib and iliac crest while the subject was in standing position and after expiration. Hip circumference (HC) was also measured to the nearest of 0.1 cm with a measuring tape. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured in seated position after a 5-min rest using a digital sphygmomanometer (BC08, Beurer, Germany). Hypertension was defined as SBP ≥135 mmHg and DBP ≥85mmHg or more.

Estimation of body fat mass

The percentage of body FM was evaluated using a bioelectrical impedance analysis (BIA) system (Quadscan 4000, BodyStat, UK).

Laboratory investigations

Blood samples collected after 12–14 h of fasting were transferred into two tubes, either with or without the anticoagulant sodium fluoride. The sodium fluoride–anticoagulated tube was used to measure plasma levels of glucose and lipids in no more than 2 h after bleeding. Sera recovered after centrifugation of clot samples at 2,000 × g at room temperature were transferred to the fresh tubes in aliquots and kept at −80°C until the day of analysis.

Plasma triglycerides (TG), total cholesterol, low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-c) were measured using enzymatic colorimetric methods. Serum levels of hs-CRP were measured by immunotorbidometric method. All of these assays were performed by commercial kits (Pars Azmoon, Iran) and with the aid of an auto-analyzer (Selecta E, Vitalab, Netherlands).

Serum insulin was measured by immunoradiometric assay (IRMA) using a commercial kit (Biosource, Belgium) and a γ-counter system (Gen II, Genesys, USA). To evaluate insulin resistance, homeostasis model assessment of insulin resistance (HOMA-IR) index was calculated using the equation HOMA-IR = fasting plasma glucose (mmol/L) ×serum insulin (mU/L)/22.5.²⁶

Statistical analyses

Data were expressed as mean ± standard deviation (SD). Normality of data was evaluated using the Kolmogrov–Smirnov test. To evaluate correlations for data with or without normal distribution, Pearson (r) or Spearman (r _s) equations were used, respectively. Multivariate regression analysis was used to determine predictors of hsCRP and SBP.

Results

Insulin, HOMA-IR, TG, hsCRP, and WC data did not show normal distribution. The mean daily energy, fat, carbohydrate, and protein intake in our population was 1,953.3 ± 512.07 kcal, 57.2 ± 19.5 g, 265.5 ± 65.0 g, and 59.9 ± 14.7 g, respectively.

Anthropometric, laboratory, and blood pressure data are presented in Table 1. The frequency of overweight and obesity among our population was 43.9% and 56.1%, respectively. High SBP (>135 mmHg) was observed in 28 subjects (14%), whereas only 7 participants (3.5%) had high DBP (>95 mmHg).

Table 1.

Clinical Characteristic of the Study Subjects

Variable	Mean ± SD
Age (years)	42.2 ± 7.2
Weight (kg)	79.2 ± 11.9
WC (cm)	98.6 ± 9.5
HC (cm)	106.4 ± 8.5
WHR	0.9 ± 0.0
BMI (kg/m²)	31.8 ± 5.2
FM (%)	40.7 ± 5.6
SBP (mmHg)	117.6 ± 15.3
DBP (mmHg)	74.1 ± 10.8
TG (mg/dL)	144.3 ± 70.0
TC (mg/dL)	182.4 ± 38.9
LDL-C (mg/dL)	98.7 ± 23.9
HDL-C (mg/dL)	46.9 ± 10.6
FPG (mg/dL)	96.5 ± 12.7
Insulin (mU/L)	16.4 ± 8.3
HOMA-IR	3.8 ± 1.9
hsCRP (mg/L)	2.8 ± 2.8

SD, standard deviation; WC, waist circumference; HC, hip circumference; WHR, waist-to-hip ratio; BMI, body mass index; FM, fat mass; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglycerides; TC, total cholesterol; LDL-C, low-density lipoprotein; HDL-C, high-density lipoprotein cholesterol; FPG, fasting plasma glucose; HOMA-IR, homeostasis model assessment-insulin resistance; hsCRP, high-sensitivity C-reactive protein.

Although serum hsCRP correlated significantly with fasting plasma glucose (FPG), triglycerides, BMI, and WC (Table 2), its strongest correlation was with FM (r _s = 0.326, p < 0.001). There was no correlation between serum levels of hsCRP and insulin or HOMA. Also, SBP correlated more strongly with WC (r _s = 0.350, p < 0.001) and BMI (r = 0.343, p < 0.001) than with FPG, triglyceride, WHR, and FM (Table 2). FM expectedly correlated with BMI (r = 0.594, p < 0.001). There was no association between SBP and hsCRP with insulin, HOMA-IR, and HDL-C. However, in those subjects with insulin resistance (HOMA-IR ≥2.4,²⁷ n = 146), HOMA-IR correlated with WC (r _s = 0.266, p = 0.001), BMI (r _s = 0.170, p = 0.049), and hsCRP (r _s = 0.245, p = 0.004). There was no significant correlation among dietary data and other variables.

