Experimental & Clinical Cardiology

Non-HDL Cholesterol and Triglycerides are independently associated with anthropometrical indices in a Cypriot population of healthy adults.

Authors: Eleni Andreou, Dimitrios Papandreou, Photos Hadjigeorgiou, Nikolaos Rachaniotis,
Christiana Philippou, Katia Kyriakou, Thalia Avraam, Georgia Chappa, Prokopis Kallis, Chrystalleni
Lazarou, Christoforos Christoforou, Rebecca Kokkinoessayghostwritingfta, Christos Dioghenous, Antonis Zampelas,
Louis Loizou and Elena Aletrari
How to reference: Non-hdl and Triglycerides Are Independently Associated with Anthropometrical
Indices in a Cypriot Population of Healthy Adults./Eleni Andreou, Dimitrios Papandreou, Photos
Hadjigeorgiou, Nikolaos Rachaniotis, Christiana Philippou, Katia Kyriakou, Thalia Avraam, Georgia
Chappa, Prokopis Kallis, Chrystalleni Lazarou, Christoforos Christoforou, Rebecca Kokkinofta,
Christos Dioghenous, Antonis Zampelas, Louis Loizou and Elena Aletrari/Exp Clin Cardiol Vol 20
Issue6 pages 3682-3692 / 2014

Original Article

Abstract

BACKGROUND: The prevalence of overweight and obesity is increasing all over the world and is
accompanied by multiple cardiovascular risk factors. Anthropometrical indices are closely related with traditional cardiovascular risk factors. However, data is limited in healthy adults from Cyprus.
AIM: The aim of this study was to examine the relationship of different cardiovascular risk factors with anthropometrical indices in a healthy Cypriot population aged 18-80y.
RESULTS: Age, Body Mass Index, Waist Circumference and Body Fat were positively correlated with SBP, DBP and Non-HDL in both sexes. In multiple regression analysis, BMI, WC and TBF were found to be independently associated with TG in the female group, (Beta: 0.009, %95 CI: 0.001-0.018, P<0.033), (Beta: 0.005, %95 CI: 0.002-0.008, P<0.01), (Beta: 0.003, %95 CI: 0.001-0.007, P<0.046), respectively. In the male group, age and BMI were the only variables that have been found to be independently associated with Non-HDL (Beta: 0.527, %95 CI: 0.209- 0845, P<0.01), (Beta: 0544, %95 CI: 0.44-1.045, P<0.033), respectively.
CONLUSION: Non-HDL and triglycerides seem to be independently associated with various anthropometrical indexes. Public health awareness and nutrition education are needed in order to monitor these CV factors.

Keywords: Cardiovascular Risk factors; non-HDL; anthropometric indices; adults; Cyprus

Introduction

The prevalence of obesity (OB) throughout the world continues to increase with a fast pace. About 1.2 billion people globally are overweight with almost 300 million of them being obese [1]. Numerous comorbidities, including hypertension (HTN), type II diabetes mellitus, dyslipidemia, obstructive sleep apnea certain cancers, and major cardiovascular (CV) diseases have been found to be related with excess body weight and total body fat [2].
Cyprus is a small country, with a population of about 600,00 people. However, in a recent study the prevalence of overweight (OW) and OB was 46.9% and 28.8% for males and 26% and 27% for females, respectively [3]. These high numbers have possibly their roots starting at young ages, where the obesity levels are also high [4].
Despite the fact that the rates of cardiovascular diseases have been decreased over the last fifty years which reflects advances on the therapeutic models, there is still a lot to be done on the primary prevention, since the outcome is not the expected one [5]. More specifically the incidence in some European countries has been decreased while in others it has been increased [6].
Excess body weight, abdominal fat and total body fat are well known risk factors of CVD diabetes type 2 and other diseases [7,8]. In a recent study, [7] visceral fat was closely associated with a cluster of risk factors irrespective of sex while abdominal obesity has been proposed to be a high risk factor of obesity associated with the development of CVD frequently accompanying cardiometabolic risk factors n Western countries [8].
In addition to those factors, elevated triglycerides levels, along with increased waist circumference, elevated fasting glucose, elevated blood pressure, or reduced HDL-C levels are metabolic syndrome risk factors which are closely related to CVD [9]. Moreover, cross sectional studies have demonstrated the value of non-HDL cholesterol as a new index of CVD in different populations from Europe [10]
The aim of this study was to examine the relationship of different cardiovascular risk factors with anthropometrical indices in a health population of Cyprus.

Methods

The current study was conducted during 2005-2009, and included 1001 Cypriot adults in the age range 18 to 80y (48.5% males/51.5% females). Approval to conduct the study was granted by the Cyprus National Bioethics Committee. The sample was representative from all main cities and suburbs in Cyprus (Nicosia, Limassol, Pafos, Larnaka and Famagusta). The selection of the subjects was performed randomly using the 2005 telephone directory, and the total final sample was stratified in full compliance with the demographics of the Republic of Cyprus. Out of 1001 subjects 351, did not agree to blood examination and were excluded from the study. Additionally, 124 subjects were not included in the study if they had any of the following disease (hypertension, diabetes, dyslipidemia, coronary heart disease, fatty liver, cancer) or taking any medication. The remaining subjects, 101 men and 425 women (total 526) all participated in the study and signed a consent formed.

Anthropometrical measurements

Body weight (Bw) was measured using a scale (Seca 700) with an accuracy of ±100 gr. Subjects were weighted wearing light clothes and without shoes. Height (Ht) was measured using a seca stadiometer. BMI was calculated by dividing weight (kg) by height squared (m2). Waist circumference (WC) was measured to the nearest 0.1cm using a regular tape. Body fat was measured using bioelectrical impedance analysis (BIA), (Tanita TBF-215, England). Blood pressure was measured in a supine relaxed position using a regular Hg sphygmomanometer. Three readings were obtained by a physician wearing normal clothing, each with a 3-min interval. The mean value of the last two was considered to be the BP.

Biochemical Measurements

Blood samples were obtained for biochemical and haematological screening tests between 08.30 and 10.30 after a 12-h overnight fast. Professional staff performed venipunctures, to obtain a maximum of 10 ml blood. The fasting plasma glucose, total cholesterol (TC), triglyceride (TG), low- density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) concentrations were measured using Bayer Reagent Packs on an automated chemistry analyzer (Advia 1650 Autoanalyzer; Bayer Diagnostics, Leverkusen, Germany).

Statistical Analysis

Statistical analysis was performed using IBM SPSS for Mac (version 21) package. Testing of the normality assumption for the continuous variables was performed by using the Kolmogorov-Smirnov non-parametric test. For the variables that the Normality assumption was not valid, a logarithmic transformation was made. The comparison of continuous variables’ means of the men and women groups was performed by using Mann-Whitney and Kolmogorov-Smirnov non-parametric tests. The Spearman’s rank correlation coefficient was used to examine the association between variables. Finally, for modeling the association between dependent (or log-dependent, according to weather they satisfy the normality assumption or not) and independent variables, Analysis of Covariance (ANCOVA) is used in the case where ordinal independent variables are included in the analysis and multiple regression in the case where there are no ordinal independent variables. For all the previous tests, a significance level of 5% was used.

Results

Table 1 shows the anthropometrical and clinical characteristics of all subjects. Female subjects had statistically significantly (P<0.05) higher levels of triglycerides and waist circumference compared to males ones.
Correlations between the anthropometric indices were very strong. More specifically, in both sexes, BMI was found to positively correlated with TBF (0.601, P<0.001), (0.679, P<0.001) and WC (0.838, P<0.001), (0.841, P<0.001), respectively. Additionally, for the same sex groups TBF was also statistically significantly related with WC (0.491, P<0.001), (0.547, P<0.001), respectively. Data was not shown.

Spearman correlation analysis for male subjects is shown in table 2. Age, BMI, WC and TBF were positively correlated with SBP, DBP and Non-HDL. A positive correlation was also found between Glucose, TG and age, BMI and WC.

Table 3 represents data for the female group and how their anthropometrical variables correlated with different CV factors. Age, BMI, WC and TBF were strongly positively correlated with SBP, DBP, TC, Non-HDL and Glucose. Statistically important negative correlations were observed between BMI, WC, and TBF with HDL.
It is interesting to point out that about 30% of the whole population was smoking more that one cigarette per day (Graph 1).

Table 1. Characteristics of all subjects

Males (n=101)

Females (n=425)

P

Age (y)

46.2±12

45.6±13

0.348

Height (cm)

168.9±8.5

163.1±8.6

0.714

Weight (kg)

71.9±15.8

71.2±15.7

0.612

BMI (Kg/m2)

26.8±5.5

26.2±5.3

0.301

WC (cm)

88.9±14

92.2±13

0.009*

SBP (mg Hg)

116±11.9

114±12

0.052

DBP (mm Hg)

78±8.9

77±8.6

0.592

TBF (%)

28.6±9.2

30.2±9.2

0.965

FFM (%)

71.2±9.1

50.7±10.9

0.061

TC (mg/dl)