Table 2.

Correlations Between hsCRP and SBP with Anthropometric and Metabolic Parameters

Variable	FPG	TG	TC	LDL	BMI	WC	HC	WHR	FM
hsCRP	0.176	0.188	0.216	0.217	0.314	0.308	0.264	N.S.	0.326
	(0.015)	(0.009)	(0.002)	(0.002)	(<0.001)	(<0.001)	(<0.001)		(<0.001)
SBP	0.169	0.180	N.S.	N.S.	0.343	0.350	N.S.	0.141	0.206
	(0.018)	(0.011)			(<0.001)	(<0.001)		(0.049)	(0.005)

Numbers in parentheses are P values. Correlation coefficients related to hsCRP (first row) all resulted from Spearman analysis. In the second row, however, only coefficients denoting TG and WC are from Spearman analysis and the others resulted from the Pearson equation.

hsCRP, high-sensitivity C-reactive protein; SBP, systolic blood pressure; FPG, fasting plasma glucose; TG, triglycerides; TC, total cholesterol; LDL, low-density lipoprotein; BMI, body mass index; WC, waist circumference; HC, hip circumference; WHR, waist-to-hip ratio; FM, fat mass; N.S., not significant.

To evaluate the relative contributions of the different variables to hsCRP level and SBP in overweight and obese subjects, stepwise multivariate regression analysis was performed with hsCRP and SBP as dependent variables. Variables that had significant correlations with hsCRP and SBP used in the regression models as independent variables. In the model 1, with hsCRP as dependent variable and biochemical parameters (TG, FPG, LDL-C, and total cholesterol) as independent variables, cholesterol was a significant predictor of hsCRP (p = 0.011) (Table 3). In this model 3.4% of the variability of hsCRP levels was explained by cholesterol. After excluding cholesterol from this model, LDL-C was the only significant predictor of hsCRP (p = 0.025). In model 2, we assessed the contribution of anthropometric variables (BMI, WC, and FM) to serum levels of hsCRP. In this model, FM (p = 0.015) and WC (p = 0.017) were the significant predictors. The variability of hsCRP levels explained only by FM was 9.9%, but with both FM and WC was 13%. The predictive role of BMI alone in the regression model (p < 0.0001) disappeared in the presence of WC or FM. In model 3, by inclusion, anthropometric variables together with cholesterol, only WC (p = 0.020) and FM (p = 0.015) remained in the model. The variability explained by FM was 9.6%, but with both FM and WC was 12.5% (Table 3). Following excluding cholesterol, only LDL-C remained in the model (p = 0.025).

Table 3.

Multivariate Linear Regression Analysis with hsCRP as the Dependent Variable

Independent variables	B	SE	β	t	P
Model 1 (FPG, TG, TC, and LDL-C)
WC	0.14	0.005	0.185	2.55	0.011
Model 2 (WC, FM, BMI)
FM	0.112	0.045	0.208	2.458	0.015
WC	0.065	0.027	0.205	2.420	0.017
Model 3 (model 2 + TC)
FM	0.112	0.046	0.208	2.450	0.015
WC	0.064	0.027	0.200	2.355	0.020

hsCRP, high-sensitivity C-reactive protein; SE, standard error; FPG, fasting plasma glucose; TG, triglcyerides; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; WC, waist circumference; FM, fat mass; BMI, body mass index.

In multivariate regression analysis for SBP as a dependent variable, FPG, TG, and WC entered model 1 (Table 4). In this model, WC as the only predictor of SBP (p < 0.001) could explain 12% of SBP variability. This association remained significant (p < 0.001), even after introducing FM and WHR in the model. In model 2, however, following entering BMI, the effects of WC and WHR disappeared (p = 0.43). In this model, 9% of SBP variability could be explained by BMI. In model 3, with FPG, TG, and BMI as independent variables, BMI (p < 0.001) and TG (p = 0.028) were SBP predictors. BMI and BMI together with TG explained 10% and 12.5% of SBP variability, respectively (Table 4). In this model, FPG became a predictor of SBP only whenever WC and FM were not in the model.

Table 4.