207±38

215±43

0.984

LDL (mg/dl)

95±36

93±37

0.607

HDL (mg/dl)

50±11

54±13

0.402

Ratio LDL/HDL

2.01±0.9

1.8±0.8

0.113

Non-HDL (mg/dl)

157±36

161±43

0.874

Glucose (mg/dl)

87±9.8

89±14

0.469

TG (mg/dl)

94±46

108±84

0.003*

Albumin (g/dL)

4.6±0.5

4.5±0.4

0.433

Creatinine (mg/dl)

0.8±0.15

0.8±0.16

0.099

Data presented as mean ±SD *Statistical significant set at P < 0.05
Abbrev:BMI: Body Mass Index, WC: Waist Circumference, SBP: Systolic Blood Pressure, DBP: Diastolic Blood Pressure, TBF: Total Body fat, FFM: Fat Free Mass, TC: Total Cholesterol, LDL: Low Density Lipoprotein, HDL: High Density Lipoprotein, TG: Triglycerides

Table 2. Spearman’s correlations (Rho) between age, BMI, WC and BF with different CV risk factors in male subjects (n=101)

Variable

Age

BMI

WC

TBF

SBP (mmHg)

0.408**

0.418**

0.423**

0.278**

DBP (mmHg)

0.310**

0.430**

0.421**

0.328**

TC (mg/dl)

0.300**

0.188

0.153

0.246*

LDL (mg/dl)

0.071

0.044

0.136

0.043

HDL (mg/dl)

0.089

-0.162

-0.191

-0.072

Ratio LDL/HDL

0.028

0.103

0.203*

0.079

Non-HDL

0.287**

0.248*

0.221*

0.237*

(mg/dl)

Glucose (mg/dl)

0.335**

0.204*

0.283**

0.068

TG (mg/dl)

0.306**

0.282**

0.251*

0.171

Smoking (cig/d)

-0.087

0.093

0.075

0.220**

Statistical significant set at P < 0.05*, P<0.01**
Abbrev:BMI: Body Mass Index, WC: Waist Circumference, SBP: Systolic Blood Pressure, DBP: Diastolic Blood Pressure, TBM: Total Body Fat, TC: Total Cholesterol, LDL: Low Density Lipoprotein, HDL: High Density Lipoprotein, TG: Triglycerides

Table 3. Spearman’s correlations (Rho) between age, BM, WC and BF with CVD risk factors in females (n=425)

Variable

Age

BMI

WC

TBF

SBP (mmHg)

0.393**

0.445**

0.458**

0.256**

DBP (mmHg)

0.320**

0.431**

0.444**

0.273**

TC (mg/dl)

0.238**

0.181**

0.185**

0.238**

LDL (mg/dl)

0.091

0.027

0.106*

0.090

HDL (mg/dl)

0.019

-0.210**

-0.292**

0.107*

Ratio LDL/HDL

0.072

0.114*

0.223**

0.155**

Non-HDL

0.230**

0.245**

0.274**

0.203**

(md/dl)

TG (mg/dl)

0.99*

0.280**

0.311**

0.101*

Glucose (mg/dl)

0.98*

0.161**

0.180**

0.044

Smoking (cig/d)

-0.57

0.024

-0.011

0.067

Statistical significant set at P < 0.05*, P<0.01**
Abbrev:BMI: Body Mass Index, WC: Waist Circumference, SBP: Systolic Blood Pressure, DBP: Diastolic Blood Pressure, TBM: Total Body Fat, TC: Total Cholesterol, LDL: Low Density Lipoprotein, HDL: High Density Lipoprotein, TG: Triglycerides

Graph 1. Percentage of smoking between males and females

In multiple regression analysis, BMI, WC and TBF were found to be independently associated with TG in the female group, (Beta: 0.009, %95 CI: 0.001-0.018, P<0.033), (Beta: 0.005, %95 CI: 0.002-0.008, P<0.01), (Beta: 0.003, %95 CI: 0.001-0.007, P<0.046), respectively.
In the male group, Age and BMI were the only variables that have been found to be independently associated with Non-HDL (Beta: 0.527, %95 CI: 0.209- 0845, P<0.01), (Beta: 0544, %95 CI: 0.44-1.045, P<0.033), respectively. Data was not shown.