Multivariate Linear Regression Analysis with SBP as the Dependent Variable

Independent variables	B	SE	β	t	P
Model 1 (TG, FPG, WC)
WC	0.563	0.112	0.344	5.034	<0.0001
Model 2 (model 1 + FM, WHR, BMI)
BMI	0.978	0.231	0.315	4.234	<0.0001
Model 3 (FPG, TG, BMI)
BMI	0.925	0.219	0.300	4.228	<0.0001
FPG	0.182	0.082	0.157	2.215	0.028

SBP, systolic blood pressure; SE, standard error; TG, triglcyerides; FPG, fasting plasma glucose; WC, waist circumference; WHR, waist-to-hip ratio; BMI, body mass index.

Discussion

Our finding of an association between FM/WC and serum levels of hsCRP supports well the hypothesis that inflammatory adipokines induce hepatic CRP secretion.¹⁸ Obesity-induced low-grade systemic inflammation has been considered as a contributing factor to such co-morbidities as hypertension and CVD.^9,18,19,28 There was no association between dietary intakes and serum levels of hsCRP and blood pressure in this study. Accordingly, although weight loss in overweight people may cause lowering of blood CRP, it has been shown that this effect is independent of the amount of dietary fat.²⁹ Independence of serum CRP levels and dietary carbohydrate/protein ratio has also been reported.³⁰

The association between serum LDL-C and hsCRP is noteworthy because in a cohort study on the pharmacological prophylaxis of CVD in 15,548 initially healthy subjects, it was found that a statin, resuvastatin, lowered both LDL-C and CRP and was accompanied by 55% and 62% reduction in vascular events, respectively.³¹ The predictive value of FM and WC, but not BMI, for serum levels of hsCRP in our regression model indicates that in the Iranian women both the amount and distribution of body fat, compared to BMI alone, are more important determinants of low-grade systemic inflammation usually seen in adiposity.³²

In a recent study conducted on 833 women and 486 men, central obesity was found to be associated with serum hsCRP in men with central obesity (low BMI, high WC) and also in women with generalized adiposity (high BMI, high WC).¹⁷ In accord with our finding, in another study the variability of CRP levels explained by WC was considerably higher than BMI (15% vs. 0.4%, respectively).³³ Extra fat mass commonly presented as overweight and obesity has been already considered as the major determinant of elevated levels of CRP in the subjects with metabolic syndrome.³⁴ However, the location of the extra fat mass is likely to be crucial. Abdominal obesity, even in nonobese individuals, may cause elevation of serum CRP levels, and it is independent of BMI.²³ It has been shown that CRP levels rise in parallel with increasing visceral fat.⁸ Experimental studies suggest that abdominal adipose tissue is a major source of proinflammatory cytokines, including interleukin-6 (IL-6), which is an important stimulator of hepatic CRP synthesis.¹⁷ Approximately 30% of circulating IL-6 is estimated to be from adipose tissue.³⁵ Moreover, visceral fat produces three-fold more IL-6 than subcutaneous fat.³⁶ It has been shown that human adipocytes could produce CRP in response to inflammatory cytokines, thereby suggesting a new link between obesity and vascular inflammation.¹⁸ Both CRP and visceral adiposity have predictive potential regarding future cardiovascular events.^11,37,38 Prospective epidemiological studies in healthy subjects showed an association between elevated CRP levels and the risk of cardiovascular events and peripheral vascular disease.^39,40 Central fat appears to have more adverse effects on cardiovascular risk than the fat stored in other locations, such as the peripheral depots, arms or legs.^8,41

The importance of truncal obesity in relation to inflammation lies in the observation that the rate of lipolysis in truncal fat is higher than in lower body fat.⁴¹ Saturated free fatty acids (FFAs) thus released can induce toll-like receptor-4 (TLR-4) expression, an innate immune response receptor, which in turn activates nuclear factor-κB (NF-κB) in macrophages, leading to proinflammatory cytokines secretion.^42,43

We found a significant relationship between hsCRP levels and cholesterol, TG, and LDL-C. By controlling FM and WC, however, this association disappeared in part of TG and cholesterol and it decreased with LDL-C. Therefore, this relationship between lipid profile and hsCRP levels might be secondary to the variation in fat mass and WC. Some other studies showed significant associations between CRP and hypertriglyceridemia.^16,44 Likewise, in another study, the associations between serum CRP and lipid levels disappeared following addition of visceral fat into analysis.⁴⁵

The prevalence of high SBP in this study is comparable with the occurrence of high blood pressure reported from Tehran Lipid and Glucose Study (TLGS), which was 15% in the 28- to 69-year-old population in Tehran (16% in men and 14% in women) during 1999–2000.⁴⁶ In our regression model of SBP, plasma glucose became significant only in the absence of WC and FM. This observation suggests that the effect of plasma glucose on blood pressure was likely to be indirect and secondary to the effect of abdominal fat mass on blood glucose. Opposed to the significant correlations between HOMA-IR and obesity indices in this study, it has recently been demonstrated that even insulin resistance, although in strong association with serum levels of CRP, may not be related to abdominal obesity in some populations.²¹ On the other hand, increased serum levels of FFAs usually seen in obesity may bring about detrimental outcomes by developing insulin resistance,^47,48 which is the common finding in metabolic syndrome.

Our finding regarding the disappearance of the effects of WC and WHR in the presence of BMI in the regression model of predicting SBP is consistent with some other studies. For instance, in a study 5,296 initially normotensive women aged between 20 and 77 years were followed up for incidence of HTN during 1971–2004. After adjusting for potential confounders, higher baseline BMI, WC, and percent of body fat were associated with greater risk of developing HTN, respectively (p _trend < 0.0001 for each). After further adjustment for the adiposity measures, the direct association between BMI and HTN attenuated, but it remained statistically significant (p _trend = 0.004). It was concluded that the risk for HTN might be better identified by obesity defined by higher BMI than higher WC or higher percent of body fat.⁴⁹ Positive relationships among BMI with SBP and DBP and total cholesterol levels have been reported in middle-aged adults.⁵⁰

It was demonstrated that a 10% elevation in BMI was associated with a 3-mmHg increase in SBP.⁵¹ The same association has been documented, indicating that the increase in BMI accounted for nearly all of the increase in HTN in men, but some of the increases in HTN in women were attributable to factors other than BMI increments.⁴⁰ In the present study, BMI explained nearly 10% of the variability of SBP. In another study, BMI accounted for 4% of the variance in the lipoprotein, lipid, and blood pressure measures in men and for 3–6% of the variance in blood lipid and blood pressure measures in women.⁵² This consistency in findings presents evidence that favors the consideration of the BMI/adiposity increment as a causal factor in the etiology of HTN.

In contrast, there is some evidence emphasizing the role of body fat distribution in HTN. In the National Health and Nutritional Examination Survey III, a progressive increase in the prevalence of HTN was seen with increasing BMI, and this association increased further with abdominal adiposity.⁵³ In another study, the risk of HTN was higher with increasing WC and age, and WC was found to be a stronger determinant of hypertension than BMI.⁵⁴ Association of blood pressure with weight and body fat distribution as judged by WC has been recently investigated in adults⁵⁵ as well as in children.^56,57 It even has been suggested that WC, compared to WHR, BMI, and skin-fold thickness, is the only clinical index of adiposity that is associated with blood pressure, especially in a community with a high prevalence of obesity,⁵⁸ and visceral fat is the link among inflammation, blood pressure, and CVD.⁹ However, a metaanalysis of weight reduction–intervention studies showed that a reduction in weight of 5 kg (by means of energy restriction, exercise, or both) was enough to achieve a reduction in SBP of 4 mmHg.⁵⁹ Hypertension is the most important cardiovascular risk factor worldwide, contributing to one-half of the coronary heart disease and approximately two thirds of the cerebrovascular disease burdens.⁴⁰ The overall prevalence of HTN in Iranians aged 25–65 years was reported 19.8% in men and 26.9% in women with 59.6% of men and 44.5% of women affected by pre-HTN.⁶⁰ Interestingly, the age-adjusted prevalence of HTN in Iranian women was found higher than in American women (35.7% vs. 30.5%).⁶¹

Visceral fat has been considered as a link among FM, raised inflammatory biomarkers, and elevated blood pressure,⁹ commonly seen in metabolic syndrome. The preventive and therapeutic effects of weight control on HTN have already been concluded,^50,58 and a recent study showed that weight loss in patients with type 2 diabetes could be beneficial to glycemic control, blood pressure, and dyslipidemia.⁶²

This study had however some limitations. First, it was cross sectional and therefore any causal relationship is very hard to determine. Second, this study was conducted just on overweight/obese women, and confirmation of the results is needed in a larger as well as other subpopulations. And third, serum levels of adipokines were also not measured either.

Altogether, it was concluded that adiposity in Iranian middle-aged women can affect both inflammatory biomarkers and SBP, thus predisposing for metabolic syndrome and further morbidities. We identified FM and WC as the predictors of serum hsCRP levels and BMI as the predictor of SBP in our population.

Footnotes

Acknowledgments

This research project was founded by National Nutrition and Food Technology Research Institute (NNFTRI). All laboratory work was done in the Laboratory of Nutrition Research, NNFTRI. We thank all of the participants who assisted us by donating their blood and the staff of the health centers and schools who sincerely took part in the study.

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