Discussion

In our study we examined the associations between anthropometrical indexes and cardiovascular risk factors in 526 healthy adults from Cyprus. The prevalence of OW and OB the whole initial population (n=1001) has been described previously in another paper [3].
In both sexes the cardiovascular risk factors such as SBP, DBP, TC, Non-HDL, TG and Glucose were strongly related to age increase. In men, this increase usually levels off around the age of 45 to 50 years, whereas in women, the increase continues sharply until the age of 60 to 65 years [11]. Like serum cholesterol, blood pressure also tends to increase with age, and more prominently in women than in men [12]. The increase in blood pressure and its different relations to age in men and women are probably explained in part by obesity [13,14].
A positive association of BMI with blood pressure, triglycerides glucose levels, and cholesterol levels have been described in previous studies [15,16,17]. In our study body mass index was strongly related to systolic and diastolic blood pressure, triglycerides, glucose and non-HDL total cholesterol HDL and LDL in females. The lack of the association is seen between BMI, TC, HDL and LDL and male subjects may be due to estrogen and genetic effect.
The importance of WC in predicting cardiometabolic risk factors (eg, elevated blood pressure, dyslipidemia, and hyperglycemia) has been examined by several researchers that have concluded that abdominal obesity is more strongly associated with cardiovascular risk factor levels and it’s a better factor than BMI [18,19]. Results obtained from our study are in agreement with these observations.
The relationship found in our study between TBF and SBP, DBP and cholesterol has been demonstrated also by other authors [20]. Body composition analysis using BIA has the advantages of indirect measurement of fat and fat free mass and the ability to evaluate differences in fat deposition by region.
Evidence from epidemiological and controlled clinical trials has demonstrated that triglyceride levels are markedly affected by body weight status and body fat distribution. Data from 5610 participants ≥20 years of age from NHANES between 1999 and 2004 reported a relationship between body mass index (BMI) and triglyceride concentration [21]. In addition to the association between triglyceride levels and BMI, the Framingham Heart Study reported strong associations of triglyceride levels with both subcutaneous abdominal adipose tissue and visceral adipose tissue in both sexes [22]. In our study, we demonstrated an independent strong association between TG and anthropometric indexes.
In addition, we presented an independent effect of non-HDL with age and BMI. Similar results have been reported by a study from southern Sweden [23].
Moreover, our study provides another piece of novel information that non–HDL-C was superior to LDL-C since it had been correlated with all anthropometrical indexes. The better prediction by non– HDL-C than LDL-C was also reported for the general population [24] and in the cohort including type 2 diabetes patients [25].

Limitations

Our study has its limitation. Firstly, we did not include information about physical activity and genetics of subjects, which would possibly had an effect on CV risk factors. Secondly, our study is limited by its cross-sectional design, which precludes causal inferences. Further longitudinal analyses are needed to provide stronger evidence of these associations. Nevertheless we have provided important data on the relation of adipose indexes and CV risk factors in healthy adults from Cyprus.

Conclusions:

Obesity indexes are strongly related with traditional CV risk factors. However, a new index, non-HDL, together with elevated triglycerides seems to be independently associated with various anthropometrical indexes. Public health awareness and nutrition education is needed in order to monitor and lower these factors avoiding future possible metabolic heath problems.

References

[1]Wilborn C, Beckham J, Campbell B, Harvey T, Galbreath M, La Bounty P, et al. Prevalence, Theories, Medical Consequences, Management, and Research Directions. J of the Intern Society of Sports Nutrit. 2005;2:4–31

[2]U.S Department oh Health and Human Services. Overweight and obesity statistics. National Heart, Lung, and Blood Institute; NIH Publication No. 04–4158 Updated October 2012: 1-6

[3]Andreou E, Hajigeorgiou P, Kyriakou K, Avraam T, Chappa G, Kallis P, Lazarou Ch, Philippou Ch, Christoforou C, Kokkinofta R, Dioghenous C, Savva S, Kafatos A, Zampelas A, Papandreou D. Risk factors of obesity in a cohort of 1001 Cypriot adults: An epidemiological study. Hippokratia. 2012;16(3):256-60.

[4]Savva SC, Kourides Y, Tornaritis M, Epiphaniou-Savva M, Chadjigeorgiou C, Kafatos A. Reference growth curves for Cypriot children 6 to 17 years of age. Obes Res. 2001; 9:754–762.

[5]Banegas GE, Blasco-Colmenares E, Jiménez FJ, et al. Excess risk attributable to traditional cardiovascular risk factors in clinical practice settings across Europe – The EURIKA Study. BMC Public Health. 2011;11:704–15.

[6]Helis E, Augustincic L, Steiner S, Chen L, Turton P, Fodor JG. Time trends in cardiovascular and all- cause mortality in the ‘old’ and ‘new’ European Union countries. Eur J Cardiovasc Prev Rehabil. 2011;18:347–59.

[7]Ryo M, Funahashi T, Nakamura T, Kihara S, Kotani K, Tokunaga K, Matsuzawa Y, Shimomura I. Fat Accumulation and Obesity-related Cardiovascular Risk Factors in Middle-aged Japanese Men and Women. Intern Med. 2014;53(4):299-305.

[8]Kissebah AH, Vydelingum N, Murray R, et al. Relation of body fat distribution to metabolic complication of obesity. J Clin Endocrinol Metab 54: 254-260, 1982.

[9]Ninomiya JK, L’Italien G, Criqui MH, Whyte JL, Gamst A, Chen RS. Association of the metabolic syndrome with history of myocardial infarction and stroke in the Third National Health and Nutrition Examination Survey. Circulation. 2004; 109: 42-46.

[10]Gardner CD, Winkleby MA, Fortmann SP. Population frequency distribution of non-high-density lipoprotein cholesterol (Third National Health and Nutrition Examination Survey [NHANES III], 1988- 1994). Am J Cardiol 2000; 86: 299-304.

[11]Tuomilehto J, Tanskanen A, Salonen JT, Nissinen A, Koskela K. Effects of smoking and stopping smoking on serum high-density lipoprotein cholesterol levels in a representative population sample. Prev Med. 1986;15:35–45.

[12]Jousilahti P, Vartiainen E, Tuomilehto J, Puska P. Twenty-year dynamics of serum cholesterol in middle-aged population of eastern Finland. Ann Intern Med. 1996;125:713–722.

[13]National High Blood Pressure Education Program Working Group Report on Primary Prevention of Hypertension. Arch Intern Med. 1993;153:186–208.

[14]Jousilahti P, Tuomilehto J, Vartiainen E, Valle T, Nissinen A. Body mass index, blood pressure,diabetes, and the risk of anti-hypertensive drug treatment: 12-year follow-up of middle-aged people in eastern Finland. J Hum Hypertens. 1995;9:847–854.

[15]National Institutes of Health Consensus Development Panel on the Health Implications of Obesity. Health implications of obesity: National Institutes of Health consensus development conference statement. Ann Intern Med. 1985;103:1073-1077.

[16]Chiang BN, Perlman LV, Epstein FH. Overweight and hypertension: a review. Circulation. 1969;39:403-412.

[17]Kannel WB, Gordon T, Castelli WP. Obesity, lipids, and glucose intolerance: the Framingham Study.Am J Clin Nutr. 1979;32:1238-1245.

[18]Freedman DS, Thornton J, Pi-Sunyer FX, Heymsfield SB, Wang J, et al. The body adiposity index (hip circumference/height (1.5) is not a more accurate measure of adiposity than is BMI, waist circumference, or hip circumference. Obesity (Silver Spring). 2012; 20(12): 2438-44. doi: 10.1038/oby.2012.81.

[19]de Lima JG, Nobrega LH, de Souza AB. Body adiposity index indicates only total adiposity, not risk. Obesity (Silver Spring). 2012; 20: 1140. doi: 10.1038/oby.2012.3.

[20]Berns MA, deVries JH, Katan MB. Increase in body fatness as a major determinant of changes in serum total cholesterol and high density lipoprotein cholesterol in young men over a 10-year period. Am J Epidemiol 1989. 1989;130:1109–22.

[21]Ford ES, Li C, Zhao G, Pearson WS, Mokdad AH. Hypertriglyceridemia and its pharmacologic treatment among US adults. Arch Intern Med. 2009; 169: 572–578.

[22]Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, Vasan RS, Murabito JM, Meigs JB, Cupples LA, D’Agostino RB Sr., O’Donnell CJ. Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation. 2007; 116: 39–48.

[23]Henriksson KM1, Lindblad U, Agren B, Nilsson-Ehle P, Råstam L.
Associations between body height, body composition and cholesterol levels in middle-aged men. the coronary risk factor study in southern Sweden (CRISS). Eur J Epidemiol. 2001;17(6):521-6.

[24]Di Angelantonio E, Sarwar N, Perry P, Kaptoge S, Ray KK, Thompson A, Wood AM, Lewington S, Sattar N, Packard CJ, Collins R, Thompson SG, Danesh J: Major lipids, apolipoproteins, and risk of vascular disease. JAMA. 2009; 302: 1993–2000.

[25]Liu J, Sempos C, Donahue RP, Dorn J, Trevisan M, Grundy SM: Joint distribution of non-HDL and LDL cholesterol and coronary heart disease risk prediction among individuals with and without diabetes. Diabetes Care. 2005; 28: 1916–1921.